From 679b71adb0030626a0cd774d338e046e56e58edf Mon Sep 17 00:00:00 2001
From: Amir MOHAMMADI <amir.mohammadi@idiap.ch>
Date: Tue, 10 Nov 2020 15:26:54 +0100
Subject: [PATCH] Porting to dask pipelines
---
bob/pad/face/config/algorithm/__init__.py | 0
.../video_cascade_svm_pad_algorithm.py | 271 -
.../algorithm/video_gmm_pad_algorithm.py | 258 -
.../algorithm/video_svm_pad_algorithm.py | 48 -
bob/pad/face/config/brsu.py | 14 +-
bob/pad/face/config/casiafasd.py | 5 +-
bob/pad/face/config/casiasurf_color.py | 14 +-
bob/pad/face/config/celeb_a.py | 52 +-
bob/pad/face/config/database/__init__.py | 0
bob/pad/face/config/database/batl/__init__.py | 0
bob/pad/face/config/database/batl/batl_db.py | 54 -
.../config/database/batl/batl_db_depth.py | 54 -
.../config/database/batl/batl_db_infrared.py | 54 -
.../batl/batl_db_rgb_ir_d_grandtest.py | 55 -
.../config/database/batl/batl_db_thermal.py | 54 -
bob/pad/face/config/extractor/__init__.py | 0
.../config/extractor/frame_diff_features.py | 12 -
bob/pad/face/config/extractor/optical_flow.py | 4 -
.../config/extractor/video_lbp_histogram.py | 23 -
.../config/extractor/video_quality_measure.py | 14 -
bob/pad/face/config/frame_diff_svm.py | 86 -
bob/pad/face/config/grid.py | 50 -
bob/pad/face/config/lbp_64.py | 46 +
bob/pad/face/config/lbp_lr_batl_D_T_IR.py | 103 -
bob/pad/face/config/lbp_svm.py | 113 -
bob/pad/face/config/maskattack.py | 19 +-
bob/pad/face/config/mifs.py | 54 +-
bob/pad/face/config/preprocessor/__init__.py | 0
.../dictionary_front_10_5_128.hdf5 | Bin 27744 -> 0 bytes
.../dictionaries/dictionary_hor_10_5_128.hdf5 | Bin 53344 -> 0 bytes
.../dictionary_vert_10_5_128.hdf5 | Bin 53344 -> 0 bytes
.../face_feature_crop_quality_check.py | 490 -
bob/pad/face/config/preprocessor/filename.py | 7 -
.../face/config/preprocessor/optical_flow.py | 10 -
.../config/preprocessor/video_face_crop.py | 66 -
.../video_face_crop_align_block_patch.py | 131 -
.../preprocessor/video_sparse_coding.py | 39 -
.../{vanilla_pad/qm_svm.py => qm_64.py} | 38 +-
bob/pad/face/config/qm_lr.py | 86 -
bob/pad/face/config/qm_svm.py | 101 -
.../config/quality_assessment/__init__.py | 0
.../quality_assessment/celeb_a/__init__.py | 0
.../celeb_a/quality_assessment_config.py | 50 -
.../celeb_a/quality_assessment_config_128.py | 162 -
.../models/eye_detector.xml | 12214 ----------------
bob/pad/face/config/replay_attack.py | 54 +-
bob/pad/face/config/replay_mobile.py | 54 +-
bob/pad/face/config/svm_frames.py | 32 +
.../face/config/vanilla_pad/replay_attack.py | 11 -
bob/pad/face/extractor/FrameDiffFeatures.py | 318 -
bob/pad/face/extractor/ImageQualityMeasure.py | 103 +-
bob/pad/face/extractor/LBPHistogram.py | 112 +-
bob/pad/face/extractor/__init__.py | 2 -
bob/pad/face/preprocessor/FrameDifference.py | 433 -
bob/pad/face/preprocessor/__init__.py | 2 -
bob/pad/face/test/test.py | 534 +-
56 files changed, 211 insertions(+), 16295 deletions(-)
delete mode 100644 bob/pad/face/config/algorithm/__init__.py
delete mode 100644 bob/pad/face/config/algorithm/video_cascade_svm_pad_algorithm.py
delete mode 100644 bob/pad/face/config/algorithm/video_gmm_pad_algorithm.py
delete mode 100644 bob/pad/face/config/algorithm/video_svm_pad_algorithm.py
delete mode 100644 bob/pad/face/config/database/__init__.py
delete mode 100644 bob/pad/face/config/database/batl/__init__.py
delete mode 100644 bob/pad/face/config/database/batl/batl_db.py
delete mode 100644 bob/pad/face/config/database/batl/batl_db_depth.py
delete mode 100644 bob/pad/face/config/database/batl/batl_db_infrared.py
delete mode 100644 bob/pad/face/config/database/batl/batl_db_rgb_ir_d_grandtest.py
delete mode 100644 bob/pad/face/config/database/batl/batl_db_thermal.py
delete mode 100644 bob/pad/face/config/extractor/__init__.py
delete mode 100644 bob/pad/face/config/extractor/frame_diff_features.py
delete mode 100644 bob/pad/face/config/extractor/optical_flow.py
delete mode 100644 bob/pad/face/config/extractor/video_lbp_histogram.py
delete mode 100644 bob/pad/face/config/extractor/video_quality_measure.py
delete mode 100644 bob/pad/face/config/frame_diff_svm.py
delete mode 100644 bob/pad/face/config/grid.py
create mode 100644 bob/pad/face/config/lbp_64.py
delete mode 100644 bob/pad/face/config/lbp_lr_batl_D_T_IR.py
delete mode 100644 bob/pad/face/config/lbp_svm.py
delete mode 100644 bob/pad/face/config/preprocessor/__init__.py
delete mode 100644 bob/pad/face/config/preprocessor/dictionaries/dictionary_front_10_5_128.hdf5
delete mode 100644 bob/pad/face/config/preprocessor/dictionaries/dictionary_hor_10_5_128.hdf5
delete mode 100644 bob/pad/face/config/preprocessor/dictionaries/dictionary_vert_10_5_128.hdf5
delete mode 100644 bob/pad/face/config/preprocessor/face_feature_crop_quality_check.py
delete mode 100644 bob/pad/face/config/preprocessor/filename.py
delete mode 100644 bob/pad/face/config/preprocessor/optical_flow.py
delete mode 100644 bob/pad/face/config/preprocessor/video_face_crop.py
delete mode 100644 bob/pad/face/config/preprocessor/video_face_crop_align_block_patch.py
delete mode 100644 bob/pad/face/config/preprocessor/video_sparse_coding.py
rename bob/pad/face/config/{vanilla_pad/qm_svm.py => qm_64.py} (53%)
delete mode 100644 bob/pad/face/config/qm_lr.py
delete mode 100644 bob/pad/face/config/qm_svm.py
delete mode 100644 bob/pad/face/config/quality_assessment/__init__.py
delete mode 100644 bob/pad/face/config/quality_assessment/celeb_a/__init__.py
delete mode 100644 bob/pad/face/config/quality_assessment/celeb_a/quality_assessment_config.py
delete mode 100644 bob/pad/face/config/quality_assessment/celeb_a/quality_assessment_config_128.py
delete mode 100644 bob/pad/face/config/quality_assessment/models/eye_detector.xml
create mode 100644 bob/pad/face/config/svm_frames.py
delete mode 100644 bob/pad/face/config/vanilla_pad/replay_attack.py
delete mode 100644 bob/pad/face/extractor/FrameDiffFeatures.py
delete mode 100644 bob/pad/face/preprocessor/FrameDifference.py
diff --git a/bob/pad/face/config/algorithm/__init__.py b/bob/pad/face/config/algorithm/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/bob/pad/face/config/algorithm/video_cascade_svm_pad_algorithm.py b/bob/pad/face/config/algorithm/video_cascade_svm_pad_algorithm.py
deleted file mode 100644
index ac43229a..00000000
--- a/bob/pad/face/config/algorithm/video_cascade_svm_pad_algorithm.py
+++ /dev/null
@@ -1,271 +0,0 @@
-#!/usr/bin/env python
-
-from bob.pad.base.algorithm import SVMCascadePCA
-
-#=======================================================================================
-# Define instances here:
-
-MACHINE_TYPE = 'ONE_CLASS'
-KERNEL_TYPE = 'RBF'
-SVM_KWARGS = {'nu': 0.001, 'gamma': 0.2}
-N = 2
-POS_SCORES_SLOPE = 0.01
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_n2_gamma_02 = SVMCascadePCA(
- machine_type=MACHINE_TYPE,
- kernel_type=KERNEL_TYPE,
- svm_kwargs=SVM_KWARGS,
- N=N,
- pos_scores_slope=POS_SCORES_SLOPE,
- frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-MACHINE_TYPE = 'ONE_CLASS'
-KERNEL_TYPE = 'RBF'
-SVM_KWARGS = {'nu': 0.001, 'gamma': 0.1}
-N = 2
-POS_SCORES_SLOPE = 0.01
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_n2_gamma_01 = SVMCascadePCA(
- machine_type=MACHINE_TYPE,
- kernel_type=KERNEL_TYPE,
- svm_kwargs=SVM_KWARGS,
- N=N,
- pos_scores_slope=POS_SCORES_SLOPE,
- frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-MACHINE_TYPE = 'ONE_CLASS'
-KERNEL_TYPE = 'RBF'
-SVM_KWARGS = {'nu': 0.001, 'gamma': 0.05}
-N = 2
-POS_SCORES_SLOPE = 0.01
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_n2_gamma_005 = SVMCascadePCA(
- machine_type=MACHINE_TYPE,
- kernel_type=KERNEL_TYPE,
- svm_kwargs=SVM_KWARGS,
- N=N,
- pos_scores_slope=POS_SCORES_SLOPE,
- frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-MACHINE_TYPE = 'ONE_CLASS'
-KERNEL_TYPE = 'RBF'
-SVM_KWARGS = {'nu': 0.001, 'gamma': 0.01}
-N = 2
-POS_SCORES_SLOPE = 0.01
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_n2_gamma_001 = SVMCascadePCA(
- machine_type=MACHINE_TYPE,
- kernel_type=KERNEL_TYPE,
- svm_kwargs=SVM_KWARGS,
- N=N,
- pos_scores_slope=POS_SCORES_SLOPE,
- frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-#=======================================================================================
-
-MACHINE_TYPE = 'ONE_CLASS'
-KERNEL_TYPE = 'RBF'
-SVM_KWARGS = {'nu': 0.001, 'gamma': 0.1}
-N = 10
-POS_SCORES_SLOPE = 0.01
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_n10_gamma_01 = SVMCascadePCA(
- machine_type=MACHINE_TYPE,
- kernel_type=KERNEL_TYPE,
- svm_kwargs=SVM_KWARGS,
- N=N,
- pos_scores_slope=POS_SCORES_SLOPE,
- frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-MACHINE_TYPE = 'ONE_CLASS'
-KERNEL_TYPE = 'RBF'
-SVM_KWARGS = {'nu': 0.001, 'gamma': 0.05}
-N = 10
-POS_SCORES_SLOPE = 0.01
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_n10_gamma_005 = SVMCascadePCA(
- machine_type=MACHINE_TYPE,
- kernel_type=KERNEL_TYPE,
- svm_kwargs=SVM_KWARGS,
- N=N,
- pos_scores_slope=POS_SCORES_SLOPE,
- frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-MACHINE_TYPE = 'ONE_CLASS'
-KERNEL_TYPE = 'RBF'
-SVM_KWARGS = {'nu': 0.001, 'gamma': 0.01}
-N = 10
-POS_SCORES_SLOPE = 0.01
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_n10_gamma_001 = SVMCascadePCA(
- machine_type=MACHINE_TYPE,
- kernel_type=KERNEL_TYPE,
- svm_kwargs=SVM_KWARGS,
- N=N,
- pos_scores_slope=POS_SCORES_SLOPE,
- frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-MACHINE_TYPE = 'ONE_CLASS'
-KERNEL_TYPE = 'RBF'
-SVM_KWARGS = {'nu': 0.001, 'gamma': 0.005}
-N = 10
-POS_SCORES_SLOPE = 0.01
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_n10_gamma_0005 = SVMCascadePCA(
- machine_type=MACHINE_TYPE,
- kernel_type=KERNEL_TYPE,
- svm_kwargs=SVM_KWARGS,
- N=N,
- pos_scores_slope=POS_SCORES_SLOPE,
- frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-#=======================================================================================
-
-MACHINE_TYPE = 'ONE_CLASS'
-KERNEL_TYPE = 'RBF'
-SVM_KWARGS = {'nu': 0.001, 'gamma': 0.5}
-N = 20
-POS_SCORES_SLOPE = 0.01
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_n20_gamma_05 = SVMCascadePCA(
- machine_type=MACHINE_TYPE,
- kernel_type=KERNEL_TYPE,
- svm_kwargs=SVM_KWARGS,
- N=N,
- pos_scores_slope=POS_SCORES_SLOPE,
- frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-MACHINE_TYPE = 'ONE_CLASS'
-KERNEL_TYPE = 'RBF'
-SVM_KWARGS = {'nu': 0.001, 'gamma': 0.2}
-N = 20
-POS_SCORES_SLOPE = 0.01
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_n20_gamma_02 = SVMCascadePCA(
- machine_type=MACHINE_TYPE,
- kernel_type=KERNEL_TYPE,
- svm_kwargs=SVM_KWARGS,
- N=N,
- pos_scores_slope=POS_SCORES_SLOPE,
- frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-MACHINE_TYPE = 'ONE_CLASS'
-KERNEL_TYPE = 'RBF'
-SVM_KWARGS = {'nu': 0.001, 'gamma': 0.1}
-N = 20
-POS_SCORES_SLOPE = 0.01
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_n20_gamma_01 = SVMCascadePCA(
- machine_type=MACHINE_TYPE,
- kernel_type=KERNEL_TYPE,
- svm_kwargs=SVM_KWARGS,
- N=N,
- pos_scores_slope=POS_SCORES_SLOPE,
- frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-MACHINE_TYPE = 'ONE_CLASS'
-KERNEL_TYPE = 'RBF'
-SVM_KWARGS = {'nu': 0.001, 'gamma': 0.05}
-N = 20
-POS_SCORES_SLOPE = 0.01
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_n20_gamma_005 = SVMCascadePCA(
- machine_type=MACHINE_TYPE,
- kernel_type=KERNEL_TYPE,
- svm_kwargs=SVM_KWARGS,
- N=N,
- pos_scores_slope=POS_SCORES_SLOPE,
- frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-MACHINE_TYPE = 'ONE_CLASS'
-KERNEL_TYPE = 'RBF'
-SVM_KWARGS = {'nu': 0.001, 'gamma': 0.01}
-N = 20
-POS_SCORES_SLOPE = 0.01
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_n20_gamma_001 = SVMCascadePCA(
- machine_type=MACHINE_TYPE,
- kernel_type=KERNEL_TYPE,
- svm_kwargs=SVM_KWARGS,
- N=N,
- pos_scores_slope=POS_SCORES_SLOPE,
- frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-MACHINE_TYPE = 'ONE_CLASS'
-KERNEL_TYPE = 'RBF'
-SVM_KWARGS = {'nu': 0.001, 'gamma': 0.005}
-N = 20
-POS_SCORES_SLOPE = 0.01
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_n20_gamma_0005 = SVMCascadePCA(
- machine_type=MACHINE_TYPE,
- kernel_type=KERNEL_TYPE,
- svm_kwargs=SVM_KWARGS,
- N=N,
- pos_scores_slope=POS_SCORES_SLOPE,
- frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-MACHINE_TYPE = 'ONE_CLASS'
-KERNEL_TYPE = 'RBF'
-SVM_KWARGS = {'nu': 0.001, 'gamma': 0.001}
-N = 20
-POS_SCORES_SLOPE = 0.01
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_n20_gamma_0001 = SVMCascadePCA(
- machine_type=MACHINE_TYPE,
- kernel_type=KERNEL_TYPE,
- svm_kwargs=SVM_KWARGS,
- N=N,
- pos_scores_slope=POS_SCORES_SLOPE,
- frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-#=======================================================================================
-
-MACHINE_TYPE = 'ONE_CLASS'
-KERNEL_TYPE = 'RBF'
-SVM_KWARGS = {'nu': 0.001, 'gamma': 0.1}
-N = 2
-POS_SCORES_SLOPE = 0.01
-FRAME_LEVEL_SCORES_FLAG = False
-
-algorithm_n2_gamma_01_video_level = SVMCascadePCA(
- machine_type=MACHINE_TYPE,
- kernel_type=KERNEL_TYPE,
- svm_kwargs=SVM_KWARGS,
- N=N,
- pos_scores_slope=POS_SCORES_SLOPE,
- frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-#=======================================================================================
-
-# Test the cascade of two-class SVMs.
-
-MACHINE_TYPE = 'C_SVC'
-KERNEL_TYPE = 'RBF'
-TRAINER_GRID_SEARCH_PARAMS = {'cost': 1, 'gamma': 0.01}
-N = 2
-POS_SCORES_SLOPE = 0.01
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_n2_two_class_svm_c1_gamma_001 = SVMCascadePCA(
- machine_type=MACHINE_TYPE,
- kernel_type=KERNEL_TYPE,
- svm_kwargs=TRAINER_GRID_SEARCH_PARAMS,
- N=N,
- pos_scores_slope=POS_SCORES_SLOPE,
- frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
diff --git a/bob/pad/face/config/algorithm/video_gmm_pad_algorithm.py b/bob/pad/face/config/algorithm/video_gmm_pad_algorithm.py
deleted file mode 100644
index 3e132c9c..00000000
--- a/bob/pad/face/config/algorithm/video_gmm_pad_algorithm.py
+++ /dev/null
@@ -1,258 +0,0 @@
-#!/usr/bin/env python
-
-from bob.pad.base.algorithm import OneClassGMM
-
-#=======================================================================================
-# Define instances here:
-
-N_COMPONENTS = 2
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_gmm_2 = OneClassGMM(
- n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-N_COMPONENTS = 3
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_gmm_3 = OneClassGMM(
- n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-N_COMPONENTS = 4
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_gmm_4 = OneClassGMM(
- n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-N_COMPONENTS = 5
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_gmm_5 = OneClassGMM(
- n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-N_COMPONENTS = 6
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_gmm_6 = OneClassGMM(
- n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-N_COMPONENTS = 7
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_gmm_7 = OneClassGMM(
- n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-N_COMPONENTS = 8
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_gmm_8 = OneClassGMM(
- n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-N_COMPONENTS = 9
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_gmm_9 = OneClassGMM(
- n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-N_COMPONENTS = 10
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_gmm_10 = OneClassGMM(
- n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-#=======================================================================================
-# above 10 Gaussians:
-
-N_COMPONENTS = 12
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_gmm_12 = OneClassGMM(
- n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-N_COMPONENTS = 14
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_gmm_14 = OneClassGMM(
- n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-N_COMPONENTS = 16
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_gmm_16 = OneClassGMM(
- n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-N_COMPONENTS = 18
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_gmm_18 = OneClassGMM(
- n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-N_COMPONENTS = 20
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_gmm_20 = OneClassGMM(
- n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-#=======================================================================================
-# above 20 Gaussians:
-
-N_COMPONENTS = 25
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_gmm_25 = OneClassGMM(
- n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-N_COMPONENTS = 30
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_gmm_30 = OneClassGMM(
- n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-N_COMPONENTS = 35
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_gmm_35 = OneClassGMM(
- n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-N_COMPONENTS = 40
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_gmm_40 = OneClassGMM(
- n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-N_COMPONENTS = 45
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_gmm_45 = OneClassGMM(
- n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-N_COMPONENTS = 50
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_gmm_50 = OneClassGMM(
- n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-#=======================================================================================
-# above 50 Gaussians:
-
-N_COMPONENTS = 60
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_gmm_60 = OneClassGMM(
- n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-N_COMPONENTS = 70
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_gmm_70 = OneClassGMM(
- n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-N_COMPONENTS = 80
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_gmm_80 = OneClassGMM(
- n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-N_COMPONENTS = 90
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_gmm_90 = OneClassGMM(
- n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-N_COMPONENTS = 100
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_gmm_100 = OneClassGMM(
- n_components=N_COMPONENTS, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-#=======================================================================================
-# 50 Gaussians, different random seeds:
-
-N_COMPONENTS = 50
-RANDOM_STATE = 0
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_gmm_50_0 = OneClassGMM(
- n_components=N_COMPONENTS,
- random_state=RANDOM_STATE,
- frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-N_COMPONENTS = 50
-RANDOM_STATE = 1
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_gmm_50_1 = OneClassGMM(
- n_components=N_COMPONENTS,
- random_state=RANDOM_STATE,
- frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-N_COMPONENTS = 50
-RANDOM_STATE = 2
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_gmm_50_2 = OneClassGMM(
- n_components=N_COMPONENTS,
- random_state=RANDOM_STATE,
- frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-N_COMPONENTS = 50
-RANDOM_STATE = 3
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_gmm_50_3 = OneClassGMM(
- n_components=N_COMPONENTS,
- random_state=RANDOM_STATE,
- frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-N_COMPONENTS = 50
-RANDOM_STATE = 4
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_gmm_50_4 = OneClassGMM(
- n_components=N_COMPONENTS,
- random_state=RANDOM_STATE,
- frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-N_COMPONENTS = 50
-RANDOM_STATE = 5
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_gmm_50_5 = OneClassGMM(
- n_components=N_COMPONENTS,
- random_state=RANDOM_STATE,
- frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-N_COMPONENTS = 50
-RANDOM_STATE = 6
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_gmm_50_6 = OneClassGMM(
- n_components=N_COMPONENTS,
- random_state=RANDOM_STATE,
- frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-N_COMPONENTS = 50
-RANDOM_STATE = 7
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_gmm_50_7 = OneClassGMM(
- n_components=N_COMPONENTS,
- random_state=RANDOM_STATE,
- frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-N_COMPONENTS = 50
-RANDOM_STATE = 8
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_gmm_50_8 = OneClassGMM(
- n_components=N_COMPONENTS,
- random_state=RANDOM_STATE,
- frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-
-N_COMPONENTS = 50
-RANDOM_STATE = 9
-FRAME_LEVEL_SCORES_FLAG = True
-
-algorithm_gmm_50_9 = OneClassGMM(
- n_components=N_COMPONENTS,
- random_state=RANDOM_STATE,
- frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
diff --git a/bob/pad/face/config/algorithm/video_svm_pad_algorithm.py b/bob/pad/face/config/algorithm/video_svm_pad_algorithm.py
deleted file mode 100644
index 7d7e0f09..00000000
--- a/bob/pad/face/config/algorithm/video_svm_pad_algorithm.py
+++ /dev/null
@@ -1,48 +0,0 @@
-#!/usr/bin/env python
-
-from bob.pad.base.algorithm import SVM
-
-#=======================================================================================
-# Define instances here:
-
-machine_type = 'C_SVC'
-kernel_type = 'RBF'
-n_samples = 10000
-# trainer_grid_search_params = {'cost': [2**p for p in range(-5, 16, 2)], 'gamma': [2**p for p in range(-15, 4, 2)]}
-trainer_grid_search_params = {
- 'cost': [2**p for p in range(-3, 14, 2)],
- 'gamma': [2**p for p in range(-15, 0, 2)]
-}
-mean_std_norm_flag = True
-frame_level_scores_flag = False # one score per video(!) in this case
-
-video_svm_pad_algorithm_10k_grid_mean_std = SVM(
- machine_type=machine_type,
- kernel_type=kernel_type,
- n_samples=n_samples,
- trainer_grid_search_params=trainer_grid_search_params,
- mean_std_norm_flag=mean_std_norm_flag,
- frame_level_scores_flag=frame_level_scores_flag)
-
-frame_level_scores_flag = True # one score per frame(!) in this case
-
-video_svm_pad_algorithm_10k_grid_mean_std_frame_level = SVM(
- machine_type=machine_type,
- kernel_type=kernel_type,
- n_samples=n_samples,
- trainer_grid_search_params=trainer_grid_search_params,
- mean_std_norm_flag=mean_std_norm_flag,
- frame_level_scores_flag=frame_level_scores_flag)
-
-trainer_grid_search_params = {
- 'cost': [1],
- 'gamma': [0]
-} # set the default LibSVM parameters
-
-video_svm_pad_algorithm_default_svm_param_mean_std_frame_level = SVM(
- machine_type=machine_type,
- kernel_type=kernel_type,
- n_samples=n_samples,
- trainer_grid_search_params=trainer_grid_search_params,
- mean_std_norm_flag=mean_std_norm_flag,
- frame_level_scores_flag=frame_level_scores_flag)
diff --git a/bob/pad/face/config/brsu.py b/bob/pad/face/config/brsu.py
index 550124fb..29ec93b1 100644
--- a/bob/pad/face/config/brsu.py
+++ b/bob/pad/face/config/brsu.py
@@ -1,10 +1,10 @@
-#!/usr/bin/env python
-# encoding: utf-8
-
-from bob.pad.face.database import BRSUPadDatabase
+from bob.pad.face.database import BRSUPadDatabase
+from bob.pad.base.pipelines.vanilla_pad import DatabaseConnector
from bob.extension import rc
-database = BRSUPadDatabase(
- protocol='test',
- original_directory=rc['bob.db.brsu.directory'],
+database = DatabaseConnector(
+ BRSUPadDatabase(
+ protocol="test",
+ original_directory=rc["bob.db.brsu.directory"],
+ )
)
diff --git a/bob/pad/face/config/casiafasd.py b/bob/pad/face/config/casiafasd.py
index 0ee9b257..6d8d4119 100644
--- a/bob/pad/face/config/casiafasd.py
+++ b/bob/pad/face/config/casiafasd.py
@@ -1,8 +1,7 @@
-#!/usr/bin/env python
-
"""Config file for the CASIA FASD dataset.
Please run ``bob config set bob.db.casia_fasd.directory /path/to/casia_fasd_files``
in terminal to point to the original files of the dataset on your computer."""
from bob.pad.face.database import CasiaFasdPadDatabase
-database = CasiaFasdPadDatabase()
+from bob.pad.base.pipelines.vanilla_pad import DatabaseConnector
+database = DatabaseConnector(CasiaFasdPadDatabase())
diff --git a/bob/pad/face/config/casiasurf_color.py b/bob/pad/face/config/casiasurf_color.py
index 4d7cde92..d4495930 100644
--- a/bob/pad/face/config/casiasurf_color.py
+++ b/bob/pad/face/config/casiasurf_color.py
@@ -1,11 +1,11 @@
-#!/usr/bin/env python
-# encoding: utf-8
-
from bob.pad.face.database import CasiaSurfPadDatabase
+from bob.pad.base.pipelines.vanilla_pad import DatabaseConnector
from bob.extension import rc
-database = CasiaSurfPadDatabase(
- protocol='color',
- original_directory=rc['bob.db.casiasurf.directory'],
- original_extension=".jpg",
+database = DatabaseConnector(
+ CasiaSurfPadDatabase(
+ protocol="color",
+ original_directory=rc.get("bob.db.casiasurf.directory"),
+ original_extension=".jpg",
+ )
)
diff --git a/bob/pad/face/config/celeb_a.py b/bob/pad/face/config/celeb_a.py
index 6e17727c..dd104bb0 100644
--- a/bob/pad/face/config/celeb_a.py
+++ b/bob/pad/face/config/celeb_a.py
@@ -1,4 +1,3 @@
-#!/usr/bin/env python
"""`CELEBA`_ is a face makeup spoofing database adapted for face PAD experiments.
@@ -9,48 +8,15 @@ the link.
"""
+from bob.extension import rc
+from bob.pad.base.pipelines.vanilla_pad import DatabaseConnector
from bob.pad.face.database.celeb_a import CELEBAPadDatabase
-# Directory where the data files are stored.
-# This directory is given in the .bob_bio_databases.txt file located in your home directory
-ORIGINAL_DIRECTORY = "[YOUR_CELEB_A_DATABASE_DIRECTORY]"
-"""Value of ``~/.bob_bio_databases.txt`` for this database"""
-
-ORIGINAL_EXTENSION = "" # extension of the data files
-
-database = CELEBAPadDatabase(
- protocol='grandtest',
- original_directory=ORIGINAL_DIRECTORY,
- original_extension=ORIGINAL_EXTENSION,
- training_depends_on_protocol=True
+database = DatabaseConnector(
+ CELEBAPadDatabase(
+ protocol="grandtest",
+ original_directory=rc.get("bob.db.celeba.directory"),
+ original_extension="",
+ training_depends_on_protocol=True,
+ )
)
-"""The :py:class:`bob.pad.base.database.PadDatabase` derivative with CELEBA
-database settings.
-
-.. warning::
-
- This class only provides a programmatic interface to load data in an orderly
- manner, respecting usage protocols. It does **not** contain the raw
- data files. You should procure those yourself.
-
-Notice that ``original_directory`` is set to ``[YOUR_CELEBA_DATABASE_DIRECTORY]``.
-You must make sure to create ``${HOME}/.bob_bio_databases.txt`` setting this
-value to the place where you actually installed the CELEBA Database, as
-explained in the section :ref:`bob.pad.face.baselines`.
-"""
-
-protocol = 'grandtest'
-"""The default protocol to use for reproducing the baselines.
-
-You may modify this at runtime by specifying the option ``--protocol`` on the
-command-line of ``spoof.py`` or using the keyword ``protocol`` on a
-configuration file that is loaded **after** this configuration resource.
-"""
-
-groups = ["train", "dev", "eval"]
-"""The default groups to use for reproducing the baselines.
-
-You may modify this at runtime by specifying the option ``--groups`` on the
-command-line of ``spoof.py`` or using the keyword ``groups`` on a
-configuration file that is loaded **after** this configuration resource.
-"""
diff --git a/bob/pad/face/config/database/__init__.py b/bob/pad/face/config/database/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/bob/pad/face/config/database/batl/__init__.py b/bob/pad/face/config/database/batl/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/bob/pad/face/config/database/batl/batl_db.py b/bob/pad/face/config/database/batl/batl_db.py
deleted file mode 100644
index 24e6993b..00000000
--- a/bob/pad/face/config/database/batl/batl_db.py
+++ /dev/null
@@ -1,54 +0,0 @@
-#!/usr/bin/env python
-"""
-BATL Db is a database for face PAD experiments.
-"""
-
-from bob.pad.face.database import BatlPadDatabase
-
-# Directory where the data files are stored.
-# This directory is given in the .bob_bio_databases.txt file located in your home directory
-ORIGINAL_DIRECTORY = "[YOUR_BATL_DB_DIRECTORY]"
-"""Value of ``~/.bob_bio_databases.txt`` for this database"""
-
-ORIGINAL_EXTENSION = ".h5" # extension of the data files
-
-ANNOTATIONS_TEMP_DIR = ""
-
-PROTOCOL = 'nowig-color-50'
-
-database = BatlPadDatabase(
- protocol=PROTOCOL,
- original_directory=ORIGINAL_DIRECTORY,
- original_extension=ORIGINAL_EXTENSION,
- annotations_temp_dir=ANNOTATIONS_TEMP_DIR,
- landmark_detect_method="mtcnn",
- training_depends_on_protocol=True,
-)
-"""The :py:class:`bob.pad.base.database.BatlPadDatabase` derivative with BATL Db
-database settings.
-
-.. warning::
-
- This class only provides a programmatic interface to load data in an orderly
- manner, respecting usage protocols. It does **not** contain the raw
- data files. You should procure those yourself.
-
-Notice that ``original_directory`` is set to ``[BatlPadDatabase]``.
-You must make sure to create ``${HOME}/.bob_bio_databases.txt`` file setting this
-value to the places where you actually installed the BATL database.
-"""
-
-protocol = PROTOCOL
-"""
-You may modify this at runtime by specifying the option ``--protocol`` on the
-command-line of ``spoof.py`` or using the keyword ``protocol`` on a
-configuration file that is loaded **after** this configuration resource.
-"""
-
-groups = ["train", "dev", "eval"]
-"""The default groups to use for reproducing the baselines.
-
-You may modify this at runtime by specifying the option ``--groups`` on the
-command-line of ``spoof.py`` or using the keyword ``groups`` on a
-configuration file that is loaded **after** this configuration resource.
-"""
diff --git a/bob/pad/face/config/database/batl/batl_db_depth.py b/bob/pad/face/config/database/batl/batl_db_depth.py
deleted file mode 100644
index 8170395f..00000000
--- a/bob/pad/face/config/database/batl/batl_db_depth.py
+++ /dev/null
@@ -1,54 +0,0 @@
-#!/usr/bin/env python
-"""
-BATL Db is a database for face PAD experiments.
-"""
-
-from bob.pad.face.database import BatlPadDatabase
-
-# Directory where the data files are stored.
-# This directory is given in the .bob_bio_databases.txt file located in your home directory
-ORIGINAL_DIRECTORY = "[YOUR_BATL_DB_DIRECTORY]"
-"""Value of ``~/.bob_bio_databases.txt`` for this database"""
-
-ORIGINAL_EXTENSION = ".h5" # extension of the data files
-
-ANNOTATIONS_TEMP_DIR = ""
-
-PROTOCOL = 'nowig-depth-50'
-
-database = BatlPadDatabase(
- protocol=PROTOCOL,
- original_directory=ORIGINAL_DIRECTORY,
- original_extension=ORIGINAL_EXTENSION,
- annotations_temp_dir=ANNOTATIONS_TEMP_DIR,
- landmark_detect_method="mtcnn",
- training_depends_on_protocol=True,
-)
-"""The :py:class:`bob.pad.base.database.BatlPadDatabase` derivative with BATL Db
-database settings.
-
-.. warning::
-
- This class only provides a programmatic interface to load data in an orderly
- manner, respecting usage protocols. It does **not** contain the raw
- data files. You should procure those yourself.
-
-Notice that ``original_directory`` is set to ``[BatlPadDatabase]``.
-You must make sure to create ``${HOME}/.bob_bio_databases.txt`` file setting this
-value to the places where you actually installed the BATL database.
-"""
-
-protocol = PROTOCOL
-"""
-You may modify this at runtime by specifying the option ``--protocol`` on the
-command-line of ``spoof.py`` or using the keyword ``protocol`` on a
-configuration file that is loaded **after** this configuration resource.
-"""
-
-groups = ["train", "dev", "eval"]
-"""The default groups to use for reproducing the baselines.
-
-You may modify this at runtime by specifying the option ``--groups`` on the
-command-line of ``spoof.py`` or using the keyword ``groups`` on a
-configuration file that is loaded **after** this configuration resource.
-"""
diff --git a/bob/pad/face/config/database/batl/batl_db_infrared.py b/bob/pad/face/config/database/batl/batl_db_infrared.py
deleted file mode 100644
index b96c9f33..00000000
--- a/bob/pad/face/config/database/batl/batl_db_infrared.py
+++ /dev/null
@@ -1,54 +0,0 @@
-#!/usr/bin/env python
-"""
-BATL Db is a database for face PAD experiments.
-"""
-
-from bob.pad.face.database import BatlPadDatabase
-
-# Directory where the data files are stored.
-# This directory is given in the .bob_bio_databases.txt file located in your home directory
-ORIGINAL_DIRECTORY = "[YOUR_BATL_DB_DIRECTORY]"
-"""Value of ``~/.bob_bio_databases.txt`` for this database"""
-
-ORIGINAL_EXTENSION = ".h5" # extension of the data files
-
-ANNOTATIONS_TEMP_DIR = ""
-
-PROTOCOL = 'nowig-infrared-50'
-
-database = BatlPadDatabase(
- protocol=PROTOCOL,
- original_directory=ORIGINAL_DIRECTORY,
- original_extension=ORIGINAL_EXTENSION,
- annotations_temp_dir=ANNOTATIONS_TEMP_DIR,
- landmark_detect_method="mtcnn",
- training_depends_on_protocol=True,
-)
-"""The :py:class:`bob.pad.base.database.BatlPadDatabase` derivative with BATL Db
-database settings.
-
-.. warning::
-
- This class only provides a programmatic interface to load data in an orderly
- manner, respecting usage protocols. It does **not** contain the raw
- data files. You should procure those yourself.
-
-Notice that ``original_directory`` is set to ``[BatlPadDatabase]``.
-You must make sure to create ``${HOME}/.bob_bio_databases.txt`` file setting this
-value to the places where you actually installed the BATL database.
-"""
-
-protocol = PROTOCOL
-"""
-You may modify this at runtime by specifying the option ``--protocol`` on the
-command-line of ``spoof.py`` or using the keyword ``protocol`` on a
-configuration file that is loaded **after** this configuration resource.
-"""
-
-groups = ["train", "dev", "eval"]
-"""The default groups to use for reproducing the baselines.
-
-You may modify this at runtime by specifying the option ``--groups`` on the
-command-line of ``spoof.py`` or using the keyword ``groups`` on a
-configuration file that is loaded **after** this configuration resource.
-"""
diff --git a/bob/pad/face/config/database/batl/batl_db_rgb_ir_d_grandtest.py b/bob/pad/face/config/database/batl/batl_db_rgb_ir_d_grandtest.py
deleted file mode 100644
index 6fc2381b..00000000
--- a/bob/pad/face/config/database/batl/batl_db_rgb_ir_d_grandtest.py
+++ /dev/null
@@ -1,55 +0,0 @@
-#!/usr/bin/env python
-"""
-Idiap BATL DB is a database for face PAD experiments.
-"""
-
-from bob.pad.face.database import BatlPadDatabase
-
-# Directory where the data files are stored.
-# This directory is given in the .bob_bio_databases.txt file located in your home directory
-ORIGINAL_DIRECTORY = "[YOUR_BATL_DB_DIRECTORY]"
-"""Value of ``~/.bob_bio_databases.txt`` for this database"""
-
-ORIGINAL_EXTENSION = ".h5" # extension of the data files
-
-ANNOTATIONS_TEMP_DIR = "" # NOTE: this variable is NOT assigned in the instance of the BatlPadDatabase, thus "rc" functionality defined in bob.extension will be involved
-
-PROTOCOL = 'grandtest-color*infrared*depth-10' # use 10 frames for PAD experiments
-
-database = BatlPadDatabase(
- protocol=PROTOCOL,
- original_directory=ORIGINAL_DIRECTORY,
- original_extension=ORIGINAL_EXTENSION,
- landmark_detect_method="mtcnn", # detect annotations using mtcnn
- exclude_attacks_list=['makeup'],
- exclude_pai_all_sets=True, # exclude makeup from all the sets, which is the default behavior for grandtest protocol
- append_color_face_roi_annot=False) # do not append annotations, defining ROI in the cropped face image, to the dictionary of annotations
-
-"""The :py:class:`bob.pad.base.database.BatlPadDatabase` derivative with BATL Db
-database settings.
-
-.. warning::
-
- This class only provides a programmatic interface to load data in an orderly
- manner, respecting usage protocols. It does **not** contain the raw
- data files. You should procure those yourself.
-
-Notice that ``original_directory`` is set to ``[YOUR_BATL_DB_DIRECTORY]``.
-You must make sure to create ``${HOME}/.bob_bio_databases.txt`` file setting this
-value to the places where you actually installed the BATL Govt database.
-"""
-
-protocol = PROTOCOL
-"""
-You may modify this at runtime by specifying the option ``--protocol`` on the
-command-line of ``spoof.py`` or using the keyword ``protocol`` on a
-configuration file that is loaded **after** this configuration resource.
-"""
-
-groups = ["train", "dev", "eval"]
-"""The default groups to use for reproducing the baselines.
-
-You may modify this at runtime by specifying the option ``--groups`` on the
-command-line of ``spoof.py`` or using the keyword ``groups`` on a
-configuration file that is loaded **after** this configuration resource.
-"""
diff --git a/bob/pad/face/config/database/batl/batl_db_thermal.py b/bob/pad/face/config/database/batl/batl_db_thermal.py
deleted file mode 100644
index e84c070e..00000000
--- a/bob/pad/face/config/database/batl/batl_db_thermal.py
+++ /dev/null
@@ -1,54 +0,0 @@
-#!/usr/bin/env python
-"""
-BATL Db is a database for face PAD experiments.
-"""
-
-from bob.pad.face.database import BatlPadDatabase
-
-# Directory where the data files are stored.
-# This directory is given in the .bob_bio_databases.txt file located in your home directory
-ORIGINAL_DIRECTORY = "[YOUR_BATL_DB_DIRECTORY]"
-"""Value of ``~/.bob_bio_databases.txt`` for this database"""
-
-ORIGINAL_EXTENSION = ".h5" # extension of the data files
-
-ANNOTATIONS_TEMP_DIR = ""
-
-PROTOCOL = 'nowig-thermal-50'
-
-database = BatlPadDatabase(
- protocol=PROTOCOL,
- original_directory=ORIGINAL_DIRECTORY,
- original_extension=ORIGINAL_EXTENSION,
- annotations_temp_dir=ANNOTATIONS_TEMP_DIR,
- landmark_detect_method="mtcnn",
- training_depends_on_protocol=True,
-)
-"""The :py:class:`bob.pad.base.database.BatlPadDatabase` derivative with BATL Db
-database settings.
-
-.. warning::
-
- This class only provides a programmatic interface to load data in an orderly
- manner, respecting usage protocols. It does **not** contain the raw
- data files. You should procure those yourself.
-
-Notice that ``original_directory`` is set to ``[BatlPadDatabase]``.
-You must make sure to create ``${HOME}/.bob_bio_databases.txt`` file setting this
-value to the places where you actually installed the BATL database.
-"""
-
-protocol = PROTOCOL
-"""
-You may modify this at runtime by specifying the option ``--protocol`` on the
-command-line of ``spoof.py`` or using the keyword ``protocol`` on a
-configuration file that is loaded **after** this configuration resource.
-"""
-
-groups = ["train", "dev", "eval"]
-"""The default groups to use for reproducing the baselines.
-
-You may modify this at runtime by specifying the option ``--groups`` on the
-command-line of ``spoof.py`` or using the keyword ``groups`` on a
-configuration file that is loaded **after** this configuration resource.
-"""
diff --git a/bob/pad/face/config/extractor/__init__.py b/bob/pad/face/config/extractor/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/bob/pad/face/config/extractor/frame_diff_features.py b/bob/pad/face/config/extractor/frame_diff_features.py
deleted file mode 100644
index cd148678..00000000
--- a/bob/pad/face/config/extractor/frame_diff_features.py
+++ /dev/null
@@ -1,12 +0,0 @@
-#!/usr/bin/env python
-
-from bob.pad.face.extractor import FrameDiffFeatures
-
-#=======================================================================================
-# Define instances here:
-
-window_size = 20
-overlap = 0
-
-frame_diff_feat_extr_w20_over0 = FrameDiffFeatures(
- window_size=window_size, overlap=overlap)
diff --git a/bob/pad/face/config/extractor/optical_flow.py b/bob/pad/face/config/extractor/optical_flow.py
deleted file mode 100644
index 895a4d9f..00000000
--- a/bob/pad/face/config/extractor/optical_flow.py
+++ /dev/null
@@ -1,4 +0,0 @@
-from bob.bio.base.extractor import CallableExtractor
-from bob.pad.face.extractor import OpticalFlow
-
-extractor = CallableExtractor(OpticalFlow())
diff --git a/bob/pad/face/config/extractor/video_lbp_histogram.py b/bob/pad/face/config/extractor/video_lbp_histogram.py
deleted file mode 100644
index 7fc7bcb1..00000000
--- a/bob/pad/face/config/extractor/video_lbp_histogram.py
+++ /dev/null
@@ -1,23 +0,0 @@
-#!/usr/bin/env python
-
-from bob.pad.face.extractor import LBPHistogram
-
-from bob.bio.video.extractor import Wrapper
-
-#=======================================================================================
-# Define instances here:
-
-lbptype = 'uniform'
-elbptype = 'regular'
-rad = 1
-neighbors = 8
-circ = False
-dtype = None
-
-video_lbp_histogram_extractor_n8r1_uniform = Wrapper(LBPHistogram(
- lbptype=lbptype,
- elbptype=elbptype,
- rad=rad,
- neighbors=neighbors,
- circ=circ,
- dtype=dtype))
diff --git a/bob/pad/face/config/extractor/video_quality_measure.py b/bob/pad/face/config/extractor/video_quality_measure.py
deleted file mode 100644
index fc9af42b..00000000
--- a/bob/pad/face/config/extractor/video_quality_measure.py
+++ /dev/null
@@ -1,14 +0,0 @@
-#!/usr/bin/env python
-
-from bob.pad.face.extractor import ImageQualityMeasure
-
-import bob.bio.video
-
-#=======================================================================================
-# Define instances here:
-
-galbally = True
-msu = True
-dtype = None
-
-video_quality_measure_galbally_msu = bob.bio.video.extractor.Wrapper(ImageQualityMeasure(galbally=galbally, msu=msu, dtype=dtype))
diff --git a/bob/pad/face/config/frame_diff_svm.py b/bob/pad/face/config/frame_diff_svm.py
deleted file mode 100644
index 53b500ab..00000000
--- a/bob/pad/face/config/frame_diff_svm.py
+++ /dev/null
@@ -1,86 +0,0 @@
-#!/usr/bin/env python2
-# -*- coding: utf-8 -*-
-"""
-This file contains configurations to run Frame Differences and SVM based face PAD baseline.
-The settings are tuned for the Replay-attack database.
-The idea of the algorithms is inherited from the following paper: [AM11]_.
-"""
-
-#=======================================================================================
-sub_directory = 'frame_diff_svm'
-"""
-Sub-directory where results will be placed.
-
-You may change this setting using the ``--sub-directory`` command-line option
-or the attribute ``sub_directory`` in a configuration file loaded **after**
-this resource.
-"""
-
-#=======================================================================================
-# define preprocessor:
-
-from ..preprocessor import FrameDifference
-
-NUMBER_OF_FRAMES = None # process all frames
-MIN_FACE_SIZE = 50 # Minimal size of the face to consider
-
-preprocessor = FrameDifference(
- number_of_frames=NUMBER_OF_FRAMES,
- min_face_size=MIN_FACE_SIZE)
-"""
-In the preprocessing stage the frame differences are computed for both facial and non-facial/background
-regions. In this case all frames of the input video are considered, which is defined by
-``number_of_frames = None``. The frames containing faces of the size below ``min_face_size = 50`` threshold
-are discarded. Both RGB and gray-scale videos are acceptable by the preprocessor.
-The preprocessing idea is introduced in [AM11]_.
-"""
-
-#=======================================================================================
-# define extractor:
-
-from ..extractor import FrameDiffFeatures
-
-WINDOW_SIZE = 20
-OVERLAP = 0
-
-extractor = FrameDiffFeatures(window_size=WINDOW_SIZE, overlap=OVERLAP)
-"""
-In the feature extraction stage 5 features are extracted for all non-overlapping windows in
-the Frame Difference input signals. Five features are computed for each of windows in the
-facial face regions, the same is done for non-facial regions. The non-overlapping option
-is controlled by ``overlap = 0``. The length of the window is defined by ``window_size``
-argument.
-The features are introduced in the following paper: [AM11]_.
-"""
-
-#=======================================================================================
-# define algorithm:
-
-from bob.pad.base.algorithm import SVM
-
-MACHINE_TYPE = 'C_SVC'
-KERNEL_TYPE = 'RBF'
-N_SAMPLES = 10000
-TRAINER_GRID_SEARCH_PARAMS = {
- 'cost': [2**P for P in range(-3, 14, 2)],
- 'gamma': [2**P for P in range(-15, 0, 2)]
-}
-MEAN_STD_NORM_FLAG = True # enable mean-std normalization
-FRAME_LEVEL_SCORES_FLAG = True # one score per frame(!) in this case
-
-algorithm = SVM(
- machine_type=MACHINE_TYPE,
- kernel_type=KERNEL_TYPE,
- n_samples=N_SAMPLES,
- trainer_grid_search_params=TRAINER_GRID_SEARCH_PARAMS,
- mean_std_norm_flag=MEAN_STD_NORM_FLAG,
- frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-"""
-The SVM algorithm with RBF kernel is used to classify the data into *real* and *attack* classes.
-One score is produced for each frame of the input video, ``frame_level_scores_flag = True``.
-The grid search of SVM parameters is used to select the successful settings.
-The grid search is done on the subset of training data.
-The size of this subset is defined by ``n_samples`` parameter.
-
-The data is also mean-std normalized, ``mean_std_norm_flag = True``.
-"""
diff --git a/bob/pad/face/config/grid.py b/bob/pad/face/config/grid.py
deleted file mode 100644
index 74ba0141..00000000
--- a/bob/pad/face/config/grid.py
+++ /dev/null
@@ -1,50 +0,0 @@
-#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-
-from bob.bio.base.grid import Grid
-
-# Configuration to run on computation cluster:
-idiap = Grid(
- training_queue='8G-io-big',
- number_of_preprocessing_jobs=32,
- preprocessing_queue='4G-io-big',
- number_of_extraction_jobs=32,
- extraction_queue='4G-io-big',
- number_of_projection_jobs=32,
- projection_queue='4G-io-big',
- number_of_enrollment_jobs=32,
- enrollment_queue='4G-io-big',
- number_of_scoring_jobs=1,
- scoring_queue='4G-io-big',
-)
-
-# Configuration to run on user machines:
-idiap_user_machines = Grid(
- training_queue='32G',
- number_of_preprocessing_jobs=32,
- preprocessing_queue='4G',
- number_of_extraction_jobs=32,
- extraction_queue='8G',
- number_of_projection_jobs=32,
- projection_queue='8G',
- number_of_enrollment_jobs=32,
- enrollment_queue='8G',
- number_of_scoring_jobs=1,
- scoring_queue='8G',
-)
-
-
-# Configuration to run on a few computation cluster:
-small = Grid(
- training_queue='8G-io-big',
- number_of_preprocessing_jobs=8,
- preprocessing_queue='4G-io-big',
- number_of_extraction_jobs=8,
- extraction_queue='4G-io-big',
- number_of_projection_jobs=8,
- projection_queue='4G-io-big',
- number_of_enrollment_jobs=8,
- enrollment_queue='4G-io-big',
- number_of_scoring_jobs=1,
- scoring_queue='4G-io-big',
-)
diff --git a/bob/pad/face/config/lbp_64.py b/bob/pad/face/config/lbp_64.py
new file mode 100644
index 00000000..a8e60ab3
--- /dev/null
+++ b/bob/pad/face/config/lbp_64.py
@@ -0,0 +1,46 @@
+import bob.pipelines as mario
+from bob.bio.face.helpers import face_crop_solver
+from bob.bio.video import VideoLikeContainer
+from bob.bio.video.transformer import Wrapper as TransformerWrapper
+from bob.pad.face.extractor import LBPHistogram
+
+database = globals().get("database")
+if database is not None:
+ annotation_type = database.annotation_type
+ fixed_positions = database.fixed_positions
+else:
+ annotation_type = None
+ fixed_positions = None
+
+# Preprocessor #
+cropper = face_crop_solver(
+ cropped_image_size=64, cropped_positions=annotation_type, color_channel="gray"
+)
+preprocessor = TransformerWrapper(cropper)
+preprocessor = mario.wrap(
+ ["sample", "checkpoint"],
+ preprocessor,
+ transform_extra_arguments=(("annotations", "annotations"),),
+ features_dir="temp/faces-64",
+ save_func=VideoLikeContainer.save,
+ load_func=VideoLikeContainer.load,
+)
+
+# Extractor #
+extractor = TransformerWrapper(
+ LBPHistogram(
+ lbptype="uniform",
+ elbptype="regular",
+ rad=1,
+ neighbors=8,
+ circ=False,
+ dtype=None,
+ )
+)
+extractor = mario.wrap(
+ ["sample", "checkpoint"],
+ extractor,
+ features_dir="temp/iqm-features",
+ save_func=VideoLikeContainer.save,
+ load_func=VideoLikeContainer.load,
+)
diff --git a/bob/pad/face/config/lbp_lr_batl_D_T_IR.py b/bob/pad/face/config/lbp_lr_batl_D_T_IR.py
deleted file mode 100644
index 3e6b0fc7..00000000
--- a/bob/pad/face/config/lbp_lr_batl_D_T_IR.py
+++ /dev/null
@@ -1,103 +0,0 @@
-#!/usr/bin/env python2
-# -*- coding: utf-8 -*-
-"""
-This file contains configurations to run LBP and SVM based face PAD baseline.
-The settings are tuned for the Replay-attack database.
-The idea of the algorithm is introduced in the following paper: [CAM12]_.
-However some settings are different from the ones introduced in the paper.
-"""
-
-#=======================================================================================
-sub_directory = 'lbp_svm'
-"""
-Sub-directory where results will be placed.
-
-You may change this setting using the ``--sub-directory`` command-line option
-or the attribute ``sub_directory`` in a configuration file loaded **after**
-this resource.
-"""
-
-#=======================================================================================
-# define preprocessor:
-
-from ..preprocessor import FaceCropAlign
-
-from bob.bio.video.preprocessor import Wrapper
-
-from bob.bio.video.utils import FrameSelector
-
-from ..preprocessor.FaceCropAlign import auto_norm_image as _norm_func
-
-FACE_SIZE = 64 # The size of the resulting face
-RGB_OUTPUT_FLAG = False # Gray-scale output
-USE_FACE_ALIGNMENT = False # use annotations
-MAX_IMAGE_SIZE = None # no limiting here
-FACE_DETECTION_METHOD = None # use annotations
-MIN_FACE_SIZE = 50 # skip small faces
-NORMALIZATION_FUNCTION = _norm_func
-NORMALIZATION_FUNCTION_KWARGS = {}
-NORMALIZATION_FUNCTION_KWARGS = {'n_sigma':3.0, 'norm_method':'MAD'}
-
-_image_preprocessor = FaceCropAlign(face_size = FACE_SIZE,
- rgb_output_flag = RGB_OUTPUT_FLAG,
- use_face_alignment = USE_FACE_ALIGNMENT,
- max_image_size = MAX_IMAGE_SIZE,
- face_detection_method = FACE_DETECTION_METHOD,
- min_face_size = MIN_FACE_SIZE,
- normalization_function = NORMALIZATION_FUNCTION,
- normalization_function_kwargs = NORMALIZATION_FUNCTION_KWARGS)
-
-_frame_selector = FrameSelector(selection_style = "all")
-
-preprocessor = Wrapper(preprocessor = _image_preprocessor,
- frame_selector = _frame_selector)
-"""
-In the preprocessing stage the face is cropped in each frame of the input video given facial annotations.
-The size of the face is normalized to ``FACE_SIZE`` dimensions. The faces with the size
-below ``MIN_FACE_SIZE`` threshold are discarded. The preprocessor is similar to the one introduced in
-[CAM12]_, which is defined by ``FACE_DETECTION_METHOD = None``.
-"""
-
-#=======================================================================================
-# define extractor:
-
-from ..extractor import LBPHistogram
-
-from bob.bio.video.extractor import Wrapper
-
-LBPTYPE = 'uniform'
-ELBPTYPE = 'regular'
-RAD = 1
-NEIGHBORS = 8
-CIRC = False
-DTYPE = None
-
-extractor = Wrapper(LBPHistogram(
- lbptype=LBPTYPE,
- elbptype=ELBPTYPE,
- rad=RAD,
- neighbors=NEIGHBORS,
- circ=CIRC,
- dtype=DTYPE))
-"""
-In the feature extraction stage the LBP histograms are extracted from each frame of the preprocessed video.
-
-The parameters are similar to the ones introduced in [CAM12]_.
-"""
-
-#=======================================================================================
-# define algorithm:
-
-from bob.pad.base.algorithm import LogRegr
-
-C = 1. # The regularization parameter for the LR classifier
-FRAME_LEVEL_SCORES_FLAG = True # Return one score per frame
-
-algorithm = LogRegr(
- C=C, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-"""
-The Logistic Regression is used to classify the data into *real* and *attack* classes.
-One score is produced for each frame of the input video, ``frame_level_scores_flag = True``.
-The sub-sampling of training data is not used here, sub-sampling flags have default ``False``
-values.
-"""
diff --git a/bob/pad/face/config/lbp_svm.py b/bob/pad/face/config/lbp_svm.py
deleted file mode 100644
index b42dfec5..00000000
--- a/bob/pad/face/config/lbp_svm.py
+++ /dev/null
@@ -1,113 +0,0 @@
-"""
-This file contains configurations to run LBP and SVM based face PAD baseline.
-The settings are tuned for the Replay-attack database.
-The idea of the algorithm is introduced in the following paper: [CAM12]_.
-However some settings are different from the ones introduced in the paper.
-"""
-
-# =======================================================================================
-sub_directory = "lbp_svm"
-"""
-Sub-directory where results will be placed.
-
-You may change this setting using the ``--sub-directory`` command-line option
-or the attribute ``sub_directory`` in a configuration file loaded **after**
-this resource.
-"""
-
-# =======================================================================================
-# define preprocessor:
-
-from ..preprocessor import FaceCropAlign
-
-from bob.bio.video.preprocessor import Wrapper
-
-from bob.bio.video.utils import FrameSelector
-
-# check if a database is loaded first
-if "database" in locals():
- annotation_type = database.annotation_type
- fixed_positions = database.fixed_positions
-else:
- annotation_type = None
- fixed_positions = None
-
-cropped_image_size = 64 # The size of the resulting face
-
-_image_preprocessor = face_crop_solver(
- cropped_image_size=64, cropped_positions=annotation_type, color_channel="gray"
-)
-
-_frame_selector = FrameSelector(selection_style="all")
-
-preprocessor = Wrapper(preprocessor=_image_preprocessor, frame_selector=_frame_selector)
-"""
-In the preprocessing stage the face is cropped in each frame of the input video given facial annotations.
-The size of the face is normalized to ``FACE_SIZE`` dimensions. The faces with the size
-below ``MIN_FACE_SIZE`` threshold are discarded. The preprocessor is similar to the one introduced in
-[CAM12]_, which is defined by ``FACE_DETECTION_METHOD = None``.
-"""
-
-# =======================================================================================
-# define extractor:
-
-from ..extractor import LBPHistogram
-
-from bob.bio.video.extractor import Wrapper
-
-LBPTYPE = "uniform"
-ELBPTYPE = "regular"
-RAD = 1
-NEIGHBORS = 8
-CIRC = False
-DTYPE = None
-
-extractor = Wrapper(
- LBPHistogram(
- lbptype=LBPTYPE,
- elbptype=ELBPTYPE,
- rad=RAD,
- neighbors=NEIGHBORS,
- circ=CIRC,
- dtype=DTYPE,
- )
-)
-"""
-In the feature extraction stage the LBP histograms are extracted from each frame of the preprocessed video.
-
-The parameters are similar to the ones introduced in [CAM12]_.
-"""
-
-# =======================================================================================
-# define algorithm:
-
-from bob.pad.base.algorithm import SVM
-
-MACHINE_TYPE = "C_SVC"
-KERNEL_TYPE = "RBF"
-N_SAMPLES = 10000
-TRAINER_GRID_SEARCH_PARAMS = {
- "cost": [2 ** P for P in range(-3, 14, 2)],
- "gamma": [2 ** P for P in range(-15, 0, 2)],
-}
-MEAN_STD_NORM_FLAG = True # enable mean-std normalization
-FRAME_LEVEL_SCORES_FLAG = True # one score per frame(!) in this case
-
-algorithm = SVM(
- machine_type=MACHINE_TYPE,
- kernel_type=KERNEL_TYPE,
- n_samples=N_SAMPLES,
- trainer_grid_search_params=TRAINER_GRID_SEARCH_PARAMS,
- mean_std_norm_flag=MEAN_STD_NORM_FLAG,
- frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG,
-)
-"""
-The SVM algorithm with RBF kernel is used to classify the data into *real* and *attack* classes.
-One score is produced for each frame of the input video, ``frame_level_scores_flag = True``.
-
-In contrast to [CAM12]_, the grid search of SVM parameters is used to select the
-successful settings. The grid search is done on the subset of training data. The size
-of this subset is defined by ``n_samples`` parameter.
-
-The data is also mean-std normalized, ``mean_std_norm_flag = True``.
-"""
diff --git a/bob/pad/face/config/maskattack.py b/bob/pad/face/config/maskattack.py
index 173522bc..9f7cfa02 100644
--- a/bob/pad/face/config/maskattack.py
+++ b/bob/pad/face/config/maskattack.py
@@ -1,14 +1,11 @@
from bob.pad.face.database import MaskAttackPadDatabase
+from bob.pad.base.pipelines.vanilla_pad import DatabaseConnector
+from bob.extension import rc
-# Directory where the data files are stored.
-# This directory is given in the .bob_bio_databases.txt file located in your home directory
-original_directory = "[YOUR_MASK_ATTACK_DIRECTORY]"
-"""Value of ``~/.bob_bio_databases.txt`` for this database"""
-
-original_extension = ".avi" # extension is not used to load the data in the HLDI of this database
-
-database = MaskAttackPadDatabase(
- protocol='classification',
- original_directory=original_directory,
- original_extension=original_extension,
+database = DatabaseConnector(
+ MaskAttackPadDatabase(
+ protocol="classification",
+ original_directory=rc.get("bob.db.maskattack.directory"),
+ original_extension=".avi",
+ )
)
diff --git a/bob/pad/face/config/mifs.py b/bob/pad/face/config/mifs.py
index 45c29fa7..b1a8572d 100644
--- a/bob/pad/face/config/mifs.py
+++ b/bob/pad/face/config/mifs.py
@@ -1,4 +1,3 @@
-#!/usr/bin/env python
"""`MIFS`_ is a face makeup spoofing database adapted for face PAD experiments.
Database assembled from a dataset consisting of 107 makeup-transformations taken
@@ -16,49 +15,14 @@ the link.
.. include:: links.rst
"""
-
from bob.pad.face.database import MIFSPadDatabase
-
-# Directory where the data files are stored.
-# This directory is given in the .bob_bio_databases.txt file located in your home directory
-ORIGINAL_DIRECTORY = "[YOUR_MIFS_DATABASE_DIRECTORY]"
-"""Value of ``~/.bob_bio_databases.txt`` for this database"""
-
-ORIGINAL_EXTENSION = "" # extension of the data files
-
-database = MIFSPadDatabase(
- protocol='grandtest',
- original_directory=ORIGINAL_DIRECTORY,
- original_extension=ORIGINAL_EXTENSION,
- training_depends_on_protocol=True,
+from bob.pad.base.pipelines.vanilla_pad import DatabaseConnector
+from bob.extension import rc
+
+database = DatabaseConnector(
+ MIFSPadDatabase(
+ protocol="grandtest",
+ original_directory=rc.get("bob.db.mifs.directory"),
+ original_extension="",
+ )
)
-"""The :py:class:`bob.pad.base.database.PadDatabase` derivative with Replayattack
-database settings
-
-.. warning::
-
- This class only provides a programmatic interface to load data in an orderly
- manner, respecting usage protocols. It does **not** contain the raw
- data files. You should procure those yourself.
-
-Notice that ``original_directory`` is set to ``[YOUR_MIFS_DATABASE_DIRECTORY]``.
-You must make sure to create ``${HOME}/.bob_bio_databases.txt`` setting this
-value to the place where you actually installed the Replayattack Database, as
-explained in the section :ref:`bob.pad.face.baselines`.
-"""
-
-protocol = 'grandtest'
-"""The default protocol to use for reproducing the baselines.
-
-You may modify this at runtime by specifying the option ``--protocol`` on the
-command-line of ``spoof.py`` or using the keyword ``protocol`` on a
-configuration file that is loaded **after** this configuration resource.
-"""
-
-groups = ["train", "dev", "eval"]
-"""The default groups to use for reproducing the baselines.
-
-You may modify this at runtime by specifying the option ``--groups`` on the
-command-line of ``spoof.py`` or using the keyword ``groups`` on a
-configuration file that is loaded **after** this configuration resource.
-"""
diff --git a/bob/pad/face/config/preprocessor/__init__.py b/bob/pad/face/config/preprocessor/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/bob/pad/face/config/preprocessor/dictionaries/dictionary_front_10_5_128.hdf5 b/bob/pad/face/config/preprocessor/dictionaries/dictionary_front_10_5_128.hdf5
deleted file mode 100644
index c45383582a2741a9ec7b4cf72b3e4b0789ac9b26..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001
literal 27744
zcmeFYc{G*Z`!|jTV=AISA}N%}&>(cB5E4lxLqao|l8A~T6=jIXn0cP(`8tPlj^j9v
zbIemRRZ=M$lp)XgJfHV_t>1e7{H^u;@ms&|IcuH0@B7~OzW2S)wXflIz4lW*ZQWIT
zoA@~X^Wov)SkAHLe^>wgS^DQ=bK}2f{yqNpy3Ico&VR}$|CE_r|E+Lx{O8NTvEiTd
zYyav0|7>^ql#UJuyXC+A|6BfZ8^@mI|E=--d*Xk}|6k33o{rYZ|Ge<If8P3EDW(1+
zXa7#dCEHuJ|Ed2gk9z-J_<vb)a;*GsU;lK-$szl{d*bB!?+M?3`rY_X`F|cSV@v*j
zTCSA@j{nN;VGdP(4%MY)|Gi-a`|N+Nb8-E*L!N)yFXOcNSJW^4x8;9-h5qT~Klc#V
zwwiNrT>F1s{=Z%~H8R%z&x-zcaFYML$N%*Q|IY*dX9oUf2L3-c1IA)jF`Unvp}r{c
zWu$L2xQWZD$XPdo-+)C%@B$TRDc=G`_ccQ^lRv|{l?s=y1XikFXa<8f#O}7>W*Gjk
zN}6${8Lr+e{9NPS496<|ymPf{hK-NAqkosN`-Sp2-GpYa+_+Ffd(aH3@el4^)oF&U
za%0D3?>57ro6`%aQFi;Vn_(dVG<aklx++(m1`gMHu7td&!U@BN-^TaTfVlnOZk7ZM
zJS2%l@_IB_xBPX{zO6Jc`m%k4fOj+GA5d7a=PUy~Te<?5(-;{1NIbtXn}H?jhplM4
z8F(YyO1d|w6%X%T+5B^ihW8hEPk46HakE@No@YEAFA|+{vOMT`YPy-<C%pw{C%$IT
zk2Rx|>CBRtax-$8B$-`$(~P@E=4zt%HzTb;Q^jQm746cbyF*S>a6wjr{}F!^UJ|bS
z+{s79gS{i^AsrMfd0P2|Kp>;?aQ^VjI2qeMUY1ssC!ze5spR|TBsA|n%b&H2gp8x3
zJAVr`;*EO>HTykUap%D20h)g+5@}8W!fP3LbN4f`eFIE<)e_spd!Zd~?wU)>_HMze
zcy4iLB@MgQ^^QtRw&2mbYxSbX7#O>8%eN;y?PxRq>vpd{886wTJlS=CiWjr{9vshW
z!tcIs@*k|CA?eDHiO*vOTH6Zw`b0OPzlckh*WE^Zti`&f`K%E)oj2?9KTX1*8-Ipf
z8=LSV<+<_e^m=qi^mt<bg@{iCN(Z;k5OC!}n$~m6dffGcqoRLr1KyzbUvyM!L%*cx
z_Gjs>SYmzYfbZ2-lx}U8kNiW&DNUcXDzS8Y^x%hfQ!W#~Dw*F-|3JqhKrq*fW?*=n
zx#?}L7AzA~V#vOx;X97J)-%WHxO!|T>P{RD&()3n4ctq`*(c&+H~c9WqQ5x!`cM<<
zr_wYMCn@M)v{l;iA_)@)f(_-&Nq9=$>3-O65-RZ7y^NEg;E&dhgSHVwbiPGCUGC9<
zS^CX^9yyKJ#w&EyQ@06uL^t&3v**V!I8ZlFzXeAP7VPRRS}-8sQE}XI8eXZrx#XAC
zgwGfeVcPe}*fsuVt@{!aPX)K8dmN(U-Rn`Lv4;#?X*a#{+h8+Z(YzNmW8Q+{Pj-p%
zWVGS1i=^A+@iyEtPCU@()QWb&Q#bkLT2Pgwwy*DKGj{*kYW!tID~5|@kCXS(u#jQd
z@V<wNJ0r3-7*QzLLjLV8#Ye)AS+C280gbr2XbW)*jf@&fMWI2)btszuOTEdZ9<ScL
zplf-Qh!IE99&WzXitBftRqV22k7v)qv6~OcxPNbStHCNF&RKUI+smrQ#@yG1T!Acf
z+PSokM~sOSgPW;I(d=<fYQ&yuprM}r#~)u(D46PEctc`<g?Bu|pDjFO;Y)v;@2i+?
zxMzCbUnyrgdZwn9IX!L057lo@KZ$F`-jb@n-({)D96pc};?abXOzp6mVk#Q-$InJo
zH({-$!rVNOh=1;ONCuS?@lD&ZkB@E=QDI)F+<T6IT7OS<Y?i6VQV$DT?fO(aPWS$P
zRh@wQ)VZA64z!}%kl<C(D{V+@bkfaFWTNE}IpfJQHMn>B_-5H~0&XcXl=Hs8LeD$X
z_Y_UrkW##yypPJnpy=(jy^}Tg@zA5>n-^$!+Fknc^(}PVRHpaIbUO<L$J@v**p5|I
z5iz_IO_)FDBrD)f!6cK0(Vj&D?$oqAmEA_ej-LFDQ>&RccriHY)-e)V9nxHDtVBR5
zJ$E<3$Wm1AQCcuQOhm!Q-eX&}8K}pUsJQmJ8mI5`U()|Yz?m`WL-&oS*c70VsQ!S6
zcWzwd3D<4J3zCDAGlgW_Mcpgt7)ZvdTMBQd^*hmP>zUbayV>%2(o{R$pDowtZ9FN@
zTF~v<i1Iph7T&%pZ_&DriT?4*R*$|gabWJR;t%dNT)LspbTMOK{bC^Rrn(kfZ|X9u
zmPN!3K{w`}zM-MzkKjL<Y`qz&by72CmV%clwz`Za0zP}<yecxN6lJFFk?8dexak9L
zL{<p{_e5`uKax~~#Xpb5KK)8U*LhZ~fnhTiXTxjdTWmd9VVw3@s{wO!6K|Qb^_g;S
zMppA}DjsltUh%BC3w6W}uQ@YA!m~f$e{kX?;qUkittmAsKH++ydNF~8a@D1LS3TR%
z>k{u`{6-cUw%KoD*|6}(s_WSi4EFnabjghmP?1BhRZX{_h<^_<=kGZ+qud97^Nb`q
zsuwQrjnXILPiNiL_f}P+YHEZz(XJ3JjL$!y<r2~Ss<Qvd6)kweStIR}Up<)gl_*U(
zlVI86a~oDq6GX@ti|zl}2=OQO&Wngqz&f>Hjr@2E+%;zwo|I?-ZI`td9WS=S&BgMd
zji;I+p)E7eY<DwoGtI*U_1j?El(q}+hjxf)ZX5{JV1cUfNY2%*EO4!!4D5PO2Z{Rv
zD>itrAT;s>OQyaBbl%#V`K@h&G9`=CLM$q{%J_Q5^wZ(~5RaBve-ez-LKe&Hogun(
zpn>mNIA%D%el&I_AAWPGSxfhm!G2fy?YnF_+AgbeV!0{}r{v6UsTH;2j<KC6YEy^(
z6Cti+VFc`JDOIz(-+^7#JfG*6b>S=FzFTW2yD(R4oli?V6Mt?~(HR_W!-QuR$19^*
z_*9?xOP;|*&n?L%>dx($w92<qh_?kVZu~BIU5JP(A5>=k@>Ju?^@V!%Md^6%#H_s8
zQZ3#`y2Wwv25gB8eyG<}fy%7suRLk5@Tl+6O^5mmLF6%qw0bHDUK{>7)6`fClWN<q
zd^=JANBmD;Q$3WA@(ssKKku!>AF^wT6K)aFO6=Ulcw@xXZ|KKvQA>~_I-M{g!?ueW
zTo+_)Tad}QnrriG7WV0=$EUZ`(5(3v-x(Pi{*4wVEseC{Bs{3h&S0T7xo<FTj)|K0
zJ};B<VBm>xi4(iC+feh)L*DdQCTdAsQJ1*bg!D+&gKa`2^gp=bN%43K8f@+%b9A)g
zXR8t~n>}RI4;J2Nbe4d64NXCN^vf`qe)9JIj|~{=*_IPRZ9#c6*Y-cPOnjOr6O(?A
zEzh*;8>P>7As6l3xbN*&eCGbVH0mx3t=GM`EFEh`mRixIK`ITS@>xzo>w7RiH6UDO
zWeZvmOQp=%>z6TL-$8yzK;oI)HU4Yb@T*Gy9dQ>Lj>(Uz=Lk0AUbkI`*5?rM;bUi|
zEYU{PwQ}c-h>pZ~X$Qvg;B2JGHhz!3gZS(A+v_j3S0I0G(zSQnn$U*BsN<(aEt;F$
z*gw4(ibq<i_4vFBP*dLPA-^yg5Be-DySSqiw;z++<y=>aTTXxPJnLD8;|D9}Y!!-d
zR>V4S^^h0py-VVI>($0S{&~Jlz6l>ZdwJ!+4hG(*cx<U3rC>$YQ~z&A+4iB7sGVik
zjyLYeAHz{Pwr-!YEdAb&edkX<;)-j*spEZ<4`o>RZtk{YAX^?K8kbw#o@L^t$_Hj<
zV@<d}Bj-}g3>BkXt>;CJJ8+3X-?VQZ5r=L5ZW$`7LHbU^vqF!mut`n8#oLyGCX1KG
zDesza!BeqlpfMD77GHZOa-|#+HoEGat!;pXv9jJ<L^=#SH;VnWt`6K+hnVaOsDvGd
z6SL_Kg>bKAU%Ve*Eew|n`pD`xf`nxHr}E!TknXqFCG|`*Bu>d_8J#<WFU*q5tHUW^
zR@%{0u1tiWr9u0YTkVj(hxg;XI|RtODR?c@y9qu9zPDKGQVX+9j?6#ny1;%-JmbUV
zA^3Gqqx|phQuv_y`50Tl!jjH6jx#)Dm@A0y487C|)N`kaJw;t0AW026b}beTINp3%
zTw4l{S8Xq5{%8RCOm!QvnpP;z$T;*`q#W{l_pNypPKUKR&EG^X5g^)>JXu5{z=o1I
z^@)|u;2cdBKWWem6PxuXrTN=n@zaKnIksgW89a69MnwyJ7X7p=Xod=p5`N^4t?vYj
zC4KRlwFJ13`%8i0&<^E0A0&?4sRjq9%~A)O8L+^u%~*eD01njJFFqVi2A|<Um%BUT
zKx3z~jIMMY>|7Vf=eV2!{G)L^q_-?^&yL-9Ydj5Q)uRG?Zdc&MNSxt=-PL&ARIl{N
zEE#j0Vx<mKqjB$nn>(|wwBYai_Ranr1U&QQ_Yv<6L=;t>*x*k?^b;4**)CLt2?bg$
zd>$RB-kQB*P_7iyBkOfmE;Xa{XQ5I*M<U+0+!s+yVc~kq7yTVebUeH9`{|}?CLTTV
zXZi790#-|@Fm!#GC{Q9|6nAL=sbw}tIFD7}@8URMbk*RFS7ynXCn;!rZEx4FE*3iQ
zuhea+?ZBkh8v7J|i*X&<aw5905ZP+EKlf%b=4eaMvM0*f{$S}LwcbWR#T~el!oYon
z^Q#%!Rk#qeNz5%b8~G2~jC+qDc8u;NN_$n~n!H?ri{)&69MjJ1o~XhYvGWG<DlPa;
zlu&wDtR4rYhDNT%F_CUJR!#0@pz4#j=c>tVc(*Yq$>UrT{`P78mKM%Ll^?~wEQkBC
z*c0QjOG|O&1@}G!sXP?gyrsXOm4bWsUnGsyx8s`m{x@$4?YOwVF8P^WHrOvE9&sBf
zgqrPJmn=TzL2DLi^VPXp5bN%cZQmY&;m1R4ug)_-B|l7|Cz1q*T!droQc6LtQ<s%;
zoB)?p{^WeEA%KI!$KTfGy|D7{h^Dp)3FfbUnX@o$gSyQ*?w+<3sMKL@zn#s3@o+Dv
zT)S2<xi#|Ru_+U@WPRNnT$*90L7j<(1QRH*_S|lle&~%nxalwu@JT@J_WRngm@Tuo
z=8jZ7)F;&hT9Rqd^W!j`7DR{jqPE7%g_w}h_%89nG#TQBl;1oEr9pF%V(;rrBDi_|
z&|ETMf<|7O>ZsN$q(9WwO50ryWT~A$qP@zYwD{d@p++hsu0L_s?<*B*WJ{I?xtlRo
zS6eY=oWizaQKfCYWORu(KHrjAiY|^E*VV-+$n*D#P1X$>_Fleqr|DiB_J)S<;o8eY
zzGJ*8HPuwiMXF)$+IF<awU~B1O+@9bkIzc8^?HP{=3|fCPOO<1FIEcZM73z~@-lC>
z-Bd~*oK1So*5_H|AK%h(Q_`RBt|4`>z3~B6WFiBssqZA|f2mMtTmIRn;tmckO^N5$
zS>d31w+H=U4NlZ~p5^>@7kAnT=$6}5VqD*#+~r6rt`t4X+nAh##J-EKSF_`RgK_>3
zC)X6BrO>0>N7!<_H6Zf5%c~aLUTYyt86)8l#doI@N4wB}@#k9g1O`5s&_Ce#YYc5C
zX|u(ILin9<oW6?^k9#*C5#Wewz|w2lf@;Y$RBFifw3_b5nXTeo9|RRZesu4XKRd!O
zsoY(1w^|`AoVPjVdZiA7c9zfGS!U1n8%Se+=u|8WbG~@3gMb5Z0;yr08OY#GwX0#&
zV}F;xvhm3xj7P&)I||#7xiv)Km~J(Setc=VT#<rNTbJ$A6>dfyot}v&;Vh&(rTLFN
zrr{A{V(gtSWZa~mE#?|d#H>?0ra!K!#|sZ)0(UHpqRrDOyI0)BK#_Bw;C;uoXCEr7
zn5B()swvnapS|wf%-(r2w)bJ)1+LeB^$UULc~|mj*+$^x<NPUlgAVPtML*cDDT2%}
zl3kcQ5#FDweEy@Ch7u3LEW3w_@Dg2BY`7p5?fs2q$o*O1)-NJvkY5Z250_e^_OxS-
zVfa$mObc$9PmzuvZbXO6x2(sk={S*Ll^B*o#kZQ)Tl4G~_{zen?bC-A412r6*_Vfg
zyYzge?vdE>t4Psyfjw`K*Hz4<y1od;w6=Q_%SzFUf3xluIsrH4jYxAIrsKge!_4HR
zUOcRSBFN!w93=ciLn-B4<bFcxb<U#Tj*>MZwee+WooxK_T0a@9)5VT<S2E$MTj`}P
z1{vt~*K#|Llml_~Z*jUu5v1)~>Kfga3LX((zJ5~fhF~2z+2p?zkiL?1sCS48vHf=6
z5BW4fZ4EEu?=TIHxz)aXdw~f?-bYVMoNa-bsHZ#%f61^|wg0TG6dArOdZqDQc>{SH
zo(tUH_8Mo@xAjX|<w5zXmu|#b5|ox*7&l_uY3|5Jirz9EVA%Dw-iwVd_-ykzH=aep
zeEr|+#RLiXQQs)3+8_sN(&IQ^%@o3Xie;8jRxRqBQtf=njw{0gVEpN122R)LJ@&m9
z4%eMYRfbv!XRgL6P<-N1e98Hs?87X)VEJ8HI3f)@_6C<lkV@dg+v|r$3fe(^b=|pZ
z4_<*csoXSXsRZN<rV<Ws$%e`B=Z%Eo1|XHmQ0LbTLt=0Nw{KK7rc|3*g+-TxamL}t
zX8jB}H|#MpHP-_Z8@-NSHXR0~@x`GJ{BPl=g6uEOb)B$5VCxpsBr51K{L4Sr69Bp_
zDUZcMVSS!@xR6&5h_rCI=mn9X=tVZi_gpG`4iy~kD<Xo~#c|_RH;XW9OvWU<s2#jA
zzCSLyN`suyIQ~W@Djd6+w0&;A95gz&9bWnw39Ho)9?7_$1Ds0T?v-X;Q1;YfaM@T3
z(7)GcHL%$BN<Geca;_3XTpQ?8?;=4Y54>r&3sGf$A=(5J0Qk7=ubqg1Ta&w1x*kCk
zs3|o4XjcOYiPmbH)SEDhd+~dlHWS-h=d)jUu`n|E$bijA6GHhb$IG7@pqm(^UDw$P
zC9T=q#~(k&-LbOM3T*jb|JCoOXJayKY@Yf)6GVU;TEpwLMH<-atH8%^E(WHCDgDAB
z9aw$N_flmc73;G?1IK1rU{^j(<6Qp~Yf|6w@V{?DrO9_oS?>{3<@3&;Insw0DvLAT
zysiZC?_MK|=~Xa7|L9OsRgDWol{hDD8avMTwSBcrA4)IA-AJRpg5UA$x%qZcp<V}y
z;Cdq1{aIw~dsqqk^*L_{T&P56h9JeFwh~1bY0phU6Jaj-<(4)ZCK?K#I&%3Y6DtC$
zOJ`eK(P^LZQH_JaSUlXP|F!cW?2-D_-6g?5#!BCJDn%?zOqlW={6<Bk&1TWJ5=udT
zn%7>^xCbOSrgY?Y*22ZPgnX@3TTDuRV|IIgFtnZeEYrr-4Q8iPgS0ny!uz`YJ@GYN
zAbKIe)+nU`g7%Vmg-^4<K6B)f+4K+)s@`sVw7ebeoEZy0GQotr2Wl(K<?G>(j#rvf
zY7O>_uP`(C<AmFfa^x5&C*!TA%G`J(5}dqW;^`dfhwo6Z?C!>Lv{qie#yPwfsRzpc
zI&Y_;dhD^vm3ba`s7A%^AX`t`^gI!>S1H88QBkQku5^5@N%{8eJ_{EYghaNn=eJp{
z+kfZg2*{bf+?V-`g1bw;-&~$S!Ze;Bl_|EL&vA<-ypUFfvCgLTBdP@abR$~a{#6Fj
zhn@1(goa`M?rV$Zi!|uB3XpJqNP>H3om7OJDj`j4rD1s-1){4M&0qZ6K*l*cP3?FW
z3|%Y=n7$nbQ@!mG2e2Ggn54>WH_O1k9n!2fIUUd&@A@kAPy+-!y86@i@*_OjJ3X*^
z8xi{^_}(NMHQ>z)0jXX)OF(bhq_65y95^3{=RPPx!ksbVHkXf8Vs0Td=h?GDcp>X*
zIjGo)<ieyhpU75x;dp$K^A`mnvhjG1AQO46M6|yftwOaiNA*0vcIcV%AWf6Q!T!c5
zZTW)?&^MVm$C3(%=xbs(MkCvRF?x(E&a4>*Gz7M}m{(%675TmFt2$VFReMx9<_&~L
zs<wob^uftPmkRY0tHDfWW8C7aW}H0Hx@?)u6EN}L)n9s_2tw!GmwP^_hPE8LY$@YL
zh&Z9M>ch2G9QL<%{xVUC?Nn|q>BSb5Rh|mB_q7GS){{r~EbjtN`I*#V#Z)$qyTOmA
zlZoH1etl9cSr3ZFf_sAds=#<qsr@4apyu)On$kX<pfd5gM#4E8(v!pIvgkw<NU;7|
zv5NwU&wX#Fz6k>v`8N96ZU?LlzHYl;j{qe%b}LcY^E33<ujl)E0u+ajZ?24Dq5F;<
zx>H$HoUpuoGWR$Ovh78Tl(P}i0+&VP7dE3)!S_R+E8FpY^n}?nw?e#m`sTuVn>bvl
zW|*zbL4_YC=E7sjm0*-;lI=g!i9#-w6XvQfFzVg9!4|DfXuAJ9*VuCq4vX{4_#2m?
zE2UpVHKZMR^zutiULoSjljNP|CIp!O!I+niCPRj$e3zMEA`I<1u_DE)1CJN!X1tP5
zLFUrQ{QKXNaB~=TS1=Lo{pxK^ac96wHHXjcFMDyn=;<lhk!0-tIO(CDQjBf}jGGUA
zs&GY5tAh6Jc97WGV0pohioyJMdiCb>LBwBVN6puVV7X|O>3_EkpKQ_A(>hj$?%L!}
z+q--5ewU2zTuu)vr4{dZX-vXp?m9X3J1bC1aM)$yZzc9Wid{OM{uHkru6BR(Hx-xf
zagx<z<Bd*HZ6|GZwIjE@m*?`<WLT^H&H3h9M4Mxq6wm4v!jA3jSM%=YLbUR3%4Y2%
z;1442U*FM&JoO_DT&<08<-7g-(}Q(TvRc!7<+C~%3J(*97AE?>QA*41=s^Lp`T@0N
zwV)XM=JU2KZJ>PTL`F^eD-<>#_u8z{jPC-=we0%`aQlU!Co#`yusb12g<H4?PjI*y
zUBXgqjV*gre2IcuyGx#LQ|iaDJ<y<K)Q!sihnzXY8!@-vL_a3sDNe{1ainSmVTMrv
z_udP1P*+U6d~3r1MiK&f2QIYWk2N*!F)M0uZ&7cVB0F9u4V!aVxtG9}$%sth`VP#C
zcI%cB?7){4dzH*11UyCY&zQf)&KuZQuim{Y8WwDnI~O7u*qf?w<tk?`>fGa}*o<^y
z(Stp@Cj8~Fn#(>s&>#`Qu75kiJ;wH@){T|E$V$Txj@n07G^K;dtHVe3YjweSL37u;
zn;K!V>RYaG)(epTv2>%BoB|~t);$L-O5mL6D<RphY=IV;9G|u7L&Fclr=33aLMJ!3
zj*@LXZsgYrvWtvG@Vj`&-K+$^?m6_9zPAu^sv892?v%sC(KLQd6EZAws59pI%*Op)
z1dBfglwqH{`MOQgbR2a#CS;-21NJHOQm(_1@G^pXXzqCy{<W^RA-NK8cTeCH-}w%7
zp4Vnv&hCNa-Si*kD;RKp>hJk}V}yeR8^@Vy9c+A2*p<^e1$GZ#v35S225JxYo)I}+
zgJZm=di;K5%>27~C-r&<?h%w4@c+p`Lb-D5@~<5@P$$*yJwZpu3ltVRM8fIR#<zF-
zS(s_`i|@!*#1)seXB0fB#ZH~}$5%LO@pzl*al%>x`uCjd9p2M}=Z@Zg#C4j5(rRAT
z-KwQXIrhw=NEK0*^(Fl3M*{FG7CUAP7GwLXem|LxZgi(izN%&8qbqe4<KLx_Q6_r)
zA|v1dX4b0OeV{bsV%tSuYh4niuGqeU`neS@-jZ`0is^)JPrsxV(OaN0<X7p858Hn(
zbJzRD&R^_!F~4jysvJHr92NL&>S0;blO9F?L0~-=*?P2?iSbu|zP*#sf!kli^Y}^@
z!q@xhO5?Xu;gs^){`-$xK{{o}w?AugFqB!iTaBGR7&Y@P+{lp*2e_1-e+iXB__B1-
z-DM46%%^<IJD-R;cGBCrIh)XG<BRVvFQ>v=FMh6VCz9~_*DmR&4@;pGa$^R%YtX86
zQ8F^K1AiKRIA+Ao!|B?kz879r2q7F_Bm0KhQS#AJ&Kt8r_*_9Bi)+flACjj6qSr8S
z%e769N-kBwmkzUmR{Io4ssX))b!jlQ;#{<=P(Loe^6cVP!Dskp`4-7)fle^!M#ZMb
zZ2aPVW|#5VGCW7vci`@+G|*oaC8{aX1z)9J9J11&;^*T<Ppib6;rEugcPkqUpv2^`
zcp@9m+aOjt*L$uBLUK&ha@X2p-AcRKsRlZ{iHP<%%4mmESH>$BUa{b~D?zDhj0S)A
zS@RxKXonZ~>u-w-4}-hO<+&e^x?!?3)X`R)jdML(c=O&N0$o&LPCkJK+a7Fj-{8>z
z%@)jKfuEvazx|d>fsPEk_+InYqdiTCb9tkjTn%_4DYA0AdksEYnNayDx&dw|{uYYM
zro);|-<&rlYU0^aohyQ|o>-jcDD4sU3@5+6B+93jg2vCC{7&s1Kz%sRo!L#r5N_Xu
zfSd{pNWXWby{;Pb)_vSpG9LvlpSD<qZ6(9gJcmMATPA)nF}7L?iH29cE>~}VzXR4E
zJQt^)(ZGU-bK52bGOW^$laZ>+hH&W=gZ8Ql=-x+`d})Dr<&MWy&Bg{CT_zB>0~@e&
zxayVdVj*~R7bvYM@56NI;*52+<=DhiP<7&PErc6sI;nKEV9$uymQrdde12Kc^?3@h
z)o2A_|6>v+*{m04@`j?@az4Aq=?!>L+r9qld>70q=4jkn`4Z%#*Q-wI4Ws=gegfI4
z7(3iIz_45w#?ibs#wNGIUhQ52@Agv2F&E*e9;*iPZRLmkN9wTk8SPk4ST=}i-cnm}
ztp={wr>`5;XoI`D3;}V$U1<1w;-<uDR|NYL!mBu{@nU?S<zj0#MjbXO53hOyW{&+5
zPrK8>do)MF@!%bl+nDEZJh2Lr-&gWSXVim}w_l*Z_hyKFk+&_exD-xJCcOP^9fLav
zJ^nMpSr{KWtoe}b$2`ewtu%Zw2968~%`bBuaO}LGNo8;e>g~@ww)IyFt~CwX%z28P
zC*%C{GdVOJg9<tEY+(!*g!560FGS%JcfZsyn{brhcH;Ap1RY1(HRwkisVMHGU7M>*
zz)LHlMS?HXV!dgmkAV>4!_+>pQ=dC9^^9ns;)ZstzxjeOFU-OOlS90;H=W2zaAGD+
z4C49k8f$bmH{y?LLx!d<6x^8ZpCfNC15-X}cO`O}!1-!lrsG>CuyoCHewrr2;zO-|
z(NizszVPb@{2f`qEqe6Qp@|x}Ixn05EQbZcvGYGYjF}Ka3%#fEy#?-sU$6X0r-N9^
z#gdmF7+_Rl|0imU3X_Ua2kmqbev6qk?OxXgbBFn3_EPFWf6ET7YX!})`(mK^ri4z=
za?!db<5~~ftbM+6a+RQ{`03|%wPg^jFR`mljt-0FdYO9FE%0Iexsd4+CZc>-*%B{X
z{#$O^cDBF9ny=m-0iP*&l@@<DrmX=Nm%k4i+eO12HU}LdOB&Jiw780^YYi%JS=UKi
zq2LYYN51WQyKy(K%!$;inV`y{LNny*g3|}pb{JF2;n8!&bEhhbppbs7awF!$sYiEr
ze=}ymk;pl<b*o3=slIAEbA15V7~7ok9883I;-V;5dLtf~Q@G20x)W@#9zD%CSi!~v
zBiA)Wq@x(a_vG=14Y=@j{PcPDc*1=$HhH)WL0jnT+<k^O@Vfg|A0BQ7;|;@>78Nh>
zq>1ifBiBR}QWa4BA({qKN@Ly%IWhR2HR&3&kpKsiZ}xTg)S{f#8*icFOn9F_cIW*4
z5~XByrOjHaQFgaWQcF}Nyt7P_rgEjh%OUc!^$xjcO#RWva-f0jd&wUcd@4}2a^Pc@
zVkEq=DYfCwXaDbhRoi$pav?SGO5`r?VDQS;OWniv=ZxASl#Fjbhf%j>f2Djn(BpaQ
z7OT6RD0Nb0C~P$Yk9$n+TF1t{2ZohIo~_M7^P`Q2<m1Y*`uAh~m{--vC6Ze>CCkPo
zD15^{j2`y?KjRpX!Gy*al-#Qx-SA`OQ^P|S27zlXHFs$247w@u?JwAvfX*%}{2ebB
z;ZobxRdt<Y)a`aVdTdJ#ESx%!p{Y59I_AQtz~*BZO{PjpY=3~~J7xvD*>MrgFY3?P
zr$tb)Sx+Cgbl{95KhF?54xg>}?o*v>!ve!^+LQV%xbEnt`ET0IxYD97^a|TQ5MLMA
zQD4Bo<pF4;l2L;DWRia!A>?AblO$!&iykZ=k|GsfNyLN|*)FBmsF;`wq7UDdW6~Gm
zFQJGsjCe?MxX$KjtbO%j+D(cCn~F#$*LM-XjbOXr^N9*8%`0jRQ~TlXU9Z$v>qbCC
z?}qKtVgXpaB+_@V@z1S^8dvqiThQm|%CA{IR8-urC;9t)GZt~Q{Tl2~LA{Ah6#<`#
zV88Z$t+aCuTt4(UQG(2by>IyQe}}X4yH>O5`Gj)Rls>#7-LDyzeYT?MOjTgp#-D`-
zKPu6FWmoX?X(C!UezntPHX<c<wk0{N5|?e|wWZfnahFWnJJY$x*z{mmaHKN@{@heP
zJh+~UexgE}y!uQ`m|cRCOI>KN>SOml>n1eRn+-n2_Dh!UdjI`IG!Z5hhDBZ%MB>UI
z;XYr3Z2ZXNYh<IKsQB{DZ~uF(cw*sQpq&H()H*is3jZL(wWL+-0VYD-uDhG1WvG}&
ze)i$Q0|sXCdRuICV8CkOP0kWK*m)h+qt?fxEZFSzdoh$$0GF4(kOKK>xMH%Hnw!xE
za`)!LJ1Jz?9Ny%*eLe^DPDKvJ%aAcbpg%WqsR)&7Z$y7EYX??O71iNn6YS5|Fr{s-
zg+EEx^AmYB;LOak`7&V&=F8U#kN+BkH6j){qYh25*(X3~-A*?CpH(4!dLk2Werljm
zs~vgwEax_4AbdEO=bGxz0CUmbUPE(qXg$VA2|rK4FLw$9v$&XOPnedJX=TEL-Q>X(
z$wp{s+!GhRxemq>m6xhR1F-CRr}(w52yEK=SJ#1_kKRkap6*rc069CJ>Wdw9u&$XV
z_OhxBNRrh7M8i6CR@?D(6(JU$FPnd@mfneiT=JyQ)-K%0OR&E$L4}fr#_T=9Rj|?2
zx%ld`dORz*De!FHYc!{43HaPV2uxvW6)j_c-@9GW@@Cz@_l}}&-dc>pp$Z36N%gpH
zN4M8`+Xf(OYP?JP)C8-ncN@!k(SUsL)t5sD@=@`E_T<=CBAzi;m%1LDh;)aas~>*0
zMzz(-jj3#TFE$n!sFv))?*!+-p=~TQu)D$EAw$AaV{f0!Z2S3JIi=V#q6^kFSlZrI
zVC#`-FIk-@g}7^Nq<DpQCOoa#GeW6s1c%%Dsz>(~;=R@JUTQ}(aK-2HSZPu-mMHVF
zJ#-qby{$!`mm=cV^6={yV#AP{MVx-W?k(O@=w)YL*#WPsq+I2mYCIM7fSDCQfYBSX
zi38>=NdE0IoZZ@um64eeZR~Z1<2kSRE3$CPZ<2T8oni<XGnjOyHA6%DSpKg>8qV&c
zOgJPp<CA!;lXaVmfF(fQlBZG+aSFXW7ufs?>0gFYLNncXIIiOJ^cf0rU8ya(vcDdi
zu5&8Ay_N@U?wL)`1W2G{Yaslr1L5tCNOP%@0tg5xqe|X92EPnr=A}<OgV;lopKR_o
z0enAvj-*-z8s+Dj?2i_LsY52;HA@x@AKs}Wm{bBQ=!Em0XCA;Fhv4hu1QHV31qO0F
zs?qbep!I}g33`>CIKM`=1}O!KU3Wer_`3ZPoK8puy)fGqW&#bs-_`w^!C!%2v+jk@
zR(wFAU)5f<az)^{WBbvsik(pX>D&~fqY<*x-aLEKAC7!aCSrt!i{Xigx#wS#5{zpa
zA9L#-0wIgG==~#f*!jxk(@0_wM5}BwO(78A)agW>84XiZ5wNBy*bRVw*jdu7T_Y6n
zPp5o{^v8`;4|AJ*k|1laFCsIr5R3#>E^6KHhahvM;6@%6wCZGc2vnH@Z$M4**Z0{l
z(ABeJzkD2=4>;#@_cIMv9CH(#GARY>lS3sN)gn>-PsD0I^#;86Qse$fq@cEm&|7xS
z4g!1SNMaK`_`$i*v;S;3d|RUtFEd{O3-q2KW8M;Ie&br;v!#x$<nKq{+sVX(R7DBS
z!9pwuO&XRfVDqZnNolDnL`+FASxq;f;rD#87prejF}d};7i()RTAeC9ytTFfALMV7
z_bSi9XOB&!+O>LdP>!*F!$LBezYg)Q)_sD@7e+t6*i?=+d$Von$_2npB}qCK_hWDG
z={fULDd20pIps!fJ*+R?Y9w%-3Ew@`R*>Fw!1I*V=Wn?`Lz;4JN2t?n$W@Msscj{|
z@q)Ea7dl+=&^OwF!h}k8d}!TQw3&g%2i<0B<ys&i^HGoS8z0=T%;odW^=yAcze+Or
z`7LzM=r*;nCZlV6zh9YUEi5~AVn_Fj4v5{tclHJw7vD_y)bhH$3*GN`O4@bi;D!$U
z6Sb9%xXSqXP7Ct^G<mdEk*GF+4#G1!ndWqyiE8y>XCX0{^+=GL^%R{R*w0`0jK!-X
zDy_1A8<0<I^84V&E@)J+-R<2=f+eoXiF-07K+}{cu=6X2{hR8<ACEU+X8mfJ%=g{6
zLbDO8&UQjhl|h$DE*WIssGphTuZIO0!|8?987NTyvGt{XJ04dw`Jocl0iNChmXCNS
zP*b8$9QSnt$TGMVlNSIR_Fp=p7D+>kel2Q<R6F#laLMOu&|rO}WB%ofL=YgpS;e3?
z;I5p&^ohfbc!XbQMn<XwmXnOGEz@cT%2z+uGIl<~==|v&4UZz>qGK!d)qWywOiruq
zZ`p~rr0hzqFBPETd5a$Rw>?;NAc7>}--65c-81S-ZG-8V#RJZj-S~R{E>iu*QhawM
zV~^0jYE;imoDt-xL#HE6SHJLffKOMQ`I$S_IQ0$J=sGr_-=)i^9}m)zwQ7im|AseY
zb^hi)Sv3fkJqkm93Y6ld-+T5*pKHdS-`XbX*|^zG4(~wmCneCI^32vMqzR>7(QbyV
zsllP^t(!KNQ&HCGY9C3u4$XVAqYIDGkzaLbaD9CR$gTIE6S~XJ$M#?TCH=e=_#5r1
z(+Xu!Bk!3R@|*#=O~;e3NH#-<Wgy3^Ck>G9A7!+q`~{?+7Q6AYkO&b+w~Q<<C&Fzn
z)3-sa9DF(}*<gOD1~&J<KPr{R!WDPE`owK&hYMx;l;i4jP_+!XQn<z&pO31ZeB;!O
zv%6iQ4KI?>%T&$kr9~w~-s(HuyQ&CR*KIF#nexM~R_m%lhqovartneDv;+>E-Fc~e
zv;$sFy>fe#8j6!~b?>~6Ho{4(lpK|mX1v(%kQ)5F0g7)hnm@elhBG2RRu!*ofQs5m
zgO0pmST<==ok6+=eJ8SmKdo;;bDp#nZ?ZGlevalRjHm<&#YuxvK^EM4kVn>!Ww6c0
zua{jSY+RE=yI1gLId0~QAXgkI1Y_H-$aJF@(B~%M<;h;p55g~y-3}z7yuZ+k`bQb~
zklHF&pH6_&F=ib4zhiNHZl;q_)`c#|SDih`+kt$%4$7;hDLB7))V5?D9WU8SS__;l
z#G25WLZg^u_%ypBz)d#==;y;Fb=!;JgOR|)+Ge)CtCFD%&Ub>UyczROM;%&lgc<eJ
zuz4#TQHk0mg|Ih-Fx;{?4LWZsbdGFofQkE84F*VksJHpS2Ze22*kd}q;U}vV&BgBg
zQTbK|1Wte0Poo2sWShLLn}VLdDn^8CyYZ`q%z+^`zx2#jC4ZrhR5(&~F?G8l3rKED
z9#lgnmiXJ2bRhvWnwQ+&?JD5_H9}Hvu?W2PJDuMhSPy67UtL+fq6?PXE8lsTJchlq
zyⅅy8)tq=suQjM@wC!by9!A@v`?q@yuf~S|}8IuHmbL73Xs(`l(Db@e7$+^SKw-
z4Cg0r+u8){N9Q!QJt1Jq$jcg+fI8@0pa+^&mV&19le&AiYasd05ulOEpmL26;cDL_
zj4@0+Q|&;4KJG{I1|c;-9#B*1wPwO&&8W}G@(;nufJmwSF$jqnWMQtkLX4u>I&7(V
z2`$cLGO=G9FzTb=W14*hgvIL!637+k&~>_Rq&)(DtIBQ`31GsY_v*k$Ry}OoMU4=Z
zHvosYE;P&BA;QbUBJ<A+sW7)(@Z26hgi4JCHZzV1G9p_~$v3lLf0%lnf|COZ`%vRv
z%co*n=DivXnF6R@l9vnOXn~if^ftT5F+qSw5O}Hzp$+@E54)#=yOW2cqHiUdTm9<T
zEgFP#LHb-p&$?kB>k}*VBng9WA8O>;lnk%lo8DUew-A`k3RUwp6~HL7c@eVM0kYRA
zuP6RCLcsbz?))z7Joe;4$+kc`4BksOnxQbjG%0tHYa|i={!E^-o9zU-Tc1nQ#;f7P
zb?q1KPyOMk!E(i=_+s=Iddyd4$KE&a`+efa14)pvqcujCt<OmrKWszN`@pU8R_Fvj
z5l$J%e<uwg7P}VQ>0aLmA|>uT0nb@bLrYw{P@T`_jdaNMvi;%qr$>JaLK3KcQA)q8
z{~Ur?BTooyd&i$?sUOJM3%}dnxqqI|0IqGi)_hA;Sd@FbcXMqE9_l>Csm{#6b>_+g
zosKMUkIT=Hl^cL)Cu^_u7kj|Glvi#~44ePm^_C@)nvVYaYwfhZ=EIlO_jIg8J3*U2
zA#ppQ57rsf?AcIMi<O`LCLbEF#Qa^e?FZeQpkcB!aNqq#$gy_+=w#Uq;|qRb8saB#
z)+D)wXZ9v~i}^g%uP#QNi$}%ZhZUi#!zw%fi6$VOqi(%-rU2LF*S%bFEy5<-wswWL
z1+d5cwV`iH3G!8~I+2{%hzA9@62qv~=oHai_nn7@0X9ygaM>2T-dMJLN~H^HObyP5
z?`pxE%~hP1jfF_5pODtfW#eDG*?Wr)lw!a8Vb+PqY#ytn((37>d8i-%%1R=>1ApnQ
zYIvepfqODJbb=S_@o-GAvq%l1=xXgEJ%d^x-`MhL*rpvrpM<U9d_%&xV&Rnhm5;GA
z?CA!nrUFPit4YwwXau|PFA{NU+4?B@wn6A9n`ixkaPW6;71qvwa(*!00K@tF`6@+6
z;ry<@7Q$le_$MThn7@^RYC}e$MS^iK^7N$Bm%T)Y@^Aj?>`s9d>DOoSF1JIVC7*7i
zFBxV$G@Y9H%J6XmGdgcF5#HzMOZaW5gp=D#rA&`@!merPcyW#lm(Cs+s?KNQl9OhM
zC7QLs^OI7Ve3PAypW?6iq}LCp&rT2Q^kqSkw$B%1^E}Y8U9L^%D+0z>^55HeY`&w+
z1|Ei96Ij$9r8icw<y7}{p4$C-sF=E>=YP2zoTlGOU5V_)kfQM1JFbJM@s~p=h}Mnk
ze(=2>7h&Smsw9aZB@%Mq)eK1%s6z+MWzPF&=oquwg!|}gBF1VQJioP_9Up(?r>F#w
zVDPzVjmy<`FnXKr5?2(90h@z2e6=YBx55Fw%iAgN<Bao3HLp(iY&U!L58J=Ecfn@+
zi<>17x+HtCOMwV`7SeY$bd$is=4^TUGct(eke=i_mjTz;*`)A_6iA6*9Or3&05|Gw
z%V$%|abiB|^h`b(XT7#939e)B7h1uc_`5U*f*lD@Ofng8OE=`yRTdlfj9=a$m(&m4
zoS`#zMFTKz?{@$3{5`yT>~chg0||5YoqoRcP6?{2{1E%A*8;pZl+&||JHamEse@rr
zJqAnt1QYIB%-@&zD=4@W?{xDG9#yi3z8kX8Z^_OhtS)sKS@jYV_uiHfiY!FqUw8V7
z4zuTPa8JW%e<@n%Mj!U^qoC7?Yu>XLqrfx>uIJuwhY?@CdehbxSfv#r*8Y;s<2$SP
z;lOYeI&8G|KBM~v6E}zKbDSXHV*N?CE$q1Dqh56V@kqA)JwI|L(t?6Cs_w2Q@ja+h
zhsAMiY+mfL=#P|0DvX^v`252Kwx3Xz-{f&L3%=KWp?i1sBB`|E-qr(IQ2oA@Q+7E6
zu6~GdK6yVIa&7VwHdK{j^kt!zi4IS+xaBvu{Syf;6AeNS<dnd@C%60(njC;<_wSQI
zJ`^|*YxeU)Z3?EorIkL)C&6Ba&>PhGZs=QWcGQ(zg5thy<>7=3lzm<(oV95Xzl)dk
zw~Q0;?WglnemrD68L#Nvxv>=$Jx<-e_`U%rYi$oLy3^2iD|V{xpy2u0pj&&&OEKnS
z0*kzxfW5*#U%Yluz}rDmE_8^3&jvn!y!Jf+)}1Ob+;jICM(6B3F>U+|?{*P0Y)l*A
zU9bH2q4$0Gt<rAqhXyj9c-KB-GhT{KcXgKQ9b^GzETT}ps{sN(rcI0IbfD|^Ctm_~
zm*9`bA7_3!641Btz@+j@Dg@1!{mOA>!ph;64W}NoVMEyazX~}7T<Q~ym|qqKkrl%u
z9x*+zE|OqYmD&!Td9PetpdTw8m9272p5RaTNxt^C1f$oM$6Kvx2Ls6$fyeK6!d-nr
z+TkB{xMLt<IEz6?Zp#<<erPA*=QDBryP}$4c;b~qMfYp`alAJ>c={ERy>Gn>TWG>%
z-ziB+2g}%b>MB0xVs>0-cQ;bs+#B-SckcW^s7Ad6=Fw?VCkD!l>s|j70y;^|%lTIj
z7A|RxYZYg}VXBf%(=K<kU3&A^@jwdP?)I#I&FaTBo7T9GWpto$?UAs3+)2psw{7x~
zV>{ZWg+F`-*)TO*%X^95hsRwrLqAU4h1iSw8&@p0foNyJjK%Xp>@vE$Ytpd-Q@#=Q
z&5+vQh1^g)X=MlekloT)>{WqAc>(c%DV3nQZ(Fm$o*4KhYje46J_+5(CF2j}o54E6
zx+v@~n<t-J&AT;%j0e&c4L=xDVQJiISFuJ1eEd*-eU||dD!HF(TxJzRH~+T0VXH#q
z_(@HfdC>!08bT6U5*<)jl+bFtZU|V<{Jh3DbU}sN&mRkEO(1_L>1J|fGl;jI7q(s7
zfDTGG@f|Y{7q0Vcd#{ua+xSnu9{pJggqzjs>_`ua##5*M@({3~+RnX9>nXNoFk@BH
znON#@#9MDSn?DrH>$)hHhP)>@c9)+FLFc#H9eNvMki7c6qi}B%e!kHq>LSvFmQ$3%
zKkR-tJgPrGtxkbG&TbRhy4_%LOp9_^APIiG<vC*D(E=x2cUWqM5TQLIU^)NKP{=Q^
zw#^rQ3d$TQlm?e9+;Z&m{OUDqJ!9D1u)m-KmMPz@s=AVbZXs8tv~TsG)(7==g(xy^
z>mPCsWaqnY%hEeG_7HH#hm?aMOBCQs@xIV8WdND{j_=0pU*h?n-z`bbeeghP;l%bh
z1kdx|#%#~#!=)ph_w<i3Fyy%7h)8ZJ%y1fMEo^)PQcc$@-)?P($G0!sGFKu%(CfMS
zv!AO$-eA{IxPKD<e3yT_YW*-?m*y4Te0>B<wL8Rv`(x11-Fs;vDj(-wDY$Q~iGfQM
zPIoo@JCPR36n&UZ1Se~Q-EzL|aCc%yu&6VY%}|N$NVRJLixQE2aa3OjnM*rFH6cQ3
zs%#PW5D|vjukqeKQ4Sf0%A+&~$k2J>nyIj89a<3$T@Q*S!MwVtgG8k^tZPy_r~RAF
z`(ngZ?@O<TwD~XjCxu$@h{$1`J9~|h+g#-9sQ4|^<`~$uEiD=d6`}iW%wn<JT9f2A
z%!2ZLdc?a!`Iu-MHhjjb5$1MF`2HLs1NnI8H^ueIz`H`NIQ=CR_{{iL)EpZG6@T%B
zt=efg{pKo@`#gIe%6#YSrXys~U2-&7rN@AWOJWOZkC|X?#T|Ku?FVcPJnO$*pc7Y?
z(&nHk1-2XX|5TZ60@es4c$OXSILP|lRt;`}K}p+By?JarXLG@gIXWA+=h2gJSw@6F
zLwK!gl?F~afnhh5szGu0hJ>aK6`(rL*}4iFG5^ld$^3*N2z9h{Q0`%X<L-R-W$#+R
zOyHb8$DM5aD>vjIc=r()oodndJk$dPD>FipvOD3mW3%%~r7|qj7EtiMN5Siaw)Fd#
z8c`?ye#DqVA2c`|x#1qz0{!t%<8G{uK*yuIX*bqaVr<oI%eAKx@nmN!ea#>XR#dud
ziHt3S#qTPkPm^C_^|7v`vK}@bcB1=?dO{(r^|Pp@wMBu?19!i=l4j@)&S;bm$%Nlg
zrFMNf5xBD3t!tW{f8i3|GodIygmY~buOd?laaDoBg^sdvG)*}B_?Ax&=6vhh-zeOU
zLVT3%yRWri!OS+^!XPT%R`Z<Lb(;u_u@7(E?T^O?n>(+)$|K_C3++bbOC>1Rz5APZ
zLk1|mJb64hI|<IY@JRZW7U7mQsqm-o$oQ>kvE6N$grw08nt`H4;MAISXjGva)c4s~
z-DT%PPItc%63*$v4Q8SWEr)Ah<aNIK<6;7|aNGP=;w;1u4!)$CEv+axCw#%nfQh=;
zonXo4)9~nSR^H*tj>8pnJj<B_;Cy7gjM%*#oITRCcCx7*C#H2>O}lGAqTBr0sh}!&
z-BA&fEmweNIX{?w+FB1nn_hAV>NJ7S<<%KGu^6=GFUKrr=l^COZ{pFT5-_CB)l1tn
z5lg?QsJ~_V^(%9ZZT0+SgQ@pyUP^nAf#|Gp<r7aaT)UC2ZDL5q{3wZOse)!~zGR{C
zXfPR8M_c+1`H|qnOcrb#D#rms0m)>Ar`WGEO+Mk#2}{-Pq0b*tK$&#Kp+de1`p%98
zH6-^ykSMw3O57VTd@Hm@?qwH5Dfd{r-8lfiW75;!ZlR+7@Ttk>*q5*(>tg;gl|tB~
z0r!GJE5RTFTg1DG=)N{ydwokC8kj7t6~B;)*H5?_4^36#?H6l!>jT*P+4BT{4XLsB
z@2V$tcsdjCtRz>#U@!p_L^w~Verm!9hx2@b?B|N@VhzdEk<jI@q3+MiBs@R3dZ%<}
zB3{_EucK=j6(wSQgXGz~+H0bxY_^S7!r;d=2Qkfd5FRi*0|&E!%ZlrraacR#ZS@`9
zQ`ipUM1zB+!40s~U-Ndgdjo8`P6;qPz>Zg)C43$4Re`FDw&j2J(G*6#-DA(@I~2xl
zr8oR;hy7QA91?cbpvE&NeYb~9&`f;$+QRe^@~k*GQE1i)U6(zSGzKfdgNr7!rac7w
zPm2e`9DBb4!?}u4P=h)0f<8aOy1~Do?It@Xft5MO<yLYvp>XM^dl!xBaMRL`ecB;(
zbSvF@FDbWzy<h8`sk2E6gmEhG-JsWukuxewG(sD=wIsyKhA_eZo=d`6`w@JrZRXNq
z%;t^%(YjeI7R=^7`ejp63PE(YQpe}idT`Ft6z5*shofamoo*}H{3D%eo;^N&;4~TJ
z&^3qf=+%RVJZe<fVZ{|B5Jy4M?X6-<KGonMK-1>GS_*|jb5Fl%<l;lldJ8rV58UTv
zloAg$!5ZnJLD~OR!FdK!`Mz;nh0LO%B$R}Tq)qBVqO3$p8KEI1$tW2~gb*QnX76=u
zav8@s=CMaa5k(3~W&Qs5|K)jeUYzH7&U4+@_xt&zm_q5nhBLJs;i#iHbmN710&qCR
z`lfu1#CtYt6nh&<|37t`M;DFsRf8F<CXDmoPE2S?erXPjq*)|hFKPpc13tR@wh*3i
z_QCM8xfb*{^G#f$1j6mL+gz!vBq!*1k_*eNhDc^kR@dw}Sfb^q2dXural1$G>|i^l
z`R0x8JCX?_T3WmQ#8rZ`;;(eR$pWwwT0NutJPDY#WP6D|tiuDr8+~PlC^%jeW}o8j
zj=G^=!gO_dac4%(MLm%o2v(7t?)=mUQ~V6WjJgeIvsT9Q!*~&v6`3l!lRnJroKHK=
zL<oo4p{1kenG1gVj#uU$s|B?ZwkEb`Z6I>4y6_ll8<<(jzqt3f9^@7q@pj02V0}Ai
z*Ydp$diunat?zijljVy7H={`Z*FZDvfOZ}<#l^4}d`iK6%2D-0+~u&>S0>}8emz9-
zO1WPBuL{EKw<=ii*TbKJKLQt&25@26c>^cm?9~;DIJC4|LAjFc>5;x-XsCBGUMBNs
z<bPX6qyrk!COiE7o}O}4dg;X{AzFo9+(wzJyo2yIO;pF+E)$lLW+KGzS>reFa1&c8
z8V2sr=28pkLDncWu9yQo5RnvoP4LuvIFei-9eOw%P9-Ki<NsEITNM=8S^xIn^X$i~
z)cz)zztP>Y^#B!4ADUdBtzm?t(lWHC<aw1e9=4H~?ZEMGxTboT3bo0c@0<lIfciNt
zrC;v~9yuJ$Ut*Yn&ok;8Elp^+<@ieIe^FK7!^b4Ptd$Q3VgsG)dr96S8>&*sSB>S&
zbzF+GO=#zKOvK5p8kO&wYkd`><KkQrN^6k5^&##B>3dXsVLg1(E-eQC`L5w-O07iU
zJbwLhp-A+JmboudLB}iqZgU$iMic%zODuT&CRq2Uuxzquz}@fl^@~-TaloCwF-flm
zt>q-zQfUo%<lafoUr8i)ywuvbk{E}sQP&fG2o$58$99Itp)^R-RMfAK=)#>=HlZK*
zh9Gt9qf61za{Ruc>MZ|+0?p_Aw3dzz;DBQP+=h*n&?-C)&C@jKFnem*c+eldY%+>7
z7jMKaql%4%jUSOQ@2Dwvzz8lB-%vloJcJFFW2=3iDd>7)&EdaX9hiBUUaRC+go2z$
zHgJC<J=EPt=X0c@K)mM&tEP+{R%D$VJLQ@QFCr)HpBP8sm&7ZRk7VCKRn`ajYro=g
zht0W?>agdqN0L!e_;?C1o-}k)sn3Nj(dAy&5>K#^DG-|boD5t2suUVFwL(N|j{3>2
zN)QbUzVn{UkK>J;0pBR)uypIx!^+kg=w)`fGt8EQk0<I<*BOzyb&C<UKWM`fQPo~4
zo_<jGLsHhToaA2H`<WhYuY$2p&wCtyP~a9*4~xMg(pyXM8ckuU0S84=xy@^bpO)T9
z?LrM`eI|~qZM!j1hU<%WE)DbBbf-SO%fR@rYplLQ6}}hDjD2HIM+fEJGvfZW5PEK=
zOdzlYc$|MpPnFhz`&Ky}`mr=z{!wt)hPNGN1r)_ArKzy*&9@i+eqHcmf-fne<~5kx
z4$sV`WI>Y8k<$U<mC$(7JC_n15B)FqiF)W!p+J9`Z&!UQJe+@8`I$TyckQps-Zm!Z
zsq!12Xx|ap<)4VUbJMWKzIB&fcrNJv6gVg#n~g$C>fTQY7d~{IMU!*f67TmNm}Vy&
z&aQ=NRyjo~Xb-(?Uf!6BYmPDN^X+K@8!bm^m4sB-Y8ulWT9AmhU-6G0nx(+w%A-;(
z@dL1aVdLIiLq%w+u+C!p{t)Q0WBV;BR*a|LNxaf6?S?yza@pd?KS8odW~8^SB^LZr
zdl1vpikH@$DG4ks#l6e>vYSk*;NU&g6{+BMcwim2(A8T6<InoHvuBe%-B+fFry?b=
zx$&we|CTD;bADIJ0J)B{_h+k5lm0)CwY-X<9_eY8S5Kc8AYAC-KVCOm(~x17UUZK1
zKeD(Cep#@R-{a6c14~;gTDVe%*0j_?*Y=%`YQtoH(z0Vyt1gAkmJ;g^E&X6{pZlX9
zTN~_p*(|48&<Qcmv?CV_`oVR@Kf9LfDVSCFYWSE`!g8Tg)z>Sv&>uG(w3GDW-6W2z
zSQqw!`2Nsa>|8w{QqiI~)|7{f+%9@qE@hzm>dl!cjV^Gm6YXSj>4q7#iS7SttKmAI
zR9ny-;nW@L(`<;pz;lVcd@JEUdW5A;etc1fC8ZTo2c+sS*piWN#IzcMzvn)n+{%Y9
zHZHG2Np`pEmgXMEz+&7Z*IU<O8H-|*vp4)MTSD4&3Uu2<A&cSKJyK6{0O~CJN46sf
zFS2%9H$BFWw`A=p(-rVQ>i5o@CKT{kBXvUebt&xsZDMh=fDWTsG$prJ)tK2dA9z!q
z3X2ATHCxE{9BXy_#N$>=@}93s_gcw((^j0}qTh(^q}nJEgh0dx!c6ix&?LF}Z{%<_
z)XUW<=||^c?cs&g!;vlMk^W?e0v}*&vTdKuKsV5Yv~%uN=b=%#!g%yt6>fUfJ9BVr
z2ab8`wFg}5fY<jtRude$!A@t*sm7jkjNcvgJBSPM!6Dx^6FuT7;Yqit80yBm4~sX6
zdQyRAD7x3MwH*$Y+ul^V6#y3>QB%VRS3tMrh%6`j<IRUyN*oEtlskLgQhHq_oX%UJ
z**&ZxxzV7ol<*snS6nx?>DWie3jFG}6%j<(3Uw7#>)>4du=VEn0esq0Q@ftz<xP6d
z*KT!Jf%ex14tXv*Jkv?lXx2ycv)Z%Bqg0F9Cb2zR8DGQs-3R*|N$<uWfZC<lod&lV
zf<{D((s9$@u^tY^5<G7|(*3%%5C%urJXE=B3nFjr_62jcK}o8_;gH{Fka;jZhv6^j
zh1%>Gdt{Iamg5G^k90o6)XA&dJ9`>|)rO7Pt1${Yu9xv0zefk(goWXUwKYgz4qbn5
z)eXumKD!011i&6Me*EKGfwGU*s75E!P<}CTMp}ddk^5mS=k{Stl{sMa<tGjOOxu5{
zP+joDD~IKXZDhWC6t?G_N<MgYtTx$fE5Tt~)3259+mO>kSDDi=2hBRdHF6~i(Ro8^
z%6GzL`E`m{*`K82o6n{unr_6?({nB1^2T@AUwl&R206DVSO@O>LO8yXKBe`#Ed6-L
zCTo4xYA5D{%<La`!c$tiKW8ovz#wWi<D<?ftUSoJ)sPy9S1Sf>OVraL#{Xe1qgW08
z=FCdICEtk>yBSV1D0QQ}Q^t$y^dfW^xA1a)R*QlT(c&v{)i~g48a=dX2^^<c=2xQL
zBlol6#49BCV~Q@?oe@}zjq_h9S{v#x!(m_lrVk<Tk!@RyWh3F6qsD?OD8-<E_VAEj
z%{#Pn^6u|f?LnpZaCh6BPSV#AzqS-e#UFO;5gZqC@va$tOnIpnB^JZYf65Xc!LtL6
zSLw|-GhAXS_M-)rM(Vf%nyO(q{QEN@(<a<ewkj+6wi1u6b(L~?-+}@x8<!-Ns*$#~
zwAP<^0p6R@I~0oZpjAP!@G@^M+J?3$$r~o2%<i3EnmCGapXR(}9}^weH><I4U}}Nm
zLb{vPTJnIq*(`EFsS0+p%kI>cugCZuVsq-mYh=;VdrCk#5qzI4^_gDl#m|GKkx?;S
zc+gt%)}zHvT;aAYW^zad1$(Xy5(?dLtf=A=uNKLd>?JInrbte<=~0cVW-ajV4hjp4
zcnu3m#+o*yKl*fLNrXv|^tZ?KUKlsj!sAWnQU<P8LO*xjbaTWrT=3+v3;Rt$-%O2G
ztG|SMb~dp55LSwkp<j=;83dus^H1&D_#IGBG}iW|cp9dC%B-3)ti~`Ytv4zSfp~qd
zaMeGBb{uJ6=eFVeI}EgCb&<IchjzOP4pUbfk!9U0HvXn2R2mJKQn~#R*R-3Yj*|C<
z=G<Yyu3p6Z8(Mdn`_|&=ZANsbD|GbLd7R+8T7bJIWUp7I4}#RcNZy#6P2hieJ}$1x
z6dm=l+1Uo0&>-wa>(jYx)X`v%?vQPTn#e6*Qg##XgK<t#sdz3N+QXUdWSI*ee4m*0
zB`1JSYYO*syDqda8a<<A)I|J2Fn%#07yo?;P`jyt7%-l^T#!NTKhd6W^*_;2>~`tF
zxAP^0S5o>iNc;@nbHk%86)13HA$m3YTP8Xr%<+C+ONG8|VwzVR2Ot?)6qNSV!tq*{
z6%RTc+$Wh;?u*r;#j<j=hfocuv)d%P*5o7i>_T&%cN=gUjOPUF_drfuW}*IkJEZq9
z(9KweAo$T<?$<RrutK4UOddku-I#r7OtS%gJ(IPlJ4D5Ks+9K}JsNx~-*0!cy@VGn
z?=!WnDZt=||4h%zSD=r9ZRo9^G|)c$DtJ$S3v{Hd>>LSj#9ZSap(DPH@MqsdY+^+Z
z__ydSEse(D!o|0tn{0+aUbdHi&qxP|)D{|TU&+Mp%0*QUb!{*e%>BxhaQEMoPsTIM
z)uFzD$>cHJ28b0kKmA{SAf8=XV%+iA4`Vf@`1g%AqYg#GZmBR0?BlAoY1x)Su4Y3n
zt91o-wm-Zd_O2ZSgSY%tnR^N<)8XX->IEP?rFrMFZZq7z<h!}LArU`c3YW0zD8r^`
zmFsGfgpYhGTsv0R0Tf=#iKCM{fr(`c{{@vUOc)Ytc+{Coa{s->;WHH|#m+7?{B;N>
zy`=uRK4=EhulBS6=4!Nk$#H&2CleVb=OYb`DaiY_TkBvr>B;1O;K~RgeWJRrGv1bw
zxXWX8pETbqaH9V_F(gdE+5cRf8Hw*_oxj2Fd-?+qcQXCe`J2Q)QcCN{EVcpP`z#_)
zs5$W5O>^R%Qy~Ou53V^u@^0&xl9eD{!ecF5m;7}$A9%VCtI2*(ggvsk!4c(jFfJ^R
z(~BI1*EMeDdnBuHPE%{|(EA*aO1gTO&o2yjH5^*_=uiyinnjr&1+Ad-xNpvC#shb7
zk2dczcn!rr)(u?m&%p|tEY$|GH(fcbF=)oq4%6%Y&OgsZ!h6(hY`f%+*W~+0w+O_6
z<T*Dfp|~QDKOx92_g@o8NKP>7v-;u7N;9carFQi19F}N2Lb$~}dC>~K#c-8jE;v0m
z63XuTHZVNy2K_F^($3puIKE}C=;G%(&<Y847=A-~;v6AC8)FjTYExF7b#EfrHKdx}
z2`k6n=5JE0gD6-(;mecb(S<Dgg#LP!<b(XDXIgr5#lY>KFvYZJ28A*ozovx5;_gk=
zlxW62lo93;8fS~c-^z9I1u{v{$$l+Gro9Tjiy8^eU-QJ63fsqeeS^p-oaP(lTaKb<
zfAvL>dE)3%KaSfg^|0vZ^q^{g3YIacMOpQK#&^jE)AD;OaR;-X@3EW^Y<_Mo?=e{g
z5mld$ink?Vu95E^GnpC`5SE`b?IHX5)92Y9s{Fr)vS^&W*n%@PX@bXmGa-CNPNn~4
z68H+F=aySVpj5}!jvp&k$SECvc}goCRPXn#gkJW?2h4*02A$-7%ami-Lp%|>Zr9KL
zmrwRj-al2lWvXHBl)*attyFk&_H@2kd@gtmnR$lfWPreI@NUnbe5iiK9`*UUGghoB
z<&->4!Rd{Qg0_W&;2>L>&2el5GB(}x%bV$iE1FmSaXA!0@{0pkYVOqoy*#dOdvGQW
zF7vVnhSXp{<>e>0wQ7(<&$H~*<^d2Z^UI&!&=0@=3AEDd+%V?x1+Oe~;^B~+c|<cH
z9<&F=?_SOX;A;8f#cM*Jprzob<z~HVC><@BO`sNF_?t8v`t53rY^(XXt+*S5RP6th
zDpW&7^{HXj^eC8rsHz>2nFF#xEXkhaTyaa2dZ_YgG;(^~`Wn9>4`^7H5&a|=28RU)
zD0v~kk$Fw1|6L7krd`-BM!a^ExgB?7Us6$``Cy5|t{!NO-^zXLMhEmPN?LG7xx=&g
zJWX+)KIAy)yiVM>1OM|g`{T5~1p-6ay5|4S;Raj()jXaHMp54CVkP~!YwzDHdzh)X
z_B8)QsB#lLuZoH03ur?ot8G8-9eM%6mjgFni=iT=>6aL55e2+yl2bgBaZsfxtz_Xy
z!`6O1@y~p2SksU%*WpkA{;`~)X&$ZEa&4hx)$%=7ZiIM0vo;JrH?(u^WHGK#858Mk
zsrXTdS2KVk9R<?N{WQl)ku{}4OHi74HRSU{q!0AN#qXJqZ)uSnYMA+!N?bQYJovpm
z`e_9$6w?kxL?l9Vf%&Jv_6{hzc2}>^p%pGvxWo<WbQ52J>x)U9DmZJi!_7M-3M<yf
zyfDlqy=j9L`nYO2%C$%h4$@0-=Zw8iph-JO1jT<)UAd1Z+XFrO-dAGh=93q*?vtMR
zljzV|GRIHGMXUMyDdU+io}0=o6?kOe-i<iH8t7u+le*-ej5AleW;_nm;;em8#s}G2
z$X<~Q-!WATuI(>pN=icD4UZf1^s!!$FLr*wcdZCVE-fu9|E|VZ110gB?+Q@ef|l-(
zREdf9I$fy>O%P9K6HZAYy=kMP!!buj;r0oYQ^^BlpL#n;y^in{=M<P3%{rT4R=ard
zL}Myi7kYt4d=UzNesw~NoYy6@Ue_3>#3FDBJ~&7A@I^mc_sT!b0@uLr%qea(@U^D}
z(%I|5Oq;TgF`x~E!v^#v^b^pLUH8rw0W$wTmeI`}i$E3OwG5YzCxK|<+-8HWU|34G
z&f3>Thl5A`Hu_ChLA}|fy*Wn-&%d8-=5$^q9;Tc$|C&>YHIapbb5fNk=6gY@^_w$_
zlwY6y+ctp4-@n|eQSLyFZWmR~q&O4{lP=CpO@XF}N7`$*P$22fD@UqaJ?weYP;}3q
zis?PL;pw|tV2*H_l_q^2iKn}BK2rO@w(j8@&xvYSvJ&Ou>&^h_yL<t!OAw1K(nhXt
z>cW$LgM4ncGceVx&Nir_9fu7$wtj2t#YmQtZZ*>L-<IRE{lbk_Ov{K@r{2ke8SNcH
zy3Sqb(^9kPIcEpj`}&3PE@xrVasd0=&*AW*z(L_;63Gij=KfqE+<(os1aVUlI%-oM
z&nT;!z?N}I8>{xC@ORhE(>uB=AmGpSo8wn|P%Hd}5Z?>JJ6v~=_w~$%Q6rZd(k}|&
z;wht?+t`l!5e6G_A{(*zS9Fu_rUrbu*Cwrjcm^tEFUyF2c@6z1He9JNd54wR>kqV_
z9>nxdu68qThR{b&b;h@a^c9*cdbe7WJ@WaLS0{)Mva4Ox)qH*k1HT`8_-}nP&NlV&
zwLi<lq;_!UmPv<^=12Wy24<LqSCp@AibCG-i90;e<w%M6Qm<rEjUo+qz6cw)z?G4y
zgSBfY(3^2{-`b~C7=0XOea<og_%m(iUc_g>P5IW{8<QO{XK8+4J8uPK8h+m;BNGTM
z@*Ovh1%)DWeDre0WDV^4RC}oCLLTZxj!c~zY=ErU*i#i@#GhLdVD*Ki1=LF_YtN+C
zgVRYyt(Hf{U{U$uzf$7cRJc?u^zB$5aAnrZozLrsMXQj|FU>_z|8;%e+B+HGX4EtM
zp|Kk0jgvHDlRKg4<SQ91t2Ve8On@4nQV`DL@YzayO2Zc3)hEVWVZ@W`pPX1H=y*1T
zBweP1h1?yDBdkx+LoDFVd8R}>Ti#!3Gf9VknnelTRVh%TVK`usR0#H54;<563c)n=
zmk+N<3dp)xO3jn~(f20aLyJZQSaEJ@uJC0GmVX=<7Q5AsA&d-Db0H~c{7>KS@2@6o
zw*R7X=n@Tz6)!T6{H}w2gNr%r9(k~)KqnF;V^NoWq4BOr1&&?kJ*K5k2UXk8jO-bW
z;Mms~Y!yZ)-i!}ZBTYRZY4u5PTCW%GcGhe9EK;#&S4AGvN+R?t8JCt2&)I(O4XNt~
zs~~IB%->fRvteWYtNlC1`jFkq*`=54%gW8h$N!zEMbF<8(~tNwaBz_|B8y@T$GnoK
zi|eXME~lY!zqAUeW{<x2ji%$2lC*Vrd<pWcM%(4Fw}NkNWV(h*Ii7KJtUYepiz^%U
z`s)!cg7-&MPl5vtN2~t|%MkzGsrnK{QJZS8`p>CjrmYzs$o-%MjuKDWg=np-8(yGT
zP)s~$LmSE|^4ZP*4h4G+{k5Ka4RGZ|>0rX?R;V^fGth{yfS6-HY<|f$<3z-|>c3a>
zAmH%@)AA`q@95WDYu|LC!`{WJ+YFt!BtIV`Y(;!vjns2&r2oyEwR0bn4dF13R_~sT
z42368w9miaNQIZ~rIjDm$)1aM?S9U+b+}14_H$5uI9zVO6Rf#E5lsGsyli3XhkKgx
z`5OtJw61h*R|zi-RYB0Q&pI2t1Px>CkP38Af3x6C#f9aEU%$<@k@se!+``#%RL%$s
zJCk1qFRFT5Cr@|70Ot;ar%^Sq;k@d#Qb{#<&T(dE$BuIR#`RLP(zG2PK5lYPeoVz0
zfv0>2-TUxrL1Ms-t(_RNIq5`IR44ez`YdS2rb8c7oW=?*7xxR9?Ny6J{B!TN!#QcP
zPx$+R!nurKT=m#6^kN@88g{L-`b~$xvt~99*?Fi>-Q@jFs~AP^#;70kj>EA^$JNr<
zINUP(?8~EP1H?}?9bI`V5zYN9=N|e6;e)$e0cUSlV20Dq?iuB1_@XGj)t}5|TbP2F
zv=0^HbmH0HtJgd5RGjfRb59HAQBO>E`+Y#w_oL_Z3o}speGkXP4w6q;8(%)nn*n0A
zjMXFjRk-%xqsX>c8tO>j_nvF2!MA4l4qtjGupzdW<LyWnXsh2Iopz!j#+&W@`>_Z+
ztWBb72-j$;tRq!!K!IE~?pi(?4OrD3u37&g`9l$x0&im#&L3lD$!je~|49Q$Q@1?i
z=I?bh*xd&LoSQEFc-f5F-zF>%-=U#X?5KQn1>x}pXHF)>CE?7)bLWK~kn4>0@nfnk
z=pt2ghKYFedIJB{+V9RH-r>a+b(S13?C=>p%hW~kBV`_!>|99t)FHHm!3C|($2@Gt
zEIem^SJ3)JK1O?Qva%Wf3dy#+nKW$5VVLD6Z}8r5{N=a4L(h=(UzD~mGfC5NrRaDm
zYZLh&&|q;2CB27QZ>jv*>q*4Z@?@*wHsa&GZGW}Xy%QI$=j%&J4xM_}?N-k52H-ds
z^DX>JDc%?{d71cI7B8Q_u@Y}b!Pl)$nnF6{{x<(K`HCxq_yvC&nbbL;oM#)0Mlltt
zj^>x@e9wc1lt12SF|SFUW&NV)NiEv^WSi9+9K^$xJK55oKSxHk;wy4XWmqcxGrnG+
z18P4uiVgg2f$;fXOiLH5KtkVf^YhvQ3>AKTvFKqOT)x;I7Ri!|rPs6Rw!aDgF#RUu
z*2^+jQkN(a`ZElXhiBUCv-_at8<)KQN8&A)HVFT<JptXQ5$By7i@-{j;dy-m1@0A!
z2OnoIz<^LaNv_kxV;h>}>;8=7J7H;7F##Fyulrq^r%w%3EeEnRe=bFz9#g~58sU&9
zo>d=0>xGBWca8q>ha;EV+|g9#W_-TGc5uh_9Nax~??*%#;o={4C1o}CqS>Y9JhlEd
zY+}2#UR{~&&u{YGu=rkvI+H4z@01!)U)Mv<f`RlKBBo4O-n3(aAWa};mWI0{G<o(C
z4~+Yr0PZu5wWyfDapKS03XHlM@5S)60(E<?ta09wgFJJq(v)K2$CH{^e};Pq3z-jz
z9hW^0^WCS9YgXrgK**oRZOf#uw{PsU`uki+UR$Hc;^K}nZGy>7rMYNUQXY0oz7>B^
zl)r1e?nhr0B}&)TUi|sTwlQm4H?&srgc~Ff@2A%T=jWFj@L9CA<TA<m1edaU^BQS*
z-+RBvQfwcb2@%N45w1b!*qdIN=7nhf=f6J}gULCrKi-h#P$Fszvogz#XTwj$&f(_&
zD$p(9W?;nIOAxf6<oAKM9G)x2g;O{SaH!Jq6RkQO>I2^}D?K4O`r74jVW~WPzqch+
zzN`>q4y?QD{yq}*H-@dCQ#&YseutAh{a|}RW~#oc9YT|Qa`uPhKySFc^kth?6u(|~
z=is+S^zd>KWShyxZOk3&9SkX0tyRSpsa%V!GDjH-KfJ`QumfW38SZ#nx@ecyYAn`g
zr;Td*Bx3AmU+v3r?U+41Wy~bji~UjHorRsiVtdC);}!AlUjCNryOa2tuN*$uAxps>
zYTri~<;Ywk-)y+<R|f>gTwHe`hwy>UB{aVih46v>?1s><MfmHBj+Wj};y)_uJb1Rf
z9rh+nyj5FI_`4^<^{YZfaMBa>+3O3y;hlWbx$!*6aHj`xZLWuVGTU2wFQq}qst=b$
zVHr-O`l*-7HlpM5e>aBxhaqZ5@3aGJ95_zIh-}hoL2+|R-N)qjkV@tHrF5YP^w*hw
z8=TBSP1R??_j78&xqqJD+p!qgS@x6)xDamPsQr}a)ou_i5<BKH*#!bguY#^-MS+#c
zXw(`3I_{_Z<zIfAi7tu(8|^$B;N69#d=Y9Vq!e>bXa9MJ4K6-oYs6ErL46H{x~>df
zOGI5#Pwd7OYb}c`(zj8oo3l<MbD2l;(=Ew!?J(%Q^XSOgTufuU-7vAf8OrYT*LVt7
z0}+tq4Ej-khxf)!&vgj9UK|M42*`xP`I|LOixcpGZ0W*SL>)2+$^KLxD~CJfc7bOj
zx<PaJfUd>cK^Q;P9(DCqDSThInsH5}9CbApL<>Vo@v_{^{u>R%+vdv6C^1Ivr>M{G
z{32y=+H~b4)13@tC{;*M-r5OKO$Yc5s4kG39;uMSR|RXkpFNY13qi)ySqF_tkII$)
z&`p<_@O8%p89t`JCEoon=E;Qn>N?Kf!CFECNp>k^%XMwQFjbZ{-Aep~%kRs1_SXQb
zwLdyZc4Eni2bZEND_{+c+3pmxGa44&^}kB^)$Q35PuJF`!Q&XC!o|&9DArfp^V9b(
zoOpC$jdDW=S}JP#WlYe}_!Xy<>ft&({xRZOyG}fs+j*P6p%ac(-|J?mR~aspdBk!#
z)?>T27yn`=@p0(cp4#a_Ja4B2&cAj_M7z%Mwxz~8ywPx$C2P4Ab@+G#F1vLAO(p!q
zT`xMO>i60<ki9OCyYtSetaqs7F_GJ2m5FX?G5ckCit%4deZl6;TAXk}qo@lBFwktV
zlS!}`9-jYAZ4Rg;{Os=>x0ybOK1vZeNb(KJhUYAoFSnuBLx~rieU(^gTEBn#N<7d<
ze+7K^YeJnJXSb={=tIE=OZU&wx`8FojO$-r4fe(Ljr__aeng>5`#y-Y!8qT3=d#-+
z;24;7F<#<5@s@1t)S>5s$$*BWqC*zpJE}!~L^pwWWZUKmo_bhcx%HMPe=*o>`7m~+
zG9MBInTlqWbFp!OzeM(HC+HvhS=Qit3xpf>9<eQ2zzIjs$UiyR5MX=xNcjrs|IczC
z67FtBH9ou`|6u@07V>T8OcxGYo$dK~B?NWUwYZsmVu4jg>&T{&5jdyn&`&Whz`oME
YYHsga$!DztA1o0*9lHN={6;qZ53HCP(EtDd
diff --git a/bob/pad/face/config/preprocessor/dictionaries/dictionary_hor_10_5_128.hdf5 b/bob/pad/face/config/preprocessor/dictionaries/dictionary_hor_10_5_128.hdf5
deleted file mode 100644
index d845c33459faf3a4574ff71d35fa8f7f87275448..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001
literal 53344
zcmeFYc{En<_cu<Hq@<KFqLh+kmZIH6(kLNhib!)(hRh){l_`=!WS-}n+rIB}Pj2&2
zDItmml@c<gp7VY_@6TGl_5Ar;>-poieqYvdj&rVao#EQowO{+SUpv%bzy7i%5=(gg
z`B}V}X917U|K9!YpTGb9*tY%W$bbL;?|IvQcX<EZhWxwDn)ja_UY>t`d3ePBJ-*`K
z{{PQ*Cr{|<@sKV5+y8&J|6azUyx>23i~oDzf42X>`T_=edyfC}!l(Xy>;GD5{l9kh
z-;d#7=W6%Q{(tS!;J+vSUv7DM7X7EMe>>#m+4R4A;+^-O1AhPZtNQQu|NMVGY5D)O
zTp<HI|FPZOJn8~G>VN0|=Yoag(SOd*oA;j%7ysLSKCkV6O}+j<E&ua(<-fiBa}9k(
zvpEmXx&P<s|Lgn4N006QXGZ_`;bi`OkN@j8{+}EC&lmWgFYy1lFR;OZA|78whYKgw
z?2JkX$g^1>$!ASK{@n;&Wm^J<6D^x(P3W+5pfBiFJsm>LPDX382<V$Q?cjBifJ^SB
zA96PnATj*YkN+qg&It?Ys50pAX7Ae!g);;g?MRSbb(nyO?x4;BH3AYStxK8`>0lU^
z`5=r&hiCSS9&A2AK<@R7jgeXeeCkoX)-g>7alglXns4Z!YU2Go%aIOitPOp)Bog3o
zqIGnQBiTMlYva=^1T4_E`@4+1Z@;doYc+Xa+PmwYH#HMrn!;1DHHd(dnOwgX@;OpA
z9n73`r^9}w{&;Z{0_a&GqdI~Nm=(S_7u-$&54T4%&5(deFEQoqgEaVhC8$R=gaGZZ
z-K>~k0^Fbb(5q_+IG+4Fh=**Kcsnw?^#cv!RRZr$6w_gEzraO?ECCVfYkSn+5a4o6
ztR5o?@YbRVZP%lNPW=Wcp|^BMfAUb7Zz%!N0*ttDZ36s6oTQT05zrJX{bAuAItVsb
ztQ#7vhQFWm+^>-Rzo=by{WbYra)TlzOqR2tX!>NJiz*9h4Fn$-o}$2~_k_%{HX1ON
zt1i_)N5C6C1uNPYCLr(UDWPpFn9@4k`R;2qEUGzi-9(-S^W6d%tCa~D*mim3Ya$aA
zHQ!7M+OeSF5OoV*3k8Z^x8Eu9q5*`N{pyz^K&8RXUcie9dtPpj-usXR*R|W{TdPvw
zf=0=PRY^1$O}8oS_a#8&6nA;u2oruCnU3r0WWr6qn5C?A3M4!@f9LlT8pORiI(>UP
z0Yhy?=VPpy&{}h>a+?qfrqB3p-$bo}Yh$O^&a0q7&au*~_7Mcw#FlET_{ad$mC=0z
z@l4R&cHvN_buA3)_a?<k)1XDMDq;IJa(vHU_i}v2fW()7&g6G9!90jH>l03affsva
ze-_k1&Ad&?8@|(_-uFh(g#%0|7SS14zn%pXr!*$G@iow3{X&*s3*oA2T7;A>9W;Gc
z>79Gd0Ijd8A(x*sVUfpS^Cfu<bTP1dx*!+P(Zj*^iUbY!ilrN!8X_>vywp?Il|U^s
z(Q2ba0###Q3_35M;l!6;53;gph&On74ofod?ZMY;qVovcs9$WDEka;|dfmZ|2WiNX
z@(OecpkX?%N6Lyo0*&tGEj3=mz>;1u@#P!>H+61xjcui2l9k0;<`r_gMAL|yL!kbJ
z6VrcM8Hhov-_Fz%I4b`r;8RW=-dt-f62Fd)lKf(E{7eExo`z5Vac1Jiv*z9f2?W|X
zXiw`OqT$?%MVl_E((qLDtX<$O0^8yqZ>=HQeW*MW=Q)poo=3K!`xFho^x1te)uQ7{
zb>*Cy&jfC03p}m2jD>*>%5!^E8QA+kE_U=N4L2{9xVMBw!}jkh>833NzTfzrzfOgP
zmPG`$Ad`WEANK#2;UN0I`%!tP2T{vy*-hmsI+`^Kh&QsBxWJ@On8$*^EnwuJ^8?Y(
zBJ5R#5e@H+jf5Jj5-4#g-Tlow24>BgZ8r8pEavO|lKGf{3c(8BJ{Z-aTb%TYv7fb=
za&&IR$|x4%_v>44sM66!_+D`f5_o#=rPitbauk?YJ}f9-gNwG$IBphXqWRN;V_RPk
z$Vr?EYC6ln4+}Ru|9rXzt>R)TT`nP>UP(MD;%B2z__beK{t~#DEhFz)LBpv6gZyQe
z=@{Do5oK(uk-pFX7N&DB{@?~5S#ugjY&mO|zJiI%a}~r=V`+GJ|1D*GcPi>Tb0^;&
zU}E+TZ^=|6I`W6{dB@l>P)mKD+j<8&9<d;jP7G1eckRC7m|JXoZ<$lvUPs3@A=I&@
zo&@&YA1>HmO~c=g3f`ZN)uN<hQ+HGp10}T91h5aYF#7GYmhvh(YLzr}MyAnmvz*mp
zpB;3}GFk8ayp)4lFIx^ah}NQAe<9C9a{T4JKN(YN7|7IiOwU|M$3Zo-;4yAJa{l-n
zyCF~DEqVSv**&D)$FAwV^_0Niwf^!t1$B61SDeKOvOgcgsle@h3|zP+v{$E-z@%qk
zXWq5aam=b!?mlT}Z`OW|P!M8b<C-<Dn_?N5e`&j6?F|Co@Jq863eiyQPaNw;Fk)?L
zmsHqM25ug|XvP&_;)Tf;+$~CuTlUfFOJ`_!$G&XnAZcHDN~MBsI!t`En4{8f&&1W2
z6<+1W&~am_r)#qz9q*f5Rj%l&L$&x9KTaNDptjM=`mqxXycj)Wo4TKgy1#Bd`}!Wy
z_U`ed6<-mj+%(V0<uXzGSNlirBTRHRce#E27lDOiaEuV8<NDK;eoNC4$H!m2GF;5S
zrM&|UB8p6mI5Wgu>&-yDSy52~F5)AT{BxXU8m?D7M|+q=VB<3<C0P>&4xJ4DIroW<
z%YWXG8BIg94vn8XLZjh^it$u-2?KLV=8FzZ)6l8n(5v<fbd1b@N?HDzh7y!uUE(AS
zy~T8`(+3C)Ng6&dJVwK}Th^~!N1<V4><teVc^&WPBdRm~G^{ZaA6cfrMDd40bBt6P
z3S7~0_DZE=|4RQKZ>sB1LZy69i$8(NssRf`TM0aPU=w=nqN8whO~vk10*@!=T&|s_
zqBTwP=|=Ls4`lkA7m@4L;$Pmjfv@Q(HSg4c7Cr`=?G~^yOhU{P>T5~3PQ&02xq9a=
z5||pH^r=&cj%iL#*3_SLoKuoN<l~Jv?5Lb$r$9$rqqJ>S?F?+)7``^ToPpb`oNsI(
z`<GCi*`8-cj%%m-q^vF-SD#vt^o^YFA=ZjJ7R3;lcgkiZhfd&+MYntTrx7C#K3Hyi
zgO0g7+1o71d1I#;m#w5mV0`z1O<J!A<o+uC%5p`#VC!V|WSF$Sx{JcJM@)=y<r-W)
zz(CU{1)ujkWnf>LR$x>b4K1&15b3p|q5na(m8%{yu;sAK`HT)GChfCKPfBBA)AEX6
zo}M)1+278U`a;8v!^aLUtYYGh8>(713s_i0x9e5RVq(Rk&TZMeG_+IvTISnAL)qKi
zV>ifoZs%t3yy-XTPyX6179XyG@Ndhv4DV#Yc}Nc5xtxHTt~%LQ1t{RUbj{blvLrr8
z-_ewrTn7ge{}#8lvmmGcqiPL{0qVJjnXA&N5R-0X|9Fb@zakY1aS!N_HTponBANkz
zuPOg&R%8Ir1;5m!NeYBH=hKZ3QDErz^NWiE>G1GJ;+0niS@1=sz~~;?-}^=tZy8?-
zT-m;2arN$MpgSw|@oudJ`#+hz8}!&vZ(y_WgeDUXP*i4g|InaA`{C?{%qpm0KW|A|
zLW2`JZ)6mQ31DWJZO}s+w3X8qTp@AcV4&HHp!u~R=k;35M~Z;x4_8VT$uWT|E_J!!
zI{~pn-$YJDFyO+yA@z%2D9}pyj;;wOaiqcPyIVV0;7}^A;ZVW?8NPJ_4xS9So&SE7
z)F8qMZL8}G0|^jwEx&8=n+bfAU0|2Wfb@yTw`-4)&&lsFU`j{WryAa{MUn}chnn3=
zSF)koTy^1KHXBrTYaMO7NaBURyJuX~>)_~LYnMpAS^y_TxRyErdX09wuNV+u;358P
zntYz9h^r+BjcS4KMafntc`8iV2TfU3Goa43WH`v30lS~7{;X_ef=BTkpNNGNSh``u
zcLTB?8+ZMXKOYIG=nwhz>@ET2Zf}%J{g^Q5VP9u@nhJbo#Z@cz(xEwandodk0alLT
z_fN5yAUpOeQcQ<@Ux&V{&)3w#bL}@@kLK6FvCWe*cggQ~ylNnSvWLXaxn?yF$oI4V
zw#_9@uNF2w^>PZ~t%FDJo68DGoEfH-dTHS)0-p3LwgHC?e#O34)0P!baX{N|?rI&l
zPbHa&JRsor-W;{x?+I9>S-UCCu^!$&s+3+kQ39_#eGl)SL6CH{Cr+3%V3*59s!cor
z;aNY`m!4<CtenS0-YptDHPe<FzCnk<Z(4;|%77~e)ocV(2msgG^0>Pkc>PgGtwS6^
zUQYjoqBM!Gan5Gxd={uwd>I)@XFy4zz%fl5HV90+oY*Hq!1V^v7{3cth!&|*YRsg;
z^(D7A_9U~wF7xL1pEU&R6ZAporof#`PfCv;r-R;j8g;`y0^$q^t87gM>@d2yQ`(;f
zpB`s#VhUA(YT3&6_rqkoQa&;9^b`%Q(xS|{iy3hJqj`p^5*-!^-VQxxTn#@=%SyxF
zQQ<)xH+(*6AN9eHF2A9WIMQ!NmyTT>Os3n;3w=-yN*^Wp-eM*6n)U`%lQ=&rVlnGN
z1Pku2yS|bC4g&(Nc?|weuYwa@jlWm%Bb>gvK4aAq3M`qh6QnCKK(0{q>kkq~TW8%n
zsk5~jZlC`qx1Ee<&K6&o$Gy*lgHh6=O`FO28?V<hPl5)f;hu(aNCk0o{OjV#fP;P+
ze^#7jLX$z3l>Titum?-N_V_X(HAyQx_Ixc|&`F>=s&QbyajJq0mjPZo_Ft_YWr5YQ
zY;USmJ;Zmqy;53016~`^U(Fj?FhrMi|4_k#g!H2W7vq=^EzEaorvwZB%pE@($7SK3
z>%Di68&j~u&pb?9lZ{Vjhx*z}36y3NR*y-1TEAm^M&o@NPTQaH0lqq{iWy<2JE9
z&a|5eK?Lqf?5evfK>Ew=MS-b2r2p&=S-n7pK+8>8%j+UZe9Cbj6<9~&hVJ@xW`}85
zMW^l46{KR#ng_`;Wi%YLt@)iu;_JI1-p3X{WMfV3yRML)8dOYb=QAR4tjl1;_r8HD
zJlgast#LgI&+Ke%y>psC>%5$Z?o9+faUQo0B5~b4)vVeZN{Fj}Xbh_B&~V3DE^|#d
z9ZfYinLSizBDFoJp5M3{!@5Fli)7cLcRHg;H;cffh7U4+#}n9lGqP24EeF4dCm%c0
zR)IVZO0yM{=*af9z0~@Ijzm}9`_&}wKkXDIUtYmNeZ5@q-%A;I>Ai%3ml+eiR*bSX
zGV8GE0T1n)3k{d?4?Xs=Wuw;K9DcuVh}sTIZCnfqq*$-6HQdF-vqa<3oc#>+KC~(@
zN|=GO<2{j|Xaqj`+io?xtO~EcD3*Ld`q3^!yRMab3=Hjas8p{e5H$i`h(^_+!*|8Z
zq2INbHJR*DQq4d`A*q4YByKzua(&%qeJ1K(e!sbzT8(XKcONs4lm7QcTdYL}>9>dM
zh7}CRxa3~l>j4sXxZE6;Zd_H1n<s+2I!);4NwG0fBjcR!vXgEBMr<tX+PcP4pa$cd
zB1Fas8m8szO{x5@#N=;7*Aj2C@O_`Dl(P#N?`#rlA!tlo^<2IG7e523;S=7DB#yiP
zWNvIqgofvHY(C1|q2nUINoh+#Hm1VjU%Y&kn76KKXp&Wnp3<4+<q@Rao$%LsNyal>
zGY?BXoM)rF%h%7*!4=qZB6YFNARW(08wU!J@sr)h{*yUU3{>8K$Fwb;g;CYdo+y!V
zN-Oi7@Gd(hF6K##-{wL`pKg^m)KNO#TKX});~obu*i5`ROpcFc<066hdjt+t8UH?3
z!9f4LyVC6zF;Ov3QF_f`1{R8y&a0r(@$&+Ec6VbHz8%qfvi3WPE8wD=8u@(N+2?=m
zImE>Jzc0CuC2BBw?U<u$9}V5}Z-2|`rDM*6n48N<ydb9#>)o@Afnm%cFS|EYm{YE^
z`j;$$-LG2iw2#zc@q9l%1wIz~?v)%$J3>Q^&piXirghkQ%OO$!Y8`HfRou*4O2$Ww
zus5JV#vSD*a({Os9`Rhh>&mifG;Vd-{rP<fp0tyoR;^+nhb^MBWT+OCU!4poBIn8E
zE#{Z5Mb-Gj+?Kmai-HTy>wn&LszWh>9}j9B>8O!U6%YyLqVL1f!c+DoXeho)h#=>s
z`NVv`g=AbeKfJ|Bvw?+=ReRMJ-(jP7p~IPFM=MdJ<9lM<ayl-!cC76@iH~Y{92<mt
z*r+$&HIaO$0cUT1)2a)o#YM8mi<>+c_)DaTf29T!cWrLb<1uHU%F^Rg^)vO@lhnO(
zUjP-u-d2ibc+*i)=FKWmHzxL&@)wJd<CZ)9*#9kwzZWf(*jYx#19KbS=&kRp#JHm}
z`Q@ir7%zL@U7EC4o>+aWT|9K0p8s36#*>QIvl6%wWPBc+qZ-KYV&V<&v?n`4=@@4A
zaD&TsI;zG~j^|s{px7zXMNgdxtY%)B(c_Z%F6v&1$R7e%|7FIvj#IIJ&sVhr)0OyJ
zD14+>7x8aIwMLX63$xw$-d727ut!Re8T+dOAKbYU{mz1p6?q*2B_(Aj$oJ|8bsrN|
zE+(GSA#v`WjB%5`awZB~yP&kEkcNLA-HFJSsKtG&>w6=`X=rw0jevm?8F!bzZ}KDK
zj)x<aTGASon8|0pXE>IMtzneF0d)fXd%v_2<oCJs&Th5mIW~H(xU}u{%`#jzt3OfT
z%0MrncbeQz1_njNHHPLh(BqVQ=eQIbg(G(@Jz+^;y=UCJYr9xD^GIGIfaDo|)C~@v
zD5qoF9-8G+HXENEO!`TzqM=VmFx^0wfpuqZ^Vj!~b`YAewe$=VBX?=!b#^e&;n$``
zD+IW>tA6CN9G8M8lAY}O3>bLEt2#{DgFxB2wc4NNNZek#M8&<0ip(?Z6h$WnraOnn
z9Z}}sQ(+IIAQF%3DjI~JwP0Yf4l&pxLczV?6W2w@(s8TC<<aLandltk^jO9cQL=tu
z*8F@e(l;L3QN&w~AHC*TJD#UwUpMcy<NyMD`dlv7#jr5gUhSsSWs;7uQ93#Jz6Q6S
zc<V;lQHr(4JoIHF8E85G)3tM1BrkDDUdNz>i3!0+k}rkT;d_d7$k(fgdo;TXx(X<$
z5oa}Fu#t|Zg0F~rzGGp-^Ph*dG*#e6;lq7rbV*+2l0c7G7lErj{&+W|z`*mK-DVrc
znK*s<z+J1u<w*G=_rW)khT9jkNZIBRC?FitEKjccYu9*uYjouzTPaZGG%p!Hvs;%{
z93a<G5m&#(GGu$7y>oZSI9~d}?WRIa4*m#Q)%mls4n2Jpq;4-_A^%n0+3tVhk%6n<
zAKMVPkXAB1_>6&PKl9W!>N9{P8e^|oT?zM1txAtHFd^{0{E;3FgtKnlKk_&<@Ohpu
zuf3@bPTsb89L-k;6Owr)IV2u0+0gy`cMA=y{MRY2t|#E7NS~5Ha}fxnkDM%3pn}he
zUcC(tbePVRvU*PP#`{th8hVA-z^`8!F-uNX!u(o`mP;g`+TeN9mP7KBLq>@nQy19q
z$Ld)Bjh0#{a&S_${7!+?iW?I)8;Zd44wtD@$b^@!F6Ru&5Og)8m~{h8khaOm73r^r
zW~SMv>CP%pd=>onz!HRD*FkeT0XhWzNZcfIlnKgvVQ_<c8Qe3fB)%T3fZI*V(}^Um
zi*MtIh{!M@$|H2((q|UbkIf1>wbX#*@v;4jG+1!(<LEVFoCShc3C&BkEU1dM*J7$P
zK=Xwf{--WXsN`rFn%i<9`oQpPGRcEq=M`uU`^kXKe>d!&SHObjtK-e&ZV|BDzW;@Y
z6dU4V8GC(p*Mps_o{?`C18ng^M2Zay+?#($iq3DuY>O9~_WY!sIG>FfI!?y>C!!8{
zt}eqnISMBpbXB2RWaY#iu?8fxJNDlo`GZff0|$6X-sEmX5)W~@3VnaZTdc~UU`@x9
z$Kg?2?ASTl{E_4nj$PhrO|c^9v&Q2%ZBq(9PR8y7$>nHZ{P*x^LjzXwjaDy<VB-&m
zhCTfA8EB}pIXWPcg45n7<K67a@#c`bMa5Sx&Phj&H|=2Hj#KuNH6%|k>hNTw<YNWS
zCC6-({auCI#D>bhJ*Y>K!Z|8$8G%ZvIhu+yBz~)9AKE)pfliwGVyEU;V;60xDKn%V
zSJX>eIG&<mTiZ6R`P*tx_x&Qnu_u)zPbF`kUsi$5-;%xii@0djaa_>Wm5CywVfSMS
z>##FUNOwG>8dcec4d43L;)-tNG6$nZ)IMV^Mr4rlPwJe=+#V{XQ2bK2?XSSCA9l=r
zY%NDG*`Cq7hmCkzbSh`Kn2qzk|8+|s=g;RNim%AR3XHLpzEM_Efg_owg0rN5Pi#tc
z{u^G4olP1RrHQ0p^^eGso~ywj79{nIlDx-8SK)QVEMzH1ZtB=ogGZ9uz5i_E;OQyl
z>A3GC?&Kegj~8L1^G_KH&j|+hSRDG&n?pmxKWEZwN&i;p=IX~yp`%N;Tj-iqbo6e<
z=(aXC=7{TlOg_%RgIn5#c+WHNZjXlMIg+moXVe~bajV7SgB{6hcGn|)mF6AA^;DEj
z7*9)+Ah6|}<qh-YHOMj0{{H<r0~fsJcgonrzzOe=yc*KaE517FnUqq8eu<YgQ??;`
ze>_(6;v<QhU%#8Ywyq9w&Dr3<nj(}MP??O|UX43;$DPeVDlRLS|8eC#CW@Yo*?VD}
zf}Rf_j4UAiKzjHe7aEC=>n&73ov6e8Yxc<=N~lK_!P55o=UI5|$Gr1A+ep5|>q%sy
zLIpAfPrrJ#q8bC`*Na@Yr=iu(7ezcIZd0S(DSIx>MTZ6cE_#_H&VKJJtki(m_~Ohi
zod7y^IgI2SKFtONDdo@JZFS%|7CRJ2#)F)k?Tg$OR>9sGhXaWg43PN2-x;}<4W_LV
zmrmQ)f{T<G<rFzsF=1ymOYR{cw6%HjZFLq1KPu+Li?ZP1`IGMFedrJ;wX^+Y83RnN
zWpC5^NrMBWuM$$oJjR-o8RxKcHuRJ(PF^ve1A#FeZqK5a5b@PqKTC*=r?RRFdu(e#
zDste-?e|>hIVc~u>naUGyFOuuCIMAu8z(3|)o^-Ce~>sqz>zth<zi%<tMuX%TSbxz
zO-!?rmRf|nj5;R1rb2den4ZpI8u<QvA}<$;z_l)#x2&xQlsfipGA^!!fDM{AL;34q
z__a&QO+F?xhj6qtzt+N)=jY0VACv2hkdWGe{90)HR-UxlwHDs0?`ReN$%cUMADYxU
zI8f2`t=Y?)To0EokNj&}0mHY`{boHXA$-L`dQn0xSoA1r$YgQgXTjsCZ|k{`&zEv|
zQxdtpU-3A-c8Cgku4gu=k^VvR$FHkjTsXL$GGlcjj=-(uO%Frg)#1afddA6n$OT%F
zqhR-d^zSpfmEyZNc&+>UyS;9R{NwQB?K3v^?z~cEI!p59af@R9SaVR#>`d#kOKg;y
zJU(^#HXUOxJ@Ppl%tVcA?}d+kqu>SE;IU&z5l<+W(pMt~H_Q#Lvnt`@(UT5$?vrs{
z=wIi4cQqO=*-`$bcQFN3MU$vvR4!hw4i}0c^BCRb5VbFw#KG@!f)|yN@j=#-?d$o;
zG>Ogf=N4rQTwlH3-{cn!B?BiuS)Q%Ih4E&+8cx-C#n5np!Qom=M90&t6%34P9FvPP
zEk*g5>z7<Y$#wC1gk11=HF<vFXVXOldM4k~fA39T(B12|#>jk(XTkkr*LACr_Q3d;
z(vM0!dEFtZDV>2Ue;I7muB*p~?Mw8o+Osh8W<%mO`7$(#jfw2zt->7%yJw<YDVP|3
z;@~p%Ml@{q&}g4-z#_XD>M$FTb}#IRyi6Tx$sF71DPD&Q?n!?A;LL)LFVhcG+!!!&
z@?N0&TPploo_~$UsT|BYUbY!>2{^b#==jlJ4d76&Tflpn1qa$5Jv#Z036#evQPq=l
z;9NW@G#tZ%EtV@g3p2Se63V|n`3@a&#wWT``UsedjCE2qqCsnCtmN5J8qBq?3z(rb
z!f)Mc*(M|pv|`V_HtP|R&-i#tLwq$Ab}n&i%onePp^xdR^VI6$T9sDN-w*;AH@z&p
z?dV|ImR}yBQwde$^wR5=wZPqcpzKEr0~%F?PCgc?g96()ZsT4WP*$H5D|lN3vz3&_
zPP0nby?fwS*$@*Jwm<rAeYY5PPkfdMSy~EL<`pb(Xf6k~M!U3sY&p~oPD~o6u)uYl
z9^)H1zx{7@pNu|R1)_RO7O&&4g3D3Qa`G?HAo0w_Ck~cuxcy?5H$R*OXV09fUocP(
zD;ftnp2$?dbg27++jlbHN89HUaqnBfL-3bD_(T(ou8$cLnxw%EMcG50(WS78@5twB
z_aa~){o`18xCT}pnP{0_!-PCTJz~pU(g^+Cm9AQ{z^pM_cCw!XUmwg%F%RNG-?x<V
zFb@`_J^3UTnM{S3KSVz5Dy;|N{9gO;yUoB@WgL_Kmjy<dlZDwNpLS`kaL0ZplDDxw
zWGu3z9=cK;U8SEjLxA|sO{0Ar_}%{QXtp>5)^I#|76g*-Q`1bDcd`buHJXjxUvj}w
z%#dDB@?GPVdVx|#bok!2VSC>blFxM5W?_?12kRs+SvgCxAkcKJx%Db4cxqq0)cd6r
z9#lMxH^?i5BcFRV2CQSiy<oF0GY>XY8!}D(C?p@2A;@;~&4ueNn`EhV84yj08nUjb
zgLgN#aF#7b@ZU2zyx|)G%bb`hSN~8!$6j9HIW-4LlVwu%dg~zB|6x{FEDhF;I(8;B
z)j&v8B%^k?7TRy|tSrmRfGtrn<Ike$VDd*+>r_)6@YG+c;C1JMOdIcg4}KPm`Ua>r
zZz_Uk&gQgf<vg&P9~ALbg3RA`^Nre9Gx7b0ipUpMblh_wa7P)5pZ%k?PUVDD;x(U-
zyJkq9pv`;nSB5PY<+snTa%I<Hl5qdBJwgl|&aE@$5_EJf_no*z@)kU5hcX4i>oL%d
zSz%12qnl~7j}M!P^ZqXVJz-8m;nkaVKRHO^a{ax2`-yrSP1?FNewc;jRO`eIzgRe&
zqj*1@LB*5VYp%$-lktUv;P)dnT)fwxem6dujxFovufG1BhB?p8cQc<=pr()3YFtL(
z^`FHapU#r;uv+u#=ITmRmb$k!z!z~ZlRbTOH4Puz$W``ir{d4}NtfH}s3_ih^mdv@
zK2{O$^M!rOP;$4rw-=e;@@jrA+p5b%n?|_|bSLw}v+=JC)JR^b)K2J0OBM2WKUC}N
zt;Xy;L8<j*d?``AFD$E=gU&Hpv}d9$4A&INNwq3QtGiDJK515Azg$ClPf<1Af3~Gb
zIidj*ni6K7i!@>_wft_(Q3Ca7Pj~}375FiAvh)Lq_ujmgo!q{J32~u(>h&Z)%RVf;
zWxxX=)hRK|kW~d1YOkJr%V5CDbtSQh)?8Rm?bjJ~rov$82D=-iUZRV8;7R#79gMh|
z`EyEah}zUuy=;9w+|BXPl4zyF;g7p49gj2No9m0B-BmOwifrun8KlFi>Yz12%NpRc
zK%Z)HKND8_+GRDZXTzcU5-LAOsqir+tmN)zDzJ9wjk@GEKy;!&<`r*J|ME+1ftLZP
z6Uh~nS+7_Le_D9gH(JvniQnzoh$uPVmgvQ8{#Xf0%XM_ut0Odi&|4$tfS_`wZ5{JD
z6~0z2jTm>LLQ#(-A$UCx&bLbwmt@Oe;|}U{s%ssjHAIXPG7RuN^)`V_v%zo46A9UE
z3`kC6>bEvl0aJeK@94D@C`?Z!5=kES(X2V|`UVb+MK<WmWi#P!pTN93)x|&?J2+OT
zRtal^7_>ggYFNEMB-py80lrfNnnmIopk&@fyZwR$=+G`dbN^le0o=82HD**;#23MR
zlgozozl(NEOw>Ygh<;cb`M%rabnd&8IKflnx6U-H0)m1CDHU}sAgLxvE1hA1)TfT<
z<w*!PcL(%NNFz+gs+24^PUbTVT1q4GTA+E!{h1Ug7I+vv$$l_JKqYgZU+JG3*!r{H
zHdeU?-s+kPzWdt@E@7<j$a^d}dNr=pvVaXXh5ny5>gGW~W&2Wol^RH&^bTPQazRkc
zS6MNg1K%BKl$HPR{|;YqK}t4E%xoRn_qPh_+eH#x^tcfJWTY%bnGQE8kGyNDD`5Qa
zYtE5JMPR~mJQ7k>4LeMash##?!J-+9cdKsKLI2sj)`{>;@cI4yYPd)l46NZf<ujEJ
zP0gGT`#dh}->%dmK=SLiwnNXJg=H{hafwRA7DIi+WdXhL68Ny>_SMt!4UoG-RrJ|n
zHauNsS-bUI5p<+ozi}ux9~P`W-(`6(jnt(G=fv%5gSQ`!&z*H@fG);b$@ph9C~Vdh
z+q`WZZhj`=wX8M^X13mvwzH_iS1|rYKDQQ~IyCCHTQbl@_5_jON8ot6kZ}1lhm04T
zyeyXxsQseRa;OTiH~+`!A~NqD{3t(1=s5=;yHeBk-sfPFY<lPZJv5a0@$TXCxjMXe
zSEBd?jl|pRGm}l{IXGB$S}2EFk2$)uri-h|_2<Kr;FVgH__06egI+urE1yYxO@Bng
zyHEKpBovUkfPE9fm+Q*$>P#Q+*m(+?WP0CZ^03h|S7!^2<h}fUW<E5GuSYMpByLq?
z35G~Xb$-jv#HUN_9rM&l-N#D{#p_Escr#cVL`dH0^S7_3*vTPi{-@nxW;hc|e>$f3
ze6GTP?|&w5G}j|bsjZ0a!^Fv=J8Bob648G^$xu|P3TG01tIpm*G%L?97>}bPXQ=er
z=bKD43_sD>B~y$~lf_-GO%v#QhZ$CV0`Vi~%(`PFFDp9pbj{<lR5Z<YXlJV;dghJT
z4`pyjp3TBO{w0g#kAeR%$zy-@kPm9TO6L6yMLY*3SxE35N>?NK`EkD&29Xt{-sDFA
zRNDdyX5D_9`Gt(LS6@ij^&z1Nox_%PFWyE&ui~d)RZ0KwM`$)r*PDrhB`Qa`6c*Y|
zeqE*I!6o@QLA{3sB!6w?q~=fZY->c<Hpi1Zdyn|u=aShZkDsvfu)w)yEDt#yps&xu
z-?Z`6IbSwX?5=fdHB_Vd+?}c)DK*I5b?&8c11UJ@`d|_uL-IS<_G<~;A@JSM-~F#`
zOHhY7aL>PuhFx(bvy}%pnA<^XJ<KY@Ios&8(Qi~7il`ggtVhK$kJFXC|HR=-IH`t8
zwWz7VrN2<mz_wdl$64NNw5jP%X(RdOk-@^$eOE{wiQXg8*4s7sRB>yj-$Mcwy)u@M
zY^}g#sgZGhkt!_T6tIQYf<W`{Utinb;^4PK>OL2TNWO7(>G9b+`FKL6^O}TZ1)3Qi
zYCid{6z|Ma-by+pjLy_8flrMntRljAaEQQbyAKxl8J1zxx`_OJ2dmL&_eHl>fp$1|
zbj1NuKLo1Q1NFDF(;zh8Ph0F-CB&ut-cok52wo|Qtc?BG4(<Z(UpYxkaBPXNk1{C*
zud|mbi9gj~^Xk@2gl9Rpaqgaf#M=%VI>QoP?Id-Ai9JjY-CEF;Vubm=$%Ax*H7Be#
zRzXHj_sYSH4!C4_ci%_#2G9&Xoj2Q92?px+K_%xB!Nq;Aq+@?EbPQ%3KDx9SMhpa{
zDp)KS!6M$qm^u)A)>~@zEDxw>T!bqu3SgN^hUnGdCg`A@_f6Qtg09`tPo(F{K+{e(
zBh#-GM83FYrs<b~yM^La&xm@^_qZh{?M8sd-hn#{cNf6lOAFY3&vW3;`StGv0@6XM
z>q^sQH8$|I%2@xnONTEiZtVVb8Iae~u*bpoDKtupGbXz-Ahp^zW~{jZK4(UMU))23
z+Ak|Nza1_H)2d`=%9%K@JA5%MN;w{qh923kk!gqChk}NFDNV3v>Fk$~<yBzh9PZRE
z8VxfS)sEeCNCIlx!ljGWwBY{E@9&o<H{#EqL*6mFi_m=Yl?h{ySUl%EMaj0y1OqG6
zo2*0a*dJ}YevLc>mC8QR4=Yt+9k2h%ah-fzwf=OsoYEt(UkDi+B^$8wsiT%b0vjJw
zTD+^v35;y)crkwC8K%kK;i+B~if77o2Uz}Xm|I}ep+44tts6f2kCXW;<NR!wLwu>&
zr+noZ&&d>|O(vNPb++RD#>nN*cCgVXV9!1C`)vHwb*kAgGyy-D&X+i)TZRc7K3dDC
zW>hm4&t@yL&`dNZ<Ht@m9@d!6?GP+Ljq{9zqep$Qldcf-bukS~`cJ#ArZccMauHkl
z6RB4^Ci8auXcCspnV8P|l8q~2TfC1E6)$`7sy{TX#}lu|84rd_u(uTs`yD95pZ4dK
z<-LmWvQ2H!!FhBP=~Xxtv5AYdYv~U(6!LHjYs^w<q8O)abp!MVQqVhYL)7iy23*J*
znK~fYh!5tUl3P?j$HQ6HHU481)HjKs`p-VbO4gb7VwZaC)V-IYbex5IEnXdMc~ygb
zd+jzZCFAo;%F!0JSIe;WqZ0r2uPvC{zsYjmgIa8jq<6gEN5jCO*jS2u9mW+ZZ{2yf
z3ZtTU&${m;<JE+B?}K#6{H;oTs_-Kw3OcuD5ki%it>Y#CXNZC>(S^D{%$m_F9%R-=
z)g!l}#nr`>%mbc1W^{q%(Y_^@((@OU;!ib&d;H;T7_&8TQp=iw_r4$cG(+-Bdn45i
zHDmJe@n!Q*Hzese`F_o`?Jm-u#J;T9EJx~reoy&s_95`{6z!&MbQLZU4LRnjT8iz$
zf(J6RDsa}@V$B$}9C>s7d+*b$vAQ`XipZ=$RTB^4-8)DfRO@l4g*%F{@^r!#i4i7J
zWazS|OiD2G!bk@-rvi^X=9DbDPN3nJD=vGoSlFzzux=`_9yf=MDqO6}!)D`8DCbau
zLXRXfAL~{kQFzkIMu&@bQmlh2H=1y@=bGLZG2}WZ7;3h=n}+!<Q%g!Vkn40^+vutv
z<)F6gMa8|>h`g^1Y+17ju*@K#;QkZDepizx(_}u-dt-cbtsfmOZN0uj<Y*ZrO1-_S
zVovIXZLUA0JCvfV^zrqZL?5H{J}XrX?KVixYPwt${0MTlX9wAik-Yhmock#{R5TM?
z+jh%^%pZjJ2Rbw}pv&Z(WO73}oZt5{{I+8>$PW~>${UyB?LxWx^E(L4{}6^&HjS`S
zPIz{0d>)j$M^yf~S%KY|pLP(V<odf-FgL`If%@@M^-C+6;PR`qa8juNh<gFjChatw
z&wRaWbygkLo(UDNNhb50io;1ioH)R&YCQZdAO(#>R=T{ZChZ{j4Kpl()Gb<E;QvnI
zk>6vj--3iWAS@?caM3#+Ukmc|E`LYEj?@>7M-Cj67s#VtyjqWH7mDWDdT>FJRTM>0
z&cZBddFRJODehWhIRTj+xGk!1%w)6;ofWt{7)@+I&A&Xv{SsKLt^4b5X9<1_*g3mm
zZxf1cwi-I8&cfQ=H_f(e;liY1>}@e!F09#O;BkDe3Qj&3PT#aR1AK%iTHfokK#2dF
zK<|eZFi1UgGVXINSl8&>w7X4#IA^nqNA{FKf~FJ8=|Lel_}HmF+Q|XmqN=VPV+1s0
z&Zrr9(x7=!Ezj_>Vn}KC3$4p9fjcf60(F-)0Z*q%UtJ;>j;?Eb#`A^-v>Lbk;_508
zkJ<N2(4!c>zuz0>>(~w-o!8fGZYOmj9e0oEG_j!Y(8JSW>q!5>b=lf$N5F;?2hTp)
z+X(!nDS_)qoroVbgDJX~2}daFbsdX~fmbYR;y_{^7^_KI$fj1nmpezQYtJHieR@QS
zQ4RdadFpUIy8wO(8d81s6X3k!*%_zprC{tkYk44s#M!+Gm&HGp!X{iUqw}B)=(p1@
zRxbEQ_pY4r=@lD3j+^tde>4KeJ^JNdW;V=tIH{=#mB3H`7vn-D6rcq;Rb}Qkz)tVb
znTXq@pId7Zle>uyp1;Q<`y%LIm!Q$VaeoCEMK$}q5$E8#!LZk6CC||)@5bx_^%@j!
zPaNH}{v28bZ2NTZYyf<o-fDA$#8cg??3eN|m{|3wZt2gzx%eXGg0XM!1E`Nw@xP^?
z4w$7DqWgu+L+*Z;wEq(mf4X%H3l@by@G)auHGfwe<~v$m6&D9<1vSr29bw~vTb^RC
z%-Lu?Hs9~HRSvG5Gw$M9UWP-;Hi2h+a<Sade4C?nJ1!FPZgLiD!sV%t42{V3X8dEA
zj<<Izwu<cE{mrZzcWM3c6(iSy=4*}$+jexKP!R9F;Ug?ke|gp}G9(Q)M4J6jIa3Vj
z)s~O>r&)M+G`f3na1+W=?}t@?<>1h-=;a4>qM<J&r%Qw#4w@MSj8qbrj=j6rq$110
zq$JDI-;L=gJ@@j8*kez4<5czK<y;mnQ+!}`i`2<39&{+z+|_{HrFQc$kb=z%__~|p
z)4)|!SCMX$jF<JQ4=%gU#i=!3GP6yj-Xi8ML(!PjaqJxx9du_P#D0z*$;&|V9M=_l
zS*5sX<J^}*`3AJMoHJRvl7d?D0mW`cqz-)F5s`GF4ORI{Yn=qsaj)rX6`DO2xgXZ(
ze-Meo-)rie%T|&3;L`$n9!neW=l1$iqNEP3N&~mv`bFlAOysSuT(kzmsmjknZwXwW
ze%ki9c>^ZT-FamcLc@`L7rUL43s5K{CS<6m0GCd%+Jz*Uc(kbVy7jYW9LnFSQb^iM
z1#Lz(Mw^bV5|wwC(4tY=E7p)#tO6+yjn7=wZN%=Hi*i@oigDp(&);L?bkzLhuj99%
z4hK&TgsPJH^zJvIN=f6nsC|nYHD~w~Iola4d6SAU=FrU5Wn^AF|G|a(M(RyyRN)^l
z&qu?~a51BEn~PC#cAu&(^BH<emt87Tr=iJ~Pp8@~5TDuzJ3l_&f|;B=BRhd|ydmwN
zdFOLA>X*%*mguNQx3vXh#kUGkX~;cwuVOuB%>>7d24~~)gK@H{LS?w1^EcOeB?sN=
z0<6ypw4p{=TF8%N7RogaAFrBV!A}3kFE%C5p+B)eH2qNx><aGqqfF}YldBJ{I{vX1
zmFhJAWbjo(vW8MZkP!#22QB@Syc%H>b;qMgooX0e_35gz7ZvYe<4lcZ190?bWloH6
zL0mKURa;8|cuhOK2}q~}EwgN)U7xZsuHsolE=lLN{~CEKu(S?L-ndIfZ)<^Q-j5vS
zgIus&)io8Qeh`+VA2qvc+W~85`d9hfL|9@xnpaBJudH?y5Q|AD1@X|Ly=s+J(7$Bs
z-92RdJ+PtUi{WlMJU8U~u<;TTl3s>eJYM%0=Cn&uJ(+{Ul6PB=U*$r|0@;q92sR`g
z<cKM$R>P(b0&*Tt>!9Pz=}678G_2od^@t_Sg8p0$OQX*e5O}2TA8@b)5(V0Vv&j2}
zw&~d{S(}JSyY&j33+XU6k1lOu&;qIT^vWrR3ZNcq%!n8b#*4U2!C91wwRMA=&rt~2
zq+su^!C}MN2XZHlU1PyZ;?a9YN;Rkp8P@L63j<dJrGEWYWF5-&(!XEpGeG^D&UV2#
z65kE}nXo@b>aM2FZjt*-#rb=WrTwm9fb{N7E^B)IAm#AcdlyJOP$Na2>6B0Et}a>K
z-r`8bTW1v2&p)M;btu2LhB9w}4)5D{XQ>pddbgx-wwTmM5Z4|AsZ((I481e>0|S(u
zy{TuUD!?Jy;B(wQHZH$-$hbIwi$U2q_}#M@8?uB-Ru7YU#$R3G`}Spmv1|A(z7bNt
z;j{X*I*GU1OD{I&l6sZKgqy1EkGSy5=C<6%1R6ZE>DqjLR}SjV7p{_c!^GJIEw9Iq
zRbqrfb9`WY6P(JVHAy7bz?SNfn#uEJSQ>u&dQ(3OITpw4{j>?RaL8@^w4((mCj%cT
z6q4gyw(WD-SSfaWwDQ+ZWusM1jeE!#6Tcss_DNsd06HGxYoym^z&?|Sm!1ivZtP>l
z&c$XdynQc<8@-2tZ|X;)&+@my)sR<>PhOOO+9o^p<%(*I-uN&yo{WPd<;@E|ka=xO
z@hYFj<;`IKwYe#CemgvKLgB`aWT;<pB0T)YJygrS<-Fr8sk6G(z_6NX0SA*@dj;}2
z-&egg8rLWWr)8O6#fGclmVofxFTLb=NI$Kr+f@(0*9{&$#>0iKLn93~*6Aq2jGf6z
z$%G4i6%{j6)yNqTIJ;bh1Nxs8Hvc?I1^dvUGIf>%9_h5CY?|@F81K+u$trpH^Y=pi
z#2c+JGrLhMEBpqSROjXXxETyQntK%5k49tFRXu@m`y6zV7=Ibz$%Y5YrF+~(GC=h9
z<BLzZMcBXXX`BW*eu0gDnqKZ`K#^0TA&b=-fV)=WW<e)em-evl(6%Sh$lSs-n<*+l
z?(n?ia*}_x+(i+RyTOHR3dJuTXR<*_{Oy)%`D{F5Ug<KkIRo?ONhCa+Aa#d{5la>N
z8bN0$j>fXD2kkwQyI)I_e#U#rhsLN_WXx@>Je<~u<zXjpKQ3>AysC1E<VGgAFS{(;
z(;bFCr5An_>`cd`D^YPKY&!OY=M`PPQ3rk9d`XMMIWYaHzeQap3~KH2*KaRjp+Vh)
zZu!VYeBaT2S&y#@9{V|OD3wHbJv1!CFsekO<B31Uy-B@~U|v#caT|_|Xysfcc}%CD
z?J3f=HE?KpZHlN$1==q;_dI)&)M-{Yh%_|Nk+P5f*!e3|(5(nHa-oNS{QHB<8}Tuq
zbUs{$a<&RjO&7lY?o*DLR`-@&mLT!ha;|k{C><#CT{_;&1%RqlYPavfG+f8sW_W5d
z6$SET1T4RiI*{|*#mfuHy1!QLvF@lcQ0R%gH=a|2b%&m==-b+g9=V5G7SFUp<S@@N
z{yl9_EgaGit4{&vk<;=o4&@<LyI#66qaN3ayfN?+XoHeLw?8~X<sfM-cF8&215TQ@
z4K1C@!Toy^Dd~1iSS7Kze#?1Mmp+m!Y{^#+o9^R^UtSfUv7)?N>o5iP36D{w$#I+N
z5M1e>Nx+4!eQ_2^w(#cclg)-UDPUk}>7^f@i!O3o6$ml1K4*3=N|}rk1)Q>a7GH11
zE$5<Q2vT?F(dBHJbc>3ChyB%#2-f3N?1|ZTaSRmGS}y;KkBL_gI?0WMlf0FLv}fP7
zCfvz$Om9$tj#i(f+`|T%P-1Fsr)XXS@&&!ClO^@NgBfQWty7t3drQ{g7Ri5AHtAkH
zY|X-7mv}@Cve~#gmplLW>UQ*bND;kC>UGa2sUO<<BNyEUSxHHKttkHHt&4a#1Fb#I
z2PIJ1_}RGOOTb7KCK;Oes{Wv2Z%CurI^RM(z2{xbiR33}r5q@~B9V?xJAD`Ibxy}f
zclFIniqpV-Y1Og50aE`pbSl-sGyyj+lbC#%o{#=t#kRxCQmh7(<LWZiDBlCNlGp3-
zva`XFq%%)3c2G9VUnvXoc|9o?*+rP%TrrjUvL0=tsHQv{3H10KvTI2!6JckS>frt=
zwA2w<{P0Q~sH^PW^OaSM3ufkVn~G{M9W~pjQ8b**HiMDBbQC(!waZI9A1C5&jx8ni
zZRbn&G-n*30{_I}eT(KOkeHV7*yJP??z~eQ7(Gtv?^#ccK40KK&h>q}4W(&t^wEkv
z*CZ(re*D?%^rAWlf0EeR9nAvctvRzQv24gZyx2zT6B$Pc6>X`Wt%EOX5B6)4b-2PU
z?{Z!H8L)BDlhAZ67UZ{E^v>Cl^^^w>X59H$2lU{wN5%$BfO9>%_I+d>16gusAk2Wb
zS0~FyX$%<sQC=dfO#>}k2Z>c046wd5;+n`qK%{lr!FP%*SiUSHbWDZ@+IL5o4x1>j
zO}xWs;0^&r9pjb7tH`>Kxn?DOGViipNkOzMiw@W9Z1|eB$@<jAsV<z)2=|tT;*}OU
zd`J$>t<Yz|cdG%f-?cR8eBLjqvV_#%yXo20-K>M-R~FsOJ3`jSM%$*Tm2;rDFYp?Z
z)Tf#3U(4(qp~J$(Cc`469%Q9uk8LlD)Y%8`7ruRvMJD~V12QTQHoVPqs9ev0*c(%V
zy$Vd|R&X1Ca)beg9Xqz<%5lJBpHKZ(-#jqXejG5oq!p|OWyQIdDnPU~aeUYEOqgF>
z-tv>wk?tdS9%dy{;7C+Ojorsqc>FBw?@Mj6j@0p@agIX*XxsBGS2Q(($o+bAwBmaB
zwzDgre`h=BH17XtPUc%)PbM_+<zzwi!&}0VIgWTHLWaikq8Y9o2womP$br9YO)PP;
z4mHVZrSbhC4v1W33s*`6gJbp1yeEHN!nA9Zv42fHY<!iNHDFJJmu1aC&r29^-jMlg
zXe=G97nV24-lPG`z*TIV)U{aqEy_)<pn}}Uo7#Z@0xav|+7}s>VQ7`OM&eEe__Tjj
zPnD*F`R`>z&sMiVxZc9g?gM0gB&}#2lm)=#4O?ZswKWiV@ozk(Lm8LOo!+^wuoP@f
zcDZ?dErQq}Zs5$DgE(d&BwD+p5{^y;%>5}Zfj~Cw^(XB|yT`Zkk69$7-LKk`tB?VN
z@^Q|a6-gMQC8*zWz77_@-KKfHiVGF~dFIcr)j`STJu*)dS>XRDbMxWW7&I~*$@Lab
zgXf1OXqQu}(B0|=rE9nn8F|IOwg)2Kd$_7C<J4_jb;dK=An_sAmI|C7Aa$#40k3@p
z*3!@~;=RXUEC+uQ79EKR&(PB6*12KFdqCZCy~f<F0=>^I)M}rhVPR>U$0*5*TN}@p
zUwA7KSD$&MxG3-`3jJNTRd%`z5A47C*<YKBZ9XZ_4oEcPl5Lc2B4pix!V~pymHbK^
znD!}ikgrA46?;ncxsCWqF{^yPa2u-aY7(CjtHJz3DPG#nbj;_=kd@Xh$8({Rr}M12
zSgJBNd*ebg-fz`CvyRj`O^Us>IXz73dQ%jAbv<dgGu<uh!u<wpo_nP;akm-2^1bV`
zs4Pcz8uv$8d@0i1CFZC{%W&k~2hZ2l4LGMeVMhJV!B-MoC60CxeweSF$k&yCSHyZJ
zI&w-$Jq&&0_RDOPJ~;c_y^MngGFG>k1(jg-OWFNu%rbPHd*$yqpVUi#S{69!-bCh4
zo}~x`w4qjk#I!*k6&_Ao*BY2Lz|bG<%eSNoftmXCug0SY7?k>T+|wrz*^2`g>hu$^
z+rvI*l_M8!=n0y>F+dnQRK8*U=@_{9I+FLLSSoPJ<~F<w;6mFYMTkhR2Z;p{H{7hM
zp-WSIN`EvAHp<OCY*>*2do)%I{#{WIJ9Cd&Ws;>ns)h<rPM##!i|s|aC8jheu-aI?
z>25l#YPL|4H|>TTs-=6jJsqZQf0i>K^&>1jPT#TPC2)Se&4L#}ndEwE`uEv*6X;6G
zgmXr!!P&>fI7W{FPaSnP^NK!%6BnMBiT7pVUa5L_X?ZRTMjg8$_m}khm*nY-thf-^
zY^5?5Lxc2fKI2Fw>&~CKifP}g1yLQHoR_a?kUXwXkvmibZ|>ZGw1x2qPHHZi(bP=G
zGm<l^o+T}y@tm-!TEGCE(+Q(ESPl&gZ_AY6B#7kAe=?s)g$Tp5{tHzQ9#kFSvsu^#
z^Zf(GG_W2fPYye32Qy$T%~?QYiV8sjJZ^mE1eSN~f(-^OSTe0y6S}z$iO|4#hep{L
zX4IycDS00{hKi0Cy(Z)3qP_kWJ#8p>K>h3V0VWpE?$s>JD#M7MyC%ACro!i^Yrm&l
zugCIY<^@($C)%Xv2Fo}^V!*iQ`elBhFq0v3Rh?4=aibD-|BIsY@aOXF;&>#ZQYl#(
zQ5sf*G^ArjLWNXBS~i&>va?4KviIJ5JHGawSxHv?q9RhMB+vEy1HE2--S>T+>zw!J
zecV`E@nZgWJ(2NtJVU1_#4tnVB)=J7ze`Gl$_rCGZD$a^ZnJowz}Aa0k=+ep6v9Px
zQ2p9$5d}NDmh6lc(_y?$Hd^{L@e*7wk$flKj03-nw(Tc5OPAG;$IPevFemVb-7DGx
zl%$iZ5=x2!u6*lisyA&o{+pvJbhHV1L#WIH1Zq%1jCCzZD+N!Tkr(IHOvAn`>7w)G
z{kdP?=o=N^fIMy@9!7+#x4QX>=4Y@09INbMtRwk3Da%EMmgEks51TUg`r3x`@&`8W
zI$DZ+`7>RI3zM<Dz0WUF!;ts~q?bRnCg8dEZ}_tQbf8ImlR{c-26Vo-&f3g}C?{^Z
zXPbU8m~ETNrf&&>gGrHkdvppQ`kB7Qxxju12}~T_(%A?n-){P&5!VP8i&MYSREL0S
z_Pg1GzAbR`tFS>z^e~ugv*5AKX@)`LiGAbUosfG#(plCf2@Ib7_>@5I7a$g=vz^il
zi?!hm_r@Ba=8oElN9S9>0o;vhBq(U|`I&paRWfLsWf}87@q;$XeHHJgsZe(1bAnfG
zDLig(_rJtbf)NaMYZ+46c*J%fCge^76n$d-W__p@=Kfj=8u3$L*O0`>or4MZ&dB=Q
zLylya2rxG=%B+Vn871~4N)|A#D*v{<H4Ig4_k7%af-t`R3PZuy1Tf8Fo~-q+fXRz0
zF@A0x<b6oA85iz@J##mY<xf9H{r(?Moevg6qdQH>)=0uX&dyKq{zrIBr_!PW@@25g
zl75@;iCo;LuFXP~lnlXf3*Htb$zVDql0@rUMsi4d#3vrKf{*`g|BkeLd=XSt$i1x&
zDyvSZgx*PkAkK;qn&kpeQDwc=8Bhx@W<kys7gKRn?T$+C_I8+v%@oWrX@CpbrA#-K
zh)*QrEHk${0yW3`n9Ya#FnRxW#m`z482B7B;rgKj1REt(u{0F;w`;z0JbxR^W&_3c
z{OiJHhr8C9w>#j4UsI(m;VM;hMV~QpRsd5SmwW4Xa{u=#58QR84gcC4xy!NK1+>f_
zoo0-AFvr9p3X{zcl3-lCm*g;blTv$y4YE+iWNWi6;pLfmm#J2&I^aGCo-HHXxZ&8M
z;`@PM^wZ0)yW~`Xtiwz5N4|x_CPSg=>W?)bSk?ILb4(hf=Dq3*94B>0=w4b%$N*9~
zznaY;yx`fv;4UVCBK$BFW+dn74Pm!;Lg=4rd^B>~cwfyBE@UPgzq~sc=bmd`@wZKe
zqwjwon0{stpN7o3Qsx?vA;+VNKb(S!(i}I;*OIV5I$~ooAp=>r3{d}SE5j|4Da+d}
zI?<<qw{84&Iw-%eKN$2r9=8R|KK-Iq3PN%dU-v8};&uN~p0hs+uplo)cWYDx(jL3L
zOU6D3st2_0xG?77Ug5@!55ES`@P@mNj7krd@rZvoV`z`tgj&Ln7*)YD*Tks90k5EZ
zy^8tx)*);-tj@#-?MUTbyb|=b6q<j#Xi)A1LRHAT$D4B`FGc&L4(Qr(q?PZ((NoW%
zan{EC+K&RLo$om~XI+eI``*zFlJ`A;k@d`oTQSm=ZQS2G<O;segrlEV6#?6zD78#M
z669FtSCv#%V($3f!+gdKB!3szLNl2UPQmhO-`}*tjbOVGN<=sCN@NB;KY+kxA9tsF
zdk8$^Q!Wx^DuSQGGPkpdSEiku&~G1hg6>MXNZq+w;A!p<*hl8i>9^qDwcF`Xs@@;i
zoB9$XOP)XV&*_D|5iBNi7kXhb>g7OmMm1D(w+$!u)+4=r!R5pCbs%sZT$~)s!H+7R
zdxO3X+LZ#5lsMbrP~=hOVk#YUd>6-ELeAfcZ`G<^M>;@;F+Et(whEjyWM3{uc7a3J
z`G&s6Ft~GlVeNoJ4eWMdkd=NPP39upnW|ndagIeG>ThZxoXw;&Y9aMFENF-poUA~H
z&g@B+g%>cXs5TXpR{}Y|&fMR_OzOt;Q+rL=2O+*J-p!r#KQ{&=HCopwu;g{GaI7&O
zsyXAk)bAIAqy%MZH)Vi$SPCy5F(EwI8M;S!CI<>{b@V!(4utOaaZz4ViBNMTFM_M`
zHPBd=+zTx1!ntjwDh2Fis9|FA<kia>q^{~1kc_E-Uo5LRG*1WNxWWO+za*!_?~pqc
z?;8cGf^+|QZt27TzG(KQ8)3jTKjr13&<Fi_o1;2u%CP55hC%<obUf6_`$YT!$q(+J
zN!Xj24D9b%6&2PxK;9_Qc;;adDJ&azs#+9cf?wyHaE?C=Nan0(Ukk$7(kdk?!kf=|
z{uf~+Scji`Z)FN56aFFp<FV-3aHyCWgIRKq_urdSC|}qFe>1O*D2h;!XTkT}Wnl^y
z*roh;^&ZKo2^a4EN9xWeT!Z%lV>`fJd)Sv_wgvAyM`(>q*WvJ`mz(v8=c4&n>)|}p
zI-LLa(Ob#08Oxa-j%00a!!E~7XUnfw;EdAO+-|96r2lsKx=A|XdfSjSeRm7mmK~ni
zz1oKOGIWEfx*Gp97VloCBmJ~1W-cEqNWP3?_{i|hCS2&4`u5YM5yjX(ohZ9siI;u<
zCMpOa)?K3GvJz-UA1Ri3ag|1-NJf?!l6m6$pX_NihqJM0d#K3QlXX~jb1mpJ>1S%m
z=LW?8A#=5g@biq8jTo-8SZqZ&>FwTc{?<=YkWb%!#%jJ1<JB+e36tE_=ZJRdb>gki
zt450zzDUe!X}R^7y&en7I6N;=HR9#wS*{O{nlYR9ko##t#1(Dnc|+M!{Px9Nre?AZ
z59tS3)QUFYi>FlY=i&(eBaoFHbg~KEI0AV6{#4>`0fibn@@M>dS^m7ARWq_;u6R}{
z@v>+(o9wD+#*aHh9J8Fu&|19w;bu?5PqX|uHnY10r#x$gHw+rlb;kM9`Kxs>S!zW6
zNw5Z-Frzowr5@teq~m3L8bK{R*NEPy6|Qrh^AZ}bgI6mdbfNZDaM;>t`88)fd`tUH
z_nhR<FU@yv{>0o0Hjlm>rYzLL9~blM&XQHoT|TXGB?aM2YUQ=Ojz+Mg>m92k_t4ay
zohs}j)lkZK@ykErbGX~J9Hkyo3(Gp^gFilPgdx@^Uowuj!WyGsQQPK9@T9#m@MWS3
z1bKGWA7ZZq2?crU^2beZ&;9b~uaOqu4EYzna-<qqo-KF%+guHA_1(KJyVk=_eTlc`
z6HRb&ALUT&N(+24X`;%KCUpRFj@0+Z#DBDGRB-Sd1^%jBhMUj~^dgV=^_W_~j?UKn
z6Uhl|5&8b6cY7@yG5WBPPWt?m@YT!rPPc&kuvL+cMKkz(CNvrO-J$}154%~{LH_>r
zTaLs-A^ufD@U%)RY_AeLutw$?lA}ZPba^%Kwu$=Mz`I)b@OG}p_;~{;oW0h2p0O3)
z^mje#?`(lUnE{m?;S%`Fo&U&LCI~-Zf`o==J#5@<k~`)%2=W^g4oUw)r25UTAt3$|
z1gg_$HTd+w=4mLKr%@p&Q03*X291ECRa+gWa~`@o)3E+{TMK_#?;m0&^@hAh$*syy
z&yd&f!St*C5n$f!YQiiT0(V#fH?eL`f*0~fmAh}d;rdtxCu>d){y199<k0mRwnesC
zIeS%O*NxYfTN<h%wudd6=f8B|@w;);rOOQ|5$qnrZ+ju>?Yl+&)J$;YTuHZo)(W&X
zPsN9KH^L2lA8iSRQqY#98+&g%01T3k85s7Hb3reL%UQGpK+y2pPsJ$oS{dg5xi1N$
z)HihQU#SJ^Z{5z<Bh|r<e(9$vZ#{^d9xikWD*#!#ps1tbxnL7sJs3_$IC19AE8V?G
z*zWv}a?Za2eC(XdtvfBin6~s3y-*Q!@p;X0vb2Dyw8Ja*yDiw`tx;!ts1c?)?y3j&
zXX8ZjzWX`XDxrxwH;QK$1-8+f(SKHK#;%t_l62%Af|9nPm;8yhgJs`^<AlfL%TDIF
zeX|~pX?L@2|5FbabW;`-HA*4AZFK6uuM`;m5q?5&AQy^!HjCx))j&nN<Qe*%sgQps
zvM;5C@a2cDiNyz3!o$P+Zk<=FhcV{r|Ni<D&s(=%635+V$Q^27=c1Ae2ew`h2#~0O
zRCmkw-<?ZgF31u^$p0tohxlqqMlo!k+dH*J`d0h*Z&Yc&Y=@)VKVEIlO@%{WQh5KP
zYJ>ceb>RuZZ7udX)l3+@2BoBo1}-GIgB6XQJ+bwW+2O7?qd@rEM-eyEbF&~u?9>HQ
ztqx!luZnjvrGTXG#l^py+o0t^*+u>1C184BL07G{52&^N<9r@K{0$2!u@^<-L6Es&
zu~N7d#0Iw&=aHO%Zo{(~t35Tqi`8-=TRLH8tg+5fjRLANvw|m2c0u3$BnDyCG-y+!
z(J{YQ2Mh&Zn#R`#VkIFn52`8PDtKiNt$8mnO=GouaR5}j-gUKBpapCX-*XlW_QnxA
zZxu7kCQOf}u+oVVj)?u5*!;D2q?5_CFLzCVUr(!2w_B6`7+*@D>()fLq)@aLPjWW5
zPt5psmDOY3=)v0C<(c>*C;H)FU>@8!0?~e_(?H!}GTlIhg3<44!dZ#G$zQ+nS1;jB
zo<4EQp?XaLmIu@&9w#2dT5s$7ot~W-RW;vnpK#%n5x>cp+<I(pk^D{l-WQBnA93v@
zbKYC;>I1Y#Jkh8kwX1A68&6&^liuW0h^i&;ol`tou;H9m!qRFw^t9Y;<KNSdO!Hh)
zj{50nD%GW05%Cf=*LTk(q>{X?@q=G?^^%c!*GjPNBI#dm{(f*D;Z`LWmh%i}TJV9g
z4((3)d?>I`EP1xO7==NzcIZ_Hc39mwjp{>~G)C=yNwy!)y(oB*yg2|__P+E!9?^~x
z+qPa$BYDT!r3z2JuhqCD7WcV&M-qCU?hCdpE`{<7U$!Zhw_-bd=cS|W#^`SwckM2h
z1ATBsp|3**hB?tIq=fY2k}^%$=#>uiFRI};y;+00<J()b3IpKu_!Vn*?>BI~CC|6;
zLLZ79=xR@=Y6q>ue2uv**Krf|)msk@{BUA(W#HJ(c*u$p@Em6<M+4t4KcoWd;ZpH;
zH3h2*4DU!_Y*LN{4wZ59RhBa1v*~j&#ZEkDoRWUsk9ZTVOSjKoYC)%nIL67_QLtcd
zQr+EH43S^F9&ICY@(tlp?eiYBxTe5GRoU5$mt)Q3y>lzbIeN}*P{tjKXhLrrw>F?j
zg^%drf)b2$_0AntOTp5Nq479J3Vzu+OtYKx#XYVLP0_d2;I7}(CxSxCkVl8p_t@J~
z{P{wMj@7;&(@xjg{cWwrE9cZ`Z9Vg0@&s*2i*`MB8S#d9J|gvpVU5Gj)ll;9Y(BV+
zJ`R=4)!*s{=YYS4=7e!%1D3x!c;q2wfor_YNCI;MD(v0oRH$1HMj@wcxQ-V?Z^hoM
z9M^u_dEC}1W;p}JrB3%;ZmUK`S(z<6ZlvNYBh$A%t+|lXkz<mpkcxCdDLh@rnxQes
z?B$VtzF6_)u65SSVX)^_+3&<d@^B3Qz0M%{+WFUw_ZWwgVbf5?ZnLTia1OdH^^fqE
z14Z0v8u|`c9HS$<ld%x*jmmRx^=yLz!}~`x73<-N%#|yVm+E2a(Bsfggu7D9bv<@K
zj^uD2$g7SDxkE>(g);}?b##RyE*81efRWVBC%27U(Ol%^J_(<6<mXa&&(#=(GGcB1
zRqFLHZf2P+{a-T#F+bVfYC*V!Jc)f3TJ88(-dgXSG6g?)|Dk=%Z;E3-3?$U{wnMr7
z#@=TpwP?8HdAj|04Q~CVmi*;5@!qMF|E*4<z-rzNkGi9Su*p;5J)Ki5#DzVWJ+Dzi
z>hz!mH;)_)vib0Vb(nDarFX~7|B|`ZueKu<>z>g4&o%3yTP5(VL{C|5Prz*tvwXfd
zlfS3u)}o`7KIn;A^;J3e9Gtr^ZDASgguNeLM=eoEf30$kwf9y%a50b3=-lss-2E<(
z2f5?H#rdgUkC!ff^HJPQ6-L4Qjw(i@j%-Ml>JE)*LQL^J7FhDQ2m-aN7oYbALnl?X
z`_(TI5G_JmT=b^^VjkR_XqS3`BIdexPKzhthtgZz6TBJdEKx$GIF^H~n>|?463N_@
zS%CU<V<p<={mPr4CcZt}=5m=Paxc}Lie!i*-hnVt+OK)f@#iP6J2^*QA_<(|KHi!F
z+Er&9PsCTD*jkZMl~g+_T=VQ3s}IA@oMum#zSppkEpA3flY=u0RvxOi;z3-j_v`PG
zL5z866nAto1&{mbH+82s!sBn2#t&H=VB%;-gQHLZ=wF_d?icI9p$7s^mW+zH>qYTd
zy46<TI9vQh&bSOLeyMKiBzZY%RZgz6%OvN^$!Bq=xe<~A1Fh=sG(z0-8$E9KTQRSt
zS+Sw5mhd<`9frR4p<z>99Nlli+gJ0aT#2ZGb%vkKuk2zVQq@5vNBlnUl$M#^yFG;0
z<WGk?Y;=<EMM+i$YY$K#e3IR`H4Rrv9cm|I%iy?cKYwOD@lWk4EiJ#5NbWUO@1(t3
zLFo_Y#A62X9a-efc|P9++dXd^@U8ZMmuBd(h3gJ@tl7+$nzt1c9}2Q>VopS()f&#=
zv&~?2^8N>jp+RWjDN@+an*(J!2C-}0t<cd<Nf~-njwcGYbDsZG2bh`dZQ#%kJDNYl
zmft7@!Ol6idl8i|5#5`h?5>Ioe@=xNsP}=|q_ueBfqFPaqkc~IR5{$785PiE>4%N3
zTReP@naDRQ`NWE;45&|77A=@oK+b4JlzmG*EM2_fFGM_}bZ<C%bS2YKK2muj(zp!I
zt3>ffT}_39nz@03QF(CsZ20{&z7`Oq4d5|le~l)mwd36?p2L53&gb)gWWnE4AKT7b
zXW~+Cg2I+3UU=7h-vti^ME@CfZ*`X>IKaB+X#@FA|4ZIfcD~;q_a5HH?5vj!nTjV4
z(zthH%j}`ShW>a+^;~>0UPt<SSL}bKhNj}eR*H~pPdkcmJ2>W(`;@2U2B&0K5yo~I
zZ#W++fk_sc67H1>SYBZ$8K88++4(2IAYP6t?2d9NKQf_1D=#bXUO8-QYaRSLVGkxN
zl&3h}2R?;&>~dBff@jUeH0|0Fkl|esStN7EPdCgD+#`9EGQ&L-)`1cD+}H2nbGi~v
z1Xk*K5ii#l4Q<tSw_2ban+joRBKb?XQIGik0oe8^MCx#6CGsqzF@?-~9_c6O+7m8h
zUAXw<<@+UI`Q+V|+Q>IRyXX7Q3v7gIozrq@*U$%-Z7q629Vu{2??bk&i4D4^MwW)G
zw}7EW*5YRVR%|}UetaJf>B}Sw9*%ue3Bk4TtwAyVc=PYfP@9l9u=(bh{Tw0ZZA<bI
z$x$oVr@n3Kx?%(jNvak|N0nh*qkG_K-f}o_b#d6tG#8kE9sxfQXYjfCG%fy14$PKC
zJlP*g{EXGJdmrhSV8`?AoK(JTSaiE9xbt=-Y*DJ3QTdnw$_rWZ;`5;}XXV{#>l_I@
zCNISdhMTao?O&IW?`wRTf2b_+#Z45kcZkmVlm+xmc58gQn;_t%I;*{|G`RklmHqDs
z@d6$cp}XNq`a^b|s<LF>kRdl7zVxdNwlXH99kGjoAX*;T{G2G5?EK(l>X--@wRXqn
zNEU<8daeFPQa7GC<;$NYlnnmQwtmbMBXw-%^WmKs4+6YbnRk=^nDib-rbn-9KrQmO
z=o(WBY_~5K`x0Ca=90nx8XYS^a^GF!(+@I1l6}j6bZ4K#XZa)SXD=lp^<Byi<F#7i
z-+gOm_q!dQ6znjvIGzG?QyzK~ByVxoAYYX2Bn9gqJM+ZwJ;qa9agxsDo|;W9chzNY
z6_iXdUA<yVfgYt7-%mCXU*(;FGolab;m$?LDXMJ3dq^qq36r}0rT?6rxOD@>N*pRU
zA!UM=k3*d#clN`jpS)LgHnah53ifdoDTa>q?T6_ZvcSkN)|q`I5|quqiR3W7hQX$D
zAEKmsV7)Bi-@bz_;Co42Xq~wa#?N{Fi*W9Nw?Aoi_%_zyZdRwGuOH>3m>93F*M16I
zwR*SLeQOo=7YaRFY#N3q96h#KAH#5lCO`5ucLR#=aOk=aZ-F8!qO*#>Bk`T=`ghyh
zM%Y<u`AYIs8{P~!^I%=D3rokYD=6Hcz{C!7!L_k^e0(|Ai)G#t545mRpOxyzi!b+2
z?jUmpuHHj5zjezn<ax(Ii+{;*+FD(&W7{*NrII`Rq`w2zPP*9-P89&}40Wc!L^Ftd
z+j8STX%xgI2juD7C4$#_=!@fI{^&^~+V+~c6f<-N4J-A0;K_Q(x62cyn3KEoO2jM<
zL^gOXmJ^=-%;mY1xgAaLWwBa7GARdh3*SpuX{7^Q@h!e{H5B}#J#?t2tsG_E37CHi
zs)PNfc;A@kW<s2^ns}9hHr@_ME6%;&joR`gIrX3dKR>e!ijs}PC)-;(I^7AqU@=S+
zO}uQy+fvm#TYJ%`ajw~%eFSM5@2j2rl!<gEw?hN63(-IEze2U;Osp%qYF3uhi4<$)
zw_bZQaHyMN786IjUw_!oE&MG(m6YEy5Bi$%^g?Y`r*<pa^#1t8tak-DtDcMYa@Ar5
zb<oZkmkJ!$Ww%!zD?>d=Hq<4Y%Kk_7DT=RhkZ~*H4?!n#@3`!_D0Zs=E4>O%+p!nm
zLto`rnhWiyD-m4neZCT*A&p<{G4XhD*ks@NQ-^ViCn9*>g+n5{VW^JOYqVBR{qf~i
zE%sCnhWNIYV(33BizL;TxRXZptyn+>(w_=@#pKwFnOoI)jkdVsnT11^hlksd&Expa
zOZ#ik&}O=Vd%YN6&5EXa93b8e4es6ZLTRw5rFEclJPGe_e`6s>`egJ&)Jh{6UAPeS
z-nsiq5uT&3di<R5D|L(76VBQtxS;UTIFme2*Q|X`PIGtT;&K*EYeYY0h1Hvxk^DeZ
zes}GpV=QV~#O>)zC_vN9LSqJ!3+po2^6c?c6Zrx>b9Y#1!-(~ixCN3|cvY+RUV*y}
z^Hkyusq6F6S1mhgIJ**J?(O)Te6<f^l=lAQ`&NXO?6=G{R9n${Y36{`NGgtLHe8nf
z(Fjis+SebYw}MlCw1*5N;g{ue!UxHBAnV$eF@f_jK*gVbW1yrRWM4$xa0cRa4@k0^
z_l-gEkMv_J8}(QfD{yck`aa6j1zhQy?*`$$heCz(^1<!>%`v}nMEWlkj>@OAaZN_u
zeY)i_9-iGCgV{r{{PEC3ZjDT!qU0V7m!u%;4`o4a;*Baaw#>=c{0fyNE39YAhM}8P
zQTrh)IhRy2Y@Rh2kh^}usz+-r3cHR<xrLVDf#DlFA2d+F_|SIG3Jnh=Bhg((-Oq3#
zY-eZijtWfvUfN?+k_Hz(4EV2(6~mBN-F`d9Cb)9STId~96@K_~_o-}4Iqu4fm=H*;
z!~GXe{x)B0gK|%6O$n1;2xLxA9w%PtEw=gF&ps|i)fBtKl=T|SJ-bkImWsU3g}Xi;
zW$J{qo2!$Xj0wjobXR7!EfpuD|0G4E)uD{)o$D8D2H<mXZ}5q5l4Ip}EBUIQ2==bu
zOgF5GLH@($XBJsi#B$wc`X`R~M%Yz2&yRM&se~i7D~80kaZ2uyS$-{ieHk}bl-C9F
z6ApO|<lMZmQ|jOC;5N`+PGIQ@^u+hmA}N-hRlxj%NvWTg_)le6RBZP)L(9p18Fy^U
zaodOM-G_T3&~7OqE@ZL_Y$vT|eYRD=y}M0w|CN=(Q1Sd`MdHbc)d&*l(5WMRYwxa5
zGAGe#OF7Ch+k~k(xy}8fJ>Wbzvn%OKGcL9*-B}lI#GR8b<ipFea0~NSpP5TNSYN3&
zCzw+NEJ?`?wwH2Ao~gO#QBMM1OfYPgPp*btid3~7o0`y(A{u2)6@XR8vNB5T+tBQ%
z^ck+J#rQ&J$&S&s3C_P>jLv`9hmHXbo0l2t!8J}h@DIs%(ff3`(4MNtW%XmxVlFX|
z#`*IXR8}Ihlbp6%S2h#}yR^7b$UOPrkZJS~;Xo`mRWZ$%0Gn5nlAr_PT$Se46@z57
z)Vb7dc_<k;KaC0`9V5QcJ+>C~rlU}09XzI`-vhEQueGu;)#9DREo-@y6b#?h_i*xb
z6VQLx{4A6I8iXhUW`Eba;J}%sJgzL_F@&yXfy{_OJhZbO^+j-(Hd2S<N;?!+pOO8>
z-2t)F0f$_S8Zqg+H`T7Pm#FZwKuzRTGIp0|ZW45Aggsl|`^0xN!4tY?e&3If?~DlF
zi9WAX438dKS3W}KYL~;u#nu|&if+J)=I#!-NI!dNIK>(43{GfW7jwf+UvwnA#L{r>
zobnc-k$TXJbq>Bocp!>-zJFtUDsb@@W=<V?j{9d=jbpg$(f4v^<zM<jhzS&vkT{6Y
zD>4=1UKk4H`|hxE3uNPUv5+tC%ewJYjYrnjxo~v3$0B;-TQih=7@%J6DaGFzZ!&E5
zRH4v?tVP}feK>Ss#Ygr_1Ul$>oHB2<gR5h|xpX*kP%T-%`QEKWRAx}JJm=Yi;+twu
zcOADvRjqaYKkhG~B=pAK=LKaDJw@rUVlM*-50IO`i@1OPV~mdRMUGh-UsIJ}>~A!E
zJhoZ}_gk9AV1F}|9y=uWy_R?!f4*824hX`3QG2b1?Gw@E+GlDW{t!4x7kuE4V<Ska
z9d*cFAUWtex!iMINjTKR4At&|V8nL*R7wEp_nq&&xJkbm)MUA2cExmKg1(d_cS{UL
z>ADKUoezY=`y}oTSeL>tR|9R{t;92Ou~6fLTrXA{Qjc#+zku>t8gk<?mauKE^-OGB
zFPt`^G4CdQK!FVNJENTgI4^R6E;P6RYbG{ndn)E*<{lq6s>lIg56sY{i9nd-3H+Kb
z)PuPLCmC|YiMQ};lou1>v`#d9z0kO)6SO3wC5NVRAg(my&_ff#EikJXRbFnz`G@D6
zQaXsw@5;BAv)Oep>S$-{-zbVlLLYcMa3X#!o(4mUf0Z~JQAM?d<kqZdyZIK~2wz~x
zdHn@-8ZbwGW)Yj}L`!c@28YE46bSWpt;&hUYwr!)$BB0$&d10&`&A!)q)oWMMw^8#
z6PMoqM?BBZob@bdLlF<2ddOGy%n6*Mvta2>4dw~Ik@&utf+A-13x+df-gogi13mFY
zTx8ePcyXf@993POUZ_riwY$BUoUwV}GTS{AKui49ySz)6HLGx;q>M8wjQF~E1=L72
zfnU6z<Q&NBgp68CcD<N<$gXw~HMe{Lv;S%KDa-UCZQr4@zYHiS+s2`AR5K13XJG>W
zSwKQ$Y31;%9B5@@{4*2Lg!dlZ&VOLogUKOd!DDf;D5@7ower0JdXrW^$!<#mW#;?T
z#eJQqQ}o&W%aeZOZkiU*;3z_&l!`plo6T^!{KuO^-6_zw-`-~VRv*T?2h!VxcjDCa
z%zr0Mt5Emd>Yc;!2scu11^tt##!pn8UzKBf(csF8$GUDI9$VGaXXMjEHi>?_p|DKk
z82C-kQPqSqTq+)wsp;UNS3O2sS^&FO6h^=NXa!;}pFR@)0N(%g3#zLkIX$E7hXZwS
zaQ3hpM{{lh`nWvkPl$5B0t<B&ApMkK?~t>e+_{jce^9UChZFI7#qUVVipHo_pYbn>
z{jj}Z%5iS60R)RCgnyJ(fc8P1L0#c8uo?DUEhsdBgOA!(8l<}5W$%`<=0OTDQcTWN
zoC^mglcCRFgepPWTIMJFIPui{JzT{29pS=iJnMLK4fuUc{I2vX5zbk^)v`M`2$Qbu
zu|2+VI6&{E^80Q-s2sdEYWS%cn9~FYMVX%CIDOUWdImpad%sC(bz3Ny_-M8sTJ3`P
zwOfe{&TXI`X8S1qqZNp+ejlpijz+J22eQMTKSysij@nesI*6x!c(5v{3toHdzn(hN
z2ou-j{^_{AfZ$F(X|aq1z&0oUQ@dVa%$V2Ul0+AzT|Be$*s_+)arWD^@)sh<=APu+
zH`381^S2)x;Sb6Z7>&BzTR<a-LC?d5aH|(Mx&sDB35V_ZM@70CMGqYxTmC=qsI8;L
zn_LLN^I~tPi3jNm@BD%dd9K!56Ab$HwP6MG25nDS7Rm}cdYoD_$F<&-Fe<Bjm@@H9
zb*v0WzWuK)I^GrH^K-&9_m=(8gU#rfszp5h1SbFc(g?nSge5Vphl5u4lPw&(AfkU$
zP>oYF@L5!T9yJewEV^{Ir@!x`LAqbq?o)wa(`a~G?`0>rG^{*2TSDf?Zwp+OjwgWB
zh2oZA;`h0{Hc^}xM&?_hXFVvL6>xNY=RzOhRJcOkcE}A}L#<u0?+m7Tp+i82V~RQf
z&OZEAKv!1}ej6@1A9Y{D<7N{rbDBbUH^yBtr(OV;H~OuL6J8)ckI+#q>2gTZ@i6}<
z&<Ql@_8l?Q?O;}|*+H+;4+<qKTWVusLBOgVZpRbO*EolV_Et08HtbXSbTtaa%te0e
zmhOd;KLxV%joDDzd0uXo<Qb*=9#vhw7mni}6}oIJh@U;QG*f1g_^)d@><%TwqJfWH
zbK+q)V2hJTSiao}FZjRp->d5-esW(k_vR+x*&=@R<YW`NyV)z;vdn|mCv&~8?i|2-
zu>lWS?u=m7*sD`^Pg}7?lRqr<Rt~DXjC=I6n(T|2t$wF2-iCbaKVH_FC1GP@O2!dm
zvSXqtR%k29wHoTkb_GaL@P2sI>w4ElT>N-XM&)%l1}dNZk7k5;qE*A0q~_Z2b?fH$
z>tx{kYGYEFkBRsf{v1i_ZY;pwkM_T}GW4U`ca!}?|N8Nn_PYIN`y$M^6S{*#tp;nJ
zxEu}rmH_VmUSy#b;dWGOnd1iwP%P8@_xlrM56eyCfBPLWk)1Ps{JMi3%KE+O<yq*(
z$mC(Mxp)u!F-ny~acRTV<eLgEQfbI0$->anOuXi$%1%?()%auw_c2Ys8oYQ;_f@A(
z1gI75*bjc$$e}&Cy~ZUU-W;-Hw9l;o|3yXlFS3<Lm2}QZ`DQQ(UeIa&p8uTO(*&2E
zcEv+VwH-sp-AK4m8g}Ia?M1?UtBQ=%R+2ugzfjuAD3JGAdNk?U2BR{2#3IXsQIN-_
zD4}@>tM@x^QX_fs!IMdXfoYkfAI4<+N~RAqH^ekqqYGg#qq6<pYrR<bZC~8da6ht5
z!M)#P@4yFMA;qIS#5-P;{H*eP4HWyII*+5pQ0uT~iQ}C-X8znW@@*{_l9c>+cpS|E
zk+N0pV@rhZ&}r@S5vYd)#vGRJzjAP%>v-v1)lBrQ`laDv6%KY(skJrR3EuJh<Ywif
z47hl3<nM^kV?6F(Ie61539gGSYa|`>git*mi8zO9Ow;W>maEtZVZEERFFf_aLnA8+
zdw)E{Z;|1<jM}nLUN<VmlDZdbN_9h<!x2h?-){C+>47`hYkaMP^$^by_fKlx2pvmo
z2B!uF@K8yXw}4m*_E5Hbl<Pq_v_Dt-UvfP#9X+PtO!{PhHa2K4Ds|(#M>&_Or~NUF
zGkEdEToojoZLo=83&UNt*C&<Qh(G(phIXn0@kFluOp$)u0(oDzB%JwD4kr)j>CNrB
zgjI^AH}?@f^v^7F1kYp#79Q;?HZ&*!`K_OONr4RFrYs*WK5Btu1A>_}V<k`><L)YN
zPxeW980;!0Jhoy>3Ej#_BUEpGllm*95v=B{&&eL9fTwZbH8yGTdp!TIs&=58)XiMy
zCT~|l<*%FscA;?mo^Px&_Ba+M^}j7ybmzl>s_r2;-UA0tZ0a`dOoTq|p#wdu86c%x
z6LI))1Khi&ICzVApdQAUQK;<NVCen3l4+f4FnHW&8$6K?wT64c9SPVXl*8br<#iXm
z^tbTsDI?z{<z6;d9n#kr%(B%hPl2WB=Z7cv60fgM9FN6Z9TaRTnD6y$2UkU2-`_G_
z@F>Vg*lOezK5wNQ-?OU+Ekvn2sBFpJt4|vdc^kE)WcU$iQ(q6l_1ooKw6cIrUH@vM
zZV$4Ley%WxjD+R6hl6!NNuWMIoe>inf()N4;?|zJ!H|0LQ`(Up+_$NN-gP+<zFjJs
zyOENH>EBK(w(1r@l&NmreTzDfRO-VOlL8Q^kO)7yF#;avK22wmKH1a#zL$RH6vMv@
z$Lx)u3*I;Kp4}KGd#l3Fta84}$35c1?4djrz;CJ{cY(YQ7o}BI^)<Ud>)@zS6E=as
zoj~_&g-DRPeW=ZZHyz#N{bo0R4<Y-%j;YkTR>3K`-4%ncNZ(LyGV}?D9e#N5CE<cl
z5k|i$lrA}00*if&E$=Z4A6_oF=V|SNN9t9@tGfyaPe#>!oFf<-cyG}BtjPsu$Drqp
ziy`n}vsIb8TM?Oya!h4!x4~tP`pZH>FThK-CBvhw8!or8-Z+ulKyqtcsbUEQAo}~B
z&Z8s);Foh%+Nah>!X<~!E_o9Un~H)bcG@FN!1;Y2jBG$*UtB?$js-m2!S0l0*@|>G
zgcsKy=OFiOdbhtz#h91+2py@z;pV|*qCLq({Zi<rGaf>ZVR^+<VqvIjTQEzVNWmJ;
z>*B}P6M)Ic?|jr;KEBPC-hQcJ22Vb>N=j@eecZu+jp@t8pHR5Y+H`{CV7!HMnF3!x
zX>_qw2FD;I>uFkEz19Un;Vv=1`CbF%Y*bIReGB~gIIA2%xP%cs7quVtz3_GVN8bbD
z1yk7SN0A*W2RA1R7TqJwu<xd+@%4d9_)FgsUQhUOIx*Qb*6IpiJY8nkV@~>~Ukc^!
z6Yma#y532nT><cuVMaD?R}-8(o+EwmQ3cYR6=Ml@Oo8Y)A%lA{l^8#E>y49PAqsm4
zBqoHl!Mh_0RnKR#p)PasYn66COi5HFM?P#ueanpJ<`?5}OZ=>gJY<8(ulGE3nx){z
zob6>{+yqXor#bv0yU^?b!((ZkW*j#=#PwJp2_Ahik~zCS0on&^+Rta#g3g}S7+oPR
zbeA0q`X^EjjQ9L}gSkraqcP!%hP}WbB-fLdCJ+2x2Tt;uHGz*)@wPE};(55SmnMcY
z56Z)++*GPcpld1{6t|Uv)7?_5j0r6)E!`tQUt<pchQyqvQ;83IFldLhSP&%3dbXE-
zPKU)+tM8-cq)(U;qHsB=ANi#o+#Kf5!_L)#R+ITG+{bZu@L@+EzTvoXFEpYKrKa<|
z4UbaLR=(!@vEY7kFNUv~^%0oF!MFYP)f&t(*xGVIvk(Q(GI?oHwIV}_@LQS>J)pIf
zW8(Uja5NtI)|Q{2iIGO9#+R~^(4O|gD81DyP|u_GRNGPm<J;JJM!%Ci5rz}$Qp*GA
zEfO@fPWlFK0`%4X^n^efE%ocKThk$6TCy(PyA-c_#b&s3btBWnRd0dI2xcGR_-<KL
zp#I>m>Hp_s-kn;cs;I33*_8gzqS2$cpD|VQ=gR`jp85Q-(JuopXKB*^93*}T%O77K
zAFhB{=G*?K6l?KgQbl&^XdafzSWFHZlR2RD*M|DvZEzzr@LyMkH5~YQ{NG{bT%>6$
z_^sI4gy!2k#MvH_@4m1_md5dVm={WZd!?xeU8HW@^sp>|tC##k+?7fGIOSIdf%;K+
z3%89?Mk@?DA8EgPx(sF;&A(D+6Cibt|6W>9J@KHPy07Kbfri`o=J#G~!@IAJtuKBe
zey{sp8+IPmc-;71X*MVEY$Vt*DJ%8k{kIk^7pKe7U-a`y$?Iu^gIuXu2`9dpJFT}i
zrDcQYVtm3a!X+lku_Y-*bmJbCwcOg@X{aDvpsnLrfnV>+os3=z$Le+Szw?(m@P{7v
z75~b1w9|8JD4lo=o%%~#qI`0&<=9E*DW-ZXj@qls{<H}XM}O@;@RsEJsV%;5b!>oL
z@eW@rR-;iySWn*PQ3)DNFSM>*q9C2%825=O;-MGy+38x}47xUNgsR?W;HQjCzXa7H
zbQb>LD5>0rHEc&1+@2+YaqI0dW1mcvRhH-3{j(0(<mlEkuH>QS<@pAV^>VaOoRXUz
zZ-P2kWwp;cdU1Q&>zdP~KT~ILs8fyb3gri*OWKG}o{P)O``FSj^aSSr5mfI&(QPzx
z0%7F$xo6Wj7v=-L#z!_D$QR*oob?}(|GI#M{kQknayu5qg;4fTBEV3wS*WbM7{1qh
zUTm%KLGPTPn`OTTVg8MWO2kSX@tqY&T`y0>iG^g|i*G8C?n|wd3R^k4`8*HQ+DZ66
zE=fr)AHuP8_HR*3MpzFkFwv5#!W{=>X}o?k<MQu;@F(xOVcY1lc)vHL$f?t`W8qaW
zes?;r6kb|@^w++f--x(}pXgns6iJTo1d$W{+18C3eG?*2J4n7`+r=_>;`<SqyE}PV
zs1%e~%AN@wCVrBbSzD@@Li}p0R=J<-0jaWE`!?E=0OD`$-+Df*0kfL@&Yyxq@M2BB
z=bBz6df6E8eT%L{HMxTqe#KSee&drbvlgweBDIbCM0*;X3ezg~GpWP#MV_`zdn#c}
zt$d{1qzzYkk`)48bfMc@)tfAzn!xp&%&W_O9Vo?X*uFq-3!P3L4;~R85AWRAfhMnJ
z?Cdt~<1`O|%t)Hh2Tk?(aMKBqUMF(SaIMKbTa3fM|MuAbXl+L&-fo_fl1AA3Tw$Us
zp$(08eKV=*tVbW~L+5t|q~O+&BO%h$uTj5!>1^e;0eGpXB3CY71q`}%GqWxQWWQqS
z-c{2aw7o2IM}h4bh$&4+iis7$P3>Q;G#{ElyN6xg^y(`-yKTy=&ms>@81`P9F-QX8
z@99?5%+;_-O6AEt>q=NXbMjYKY7vM8>n_|c&4DYAS?edxHN#Cy+2+=ULZCHFrJ6XD
z0UVagQU+FyP?6gpQ8AGQnGWoK>bDethA?lil3yRx>@`06VwvPltXx!*c9uh5h5VwV
z1@VT3nVL#DXM<F&c#nm34~VW<Zo7ZJ9`@w)yRB?~ju|%vz7I%OAR~WmrS-*POw?o&
zc>kNskymbO9eCXVIaY>qy`e2oPU}_Q`?>)csV){o`j^Acpk;ao_X?0pNV=UQ)lc%9
zoRr7J!<~EN)cf@po#?c0N&AdE;oO-heLe0Zk7>(mbB?1QB-SR?Im7}{iS|zDgGVh`
zS9kEi0jCxyJ8^gKGV#4tsol-JoY)T5BUNc(&W#u$nz^9F(ujfTi;KTGJK(qR@Q!E1
z+xH}O-`0IwsxkG`!KiOcEy%L@a-S;UySf<1Thz5FxY@Cw_^NO#K6IA5=uFOuPvsXg
z-;p^~9`gXz&$$8=oU8ZJI97#A3YJ$Mc9J|_*^c|CjwGXS9=&qCRW**}-OZTre~!}U
zd--l!6VEmOLWI&pJN`D<QS#Zx01xuLk>*H<$6r@1EV#X5F_Xez;~&#b78uOQAJXhV
zt1^1Zcf(SoTiMsxky(Kst(dlYKP*HMPpK@aU+(bl=GCq&pB_|rxlB2+^CcY49ErK#
zl8>zaQ8O^wmICXG^Y8Y3t;4I)Cx@0rTTo4eYfAe@8=jTYse2rofDfKl@7fz14ej58
zPq(Ny;DV)o=9MQs=w*Ds@e)-j&W4|}dEr`!T;p6T^KS~_lhzhp5C0@w44W0Vp&vw!
zg;<q0#3Ro7#QJTuzBl|mKFUqmOt^=)gHI{--DpYs->Ca^KT0Q>GT1wi=VA4yD0NIa
zg#L;OI)5o0w;G!6UYTvf!<GsTo7*YC?_2lDhnwu3vU~eS+bA1On~ek))|Wu)Xby|_
zj%Mhuq5ZFj@T>e`vZ^Wi2&Y(hyxSRbU~2HJi~nc=n2ZV9zP;H3q6)`(-K;6VyD#98
z)wg(9k&J#6wAcU<zaFV*^bo(2qO{ER>RPgQ!DebA3!%$n>&c^Q+3@CqTw+OZ2`**K
zbWJ7?Lg-eR@``^A;JLEqd!6veW`4|c-8mGJ(^6xXawtP1x9=RBq%I8CPkUdKS_RfD
z$tP|wBRt9Z@NoA+IV44IsoNm=tN5@{Hvy(@*y)gZ=lgUXSm*wD=rCFWeOK3*)lbC3
zg~hfVXEr)e^va3K>7DJ67)*cnL`g3s&%_4?Rzv{zLypktzZB?NI?*}t{snygv9W!0
zwhW4#Uj)pqBE**)>QB6Gf}N?YR}@Ge?uV!T=wwwI*&FpI=W7`0qh8s1Q;)R>GVA2m
zejY4<t&t^EO@m!vyZ-B&1zk2MUsl<(v$_DTwRp6B-b4WppA+(<A$`!b7Ct*CL+Syi
z|3aSV)?rYo*`E7n6Y*O>tKU_Ldel^uzDjeIcpyggSMpw#;nl64^=>2CIGwd(Ale)N
zN&)vCKPFxs9)TS=quz#6mv^|<$Q6OO(r~b|LqGa26oyr(d*c0awiO|(aLhd{!F?mW
z2&%bu9_(!o!QZAn_e{?<p}%uMV9snMZZCL8)3YNEi$3YBxW+1@S^fuVQ!fvY`k))9
z71WGppKVeVvP^-~no4XtCOWaY-m!C%z8GWknCHnJQP7kdsmdaGqolRZ?7O!TZ|s^w
z*_F|Fy!Y?@n#0>D$SwReexz;~w)qPeh9nnZ@5S>=?zN$CjnQ5>U8w+%wzC>eZ?A`7
zCXF+b!i^9wukh4tu?tuK`EOj+tH)g}rJH>oc0h2k!%jW2zpR{ggz@NL8eVWYq4l6|
z1ivV4nyWDBL5>c-a1mkhUG)$eTK!1Dvz*sbd@@q-(pJ$+YcZMlm#wWzp7_nImMQ~v
zRBEx5BYZwkx*h*{4}I%nD*z7PQGs6>wGhZBKU_DO2ofn1Zmczpz!xIeD7%`6gF4F3
zD(4z8JNe1X74jaunsbF4K4s7=qxZdt%)?SVcd^{+DuVnf&jnHKDqPomx3==G6jWFE
z>ElWNo`$+`E1gk0d{%suA4Sf+!h2fuN2OzNm4SWRrM?O<yeb;ay(<&<h1LDN7({%M
z#}b1orGxR@mQ(z$7vu1^k=gKBqk51t7+2~3OF`;%g9_s#{cyU{yP22x8YUfdPtW*P
zqneaa{ic>C_@+r~VU<t}ioKovPsLina7lpd?QTcenY{(6j3t<H#`Vs!V-l#+SY{d!
zAIFVNcU??h^+Df-s1v6gDzURNimZsLMEi2)u>~7HY^=;2LXsb!XDr?2?%W58Qbmp9
zRjn{<dxfHZG!IVv4SS@z;sFx0z26-93W52K=;I&tEpV{Vpn`3&ABJK=n?AEuK-SS$
z>r&$>Ape#138PsD^j2-=xTEqK%ueNbgmShM$a*#TzkmWrf1De|=$QhGMW-HoRwdu<
zX4TpS)*KAK8X#~Yy9JMS4l!E~q=E|H&Y=qXauD@w^QOy*MP@NE+H&(GxH$a$M8Tdq
zRC)Zltn+IrG$}t|>`P39^G{BSUdYVFqoLCt$5;~anZt8)E$IPVi=b#}`nH0?O#Ij5
z4(Tw>u&)G(Z^BN3$3B5S3)gq6^79N1;Eoh-^-|6T@OVOf)SV>@TIJ7vRPt=ZlP^}+
z%n280{*KK#wrv0h8}G?4Ce?#HfAxX)_Bo(j62G148e+h?Rih|#vTqFfMU>Nf@kl#M
z)sqj6KwWcnqkFj!Y%FdqA803jt6ZB5uj?7$BQDVA=G})I|IR*i1hUutou&^BWPwC;
z-?NXxWFE}I-N>PniCnMQUUP5s;=bQ5(KO^-S^oRLo%b5Kw;tjAY{gZNXA?utJ*Gp<
z>M`awdC-fkdl(D%-$5|A&h#qiQyBD)|2rYRFAF++uBs^-AzHAS{7h-L!AR<Wzs_+%
z;Ie<rvH2R|b<Np18m`B|FWzt2bu(>f-ZyQ*|D_e`%*Sn6{TncneSn&p^r_?qvY%~>
zZ-&%uiaBN`oj4hHOOV!F2|xL6Nne&Dd&utx4oXlBf%%KjWizg3m@#{=Rg_we@(tA2
z_svnro{Zg2$93wluJGr|jUZceFO`az`%Cr%yc>A&lH{}$ck&PRl$3!e!)QwTyE1&)
zJ7#`WF>kKfK6p@2<SS>j8cHWW__`_W!Vfgzdvt;HCI#*A=XMJoqBaG>!^{z~Xn
z&Ge!rxuskBvYGzdLvcFJ@>;WgCLW%$*m!=i3__AD_+ytE!1y!wT2)dJR?mA$YmBC0
zeFA$`&h9p}Z8=L<7fR|Fn`zT6mlB{+<Du0V@|?VVZnx_s;p!qR!wigUT5<b(%m37`
zML^jf5v3-vHYmN)EV$_!;V<54QFHbuz!@#$d_Tg;bpDq*!15#wO*YQW*k?yT<ezh_
zcOMnPEhZQL3m!4xIzQW-{xbt^^0W&oeJ(=JCk#v+yOZEb*hv9l(-`n=NP4?TBp*LL
zs1wvZUyJ;bp{+(QYhkOy>olY23J}WR<JKVWi);I`qt&?%Ed8CXZupw`{Zj2te6MrB
z8=IV+uSX={QGZEek7?3xRaFhA4IMxR88H=ClOp7EkotJ$KoRyW<;_NW5YFE3@9V)2
zYUm;KPA=%yApUu7ne^VN5|z8{d)1lR@YS27cZGtj82`OoB0i7!n<fq&@3rX18_)h+
z5MpaV_xz>vL)(b&jZ1`|tG*Y5BpT++cebJTMlaiq!{j~?|5ERZT@M})&RlG~-GU{d
z>Sf2zv}0}79iv#{=WCt!V$@<HdtQ2K!+MVtk^E|Q=?}Uj2rk>CPbcDv2bMGwTrOR~
zht?a~S+!)Zi@n2(GY`WskmJ(%p@w46C`y&AT`mK`BSzj-a%8UdadPE)Q6cy#efaZM
z!wR)nV*gY^3iS3^{#BA`gQRgzmqz6l@a)<|4y7zGuyOd3A4zg|B4dNWpW9LU+TKyO
zSKaUf^Smn~o8YF#^lh__N@(~j&9AO!0n)L0bjqx~_~h>Go99W6<UrHa|4M)6LBbm&
zj_-2B8*`#k_JMXTHoveMUjnjEu@}-`=y-wOebwju&+_nqnzB>G$#(dnWN<#PwjMff
zXDi+RQ;X^!PbVHOYKK}|#=%FTh)31pIWm2k!Ml?~<*#TX=)4J$swO!>4)F_O92#Aa
zx24NAWMP1CQ3Bc7pM=rU)!~hQZxS57ru**6k#b~s^~QcCm;$FRM;{+iD8OkZsm*mY
znc%=RQE5WvT7&zHHC<O<B4?JYG|k}_kX;nh`$+<JZe5K}`=gSOMJAT}^n4WX>_6)N
zhcyLXJX$LlIoJa{DbMvDnG;XmJ4jCd)Cie#doyG@y+G8yg1P!-3>b&s(DC{H8tOSy
z3rml+L%!}g?E>c#xTO2fkbi4A>Y4U_ZC{87{X1v4JtbNIQa>oOiH4)Mw0yztxO5!W
zy2qIO&mRodj_zVEZUw6}jqi@Rju4@$SGTH@fV-<zz8jboK;C;k@k?yAz^0mc^#Dsf
z{AypOi}HAdY7bQf8qZbXW1irYfo$@8B&8iVZ|wzvA|mr)r>fwe>7BKghK0Z-puQ<r
zgLs8}M^aPNi(z!TQ;_7pa)`(?onCpJ4{428zIQlwK)TbxGu0X~u(HYd!`^iYYN_-o
z)Z{yXyXPuP^mr!F=Zl>z=uC$eFV5q~YskJSj^?50`f9l5tMv2y|M#8w-(D(?As&`b
zHo7z+U1&45wwIcC>C?Sx71&pLVZ$)?YiURe>=4msx8={rf&bJg{^TC<faa$6Wwu-#
zY`HWwPxdmLeP%Y5pwUS31+mg!47xBe5~#EShw&4HzO+347`#s%exOQxs>_T{Zk1#&
z$;pvL>yNQyFW~IG>)hfmV6T+R4!zHfVCRxqE7H^pG`B2%?#gQb=JBhvXR<0$<-fvd
z-&EpZ%<v81^RB^L4i9(?k5|FN7N7D`QeOo|p88&s)dKB(n=1qS15kXBB;?3Gg7kaI
z>djM;FmUAVn#qSE=zimHR>-~@+$TRx_eB<h?DW@tFBXb{sXS5PLsJ!$EjMx4Wn@9q
z=<U{CvM)KpI?%*m$P;W%@5vh``yeFNjn4G#N`-{$V@Z3+J-b2sdt#gt$phG)+>t)o
z1j#K^S2x|rfvlaM>%};GVNdxD+n%=+U@|?Z+Hs~7ZkgWr?|&7YcQ}`C8;7$>MoCDh
zl#x+JWt8e7WrT*T><X1sh|oYNDtqs}_ugFg-m@q|M5V}&%Idw}zw78YeEWKy`@XKv
z=R7?v@_@<J?XP$r@l!4RZnh$Qy1v$I(~S)$kY%^p#xhw45}pCqG!?qQ7*<=Y>uccW
zI}<Heqnoha(HE;YnS$Ol4y;$3Bj7DRivWXLJM7dm))*S^h1Rj?KQuKZ_`!?$IwR?!
zjqv!H%9R#C@c@tSwfREOIFKWKe7*zL0$D7wwE9U7Lq=t3vj-9<AC0S#JhjcO*(udW
z#6NuOzu5SRX2@`7K6j3pz<-Ll(|;5;K(5cRw#)(Ixslt+eBewo=-fO|vq?BrZ2V?r
zd#sz_p6y)V&){4rY~IS&%pV8h(c5TpFIVD)u^Vy$OL_32W=lhMauvM2uU5%_BNP1i
zA3PJVEQi`2XI2FX(qrAY&_18j3%-I~`77(?(B|;dojoKO`lrorTsP|@?=gx_Vf9Y<
zD}F94U9JzxXNDN^3D1q%$(OHHI~pHpP3c-LH9}ghfj+-zJ@Hn%M&FXH1rB>th;ZtK
zYJaUdZ_hw<Jfs_Whvc&;dWNVZ8L}bA_}4j700ZGqYkrRmYT+WSmX~aC74f;9h}l8%
zMBB|KdHUz7;kwasZ|#*B*l}Wf+Y{$dsFxj_Rw%2%N7c;cW#oMC<Z$+Aid8vmV-Yo>
z^K1sL5e*rkzsd0M*5jtDA0y!#Uo6i<>OxfhY-*aa=n3N2HM5=5v%tbtQ=OW-4PxA!
zK0fNtfPKyHzBRtf#dA8Wu76@)!p}pgxk^V{pwpg0WL>%sc#iZaPK%|&R7(oYE{hhp
z-`3Ma*<AyAU(y=9NNTM(b8)8ZKrJ{fGroP;-UmMWe>yo6uh~q@>}$qHZ6I%S&{Lw1
z_}A}+`uaFFLEyEpgX}c5kg-=-=68iNNEc8Uh#xC}eJh$CPP|@V_uEODR+QwoGn~H-
z2FC%E<^FP5Aom!yi3Q}Vg@4~_$Gb^iY^B<?S@3=ryyhPa?Hp-D##<R$XJ1z0*o<xZ
z0^v1mJ2lhw>vbuvgrD%ae=!@AkNt~hJKl)-cXmodZL3Em%PWqH<6clV!_k?%O3wdZ
zid8pcGBD(<{gZY>M6p#L*FQ>yV5o9vs$ifM8sV{J(@;46&PYC1XBYtck6P?bFiU{w
zUv7)B)Tzk1`;dInU=_FyXBEVUwZZ88_m~dCQ&!nhAyytx2tNys-G5#G8Yh0de0+-d
zTn4W<AC>p-1nn%D{fz-V@cmm<`$6q8*g7)*TevP23j+4aZW+(OE}MtC*F<|@K|9y!
z#w5uhp1Jtq#q$)nlvNP=^HUyr2hiSX$|gBLW*yT@A6p;+_tLZkc;o9}N%L^SbntTC
zP?@JrCe+p@`coxkD8u$FwRWHvqi&tJp%q+;H-mH({FzITzh8Gfaj6Np8hnhu#08+1
z*|#6zHtmRy!;k-WguHLv#3m(4?)VI-U67inMfSHxJnDbd;Dqj&U*mEgKIrRmxS&qX
zv#Papm(t2{QAwdv&pj4peVPm$ggUXbJ95>FIS-|qom?uOHsS!i7NzN@QXH8vp<FL5
z!qS*iZn5P_=>J5WCW6dK<1;S^?rp5Zx_yV~TeNRtfQ(k;0rgg-yyi?nf6x~<chou$
zyHz8FU^%1e?P|QgE*1VvJPhv{WEELiIpJq+#wjbUY7|d<vpw-%KMqMV`RmGQqFT$9
zS5FiWTlb173~avxBG0EXhvjRrK(l`Km47RKzc3WYLD_@J{5P-HEOjA?vwXd+5r)Sc
zPOB*m6rt)yO!YAF3#4g09$-_d#i0LO(!)4vQD0j>$v2@KIzpbgzN#i(y~|1!LPNPo
zeeV3tT-qoo6h2-MQJw=Whne$C@00iPvS_vAWCF69EDzIXxI^<tnQJC{tKj5Vg6)J7
z>EFK-m^yp81hV;NilV0@z?pwkx$kxZ4xjqbF>c)se`#h`o@(@fnW^0G?{0+WyhkjA
z_hJ#mS_Rv2$X4OqM`iz=JXs3K?{fK+m3!bshdtkoA8C-1D*v$Mu|I5yIv%~bGYFg|
zdwx7Kje_^$25Yuy^)OUF6(b~`4^~2Rk<+3iA67Bf_lKek)KkL)B2HC6K-qc0ly_-p
zxgq0m^FcXSo=D%z-4O(G`=4)BXB+^p?WSWEuSkC>U|W|TZya3FmZfz#*aSuOWt}>P
z)!^Em_Tq_gJJi@4S^ri_$L;po6nZnn3lPukH<?d3X-;pG81ElNuK2r;_`~br#<AB!
zyky^~_Pwf)jk6aug9QUwt%p%Jm8t4IO%RUBjyTEAMB@${x=5duT3~ph=x;#wPf7tz
zPfeIQ@b(4z7TdWPsQ*&PrI?$5<xAhQBehfDx6XXy(%}HO;4|sO9ajb~Z=Thq5heWZ
zrJeYBt__mg3phFSuYtkCD`CwR1*oWYRG;ll5<KTW{8IEy6=>2LMOP&@!{p+Yx&BYD
zp~cj8>J0Hrn572}y6?_J){>pGlDn(G&@}U2+(nXaF>rh~#u|t(OKu!)R@Fca-h)1B
ze<SeErIn}|;*-1mATFQx2=Pg6F}kq0Q3W^L26dziQsARR&vLk2E+}N2>4=}I1Y_f`
zn#V}b-nPV=e(`=Cu(507ihL<#L=k)GY7zWns{VfIWEY%c{WkeyxQpyBoJ!d2{9)eq
z0R0Yke>~gufn8%<3vg;<?$pB;VEFTRYN53O^i+nq_mes33hPndrae{AWVOA4wpswM
z39dccBOQc_a+k^+WV7Hw={`yxmXGk3BQ2hpx)B5l9#h9eBofbkujR^n;yKQoZ7eq;
z-i%<2x~8-NNcPcCW>@Wj0>Q**PMIXH74~z7*z+np&HgMw;J+FSc3k&eDvQMfNrSly
z>wy>`JMyIaU;#v_?%RJ&BoE@>E3hp$M1d#;k8V84MTFieJhYWN0JjGx#C|C0!xov5
z?K9g`Fz^1)tm{E}sHeQ4<v{u;DQ|B+Y|}|VOZH114g$THJH>4+k?D&Rg|>X-iD8g=
z%dJ7Rlz6Q*g55Z08u3<8q1zqJdK|NeJ3h%-2mN2N=XEYOz*gJ2^Boahz&x71TJ<6g
zoBZolSNoG-@~?i^Ei!Lfv)q2>;_h-7{mmtH`FsQRNVF8oX;on=J#3L%D?_hRwHlel
zV5t47l)APv5t)2K%Xwnr;h5=_%Z`kl$e0-sB&1jdA0DO)iEyMNqrHdCa%?@82YUy0
z-R^=KzBrC6Bc$g+v0^i46oBlZ%=LIU5?<8)=J`qPv9!B1Yu}cUe3gES#|hDTJg-Cb
zor0<#1<DtKKD~(tUrEi=T2gK>$zDxA?@#8p^4xWMpAUgWXFJ!2{63r#@VYg*Egd$C
z-7BL$HN$ptaSR~6|JBPT0t4R@aK7w;+4ouEZ|lo+E4xni8V*%FbD{ZIAiO9jzqcGa
z(x0{jE)wpbO}_V~@(!>tv{#NOC+8~-qlVG0U~u1YYqG_>34J7&1RV{EAjpl$((>p4
zOnx$3mTRelalr%&zD!R@JhbP;sY8jd_2aoS(${_9AJv3H&gWJ*c*?_ei4r0BsDHDV
zRUYQkefeVlG7e0nDMrnz5zN9k8CLgo13zv1lc}l@i0AX&(+s)L9FU_c9Aghle;B&I
zxz|DDVy$CjMmwAu=pDT?Rt{;YW$cse7NEL%nBVab;RIjo8>*BqK>C#5p&J)VpgI4r
ztk8uQ5E7zsUcI9MHp&|1H71D9<7sH(w`Fp!Po7a}DTxEN6Qh6bllfus=UqQWRQsV~
zG`m48mT+*qK3M&ttbv(_#ta{WDzI_iF3LmJdHCNt^Zd%0cxaE*r}q>sLXlLtM;9d;
zAiG1FU%|8<JX4QY6I};fxx`{3^q>XkX=I$lmMW01?uWiIQw=Z-&<gim?8g8(D|;bs
z!lyIesy23^3YXo?uX@krA-mO#p>1mo@QG{>uU)AmUT?)e{)Eruba+#OljK-ywEC)Z
z($j$JX?4*-xfU3>G4V`wH~~Kf)2+8$Y{T83)y!{OXMjmrL9`3Wm+k)5Jmy2Val;+$
zY?u7VzP83~<)c~#cJ`FB&`b`)a|IW>iUSR>*TA8-f2IhI8m=k1o+Y^z#UBmBW{Ah1
z?x$I?8-Q4^?KeJ>pL1+`_2_g?5ft^Rp1wiuXQ5Wqr9*=W$fsu^8$U|8bdNLNw(cTx
z+>9%)E-lug*o#kNeLLd7?Us_k1D!-H`RHuGd$$i7&#ZDWtH<EdN{W2IbRl-x)u@E-
zs{jj`B`&M5SX895wCFMJfu|RC+R&W}2a14A_k4{AWIQCpDgWgKRw+c#Cs=tR^`C?t
z``?p1)iKSoBHkqYd$i;1a-;`F+Kr}FUQ5S)UrNM<-;`mIi0kw4b0lX=aYSmaF&XPc
zw#5yv5np_u&G+KJMIa$yxcMfl8Qe|onqFb+LuO&c9lpQpfV2DKnC$y<czCM%lsB0d
zU$rcLb^D(c{%3EO?rYqKO}XCcDz}T_W8Fbrf^R||x__6liPv+n=+CEmh6J#^!`2<L
z)Qng6*3Z8A5{0?4ooa*GVOT*U_?zE=+*?JjZr_}@!*+wMl6td4_%`LTLPuv6TBx|t
ziDra@u&`{N+<*CS`^D0wKaXR<wjy$--FX<zS&l4hpDcmLGR_-3wyC(KsBy@Gq6Dh1
z-{j+`&Vt=0=FZih8qp`H>6?XB0G`b~M#a3x4Z8Ri9t;xR{?Mks_BPGOs3$+i<92%x
zmviIJeGiC+U)66-K0S(o_ISl}TrcwA9QOsG)u}LCV`fj+`P_wF7D2x!gM+}~%;T`N
zphlG7bDLdsF9e#iiyB4toxmaUR$d|{7$^DqCI61q!@z4bn%PL=bNI#2?mim`my9Yt
zeO)UBgC6Pe|8jHjab(El^)Fd)d-sJJ)+SB(!gVmY?MwqarKmaWOPK(MK|fa2hkN0k
z;-cXcLj~wFe!AHQ74U9Wqh7a^@HE@69OkDE1r>QuQ6i`ZjsZ;@TD=aS)Rtp55lV&X
zdgqJ=$sXuepSpbdq&jBFyjML+-wV+m&DNRM8h~%thKiOM;T^5&efLXl0FKbNkrZml
zz%;u(wydQdY!U_E3tg{=Y@PnDMtk!7`z-HM*;WOuFHg*z*cFF6DJCgLecIssQI&`1
z$bK*(H^hF5EdqqR6x3qLTw+ysLy3jj6OTo91;x8JL9_A7vHPr1Bwv@tH2x$RdLC@7
z#Oswn(baQOk4C+*?~2m(p)I{Yy_I|LR#gIUGJNt9q%4Kg&m12Au}Fj`BD-JFWcXsR
zL;qf;qurop_4M1b@MiE_Hk^L-y$p7os(3N^F%P_M@o=;dKFE<5#T<&Hzjx+gPsb+V
zGd*v)+A=^>4|vc+fA&xe)P8TD3077Ir}CHC0tJmQej%XCj;#?DFZK6Dj+NoPv_0lD
zj<tC1si71BP~*~e2SLwWnII{kAYNzFhhvwt=lJ-laO6D`-|vN2p!{0rZh%ud@u(%H
z`3UC1)?;>OL<m3e@|ODe7o>l7E#&O>4KnBOgHx0D`^&(WmQm=zg+`bT3!YF2&%w((
zHtnLN3SrFlpiHE80hGter#cuWk#neU?swI4P-gMGi7ZW6a#uUYfb>1R)^CK@r|05m
zr~z-Ha1x$+l&aZH_y)0gvLA}>caZy4Q?p71;pt9UY|(m_2Ek``o?L&~2uEC&kLPVi
zIQZ~*;N62I#9RMD-O4r%E8Mj`?7l`qp5sTlA~H9)YIH`tDXt9!^snsxaiSFyZsfn{
zN>0Xi<J=no?~8#`?;$=@?FFM5=QnH2U7)aG<o0>2886nJwxZq>2lo%xznHfr_k#18
z7o%pIh)095ru|kdR^PVTWA(TaXS275Ywj(F-P+q(mR{Gx%dN91yT+Tr(QIdjc5WfW
z3KxGnKsaFTv&HnM2(PnG+xnMRbpkv;Dzf5}L_A&KHLoGq2TNbAwiJ=<{g6YE+qd0K
z!13~zBJHdLp8ApV?|5!01Rgm*W4618%<&T2?Q`qlW{QoivTPZam^fSyJJACVZu5+?
zg_YnLi`T3HS2Ey+@Pl)5GND+}eB`MWYY5z}{&$Okhz}Q<jmy9Ibl`?qn3jup1NI)C
z^iLt)wqM8J<ZW@zhj{CxM@NQ>pp~mB`@IL@922bI??a^5EmTv-O!n)V=4ukoSMz`j
z6|EWklF^6j&5{Xk1$O6NeW>{Q1^R2V9KTQ;4J;k&)4x6X2!F3#Zs&y>5P#Db%tKj?
zN0q+woR6tNhcvs7OL}RrR40A4tbp9Fx*DhNWcI?kx6s-)vs|>kG0|ryMsf|^EKQOO
zE_lIJ|BEaY;mluaIblqC3s1c-R@E=GLdJZDIGtW8`aZ~&+~%Bw%9I>uD;&uEb>EoE
z^{3r%tzstffl5658gR|2tSx|@2}bcJcKYL=U00l5+9aT-#UH;GwIW!1kbjYd?C~F4
zRNazhA^nJR-4-$|x!5H3cKiJESXfOz%<6u+5DmEg^b5H9K`_JN8{d-}V7Dzh%a!3k
zsM}F=F-4N_7vCJ1vhWSW+Ji!^Gd5%&t7>4Q-$Xpz@wvwzt#`szippnJWL|$!K~$y3
zE)dOc1;w8y?|IkS;>;nMCs53KE-CHs5Ij0&Evep4dLWt=tleYjD7kPzQG{@-8@%k=
zb#7FHUAI2NVYM>CFMlRe{e|@HBEv4aJSYGuhnrv3M6)qsO|j^KRWn3CWW4u6EE)9Y
zVjI5}5HDch!QbNviEz2_yexGa*+W;3#%+@wfUAjLzW*I71?X#jD)_VybU!~GNp4Ss
zrq(Nzf>R+ltDP3)U66u7f>-v%pKJw<MN9S~hEC8tHW;%O{0z34KT?sc$v`xvIjX9K
zn2N0{2W)!alnRlq=y$@I7}afCwYnipsq_e^HQ}&t8`7KD48Y;2;NRlTwJ6|Dd7Qtz
z6!jmZep2)<!8dzHr`vb;;W7Jar+C6)D!epAmC;Z7hbD=)tGnDG;L?%4=Ga<%-#4Ti
z7*6)^Mirh^=nG~B)PD!GdoWJm`&?xI3G8&d!{$T0m_Kv<pC$xm;i^g8x>IQ`{L*m?
zF&3ytGnMZDw&@2$Gyg<i18)<47yi+)iRC!Fmu_cLt_Qx^TpAEkA3zs+qaP~ePw?(8
z^L8$w0!(jvU&<Z$8e_~xVkz1)ppKT8!$zeGU%xmz8&Brg2J%Zg<*w)9f#X_TeZvLV
ze=Y9MD_>{0`q#+q1$z*D01*{4y|>V=_0Y8KKnR%wEmNH&d#ZLn!|q={no;?B_3OL7
zMG(x>l(O$a1r`f9(9Bw-;>;{x`j@OGJfR;O&KTT+^}qZn#rqm?S&nIBZ+kNq+WfOt
z-&u)?Z{2n}9wqkzsdNeNe`UD-zL3doq5wNGxR-m4$bG9^X}FuG5w~ST?l=(9h0Wu2
znWD~3_|GrV>&i$zyxY3FZ3kZzt`|oY%y74%+Jvl$6q&dDdC^bcK_mFUUa{!63h|F;
zm*3BDjKUE2n>VPwkpCx<6p6rhZMfH2cef~SGkUvmYAx~BfJt+0_W&R9;kihLo~9|p
z=+Bm^OMeLOf>yhZ@o5E|vexUIiZ8;v!t27yQ_W=W)#$o%yAAbO&9?KTH$vEr!Rwyn
zT&nNxv}AZS1-Im8Jk7h;3Nw?rQo8IUcbC!}RI6Hw!~C^%8ibpvn?%R(Zo&gPOKm>y
z8urEEbd3p$;R+nF2-*6Ks}u#+sT*Z$$>;6HWZ`<R9yl$-1JxuN;jp48%MTOsdFBdb
zGu|Y9)!ON*rr0J7S@@OdN^<|%db+-C|1xm?P=R0RTnm;I#g*Gp#b89-afxde>yhfg
z<h1?`a=y8iJ2_0eA4SH8{SG+9!D}&haoWK&tc-Zi`KmPlXhw8yhWA&)u_%qw*BNa%
zFv)CM<{gh+4o4=t0s^rs;QchmE8_KZaL^dv{Q}*%y|pPxuc?B&&!zrm2S&d<tzvPY
z8V5N)vAY<Q<G~85_dXs4*mJdpN_@H=w<RAbVAbv;Kc9Tld-A<Hw)0@b=AC@}+QlHg
ztIP-IoYW&9@Fb(~>X(%6vxGB#hWV*d#Y<$JI=9%FN4%LTt46k_WhmU$Gf3s{0Xi(=
zC8urbF#p@k+FuFc6IO^n+Q{93hlEvBc2*&3Y%@5h`>`3v^u{+QG;5LXLh7V&R6gEx
z5A74Q?ZmDvpE^h0H6k}Vbze7!4J2^2USY8FLFLrPR%hd*k!7@ff1^M<GKK>OzjXoX
zm1b%_cPfRGN1qjzvKE8NJe$O>SGgeiZz<T=y$lybUTI#UD?>(o=aJ{WiMUm0<HP!3
z4jh*Hb~|}K4upPeiIpJpr`GY0*N2RIF{%G@kj~*gRHF;65NC0LgtkiNvMt4UUCl2e
zq_YvX>PfwH?MXpFTd%86obn;=vZBHI-aO=e$bWAmyA?YpDp<uv8!%YR@8~Ss6pm5)
z^Ly>6!>X&4>J^o>kk5QPTi{DMPO9={zUIlrMXj)=?d2^vD;S&~I-Cc)$~@+?d+UHR
zCh}0x8S>{w(~d!oRaoI-a+24s0r$^sGGCg=gt4!?=~e%ff=8m!{_6@CK+woTkNUD7
zR2R#uNu>8<l&{1htw{x*65RLc(2GRkR~Q@2xK@kp+2`7`ymOH<*ICy{C>mX?Zv8sR
z5DfP_lJuP;i%^~B^@P(}1BNd)K4^4^$L>zWyIUv79{R^?!>dApFz`dsB;sc(D&EO_
zOxsX~PjuDS4@r{qML1VtIpGobn3g35u+)OOT!`_^%3JW&4X)dI&=R^RS}Y<7uleW4
z*9~6MWdFHS-Csks3*5$BbxtG~ko`%B&k=!4INIhY6*^su^T!{C(EHfq>HBw9g}Pec
z5<jcDL3|2q+>4fRP{~H>8R4WupUSc60&D)&=0+^CtqR*^kPRQ&?r@3BlmOw--Z^iW
z3~r~}RIin#;kDM;Dpp0(pYrWm$LpmCL1CJ|vI%dCo%-%KpGR2!_E##+jWif!G0XWH
z(hmiRg$|>qi=eq$)a>8w5-530KN3#n_ojCe4l16>z}M4uhQ~MtAdGD#b1An2es!95
zt!l*qcl;}T{9TExUioj{YjvTc(Y4<V8zh&*Y{p#^G;$d~v}0XC|d3hqAN2Hqb$
z$~(8$<HKVaXDyF(!cUjvRH{?G(0<@c@?oJ^JXy<pSU|4{2AVEhj&{pLx9bzXLOlyX
z{>#bv<17IfeVJmyR=)~*7cAEJNj}%irst&C`DTpbv>l({(TMYGg4^vmOGu70YGU}n
zBfM7pH~*7I3Emq%QrkyOys1&T41Qv**nRW1C9O^hG#n3*-SV**G+u}oaLLqRxtQRP
zGx3)XY0+(0AoE%^sS(*0mrxwXE&3<+XG7T&`6&acI`r{6#F<yz1zWx*wn$B+;Et4W
z#z!ZM(TU~t!EF>-pr+}c*OKaw-8y@?g~{GJXK($Y?Dl3*Q4o{v5K4jliRW~Uq+-#5
z^NWztn|jQqRHy3+=>z?y6Wluk$X>3?xZt7-VobD~{CHylEF2Y$eJ<9F@!4s#f<Hqc
zuzzxKhjbZOaBDc|8N`Bfe!uUl(s-1zx^za8u?7|F|1(-6IklM;Yj)S#bi&CHV$30P
zjlUZ5v}#{#V3u)Z+n*N^NOMnr#72#9sLl6Uh4_=6T=B8X^Zz`7H;S9~`DhP(-BfLM
z$nl3CrkmaL$%H3ouzG#AeHeDrJhQV@Qh-k>bu#;m(}7;UKkaWd;b6G8rYoJw#G4zZ
zsCnE6z+Y6FzH1{9oWrl48LldZ+jQl*Lz7uR6?UKF+m%dwLf3u&#;I<2;dN?*t*ajD
zjGa%uV{V7phtKA=Ovk_!G@fI%i-ipFvGP4UrNGc~)h2|@t7bR^!#~cJ<6xrH4Y|`b
z<h^$2<PXBb?6_E?zA8|Gn%=sqUVq|YG*J7jh_w&=`93qy$=8mKessm|!rge+biad5
zVLSZ3b@0>p5<(_t%KM*RNKVDImy<@g9%_%@ji}Ubf#nQO@;zw*cC9c<vFbDs9lp^X
znihakPOMGl{FR{i{A&W`FVaV%jBXJn`!#8IpWZu-#b7GF>}2~U92q8>8w(OLAn7>Q
zKoS$_BU*^d+Q;{SO4@Afzh%M!c*yf`c%u+LCD<LQup|9d-_(I<GVdOj57toLHUQt1
zr-fXi(}AZV{-(^y0-(sg+v@i`2d305p9Oh#fXLeqCCcUlKsQrp(933t)!WOHMUCRI
z^9t4Jnf4q!`0BxvC&V}78j<?wJaa#Idq!}~vN%J%rCN?kVg=TA_Wjp$y9gg~1TpjJ
z=ELiKcivqWZGbn@LEUPl9`G^iCYK{GnfHVr@qA03-)*l$3g7Ha$16U|A8Sflpnu_k
z;vicErnJx=W-Yaa*8z|0eLvNpqRGtz#ZjSfEOq);Ea~^OYV%+Ct=Wu$vFG${|Jso~
zoL_mw)*#$`wqLboDiK#G(^aNNW8rJQ<@U6H3D7CK?UMpsF#38JRotU)g?o=ZB$A$n
z!r!K&iz_q@VEnvj%cd>yz`xzg;}af%uF))~4WGWo>-4t^AJ8{J%X4P`_YJMU>CLe9
zV^|<u-SMfhIlTzzg&WG1$vO1HfAn7uIJE-B&g}+LOIA4K&nqaTmxi+RwyE;B3gKO{
z^QtK6gY9<NE7TH4a@o(B+i%Uq!i<q&h~`8qKIzc8`h}6?8D7nc>X7$9;h*kI0o^<Z
zn{{(6A^Zo*iHgd<Tp8HgGQ||imJbv(Rql<I#G_U<bmlkdp?m)l71>4RMPv0oUr!VM
z);lQ%Fm|fM(KhRS{^XpU`&q<EEu;eqqO`K<8K1%AvzM;heaq3ddRgU;V>L$cpUaii
zh{LCiDJR5C!wJ8)H|ouU8c;ZOq-}?GJ-GDT>DxH-66!n`XTOCMfIZj4_?8`M*n2&e
zvXb;zBDBJ_+d1EXG)2eCx7bcl>`X~L@_q!`d@TaK-Zfx1PwnZiHwm{-_FgL!zcaSA
z{x-5TbHi`JBIk!JpW)P!Syr)P5bik<|2T-J4R<VG9@7qdiH-VQITz~va4+R;F6zWG
zOctD(tX51!+3k_tRE>T3s&2)-@s}-dj~w3!{QvWHljBA+@jaFuEp>BJu0sKn^>q68
zeRyHgw|4MKEczXuy*sbwiSn+lr62n{G4V%#3hj?X;P~qGMqsWN-!1Y`U2gBf%YQyE
z77<^d;~pA=C9@vX;g)!||6o2O-Mh1X`)Lo--CgmvBz%{xtuww~PxazakM-~N3dB$Q
z*R#R>>KizkY*v%r*?~henhHxd67cT29KQ(hgjZQHQI*xjV@8{Ef{02!mb_umjPt9(
zsS@Md!=wk1rEc}E%d82vt~PvebI*a&x*b>VFx*A{(QWd7M%vN*GKK&0asjf=yBz;o
zl!GegJ_v8TF2E$oYmHn_3X%J+WWYJY20UGB7r{FC61}7=!Jjt;2K%hk{$tNaS4RtH
z^|&ysz8-&AxVZ+u7X3HgtDS~kO+34+yQ+{`VAw>}yavUselApf=>RE?sHvwU=PP&8
zKEfs~9_222(OWNs;5W9A!62JB?7da$%YPB^gN(vNxXw#F7N*LwK=QD9@n_4s0}?Q!
z_J`h@Z4#I>Nk39cufxEFNihS7D)5OMeZb(AhSoWCN_%z{!Ux^y+~#;cc&%dM(H|WI
zwX}D+SeffE{`pz<9n$$|Vf`=QkzN9r^%#XJP}<^!y>=ZL$Lp}g?B<&XU0o<3q3LgS
zqZs~Ud1(4Kvk;Gd(@>CIs>HCw@M!(0dR*ME?!14Z4efopBe|Gj(a<bRfPSnT^<3sC
z&x|yn&WbH{{C~aZ{>x8QbG8<*N=eHI@%7?+kCdW|l*QO7DFekH^6`Y`yo_keJ8UkQ
z+bd_=hX!wB6Td6fW2W0_%blO|G2Z+?>wSOYAn}+Q<B6e0%u+rc@sso(hrWJ0O3N3D
zi&N+Lr{1`sDO)}F*@p!{nRYCh`$-8N{mLFCFhh9pj>$ukdg<``KoI4Je`GFtifL=B
zWCln?<);<Ry1|j4%fFJy`)AFEHj{U)9FB833gopCPmFNLtCI|sXp<z;m2K64ANj62
z9aztRsShH|kzqy9l_|PsXKoUtpH_3V^o_wa`y+!xekr(ZUZC0jPb+Yr=<8a_jRSMD
zxZlmf#52}*Yu_s2=+V8iQp@Zo`{&Xnrtw34Q09Gr|0{DEe9SiDdt}!R1!iK5X5>B2
zWIu8KTt_aZzLuKLf8Pk7UsG-<X+1?j5q=IE`CPJhD)4yZlmr)&WvUufnn3BA;TeIq
z5wJP&SXNs&165V)J~#UKVd7_2gEqB7NMYp8dh+E1_)mEnPWu!=zoD^|Uvvf*oZwl1
z!_^3-3p%Sa;T0IkX!Xr=-vD%e=w5N&=?eSVHS?cNxnpl+l<EGNJ}9!mL^k6R$jzQU
zZ)jQyxr^^sDnt9BH&l4B;&D9a99|vxur(R&Uke<XTWAE!&dJVC2TMVc{gc$%wsxd>
z^}0)yaC<15{;Q{Aivg==&K{pG)Pc*$b*B+^Z*(flOx(9#haMVhueA$u@rK~vy4B=B
z6o(YLJ6$Bl-}}%1`Dt&^_!t_sd5?IPJpQWeJ`w^is(1e~eBTdTcLQDDeH;L11<ub^
zq~8@PH@MzP`k>3vz3q}h+Q1jPBa^YI6WVr_<{e(@0AK%n8%e^0p*zDLddV#h)ds5*
z1K;$b;>PjYYQr_SEAPSrb!0B_1I%_s2^VAbnP>O5irJ#$5HEEsH|cLIt^TALNP)vH
zR6A^{%ixAh5vLSgKAO@cg!(Zhpkj(yDpiU*KAyhHb?P7C82kDSaV!);MppF){(Z%`
zB+JxYlvRaG!^t}YO!MJp%<meiGrbUNyqcdb*A9|j#%ZOJ>xqwS>!~2-M$}fYX<n8i
z`TkUmYc*T}&=%3x!QAPOXJj{b`H}f#ih|C`WI8`|qGI}H#Mg|w&S`gia3J{;#zU{h
zbezG5UdoxKp&ds&s{991;_yYWhS%SyFkH9WeOgem54Wv28ZxJo9^=l2pxo4YP+Vtw
zPSp_%1OM$(pJn#Pe+qFQHmVwN=?qhjh<Xb;^(4iaOg;kY&(l;DGWno<z@zO~Q!51f
zP;sjqF9f>(N&{s^<Dh8&gTZ}dFLr_X%#~f59Y`URb8|T$65&u|=XRA)=n6ixDOE?F
zE6ROG;|%L@_kHtajpoO=WsSL<VkQGV@f}&&WmgF{{GacbjM~6KiWigZl!+K=9O)KU
z6AKiRcgELDNH1`F*KwtQ1UMn=&KbPqf!#J|cQ!UugF%WCqx}9<cvHo&#F*6wgOVOk
zS)FRIhhpE=#mmj;znrT7>2Ex~;A$GsB6F{Bj*S-uo!xN7LCxdla2p<4@c!odCIy32
zZ{?OY)#1dUHZ|?=CXoAmWGQ0oJp}VE-D5eHjNkBqsb*0F7&^4FzWkL4%P;Az?bPDI
z<r!DcUVp-;kh3}~uhWPvDL=IDis!=X6h^zQ{dG`(RekS|umrHOOdfyW90!F@^!Ovc
z4!}#p(x|6l&(Udcw}D1P8yvi-A*4H!3Eo+Op2@UtiKj$s+-rL$thDkxA13!Dv&Uxl
zmLv%mfa)*r;V@qiaPVV18lVY9(-o~Edj^2&pZ%I-WGYB(-JGMBCwUahOty!^@sL36
zYu?A10apTq{=Fr>*s5`}1ICFB5b!IssAX#uc(;WWH;_Juz}T6|fQ||XNgt)&`mP0N
zHIox;p1A^NyvF3W^VMM5BhaoL;Rlr~jAz!{BY+|8;E{jd`{Ao{X6nft;%OLWVdF?5
zUX8_f`hTCL<C4`SkoH6T<(0m(f4K%qehz;O)+-0ak<)sz2F*}&Xf8oYDi8516WzYf
z=Qy=i;@i^S1mgk^inRTLVClo8V#&@%I6Yh%^4-4xg84!Rc{1zam3#TAXq6WDGVt*B
z_Ir_#tLkRBFNOGV`!)p<E)p*4M!dV)tt>dPHGpw)svqpdw<b~SuYnKj=PRX2{^n#`
z;`h0NE>wHO+wk&mGqUAhrPjMrg*(6M%0Ir4iT6rYJzV=*F-yr@`1CyKmoT><KzhPS
zEe-kDO!7h;fBwF%c@zZ+>nHf?jhk?6o5fr^;e+25kw22;*Me)08Vt9;%>-jpm!8Ph
z7WkAmD01U=J33r@7WG208#uMOPn8~e1-oe6Z7i9(fns;^oFLg7dTnnEk=Lrn1oCes
z?;n6C&RRFDG~z*2<ZYZmWiLz?)_ofDivt_seN5M=Z$O4j<&|2|PS{UijPg|3ut{g~
zU76&gQiXq0w>q@L&wCFF{LKr1o2ELEYuW?i&L-XUnMM5er?9(IB?lY&-m~hRt%1uR
z?Ac#S_*FcT1=0c8*wLs#Y41hy;=Lyigi@2d_F2EA4f7h9j@U66+EflE$G(+K(`LYz
z;?loAejG$Mrq8#AKV-uJt+Op(Ol)A=mQJ&!TfOjx^V@Zq{pqMtWvh|HoPe(1-j5uw
z%fs(h^wibgqk!qhw9j#wD#-pWuXA#59G=+cVDy9RW4eEQ$x_HE#wXo;{p|k=!TdC9
z0Zn`vJkBMA)b%)A_X*=J5lKT9bxE1-%x;Wo8q?J#&k-Mn?Zd`T^YI${k-Gxj^<Zr1
z`64l<2&&pGuiaN_!LHFGGKp_HF!uL1)l(b^C>F7G`)lhym}{+dsr_3C*%2z^JgmfT
zG<C~fey$52KhizJY*LK^;m>z0*>}PI{Q_P2hK+Etud?^=QY?roUfT8{%nQ_mI?u{E
z$D&rfXOo}IC`d2(R9*Yi05RVL7K$aE;kbmrjr+8fxbv^sgmY08QVZvVjh6JoOS6v`
zttF!19>;+XtLP86q<3U;tEGW(wA`qO`dgfB362mx*9C+8ODn9>+3?S3{N`Q3SnPhL
zcsEHX3=Ge>%-y_LiR?~$PEKSx!TV;3m<w~{JhAM&z(8^{MyEqBt==sF*$3r{PaYM3
zoN@lA6_SVjV`8rF{{0nBP72BVi41_CbtN6%drh#5UwU88Y(20gsWF&T*5cGA?>Up;
zO6aVw3jB690hmKg|GlTq!E@O;x7kv>fu0He{A$gH2HQuHF`E(i(sjnkfN+Wqc5eDJ
zkh#>RR>#hrlX1Ao!@Y5*G6!wnyz%h6)Pxo3g|dR=Jw~Kgjqb(Gm{NMVV;fx)s5WqD
zEp;a1p6g7^-^uTJ%}on2S&cmM{gqD_bj!m1!3^h<epExzn=?C9Zf1hmpTN4SyBc9Z
zc<a~idtxCgYQdK0VgxiUZkb@nYloS>tO=dfW^|?cSvGko0UBIR+?efd!1F3vLLb-)
zu~Jn%lsDWHWgg!eR#5(cPdvD3EX<Pd5R2z$7xfwpU2D}d-Vy}a)ieQfgcHQwsbuZD
zIf@}s7qU6G*Pxu|J+U#b0QlASBEd_#3F59wC17hQti0bJKl+^H5?#LN?lsRRISQ%M
zcMHk=TeoH4C%qGHnoeaODoqB>LpuAE{wu``;XCqFx@(Xzz+Oaxs|f8rQrTORKAYy&
z`L`#fv#{o0jdC7E2VAE<>|^9rjEpahj`fripVjx|agC=9z^bZokjAA3idvLDwF`9;
zpQwCV<?~uR%#gNmerp9%%e@vc%F2cK+~uR=v~{>`*gWFRd=bct`Zyhk^1^LZw`pik
zw7~%DY2mAXhQMh#=KdL8TWn$3)?Z2HKK%mPuDh<qq0BM5JGrgBka|t0?YzZD;Ggr6
z$UKz+rGYfjhaQmgw_U74pj{@6Yrd;wO(+DG`%M><9Nq9*#w%$zVX{{lE3NpC<mFT7
zq)M)koM#X_O~w<g1ei=O+xW8CgCU&w>jybM<^QSC-F>YH9Tk$k58onuLn}rtvvV~#
z*{7SDeWU~@`Tq&{Oy|M)XLsEPM~lFyhQUDfMI64)_^sME91Be{1)U!)(@}W7xVZF$
zKWcPT&?zRAf~wNijnbHO)LXB8#}!--|7b%rpPvYa<{I9U-P%6rFZ%Ty=S&609lATW
z_`3qQE?x9EcQ6#z9a>*gSCxPrRjBIaOZ6z!bbcvq9PvS1T<!W$8WvmYsu|7~qNsf_
zcT;6HaE3;Ehu3xBBZDbvaknlEZ4+0UyH$y@H5{+hxQfyJ>t*?WG&Pv<udZy5TL;Qj
zEQeg#B-}O2sQJ_b<o+KvH@m_UL;92x&x4k0kt-#j>g`$pWITDg`l`Pk-%rnYUw&DG
zK9kd|Yl%g;U9QO1bFm5K&lH?*Rep;p4|!J)e(A#ZPrL4prRAcfS<DLKwIq_y5kCLO
zGy?KAehRYOEXQp%C7&DrYen8Cm2w061z5E2{L;6z_tl&(yIRj%5)W*)txRZJ1DXxr
z%V4ss#J90`qyiU0G5<-+{dZ4`Af@4-ZX9zD?t3(`O<!pco9%CY+Ng@gsix5FET@9e
zIGP%zN&hcUX8DqOels3y5|-W7+k)c@S)VP*9MH^nPqd>!0@hj4I|bbhfx=q}-^GFn
z_uxPXmvDJL>J*23oR}fJ711lx(cb=0Hk(hC{niPKl{WW$m2JX1Cv<jM5Dx0xjdx;O
zzr2Bz`SqmBubv>gzaXsN=mP27Vjk6P9^m3Ppe(qf48}{33GGv<!{NUp)Ak%ruq&rg
zI^Dtvl(*W1m(2IzZRI=MB4mzQFt;<v>2)p`|8}FZO>2dp-V)*0&mv4WQ?y=bEr7YR
zyla+m`55*&az5Z$3*=vX9vaLPkEYfeg|GNqz~EHW&_c^I=;abD@qXEaO^*BOxc1h9
zK;`w8o8fJ+_RRCGqGb&*D!A>KP4xo$>9Bn?n|-)jWuS?#yA{q+3et9w-p!(mRnHdH
zJalx>Rm^ZJ!+*4|a@jhE(7{}#<5^=VjNjQZ;?tgs`jhKP#vdB-VRHW5D%m42vq?<L
z+PkAy2g?mtGOwAZ=L+T5D#J&XDdO)47plbQ)+BMMz?ZETUU<zkfJ`UbTwP@%1f(CJ
z;#iHuQn~Vr^ES0u6szdg>{I|JIW3Q+ufzjt)_zXTtAmu3uagCg7FeVnzPAHX3Ey{h
zEGe}acAnGtbgv-^k2&wtR7)V=pFPexvS%Z)Xna7FVk8&crZt&Qp6$U<-tWeCF9(pq
zP3AA#auW_eI(EtTemLymRljI_`w65fuSZ8J*Q3M_J5w8hK|E5hXD7d3Je)N(<s2Zt
z*Xj!%vbqL3xHXPqn|Wmv@=;%PcyqQFKNO|6?7ou^&5lM_1e4S7o~U}A_$uNhzt7$;
z`hsx8sXO6CTq^o83J=3b9Xfiay^Z_oL^zDEPO_5z<@MgC^*P!YNL%}O{c~{zQvA2!
z$o8=lA2j+=N@jPVst8L<pU)7^j>wO?ZWLkrd}0d&Z5*_U{rqrGw+_qLDv##T=Hh|y
zEafzEj`~lrV&{lbH2OYjHV)eR0;>O&u+oO*Lu__X#-YMweCXr7U%nz1gf7s_PTfm_
zsBMmyUk>J@OW^S<x9`@VSf7E=A9rsYx+f5zb0Q5~5(m12GwWc(*>ul~+hsV!J3~2k
zyca)trAyV7XJb^j+vjcdS*Y5OwQGXc52q?Ij`MOY?hl>()sj_)!zIgxxj_y%6~Jw^
zF4={DIG-#Vzb}NHhg61-u=Jv=<79wy3h^(>TOH{xd5m^j&Y2873CH;_f8<x*zeetX
z!E=`e8d39>z}YhjC1^dK?>S#zkLLRRbkv0ZykY%I-RMyX9Nj1lVUSM(eNTCb%&TM{
za<^4Aip;qLgd{Fqoa@FyO`3OdBuCYomYsZWI0<#?rGhwo0<cH(9RE$3I9%SX<8#HT
z8Uvg@f3x6g!Vannw>kYw@%g5`o_JC%j#0@^?faUCZX2?Bu70(chOp3ZwHa#`D`;gm
zORyo8BFc!HJU2)8OPb^-pwWf8UkWbWc-mCj(c)+UT6e?dXl)(Rr3=5ol1|)nY*Rz3
zBn#?|It2P6ig3}mwUhd6H0piOUSf1HhWEL?A2FyGTVjqD>9pkI8w=mWiMASuebdko
zva1e0jZGzl7L|ZWwAf|J2gFlFwYr~Xp&fEfUh47}5$;>V<DuflSS&eMk;PPy0J6tC
z$D;)j(Xhd(Vb|L-aAz8B+xI93WhaLm^~WCKPwG#%QuK=P;>az}E{8<mpB&0LyT1pd
zUdid7Am<iz^jUkZ){AH3DMchT$o+Nc*h2x+a=iQ8!E03i2^yAJh{}^(=1@qKEsN$b
zUOgmm=>0X4AGj$TSQk`+k{Q-V#jmuY@ISqv-?;@azOwf}Au>09Fk__{z)^$sTUhM8
zf_m`m`PiFnJF2igbLCY9a}o3oebqY?S_dLdXD#!X2p=a*IQoTY3tsxlVESn(2A)tz
zam2GVfCFRhO}ejzFns1j|Hbxd%ndG6VkG&65Uqn-Q&|ys5;8rn7X%T`hobJaBQ-$3
z5&N6hEEM-_tc_SwAY7MW{?zoe5^_R+dg^IdK@F{X?CmuZtolT8GMAi_Vw|P!TDeDI
zFPD!`i)%7gT&dY<A6f-#pAy3!(>LJpOqUs-&jVO{iTCH#vnd!VEqT{`g5(q0(%Ja0
zR0GE=g-=w3$9AM+p~`23%t<<>?|*J0`-d`@myOM(__<b^>w-cD>{@v7OHLpH#hPb+
z8CVu0zwuw+Ad+w4X+Cwzy0rvfEp|U0<IRVb9X%D{ibdG#quyS5q6bfg<{oO`sz){b
z%gjO+x!}6rQ<lIcfWLV**|&rA);H;rhP{$en=^pcXv!DauUC{&7L<e1jZJt;T?}u>
zavFnQ#G?>T+@b$&_K{Sh=#`g@3AlT8PW--J7P^ZTj1LiBg0_~?+<Fk%qg97p=3K9X
z1C;IlJbbn2F7WQqtBiW&vKUf6;hc*h)(q^AMH=A4;)Xe!Sp(jW`{93dp$$10Me37o
z=i{Y%>F;7sNZx|ku=|P@`8*3E+NzQUkwN*AM~-m~nhz-7jFu<>lvCVRYMzeN@tmK7
zNZ(eEHtB}}YXmai<+xvTIvSEAPIar2^Qp$#uFu!VUiXa|+mW&6c6g(DY~OTv63Fau
zwV-}di@etF!VZg-0Y%YiZ_Y{=JjZ_F!5*^TW^887a5FE1Qi>?Y`sP~fmh8$9c;o{A
zrLg4-=psaS`{ec}mO#PrEq{02O2Ymz;{c}oV%(RD?WrS(ml^iPIEi*c()07r<aO&n
z^PXw=?lC_wtSP;7dL$pspP!c~b*aL*;SUj<0(J1q5F-E86oXWLeyOrX8eF5093>kR
z<h#BG>dw^|s`%oKKj{-reUlcaps0YP&8qNB`A{(Poad<hm4<X@%s8a>bYW1@y}p<;
z{otcyKC$gh7Eu094jj?R!f#T+oGKF4Si-zcPeuC8-M@C$SAMO7k%wX$3tXvic7%(O
zhoca)(+>Sr5RSoP^g|vW-uGbS-}W7}=Lwhpj!KHq0P&&FD&6MwO$4u~pM=Vh0Ct&q
z%03TB{*`iuTbSgRm&FB5(hry8+fSvLV(pcr(JwkAk(P>r;r|lEU-hB(^kebP>&+m0
zFuYvm_H9u4Sj$~4Ukzs!3me^KYf&VWoynxX9cNRu3cEJQy}j=;ORa4o6tgpZ7T8@3
zFE)gR3JAw=ZEY%Fv(gV6--?D5ZfV5ZEXzjDR9UdkM!W0qY7ewsfGBg~&DgB*IKI@Z
zh$5EnjLtnyC%J%8|Ks<{fX7~N>Gk#jpfNJ>q!sA|TQQMcb8!f!)?IoEN(o2@6Hg|n
ztKi6Ut=T`>Z4j`-VC((8ov`KBX(u{$;=4>{{9u)ug?Gdn-#HOp>!@M&us3Bn{NBPJ
zcu~6-=#0AF<MkBqPRP>QOA&{CRwkjxN~0h$eD`P@Q!U6-Iz760s|8HA%m~x9X5zZM
zkV@r_80=p<Qm)*R4Q96kr6&HCK*GVr*qL1}Fdh*5B^xqu{L+y-?Y31Y6f?0<B#XqS
z-m2iK)Cqe<S4!^Y+yTqskbGVV@;7J27Alny&)g{)lqDS2Ys0qeyB%79SHXB!j_?2&
z#;D}Zr&q(HL+LBFGR^2?Xks<YQ3e%z=>@7tFXa7shJ-Z!MzB(dnM>`efn&c~muYlj
zamTll4_?t$fe^c>)}?3F;GOvP?-jBiv+T<e^KK~wnVcri;B#4cljhEyr3-DSrOw8i
zx=Q*6+El+S2oFYi=e}3he%;1zeL)Q3owZo@_0q)Oxfne6_3G%<FT(Y-x*im8<UQC1
zL_W4w?E`QaxRE>l8f&8u|NK*%4!tTC7pQFsXG^GH`N7LJ=-}Eszr@rFJUcjf_K^Oc
zqt1}&Z?9(XSJ*x2exwXV#w{5*e-{GnUn2pDga)vdeI{kHB?ZparrJDot%0b|>^XX#
zA+S)`IPXsKMauc>3#%E8&^q6cEq>M$newUM`QEifoADUQ*yJpDIc|OGdrv*?`h0$Y
zr#=EHPo%cXNt9!MTgkMMO%3*G@1OrjxD`s>qdJcdyuwRq?KeXRf8j(R=Ls?US1{3+
zUmk5x26OTErncXShLIbC&vH)@-_c9&v4>)bXdrayVL!<MC3fc>Urm35`lp{p^s_w&
zu4w(Pb5bohwR*J4B)uFHWJL^>{t;jHj?%`DqfMARt?|(OS}`^-rf}wD)MASM(x&ZP
zCyG|C)jm#Y#4CJrQO~u4k?p(WFlSyYPFxherFOR%cNpr+H=M0NX<MNaZp{(+WUs(2
zfxu#T?Dyj{cW4IOUu5bu6>Y<hLzaI~sTx_BjozJMNW+4~#gLQiK`8oXr;sdX59&?d
zKl8&N9p`kFJ8xqrR!WNjpK}7nTlfUlI#<B?>8*1#qJ)<tuO?X85P-fkb~5barG(4(
z;}{6GgH1KZ+y}vC(D<wpdZ8%^f>f;?)sEI+)Y{~*#%K)K33&!S%yI@*(U0dk$-ROl
zkmKmj$b1yI|Bh0zG!rG%vk#vo9$UM(>9W7d5g<IlGJ@sJ@LQRE;0K!tglBvyZz^ws
z!tF^9OE%geu-R3US0exqgf@uN4cEb?o@nhab;NJTa&ndL0m-LoPj7ZO2jWQw-IrHK
z5W1ERIb|@r!@}naZwk8l&@Jk;p>^n66ldU4I;vBG{`=B1)!E7WxOSp=i+LePGK&;D
zY7b(j?j?r<cU$qO`F~V7a*fFM#_;8H_gdVot!$KguL>iSx>qG58_?c+<=QbOS5*EO
zcjMWgBFG#5&%>UdcmhSAn}1=>1CD@e_mUC^!F<P8b{#SgsuSB>PZ|lr-tl~a_J}GB
z-}zX5C9M`h3|Pyueq4cz6$WCjHwbsqYw=jUZynB2>t5X}U5El@EmIO~*+{de?RZ{s
z3A(%WvEC)~E>E?{+WVP=D^Y)K>d|T_1hn>>6}onS_GiKLR+9UqQ*2W^|EdM%rJqIo
zJKB!B)ZE@&^v(p`6VxfOR;AczW|pCSs0JP$^bz{oN^)H~`Ln27h{F4CQ)XQud|v9e
ziZ8ZRp-2xqC1ra7e(?UfQZZeGCzEGbyJY%tkcxJs`f)VG-#u`56Fkv-e`dXxX#<?U
zeq6(ap#d_d<aiE0%!i#)$HQf#a-o!|xS@-#n(U``rfua62NUOq2Megn$XxJQJ+F8h
c{ERT_YaechL08k_Hi-n-?C?lu&<nu-0sKV*;{X5v
diff --git a/bob/pad/face/config/preprocessor/dictionaries/dictionary_vert_10_5_128.hdf5 b/bob/pad/face/config/preprocessor/dictionaries/dictionary_vert_10_5_128.hdf5
deleted file mode 100644
index ea5f90959c7f44494f79dc63760fb0d1abb1d1e5..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001
literal 53344
zcmeFYc{G*Z`!}wV42hB`L!vZ^C_{=XQz<D;iUvYanNmbDWXzl?W0}c3&z$Sb9P>~z
z6%wf=Dj}-p{yd-ed#&Gk{`{@={PA1A?>TFoz0W=Dz3+YReGRYcbp_~ZAK~TR#Lf1f
z4<{!ZI~)K1uKxS;_n(hx$A6Ff`~TnbrvFry{Zl^vr+l&ezm;Wd|M{}93IB6^?LY1R
zpVyr`qocz{T={SN|CayU#<rLJzctQ(5ByL0|En3$)zL8c&jVll=c)f)rH%irvwtVU
z;@Z7y|Ed4G9(Dgc@&B@1#>VmAw*F~w8QYHk-O{q<|2^ROPrC~Ll>g`d6~xN_&y{N>
zfbG9)_b}Uj9=82|SN!*emBi8ioL|2DzYTK!bNz~CrvEPW#{XXV-`{orwDO;OC~I4d
z*x0Q8pQrz?*Uz3jrTw2B{qNu;{&|l7>mC132mdnz|1$&spPPZjU$^WZj8Nd2@fpfn
z7b+wK_d3))rb5}~9e2XMP~iKOC~AHo1t{@JY9&+(be6OGq@1UMX7LNpz8h5NSK7q;
zF^>Wjg*Cqwf+^s6*Ije@MJlYBAKth0fC__gAL4SnsPF`h3P+MCu%&nT+GY<5NcO%m
zX>_H6zY3<7yHnxVpjz(zdMXHn+-tcZOof)KvYF34De%*@di3I9Dm>IySTb;?LeH-v
zKK-XuAoFrPxJtC6oViEqy%z<RxxYWUz(<VB<%NuBDiw4*YRA(LP@y;BXMO8D1%5cY
zgkHHw0SBYEO9~uRXrDNel#f){t$H)5UY803p`JH|dMLolh}Xq33M^<&OVW=};RHuj
zf6^l=@C~QEc@Rmo7uIYy$fN-C<fmJ|UsGV;q2P<cDpcTYx6~f2q{1s@#|-l!3K(Q|
zW-TdG;mpU2N9_zKKpyvf)+$7t*APp%SWShgu%PlIJE-u@O!SQ38wxmYE7e*EqQLeD
z`k$$%)yQsRw^G5g9y|WN-l)Mt#iv^jdsW<}qhaftgZCwvcyCkZso)P)ctU32g33@G
z8eVdf(NCbDo4a??t(|lf9UFgG`;>{hyR2@_$kw29_tl)vSM|tMcknk?Bn3Ab1#h{)
zPse<Vh*B>TCic`$T8Sms;HLckRR8HZ%&B%TjM1ZF@u$<Je%cIVSK6bomBqxaBWBXS
zq-!uQY{cF{s~&%Gd%=1aDkj9l@rB6I@sB-iQnHkZrJl@{uH<UGVqb%AX!WRi@N0Wa
z5(P6)AG8@?q~rasNg6C+ChlC{DC2Xo8sq0~p1;E)VPtmEJBASji(RQ!3lVe-I5Mu3
z@sf$ASGj(l@vOnwx6=1|iGFo>H%CTVP|<9k>p<@?9e1z)w3<R<V#o`MnmEy~G*vs0
zJC=m`BbA2E`zW}xFj1*-D+5te<@2$_OdPK@dV}*-7^eHkMEM5^pS8_p424s0bCJ`s
z(I`6BJRf{iK|F_KZLOk&Ljwf&x;_kQBZ0Ku-xJC1)$oMXU1LWggH7$m2~l4rENo41
z(e!1&M>f45s+kD1qcMRN{nc>!O?UYk5i&&A^NllfnDDq_MTT`}0|<4f%GnE0;7sY?
z?*$IEuvug%ccTdj-UwwYOV-fg*QsEt%|<3%p1W~Nc_$T?mkd4^K3WBP9*7SQk`Q=v
zx;+GP8K4(?jpp)`0Zt`C7CSamK>U8RWNl$NT&gT!Uz|fYthl*$(>wzNnB(d-E)4k6
z`EFj{gbaJ{iu*HlOTd59<9E?<5@@%t<>abi!ijF0UnM6KenvhN+R#jfm*qk~h0c`$
zPmcVj(_9FuZl=xVhna9St-7jAivil~F;gDBWYBSGv0G7C1`!9<e7(Nbf_J9;GT7Jv
zN#=j1f3`4yXU}EJwAEyg8jp$ZkSK*4=hv5+SCT;a`raCIr3MJQz4X92h6$Zgma~6N
z5G0i)R9@AV!PhH-7HmRfXp-g2S~0_fG&`57_5F>wT*}0yu9|}S-`GqX<LmM5>arO%
zB?>Nk`!xI+u}{h>)|~D5#6+uxbt)T<Fi~?(@#(=$6g<vHyLxvAV)O<@tFam~9vS~2
z7_p6sIxj@jYhO}Ox1u31ZZieN9!i>DRHvYar~3Q}I~wLJ3##5ote+%}bz;6Qbe!J$
zjG_Fc79VY2j<vUHQ1*H7&}u^pI!(lEJ*2_JJJIcPiheZQJbnJdVZCycxtN+NbCHZU
zB;-zweIuj#or+<eT}+Jl6)0-Fn~d(;Hq4m~kkIR^w!21CJvOZSxZ=7P9mV%uG<42s
zz_HQqr5Zyd+z~sxA$AuTZ#~(WP@zx7htAu@zN{zVqwxDhqi$4850lYSUqeHm)}GTB
zH7haiX~}~HeiF7_c$ml`Nye+!e%#QEVB+F&D~gQ*30FJyB?c1hy&k^##Zw58S5*I6
zfC?2$>)(!2g`06C>hx#9feI8Asc^P!tHF??f0v&4QE<p+`SVIcD$=bq4pzrEVsB=%
z%n)%Oy_#3YmXnO@vpzfV#Z!@YetSUJ2puo%Em`tiWMY`zR_}A{3>=8F5B9dDVC-w1
zHG7(Acp}byi^*;(zNAa(Y%`<byH&qmHD04(=H~FE#%2m$;jY<Zvw?y0(`!GK9;IM^
z>zi}+Ary?M-?qmrl8$iWT*|qLT2y%05S}?#i-+Cq#I};ixO1Yrso)P4L)C^iY`#oI
zm`vNC@vRJx{$Bp*qaz)0K<1RWH!<E5PLB$9&@uec`xV6}5Q8+s6yFAs@te^->$}AJ
zFg{whKP8okMzqM?W=2dr^28zf<Zvy<hE2!3@F3&ab!#b3trXnl%r7rnO-4UX&50d4
z6kKAie{}9C6{~hmd)i*FK>FYBMR$})xNFT#5ePsuylq~^O{QVldtv8be-f(xh*f<i
zM#CGSBYu^eYf!K0_qx>{40H+}S7ll@AXBoLD?OzewYb|alYSs}?oyZ34It*nzg5us
zG!=(8==F0SZ-Oj!ksb$kI?#*fc6CWmV1=~8xlm3zDDL5@iF9Rx;(}G5!7u{^FX|5H
zRWYEc`SJG3778?9Gxnsr(t!6zaXz()0@V6;O~qj<yo~?sv#FK=)j$0lN@#?B3%`Eb
z?>HScx^CA>lca+1rz4{JLo_&k<rM3bXd`&sI$meJj|3d{_oS6J)It8R*zlS~3e*<#
zGkj0cfxYeWt`h}R$O$x{rg~R`N<+ZNrd3o(t-GTvZAyhtmp1-zTfu+}e{!X-O_G6Q
z@9Nu|is~RYu;k#i$a=`>O!kp8ZGd$THrQ<(q=RwYV)Dg_8gPGk4&R57;Kv`!b5mjz
zaC`4IK4?#c=O!9+>~UmRcKyxxBVQ`y_nH+-Z7u@YW>fOtv`QE*S({2Xq=RZL|54>d
zDjfWvzQe)07MApO1O`b}z;g|Aks-Na*lor`#xD$ji)pW;YZ%a>?e!P0l!Bh>Q%TOX
zHNd^iepE-L9%7mWPR0`bToY2>Ki=029W6T#@YgY*O8TMra%Bp9_&e2g)0+;Q6>C#A
z_BTRKz)w?6E+$M^+t`#-7%=p+qi;rm3Ww|+`duR_5H7QykNJ}fMb+yU8;SnQU%V^c
z@~jao-~4^DlCU$_#P0?w`qE$%D{`L@tro%)Sdk6eXfWqts@k%#8B~HcSY72N?vH74
z9h5DCou5KHiYMz~)sdn1WjPFZ;=5>~$4=Nc`$TeUJ91zhnP2sa8VTCA2=3CVAcH3E
zli&WA4oWAhEdyTFfdJd<mA%!~VE=lJiB?ZJOx}C@Hdmt=T7vUl<w!Ch;?I4C!?h}C
z3;F#}yS4;`n>1Z3Do7w~&NP|&$bbf~s(lH~wa~X!rI{W=h4nBrw2zSnr=-G{Y4cP7
zo&GCZo3|0Tdkr3a=%qkPiGaX%|9T*eWsP4PFNA~54^zyhUI5Qz<KD4JCP)mw;7lO;
zoeVO0Q>j`7Qwox$B>7UX76@A$dsz;b_uJZ0J~u)gJFEP3pga6r%>7xBLxpW)lVf_@
z+F)Ib+0VBWCXDl@?@d;uflt=zf=^31=rhV$v7(j)IXv}gr&8KL@o@M``+P#*XLMY@
z@d)A9hmGHDMRL*7L2B-=4+FTFD_r_Q+hKg$xu|TxW+;}9i?MAjhP@*bhwrZ=p<PS)
zxK$$Yo`+I8KHqMKLeY*?J7eNK*96>H(?*O_BVCk#Gz;I))~&A$C&8ueO#_#uXizmH
z)+;E%1g@U;lCyGUP=4uxT_LFwU+y#iQeIvRCG1=)gsmIFyVhjkK@=65B(i=asxshC
zEiC$8E<jmUSi9ldN-$?$=@m|-z(}S~T<s$oJe6D1C3>C=eHqgHyWOhs-nP~?554Na
z@A5*T_h~ZNvb}9G4rPLd^f}H$qh#Q-I?VEQF2tliJR;s6wJ@8ywC@kGuU>O{9sHqC
z2T4q$V!KW<oOH|Oo<Ez577|ZPFS=Gi%E?U|jJz0-p}7C3PHa8QZEjH+8m2?{=|a10
zoitoCnysY#wE>em{BCM3G@^B=oaM)-WPG<RjYHCyiiZ-ns+#RaY_mUUMN??N_g#fX
zuD_Z1A<9iGC8-HV1+B`$<?B!_fbx1EwH~iI3tIb&G@#a;%yHS)COnu>dGd`93&U0H
z_Dp%DAU8+vhN;gP_|4hgRym%A*Q$f*cN1AS*n3p3I3fgH%69fn#D-$E<-4fA5hN7B
zmTis6O}OTUsM@_k#P*l6bv##E&_v?MfbRP|)YLC2SMRRJJI_>{_dldy#;oz#(U}JH
zZC;w3=cS<Ba^IK(Tr7+b6LX)GtHm{r%Uf=WlJV46nSdXh4fsfMOoO+LjyG%tdLCI-
z;Rl_iOHRJ|_-FU>bslG#DF5oy(^HCMY~u>BnAaua4r|k!2Z?@nzMRX@!h95-n92C!
z!^EuQQ{1BsWSs00S2TSZiF}^A_Y$v@QK{EJi^fR8?&jB7r)+9b^-FDTYAX%Do}R8A
zCzs%#)1l{AZ>dFL+Z!W`VJxsdd%#Km0~v143B8rLQ42~(&-+QR>VPLRqk&6@0z$XX
z$+ix$;JERxM!EeI_|$i$D4USKn&)296ZR7J<?$QMqJ-R@`mx$<Gd4l#nO3c&Y(kEt
z{bI*e^^oh5-!C>ng5VRM7PZ$Qi1zB)y{~G7!k$xS<ByVIQYcQZxvvi5SCm>CyVina
z<~G|_YXsL_#Z77}S@8UvIwl5F;X_55AU`2TYVHphnXplyK!p_0lSYRAx&SAO+D4dU
z=USGdMS)*+x!f;z)`PyVvf!Obgjzw8a0;CYvZFiQ=vNw`G(>rH(vAW=bxAY*-w@uG
zySnS!k->zEJV`r5f#i2izid;O@Y(0Gb`~LTmm6=HG<`>cU8gVE{UQ8<)w6*sHWU89
z>=Ac04t^%QxBdCNsgDYqw4;<WJL_RlsBIvsqz(>?b6iZCrNG*6+yY8644{rFT{d|_
zf$VOV$JaVY@YP$U{dW-w*pE5fOw*?TpHr*H&df@v3HcE>U_k-h*L_}wiv{3NJ7!;?
zO@hM*KJQmDC<hnD%8QFvt3X3(TSmETHE;}OJ&X^^1HQ<^hn-ikV5|1#Q(k^Gus+m9
zy-SA*d4chBqHQHG^whmyYcCa2eoLH>7RZLl%jbiCf2fE3-8!@z`+T8S!)|l(&s1=^
zQSx0Nj0z5sm+8gJZ$QBxu4fO<)Iz>yiOLy;Tv(_(xzLv02qih9k>u<a;B3rjDlI_h
z@Q(Pge=Q9>%wBn~T3rlcJ*ZcBiw^vF?4j)d6|PLG9l1rUt9x?y*zepVLztwXZj!-M
zXz*EQGJUWE3a#tJJFHv4{`Ruym6kNP_s3=E%3uwoJn3w1C=LYGZ0*am9tI>^SJ<7Z
zZ-XG-BYImMs)3Wjy54lM2HKKeSJxSOfTC$e?U^VRlnu2;Sf#dsqzB`B-rIDzsk!lh
zuwy%nrD~dl2o->&f_(jjFLhuf=^wPxrx6s+EW3J5tqq11!p~;LwSY;OxK*8XIJ{H1
z68k$f3)$CpZCEW+3kS3s^&b#+iDpq{PMtw1+~qyFZXqHRZfJdO_N5fz8C<(;Vi=(z
zz)fe$vJ+}Lvx0YAsRpvnkww<gJV?;YZc3e|Aj->!H-2KkMtg(EF1td|_NrUHVTlT#
z2d`+Y>rKV3`6AZ)&uv&JnR)ro%X*l)qIuO+iUeE5bo&RlRKv~Zfi`TC*{CRhpT(;v
z5FpM5s~wuaweE228vhaqNt@<z|J4S#;taP_PclwzFu6o)sDP@mDg#D$2^>iA6v(;P
z4S~kh;T&(MAo^P>t6w1=pYqxl%GBjRulm*i*ON`Ku3IPP`Ctp!R;hdpd|v~rM}j7Q
zwAFwX&xz;w>;=BK!j|MO*#RA6iQRsZjo??miXt6S1iKElRP}SzLf(~6+eEKr!s{aP
zU8}-2cx`GoRYc5B_qOz*8^r$6`?Eq><XI|&zBqhL{B91a&nAj_$+g0&q7Qt9J81BF
z`~0ra>m+#U$u`UJ%o9GRZ!T4nsDnb0eP=&xD2IX8OP=PrWbkq2*1Ntw9T>E>gx!bg
z;6$E^XX;!XEXjlnZVjl0=r($Eyi^BV^Q}7lZEX?E>^S+w{A4=#nXOjY%bx^2GHaB^
zchf<THA->2Q3K^V>gP+8Xs|U_ETc#z6vt0<J10BrhpBN@4oRzGSZ(zBkXmgvJRhP)
z>6w;+uSr|G(7`Y~*ctnDrAZ6?S}EV~OtuM5WT?l=WYPeIMh&|7>Ch|n=A*Dx0aSE<
zcw&640Wh&@!|4rl(6wB<o|C{AJanfFYtA5)UcYVl@oqT0VXb#tP1qZMqrJ_7lRCkI
zt)`)0vH{qRcW9c~6~XUwRXK&8IpBEkoAygXDjYQ<CsT%+!Ib{-HD@vf_*V=pq!;DE
zV|f>WvM1^ABhq4-Q5qfAYx8HvNWX;VcXsZd5i5o4!b}18j%KLnTzi)xUIbMohnJ&p
zC48TKYZ)ie2G=|lOj7SO!&L9Q#>Uh-Fu6r;mkDpcS@VD+8{}!|eU0_9JfskrBwGcA
zYzhXM7i3+_EXG9}i<4&Mt+-O}gp&z@0}VA%tIhtDA^q@?C?ktD<XWZP^Qfl|j~6g+
z^{hm^SXA4-V!Rdu6w@0B<OYXYboTvSSA%Ar;l*3j8E6-I_v6y;QuKyP3-z7#xL5JT
z+`y$MRK0cSp`X}eoCRe&Z9x*g-xFy~$|2#tu%Z{9a~;?$;Tat<K*49v1&u1z=}4h&
z;eUCOf$wM^-#$G<MS*zv{FeJHBpE9$J#?(ayIehiGh>yQp?=u$eh>|Z`X00l^^_y!
zkizuCk4zNPxGvw2(u8-n9+WwfOGd7P4(uPR3-Lh7XN%(;r5I&?-p6te(GQoEW=~EX
zj?KBNTrDiX<^h!z7o2i%A&Y8|&Md);pE87;Tj==q$OX&7z3upP^~6;5uDdWU%4c?9
zqzM}~CF!rvq+;sjaq|%q#488J847O+yv!tERR1pxw{D)e>Z@LX%RExP{<dv^<iPa(
zpTnEremINrF1`x3Wy=ey5qd81MokQDz5s?ShbBF%Sdc^FEzh{zMA&C8hdCXqK(u;2
zFH5Zf>N!iP8U`g0t+w}8&Nvg~%e7dGkLW<1<&c?v-2pz{%tx1Uf<R)44-Vb)z-A8h
z<zLvzu(gSG8~-q2Z+0c)yh0>&cUJX&9}PpHxD2^n4NtM6_e77i9TjejT1&`YdI??o
zrk#t5N+8YV@P`jvIdHRzL(ES0864&b*!1N(9kRbXzcFRk2+@u*&MW%!!M8H9%;`cQ
z957c=`6Wn(&vhd|6d$!fwn(j(MSdGBulgi<vxEY)`(@D;-Q~a=stkpxsZgsJvGp@Q
z3wE}<&g&aCLvjU|V1-yce7RZ>lW**cEy@}7u3lw$M6vIVfH@Ox?rq?IajX%xnC8<j
zd|*INfz0~4(HYXjpFO%qBeLLh^P>?DV={zux8;}F)I+z+jiwvPg)nRv?#sO+16b;+
zfzgMvFu&sE`(1%FaKW%7fyy@6z}a_9aw-j`Us=iU_mbc!Nqp~b&O+!gl)CE`-vF;Q
zmYuOHYk{gwFK;-~!$E76GiiBGJ0$aP<(S^AgtD62@D`&4h)&&hyCba)R&_Of-&jKd
zl6A9aV>S(*gq)n!KN<(W`li@2<_poc_~Amtfi~ceTl3;<?|od;AF+PmR1pq$7E;Fg
z!l7o}=DTHV$zZ%avFF&QZup*F5Po7=Da`k5#OFN?aQ4aLxfyyqDDBAOk)N-GNcesG
z)=~qA+|W3lZbXA@hIwMv9Y@&C<?z;@n*~94?Dx8f*MXX*bv((G1=Hi-9F9y<!RQO=
z?2f83Q2dlyb!THHaBUFwUs)0Z`z6}<c6PNx&fRBh5*67XpwM|g_j(ZoCt1^Zm0#dR
zzPafws)694;hj)(wFxA{_*BrN4cfXc=m%();{z^tPV+l7=uX@oTyB*K$3_Moy}wF@
z+q!RAnj$pdXv#j(e7hWO4#tFdA54Q0uBK1?S0h1Vr^c33b`6l=IkIS8SPxhGgvTz1
z6YJNA6cV%H3G#c)WK-9?fH2R*(4*n)V7}^-xOxN`mfPH!m%7~uFSUQU?~#rG@|v8M
zV=aZKBpCOnT8{z#Ls7;@kG_O5^V#1I&X&XbE3QLwy&0HPq<{MK4l2HFSQ1IJYk^m*
z8yoLTWx;`*V@;fglHu5qm%lw8=Hqt<v9l7c`Dj5J`b7)x0VS`n&I+G8a98bWaTB0|
z%*fx8k=bY*eRwH5`hEfMvL$8M68>eYp2jA3T_%v9$Eg-=EQUosj!*i8U0C&XeeLYW
zO30E_?h|fn1^zADGv)&bJZP``8d+8)M6XUs{mqev!(o@$jLM^6hD%oKlx{mv1$PTk
z#mFFhqEA}8;01jD@%OP~BLmOo(a7c!-UPn<mqRnC6<+6M1haE@fcdUrrKP$8th!#?
z6FZp+D{FfeZ#;^^qI+k)2uGFytu6X|$dP(bZP+Aipj8d3jqKV(F8+8$_KEf@Y(irR
znLU>YKd^ORyU#TO=cjHhm`fpWvtNOF6}fMUz|X&Dg~gc;JY*KV<FG9iZzsJf3^bx+
ztiZB`F|SJ0{Cum87n9N5`M#fa2NhjZ)-@g3+>C`bO}9eclkjgNS7j=HDK04ej{Uqd
z8Dm@<N*>CSF*j<Rp|k}H7em=s&70QZd*_8>d2SNcEPY6F$}PhH<Lz>WK6E_)WnGQa
z8!8$Vv^fhZGjYqHf9jV{Rk&LEMiRNB2n}A}%RW^__>~QBLLb*taiQdhfQB<2cQtfp
zzHBVPH@%xe(l)!or+Z*D;nalJ!Xi4=KhThx|7Fd#ZzXtUb}yS_QaLU*$Sqs5?FJfX
zh&0HIwP1;NyyrT>26T*SSX_}r#*)a>S>rhrG~m9e`d9oZ(l7cCTjV$3FF`rpj$#U~
zOj0OmOKV1S?zc8AE5h;f6uqX`<>>C&^u_-L4L6?uFmuSF5uG_S?^uPiaJi)5sZ9|j
zIA5UcRKGkPS%PZz<sX|c(*}%_ewN}Emxnl>U5qO;I;ek#b0EWEV+5VR&2LIXD4I-l
zp(-E5y^1H{!s1xji7)vmwCa+wY*QJM4l&PqhPL5sNy6$ZRwZ(U>Vy~`YQ{Ie?%W;i
zNP*D9qrXDW({XbBS4uHQ2X6c>c3L%njAQaWmA?%tkoQ4w!o=osEL6N`xzt8RBM+6Q
z>n3{eWw{-*nV*63r&o4<c1}jAH118}DlD|9C|t6OuEO0{F=gGmE*uDOh<=kt!C;Pt
zV~^dl@Pk{`kH~;V^wki!^VGW-?Oj5qoww&9o$jDsbb^8hUWi(aEZ&ANr*g{fH~AP{
z$9O%8dAM&E?|!Fi#dz1y(%#Uf8T*@?x&;Hm@pPuiEk~tC82V|uXUH$&eGcztN#BWr
z6TaNbHpDaW&CiXsC#F-e@MH0DH_;lLo3ROex;z&RepzVSY|4YMpV8K2PYP}?F3=0s
z$;OPpo~x@$YLKnFBzJj(EAX#BH=fc`1TJ*HoT-EkY@FJ=`-Dvz8l5oHU+z?ZtTno}
zw+Vj&%XX@dJ}JiHhhOx<_CLYr23yi&R=-B0DBJjnpVfF**5X*+7XtS=vs&<EX(gPq
zb-K9gaWnF*vP{%YYQ%W{_ofU9!cIG#n_9Svg2Mez+{Xw%v8qwZ-)N4CtxrlmJ-ObC
zKbBwOT7R+$nOEL<iC(h9(7mqJBF_VHPuqp&UE(y{KU$}q<kyDR-&*hdOvD|<$^K67
z%1TlB;?5F@HR-TJI_%i-$adV89Hyf%*M#+M<0l=%sW|oAuxQ)yWNcLTJ2x}_3~7A>
z(-wCF@R3v9xw!69q~|LQ`tmVx)L7*9XKDyc7*22>=c<DXE3C8^gfoz18DnB^P8GKD
z%InhYoss7JzFFmaG(@!@4;$HB0|)0l2O9T8;V&L7`I(VsOi;06zZYH!i>2!A*Y@Rr
z@qvh@kOm@N-C4ov7pcRxDC6-{eWj4M$8NHYs}gcTv=oA$y5J*U_dit@DR6hGpXPcy
z1lE<5<Vq0w(>Bp<m}55?z3=6la&UIQ{48JY@2L_Ts}}3w+t~o}{94g3|Kwr=PyC3g
zP6E6cZ2BgU+YM`f{i)ver3yYi5WS|kz(V1}bL^hJ1U}5CTgR10K+cBQeq%ctq0!Be
zal(v3*aZik?{%p}s%hf5B1aU=zR*~~8Q6ifVzUl1Tl0W7-`V2o={87z=Dw#nJQKA8
zjOG%!{o&F9wg{UOiNMk7^Ra}`$3|v`QQJRs!d|h0R~OX6QMG4|{=DKDOiJkJ>jtsF
zv1#>f8P8a_{6O^MuOuoA>3_QOV4@t>nmQfP35$j2xvTeC6F6FwyZ@?fGdYm*PMSlH
zg9d6cB2w{z)v)qxueJKmLUiJ?q-@w)i=0cnvgaeKfLCKO^$WEYWJEbH=jfNicPG1T
zOEHa@ww3$+*)45Q9r@(=v+)Wf8%^=NTiXhM3j`DXwpC$gj+9kW0T~mcQ+noKM}W)l
z#mEMO2F$46*(l0G;QUv@xOu#4;AW1>^*5nRT%G#NuTzSSiknqWiH^15<ot|@&&_Py
z(LXsoY~BPf^s4?k5pgz}cud~z*I9V>gTY$1nai-;^G@(FnGDRD^z(Q>9gkdz5>3^?
zc_{H~=lw8p9-g|h&TW7z4Z72P&fijuM)^n5A9ku&V^V5Bdd5T*{#_H#<$t6R8{XYL
ze*Us8{LJZT@h9X=n9j*on+uKjDA6I_Ofv^R8+drlI*_4~XDQeIvnOU=Uj(`Rai~)D
zvCC_D6Glq78~l_jMpjy}$<udtkw+n~x8hPBUQv-u)4oxR3~EI8TE2R$wY(B@b$c-p
z$L8=76-ChNT9tFpBMol82+^9gssQ(`!KXCJ25>++^p>_p3^;yRW09&~3ds={6|bb)
zqf~Xr>�v@UhtLfy}-h6x?g|VRH8~6xh1+T&zAZPY3+kAMa&=bc_#Y(NH#gWw(rx
zXY0ol&A)qIzRiSp#*a8kX(YIASYO~&6@+equP=;v<w5dj>y!9cXG|5_+RAA2dl$
zW=44xFkx5Npu=7Z8EVHuHYg;Z@3q$kPSP#7sVZXa?ay?qXsMHwtxrRJ&*yyauhxLl
z^DxI1j37K&^Qu|#d?&tM#^+Z#o{i50_rj@=GAK%OejCXh2d);MI8P-7;tI)R&JNpJ
zeA)P}Ui<|KH-w2b+AD`5C1t+poNgq6gCAGBk)8)LvcPBT(t?HOxjWVi*rQXuYH9%=
z8OGjN6>XobK@Ar@`DWW9kpC$X61}qqxiq@ZcATz6ttUqgUGpo#^V?YO{T!(H;KsPw
zQF<P5PL^+eEEokJ)#uX!EXdfqQp3Z)$OompzgV3ttibnfR62Qy0ePh5`cIcK;L_8m
zHTgfX@Y|tfdE#cJxN^~K!Tm%JR@tX)3}B~&4#{!{nT?Dz_w@6{Yz)jMC$%OkJOD=%
z(eu_jULj?KZE5aQ3BK(85TLmQ@yPU&Pt=ZlT)X14T6ZD?zl%20a#MRTMee-jP!JUl
z5_E=MP9|;+dU#X7Iu=Tq6DF3gB0-=`F=n@LCP1p|nSyo}CIxXXlv}ef@a<Uf2?uYi
z+wnjs;!`4gQIQQjW}Sok-ks?9VA6rFiVEC3eR7eP_Cs!^jVZKT2zkr-Hy5C*mp6Jd
z0~sTHZn0AB$c^hnFT78Ny6$jq7cm0Y+I0AC!Q)g=;%k&sZl&Uz9}ieGhc0Y$7H}Fr
zl8Vp%B<>h-jE4;^D}tR56Y)OQuuJ$IDoV941ho_ToULk7Q*e6;!RNR<{^flI^eOqe
z4Tbn3^<3+yOCkyFeHq7;IXY3!p`YtiaURNu8on3rDg*m{g7;Ws<#1q3Ib21y8n>R&
zb?!D`VV%|T+Va+Ve8^pBa$lW@XUSz8@NOc*pjNTSi>)jS{qk*nR|OL{{mN*)q+X8;
z???Li!b{+*>%3x|Xd)c=P-0Nk;fuLn9aSq{b>bOWM%|X{OuT>S$Ccv+HSo~aZoFx#
z90J+h*eyNsa7DjLP_RrVwmAxT<(E?6vs~e^{^Tw=*Lhyh*d`OJAJoRRd6KbJJMGSb
zOB1eieI5UnR0(%G5-v$H8(^*2gY^2bK-{?{VXy0<yV!ic&F#Ag!S```dQHBv16V$i
zT3;s!{4!_c-kxhYz$vr;XKZdUGWp0~Uv00#xP~{K&n*gIS>x~NdjfT^dt+2v-SuG5
zpUT`m6($J_D-*_D*0!PR5zZaa>SgfqIF<ahtQT^>j20(8$ivg}Ek>(a$!NFo>+_!5
zR%kLT^eU3T|4HuQKAVZSXtUAGA*Nmq&bbHo&4`r1vnw}!AJ35C&rx1RTtX={7)|7m
z<yqk6nO9VrnE~QgR~Zi6CwLG0M=b+N5I(h9ypp%6g4hQpSH-8RVe7}|?%iLjplW<|
zjC^wrQUnF(%`0kA<NB(^Lo@mCm@BR`sI?VpY9(%PSJKcxNW|a)fj@~g+lT1q($U*^
zD2;uw0u-|Do=zQ3g)EV5y*De;(RA>{s;`qx2t%p;<+C(od5AseGpWGg>ks1M!!yAD
zw2~%w7=mN>c%-aKEm#<POmh4*gLl7kCRnG^aQCrUt~|nDR~&zD6L+cxDD*C;B6=4@
zefXu=%8?DJ(>fnFo@v8?=gYjOo)d9oF9mb;K@u2Gie!pZcR*Lviw2(W*<f(yIM?6O
z4vgFHRc%7x_P^uY6L!^AK=}uG<J*slAtBXJRBtW{C>hZy1A&M(tGN!!#uM>a>o<>o
z7t{k!iCOFknJ&=x3uRuL%7mVy*VddPcp8;8Qdtq^NibM4cw5x05h^EC^|)_VL5#8X
z-JdEH=#O_D>sd#`x67IX9SjIRV&<t3rym)R86-5VK?U`9rwsmR8DT?g-JGr{VXrMN
z&e;?Gi;hWVQI(V<6n$Q2V7aRSG(wK^B`B2O-za|hcxEF`7;14093-J5N0G<X#7_9V
z`{Mr5;at?Tv%J=pO9zjut2Ys7AAX|^Nkw0%z-#aCoNf<i!t7#^{7csoc#+E0_sG-^
zmLBr>jdrs@`ba=vPiGnI%a=Q7zWxdBHmw%@qLPN0AH%HX-WS1n53u6B)B`)Gw`^e$
z@uIiUzIP5wXMy9PSHA`B<v@SG-hG>Q4Cqun$-&da0#8wYQb|!Jglrr<y=T({6b?G`
zENgcyeCRTA3nq6$1!sLj`z?Z}=<01d;jW8YlbIP3717{Vr%Y0QS_7>=1Ubdo8bSHf
z{sxX%Cal;eXxDf<9ZV<A*F~Ol!8RX}#|P9Zq4h{t=d?^aWH`Izn~HQoF6zGC5||G>
zVg|x>-svDd^5xz)0$&lgQ`<HaLxYbk-G6s&X$0Z!XD-t@7Pu|g;o!)dT!>G<b4@xZ
z5^6S=dL4R2h7sczZcx?^zl3WTPqNGLDW}xVE4PZU+xjClj7o(qCx_RL9i)L<UhViX
z&vrQK%qd%J8U`2CynOGD=744ARPKiBwotdsP*|GO2sK~*qCOq#hKPjd<;|NiF+1~f
z%})h&lny*Dv*NZ13X?<jqH-;4(`xU_-@}67Y{vMvx7l#fePdeP$}~9CabMn-tsY)`
z-tfIjDFxkcse)nUZD1Mo(Oj^L0cR^73S7x8f*TungbJ)!uxmzJ_GA$iekSdt@J#t3
z=Mxk;N5scISX-W$e&dC`JHOlcRChsopw*eq34*sKlHo7%qz((;d7kYj?4=3c#EJKJ
zg3xj-_>^OMB~S|XCWYBB3AuQk-k_ZXS7K*Ip7__mbE`K$?ha(Y&Ck~z#kIS@X_-Ux
z?aPFGTDSN3hj0pTX}ie(R*Zs(%`F#K-ED$Zk6mRqc~^i$dV+*!P9wCspY4ky)j?Hz
z$mO>!0T|yY(<8oE4Ho6vE2-=2V0%<U&NI<G5PC16d{V!d*td!c4-dqFoAfp%VS!Z4
zKVL4L6w?k0la-fJ1w+xFDf6Z<w+hbuT7M;CO(LiypPckk>jJX%k!$pUP>2cWvrd2(
z=+;G-uhtC6wYt90Rgj9d^hXw5+6~}TJglja+yI@sUXOKVQQ-I+{kS_TGl6j-!+B3+
zDc)MrlM5JRz>mcX%%}YXUST%ONV=Vm2mJo<`^`qe>~`O=ulJIGg_kNBYwF?f;Z<8>
z<ESX|ZIhzo&4(~^C8_mFe-tYH%sTLSsR)nt`)QwUL|{KTx$8$06SX=gv!ahBK)(J5
z4M`>qB7YyUT5-4<8x+cGE;rGkaEn>9sX+kjURzh+Gm{P$)7z!JPNad;U%6fm;Z)G`
zFG!2Omx|mYl~PU8Ec|MiF!zqD2q~l0Y?}%jfvb<*w1mKsdV@V!>~e^K`;_&11PR`j
z@_@S%VK-mfIJxLinGD%UpDy1FAb3H(OWg84)o^0>4l6(NF8t=E?-IC+f|}#H;Q-aR
zQugmd+25V$Q~Z`|<qW}V3TnG>Pk@5Y4~Q?%x30z$`dfyyt*X(wXg*nsh}(GHt$7(K
zT?x7Va=zOMTp=XRPbX$41G!eK4SAXnJg(t=Z;l_Xz)rLF^f;+3d}%t{pUXRdw((c#
z+73<VdXE0=&Y2E4yyKc=i)0;M;7kw_H++Ugd=IC7$<%?Sg$9}Lf<I=u7Vr1Xu7iDj
z87_U(B$)pCH-B`n7%hGWtmxa?0T<i;a(PA}sGIY&-+%cO?T5p)3d>vI*yL4V(tX0N
zY;IACdrX0-Ew>lgKU4x~x7ZW6<>h#^Q<U?kdm~iLrW=HBZO6}L>sI}-dIceYIt~*x
z%`hI#+?%FWj0GAJh8p@WkS~(W<i&$J!tcwyt(e{cJpBhh9DiK~zspCD`Q0hOIyL)`
zpU5w8qr6Sf-nj}iR$=tcm)FCzUFm0a>0G26g-`SL&<HtkO?RA?kDqVaIH%b+;>l3g
zgNXwOjD1IJS8hy&gmSl%@MBq6J6DpKn^A$T+yV5}ZyL~{V{g<=o)VaH8vM0h(GJ)j
zT#MMWw;W$aKdWi1slv@MbMn4t8gUhuo?BK!6Zrhhy0f*D3h{e_pFQF<gej_Xzl}Z>
z*|`lyYmPEeeC>s_JwBz-l)vJzhFm>_O68M%8EN3T?@vSh=UR9r^l<BfU@R;w`_!Fo
zMu*PzNjZIHWN?ak{wl?d;2A~4q(90bLzxxzi|<GstZ`gw-0fZmoFRt^uv7<|^(RYi
zN#ua2M^{hX)qLpY5UQFABmCS=w`y6uy)bp}GT)gQcZif2J-3jVh0(9LSW{KL@G^2x
zcB@1QWLsKDHtDrNtI}uA`NC@WA>I%ZXj+586K@Kag%^Xvy<F+%lLX)XM|V};#xk&O
zcktwXLV|Tx2fn787NKd0km#g-EKtsGhzP%04J>_gnouhp*3Cq7ey=JAsf*=B#Y8;H
z@_Jk59>XX2+m0nRYQh2!f`qr9-UfFr7H@rDK!X4ohs5WhhzbSjZ?z1QVU3|uEw6t#
z<a~Y*`-a{Om)$*LOV>0)^VdOvw0a5(ysn=VYAr|6+Bf%f`<vmN8?TTFp>JD460-$+
zNFXlYA-pN81iKt(%c|6p@wBg>&w4^`gkC7UpjX}jl^)Rp;p=H2cw>d+U;R4RuMry@
z8Rd`sDvhifnOqEd8NdFwdJEcedwrgEtOuFtECbvAR9Mw~Z;*qog9lzqevKmh>J3?^
z<Kh)M(LbyBXOBh!Y<stU&NPq#^`0Z57JUBrn0dGnoH9{oI(bITz8iUqJKhDR6oKA0
zSy@?94g4yw*`*fe2Ugz)uGHj4A@39`@{2<Uez@9|@#As@JQ5nP<*BcRlJ_qHoqoFG
zhv9ELYYYl8%=6>&=Aa&e9~<ghVrvd@%=^oiY|G)gD)&HmH(~eBUq`OWD&*Esiar<7
zj%Ux9CXIYA1x1m8%5xRPu-W+RhcVXznA9rUd2*KE8FQzJs6z*~RIlFl%&8vkjJfaI
zbG8nIg}y$#e(gE*2hZMU9eshPo7d`i%e7&mmR_=kUom_))m42-@IG1}=LR(-7Xj~O
zzD;iVWW4ZkPk>Nz2X=QpW_)e1K(C&om70ta$bIO(V{cI+@C`~2FWe<#fVE`gx0x<H
z#cyS4Qcwv>gYU%Lx0RrXGiOSA3Zn7(Ls|kcRZwGKQC+3mj$b+6rQRg?`reP#^;;up
zSiUA=cJx;*CU3s7{^{XFRCH6m5R}r5B$qMkeSRcV`XwtpIV*vKm!JMID0+c@$A&a7
z3?;(`?aLXv`@6B+>y)88XFKX=9_<z9wZuxT*-QS}h)rcGUY3L(x59f7b5@X1Bf#*4
zg(w9R(gI$bzFUUYKCvU2h6FzJWjkB1S{iKiu=Cm~(Se~pkIc?)qhpAi?;-JDbexYE
zSH2NTLG`?*67Mh7C|{NlpEE<l7k}TX&o5MBfY7ebd#)1k(X{G2&c)$GoZ5}&;6MgG
z*3`Ox(2n5Qy8QK7JX?v3YWFQgZbf)|-;p2Z?8w-7^TC|%CK?EAul%7;;J#``A{?}A
zDqc~e>%4TV#<#|&I5Kw@qnYleBl$*U7(8=<Ge|xkDa>KVeoq>57tdc^F3!OBeP1UO
zzBu6hHu;b^LJvKec)0yL5f=#c+>pRpM(~0&H#YkXyu!XaZt<(U(s1jJ?Mc~E>G0+Y
zuYM=ZAAh;OZ)A3pP&346+IFcOs~y8#6mUKKv`m^3e)}A}{&4NMao`%9)OBQtUoONE
z`JR3!(^tsm>7F8WFN?sxtm;KJdZ1t&48Ak+$1aLmA&09oB(45qFzVceFCD`B^fa>2
zdnR1S?9@XD?^reVh*1G9s$*BZR3b3Papw8u;x4?&VQF(*#Rnyngf8wO`1$AEd3>)A
zgu~k}R^&(757BM9fYVaG1t~|pWu5(=K?Hkq$+z`2uquY){G=@lc8)R?_^c4Y%1D`+
z-H8U@H(&HshygL5n^SRZ)u3?i4*l=2J_=CNU2wh#`A+w3dbOeh`AZi#TG$eCu4q@v
zTCZ9tspOgLt}6xlpXRh&Rt13<#YE*=bs>Gv<)UETNW2}Lbldm=9g3LS(;PU;V7^jS
zQQxZ&GgsD7e+zYEgy77__#+9hM_|#R?N<$4HsX?VEGvhry8Q=|8cUJirNCHst{rY=
zu=YG6;;GdidKSu3sz6HVTwoW$_nI+SaOPjEhsTP{Z0)NoP}7(&ZOtQi&jTN~>RhdW
zl$jke>#NBSALzHH?j8wFE=FGuA$Y#~>ow(PuGYf3RdzpJ&Xz*_+PM$skCCBv@6a^A
zDh0MR(sLtcSnzB2d;z;E!S9i$2P^AUKtiEj?0eaII5%6){+q}bx+*Jvb0c39a1YuY
zJi|o>kqIBV>gO7$mznf!Q$;BL`c&Xo3X$i;5xS?37@yVZl#)qdGJM&-bY5$^7WicT
za7*_i$Xqy)!y;2*Ow3$n=f_5v?RhMjd<fxmc1Ck}APG9JpZKc8L54+dpPJ?AR0vUG
z#11TP1kl~Pkw*k!|45Zep(T+Ib76A7?Hv+SeV1dq7)s<(eN|<DB-H@fDg3HejR?My
z<$Qn>5`57)am6aBIw<{}XQnJp0fMWxiDu4%VsU1rb0-PT)m-YmE==UP^iZ=;x07IV
zzP;BydkV-M(k~zIM~GCg(w00{21C<!42$V9c%yB~Z<9^}!SbDNtlw0?X_Kh#7XvKd
ztdG9;x~UQNhx=zNl~N)64O8}|bUVm=<-go|oeZ7n-P*iDVd$(s5go2gfyG7nCX>UZ
zaHjr?Ktp#YTrfG*^U8(+LQ)raf1XQ3XVI7@dAT~+ER(L_n{WX%E-H{F<f}o=n^X7P
zI1P;QNc&G#CV}OAD(CHSG6cro&sx4Y4^l~{k7eV#!9s~|phJ-YHIRPg4uS7IzN_<h
z$JHX(@b$@&C#ft@)Dy@FEv$pIb5<2+j2dBR>!gE#Au%6cdN;F#V}LuvVJWtb3TeR?
z*9^MRV94Hnuh;q#NQLQHw(IHOp?q@M`CJ8<)uyEHe)bgBoW5hfW_KIV%{Q$HjCcW+
z_K);0Jg9(9nts^|pG<Jr&T)L-icX*(aC>L8I~;shpIALq*i4M8<(a)-HPjn-NQp-$
zLk*|k^JrxT6x1vGrn3|J_I3O1C+akieY@jfcRw8rPf9i_@)7w-tgpHrwk4o(Gw|KH
zhYe`R;w(Io+JiD}LlOd}6o~$MLrrDZ6KsvFdV4l56|1KYN}b<Zggn`Ab3-0<Vv5Yw
zrG?idIPm1K+a>2@5a2B=dtFonNeXBC0!y;d`CXVAbN4GetkX#k>(0hkFMGViqsnpP
zC!fQVl3IBF)RzBVIN@KJ9cGhN>qO&yZBq9AguNHG*uTZZ8xJW9$hU{(fYN#7D881D
zyT+Ewb^U3<uyr{U+2>KHVfdr0QkjS!$qi`LJ7+@H$6gP4`B*F+ckDj1p##mESh;^R
zUZ8*X<OkKSwvd{Ubu-Jj3iUoFOh(xh<Aaj2n?esdP?XofUvNJO-nLoh*u03x1EE<?
zXY1}@^GZJtZvP@k{y9~(t)~ll1d@E7v3cWhSxv+1FTG&NM@Vp)aS^JSo?boDM#CK^
zLV}GCG@*awctGEYT4>X^JG=XFBsMctBu)k9;PLZjI)OjP7?eBnC*V&Lj(n6@IKP0<
zsC2sX8-aJNE;9;9yH^D9!ctQ~#Qq$4G#IJ!E+4u?MBv$Y8}tfllNx?gAgjgveUwcL
zxMcCV6$f>I=%z#V%YRTH#4OV4l4=|-tlv|)_`Mj^+&0JWTdD<t*f3{9t!_|i4Pf5=
z&;;J!g6($8dBTYF=r;PJD(I+Xe9KZR!P8Q&YRc_Op!a}?-%Cjr^u&&=wz-)L-B#Pg
zLWww#WXD}ehI%pBOD*>v4(bH|Z+#Lu5)8QAIX;mXONGJ%2a?xrE`eL#+$QHA68WX4
zqkbL@BttL7Cn=fW6%X?_8V(To1ghO93(p3*AzzyQ<guzcg0I6Tu%U_$@3Jc$uRp4Q
zWVM98Pt7l&b#^iEH-Rs0OY<JQ%bA6vmB$^cJDAY2)7E&!uUv4|^=MNi@-zqIvVA*$
z-a^$wHoF7TS_xhq>)Mp`J>WO9uW`3*g8g}~vs^b-K&bM4dHr_<5H_J}72m*smb0F6
zE?iXD+*M*rCi<0<qggo}tOm!<b+yxSmKc98-1uk|1uXo!4}B*1kUcsLS-UBH;Jf$y
z^?RX(P+s##LA8hsLJqW7qg{{S;Z(|8PG>p%eR%uCHwp_Lo?)49|3M?fu4TJ%TOj;d
z3N$&$RRRO+yAK=<BKWtpIYw3lzxJNY;?U^ZR;XUsHgvGc9#qebt#VPygJpwD{CR?H
zP{t6ex;I*m-^W62&5lwb_I_69y3$f8;(50uXKV&(2h6j+&sTxB3q|EwEEyHL#ol*c
zWWfqW2d!n1WLQ7;`b~^wIND$KxiPz~5p-<Y2}%PUO5MyEy!H){&YPOKDmf8e4zshx
z=~ROBaaoJr`xN*cI8-^<-2u1Ek4!B2)Pulu_Oju1)sXsE`E#iXp`Ru4zP3XEtVFh6
z-=G6<h-=7pyDb&W=Xv%86Z~WOp>L+=LaUIhdO7G1u`Z6EICSv-g=c8fvgN3$3K=d+
zAIwVhq=RH&x&IYzA5bh1`4jrP8@LVqMB<fn@ko-nU-N-x*!-!G-G7=6?oS>$Dh8Ir
zKJ_$4NM#t@m@X|VHAux*sqrFfk2k<^dRX9lwp{qO+bH*=Hw$8gEThgV^ulLFZ<e(`
z1upIWGH3Uo2v=5)e~5A+_8G^4^WnY4P}J5LsGsl>0u%H$aM^W2+5Li1m2KtdD9YmN
zBX|vU(t{rB8<{v<e{L{t1A+4h`I|-uG{BXswhE$l58+}>`&Wrw1b<g7lk4$QADnGD
zx?8cN1^9wpThuve5aXU+y@SBPvQ(}aC$XnuqS<35_O)3U%HI3pt6MI(?>%);{W25Y
z1y?<(JxAzms)u3Q^)x8l)88@s+!&W#@&3F~Hxn8g)^e$_v0yph?b0Vd9KcrZsCgja
zcUTYnHeS0(<dd~MZtFQ#OXNfCmYvfico15VNxulZ`<PB6Nm4xq#SGsMT)$QfUTdo&
zhR=87yC+w8o&^>`x>gw}tu7s&*CZF~2bCaq41PL(AQhyZuF~8!M8@Nnj))Wl6{D)^
zIibOmbnppdX)+01Y||z`L(h$=C}z|7^k!!|_Jt;YPw03BxmzrpJ*sNp%#?Fglvp{q
z|GKq(Y<UK@gjuyI`j>&oD+&Hj&wJp8;AyqpT4nJ4)%sa};c^fw6mpMV9|&1TgdLdM
zi{QK336t_yP0)Vyhe@+}9c-Lm@##}=H9qD4b5g0W7#=jOKKb@i42DT8tb6jf4Qx1W
zwFej%z@<B@W7W?l<J*^}dwUk^;0GsJ^5?~LO!3gC#g@0jU+p<dv#5ORtQViM&MU-&
zbDqab;_4x-r{?-NM+F{Pn<px^Hy4g{MqD=0O2P5FZ3b34@mTLy{MUuA99CX1`M&0~
zIlK#Kl2_3vz@F;lk5c*(V6%cfusNNAXE)57-OVB+o5WW>nXVUL^l>DsSh53s&)V=W
z`9z}&*Y}_w85ES39b5?VZa|^&3Kxf#GO)Y(dX3(VK9rKUBrIf~iH?8a=-QzgjJwYi
zm@H-Dr%l3dT3RDOXlooPiK_#P?e}Ruv91EY#uq2)Ih9}(t8mGjD+`arx^C$b&V-vD
zujbEet$@X}kcIhoRFFF!B{7#k2W~;LTRJu*I3}#xyPx18{SvKD_>s~Gq9lp(q#Z9{
zJLzJYoMSA!W~?4DSi^$rw^lXp@utCo&l$R~^eb?hoM)I5`Pc))_pF>IT43My#u=l`
zRA`peHem4;LE?q4e{ANup{JUBlc)GDJW!U1(VZ)R>7f~&ugcMAx{ptQ=SvH8q$cWh
z(%Qh{qq^cdV*X@j@6T8h@;F!6A?b?=4Kn?%gk3xJzc@M%e=Ps+k5@{AB3p^lM1;yp
zjzUJsR@r1E>mx#vSw{AjmA&`At#g~Vy>}_8NJwQh^}D{mf55}zabMT_eXes}&*y1}
zS7w~5Gm!&u>iFZcyV@#%uX!pu(!2om9LzVa6VGGaqkXj2ZAmc6a6}?lgm~}SBx_d}
znjs)ZW<<-4=!0u#%yuPZf$t)%#@?Ofm?9-%9#K*a0ZiZY2c8pkkzIfLq4HoX&~4%f
z-rfhQ)=c`*Bv0HBk|M$&`A~3?wuvsdA3o5pM!DM)-!CpNe!e|d0;ZRD1sv2WL$eBP
zl}G=g@xQK)m8bOt54B3G@lU}MuXM7$=bo(qTWz@BCDH&rF5#E1@e<Epo_~-Kk*nr@
zSo7t`(;)~`nM?b2w-QgtI8WZbP4rg;<uf@}DX@E8wdIs6@ttpa_2+xWa<ryS;{G9#
z3y*bvZ+m+=8k6;o3wav!!KLujWxcsdeDmb32idp{QjZ^Hl{QX7_$RXS9DffuTDZMD
zL{knXL4pB~+&b~6ob-i5mMH|copp~P$_xJ{8viw}A;W`T%WOv9i8=Y%?UzKmijalx
zE49;`cDVQS=riES2lB(9MXR43pq*(_wc}$9MlW7vOlpgQn`%um3SmvCGt@S?d!`PC
zIfsv}g|tDhbgo<TG8tAL9($4es|uPl4D9tv+Mr54+s;UG0Nmmq*+(-c;(Z~i4S)UT
zD3-CsUgB1R3wN9P9~IPrc}+eOk3>K4S}ks`fmUF>Zu94WULH=?-XcpJPQj;c-8^(f
zz3{qdpV#4w&9LX5MB2=3H|Q=2zoC8Aj;;z!>Zj(?!8V<{Gk&fVYkR_#J$DwNMjf}~
zA=v?RJorfmgUitGn(Q*mMg+kX%!nTLCC>fa3;oI#)fnIOIroO!FuG~kMv({Xz(Q{(
z%=l&@)UrDF3idp~x<|Cd^t26FaF%ZA%l%=De8Hpm#XJQsnVzq{5LXIaeFOJp`*X4Q
z?SS%!oNR=<JA)6KcO(7N{XYc+(;)x*<GASSh44i3G1aZ|46GdAlNGR#jt;$M!o&ST
zIK2A2UOu4=6x<~Q<Y?pJN1`)_4r>DX+}LYV>ext}d$~TuToN<Y7LKa8rs7zbj*2Vs
zdqiB_-@jL{5O*rvX%^~l#f>mA)dS;$1jl|TXn7(4=9=@~Djv-OgU@ux<zIoW8w({d
zHAEi$-C_0Cjy_BiyG^3oT@S~b$(ny9l3?4zLljibK)%K&%WdRN?4~s21U&D@@*nKY
zYL4Z=fBIkVS&;}Vwz^#yH5ZS=FRC@xUQqCE!%f!@lf%e3Ypv7VOo9und(vYaYJoMO
zfA`#SGuRG2ImF(d183<Hj1;r_Q7vCO<t=Lo=BA7NoQSCaW6@`#A%^#X!<Ii=NvZ?_
znTNTahnC~YX7RbslU?9m-1<v&J{wQ&;a=nYOXLMMvnLL8IG{|*fjLiuJX|0t_*@{k
zlh4&t8jssan3?Y_7pYVM^k%lFq|B4B|8$t_a#26Vu2L7p7sta+z8xQ<6B~i&iLva<
z&LKz(=ah-<Y{8lW(RJJ9GBj0CzNz%H3Y<lSb}+m6!)rHApRo3Ppd4m!A~;Ok`9|0~
zIj9TWkI|Za7VZJ3nr5-?=fxNn%eDQ4Kpm)V^Sbnd(t(%Kec3CktI^@KeeP!6W4OiQ
zu=L_D1vNkIloVpDg{hqy!`w!LsQd7yvDi=wI=vM5X%H8R=_=xZR|(ERh-syA-^Uub
z?WB5uDXbMAnoLAh{u;-l%%0zQi1(z>c*`UHObbS(yw#piEQTAJMTWn5EAibCnt8sO
z4m{;l`|S9oCb(C1!$D|g7mBf-D#+K3K*e0E=5p?E2oq5`<k(602mCt~I4=?Z@09f}
zUcEuA>*y$){X#;XO(&g2mJD1IFWtjSO~S<C%k+=g8}Z$hGji+~Daa(yqy0=H98KxJ
zx4RB!V(%U4doG);Nb~Zk@W&Q1GA=Boa_(wDdGU{bJqiBfm$)@$;*L9fGHq?uKa+<{
ze)d<(Cz|m_HqW$QdM9>@3jYqsPY33l+GlCJ@o@Uq`(v33-FTLHb=QSD5|VZohxl9c
z;V1(=%lu7WRDBikK`puw9jX)@{u1-n>p@#*uD)nN3GE^+=i&h@u`lBtOe}_)3?E*`
zgXMVQhws&rnkKw>JZvj0y$s31FQSYUI?>kThsaia6&e~#N{8t@#=qfnDn5K9T>Yy&
zu}JWs(Q-<b`D6W9`p&TC;xkW3Z)4}QFK9z)cJ<Jx**dK7@~UgM??l2zBU5TQh`mM^
z&G{vK;fvAwre-J!O<7azcbsX%&6#~ipJ=yZ%EI2`;ziA9C=pVWI+BfjCd01c<XTvE
zo?RM}FM=-JFB8TA4Y+W>dgMVC!Jo-lWh94`pv8PJ$I8KaSgJgh*5*z0?JHjirrn6d
zfzjShOZ8#AJv~1E?`A%(sHbcbb<KlwhNbsg2rjB$bL<z7(_<Lxd$Ld+JB-`+9tWc@
zRe1i;$ED_36EJ(Bdtr`T06D7+GlraQsBuJnspr!Gy2%&5vJgqY=7hftA)a0E=-ufP
z)y^#-QkJiKcEk_5{|hp>8rp`>#xo3oB@4HBsX10;I>7C}EK~9BGFbQ7;UGBR4=kcv
z1#0xI7`htqD?82+>eXn(m4e!UUU=ZMOA?XmQG0iLydfET>z$6ZFcJfbxuCBsn$OUy
z{{xF9M>|Ye2_Bj@pn#W<p0jUU3+CrEj|XZ~uz~M_R6tJ#7Ddz_(wP{7OP#-ujUG&b
zFG1X`?&H2tlXkT0eP=iFs=Z&kLYsq+GuiLF_&WeMvzFv<KWPI8*EQL=mSQ~Lcu85N
zp#>G)HQ)ChZO2B}-yIf~B<$w?@5BwOD)i`?wD|q!F<ct>Cgc9L8#$G9XK%4KBOjx^
zgHKo^3V(T~$>~#vO8a+G2kU3yk)U)HN!J>DC-_GM{Cn|~`tb^5y<8mpV|nd5Q!Tz^
zH@&+0tP*Q}PNdv(Zoz|Q(gr=;ZD{kkIpR1&I%;YxRMcnx|GX`_jg*1!ZahmId)$KU
zTyFQ?^0Z)T(3$lR^LFgcHk~Put-uxLlsylpbMVnqljH9QAI0?2OdP|rF7)Be8NN75
z#*?dmO>1VK;eJao-rr<T_~x>#d6{bfXZ8291n6|&UhfC)tZpQ{>0o_xO!zsbax?|J
zvCP2cxdU3u30*ip`t^RnZ3-^)tE7iDHDFlG_E(v875HTuoP2IM;LG0Rp9<zg-8<a9
z;;Yh#F3ognDgHfpY0v4s;<3f3ba>VxH?0Vxuk<;*U>(4>(c=t@HoX{H3SVy!=cwiK
zHI}aym6(2I&uO7u>B!2vM{nU)E}XT$d9_$R4*vv(ORmH;!oxW!`P<a5;o&Rl@#l;K
zkUY>bEcqi3uIEi|b1zH=7e}77o?8`Q7%dX*C-oZ4r+3ACWk(nZSK2n9SBeAv9`#Xz
z^$=`4ezdB)1-{kW6`m*ZKFO`&dg~v2fac7$eUj}>=*L~+@0CH^Uo4wHnJ#2N-AOuE
zx|BM2opsC8S1SdyM0XtuS82xAosM5al&i7vuHmuajS^V?{eJ27pFvP#sOP$pUxI!M
zwmZN5uEp)0I<vZ@G?;JJw>2i_Xw1VoxrNWG!PAk~Nmh@9ZHnKb^`**@zm~I2@;?{g
ziHJ@yk!S`JdlR|Z^?X$4r2fjYpMnY3L0_h=7|;5p%>Fx{2EV=zd-<*wfWSqGj?-%8
z_`uRe!}vxuD%APP)}E}vuqG4wQ%#ka`KDIocqIicR!FNf911}`73Oo_-_+sER@&-=
z!Ys^O9hYOdUy2sNzB4-Fjqt8ydPPqtfvB5#x%mmd&alaw+8)h%bZ=R@@%dv1Uii3i
z>G8BIeoz;fkvGl6^;459fAo96W3x!cV66eXFxv6Iqz2SI>b4q7)O8Quc=3L&3Wx7!
z9e(f-b$D7cB{&_+u%ivm=n-7q!rlu9>oVG5vpG89v|bI|b&Xlud1nAP-=r-G68TGJ
zriV)hwVz|<?(!}>QaPj^>g^h-EP_MAIxlLs_kwPg)}8rGC-B)!{-gMUm`@p)ZC`gK
za?+!p`9i~pK5SEs*#(g{qK+_lkDIaBv2p7yLxe5Zb$WJ&6Z7{MyCO7Jo1MU6LiL72
zKn(;O{<JHUr4iIJY^C2SHesK!l!9<=7YM)Yk#;fhh4VTii&-uN=f+^SIJOauH%AQa
zY@=jidXlRS=_*3@uIoc(e<{G6c)aF>M>+Ji3e{c9Ao|cZQuub7Q;=#+Uo3!o0^ade
zEJl7paMn;|-FCJSI`{j_zZ_@=hMVWgzon-`Rq8LE8zcQ-zfoE=?pgt(@_{p)$uB@b
zIHa70mxMn?$5XF7t;5TL8Af*w8G*LdtiiD?L<OzU9~hMiy99F??#TD!4dKx7Q;FFi
zU{VM&y;jhun{;*}r47&aY-VXLjbO`f2^(4>cjYHNmU>K-;EXCtLbW!EK~9j@x&2`&
z_UZ}e#O)uzO9PVi{yoGz@NfDls^8JbPkr-v9nBCC)8jInwa5qhuX{VFTRM=nWSdYn
zr39X@2)hlYCZNNZua0RE@m)}6^mPA6FSy9QaWDK=3r_wiv6xIQ#oLPfL|h{dX6AaO
z3Tp{pOj@w~=_d^!&cprl>2x2Sy3_gVd@~tC?DC&0rai~DkH6=|W4l2{!rZ^ml?2~e
zE|{4Se2UC>w)BTH1^C+}d`9Q3J!t!M9clhW^yjW+FU1Oy!MlBBF6dA<>Z?lcaPf$M
zN&Wg>3A!dYWk8}75a*AN6PZq$r5l#@rvydjhVc^8ee1(=a9<h|=6z1W#pbQic3u;(
zJY#WQ_;L<R9qds(9$Jh0KFg`*6W{d)n~SmyfiGac5ruq^KNANE-Z<N8HQ^N-r-?g1
z3C^vv)j-2C5m$R{T))tdf`E(JKcSga$YG?=4w92mW{;gjVn8LD<&Z_JPb1#Qxixjo
zs1e$mlE>O9Z72@zdvBM;0mEm;r+?(?V4CVFGlxbm?!2K}o_M7imO?Z~_f>QPht6AV
zhR9mH)kQw9wwwoSkH3j2@uWbVO+7j4Ln*{F*JX^CmBZcv9kpQM{hTZf;VhqOhx@WV
zM+PU;LEBGV^~g*FQngA1Q4!z!Z%X8XVlLhV&(2+r_g;3x#m&W3VgF&^QLOR$PrL$0
z3OdpaIy&Kv;m!{-VWA)*yJue7vKf{gDZvM=dg0aP!{QX9YIw)v-?<jrglaBiyB$x=
zU`5{OysTn1@Qf5I75WT7_M9Uf3Ri#)>*(e;pLTfu;d11!{1_NJnfc%3`5`d<QmjWc
zMgkC<15xV;JlI>k8Z`bK{*L?;<Sy#QuBf{)lF}()Z^WB!$y0@uPs-ok{X>QTQIm?P
z)DAS<F;46Gq6!rE{e6)4xd}3+K1S;e#e(gkqR?V#Cmc1Kd3`Hq08=!#VoKBVu%f-F
z$KezO-Y81trBhX*{&aqzDXA5b+?vmyS{=ck-K*Pm8mfqXQB7&?LLUh7&Qa-`dtlh#
z8n1dYD_p(W$((y&5~o*mpRs)GMcop24!GL`>Ux(dygdl+`Q*O?jKo}})yV3liE}qb
zKK^mz+qH0<V<}ULPHKUJZCfvF&!s{@B+J$uk<WOdGR1ePB@!aWKZ!&WzvKPWF2D3v
z``~EH+waBKNw9k|#xyHF6I1*HYo9<fDqI^?52GsuRU589P)P#6i&Ce!n4@u7B$858
zWCJIIrKs4hYonby6G^@z35uls=ILq3ko3&t-_ewCtZdtgJ?xzaBR)(S6`e6~$+z+G
zg<=<!OMDWjViblBr(|8n;ybb5?t%45;acEdoNfG6nu#N_g&v)xN~G%h@7d>!K=^!n
zCj*O<4=P{c_dasl6`$QTwVt)jfq!h&Ph(y+V>EAY-bqq5din^8aAlN1nWXThVsbjf
zN|NhYUK2SY>q}mH!-@HRvB^W8<uufjc;V$qTZ+|>Lrn5NbU|wCmGdf-5pZ+|*R9QO
zWZZAAlo1#gjP@n}8IHE4fqoMut6(4iZkO+(w!Y_u!lAC0Q;XA)NA%r;*;6FkR#=^o
zs*`~a)wZaTBpu+R!FvZLsYcBD*qf=aw+3X^?Ja(~lmqSeb8*>hZCIi5!G+(a8A79O
ztj$~>#IQTO$Bz})0qOHp1#3hD2HG4s6tU5Zj+}II2R9pmVvURD#P_VrS0lON0t&E4
z3>dR}B*XQ)9%)v20~nS0^qH?+HL#sHQgtw>592bfjCwPT!j3AXw_zSWNXm@-^JCXA
zW>qfTw)~X|!JAPj22w;WZO_vRzeH!0Vd)wZ+$;mb;y{-3mP5F)_UniC;bw56Z<*es
zHi&FWg-2`})X^c-SVKLqk_Z45nZN3>LK$s=ydl{J^k<f5myD>!V>=X>oA1`4vqMID
zi(?C3KC!#;-eLyWzfI`UG%A4Lc&i;({FC6~7RN+)cP-rASi7?SR~kfVv(6o!B7@^2
zCySj8i4dw3DpYni1eRCB)0Zk6VLf0#O_7e6x5!=oy?v4Ji80N*`F<=3H8|8`IEnf-
z^LRt|`sE7z-1T6O<OT_x5;rOazqi5}{eZX1T4ZFqTom_!ZU95$&YiW{?vKgZYx}lE
zSHopCf89?%Ns#dS?m1q;0`NGdOEj~pFz<+P>)WPK2n;$>DQi{^UqoNo*)R8kI$vL1
z*ncTtepUKnj(j;#k!KiJmutZp9qC0xTi{q24WBcSkB#mVk`*O#rbQW7C8(lrqtLNq
zyUC`+T;1-F-mZ=c@Lsvdwe)BZcGvk#h~8=i>A?isidQXQUbW6}ZjlVaW3)+OE8Vb4
zGx*(OeG)=7!%y+5c7pRTUCN|FDL9|WfB!$9>L~Z$NF;Jy>hV0}p3P2ls#bI0IUS6z
zZ9EOm+;s!LQmb;$H#K-GK}<<}t`mEy>J1W#+i}n=)n7p06^3r^{GnZ50uhGa{0r<T
zI39Q5WpY9pS{3^S7+xC2-#7LJ6<8EOjlxg4U;oQfN>mKcEDfTlV9z;LYcjN}NFUMo
zRF5)o6Q{2~eu3`(O@UN*8ZheCfoS_r-ALJ~Ia@;T3RQ=jX|5mZK=!pb2-YX!A#a!@
ze-Iv8shE4Z5JVwz$)APJ&Jw&g$--jDs2eAcAMPt<!}0~{4f$OKpdxnqU*kkOR#>JD
zDkWsVS8-(rr;Kh?znpD%`9uMHwZCpG`neRUqmQQP>Ja(!*qm_P<8e?$BVJzKR)?Hj
ztFJdNm*9PV$(m%lA~?@sM4kDh3{UHI*t*r^L0H1>2Y*#+G0!-KWZK`2$EB_c?*3g3
zVU^({z2)~IlvV5HEh4AJctv$C(5VD}z5DUKuxbD`FZ!}A-KfHtN1s+Nni}I}D#z>d
z3Js{QYuMOk*$w+yVs?{`x<kR4;eTJ*$I!!m+1z8X1^?XrC`5as16~M;XGvL?fYk25
zYePyT>=+8U(RRHJSspI-?4=n-seMO}yt5ku-`CqDLU}TPJ3GN3FuV<AYfTvUKMI5X
zl#DY**XmLEirk}sus{^zDO%t>Pn=&F{3D%4Q80J3miv50CJdNNytwRyD0_7=G-pR8
z+)NKT>9#P8S4*gK|A`^k{n`6<`gJAtHRu^U$tnjMw||;6?df>y;Te&Cd=>aC$x3W$
zssd#12$ajEHXxPb_xEQV;(+Lx*WWu@i~K)SElvg#$HG<5JsLKtuueJVR1-mPp9jQZ
z=+-N+TEZ*TOS2r(XD9d7pX&!jmy=@ezBl1uJ>%=>ca`|a?VS@nJ5jHmy*?=Xzd6p9
zM_!?9D=4;X>pSk;f%BTmv0?;AJ1qZaK-u*%EdAnteEet_7z8ZcZYMkx!_xAPP38zM
z(S^={+nPDx_B%;|$~Yer4mu>{j)tT3l(}Th-h6yx#@XLQcwjFQSIWNAwaD~5+;oQM
zJ6{Yu-KaEGfZkuH3XiLoA)Op!bV^hcmc)LIlYN~JmZUId7mY$3ASG};DQSTj$;+iZ
zYCTwvm}?Q;j{72;l;&xP_igNc^8p-ymCyzjQO`y=%lA=gUc3Q28>Jh{;>&RHo>U^K
zAqoYLTu@jrj>Y+Rea1>nZ6LJs;;+=TW_(cf@1sL$3!Zf|UKSf6JOsy%g-|&njH=%(
zmL}?|S1(2ycQbb5nIAurKfQ^=jnUb^KglF`^eXd}u6__Y%Z`QJ+(qPablzn1IEE1(
zL>qnAQR4l7{d=TbqZvQsJct2?G4N<5*Ic#A12zADJV{^c(R?N(opi7P-8E12{tl=G
z4<~QIHiDC&X{5Ftj&Vn$=!BTB{!Q5Y)JyfCGzs`q6(7F2FbrCy3Hcw6SHiHv_`N!N
zPgLeg`IOB$01rh_fOjw&|L{=X+P*jfhF&j=w!%ulvsU+f@X2Ht=3~3(^UDs41W4<$
zoHg+B*JH;wCPTQdOQ`FDe+D?JCmxlY&w-cO|2~ctHN*B-qtil`weT$M?cmeKRJ7x@
zx))9Kf2rPnt-AiA3o8PwKgHVCLu-T9vI<Wi2p<(F%nombp!$BM69li%-fwu<hW8$R
z_f==}Khyy>4h_pZ^a!h7a#b8$`Dk@O-y*lI4?ma1iS=C1#P;crGaG!(pk*Ai6w*NO
zz1Ej6SKg`xov&Pid!h;c%!G2=YO@e7vG4F6u4)4&rolkn_G~ymW#I8FsR;u-8h<Ja
zW#fM%!R%vs<>;WKmgluf<gsk)I($mw2#-^_waB&>)H`{UI`ZLVpgFKasb4n7mV(mE
zK+Rk*cKqsAO5{hZ)+v5r0=+2TaddxRNCny{r`*2%J_-$a_JO}L;RhCeH1^?84@k+F
zbP7}q;o;MF(nO!6!evv&Jm0o#qTXIMO=8RjKLKm=Ft1UtIdNX%%(q@#7_&dW>q`Va
zeJaEi{=NZo<gGtzZ1qF-M+cL+g-8_tF~lHoDG%4G>~^1?Y$CWx=X&AtGN6CUu;v~%
z3@yFs0{OEAApB}OC$#{<BYoDSm9%>a#(HCxvHl65nS7O=QDOiZQc4G?)f$2StHq4I
zMH7<aE(zx!sD(-XBj(b>*~m6foc`}<4*U(A5E)Qy0@uvZKx?AkTg4`!DDd6~4xKHq
z({Ll|l1D8$pP8y*Pk{CBrGq3m`}e}B8D)Y))Z5^^td|1XCCjI!-J)QBcEBUH4;jGp
z<4Y1>dJ~j=i!PymP>y`LT17r4L=M!0wAWw13>F`JHG7{&_@2UrA8So#z>|xF>3d%%
z20e-^YAWnTW9|b2{pUMC{9=a*sj3xh56aVsd`gGvTO*2{nJpOPe=LIezb=$pV3kcE
ze0b^F2hzf@5N6Nzvb-^G0gEfQ!Y(Qj{_IDxYR6dz(fPwg!zun=5ct<E<NV173gWIX
z^tx1otA6=gefa^r@u{OdM57V~R~C6BSX-cb>fwlM1_d}9TB*LU)#15NrytL6R>P(A
z7bljkwcu8Oqg(Y8!gsT8>Fk_CG<^Rp$h?o#fuBWuU*7qbhD<FvOk;%v4`KNqYyX*G
z3^_>8;%Jrw-<0pUph6RV=~_y?eGlOY?LsP-LL-i_376SeOG30(dfgo1w@Lf+(8e%7
z9k~iZto2s%V72lDqrrz^+}+|wGm>15g37F+HBX=6yt8gbUUv`ndy>Lusxr~)%{5-{
zV=s_$tFhWvzz>+x<ahO)smC0n#uwoW#M~mn)ZEJa5%`oT*y}Gefj*~)-FM=9Dpu^P
zENw;}$T(fyML%1KtZn?>pz<7Rvt-YkCd7k5e9e>1s3;VV9o=eAYKE1YB8?h%%3(=-
zXib%~0v?|l5-638z(4Yp{l5usi(_Zq=NJeDjut74aKe9k*V4w-Dy<Wx(=)}G?vLT~
z=;T$|X9i%iFs}V+sT2+5XZ9^Y9=cyWQRxyrj;CX;{uvja#8-UfU(N^C;R>ys&}Bmk
zM*67^s1ba}&ks#1bOJ<Pp<V1>*40wn%^tLBH9!c$V?3-FkC)=xvTK#8^Z((S?$kmT
zojQnd(qizSxDfpg8!M|<)hHBusM&s!f@>lZ>4ih>@XhQzlNc!v6+BH?@7ECZF@Nl|
zD|a5aG-`e^j;+Np!-E}f%gC^QXp^csw+I{V{@Jm!y#Z3o=L@e3CxF8IMfnRxJxG;K
zm9~TMZhUfUjA;B&2##61tSi==L6h8V#&fO(=xoJvuH6{HPj|1C&s?j-skLmS*OO&<
z(0J6mRJ9KL_&}+D8{rpZ=PL>LIEflUbVc1}%_yc;f3W{u18gK{B*dO4gXh_284Ct}
z*x)?wzsHmC6WA$ohEdXRNpI5d=7UbSeUIlvuxLIM9oljhAnu`-tqTKQN<|pDKKT7(
zcn-X4`(FCt%pgcjn;)`rDuEE)Gxp4uK6q!BXkEdLc9iJ8J}OvV2+BtLV=KFdfx&x0
z^h8=Q$OLkz)^btMF4uPP$MYVvxHq13#=8+M?S}UT5?tuv5K}ku>jXIM->)VU*8+#h
z_2vc72QbouIcXxJ9ogNVH8@<Uz-fakZBmC*fmQz7D|<@<0a>7&iuq8AlgCf(iY)C0
zAueYQF1Jc-Ty9A_c(xZH#6(%$eh`}7Np)KB+4%hB`S-0G6tJa!6KgS>j!%_WF3$Q8
z9@Xcp|DF8a3HmqHzZgdi;L7V4i){z|@#ins&^-wiz#!a`F)rQ<{#j4kWP@7a`jMes
zsrx(8EGuXpwawr{P=C`4(*`W@0Jas!1`H}te(SrKh;uI2^l6I7(D5oi_#NTh?kP~v
zIC!laGeq{sd9qgFoAeP1mlDxu6Ei;c{iQjaW8NN3OR2<dDngM7pR1A0j^lLAZ8F^Y
zY}{;5@aZavqh%kc8nJ=Cd*X*+9fl0t46ZXSM&&zI-by#>(0__n`{eIV;FoL3V!c_3
zO5c?|{as41+DCL_mN<7rb_KpXu{Q~|1X}&JD)T|8kMu_F0P$QaeBz4n7=ZNu#^`_2
zqyl&6N#_a9P84mIUYp~kpx?5*)ofo6vT+#rMQtHePd7fd5zm6<lg_#4Tgf=OjaN&V
zgWx|o)5!~uQcz2}{!`t<Uf3M2xMO=a8xqU+colVYVrW;X($1h-oC~|seJLge#8o2-
z=n1~K^q%$V;a~1Z`#!I1c6%Rwlz+-{WmiAmXAUdS+MR`_I)kN{5{trSMR9y0$015-
z*0^ho@T6#Sw@>V=L;*Ll?&y2~nkI%szb55iZ>DRG_Cx~iWp#xk+G;>;E;+QzrvW{=
z8xC#RJb{Gx6S;v|WmsL=m&!uix2ebfCTUW+;OFktH`S`8I5%l`H(;L!(mn6d(v2I%
z*Y7LBC{j%z!>aJDjK3LA=3h{2UnxYTw+3Vdtp;o>O?$}kvlmOG3FQ~z17b{GuyMFq
zig$ihlc)$D%C1V;ORsSlnQARlKTdVw-SF;XygrqvlNZQIt<p%GXT6Ljs)V=GVr7H?
zNRf?csRDL4;afMx<LeuZcuptYxh;-@C&`i?Ouq<EZ(!hq=Cuy=)95@uLrwUH#9I|(
zvwG3z;E3bfu9wIG`+4G6li=&{IfkVBgs<=r$}8JFgTLKwrN0P&(>v$J`7p&k91hO(
z8<oyQXFu|e`R^qV9(r2XXWa@!St`||2wpJ;)=n=fw&CliJFDI^<=}kL@&y4(Dh6+t
z5lmkzfiQ~@Ns-|WJoB5pTdb3UU3Yp!z6P`*Tf$_d#lak8`@-nguVsrG;&yv}68ypE
zO~;cgG$b4tFkZhb*9iCcO=C5Q+~W(?@q)a(I(XKhDpc0jhM%in?&N$&L9u;1?`}6P
z2WIN`PtRWMMulfX#h$v=D7oC7<St!?=I#f_lBeoWDtDfiwj&N`X{RkH?^@uD?^V6^
znG`TIaQ<FJ<PPQziXX`R7>WOAF0wIn=Hb7A9SgIWBf#;opW27WTQ;@p@n|Jd(AxDT
zZ@M<|J#OJbxn>@VJNyS#86ReX`;1PNQf&ulZDrDAPqaXZ{~wPBOk?P#ZpF*B)CD8*
zWta8@wSeWT?TvzWGawr7*vwc}5)Z@lkIe8GtYBp;*k==k=V&_v`J+kL#B?!~Ie`q!
z#omF{KKAgaGJ0>heLbESWz?7_=HpT`rMJ#d2(JH|nut4NH7ta8i<u<+hc%KrSYufG
za3A-d^xfABK+E$^G%sB{c;4IB^wx~<a8``<AF`z2cIVp{v{r^NQ~sr^i$oiivi4Y?
z+m?qXkA7o3K%6_)j1rc=HCm8fO+SqCY7jHM-43^m<)FN7U+m9AO=vO|Y407{0e93_
zxt+$+LA1M3lYXWV-|y+8F>NVA%Wq<SM$bn<Q=jcqS9>m8@=jws=w1T4lb?sr93MbR
z;mLcU^e^y<(@D3>ipjWPbU)ll(G_!EAEcs{YQx0wafc^gOHljD;g!Q@9^*hb!$d;i
zs?>Aie=5u}M2>SPAzn)%70zU>eAWNui)N=dj|cuMfuij?qNk5#gJQH~S8lRByt0rS
zyPbC$927{2n+92!bI0wDo=7=1x8CNNiGK=NuDy&63)zq$XY)sxcLbGO$Eo-VDMZfw
zuOyebHB7#HL6ICv2czhdyz^UmAUJi3Q-7ux4fT3YYb~_lF~08L@v|?1*RQfede2kL
zx<_-Oh`SYfHx5k@6MwW7oe7H}`tzUil`cLayxNJ2QX;bIrNAOMeM9zrA*>nS{Tsc6
zSlE-0s;xxE9rd{l$(1>vJ<?LeJV-{bedofdj}kd*m?bB_A0+;M+|`<qKGgbra@hT1
zD{K{hY|#`e#5ebsH-qHU;q#(j#h02%qzkarM&dk3rVs8rz19i;QZ5IOOM_uIw{5;`
zdI*T6YMfjrx8vXBvhkjk5UAMmurTOb5!%u^l(O$eAj>AWm*%!%`}IMahn8gArSLj9
z=0Pt?OI?zw2}&V&2B+;6-N}Ua!S+dgIt5)<cK?_4iP)Pn!fa)Jq#hloccubg4sKGt
zn~xnPJYf5;zgUusfv{|rWsiwgOclvAppz!<Jqkmzd<enKe12+s<8TAP<?(dJDrSL*
zC<hu5oULVCm-uW`DU94skz^tI>(4a>YmA9LNNkvJ{9~B`V7C*`S!`><O+}sY#C_eM
z!DY<x_*elv8_`*g)$D+@ecxYgv#5d#M-J)B6P~BtS%Sfrk_q2!g%{lqEedqpJaO!g
zOB+NtTYVM^Cj22K^+xM0kMU#3=fEnKa<t@SB&mvb;B)yCL)K^d!D{*?nUh)>-NKSr
z-uqWW7kz^Fpn4f>iHM24JWu#VM4gsN<O*O^T&US~Cxh_ph)-Dz4ikNSO_`7WSs-6B
zBl_%nHp;uF-gZ{#0<RvmKld*u<9$2I7WrQWP;Tg@9O=yk!-?%mH4#0~oHE$2xsQw+
z=_hAPSXyAu)D@CEuPt(qRfKU`XTq6_BS9F~15^y_55|dH(_pVIalgETS5J%C=`Ys7
zMCbPMZ<GDVlqgCr2p@n5iDH&}9yRFfp|~cV-HHr`si*yt{o$B3kC0AA0(@C|rIt@{
zT0aA|YT}%R;1GH4i{OkistWTc=IatX!7QG*Li9;y{e;+5pSD82{JWP|l_}86{Fs50
zqYZ4#FIj|ZH{y!<(Dl9t{m@rRd&M#|9FwDYy{HFz;Pw7*Yw|x^p@Zk%*RnAZYS!I0
zr|_qM?8%l|eqx@GcYja0&!=9XbD^m^wYwGmYNNu|=T<yqS^W6Tl?tFzaTMR!o&r^T
z?&l=idf}dSpoEh{6PTCpQl|S82Ya7;VA8oJP{Zxnbx$c!=xde!b8rBdTz|J$j~XJ!
zt?vSF=(2JDTXvNgqOa9!X|OYc@P%CYJ{>9+-38lfUwet@7DLtnYiahXEUcoWg(z?J
zLO*R-J?BOTY@@TVVIjO^GLPccqrW$RR=x*osv{Y$Obz|#NSq@Z-sNlNL?2@38Tny7
ziFg=I`k3@vCIh2ej$G6>%|U8II-N1<awrc7&21=Yg3$=dLe^X)(qBGzn6qO5H*Lgc
zS&9DSo!-Q`8HQnS9FcGSGT;pqdNOZ?L@YWnKlRaBs)tYdckj<eSHQ2^xhB<q6i{gQ
zXO{8k#QhS}VlL*r@Z}+gMHM^2C12Y&a;J*|=KuD4{gWtxo9DL6J<BKo9X|T2d$Ouw
z%y2W}DsMRo@9WQ$F3p4REe%2Y1DP<Guo|}V$^)z)M>Oo~q2PfTlbFn~I;4HZR~V{I
zcow2MT2FhXLMg{8<GTwr@Gwel#+23rshy(OC>eto-#K6Tvm+O+s|G~h=o0tMQ*OVa
zc7mdE6W<mmL&4M2r~g#n?#AFxPL#_M$&f=AV-!{!fUF$j)I!(GA(Z`E^~AG85ZK;r
zs>Ixl@zojeUI*f#qrCgE+n)|tOPaT@eP0S6n>R$ip3XwusaKqL?Ay`ewM|{dks;vS
z&&$H#oemFg+GwnQX#ueq%}>U4)i_uwV5}_v5=Fgl31r;u1LY{%2Z!b=;FR3JE{{}#
zm)^3ed=S?SfA@H%9<dq6L-#^GBewZrVdHfpp&zx7GrLwLOx=P-4=1yxbi6RiW7s3Z
zu^+AO-@dPE)PUE8v;q_bTHt}jiNWspXrRBYq~_z9hi6zSGX1a@EC2J!((xss+9Ch*
zwgkuLwVn1F_s>!|f2&ND)vOYw1Nfh7+;0Y!D_Xijg!i^M>hJm^q91G8@ETcuIKt~A
zYzka)MR1%yF=n}`0S?*tJ$b)p7>w6`Yiw(Ef@7oJ`{v$}P_A_~d)+b<yVd&i8bUI#
z;eM5D*V$o+X%ISho7@8tL(es)_&Sj!NS{2Y(T6@h6OP+b5N}lv9H7#!gYUz}zbcq(
zAUrJQ3;A6hvcD{4oHgn|Is2vaU-GJu`{k)WLCuBGeo?6Tc5)T&e%5z9*oxS@161RN
zR4y=G7jXB>Fa>W;y9u~6=Rk>ZuVZ3rGqUd4u;Pj+hdXVr9*H+bK)9RlHZUban3Ril
zqn`)*ba&jW5hwPMO-I?!_NQX(rR<({h7R~Q=)de0IS5uV4(MP~1niZZtshMC;a*V;
zry<V>2n6*_pDb~OMT&;MUu-=7S)9<!ChkAO3^1;jsKb-hSDsOErvf9>cCmrQCfG@?
zy)s}(<kA8nQW>+G(PMr?XU4l8cfC{IDRVdh{M}qVA6!j@MM9W#k@zk-YVXjQbf*z#
znKWxqIt?khX^VYKvB-2LF(rA1*q<kUtmzE_Ml1GMH<Tvz;7ez@rq6ZRV7ts6w;Gs%
z=Htng^*gNKhvSRz6onAf(7CEu5!;KKvPW!{auQKR;-RiF7vb+HzUj&C9EBhGlGHt9
zf-$V^u+h3|H7*TDj9TokMkmXNSz5Pn5UHa#{#;82ukf$}2lje+Vd61V@UR8?wr!~h
z9c)I~pDVIE?9z~dXX7Rt7vUG7bWm@!PQXRh8bP-h!Y6H%W5BhPirg}vvITxLBgwDT
z>EBEbC{RAIoiiPR`1dYn<Ee<falcJcM-Q~1)}`Qv$8OE=x6zK#HGc?=ugk6KxKS{%
z`W9(@TpfP29%$RrX+b^(>W_&g-T3>9)VG_`L~f3uDp8H`Bpl9~OI4Y!gEnhl>f18y
zC|JehU7bzA8#L8)^XC1yKeJbI^NT3Ru{Nlj-&PDFFJqj>W4n?2L*=0ZG!@V)#Tan>
zUoA!`MRA(F?m}9R*~+U26EXhV?t_D>gm?QgF&uuIji#6Pf60w+#pAvwSjNy8xeZt@
zn&vmdzqMB)GyI5#@w1Kc-1+Fi{@|Macqw|*glxZ@)&L@7I@Z<31Wy|o>a_K`6?51p
z1HE|rk?+qT{Tap*{Oz-8WNFw4S=M)d8+xQ*(CZ+Jub0VqxHIU!)TeNy*IgyE9xuaU
z<6*M+R5?=XrQR#w=1QE8p$Uh&`jGCy7F&K&6$;AfMIPtR0MZ_tPwWZpNE%b2G2PLE
z{$i%vhG=W?=lu&)mk57#N!1`<TnQPuxK3RA)>jQW-tNwpy+hdZ@aOA2wHYWCp1xx{
z!41`DVn}*r8`j(jv#IuuhkbJM5B9|qd#{`tZeIM`gcA=1bJs|P__g7C`eUgM)HLD=
z<0YrUx1JJ1-@<k*qUI>t`LG3dK7VkY<5f2Pv`DWXB<9}Ewm4?qSqSRXG0&pDwWGEF
zrFZmYgdbGdhL7n@7Ap8{CY5CN;gzye9-g{>7<q$tDky@4!-c2TUJ>83-&=a~`H}<h
z?9s=m;j%-xU-Q&^?;s-Qu4G!Wp!F2v&He8^J>8CsoWcjhg(ARlA=iNQOg}odsN78_
z_Bi&G>R(OLC;S;1uXrMvDsktiZ@T|VGqIQDtqQ+IKaTQ5XK?dVK;V9#%DaIkR4Gj?
z?4avLy~dk+y*dc4f;NTbJy93ZZ>4%%R3r92mjAlrwX+Z>pP%}KYi$_O;1*Tj;fuN|
zGJV}QDM(q)YM;5(26NQcZkXSz#w-6lq%=F%;)BnrZNk6v@n4X3L!lj!7Y&>)U@k$Z
zkE!~bB$tipg}T!kLqtxKNqOdsr8&`;%+lt|E(J?l?<|RtW<0%U#>;En4k1}#A(IiY
zcy#(1!#jF1=&_3>eWL0{$-f(|0R{tT*?X^Fea#;$7@4$<;@m*V=>D-ab%G1iVN1Ai
z!4*3BO{CW4YmsB{8jZ;5T0l>BkwZfzxWUL5Wp=*}GgtmA)w|aO16(qs1&2zQJ|>${
z^ok6;e=;7HRz%`v_pMACq7Od7F!lUHek2wplIT8;RRMq9!{z0DL-<2|{}Z*j3Ve0d
z+}&3x3wu@Wgr-dsd?5e+;y=74&|Y=pX_!b8yg9YW<~H7fpGl<L-UrV3j=B6+wMrGp
zOpqcSl$s&1=gIdWBzy#xnq%gl1|Z}0w}foHAspamdcypt6Hc#2RC-&7;<1E>V*05m
z@IbJ<?}|<-Sec6Ump2moU2-!ogiVv7xA*932FnPP2=AZBv<XMrKGNBmmtAm62WUSU
z72~He8qK;j3Y>f3?!Ns(Gd%t4V0}-l6=D`ce1^*ipH-Z)z-xLER`icn9HA8B;TXHi
z6t+~j;K*!zDT?qaK8e&~b)~=s6<&v?wlGLo;mEzBYJ#Pl+^Q9HB;=kh6;2=f50?2?
zF9fwm!pOSD&*+pKDCXg8+v!WeXp2)ETw#%Dp-XoqgYf7?Pp;n?xiE}oFX~)nA7{g^
zgI@%D=(500`D~KUqXg7zy%v7&H__LN>uHEnD@3oLh;tWYs*!g1zw5R^iNIh$booX}
zxOO;x>uAUjPB!#B|0dFlNiDO1h9P-qsj8#9x;%{19`Dr*CA6^E_t`7?!~rxfG>DJN
z>BauF#ejhVA_uYKNs^Ig9V*t)|5-lp6g4HiU(ietc@sM?-x=Npe8)lcsK2NkE}QKT
z)%r>7<7}nku=nl2$k!JWb)s|7M2(%xi&TsbPSqcx3<$1!{G*(+Tnq+zH&BslD0tD5
zZpX^&5-?(77ckOo0P+^CgWivBf~VV^O`*i&H1~gUht&fiekk(KscVt&r{ZP+OK=Q!
z|8!lOnXG~DT`{_qPnzLR$lg*7l@_=(>>6$?*aQt$sT`8n1hbQ6pZshIf80mf$|~ab
z4>-^Il9Ax&pXg}Mv9uE&{z=ug4vju|*{Sk_exwOl#0zIDu9gBSYO*h-5qo5+Pq|;1
zAiOZ#iZNVXLvVC6^|?D81)9N3R_b{s9H)9ZJg5>5muG^?%Y=&wZ^2Z4x;z=`Bmd3(
zqpyY8z0@6JyNG_$XC5o<K_br(lu1XIoC~xYx^~@*!@wM$U9N6X2X!>p%-6Hq;Ow05
z>g|wx<YBek#qlB=+z+>2JC$1t$Hsp2!~f=9vA+)e<cfxue&*fA!(BKnz|(=8eMnkJ
z`)PVI5=#`$R15E{M1|RPnMTiIeDPxUK7os+_#lqk=~HV9#+nONz7k4=9hHuIt-fZ0
zmO)73PtgL1&hv|t6UarT(x#oq^vXbGK~T=wu?Pfqo^qACI|z49!(iBA8ysQmE&Jh~
zkI(rsY<)yhP|SK=GM(_9$vZLE94IID9yQ5xdmqoiajF*;9|%9FUZcRd%)5=~rWrD8
zB{ha$C}Lwydx(9IlS-?UKjnDo?;)WfwN_;8b(36J7(kokm0>fULHuP%9T|PP8G{ap
z83+s*psZG7D$RHWZpKtGg-+DNU+{UD?cPnyH*YYi94>_l@f+EecL>jg@KBw}bOH(=
z6yI}Qu>=|&R(3@v1mKMib~-s<{h`w7!?Bv868x|*cGYx&MC|8r?=zuEMZch=FrMAn
zX!zfncWY)f>|S1PSUAv*tLJWNy?oq>)SF62y(?1j-7g7-V#9J^&9)Zhco7DQ?N;0F
z68XCMs-s4ibPMrH@Yjv@v(4Cc{;U4UMWT;T=M<JluEo+9_0LbK3_u&Zu&Uxx3T~Or
z4bIySqDWJ*M_d=N&n!L7vOcZ>Q=oEN$aE6qwiXI{zU?J^9+5?twK5^4IPq7IJF!1u
zCN%G0bv91=^m>~7sQ}HrJ3l{pMBL}5k|`E0wV-BH`GjccgU;V~Re!ziLBro4|3;H5
zVMj7u_4A4{Jjs{#LMg}-ZPm`x?j`y^Z+|Jg=29v{!B;=RCf^R=qn=&OD{_T+S6yB1
zKhbI!y>rV>xU3D0f}fj^A5u{IR)b1%Y%coI-fqdrsYGS5`5>>l1b}I$Df@pEEV9yW
zs=b&Bx7pS<17*8GynXzFpL8ko)b$+jElEYOg+m9fnN*?Vook;`sUtyAzU;Wfu1lZ>
zC%9SFE1)QId^qxZ8r+QHNpABe_MA4{EI9v(jPHZ#7Wa{I@$ABf|HML?iIHF61#O2R
z$Vqiol_c`Aq&d+L7shbRV0t+~*GERzFBRA4?3<u-Xr9@=q5_`NsKQ>tCtYit6+Wz8
zi+}829l8HK1HC%>C3o2n|EEN&2}3Ofs+7!ZVu|^4g48o@8oqR};-sS&l^ew^zeyh?
z>X&qm&0{&|)qpIzy6rq<;b2JR5sr;6RNGy+`X{Iz8%!6Z6^Ok5(q+2X7ygxKYd<^T
z`n4OCUvKMP=N~{56F-OT$9hpOOggG?tOPZ)L!a1o)Z<RtFQ$W<S$L*3co)572HqPP
zAG=<ji6q@EA^IaV$V@S{+^=4W`fQP(P7=P*yR&n2YT-4w`vK42uBHS$CNvlDrKton
zcq(2w$5+8r;1H?!kteo%_2XmZPJu)BXJ1dt5cge5KMnWOGGY(LOqS)hPWWt1BWbDA
z16#&w7bfgV0CUTq?Z_GiO7Fa!&qOJB)|GAi{D+WoKFC4OWfEGIjwmGlYlav~yJMVG
zKg1L}&d(<I1Kqg>*?~*VKyUu8>)?OHK8%X>!ka5SAYOPT&vG9bn&zH(`<B{6V4`@l
z=;KCMeil@##Y}LYb<<6bS2N(SS;LDTA~|TPuMqH;@Z&o54eopSx(G#T|GT4{Tma-R
z-c#q^6MU1$Amv+E2L>Kq_*iwe2Tx?VR-e@&_Ub63ef$q^)HPp;h$MVVlF?_z^2^iU
z^@Oo-o^Ba3O*Y=wl`Mrlj(N7Hl^#Q8DAjyoSOpviphVV|4&cXUqn7Q*$@uRZm&{On
zIZ`dgYK*-rLKl%vox{bEK=W54tb+-0@lp3tgM~~S-xAunM(iuwB^c-TnWh7U&Uu-R
zFto$nNE7t`UI^4ZqY)`YKlL}w{NwG+8K5MY%UYG$kHOBuP)+Q0hWS{t6ozPg`EJp2
zR<{uje%kNSD3*y|Pn~KM-kt^f`LA>4coo30y!oL&Yd!c~<4VSw?@O!@%<pz5_EKy}
zJ&A4_PQ_FH0U!5xw4i=|Zf$pdDc*~|cIMgp0bH*Z^E#1K0bPEYfd(n9;FSB|DHjJD
z(ymyyi0*DcTM~cs=zJy0Zt^I1nU29WnZy1m#J%<=+%xP8dkau4$C?BzxxuY%+ne=V
zd@;{mNilt}1K3-<r*7zxfcx%+Uo=zpm{w%J%}FB&D;R2DXB9Pohq&1N%EETo<{arB
z^orQ8eC{jP*_0~O#!qIOJ|nPa(IL#>BEcP83hnPQ2?U?@fPW_2TLC$#omrc!k?Q-?
zn0p^C!;yV^)W+;PAZ~c*txEPVZhQSydgjOgQf+BIzV2TNC)vBobA0omQRTvs34=*s
zJ1(nq%$tHG9~xp+y{j<NC-<N)Q#y){>PTNlTY~r8HtM3#0E4NXF;6oyQSGPxk3iQt
z{I0p^|C=if4OBTboUI%1CLhbSl>=4q%}HOLo;MTUbIrJsxT>&TRPj(VTL*Y*oxkQ@
z-U4kmzc>coDg$lrt2-#-VK{qCp&>PejOY0|s@YY^FgF!Uq(j<ZuN{lf>(+Xh>M&&I
zn#zEOw=>1YFQh{kww--W?5Q*DO0vGZ)DFxqsrN2@BQ(Uhvz+d3RbU6qp;x_n;C?Fa
z`iX@`%nx{YUT!oDUp`<~9hhhWoAKttYICBFSyz5A)RctPQ4bEMS(iZnks-VOo_w77
zWMcTmtQ9{5zOVHm_O!h^E<3V=@B%VA>lyAO>X?5@Y$@g9Rv2Vj?A>tC0PL>k<PUaa
zW3bP`y^~kd;7JsZ?tfK1sQJ5c-7qQ>HsoyllDLSv$|k_z>t;0c@>{9i`;iJM$3w3_
z%V|TY#68oi*7a~#W1LfMD;9DNe|&Fzp$#|>yUb3$s|7m4ljn>hiTxpTMNxcBJ<t@$
z+I@3V5J$x2s=hA`!I6aDoP)c|z(v>WL>b}#di!KYB2QrttR9$NJ|bHHT#h3Z=BcAF
z?jm{f_*pM>U<lNXB=*KszdP#f^tl~Hkmt#joe}s=q^Zm_q7gVk6BiD9wE<uJ7f(&y
zUOZi*;z4_M6x9nQ;7ejG+!m~g9I$N$F^$++oya`gUAATa$+QU{=d(ROOVfa|`(2i7
z+7d8r!6TK$h~Rq6^&&%RdO*mM^q@Z_8-}|Yk3Ds&M(RKJ?&-{saeLy9*atLi(BgS5
zd&)NpmKyq&<^@YJO40O1r%*AjaBk>i@;BnPwCnr<8#%;2ds)^dU!o7eK#6=v%mJNJ
zQN2#L2|-!0p((E)KfN3rN&1!w`=0!iN)N5V9|M~@)L)B`UggA^VEO}iTv?L#Cb<c`
z_OAPc1`lJxVQt&lznP%+s^-6{aWZT;GCVY6i-XF292Qn1wNS=%a9+kg7x@kDpP3ya
za`#7nWJ`Dw{8N%~b<5ReJmJgs`KTq4|GU51YewIXY2NnoZ(SPU@niX9>1PzUU)ptK
zk7hg04c_mc>raClJQ5D)tXlCf!}f!PZ@M8@=Ie9uGmX%!ov-<u*lV_f*=F>6ayhEs
zYEo|}>K$_2-B(Rl%8+)i$CT(;H6RFbx!-R_`3Da*C1zcqUh6kIGx2^SY4bYM{T96S
zq}TdOQXBkGZwxv;n1Y&nuKl@TSPk9}1al*f_h5LGNd~i6J~mS$iNP!dN3KNQCdCu^
zI<}Hpe!XItXuGlUaj6w8k4w~aooR#Dn@Z>2rI+JKN^8dX;u_dUH8bJ!Abe@9dvl)c
z>c+<(3K*hqXJSa&_JGqR1g~Oglypni3Hm7^V&1W2Nd1xh>#FHEP6c-SjPb3)w->7S
zXvQ>xiuQ;@=a({QGFuAdztx1rJv@h{Nd$LQYMGOHv>CG*s!hc|XClYzpLZ+u$|3uf
zIj#5EHhdHoAXq5hjB}S#g#Hq9uS-0uumvR0dG}9Ec(D<tYJ8tveGmzqKlXp9Am#yG
zOK<Evq-x;NQ}u)^p%mc1vGmpW|0p`|N37pA3@bzmDJ4<LDiNV5(nXSrgj7mega#rz
z`r47b_uhN&aXt3lBcno+(Ll+F>b>8;pda#i?)$#3^E{4I1t7N^MgM;eQs;~Q%+<S<
zP{ecod5%UHcIW77cai@WnyU#6PhNGv-J&OgD=hV3_kPINs-Xpyw6;8Be^P>#lpDP;
zR}MmB^X+>RhzD(N!s5;C_NZH*mMX?bddGzaM7)`L@w|CdMQ}|Ua4c?rGIx;hg1mkX
zajKCXrSX<?wB@;|NKgARLRy#P!JaoK6K~1L&f|yfE)!2##Ln8gJF?-_tjBk$)LJm<
z%-*77M?5`SRPMBp^Dga`W`e7DH@L|x=m^#L!kxbphs1W1IsIqG7i~77Sgsqne|V(`
zVneB@8C6=~ZdFjGi&-8Dof~>uW#5U*M!T+6WtU;9$GX=|)ohTe%VTHK>p{1=zH99H
z0ifNp&^NnL2|jY7wA!rA#5+x;Y~B%s|1Q3`bKp!Z)`k5y7tYlTAMQ4GoD3_2ibF7|
zW=;0cEYqGoBDu&@@LuRbdooV1pxSVFDPGN<SM6BKg4y|rtuKP&@X7r6{NwEutiHkD
ze;~vjr9Hd#w5LhV>#ts>{ZShzJ<$JncEcVdl6I9o;pxFc?fehkn|KpW&N|1)Y7B%L
zr`>$d83SQ8!wf>33b1eIU%|%#EqEg-w}zvxAG;46c~~6#8aLQ~h4nnH#z(_S50;;&
zf_jC#d2V?FK1fyE`*dLlOV9tTw%9@Pq~fLyZw~uln?Z_rT4piIx;{)-5o{v!rY@%V
zGi2W5`oM|teHlLYd6${<IvOH}U!UM&tVK&{rJYu6F}RQZ(}^{b(@HyIAvfS^jG5cR
zMK{j|gYBh<ExS8wQGzq@J53;Bq2l%a){bs`^Wgm{JCzvZuR8zl-<d#IH||q)sjJ6t
zot+#%yUH>2826pe96hMP#%6OXJ07Qd!e5v+7NPQGG24xU6?jftc<h>M9cIaw9clmB
zi#$hMCFr=4(5Uo^XdX#TzY<?a*veRi7n1j>KYLz}iixiRwRYE|>UrOIL#uq$X}+a4
zs}%$XRn2wubZYU)DWUrY0~L6)Cd1b8ZaW5iIXrnSlH3RSDleP(rvrXCJtd^!gwC9o
zLc-Rnkm*!Xq8Pa^b=X+myhQHfikJ5MNL%rM;fqB-WjpGy=ZNFvC6#KtnK?g@DAk84
z<$J<wCql5C<w4)BnG(F8r^%W`^%9+W!OfX)^r^dILc7EU(B&VeTSh<$1TXiT_}CE+
zHB%BYw5+)(cp*FFQd=b&_?jI4eWwk#ZZKHZ5`W5uUe1%na~W{@&eOs_F@>lU{AsT^
z=|fgYWiH0Z4B*Mb$~@h%xghi9!KMY%d=&gQv`p<3hb3<s%|gU#Q2Xdh6NWSOXg8x8
z+$~lNzU(RUMyGVq#^?jTPGJNJ*gS9AQ&fpPGF#W{CI-;E=1#rV%V2Pv-O&HJ7=dSg
zyy@Ca=F~5nilh#Yl74xC+WqmXgLwb7Zi3+FK(fE>-DP5(kE*RYq03a2_^m5I?F=Ky
zo0?E1+787-sdKRCY+x}tXZc45%+tZFgy~SSc`*w7P1*IwMIQ`DYh=P>wSd)2Kw$pM
zRn*oPi!HY`g6^&xuMBmQ@s*>8?2#{d;QHbAPItmZeOh=laA!j?w(Z({R@5zn<QbyR
zUA@qa-?VM=H`F4rRzS@ET3Huv*J+%tC0r6`DzUv6m5J9gzG&ONigJ8h=gK#{zX}gN
zjb7^3?!n*>i$kYlt8jIGt3v;UY^<Rq1?`roLp{~BUp4j=?C)MEzqz#%Ic$u!Os^v<
zYW^G_3@O5G_LH17!u`m9`V_kd-5a!tNZ&=<5QyhzmPPvPD`75r=avT2=Vypnw)dcG
z!s}*RRe}pCxYKVqY~^Y_)+?Ep_+)oM)`dF%C2hh5+QcRDR;d&%n)k+k(oDwxGNd2C
zpHUdOn8(hURuB75f8$$_EJBmRTKU%$Lx6Tv<O*L<1bkzsF4A-v1n+HJ7Vb>scgn{>
z1-81v<LOk6BS)2x=B4`SX|X&|q2N)cPt72`c&H_~r5*G?%N2U;%!2U4^=VBy^{_a7
zV(s#$9#}V2&rfY^goZI{>S?mKVK+~+E9Pwm+3j}EEK`cXp#5wLlUEJA*K4Ml^$&%K
zNr{8B)Lm%$)%ObXp;+7~H=_BeSdZ&bKV&CJ&**#g;-5cX3-PbSe25Rp_vO|`T}m=7
z#Hh)<%N1uMux#d(`m$s_nZrNKw2G|64kbZN2I4>C2#!&TF3ZKA`kxFOHdE04z2C(m
zmLjZrIeYHW(R_3-d~T__RF49NpCzT=+7fS8xmi|j8}9UpY%3$Z_)nU{Qbwr}xbl8h
zwNtqihr*^3HiRRwJz}w;y1xxMJ!T$okLAL8+DXIn9g%o5mv2|s))1U}*OIqboB)5m
zy=ptBL3*o<`m8Ey(O??Bam(MN9W>lS6sswY;5Nc?`I=)jEQu|&eLWd~wo0xg*Y}k`
zXxTQAUuk`y{p#cMh4BDD)yIY3uZBTkU+a~M?0mv^xp?N@FXBTVNHJIY(gmv?-^Cf)
zTtL2*&Xm5k3R|h=&Y5iv0v68C13hnZp*DJIjh(Xxax=F(h98drMhp25Q#S~=_>rqt
z&ZB%(8lKzxX-5rQ|DB_s&fW@%uRMO<3W$e&wo?}ZcKPCQ(XwKT8XpXMvHYwur4*9C
zZ2!c!wHwZ`I0_CYqyp_D9lq$*JPZvWJo&xB_(gX6yhBkw2p0S7{z$wJjthQW`*f3V
zr^MUu3v*?-y*=&hD;-zThx_*`jEeX$S+(1C@9l%?uHW53-a(kd@%Ma6VI{JqylN_1
zNyoX@`vs;4N@4T6QN6z^{jl!hk=XR85I+a*iPxxZK<66OFRgr5Xpzpbra0RG`v!&v
z=9D^M&wh{bo-8L|VzA(l56H)7cYlQb6!gd7*}?E}lB?giBgnABHxD^&+c<^V^6*m7
ztqBck(vP|SG2uVLr%z4!dZvkZG|%)tl2MWLN4?N*MZ^dWbSLY*^P8(7T)tbpS<D2r
z8b&X~ybp%X3fDUMfEs9?yHxN#wiZfWho90ro(she84A^k&cN6fyRBid79B2oZJduR
zf}y)p+nV`_-^%W2>|MgM^3~iQ9D2}^+?!JvJ3kMi^x>MFNAG07!TB7H3#Su7ee+Os
zVQ~~zGMy6q-c*P?dqh`bJt$azH;u~FJP_5t8rt*ibU-Z?w|ql#US~+mFS5-C<Ent_
zY`P~R=s%cpxlo4F{Vit_m~!DVTlmw?)E4CY)1W&_myd&kEVeIi6~YJQ^?O|HrTF68
zOG5*Va5xjd7X9^S0hC2v>+ohJJ~QF}a=W!k!Q)6LPvwtT<lb2j+TL{y-Y|Wdq2p=9
z;n(5U>69tJav;C0q@W*d4<v;oN#&xj%Kn~(iW;nNugOy}B=?oWHis0-o3SD??D=bn
zF<h>`!ag8I`pr&B$KUKh%(%&5NPk!nd>6KKo+tgTMEdfL9b3!r2hXKun!H*(5PimH
zsxl2*lvwh^--f~o_V!b&e3iJ()48FusS*Xg-k@ROtHrc<Ehq6iUYL>4H!8&I2L}`%
zIMup$W7)-mE^e`8l)P_Y;PRO0p58o;p}DG!whDB#%&+qxo+~8MAjh5joqzd!@BjrR
zRxaM0kncp5O!a?v;^J}0<qVB=899#{l_!FKx1r#VYvEje*{D2Do4)@~E4r2y%*Otg
z3lS+%J@Z9{c-NrL_3Yn36o}VmSL<s;2VaV*fHj%hwF_oe9&|$SqB@m$`BW(INsr*1
zDn^f=Z)uDhn~-rXZ?uT)^E*GjVjTTcg_~*}4=L5$0>}3ER@7V++*pwF)cjq6Vikos
zgY12%mU7znseKgstcfh^>sO=Fi||wPE>(C|?2$ifXERc#9UY*O7)6WP+w9l=x&r--
z>r_E@7;NMGvF{gEHL~ben(Bm-p2Cq2)ju2wUxPAe${<Jf|NRm=X-i!or;}|g!KDFN
za=VL{zox;v=6xAXbLIHxckZ3szBgc2K`FUX(E;~2?R{|N2L%*?`O!S_>vWp7#Cr%;
zVA8Ma7bAws;dEIY1KoHY2s>2>oF<+LvrNiy+RPF-Lvu~oJ&yPjJH5h-<pUv)S*5`_
zBn#|?*|Y8tt`mbsuaB2PAl^9PlkFzhg9hBLSMv5fg{JaX4|#Os(P?=!L~>U*N*q)f
zI=zqNh)!rMi|vWVsfu>5``fF*cznY+mdqdWe`X2sc=F<Zzgq4_uE(Q-xaQk)(Pj8i
z@z%#-p%`S%a(AF_?Lt+JgAB)q!tuW|Cy#P(E5v<CEByE7oblSV0=Wx)#?W!=K>C@5
zI*jL@YZhg>4pM(vhHFR<>d;r&g(p|vVq34}suh`EYk~64_7!8aVeH?YVH}D!&Kqr8
zZ`9-Wp%(oUX<?A>=IuSb9D>w)E)BolN4%w1M_Wxj0ztrb?SPa&@!7cL+Zvte#`jrT
zw;m-E-sC{PUtC@T@_kda^%p1ka<9}MnY>M)qpp8RVe%zjrhWg%Z9kbO7XA*SlgY&M
zQN{*jKZX0HG{ZF|TES|8I_dWw(m&6<a)SLA@wT2a33?e>45tg;mu|9Z$IIUQ;yoWo
zAA0SQYW~(56q-0?{$nX0PECa^aqbL&uLVw%L!?I@x^O__*Kjv{5$%v~b_hf2t(P6&
z{j5ZB!`~s1gkNXA`>0%v2l>u9rs*jATj6EurHO#VKzPe*yz|PTJQP1pb3DJG4AJr%
zleADVs%cvt)cxE7xAD;H*W_OKPvQP-{NV%;`gg_J$*GZeT*Jq&1oxu&*4>%L+O^>S
zF~8U8sx8p%n>=<(F%dRQSi5{rlfAuy-_h!AeJIJX<K8BoYA`w)Bv>q%35hYPhc9In
z!E0TuGCiF#Tv_(Kzjn19UEJ3d{yk3tnWSs2)Ki3$Gret})5l!A8yx*-zh^vDicHeA
zUT;E9;m|XIETwpil4tRfBN%e!M22-<^q~BXzG~IbF}(aq@Q=s!DBQWr>d>cSAI>&Q
z%I)7o-j6n4PlYs2)#9<IHoXsfdy(6!sX@f66osp8ck(ji!ffyWHSehsP~?m6-u){9
zFVP>F8Q>wgc}1A@_HD$A(_?e%7M`$Y@aXVZOFi7(9Quyak$iVm7kNT^k|AG;3j;Hf
zu|S~Z#NTb{*!0YIDLP;fwsb2MoT+rh*wx<EXYZas--#IGgQ4WUa%|PQ<WCT?T<;7$
zx-A_fA9pgds6D~>FPF;0ZRe`5Fr{l*AF@G_`G__x`DfUjK*^Mr2!(W8el>$Vc5rrk
zkNF{HUu0DlO@3zBh}ZYJNBYp`!)^AqiwasbaPLb_I75mr2AlIptqULwp3g}$Iz;ZX
z1Ls9OX?s94|C`v!HhUb>+w-rmCl)fUGU*=T2?8eL<J)AV8c}N(^J2P&F9>*a2>-Jo
zzK~m+Y+i4R!MlZxtR{{<5cT{{T~<LltSj%n@p-lyUfh%IJi^%s*IFkbeJmeq$Lj9K
zK5@hO3pRl*%gGQw%69BwUNm^xs@8~{%mZtguioZ_YnGg}^mH?8K5o`h<hr#$cxlXG
z)4`RYSoZ6fD&3Yrh&hrudWkO*UvB?7NVU%ib%VCK6l9g5FO50%vE^87yUtN2@Tm_9
zLiC>bIpxBID=!+a6W&E}-=EiJN3$_W&iq#lCgUxtr@K9h+aW^mzJS7pGwe8di9ftO
z78S<MPco(SV8gTLg-u5iP_^pPH#XI7C{UL_>Ret5^DpR`2FSlBF5BL0d$$PF<{ulG
z4j1A5&JgOhExnMG@n_4NeF^YXw7=-sl!wvW0RoMi;_<`}6Iz4MKG4zzYmWDNKry2>
zkZHFMzR3*Cys}RGT5fau``v0$Nhl|4hHxqpB^DH?nd_iEswaQs4e1|ul^@yqtQ*hm
zJoC}`Q5KG^c!@|m)ncCd&GR9)5g2n)h)cqt6K~s#^xfs}K-ci7)vHmhcypxfK<Po^
zDUaR8d*KP;@GF~7aT<5R`9sUBVK2JDd5!Povs<K3B%y!1ina^B4a77!zoEc?i*i(r
z2g&^S-T-rEUoz0iRBmEiPlgPoAlAR{DnK)%E&fRFRdDB2i-?_SfMMBl#dGV?$lmsQ
zB(%O6xgKX1?j>{eM;B*&WLNW`cIhxz6nzPd&t+d@t;<9$*~QU|Hf3mj%uFy-(F<0q
zfBKv(FU5n}SrWA@B_v<c_YC%BqvAK$1g?N~oZ4g-%RE&BR5sxYwa|gP8pY>5DcNw@
z+$Q#*Y8_lLl4(~Y=dtCKvABnH5^B~6X#~q6dYip@a9X1WE%xf?UyZDT$c>SisTDK0
z!I#{pm7M_l&tE^kTe$(Zv~|gQJg7sDTc3MtAQPf;LL6Ac{Bft;LeqtD3RvB)r+qk>
z2r51G30hCg@%a{y35V}FsBfZqV8A&LjA#C`<>@rRQcA2jBf|(V+C62n(#yh3lMgwm
z+=R!)qt|80P=FuCnp*>iA81@#+BZ21!RNDIW%AuF997Yoq)3#*DDTtz!UjQ@IS`@E
zaEk1y`n(T);~>27E!`5&7)Vdz#QG-MX9Yk%TP-2%UxU_V0q@yZ8t{&=TERc1AUyE5
zeV?*JJ;@vIxZ-~&5o!u<_<PdSBO{l2kyBnN9`TLkys^=ZfBwD9)D<p5mjPz|r#a>D
zn&HWJmunOhs!MB$S0?k@{SJSe#6#dpc8$p)>N-%=Kc6jH)(#KePV@!U`63HTXi2Ta
z0NPKBh^D`dgXhN9^fuSYJnXycd>i36ct3Xf%FdDtALm&q#X>pIt+rL@X=($=^0)1!
zYi-1j-|a5m<1Zok{`b<ckqFrxY1^o7RbbEID>qEe5H7|S_lm3igfpdk=v0480sb01
z*!qP&4Ft9J(;He5u9AFZ)nrK@X#R>jQjtr1#)pr_yf|Hoi}bf7Kl&!Xp5Cmtmn*vP
z^zp0Lrvr*0zLH9xO}YlBFK;TUbo&qQnl_f&ovnnbg$wd@+0_Wb7h2yC{%z9mtVHpF
zml$gF;N^&#FBn`sn4&yd5Bd7rKXe?c10|8cVppYTh~0O2F5*H7UjOJWWaXTIt;>r1
zudkB4%t*sI{m~GhzL%95=tul>FY|>Ma}g8T4E>bLvr+8FFH5PJ0rb?lK5~OE1N~3P
z-aVJl1@}W++JouHJbd~|>SRGFzVG7UKWW{Coo)WV3JwGyi)+o4BJox{Qt6b5VW(i%
zm**oTiUuI5_-XGBw;qyrz0Ske9|jr2Gz(Hj!Kmm^@3GXIj2iP7xq~bRFj0`wzi>Vn
zo8=!uEr%pV@mUUZvLu5Wqn$=zU_6XoFTD{v-GmGu8H2Q_J8=1Q^P}hs47gePZ`33D
zP`pD`@KZ}F1s<&BC-<|I;aA(OjPvuAs8y6mzo=G><~zj3=*T`m(0aV8fN-}?_>DaO
z^|1$+q^d6}yA%G|+k4`c^hIc8I67b?Qi#nL_`k$dXTqby4YzNv<l?vKjH<ES9r*j!
zxO8kt4K`a<>ejw%#QAo~?TG`Sn7kn_5nP&&zxB6Bt!yFOD{*7l?rv+`C3euod$bQ#
zRDTcNTgt}q${b&nBo`QZsZ?a9UWP9_pIBFoG+>NC^hkj?xd)zhPJHt`8d&p>^ZmR<
zxcbdvm-SqeKyK}6X{32G#!VZ%^J*VP&MsrIwUPuBzQU)tOSlw|+hCiKq6LJDB&YO+
z7T{MJUCJBcXOj<EOub4&c=yW>!yRZd@VBvueF04|Sd7rlr8MS(nx(U@8+#!hpAcz1
zb({1_HtBvpnbw3yyIW`q1H+K7vhvs5B;nTao!gRryc=iti5*(ft;YnW5#AVqIHY~J
zC27|Nf&iZ{_ojeAl-Ke7DM|=p`&FnI!e<)M6z^@&Q|CdwL!?HLYb-3v_vblC_o7V9
zEN>5e8-||M@S@GI20MYd^Hz#sc+~sTXvlRjEKb?rFAB@WkP*jVRl8;k%G(>LMsgX}
z`%V*c3Gpb%f4pqWl8%CHW7nGq58@>CbJ|CgYLN6A)uSU^(U6k|^v;j^LhJSqYEdgy
z@OZLQJ8iKWgf@FNHK@gdUS)!;%Q=$!a^7jYH#QSxX3B>RxNBkPzr$JyRf%|#{hsOE
z#bVf3xc>G<c_EDUnC+2kH^r*O{D10$O~A`U?UVMz0gUT(&n1%gT;a;Ew1NT(_-*oX
zWu$LHTBC|T`Nhsq%XZzTlD-r=)C5$#$$oV@r^4w3$->zh9kfYeK2<#!eQ=a!C0%
zd(Ldm7d$0+-05lR39mXNj&@rTuzlIQ>jTLNa|iT1l@D)&PSH~SiP=o(xi7L?59`6t
z<-Z4-VL`xiWNwq2ZyGr-dL=w^a^R53Z;93S(dd%SI3MXoJdQS#nOt#QXfqi1fQ#f%
z52u~54VvtMT@D+e=hV~DWqDgcr9>GTPW+Ofc}pSu8Oz(;y46s~Juww2+X$JBH#B^x
zN}*-bS9*gde!$z@Vpe{=2j`E8#6C3kh1ol*OR*;X;H2v`cz|$LxmS)SKNxEP-#5aB
zqc5AFb2Ndz@>&t>@M7GY#$1Pn#d&m$)h%Q`m$J;&+5kS~8<j@$6gb;koES>xF50UC
znY>R)?kaXhB&;D3FK=kv2>n+CdzoeICdj+Y?oxXCmQ8t(_Sewj>~u1E(4GIfVCo2&
zH3yvEm6HAax8}yt#46x9IN4^Am<;o+0a@>lv?F7_x9iiPQhe3a6L)E~90Lrq`84Ni
zq1uz~vUI#Z{^$?P%KAmPsg?RQ`M+B6&G*$Sy8Vf8_cre=wOtW>vhO&$Qc(+&eNXg%
zCsT0C2LY9qyQ$Fo_VCp!^5sxg8S7y8xe5olA56VoYKB5dt?W?hR?^dMv&d>HgySH#
zD>5V%oMP_AM>QoNH%lW!iEs|wa%r*%@JaxVg?a-sg%*&k{vwfFPQlJ26I2b%IUo}x
zn57q21?-mUzeE=5Kp{Ch?6CY>@c0|>Z|h%M)TK!dt!^&CsD2lzj^r|+TG<!SBi#rC
zU-EgI`iDUFvQb7IdB@uNxla`8l;aCl))Cj`KK#&h!l2451mjnJGl!7#(LQ^vKzwH#
zPO%3GB(0U>?M+roV=5C^b0zny{deNu@LPKjDj$S@pE>pI9O%Fw`D&C-uS77+?=OBQ
z-i^!Z?5W523BSy{gHC)R0B-CGGaG#pj6dr~-Bz^9z;#GPol2$@uIdzh+MSUPDu(le
zMJ+zqs5q-!CQNvCGrD(v+7!S@m#<tP8}TQ|ir>*-&nKRU{pp5=NjMtIpU9P2f>(9a
zueo#PqWm|$6GwvyC%f_YfK_}c3|QxW2-!`1*OR-8@0wMTJY4X!Ht}ohrV4tHO!i-u
z<<ht5Ddhe1-@)~9!B~9PX@5!gT@JqWdOF}3Rt=$ghT1RAk)Esi`|fo5La;Mv{_^Fs
zIs9w&4LT=Ge3yA1<3F=FU^nxJ9bE6r;Jqm4xxu_fxTOyQxqtNWeQV||uA3AbKc83b
z9aM|n22Hg~UqT_sV(a@d=@8g<^U0!zyBV-3PPUpjv;x!0Tdr^Mt>ky1dE`>hDA>kj
z@@tCsK#S){Yj*b=SUvIU#==ZGJiE9xLhw7`1u-RS@)AzJ+GE+`hu@lDM|$7dP+u&B
zDH(rPY$(IV=_B?2Y0aq1cr978ISRymC3rR08-Tm+vfZnK3ed1Ec&O1%{17tFj+@N+
zp!?tIVwdVRu+Xj;s$OdX$xlr>zR%)u7rXS?p^+$5PJZVWC|U%vyTPtgB^Sy@Y-Y5X
zdq8J_O{0yo7P9`bd55H>!sc_#GPVj4n5!U8uR1~Y*$Y-(nS2HKQpfp2(Zd$P<3G(6
znotd5%9|P;J@bGHl~`)JYk<y#>Qx$5A>RA-`nqRFHAtulWJhs5$7@^0<X))PAgAwT
zZz`J-=(H)B^*bF0InJ#z%k8zGywX-W!5W7@&KeX5(Pv^@>GgA}llhRWnXvWa1B6GC
zoV&+~Cs^D%-ek1C9;&UyKba0$qg&Kj6@#c4@M&Dp59;j(C#w$kA2~(X@u=YZq2I)}
z^&v#=GTBeL7W~Lnnri?z)6ZMiL!0sG5%*N__&i9Q6AxKbk3|6mFK6G~J@}AQ_V*zN
zFUbDdS@q~}KQ8D~HoZtLKp_)7i%Sj^l-jQON>$Ah`~#z?FPv;f`N51wnS$*o^f)D0
zmai7N9#l6_{?=oQvvT4V9^!G6<IB%is>h%2?;JB#O$HNBgLfhx*&yw{8dlODi5sd>
z<LR-auhuMG7^YbcJIvVB?@$_$C#KEd%Q4a)+jRJzEMqdfvVOnAE<z8bzn*z}f%GCR
zzD|~#ghV5si^=h<$0=yF^VbHid>S0sk@KuxAQWrXo$q%a>q3KTj_(tVh_{xpz9C1i
z4*vCio|Pr{qB;@zZ5{hMkyY-Ky(8Ht#mg((i89n96ug%FJ`zvf(CbH=h!;28nLeIM
zz8*c?IM*-7m!ou&-ih2H;;&^z-tsSoBvvB6YI&#$r;YDk38_oNk3ULAHom8#`Q#Pf
zM>M^dZl*&0_gWI>Y5E(bDd#|pT&I^Zx&JfkuoiT9kbC3f^JlqLtDse*?^%sX11fV1
z9u{kGfw3R28pThgVWC*Zt)pwi8-1@uKh3rQR4l7y{7<za-C$tWQ*!<WRv2x1a6AX&
zPmFK*Ig&xVSf<GXq7;axT9OpZ&%=MR%azR%-Qe(Fz`qABftZnBemju17+joIFHl~m
zLE01cxN)*~R%Hz^4I}T>m*Uq7l<JB>G(NdXbEF>Sb~)Y)AI=5&=R-Ab2={rU!LDXI
z>9ZMzCDK?wD+8_e0Ged#9;|-E^mgrd4+QXq1r_(zpg@7*110TD7&LA6-oa7;&tzJD
ztR)l9g~qEio`P=T7v0RUEs5mUG$xWcr_vyE>#NXzim}LYU<=jto+fPmx!l5*S_5Wv
z;$OyuTZp$}VH>NkJ5UV;8S%6w;h?qXNQGuM-aIa{KBUzOH1^TQ8<HEan2A}%ZlnqZ
zav2<^*UP|8MJqbTqXuII-mCL_We{$HYu(z9R*Xv*pfWLU0KLy=u$_bS{14M5<V~d`
zs_hSmoqPk#l5Uk<1)Y$sBdr_ztOe8#N;!W#-ve5k?XfU31h`zjblvYGoFRi9{UL;#
z=l74vZoZ@iawR!dDmV!*!`FSk6=Me|34fmb)|UtBMJ&&Qv)_VuXy=%974bWZez~bb
z-nqvYTlBKpQb2CY@<8in(odV&c||C;3XqP=);F;dt{bTfT^lZh#&=P1orIHrW-3p2
zl;r&Q-`@%#SY^`7p5~-9wZlx3$ek`*!e>9y8T{>Z7XGf^D#v&t4v(EaKW!tNjc+dd
zdmPB>fgM!zeirK4=<j%bzyIH0aLUe>nc_)+COUiQ759LSkL{64q6t7Lr|tM1ZU_&3
zT!-zHOo92L=|{U3UoiWX`jVF9g>-yeHl=SXLFL<apORavF_Wi-U$ZY01P_#z?K<%i
zcZu&S9rWFS3LTsw0ckE6-Lj^B+#&#&zY9(2zN0|eqwfqEq_-=mZgp$PvI|;<wzZ#i
zeTyH~ZNA>W7>m}C&%Ist*Ptp<VCT}c;m-%{4Fx?7*nRczWjfMRrF59zsIn?UF1fLh
zNUZ`iJIxj-%v+C4lM>|-SISY)hw;Mr*ATq^?H2D7wQ>;GuN)t-Ovi0J7xwyn%_3d}
z^>>lHgx7Z@BjG}d2`m}j+229->jP_3#Xi#oP$TBZub@}~$ndZKkZwLu9ndVB`xA#p
z9!wpaei{Hx(>1?d6CP&lrd7_I*aUpY+$40+D+d&75_c)lx8nDj_Cu9sVK7fuQXEb4
zZv~x28pHPzfGOGCGxlE=j!XS#P4}o3S31l(e_G_D<E8gCR}DIFt75C4+m===4LZ;q
zZeD{&t70y0;qAnYA2-b>R7syze2=Iq;cqOqbh>E=cHxa<9|mgjgGsJML9>w3f_4na
zrp5B<c+Xw>hw1ZfEPA*1OcBY~=zH+pWHgC~fQkb*86}!fh!A=^4rLLqtM*k9?k+T*
ze9|n<_Xv31uYdXPm>>GKM>se%lHa}4PUm*ZW`c8Ph4yBB;$>0f`}=B37j`|l_x(fX
z0J7{<VSJ?8h+KCTyu_jd;Dh+xO~$#t$Zgp9N{}xM_&zsVh(t7F={h^_AESCqHlm(a
zypjx_xi;2qELq6WCNAYGNcPb*|JDo-wd2v>(l(-tr5Mb3ImD67vsML@dLAZ*z<rsJ
zYocTi#xNW|55~QyTp?s9e4>WT7dxo!Ez2-~wQ&N*ia_zgllP+6^Px6v$ly<U3d(Ak
z9=l^#gm1lHQ@4%<0{=DzpPiq|!GWbi+n(gtqF)cK_4N~vBe!JO-&dKKEPCZr%4$Az
zs*D=HC0scLN1wxzWZx@zp4!iuy9)y7@0e8CkbG7VPf-IoN4mDpBpypC1dBns?LrK-
z#BZGx9zITdv7cjFo)NFd(IWGKRw=?a6y48m8btOB47(#L!n>fgXjlJI!)p|8+GJvX
zvjUIjy8M&7Ru7z|i3X{84N$Gbc&lz}C2SVu?|HW`1ud#RTy+fTgbe*R{H#W9upZah
zroq;LtRDJ7G%Sg@{y6U&BV7@gaXb90B)k%NpV{2s2E==sbM3{wE6rFE;yYLCmW+Wd
z+%deTE1>IBT_elu9++fU}B1DCxWZwzY`<FETapI<N_9M_VL-Jc9gVR19zaCCLU
zC-peT(6|KX-GaB>wTn=c`^H?>cMo87YUZfgp9cq+$M@K(bwQj|=!1kp!V_W|Vb$pE
zz{sMRv4dn@#`a^aZAFmyQyZ4nHs`j$@-CY4fCI_+)A7MnW?U)CC;Q#i-fe+q`}V4+
z*pd9N&B3SA)ZNf^^V+SK4JPRB*%9o$(u(%xUYd5)uhEIBB3#9w26EzfS8s*&fsTwZ
z|IPk9Oy9Ma#h%RZw7wiUpQKfe4xvWf?R<on^@_VL{zM!2%TcEOVm1Wm^V$DstHoBi
zIadb1V&wcO^Sf;-0~jZ4@0ndDdk9X+Q~OCC@3V-&LQZ1})`m*nemqfxUn-^2*XKK6
zvZOS#W03;-$wzh5N$zW$UC1f7Cjo4C#cyyI6Q0=Ic)IUik{e&Tt9RV+Exh@o^3GP3
z@RZ+qRvq0=es8U)@~KH~K=v8sZM|AK7I>%KzuVLYx$TZaj8xJ1ob~!Eqvu6<&{|uq
zn(zW<OG{O9Ci3t@;lGPhvhC0_K9$>UoDNB0+HAt&xi~Fwyz2sakFKA3CnfCk60QoJ
zahNP?0Diq#A=w-G=ph!A@B1VKHm<ZevXMDJ?Y+C>CaO(<9v4eq+-`vr44VS!)EcnF
z$})p;)&qX8&E~yhEd}4*aR~x6<a{k)`m?3C2`rg*Q2t{|$M>@zSu-wI;TelU>vZ}&
z80a@jRGrO(s+gPWDP+IK>Xe(T@+%a(7zM)*<)xs_o;O10&K7~?)3oK&+^LW}$lcR#
znFtm&Uei9bMfl{Dl=`<A!nb3vvc3G-7vjz)F_@~Apn9q)lMb0<`b4r@4;_Du|LMNS
z3ym4T_l@_YXm@9#Q_juRV!2#oM|rb}lw8>Kz4gJ|y%tOn&iE61wGo}NoSv;am;mkW
zn)i1^C|GRw-SUldK6=&v^WLZ3gkk#{UwYq20wFH<2JIi2_^bHHY{#cJpkx)j{w;b0
zb2$#aTzu&Q&&+wpS;CU>rKih|x{y-*SpMp2H*YTKw@-Q)$(EvlOdHoycr2!MrOX`P
zUyJ&NmT$h_OM)l55<|X8m7vD%MP|OasQKu(uxor0{3pfoPLn$qjQBZkPKOqw+@!?k
zwZA1`-fQzEm$44b6FM@|UY9|}dF>C(E3WvPtE%~zX8~r(ZI*ZuL;R!usRs8+Z}y<y
zan0P!I@DrLv)(;cfgw?5JL&WZ$3aA}LsK#z#XUHtoNlJ$H_F5p(b#rqOI%@NJDUs|
zvNMvpcJ1)0=+kYEH|NkO>_TwnOd)P6@EALJi44jMiaA$G8zFej>e>p{fzNVL$)}y9
zofkuAF4f$J<6p#U+^Tc2ctYLP>tP*K{>;$hzd8(ExiqieRhdHOOjeha!XT{uw?B6N
z<OoCz6yOP?a#)#frk(m)1kE~Eww1J)quy%hGJO#R^f<&cPHwA*9Xj%3$qu#PJ?y*H
zn7RoTZ?5uG_~*lqr|Ua<_GaPIjIH!h1#+I7=zij~4}rzY*DH?`?%^|ShPL#J5$H3<
zBN7`+{HV+VY)|P3kC6SmxY46%;N7g1J2;ty$2C093;Oxu+8q_0tyf|}rrI)kq=)po
zDZ(|t6^IczZM5=V+8|m}xx;_44tUB=l+7sjz?lmaLGcL+-U~~R2q!tr2YInBJU(gQ
zSaXo=cU(L0w6ps?EKdWeCyU0;5uLcM=rrp8un|wFJ*;LtRt)O2zxNdlW`Tp_W-G({
zVaSp*7Nz^36rLWkrF>i_94F8H97R>}U}ne@BV`tb>EpjDRvr-lb>l{+Y;Y-3DSW+@
zvabxRUY%Y&(^G}*xo=k_6IvlCOoG9a*%EI*?WBI*SAZWk*tQ7!lD=c4z*3idEP!pR
z)oqS;SiGo`dcU~~ylfniJ-7j~_R=4w)2M|L9{o-$Tb!Wz@E;Ev);f6bwSedPSTDTZ
zu#DWp-v@s@^^=Ud(%_J_-bK~ea4dQ@#UW1S`=Tv}Ht(Dx+@6lFn?L3dzd@OE9v2Up
zGv1m@=qiYZ8P(Z#?TkvWv2H9gJlY0MFPf@9obCksv#cg;`UR+#FO)Abl8RfcpQQE=
z*F(I?Q+{~dN_clW%zl1P0)xu0J*5StXJwuw@MNYA+|#6!>n0n(y^=A7qoEd_AD8}I
z;*o>GPa|UnDW$;TT+YmuSqHDuoD5Dr>;k$%@t>}n8eslQ`@^pNK~VpavaA`I1b+?6
zZ26y-!D$iSq~NJWNZx3_dbXb2^Q;coiS^XtGwI^2glQv8Z@u=X!kqY-%-{4e9Vq~t
zASFXj{yZqsDeG`O+<=?(+<GM?$@gQoy~>I$0bT#F?zuqrDle9R+C8rd+BjMqD?38K
z^o>Z{KpWvBUC5f)Q<#YFE#f<v6~f_?=<{nBmI!pQaUm@G2f(zbYo`Ipqgxv#Yi}Xn
z-;QmQeq+9bD{H4XLLFKMD6B7P%ajl1o_Bhsd@EqlA^G{k{u+ExV)=IMT>+>_Tkk#T
zIt&>S^7LJ=s?b&VRc@IH$(f(tBlqi5CvIW;O}A=T3y+l*I9+6Vq22#wxvO9UeEsHS
z5u{LuFOQ3ExppE4k6ZJe5qBv8rNolVjpb4hUEI-Lr9g5E*Vdf&8kM2`1=WGJ=c%|;
ztoG@{$13=M;<Hq<KOR-GWvOd#w1e;PkLu6Q{6K84`bl&S#BswjGr(UBG$V}8%#Hzg
zUO!NsKdBxPAB<ieew>3YS6E+9XJ+EnjLh=uI~u_BSDq=4ZVvddF21}=Mdm4@Uo>>x
zo3P`#_+y!qB%jtZ^n`)k57IB+=6x!ajuc~0N_AZb&i}o!P$g6WMtquFVTO)iC^TI+
zzr!2eMA0t)UaY{Y0k-DyGVS;?qA%?L;rZpMeST<jF96tR*lvsFB$In`_v!J0Hjw%8
zxa+li0a{wjmfewTK=Df-{hp~7g48vs)$x=#OqJL#-lj@8!tg}BA~6WhzMJBR`&x@0
z>|tA8CAs0O!kLrZS@v*hPhF+a**18ZE8EMz5eHVLN{&K)X&8L$;lh^*l5;y9UM|{E
zh_7dKnr5EG!`XOcfo)@c5YGCZ-gzn$me-Y?G_6Ciow-AoS%A!ucAgD-)ky*QYRk*~
zze)dpe@o>l{z?=J*>lVzzXoZGHyv^~*99k(Mx!SZN??ifwS8<-;Zplep;|xE8`Z9r
zGl}d%;lL@^Qt1z1PZ731IMEC<FBSjWo=S2ne{1v(?JmbVuF0c6Vmk0tJ8y$bPb(DX
z1pEH0%Y)-Q{=3x`@^PLma;1H97e@1Re)$*DfCa@(^r8PX!>i!7bGa)yXsuDMoHC_~
zgC%-2GfnL{$Xe29=Tw9KPOs%fE{B5SQKksJBVI7(n)IEDhrDxFG`+t0mmphefvC<{
z9=<f3NTiJ^ft5Y(TY|%huaU1tYPU`qh)Ar_Jo(WM`z06KPA&I<km`=6O2#0l5;$R@
z`Cl^zQXSDeoZkc+0e(I&Gl^GsMC{P;i#FJQXEuxR;9FpHH`@F%I}OGk@0ESV??rfk
zVw-N{WC5STc4uK(3TPDDe;=dog(;<dPX-Ua!t4P4PkCGF!Ld1dO+z~oocFJ^SvNj{
z;EtyaueZcOub#_a$pi{~`O*_5aN`AbUdbEwAv{rA-`Oj7qg!EdP}Y)tHWqEqWW0D|
znFV>99zMJGneg^Hj1K=%Ci$-;+SfIjvLW4?qggOF3UqQMeg!Gz!rq)3=QB&WSeebi
zGHZ@-_LI9!r>q|q^qf1x<<o<qM`pSF99@8G_EK@$HG6c_>EEkC`iRdhLe-k93*m;(
z3np`U!Y`PJ{jBp$4fY?wCpnH;Fz<JSlTN(@y#Kx5acz|Noa)77-Rc}*#C7Gu@|85y
zyz|7Uk~RlCnlt-+xx<iYH|zJ-?M<-nZ8Gh*Pfh51Ai@0ig>)DbjmgsP^MXfB3x#Jm
zb3wM?N~57r1~jHK7tkIW!1}E8bO-SoH2m`GhM2ZFaDUtW^qYY-O7-;JxHDb_Nf94B
z?B)qa#-u5bnY=rWEw{W}+);$LIO!JOh*Y4^(L0A}w46ZLG4<2>L;?g_-RpY$y&QOz
zj7_CN{jlnJuIt_E9QdQcWBJR>9j#i#F05JgK|*?d&8D(Kuz6J#S2I-z<(Ii?cLq0-
zK8rm^{!kTO+-|73Fq4PX`G?MZ8zsC{<D44_fz2ph(40MQMz|ht8(C`YLNKwXb0cb7
zKemU?w9q=#!Aahdtv916cs)n-5bK^S>^nBvY@HblErXJkPXu%E%KdDg!x>p<_O9){
zP<RbaDp+5VzncaYkgGj;tpsYnkGznSB%CFNX2E+;O3}4$8~Y7PJ$Tm~o4a|l3mG;y
zOz0o0hcjtwJ0emB@Z$WXe^GzKF(*J$U^_;F^UZ)=AF&=4WPWA!O}4`0SM$1$mE*Be
zDcS7$u}b_7gY}f}*`O%j6Jl<b0726lDn1ANuuwH;$J+~Os1W4w!}C%jM(Gdv4lw&5
zgIZZ(L}oqyRhIQg-c^7Vqqb&RAMauAlI-24ja--uoOLX84<UV*)r0#iNG@0F{`5!5
z8oV=e=<w&pc1Yj3NcZY!9iCe#P_AAc!XDQSQ6A=6q<RvUEg}#CK}$-TObW?6beHQ&
zym2{f*%kg#kgAuwla<nIRuGa^Ur+2I{Q!FTPny-jl{mP!pX!=YIXq7MYTY(ni<;8>
z!Hk>y;IqO~@vR3XsBqTcwmb2fB`L>tI42O#qUFPHl}DeWt?*y2II`E%YcscQdY}jY
zDJG6Key$<D-#bG`T`5rfSX|JJyA*C_54%VY=A*gSSn$0~b--%h&*Ao!^agVK&z62F
z!6M#m%8bipSjnu{>h9K$?uS(zPP>=GJ<gW!XbQ>oMevO6b|}OI#&6p~t2(fiWpDP4
zOOMbu;?1qY>J)6!Ik0W2v=~{OkKIvFD}>C?PmZ$9*1(+kv&ws{6*xUhJ8}DgBho((
zrrk{Tw%#wgu5(4dM%rwaSfl<lY-Nx%&HP@6@3}H_jF@fF|N6+Yq1P>ND)rwdN8WZI
z?}9czt~i+cy=SXCYbuyi!gZX-axgh@Xy!dF@qKeFoZKly_#X>D47u-Cf%xjUyT#oG
z2!F|?(<@hrmn!{z1zkGufYEnXu5mK|&!I^<e<B_>XJ4MO@N5UV>jU@KbITD|WY?;q
z8?eXNqU>p21)SN!bx!6@8wQ^{QY~9U{NXYE){zD6X#Ohvu6KJjWII18pKPzhC7&H?
z&lQK!C#BNSeTjnc*KdDUOliOl{_`bN%5PxTo7;2C_1V}AUYA$5^kUk2kd?^U5@7a8
z&*Uwwft~71FBFP=VW;t3bM2f4au12kxb4tHd|_u4$9UYZ`t=jrw|)h9W<F`ZL9iuy
z?T~WN|IvhQ+$pyXyX293aSY?ZEBUw}6L$UW$3iUP+2(rlcpgk_T773cT@GSOO2?RL
zlTcjg&?9#-7x+v!e^l^qKl)xC)O0`C3{}2<CuS@Pq44-a&X&9o^cMa2OEfYT6THt}
zsI}<9DgNWUr!TjGqrsSRq(MDMR4S{{jD#WAm~@x4UIRK*$vjR`X~gN}M<uKiud(rN
zUx(r*@_q>#73cJ_LZ+OW?YI1EQJp=UxwSqO>vudY+%!;!nbZQoc_t|!!k*5z{GtdJ
zR^J)clJBfIoZ5q>xeCS9<)y<W>(E-d>vr2(62>=V3}JT~T=c$_AY9afSx1V6J-u78
zLVyXw{Bw|xedVY{ST{`P4piqCXM=Y6{y6!qB>!BqyM`l`_%0-PPKBAZpv(4QpK;no
z2<h5XIJY|$%H;2y_uN;AQuKP<R*xF+6*WDVmR=zQ&aX26EGM2h=EjY<pE>CL$)<D{
zE#b?iYq_V!Qc!(q`L!+i`|>}PRBL^&0&cfGV3_pG#Al1@s(po|4<s?+Cqvhb98>{U
zPgzpHjpM>p3N+%9r;Cib6$KwlIk|eBrJz|tyl>cIGq@yBXui{gVM7EHwcmci{d&v3
z@BXDa>=M+EsvWb!sZ0u`j`U?c+`@pnnkbF~==VAcRUuE*{uAAT^?06DCqDm<15{sr
z7nQV<1<&OkwyqWyqV?xzd)mLYqT%N2W+KOnuyS)d`>+i;*UgvY&NSD;Z{BsLhDUjL
zI_dXbwVVMQrfGP2pY+-SLzy>ExKzXPtMVGf#S#>GD`qG5D<0=Dvnf=!8<m#s=X1KJ
z;VC`N<0M81`|EyZAKBUn8U?Dx3&&FM<F4lH>&hgDYq(^psF;R$_DzW@na7>z$)T}i
zECtRZ9$$8)b|G7~!LJUNG}PO*<7^4pd)|`W$5ta#3Et_+^SVnRz!x(f@|>v$N7;Uq
znUK87og}pf%E?72I5GM4mRJ*d1(rI<p7RG6X}jeT={oF~4r7l9@5a~oDrcJX9gckG
z`hCnT6CLunS2xs4(0qhCfci}!>OJCK^S)XD_s)J3af@!kHhtz(@x;Tj@rK1ELZKMF
z6n*uqs>wW0)GH-4KO4VAzMC02-+)7(PO;Ya62E-r<d3dl3aGsm4fTG3V90RN?(u2j
zO{kyGD~hbg3I{q*>-lp0V$&3#eK{JqmfuJ8<>q62GuvwRtuWMyI$oW#?><CpR7l=G
z+6?zNeD)uv?!<R8DTyU~2^e<7rKzgq4f5u=+%{!S!`(ldp3bY1yk_cw{r$J=@wxoc
z6u)*7YWI}0vtNh>IR%fK+(JDlm}>a%(he=W^xX8@0f~0Z-Ss<o*Y--JpY)&FdbS?M
zMJ8%Git13mLNZ8J$OdaTT_V12kY3qcx7U+(wGd^hLwj~D6`go)OZ%6Gp$VN6#o;#L
zA%;}E;5gWYl9wIryS|crDAaq1pX)}WpkK5+7u&!uIA1kTtqNti8x9-^ZNe9h<yRc|
zV}aN0B-=dUGmG!=_etZ=MNy8|H@+#vfW+{Jo1Jy(_&_-H?v~#@$Rtet)K<+8j%us_
zb<m0dmJkm2VZx7Q|7PUylC2qZKJ9bzS0J3CA%}Htxl+(KdT6udSS^M`w{Fj9%fYil
z<{`z=8Mx#7fzMlJ+Q>VxN2|T75lh*6jWR{!vHwNcOm}xFu35P4vdJGnp<{3FU(;^|
zudz%mKBo>0UpePIV^o40`yS85{71OH=jJq8cXT6jrI5`n?jn5g-M^=IPYyi!EN5$S
zp&WOIo<C&ZS_8@IwZ7j;kJ+8OJI__BA4iYKDcoo(fRjB;&z49}%XXC|fg)4@A4R@+
zD>bM>x8>ulEW3L#*S7VEE9vDnb`0#JNzaF=`aq8#jkUNvr(#Ev8}WHORx#7MeII4!
z7d1O%0x&;J@G;+<C*%qEKXUxkkCE#^+ujill-YyJ6RHY9uvMmu`ekq-^oBEU5s4)8
zfGhmxsckLc8|D1FY4Q%56%*mMtagN;_|&^gr*m-JYC%DDt^gA?3}5~nOvXLOjO<f<
zQZevt+417$3RLp>G?}_aauun2FBz*u0;ND-xc6%j#uUaK(_(MHv?Z1_2i91;H+ANM
z<A1ftttT<wM||M7?=GI<&GCT0AKwJ%F|@!TsvFlYjkMsJA>TPu!ddLrF%+nZ$;WLz
zC*wwP>`<abw{Ft{@ef%=jXM!PTw~|5MNk;ok8kXkx;#kyW?xDrSHnGVl<^jaLoo%`
zgQxFD-5JD1fmbuzED0~*tkF4n($`{f?zC7pDFu1~nG-jqJt1R>TELLE8Eap+srh!N
z;bqp>PZGD5<KoSyldJR;jN|$6BATTbWOik)2@W9oWqvuZA(aR}qqpy=5=;Yc!~VRt
z0pvVNmCI-+b4s>wC$of71g7Ob&zL@vyiV@9r7_bsw7ab?f!>`cX+3;y+^7N^5=WD!
zLz?06E!C3th${Gg(zE;ArvglPaiaY;cLkOzMd>UUML|syM-KJ9X85*t%+aBP%*)N(
z*l0Z4F@;w9)ySo86f1qRyzPAkh!pNidP_I~N_G54*;IPr)3K1p47Zx#yz;&+yZ(EH
zm9>vwUwc;zE~8)AXYZuJ{c{&fmXqs%soPRCn&f!5xfa%{kvZCr&290Ymr{r~bvFB;
zST*pc$y6`Mb;7^RF+X*(>mcDiPcq|~I`|^lRkr@S7V2;PIZIy_i|*ifzt*4!8ruYp
ztjoQ@l!@B0|00SY!l2h<h1?&gn;xs`Kh1^Ycg+F+k=|nNX33p9bLwG3`sp1SVn7i;
zdhfq`H+vy6#3QF^!v=3CCDT0CYk-Yse^2Z<91nia#bu8MRf37p(-~RD9`MR%tO=;_
zhYvSe3XSu-!Q`f#_0K&4P_!aqYEFDIAFd{^UHMo6!`aIbqyHu1+ly;%rTlfE^DWye
zg8x4hb(IvfA^s|<82@c4H>>cJUyZVfIhms?G8#WQSqHx}dz=oQE5$0-UwgBoW6?BX
z)v0}<7!NQC?y7$n0&%9Df6b1RLj86oCHWH+Y}LLowC!0DYJ?0b){*|Ud5q7+=LL@V
z=BNT~Ww$?SS#iCD1ut0Q9W7mMO~cRa6>J+<Ye6T5s&tjB8pji5ujCSc&Dlzok;B;)
zaQw2cXo*1xCVW<j$%rT1{6WquX6(d&7_i>oMZ8*3;{TW`)*mDF>Wrl+Lm3X+%ck(f
zw1JtF_uSEV!hep@+jUauJwD#dG3g<wh-N>eXa)D$6L0vvU<=}l`k(~5=Qi?zBfBZy
znwt2}{dp_+q$=RKv-^hEy96jZeaH9MTs6F3)9tQ)lufvwSEyV6wc=86D$NhEX3&*R
zKHei(1h+;`xb^V{Kt$Y~_toVk!1_ovLy&C%?V@Og1|k!{zW?i6OTT)E%+4+pX0pP_
zkKy^J&pg6Eu3NJT;=53YS7HCPrerwaE$R+$o`JpTnAql>evm0cp{gP|)vkm4`-I4z
zw}4_Wm`>(sRzAD#v0h3Cr)wV^P3G$euQ(yj`IJA*6(f&$K@0w?O5@Z2ej9@xYEYc^
z5RQ51)M>4?OyK*rOKiEX8|(H)y*fmnj>kA&{5~L?PI%HM+(g3zai*?k=V#A0lyj}0
zw7Ns)ZEmaSuPqT>`?Km3`Qzcug|l)#46X2whGTkGKM>s>|MU#I)P+&qTvemxO<4Ua
z+bF~JJd{5*e17Ny@ycW#T-Zmv4xLYz94*p2@$KTp-Q{!?G-bB@IQOU)_w}&3v^7?O
zZ{{Tft#c)4%(EJCz^fPziC470W$eJYYpD-^s3xNN#ij};o_4Sw4^;Hnn+3EZp@znG
z^|-fshWi3<9-6#m{a*mR07Cy8v!J3s!o$L^c?Xz3*U3y{Cwjm?EOq+gGTF4h8@Adt
z?{dVye=sO1-obIdU`%0rq+8Oz$a4q0EX0IAB9Hh-khzP$LG>L=;vdJql>;$=XPL0S
z(a|2~5OuP@svp6M7%5vnk=|f4+?lMu9uOdA@$=KapQGv(GK;=HdBR6MMMJ1RmmSZW
zXxK47-qq-6>RHCWK8Q8CoWje$HB}b%EO5g==04;j+T~(DC9(l;zhlflc12<1mw&^*
DO$^MF
diff --git a/bob/pad/face/config/preprocessor/face_feature_crop_quality_check.py b/bob/pad/face/config/preprocessor/face_feature_crop_quality_check.py
deleted file mode 100644
index 3dfc53f4..00000000
--- a/bob/pad/face/config/preprocessor/face_feature_crop_quality_check.py
+++ /dev/null
@@ -1,490 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Created on Tue Oct 9 13:53:58 2018
-
-@author: Olegs Nikisins
-"""
-
-# =============================================================================
-# Import here:
-
-from bob.bio.base.preprocessor import Preprocessor
-
-from bob.bio.video.preprocessor import Wrapper
-
-import os
-
-import importlib
-
-from bob.pad.face.utils.patch_utils import reshape_flat_patches
-
-from bob.bio.video.utils import FrameSelector
-
-from bob.pad.face.preprocessor import BlockPatch
-
-
-# =============================================================================
-# define preprocessor class:
-
-class _Preprocessor(Preprocessor):
- """
- The following steps are performed:
-
- 1. Detect and align the face.
-
- 2. Assess the quality of the face image.
-
- 3. Extract patch / patches from the face.
-
- **Parameters:**
-
- ``face_crop_align`` : object
- An instance of the FaceCropAlign preprocessor to be used in step one.
-
- ``config_file``: py:class:`string`
- Relative name of the config file containing
- quality assessment function.
- Example: ``celeb_a/quality_assessment_config.py``.
-
- ``config_group``: py:class:`string`
- Group/package name containing the configuration file.
- Example: ``bob.pad.face.config.quality_assessment``.
-
- ``block_patch`` : object
- An instance of the BlockPatch preprocessor to be used in step 3.
-
- ``patch_reshape_parameters`` : [int] or None
- The parameters to be used for patch reshaping. The patch is
- vectorized. Example:
- ``patch_reshape_parameters = [4, 8, 8]``, then the patch of the
- size (256,) will be reshaped to (4,8,8) dimensions. Only 2D and 3D
- patches are supported.
- Default: None.
-
- ``patch_num`` : int OR None
- Am index of the patch to be selected from all extracted patches.
- Default: None
- """
-
- def __init__(self,
- face_crop_align,
- config_file,
- config_group,
- block_patch,
- patch_reshape_parameters = None,
- patch_num = None):
-
- super(_Preprocessor, self).__init__()
-
- self.face_crop_align = face_crop_align
- self.config_file = config_file
- self.config_group = config_group
- self.block_patch = block_patch
- self.patch_reshape_parameters = patch_reshape_parameters
- self.patch_num = patch_num
-
-
- def __call__(self, data, annotations):
- """
- **Parameters:**
-
- ``data`` : 2D or 3D :py:class:`numpy.ndarray`
- Input image (RGB or gray-scale) or None.
-
- ``annotations`` : :py:class:`dict` or None
- A dictionary containing annotations of the face bounding box.
- Dictionary must be as follows:
- ``{'topleft': (row, col), 'bottomright': (row, col)}``
- Default: None .
- """
-
- face_data = self.face_crop_align(data, annotations)
-
- if face_data is None:
-
- return None
-
- relative_mod_name = '.' + os.path.splitext(self.config_file)[0].replace(os.path.sep, '.')
-
- config_module = importlib.import_module(relative_mod_name, self.config_group)
-
- quality_flag = config_module.assess_quality(face_data, **config_module.assess_quality_kwargs)
-
- if quality_flag:
-
- print ("Good quality data.")
-
- patches = self.block_patch(face_data, annotations=None)
-
- if self.patch_reshape_parameters is not None:
-
- patches = reshape_flat_patches(patches, self.patch_reshape_parameters)
-
- if self.patch_num is not None:
-
- patches = patches[self.patch_num]
-
- else:
-
- print ("Bad quality data.")
- return None
-
- return patches
-
-
-# =============================================================================
-# define instance of the preprocessor:
-
-"""
-Preprocessor to be used for Color channel.
-"""
-
-from bob.pad.face.preprocessor import FaceCropAlign
-
-FACE_SIZE = 128 # The size of the resulting face
-RGB_OUTPUT_FLAG = True # RGB output
-USE_FACE_ALIGNMENT = True # use annotations
-MAX_IMAGE_SIZE = 1920 # no limiting here
-FACE_DETECTION_METHOD = "mtcnn" # DON'T use ANNOTATIONS, valid for CelebA only
-MIN_FACE_SIZE = 50 # skip small faces
-
-_face_crop_align = FaceCropAlign(face_size = FACE_SIZE,
- rgb_output_flag = RGB_OUTPUT_FLAG,
- use_face_alignment = USE_FACE_ALIGNMENT,
- max_image_size = MAX_IMAGE_SIZE,
- face_detection_method = FACE_DETECTION_METHOD,
- min_face_size = MIN_FACE_SIZE)
-
-"""
-Parameters to be used for quality assessment.
-"""
-
-CONFIG_FILE = "celeb_a/quality_assessment_config_128.py"
-
-CONFIG_GROUP = "bob.pad.face.config.quality_assessment"
-
-"""
-Define an instance of the BlockPatch preprocessor.
-"""
-
-PATCH_SIZE = 64
-STEP = 32
-
-_block_patch = BlockPatch(patch_size = PATCH_SIZE,
- step = STEP,
- use_annotations_flag = False)
-
-"""
-define an instance of the _Preprocessor class.
-"""
-
-_frame_selector = FrameSelector(selection_style = "all")
-
-_image_extractor_0 = _Preprocessor(face_crop_align = _face_crop_align,
- config_file = CONFIG_FILE,
- config_group = CONFIG_GROUP,
- block_patch = _block_patch,
- patch_reshape_parameters = [3, PATCH_SIZE, PATCH_SIZE],
- patch_num = 0)
-
-face_feature_0_crop_rgb = Wrapper(preprocessor = _image_extractor_0,
- frame_selector = _frame_selector)
-
-
-# =============================================================================
-_image_extractor_1 = _Preprocessor(face_crop_align = _face_crop_align,
- config_file = CONFIG_FILE,
- config_group = CONFIG_GROUP,
- block_patch = _block_patch,
- patch_reshape_parameters = [3, PATCH_SIZE, PATCH_SIZE],
- patch_num = 1)
-
-face_feature_1_crop_rgb = Wrapper(preprocessor = _image_extractor_1,
- frame_selector = _frame_selector)
-
-# =============================================================================
-_image_extractor_2 = _Preprocessor(face_crop_align = _face_crop_align,
- config_file = CONFIG_FILE,
- config_group = CONFIG_GROUP,
- block_patch = _block_patch,
- patch_reshape_parameters = [3, PATCH_SIZE, PATCH_SIZE],
- patch_num = 2)
-
-face_feature_2_crop_rgb = Wrapper(preprocessor = _image_extractor_2,
- frame_selector = _frame_selector)
-
-# =============================================================================
-_image_extractor_3 = _Preprocessor(face_crop_align = _face_crop_align,
- config_file = CONFIG_FILE,
- config_group = CONFIG_GROUP,
- block_patch = _block_patch,
- patch_reshape_parameters = [3, PATCH_SIZE, PATCH_SIZE],
- patch_num = 3)
-
-face_feature_3_crop_rgb = Wrapper(preprocessor = _image_extractor_3,
- frame_selector = _frame_selector)
-
-# =============================================================================
-_image_extractor_4 = _Preprocessor(face_crop_align = _face_crop_align,
- config_file = CONFIG_FILE,
- config_group = CONFIG_GROUP,
- block_patch = _block_patch,
- patch_reshape_parameters = [3, PATCH_SIZE, PATCH_SIZE],
- patch_num = 4)
-
-face_feature_4_crop_rgb = Wrapper(preprocessor = _image_extractor_4,
- frame_selector = _frame_selector)
-
-# =============================================================================
-_image_extractor_5 = _Preprocessor(face_crop_align = _face_crop_align,
- config_file = CONFIG_FILE,
- config_group = CONFIG_GROUP,
- block_patch = _block_patch,
- patch_reshape_parameters = [3, PATCH_SIZE, PATCH_SIZE],
- patch_num = 5)
-
-face_feature_5_crop_rgb = Wrapper(preprocessor = _image_extractor_5,
- frame_selector = _frame_selector)
-
-# =============================================================================
-_image_extractor_6 = _Preprocessor(face_crop_align = _face_crop_align,
- config_file = CONFIG_FILE,
- config_group = CONFIG_GROUP,
- block_patch = _block_patch,
- patch_reshape_parameters = [3, PATCH_SIZE, PATCH_SIZE],
- patch_num = 6)
-
-face_feature_6_crop_rgb = Wrapper(preprocessor = _image_extractor_6,
- frame_selector = _frame_selector)
-
-# =============================================================================
-_image_extractor_7 = _Preprocessor(face_crop_align = _face_crop_align,
- config_file = CONFIG_FILE,
- config_group = CONFIG_GROUP,
- block_patch = _block_patch,
- patch_reshape_parameters = [3, PATCH_SIZE, PATCH_SIZE],
- patch_num = 7)
-
-face_feature_7_crop_rgb = Wrapper(preprocessor = _image_extractor_7,
- frame_selector = _frame_selector)
-
-# =============================================================================
-_image_extractor_8 = _Preprocessor(face_crop_align = _face_crop_align,
- config_file = CONFIG_FILE,
- config_group = CONFIG_GROUP,
- block_patch = _block_patch,
- patch_reshape_parameters = [3, PATCH_SIZE, PATCH_SIZE],
- patch_num = 8)
-
-face_feature_8_crop_rgb = Wrapper(preprocessor = _image_extractor_8,
- frame_selector = _frame_selector)
-
-# =============================================================================
-# Extractors for obtaining RGB patches of the size 3x32x32
-
-PATCH_SIZE = 32
-STEP = 32
-
-_block_patch_32x32 = BlockPatch(patch_size = PATCH_SIZE,
- step = STEP,
- use_annotations_flag = False)
-
-_image_extractor_0_32x32 = _Preprocessor(face_crop_align = _face_crop_align,
- config_file = CONFIG_FILE,
- config_group = CONFIG_GROUP,
- block_patch = _block_patch_32x32,
- patch_reshape_parameters = [3, PATCH_SIZE, PATCH_SIZE],
- patch_num = 0)
-
-face_feature_0_32x32_crop_rgb = Wrapper(preprocessor = _image_extractor_0_32x32,
- frame_selector = _frame_selector)
-
-
-_image_extractor_1_32x32 = _Preprocessor(face_crop_align = _face_crop_align,
- config_file = CONFIG_FILE,
- config_group = CONFIG_GROUP,
- block_patch = _block_patch_32x32,
- patch_reshape_parameters = [3, PATCH_SIZE, PATCH_SIZE],
- patch_num = 1)
-
-face_feature_1_32x32_crop_rgb = Wrapper(preprocessor = _image_extractor_1_32x32,
- frame_selector = _frame_selector)
-
-
-_image_extractor_2_32x32 = _Preprocessor(face_crop_align = _face_crop_align,
- config_file = CONFIG_FILE,
- config_group = CONFIG_GROUP,
- block_patch = _block_patch_32x32,
- patch_reshape_parameters = [3, PATCH_SIZE, PATCH_SIZE],
- patch_num = 2)
-
-face_feature_2_32x32_crop_rgb = Wrapper(preprocessor = _image_extractor_2_32x32,
- frame_selector = _frame_selector)
-
-
-_image_extractor_3_32x32 = _Preprocessor(face_crop_align = _face_crop_align,
- config_file = CONFIG_FILE,
- config_group = CONFIG_GROUP,
- block_patch = _block_patch_32x32,
- patch_reshape_parameters = [3, PATCH_SIZE, PATCH_SIZE],
- patch_num = 3)
-
-face_feature_3_32x32_crop_rgb = Wrapper(preprocessor = _image_extractor_3_32x32,
- frame_selector = _frame_selector)
-
-
-_image_extractor_4_32x32 = _Preprocessor(face_crop_align = _face_crop_align,
- config_file = CONFIG_FILE,
- config_group = CONFIG_GROUP,
- block_patch = _block_patch_32x32,
- patch_reshape_parameters = [3, PATCH_SIZE, PATCH_SIZE],
- patch_num = 4)
-
-face_feature_4_32x32_crop_rgb = Wrapper(preprocessor = _image_extractor_4_32x32,
- frame_selector = _frame_selector)
-
-
-_image_extractor_5_32x32 = _Preprocessor(face_crop_align = _face_crop_align,
- config_file = CONFIG_FILE,
- config_group = CONFIG_GROUP,
- block_patch = _block_patch_32x32,
- patch_reshape_parameters = [3, PATCH_SIZE, PATCH_SIZE],
- patch_num = 5)
-
-face_feature_5_32x32_crop_rgb = Wrapper(preprocessor = _image_extractor_5_32x32,
- frame_selector = _frame_selector)
-
-
-_image_extractor_6_32x32 = _Preprocessor(face_crop_align = _face_crop_align,
- config_file = CONFIG_FILE,
- config_group = CONFIG_GROUP,
- block_patch = _block_patch_32x32,
- patch_reshape_parameters = [3, PATCH_SIZE, PATCH_SIZE],
- patch_num = 6)
-
-face_feature_6_32x32_crop_rgb = Wrapper(preprocessor = _image_extractor_6_32x32,
- frame_selector = _frame_selector)
-
-
-_image_extractor_7_32x32 = _Preprocessor(face_crop_align = _face_crop_align,
- config_file = CONFIG_FILE,
- config_group = CONFIG_GROUP,
- block_patch = _block_patch_32x32,
- patch_reshape_parameters = [3, PATCH_SIZE, PATCH_SIZE],
- patch_num = 7)
-
-face_feature_7_32x32_crop_rgb = Wrapper(preprocessor = _image_extractor_7_32x32,
- frame_selector = _frame_selector)
-
-
-_image_extractor_8_32x32 = _Preprocessor(face_crop_align = _face_crop_align,
- config_file = CONFIG_FILE,
- config_group = CONFIG_GROUP,
- block_patch = _block_patch_32x32,
- patch_reshape_parameters = [3, PATCH_SIZE, PATCH_SIZE],
- patch_num = 8)
-
-face_feature_8_32x32_crop_rgb = Wrapper(preprocessor = _image_extractor_8_32x32,
- frame_selector = _frame_selector)
-
-
-_image_extractor_9_32x32 = _Preprocessor(face_crop_align = _face_crop_align,
- config_file = CONFIG_FILE,
- config_group = CONFIG_GROUP,
- block_patch = _block_patch_32x32,
- patch_reshape_parameters = [3, PATCH_SIZE, PATCH_SIZE],
- patch_num = 9)
-
-face_feature_9_32x32_crop_rgb = Wrapper(preprocessor = _image_extractor_9_32x32,
- frame_selector = _frame_selector)
-
-
-_image_extractor_10_32x32 = _Preprocessor(face_crop_align = _face_crop_align,
- config_file = CONFIG_FILE,
- config_group = CONFIG_GROUP,
- block_patch = _block_patch_32x32,
- patch_reshape_parameters = [3, PATCH_SIZE, PATCH_SIZE],
- patch_num = 10)
-
-face_feature_10_32x32_crop_rgb = Wrapper(preprocessor = _image_extractor_10_32x32,
- frame_selector = _frame_selector)
-
-
-_image_extractor_11_32x32 = _Preprocessor(face_crop_align = _face_crop_align,
- config_file = CONFIG_FILE,
- config_group = CONFIG_GROUP,
- block_patch = _block_patch_32x32,
- patch_reshape_parameters = [3, PATCH_SIZE, PATCH_SIZE],
- patch_num = 11)
-
-face_feature_11_32x32_crop_rgb = Wrapper(preprocessor = _image_extractor_11_32x32,
- frame_selector = _frame_selector)
-
-
-_image_extractor_12_32x32 = _Preprocessor(face_crop_align = _face_crop_align,
- config_file = CONFIG_FILE,
- config_group = CONFIG_GROUP,
- block_patch = _block_patch_32x32,
- patch_reshape_parameters = [3, PATCH_SIZE, PATCH_SIZE],
- patch_num = 12)
-
-face_feature_12_32x32_crop_rgb = Wrapper(preprocessor = _image_extractor_12_32x32,
- frame_selector = _frame_selector)
-
-
-_image_extractor_13_32x32 = _Preprocessor(face_crop_align = _face_crop_align,
- config_file = CONFIG_FILE,
- config_group = CONFIG_GROUP,
- block_patch = _block_patch_32x32,
- patch_reshape_parameters = [3, PATCH_SIZE, PATCH_SIZE],
- patch_num = 13)
-
-face_feature_13_32x32_crop_rgb = Wrapper(preprocessor = _image_extractor_13_32x32,
- frame_selector = _frame_selector)
-
-
-_image_extractor_14_32x32 = _Preprocessor(face_crop_align = _face_crop_align,
- config_file = CONFIG_FILE,
- config_group = CONFIG_GROUP,
- block_patch = _block_patch_32x32,
- patch_reshape_parameters = [3, PATCH_SIZE, PATCH_SIZE],
- patch_num = 14)
-
-face_feature_14_32x32_crop_rgb = Wrapper(preprocessor = _image_extractor_14_32x32,
- frame_selector = _frame_selector)
-
-
-_image_extractor_15_32x32 = _Preprocessor(face_crop_align = _face_crop_align,
- config_file = CONFIG_FILE,
- config_group = CONFIG_GROUP,
- block_patch = _block_patch_32x32,
- patch_reshape_parameters = [3, PATCH_SIZE, PATCH_SIZE],
- patch_num = 15)
-
-face_feature_15_32x32_crop_rgb = Wrapper(preprocessor = _image_extractor_15_32x32,
- frame_selector = _frame_selector)
-
-
-# =============================================================================
-# Extractors for obtaining RGB patches (patch is an entire face in this case) of the size 3x128x128
-
-PATCH_SIZE = 128
-STEP = 1
-
-_block_patch_128x128 = BlockPatch(patch_size = PATCH_SIZE,
- step = STEP,
- use_annotations_flag = False)
-
-_image_extractor_0_128x128 = _Preprocessor(face_crop_align = _face_crop_align,
- config_file = CONFIG_FILE,
- config_group = CONFIG_GROUP,
- block_patch = _block_patch_128x128,
- patch_reshape_parameters = [3, PATCH_SIZE, PATCH_SIZE],
- patch_num = 0)
-
-face_feature_0_128x128_crop_rgb = Wrapper(preprocessor = _image_extractor_0_128x128,
- frame_selector = _frame_selector)
-
-
-
diff --git a/bob/pad/face/config/preprocessor/filename.py b/bob/pad/face/config/preprocessor/filename.py
deleted file mode 100644
index 9f13809c..00000000
--- a/bob/pad/face/config/preprocessor/filename.py
+++ /dev/null
@@ -1,7 +0,0 @@
-from bob.bio.base.preprocessor import Filename
-
-# This preprocessor does nothing, returning just the name of the file to extract the features from:
-
-# WARNING: if you use this, you should provide the preprocessed directory, as the database directory
-# i.e. ./bin/spoof.py [config.py] --preprocessed-directory /idiap/group/biometric/databases/pad/replay/protocols/replayattack-database/
-empty_preprocessor = Filename()
diff --git a/bob/pad/face/config/preprocessor/optical_flow.py b/bob/pad/face/config/preprocessor/optical_flow.py
deleted file mode 100644
index c7cad27f..00000000
--- a/bob/pad/face/config/preprocessor/optical_flow.py
+++ /dev/null
@@ -1,10 +0,0 @@
-from bob.bio.base.preprocessor import CallablePreprocessor
-from bob.pad.face.extractor import OpticalFlow
-
-
-def _read_original_data(biofile, directory, extension):
- return biofile.frames
-
-
-preprocessor = CallablePreprocessor(OpticalFlow(), accepts_annotations=False)
-preprocessor.read_original_data = _read_original_data
diff --git a/bob/pad/face/config/preprocessor/video_face_crop.py b/bob/pad/face/config/preprocessor/video_face_crop.py
deleted file mode 100644
index 5bcd67d1..00000000
--- a/bob/pad/face/config/preprocessor/video_face_crop.py
+++ /dev/null
@@ -1,66 +0,0 @@
-#!/usr/bin/env python2
-# -*- coding: utf-8 -*-
-
-from bob.pad.face.preprocessor import FaceCropAlign
-
-from bob.bio.video.preprocessor import Wrapper
-
-from bob.bio.video.utils import FrameSelector
-
-# =======================================================================================
-# Define instances here:
-
-
-FACE_SIZE = 64 # The size of the resulting face
-RGB_OUTPUT_FLAG = True # RGB output
-USE_FACE_ALIGNMENT = False #
-MAX_IMAGE_SIZE = None # no limiting here
-FACE_DETECTION_METHOD = "dlib" # use dlib face detection
-MIN_FACE_SIZE = 50 # skip small faces
-
-_image_preprocessor = FaceCropAlign(face_size=FACE_SIZE,
- rgb_output_flag=RGB_OUTPUT_FLAG,
- use_face_alignment=USE_FACE_ALIGNMENT,
- max_image_size=MAX_IMAGE_SIZE,
- face_detection_method=FACE_DETECTION_METHOD,
- min_face_size=MIN_FACE_SIZE)
-
-_frame_selector = FrameSelector(selection_style = "all")
-
-rgb_face_detector_dlib = Wrapper(preprocessor = _image_preprocessor,
- frame_selector = _frame_selector)
-
-# =======================================================================================
-FACE_DETECTION_METHOD = "mtcnn" # use mtcnn face detection
-
-_image_preprocessor = FaceCropAlign(face_size=FACE_SIZE,
- rgb_output_flag=RGB_OUTPUT_FLAG,
- use_face_alignment=USE_FACE_ALIGNMENT,
- max_image_size=MAX_IMAGE_SIZE,
- face_detection_method=FACE_DETECTION_METHOD,
- min_face_size=MIN_FACE_SIZE)
-
-rgb_face_detector_mtcnn = Wrapper(preprocessor = _image_preprocessor,
- frame_selector = _frame_selector)
-
-# =======================================================================================
-FACE_SIZE = 64 # The size of the resulting face
-RGB_OUTPUT_FLAG = False # Gray-scale output
-USE_FACE_ALIGNMENT = True # detect face landmarks locally and align the face
-MAX_IMAGE_SIZE = 1920 # the largest possible dimension of the input image
-FACE_DETECTION_METHOD = "mtcnn" # face landmarks detection method
-MIN_FACE_SIZE = 50 # skip faces smaller than this value
-NORMALIZATION_FUNCTION = None # no normalization
-NORMALIZATION_FUNCTION_KWARGS = None
-
-_image_preprocessor = FaceCropAlign(face_size=FACE_SIZE,
- rgb_output_flag=RGB_OUTPUT_FLAG,
- use_face_alignment=USE_FACE_ALIGNMENT,
- max_image_size=MAX_IMAGE_SIZE,
- face_detection_method=FACE_DETECTION_METHOD,
- min_face_size=MIN_FACE_SIZE,
- normalization_function=NORMALIZATION_FUNCTION,
- normalization_function_kwargs=NORMALIZATION_FUNCTION_KWARGS)
-
-bw_face_detect_mtcnn = Wrapper(preprocessor=_image_preprocessor,
- frame_selector=_frame_selector)
diff --git a/bob/pad/face/config/preprocessor/video_face_crop_align_block_patch.py b/bob/pad/face/config/preprocessor/video_face_crop_align_block_patch.py
deleted file mode 100644
index 634bbdc9..00000000
--- a/bob/pad/face/config/preprocessor/video_face_crop_align_block_patch.py
+++ /dev/null
@@ -1,131 +0,0 @@
-#!/usr/bin/env python2
-# -*- coding: utf-8 -*-
-
-# =============================================================================
-# Import here:
-from bob.pad.face.preprocessor import VideoFaceCropAlignBlockPatch
-
-from bob.pad.face.preprocessor import FaceCropAlign
-
-from bob.bio.video.preprocessor import Wrapper
-
-from bob.bio.video.utils import FrameSelector
-
-from bob.pad.face.preprocessor.FaceCropAlign import auto_norm_image as _norm_func
-
-from bob.pad.face.preprocessor import BlockPatch
-
-
-# =============================================================================
-# names of the channels to process:
-_channel_names = ['color', 'infrared', 'depth']
-
-
-# =============================================================================
-# dictionary containing preprocessors for all channels:
-_preprocessors = {}
-
-"""
-Preprocessor to be used for Color channel.
-"""
-FACE_SIZE = 128 # The size of the resulting face
-RGB_OUTPUT_FLAG = False # BW output
-USE_FACE_ALIGNMENT = True # use annotations
-MAX_IMAGE_SIZE = None # no limiting here
-FACE_DETECTION_METHOD = None # use ANNOTATIONS
-MIN_FACE_SIZE = 50 # skip small faces
-
-_image_preprocessor = FaceCropAlign(face_size = FACE_SIZE,
- rgb_output_flag = RGB_OUTPUT_FLAG,
- use_face_alignment = USE_FACE_ALIGNMENT,
- max_image_size = MAX_IMAGE_SIZE,
- face_detection_method = FACE_DETECTION_METHOD,
- min_face_size = MIN_FACE_SIZE)
-
-_frame_selector = FrameSelector(selection_style = "all")
-
-_preprocessor_rgb = Wrapper(preprocessor = _image_preprocessor,
- frame_selector = _frame_selector)
-
-_preprocessors[_channel_names[0]] = _preprocessor_rgb
-
-"""
-Preprocessor to be used for Infrared (or Thermal) channels:
-"""
-FACE_SIZE = 128 # The size of the resulting face
-RGB_OUTPUT_FLAG = False # Gray-scale output
-USE_FACE_ALIGNMENT = True # use annotations
-MAX_IMAGE_SIZE = None # no limiting here
-FACE_DETECTION_METHOD = None # use annotations
-MIN_FACE_SIZE = 50 # skip small faces
-NORMALIZATION_FUNCTION = _norm_func
-NORMALIZATION_FUNCTION_KWARGS = {}
-NORMALIZATION_FUNCTION_KWARGS = {'n_sigma':3.0, 'norm_method':'MAD'}
-
-_image_preprocessor_ir = FaceCropAlign(face_size = FACE_SIZE,
- rgb_output_flag = RGB_OUTPUT_FLAG,
- use_face_alignment = USE_FACE_ALIGNMENT,
- max_image_size = MAX_IMAGE_SIZE,
- face_detection_method = FACE_DETECTION_METHOD,
- min_face_size = MIN_FACE_SIZE,
- normalization_function = NORMALIZATION_FUNCTION,
- normalization_function_kwargs = NORMALIZATION_FUNCTION_KWARGS)
-
-_preprocessor_ir = Wrapper(preprocessor = _image_preprocessor_ir,
- frame_selector = _frame_selector)
-
-_preprocessors[_channel_names[1]] = _preprocessor_ir
-
-"""
-Preprocessor to be used for Depth channel:
-"""
-FACE_SIZE = 128 # The size of the resulting face
-RGB_OUTPUT_FLAG = False # Gray-scale output
-USE_FACE_ALIGNMENT = True # use annotations
-MAX_IMAGE_SIZE = None # no limiting here
-FACE_DETECTION_METHOD = None # use annotations
-MIN_FACE_SIZE = 50 # skip small faces
-NORMALIZATION_FUNCTION = _norm_func
-NORMALIZATION_FUNCTION_KWARGS = {}
-NORMALIZATION_FUNCTION_KWARGS = {'n_sigma':6.0, 'norm_method':'MAD'}
-
-_image_preprocessor_d = FaceCropAlign(face_size = FACE_SIZE,
- rgb_output_flag = RGB_OUTPUT_FLAG,
- use_face_alignment = USE_FACE_ALIGNMENT,
- max_image_size = MAX_IMAGE_SIZE,
- face_detection_method = FACE_DETECTION_METHOD,
- min_face_size = MIN_FACE_SIZE,
- normalization_function = NORMALIZATION_FUNCTION,
- normalization_function_kwargs = NORMALIZATION_FUNCTION_KWARGS)
-
-_preprocessor_d = Wrapper(preprocessor = _image_preprocessor_d,
- frame_selector = _frame_selector)
-
-_preprocessors[_channel_names[2]] = _preprocessor_d
-
-
-# =============================================================================
-# define parameters and an instance of the patch extractor:
-PATCH_SIZE = 128
-STEP = 1
-
-_block_patch_128x128 = BlockPatch(patch_size = PATCH_SIZE,
- step = STEP,
- use_annotations_flag = False)
-
-
-# =============================================================================
-"""
-Define an instance for extraction of one (**whole face**) multi-channel
-(BW-NIR-D) face patch of the size (3 x 128 x 128).
-"""
-video_face_crop_align_bw_ir_d_channels_3x128x128 = VideoFaceCropAlignBlockPatch(preprocessors = _preprocessors,
- channel_names = _channel_names,
- return_multi_channel_flag = True,
- block_patch_preprocessor = _block_patch_128x128)
-
-# This instance is similar to above, but will return a **vectorized** patch:
-video_face_crop_align_bw_ir_d_channels_3x128x128_vect = VideoFaceCropAlignBlockPatch(preprocessors = _preprocessors,
- channel_names = _channel_names,
- return_multi_channel_flag = False,
- block_patch_preprocessor = _block_patch_128x128)
diff --git a/bob/pad/face/config/preprocessor/video_sparse_coding.py b/bob/pad/face/config/preprocessor/video_sparse_coding.py
deleted file mode 100644
index ee24dbf4..00000000
--- a/bob/pad/face/config/preprocessor/video_sparse_coding.py
+++ /dev/null
@@ -1,39 +0,0 @@
-#!/usr/bin/env python
-
-import os
-
-from bob.pad.face.preprocessor import VideoSparseCoding
-
-#=======================================================================================
-# Define instances here:
-
-BLOCK_SIZE = 5
-BLOCK_LENGTH = 10
-MIN_FACE_SIZE = 50
-NORM_FACE_SIZE = 64
-
-DICTIONARY_LENGTH = 128
-DIR = os.path.dirname(os.path.abspath(__file__))
-
-DICTIONARY_FILE_NAMES = [
- os.path.join(DIR, "dictionaries",
- "dictionary_front_10_5_{}.hdf5".format(DICTIONARY_LENGTH)),
- os.path.join(DIR, "dictionaries",
- "dictionary_hor_10_5_{}.hdf5".format(DICTIONARY_LENGTH)),
- os.path.join(DIR, "dictionaries",
- "dictionary_vert_10_5_{}.hdf5".format(DICTIONARY_LENGTH))
-]
-
-FRAME_STEP = 50 # (!) a small number of feature vectors will be computed
-EXTRACT_HISTOGRAMS_FLAG = True
-COMP_RECONSTRUCT_ERR_FLAG = False
-
-preprocessor_10_5_128 = VideoSparseCoding(
- gblock_size=BLOCK_SIZE,
- block_length=BLOCK_LENGTH,
- min_face_size=MIN_FACE_SIZE,
- norm_face_size=NORM_FACE_SIZE,
- dictionary_file_names=DICTIONARY_FILE_NAMES,
- frame_step=FRAME_STEP,
- extract_histograms_flag=EXTRACT_HISTOGRAMS_FLAG,
- comp_reconstruct_err_flag=COMP_RECONSTRUCT_ERR_FLAG)
diff --git a/bob/pad/face/config/vanilla_pad/qm_svm.py b/bob/pad/face/config/qm_64.py
similarity index 53%
rename from bob/pad/face/config/vanilla_pad/qm_svm.py
rename to bob/pad/face/config/qm_64.py
index 3d8e64cc..cc4326ea 100644
--- a/bob/pad/face/config/vanilla_pad/qm_svm.py
+++ b/bob/pad/face/config/qm_64.py
@@ -1,16 +1,9 @@
-# Legacy imports
+import bob.pipelines as mario
from bob.bio.face.helpers import face_crop_solver
from bob.bio.video import VideoLikeContainer
from bob.bio.video.transformer import Wrapper as TransformerWrapper
from bob.pad.face.extractor import ImageQualityMeasure
-# new imports
-from sklearn.svm import SVC
-from sklearn.model_selection import GridSearchCV
-from sklearn.pipeline import make_pipeline
-from bob.pad.face.transformer import VideoToFrames
-import bob.pipelines as mario
-
database = globals().get("database")
if database is not None:
annotation_type = database.annotation_type
@@ -31,7 +24,7 @@ preprocessor = mario.wrap(
load_func=VideoLikeContainer.load,
)
-# Legacy extractor #
+# Extractor #
extractor = TransformerWrapper(ImageQualityMeasure(galbally=True, msu=True, dtype=None))
extractor = mario.wrap(
["sample", "checkpoint"],
@@ -40,30 +33,3 @@ extractor = mario.wrap(
save_func=VideoLikeContainer.save,
load_func=VideoLikeContainer.load,
)
-
-# new stuff #
-frame_cont_to_array = VideoToFrames()
-
-param_grid = [
- {"C": [1, 10, 100, 1000], "kernel": ["linear"]},
- {"C": [1, 10, 100, 1000], "gamma": [0.001, 0.0001], "kernel": ["rbf"]},
-]
-classifier = GridSearchCV(SVC(), param_grid=param_grid, cv=3)
-classifier = mario.wrap(
- ["sample", "checkpoint"],
- classifier,
- fit_extra_arguments=[("y", "is_bonafide")],
- model_path="temp/svm.pkl",
-)
-
-
-# pipeline #
-# stateless_pipeline = mario.transformers.StatelessPipeline(
-# [
-# ("preprocessor", preprocessor),
-# ("extractor", extractor),
-# ("frame_cont_to_array", frame_cont_to_array),
-# ]
-# )
-frames_classifier = make_pipeline(frame_cont_to_array, classifier)
-pipeline = make_pipeline(preprocessor, extractor, frames_classifier)
diff --git a/bob/pad/face/config/qm_lr.py b/bob/pad/face/config/qm_lr.py
deleted file mode 100644
index f936d2a5..00000000
--- a/bob/pad/face/config/qm_lr.py
+++ /dev/null
@@ -1,86 +0,0 @@
-#!/usr/bin/env python2
-# -*- coding: utf-8 -*-
-"""
-This file contains configurations to run Image Quality Measures (IQM) and LR based face PAD algorithm.
-The settings of the preprocessor and extractor are tuned for the Replay-attack database.
-The IQM features used in this algorithm/resource are introduced in the following papers: [WHJ15]_ and [CBVM16]_.
-"""
-
-#=======================================================================================
-sub_directory = 'qm_lr'
-"""
-Sub-directory where results will be placed.
-
-You may change this setting using the ``--sub-directory`` command-line option
-or the attribute ``sub_directory`` in a configuration file loaded **after**
-this resource.
-"""
-
-#=======================================================================================
-# define preprocessor:
-
-from ..preprocessor import FaceCropAlign
-
-from bob.bio.video.preprocessor import Wrapper
-
-from bob.bio.video.utils import FrameSelector
-
-FACE_SIZE = 64 # The size of the resulting face
-RGB_OUTPUT_FLAG = True # RGB output
-USE_FACE_ALIGNMENT = False # use annotations
-MAX_IMAGE_SIZE = None # no limiting here
-FACE_DETECTION_METHOD = None # use annotations
-MIN_FACE_SIZE = 50 # skip small faces
-
-_image_preprocessor = FaceCropAlign(face_size = FACE_SIZE,
- rgb_output_flag = RGB_OUTPUT_FLAG,
- use_face_alignment = USE_FACE_ALIGNMENT,
- max_image_size = MAX_IMAGE_SIZE,
- face_detection_method = FACE_DETECTION_METHOD,
- min_face_size = MIN_FACE_SIZE)
-
-_frame_selector = FrameSelector(selection_style = "all")
-
-preprocessor = Wrapper(preprocessor = _image_preprocessor,
- frame_selector = _frame_selector)
-"""
-In the preprocessing stage the face is cropped in each frame of the input video given facial annotations.
-The size of the face is normalized to ``FACE_SIZE`` dimensions. The faces of the size
-below ``MIN_FACE_SIZE`` threshold are discarded. The preprocessor is similar to the one introduced in
-[CAM12]_, which is defined by ``FACE_DETECTION_METHOD = None``. The preprocessed frame is the RGB
-facial image, which is defined by ``RGB_OUTPUT_FLAG = True``.
-"""
-
-#=======================================================================================
-# define extractor:
-
-from ..extractor import ImageQualityMeasure
-
-from bob.bio.video.extractor import Wrapper
-
-GALBALLY = True
-MSU = True
-DTYPE = None
-
-extractor = Wrapper(ImageQualityMeasure(galbally=GALBALLY, msu=MSU, dtype=DTYPE))
-"""
-In the feature extraction stage the Image Quality Measures are extracted from each frame of the preprocessed RGB video.
-The features to be computed are introduced in the following papers: [WHJ15]_ and [CBVM16]_.
-"""
-
-#=======================================================================================
-# define algorithm:
-
-from bob.pad.base.algorithm import LogRegr
-
-C = 1. # The regularization parameter for the LR classifier
-FRAME_LEVEL_SCORES_FLAG = True # Return one score per frame
-
-algorithm = LogRegr(
- C=C, frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-"""
-The Logistic Regression is used to classify the data into *real* and *attack* classes.
-One score is produced for each frame of the input video, ``frame_level_scores_flag = True``.
-The sub-sampling of training data is not used here, sub-sampling flags have default ``False``
-values.
-"""
diff --git a/bob/pad/face/config/qm_svm.py b/bob/pad/face/config/qm_svm.py
deleted file mode 100644
index 8f9b8e25..00000000
--- a/bob/pad/face/config/qm_svm.py
+++ /dev/null
@@ -1,101 +0,0 @@
-#!/usr/bin/env python2
-# -*- coding: utf-8 -*-
-"""
-This file contains configurations to run Image Quality Measures (IQM) and SVM based face PAD baseline.
-The settings are tuned for the Replay-attack database.
-The IQM features used in this algorithm/resource are introduced in the following papers: [WHJ15]_ and [CBVM16]_.
-"""
-
-#=======================================================================================
-sub_directory = 'qm_svm'
-"""
-Sub-directory where results will be placed.
-
-You may change this setting using the ``--sub-directory`` command-line option
-or the attribute ``sub_directory`` in a configuration file loaded **after**
-this resource.
-"""
-
-#=======================================================================================
-# define preprocessor:
-
-from ..preprocessor import FaceCropAlign
-
-from bob.bio.video.preprocessor import Wrapper
-
-from bob.bio.video.utils import FrameSelector
-
-FACE_SIZE = 64 # The size of the resulting face
-RGB_OUTPUT_FLAG = True # RGB output
-USE_FACE_ALIGNMENT = False # use annotations
-MAX_IMAGE_SIZE = None # no limiting here
-FACE_DETECTION_METHOD = None # use annotations
-MIN_FACE_SIZE = 50 # skip small faces
-
-_image_preprocessor = FaceCropAlign(face_size = FACE_SIZE,
- rgb_output_flag = RGB_OUTPUT_FLAG,
- use_face_alignment = USE_FACE_ALIGNMENT,
- max_image_size = MAX_IMAGE_SIZE,
- face_detection_method = FACE_DETECTION_METHOD,
- min_face_size = MIN_FACE_SIZE)
-
-_frame_selector = FrameSelector(selection_style = "all")
-
-preprocessor = Wrapper(preprocessor = _image_preprocessor,
- frame_selector = _frame_selector)
-"""
-In the preprocessing stage the face is cropped in each frame of the input video given facial annotations.
-The size of the face is normalized to ``FACE_SIZE`` dimensions. The faces of the size
-below ``MIN_FACE_SIZE`` threshold are discarded. The preprocessor is similar to the one introduced in
-[CAM12]_, which is defined by ``FACE_DETECTION_METHOD = None``. The preprocessed frame is the RGB
-facial image, which is defined by ``RGB_OUTPUT_FLAG = True``.
-"""
-
-#=======================================================================================
-# define extractor:
-
-from ..extractor import ImageQualityMeasure
-
-from bob.bio.video.extractor import Wrapper
-
-GALBALLY = True
-MSU = True
-DTYPE = None
-
-extractor = Wrapper(ImageQualityMeasure(galbally=GALBALLY, msu=MSU, dtype=DTYPE))
-"""
-In the feature extraction stage the Image Quality Measures are extracted from each frame of the preprocessed RGB video.
-The features to be computed are introduced in the following papers: [WHJ15]_ and [CBVM16]_.
-"""
-
-#=======================================================================================
-# define algorithm:
-
-from bob.pad.base.algorithm import SVM
-
-MACHINE_TYPE = 'C_SVC'
-KERNEL_TYPE = 'RBF'
-N_SAMPLES = 10000
-TRAINER_GRID_SEARCH_PARAMS = {
- 'cost': [2**P for P in range(-3, 14, 2)],
- 'gamma': [2**P for P in range(-15, 0, 2)]
-}
-MEAN_STD_NORM_FLAG = True # enable mean-std normalization
-FRAME_LEVEL_SCORES_FLAG = True # one score per frame(!) in this case
-
-algorithm = SVM(
- machine_type=MACHINE_TYPE,
- kernel_type=KERNEL_TYPE,
- n_samples=N_SAMPLES,
- trainer_grid_search_params=TRAINER_GRID_SEARCH_PARAMS,
- mean_std_norm_flag=MEAN_STD_NORM_FLAG,
- frame_level_scores_flag=FRAME_LEVEL_SCORES_FLAG)
-"""
-The SVM algorithm with RBF kernel is used to classify the data into *real* and *attack* classes.
-One score is produced for each frame of the input video, ``frame_level_scores_flag = True``.
-The grid search of SVM parameters is used to select the successful settings.
-The grid search is done on the subset of training data.
-The size of this subset is defined by ``n_samples`` parameter.
-
-The data is also mean-std normalized, ``mean_std_norm_flag = True``.
-"""
diff --git a/bob/pad/face/config/quality_assessment/__init__.py b/bob/pad/face/config/quality_assessment/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/bob/pad/face/config/quality_assessment/celeb_a/__init__.py b/bob/pad/face/config/quality_assessment/celeb_a/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/bob/pad/face/config/quality_assessment/celeb_a/quality_assessment_config.py b/bob/pad/face/config/quality_assessment/celeb_a/quality_assessment_config.py
deleted file mode 100644
index cdf772f2..00000000
--- a/bob/pad/face/config/quality_assessment/celeb_a/quality_assessment_config.py
+++ /dev/null
@@ -1,50 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Quality assessment configuration file for the CelebA database to be used
-with quality assessment script.
-
-Note: this config checks the quality of the preprocessed(!) data. Here the
-preprocessed data is sored in ``.hdf5`` files, as a frame container with
-one frame. Frame contains a BW image of the facial regions of the size
-64x64 pixels.
-
-The config file MUST contain at least the following functions:
-
-``load_datafile(file_name)`` - returns the ``data`` given ``file_name``, and
-
-``assess_quality(data, **assess_quality_kwargs)`` - returns ``True`` for good
-quality ``data``, and ``False`` for low quality data, and
-
-``assess_quality_kwargs`` - a dictionary with kwargs for ``assess_quality()``
-function.
-
-@author: Olegs Nikisins
-"""
-
-# =============================================================================
-# Import here:
-
-import pkg_resources
-
-import cv2
-
-from bob.bio.video.preprocessor import Wrapper
-
-import numpy as np
-
-from bob.pad.face.config.quality_assessment.celeb_a.quality_assessment_config_128 import detect_eyes_in_bw_image, load_datafile, assess_quality
-
-
-# =============================================================================
-face_size = 64
-eyes_distance=((face_size + 1) / 2.)
-eyes_center=(face_size / 4., (face_size - 0.5) / 2.)
-
-eyes_expected = [[eyes_center[0], eyes_center[1]-eyes_distance/2.],
- [eyes_center[0], eyes_center[1]+eyes_distance/2.]]
-
-assess_quality_kwargs = {}
-assess_quality_kwargs["eyes_expected"] = eyes_expected
-assess_quality_kwargs["threshold"] = 7
-
diff --git a/bob/pad/face/config/quality_assessment/celeb_a/quality_assessment_config_128.py b/bob/pad/face/config/quality_assessment/celeb_a/quality_assessment_config_128.py
deleted file mode 100644
index 3eb4e087..00000000
--- a/bob/pad/face/config/quality_assessment/celeb_a/quality_assessment_config_128.py
+++ /dev/null
@@ -1,162 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Quality assessment configuration file for the CelebA database to be used
-with quality assessment script.
-
-Note: this config checks the quality of the preprocessed(!) data. Here the
-preprocessed data is sored in ``.hdf5`` files, as a frame container with
-one frame. Frame contains a BW image of the facial regions of the size
-128x128 pixels.
-
-The config file MUST contain at least the following functions:
-
-``load_datafile(file_name)`` - returns the ``data`` given ``file_name``, and
-
-``assess_quality(data, **assess_quality_kwargs)`` - returns ``True`` for good
-quality ``data``, and ``False`` for low quality data, and
-
-``assess_quality_kwargs`` - a dictionary with kwargs for ``assess_quality()``
-function.
-
-@author: Olegs Nikisins
-"""
-
-# =============================================================================
-# Import here:
-
-import pkg_resources
-
-import cv2
-
-from bob.bio.video.preprocessor import Wrapper
-
-import numpy as np
-
-import bob.ip.color
-
-# =============================================================================
-def detect_eyes_in_bw_image(image):
- """
- Detect eyes in the image using OpenCV.
-
- **Parameters:**
-
- ``image`` : 2D :py:class:`numpy.ndarray`
- A BW image to detect the eyes in.
-
- **Returns:**
-
- ``eyes`` : 2D :py:class:`numpy.ndarray`
- An array containing coordinates of the bounding boxes of detected eyes.
- The dimensionality of the array:
- ``num_of_detected_eyes x coordinates_of_bbx``
- """
-
- eye_model = pkg_resources.resource_filename('bob.pad.face.config',
- 'quality_assessment/models/eye_detector.xml')
-
- eye_cascade = cv2.CascadeClassifier(eye_model)
-
- if len(image.shape) == 3:
-
- image = bob.ip.color.rgb_to_gray(image)
-
- eyes = eye_cascade.detectMultiScale(image)
-
- return eyes
-
-
-# =============================================================================
-def load_datafile(file_name):
- """
- Load data from file given filename. Here the data file is an hdf5 file
- containing a framecontainer with one frame. The data in the frame is
- a BW image of the facial region.
-
- **Parameters:**
-
- ``file_name`` : str
- Absolute name of the file.
-
- **Returns:**
-
- ``data`` : 2D :py:class:`numpy.ndarray`
- Data array containing the image of the facial region.
- """
-
- frame_container = Wrapper().read_data(file_name)
-
- data = frame_container[0][1]
-
- return data
-
-
-# =============================================================================
-face_size = 128
-eyes_distance=((face_size + 1) / 2.)
-eyes_center=(face_size / 4., (face_size - 0.5) / 2.)
-
-eyes_expected = [[eyes_center[0], eyes_center[1]-eyes_distance/2.],
- [eyes_center[0], eyes_center[1]+eyes_distance/2.]]
-
-assess_quality_kwargs = {}
-assess_quality_kwargs["eyes_expected"] = eyes_expected
-assess_quality_kwargs["threshold"] = 10
-
-
-# =============================================================================
-def assess_quality(data, eyes_expected, threshold):
- """
- Assess the quality of the data sample, which in this case is an image of
- the face of the size (face_size x face_size) pixels. The quality assessment is based on the
- eye detection. If two eyes are detected, and they are located in the
- pre-defined positions, then quality is good, otherwise the quality is low.
-
- **Parameters:**
-
- ``data`` : 2D :py:class:`numpy.ndarray`
- Data array containing the image of the facial region. The size of the
- image is (face_size x face_size).
-
- ``eyes_expected`` : list
- A list containing expected coordinates of the eyes. The format is
- as follows:
- [ [left_y, left_x], [right_y, right_x] ]
-
- ``threshold`` : int
- A maximum allowed distance between expected and detected centers of
- the eyes.
-
- **Returns:**
-
- ``quality_flag`` : bool
- ``True`` for good quality data, ``False`` otherwise.
- """
-
- quality_flag = False
-
- eyes = detect_eyes_in_bw_image(data)
-
- if isinstance(eyes, np.ndarray):
-
- if eyes.shape[0] == 2: # only consider the images with two eyes detected
-
- # coordinates of detected centers of the eyes: [ [left_y, left_x], [right_y, right_x] ]:
- eyes_detected = []
- for (ex,ey,ew,eh) in eyes:
- eyes_detected.append( [ey + eh/2., ex + ew/2.] )
-
- dists = [] # dits between detected and expected:
- for a, b in zip(eyes_detected, eyes_expected):
- dists.append( np.linalg.norm(np.array(a)-np.array(b)) )
-
- max_dist = np.max(dists)
-
- if max_dist < threshold:
-
- quality_flag = True
-
- return quality_flag
-
-
diff --git a/bob/pad/face/config/quality_assessment/models/eye_detector.xml b/bob/pad/face/config/quality_assessment/models/eye_detector.xml
deleted file mode 100644
index 56714784..00000000
--- a/bob/pad/face/config/quality_assessment/models/eye_detector.xml
+++ /dev/null
@@ -1,12214 +0,0 @@
-<?xml version="1.0"?>
-<!--
- Stump-based 20x20 frontal eye detector.
- Created by Shameem Hameed (http://umich.edu/~shameem)
-
-////////////////////////////////////////////////////////////////////////////////////////
-
- IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-
- By downloading, copying, installing or using the software you agree to this license.
- If you do not agree to this license, do not download, install,
- copy or use the software.
-
-
- Intel License Agreement
- For Open Source Computer Vision Library
-
- Copyright (C) 2000, Intel Corporation, all rights reserved.
- Third party copyrights are property of their respective owners.
-
- Redistribution and use in source and binary forms, with or without modification,
- are permitted provided that the following conditions are met:
-
- * Redistribution's of source code must retain the above copyright notice,
- this list of conditions and the following disclaimer.
-
- * Redistribution's in binary form must reproduce the above copyright notice,
- this list of conditions and the following disclaimer in the documentation
- and/or other materials provided with the distribution.
-
- * The name of Intel Corporation may not be used to endorse or promote products
- derived from this software without specific prior written permission.
-
- This software is provided by the copyright holders and contributors "as is" and
- any express or implied warranties, including, but not limited to, the implied
- warranties of merchantability and fitness for a particular purpose are disclaimed.
- In no event shall the Intel Corporation or contributors be liable for any direct,
- indirect, incidental, special, exemplary, or consequential damages
- (including, but not limited to, procurement of substitute goods or services;
- loss of use, data, or profits; or business interruption) however caused
- and on any theory of liability, whether in contract, strict liability,
- or tort (including negligence or otherwise) arising in any way out of
- the use of this software, even if advised of the possibility of such damage.
--->
-<opencv_storage>
-<cascade type_id="opencv-cascade-classifier"><stageType>BOOST</stageType>
- <featureType>HAAR</featureType>
- <height>20</height>
- <width>20</width>
- <stageParams>
- <maxWeakCount>93</maxWeakCount></stageParams>
- <featureParams>
- <maxCatCount>0</maxCatCount></featureParams>
- <stageNum>24</stageNum>
- <stages>
- <_>
- <maxWeakCount>6</maxWeakCount>
- <stageThreshold>-1.4562760591506958e+00</stageThreshold>
- <weakClassifiers>
- <_>
- <internalNodes>
- 0 -1 0 1.2963959574699402e-01</internalNodes>
- <leafValues>
- -7.7304208278656006e-01 6.8350148200988770e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 1 -4.6326808631420135e-02</internalNodes>
- <leafValues>
- 5.7352751493453979e-01 -4.9097689986228943e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 2 -1.6173090785741806e-02</internalNodes>
- <leafValues>
- 6.0254341363906860e-01 -3.1610709428787231e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 3 -4.5828841626644135e-02</internalNodes>
- <leafValues>
- 6.4177548885345459e-01 -1.5545040369033813e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 4 -5.3759619593620300e-02</internalNodes>
- <leafValues>
- 5.4219317436218262e-01 -2.0480829477310181e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 5 3.4171190112829208e-02</internalNodes>
- <leafValues>
- -2.3388190567493439e-01 4.8410901427268982e-01</leafValues></_></weakClassifiers></_>
- <_>
- <maxWeakCount>12</maxWeakCount>
- <stageThreshold>-1.2550230026245117e+00</stageThreshold>
- <weakClassifiers>
- <_>
- <internalNodes>
- 0 -1 6 -2.1727620065212250e-01</internalNodes>
- <leafValues>
- 7.1098899841308594e-01 -5.9360730648040771e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 7 1.2071969918906689e-02</internalNodes>
- <leafValues>
- -2.8240481019020081e-01 5.9013551473617554e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 8 -1.7854139208793640e-02</internalNodes>
- <leafValues>
- 5.3137522935867310e-01 -2.2758960723876953e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 9 2.2333610802888870e-02</internalNodes>
- <leafValues>
- -1.7556099593639374e-01 6.3356137275695801e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 10 -9.1420017182826996e-02</internalNodes>
- <leafValues>
- 6.1563092470169067e-01 -1.6899530589580536e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 11 2.8973650187253952e-02</internalNodes>
- <leafValues>
- -1.2250079959630966e-01 7.4401170015335083e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 12 7.8203463926911354e-03</internalNodes>
- <leafValues>
- 1.6974370181560516e-01 -6.5441650152206421e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 13 2.0340489223599434e-02</internalNodes>
- <leafValues>
- -1.2556649744510651e-01 8.2710450887680054e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 14 -1.1926149949431419e-02</internalNodes>
- <leafValues>
- 3.8605681061744690e-01 -2.0992340147495270e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 15 -9.7281101625412703e-04</internalNodes>
- <leafValues>
- -6.3761192560195923e-01 1.2952390313148499e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 16 1.8322050891583785e-05</internalNodes>
- <leafValues>
- -3.4631478786468506e-01 2.2924269735813141e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 17 -8.0854417756199837e-03</internalNodes>
- <leafValues>
- -6.3665801286697388e-01 1.3078659772872925e-01</leafValues></_></weakClassifiers></_>
- <_>
- <maxWeakCount>9</maxWeakCount>
- <stageThreshold>-1.3728189468383789e+00</stageThreshold>
- <weakClassifiers>
- <_>
- <internalNodes>
- 0 -1 18 -1.1812269687652588e-01</internalNodes>
- <leafValues>
- 6.7844521999359131e-01 -5.0045782327651978e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 19 -3.4332759678363800e-02</internalNodes>
- <leafValues>
- 6.7186361551284790e-01 -3.5744878649711609e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 20 -2.1530799567699432e-02</internalNodes>
- <leafValues>
- 7.2220700979232788e-01 -1.8192419409751892e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 21 -2.1909970790147781e-02</internalNodes>
- <leafValues>
- 6.6529387235641479e-01 -2.7510228753089905e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 22 -2.8713539242744446e-02</internalNodes>
- <leafValues>
- 6.9955700635910034e-01 -1.9615580141544342e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 23 -1.1467480100691319e-02</internalNodes>
- <leafValues>
- 5.9267348051071167e-01 -2.2097350656986237e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 24 -2.2611169144511223e-02</internalNodes>
- <leafValues>
- 3.4483069181442261e-01 -3.8379558920860291e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 25 -1.9308089977130294e-03</internalNodes>
- <leafValues>
- -7.9445719718933105e-01 1.5628659725189209e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 26 5.6419910833938047e-05</internalNodes>
- <leafValues>
- -3.0896010994911194e-01 3.5431089997291565e-01</leafValues></_></weakClassifiers></_>
- <_>
- <maxWeakCount>16</maxWeakCount>
- <stageThreshold>-1.2879480123519897e+00</stageThreshold>
- <weakClassifiers>
- <_>
- <internalNodes>
- 0 -1 27 1.9886520504951477e-01</internalNodes>
- <leafValues>
- -5.2860701084136963e-01 3.5536721348762512e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 28 -3.6008939146995544e-02</internalNodes>
- <leafValues>
- 4.2109689116477966e-01 -3.9348980784416199e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 29 -7.7569849789142609e-02</internalNodes>
- <leafValues>
- 4.7991541028022766e-01 -2.5122168660163879e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 30 8.2630853285081685e-05</internalNodes>
- <leafValues>
- -3.8475489616394043e-01 3.1849220395088196e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 31 3.2773229759186506e-04</internalNodes>
- <leafValues>
- -2.6427319645881653e-01 3.2547241449356079e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 32 -1.8574850633740425e-02</internalNodes>
- <leafValues>
- 4.6736589074134827e-01 -1.5067270398139954e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 33 -7.0008762122597545e-05</internalNodes>
- <leafValues>
- 2.9313150048255920e-01 -2.5365099310874939e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 34 -1.8552130088210106e-02</internalNodes>
- <leafValues>
- 4.6273660659790039e-01 -1.3148050010204315e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 35 -1.3030420057475567e-02</internalNodes>
- <leafValues>
- 4.1627219319343567e-01 -1.7751489579677582e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 36 6.5694141085259616e-05</internalNodes>
- <leafValues>
- -2.8035101294517517e-01 2.6680740714073181e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 37 1.7005260451696813e-04</internalNodes>
- <leafValues>
- -2.7027249336242676e-01 2.3981650173664093e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 38 -3.3129199873656034e-03</internalNodes>
- <leafValues>
- 4.4411438703536987e-01 -1.4428889751434326e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 39 1.7583490116521716e-03</internalNodes>
- <leafValues>
- -1.6126190125942230e-01 4.2940768599510193e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 40 -2.5194749236106873e-02</internalNodes>
- <leafValues>
- 4.0687298774719238e-01 -1.8202580511569977e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 41 1.4031709870323539e-03</internalNodes>
- <leafValues>
- 8.4759786725044250e-02 -8.0018568038940430e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 42 -7.3991729877889156e-03</internalNodes>
- <leafValues>
- 5.5766099691390991e-01 -1.1843159794807434e-01</leafValues></_></weakClassifiers></_>
- <_>
- <maxWeakCount>23</maxWeakCount>
- <stageThreshold>-1.2179850339889526e+00</stageThreshold>
- <weakClassifiers>
- <_>
- <internalNodes>
- 0 -1 43 -2.9943080618977547e-02</internalNodes>
- <leafValues>
- 3.5810810327529907e-01 -3.8487631082534790e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 44 -1.2567380070686340e-01</internalNodes>
- <leafValues>
- 3.9316931366920471e-01 -3.0012258887290955e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 45 5.3635272197425365e-03</internalNodes>
- <leafValues>
- -4.3908619880676270e-01 1.9257010519504547e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 46 -8.0971820279955864e-03</internalNodes>
- <leafValues>
- 3.9906668663024902e-01 -2.3407870531082153e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 47 -1.6597909852862358e-02</internalNodes>
- <leafValues>
- 4.2095288634300232e-01 -2.2674840688705444e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 48 -2.0199299324303865e-03</internalNodes>
- <leafValues>
- -7.4156731367111206e-01 1.2601189315319061e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 49 -1.5202340437099338e-03</internalNodes>
- <leafValues>
- -7.6154601573944092e-01 8.6373612284660339e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 50 -4.9663940444588661e-03</internalNodes>
- <leafValues>
- 4.2182239890098572e-01 -1.7904919385910034e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 51 -1.9207600504159927e-02</internalNodes>
- <leafValues>
- 4.6894899010658264e-01 -1.4378750324249268e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 52 -1.2222680263221264e-02</internalNodes>
- <leafValues>
- 3.2842078804969788e-01 -2.1802149713039398e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 53 5.7548668235540390e-02</internalNodes>
- <leafValues>
- -3.6768808960914612e-01 2.4357110261917114e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 54 -9.5794079825282097e-03</internalNodes>
- <leafValues>
- -7.2245067358016968e-01 6.3664563000202179e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 55 -2.9545740690082312e-03</internalNodes>
- <leafValues>
- 3.5846439003944397e-01 -1.6696329414844513e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 56 -4.2017991654574871e-03</internalNodes>
- <leafValues>
- 3.9094808697700500e-01 -1.2041790038347244e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 57 -1.3624990358948708e-02</internalNodes>
- <leafValues>
- -5.8767718076705933e-01 8.8404729962348938e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 58 6.2853112467564642e-05</internalNodes>
- <leafValues>
- -2.6348459720611572e-01 2.1419279277324677e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 59 -2.6782939676195383e-03</internalNodes>
- <leafValues>
- -7.8390169143676758e-01 8.0526962876319885e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 60 -7.0597179234027863e-02</internalNodes>
- <leafValues>
- 4.1469261050224304e-01 -1.3989959657192230e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 61 9.2093646526336670e-02</internalNodes>
- <leafValues>
- -1.3055180013179779e-01 5.0435781478881836e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 62 -8.8004386052489281e-03</internalNodes>
- <leafValues>
- 3.6609750986099243e-01 -1.4036649465560913e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 63 7.5080977694597095e-05</internalNodes>
- <leafValues>
- -2.9704439640045166e-01 2.0702940225601196e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 64 -2.9870450962334871e-03</internalNodes>
- <leafValues>
- 3.5615700483322144e-01 -1.5445969998836517e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 65 -2.6441509835422039e-03</internalNodes>
- <leafValues>
- -5.4353517293930054e-01 1.0295110195875168e-01</leafValues></_></weakClassifiers></_>
- <_>
- <maxWeakCount>27</maxWeakCount>
- <stageThreshold>-1.2905240058898926e+00</stageThreshold>
- <weakClassifiers>
- <_>
- <internalNodes>
- 0 -1 66 -4.7862470149993896e-02</internalNodes>
- <leafValues>
- 4.1528239846229553e-01 -3.4185820817947388e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 67 8.7350532412528992e-02</internalNodes>
- <leafValues>
- -3.8749781250953674e-01 2.4204200506210327e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 68 -1.6849499195814133e-02</internalNodes>
- <leafValues>
- 5.3082478046417236e-01 -1.7282910645008087e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 69 -2.8870029374957085e-02</internalNodes>
- <leafValues>
- 3.5843509435653687e-01 -2.2402590513229370e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 70 2.5679389946162701e-03</internalNodes>
- <leafValues>
- 1.4990499615669250e-01 -6.5609407424926758e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 71 -2.4116659536957741e-02</internalNodes>
- <leafValues>
- 5.5889678001403809e-01 -1.4810280501842499e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 72 -3.2826658338308334e-02</internalNodes>
- <leafValues>
- 4.6468681097030640e-01 -1.0785529762506485e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 73 -1.5233060345053673e-02</internalNodes>
- <leafValues>
- -7.3954427242279053e-01 5.6236881762742996e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 74 -3.0209511169232428e-04</internalNodes>
- <leafValues>
- -4.5548820495605469e-01 9.7069837152957916e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 75 7.5365108205005527e-04</internalNodes>
- <leafValues>
- 9.5147296786308289e-02 -5.4895019531250000e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 76 -1.0638950392603874e-02</internalNodes>
- <leafValues>
- 4.0912970900535583e-01 -1.2308409810066223e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 77 -7.5217830017209053e-03</internalNodes>
- <leafValues>
- 4.0289148688316345e-01 -1.6048780083656311e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 78 -1.0677099972963333e-01</internalNodes>
- <leafValues>
- 6.1759322881698608e-01 -7.3091186583042145e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 79 1.6256919130682945e-02</internalNodes>
- <leafValues>
- -1.3103680312633514e-01 3.7453651428222656e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 80 -2.0679360255599022e-02</internalNodes>
- <leafValues>
- -7.1402907371520996e-01 5.2390009164810181e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 81 1.7052369192242622e-02</internalNodes>
- <leafValues>
- 1.2822860479354858e-01 -3.1080681085586548e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 82 -5.7122060097754002e-03</internalNodes>
- <leafValues>
- -6.0556507110595703e-01 8.1884756684303284e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 83 2.0851430235779844e-05</internalNodes>
- <leafValues>
- -2.6812988519668579e-01 1.4453840255737305e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 84 7.9284431412816048e-03</internalNodes>
- <leafValues>
- -7.8795351088047028e-02 5.6762582063674927e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 85 -2.5217379443347454e-03</internalNodes>
- <leafValues>
- 3.7068629264831543e-01 -1.3620570302009583e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 86 -2.2426199167966843e-02</internalNodes>
- <leafValues>
- -6.8704998493194580e-01 5.1062859594821930e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 87 -7.6451441273093224e-03</internalNodes>
- <leafValues>
- 2.3492220044136047e-01 -1.7905959486961365e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 88 -1.1175329564139247e-03</internalNodes>
- <leafValues>
- -5.9869050979614258e-01 7.4324436485767365e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 89 1.9212789833545685e-02</internalNodes>
- <leafValues>
- -1.5702550113201141e-01 2.9737469553947449e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 90 5.6293429806828499e-03</internalNodes>
- <leafValues>
- -9.9769018590450287e-02 4.2130270600318909e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 91 -9.5671862363815308e-03</internalNodes>
- <leafValues>
- -6.0858798027038574e-01 7.3506258428096771e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 92 1.1217960156500340e-02</internalNodes>
- <leafValues>
- -1.0320810228586197e-01 4.1909849643707275e-01</leafValues></_></weakClassifiers></_>
- <_>
- <maxWeakCount>28</maxWeakCount>
- <stageThreshold>-1.1600480079650879e+00</stageThreshold>
- <weakClassifiers>
- <_>
- <internalNodes>
- 0 -1 93 -1.7486440017819405e-02</internalNodes>
- <leafValues>
- 3.1307280063629150e-01 -3.3681181073188782e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 94 3.0714649707078934e-02</internalNodes>
- <leafValues>
- -1.8766190111637115e-01 5.3780800104141235e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 95 -2.2188719362020493e-02</internalNodes>
- <leafValues>
- 3.6637881398200989e-01 -1.6124810278415680e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 96 -5.0700771680567414e-05</internalNodes>
- <leafValues>
- 2.1245710551738739e-01 -2.8444620966911316e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 97 -7.0170420221984386e-03</internalNodes>
- <leafValues>
- 3.9543110132217407e-01 -1.3173590600490570e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 98 -6.8563609384000301e-03</internalNodes>
- <leafValues>
- 3.0373859405517578e-01 -2.0657819509506226e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 99 -1.4129259623587132e-02</internalNodes>
- <leafValues>
- -7.6503008604049683e-01 9.8213188350200653e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 100 -4.7915481030941010e-02</internalNodes>
- <leafValues>
- 4.8307389020919800e-01 -1.3006809353828430e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 101 4.7032979637151584e-05</internalNodes>
- <leafValues>
- -2.5216570496559143e-01 2.4386680126190186e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 102 1.0221180273219943e-03</internalNodes>
- <leafValues>
- 6.8857602775096893e-02 -6.5861141681671143e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 103 -2.6056109927594662e-03</internalNodes>
- <leafValues>
- 4.2942029237747192e-01 -1.3022460043430328e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 104 5.4505340813193470e-05</internalNodes>
- <leafValues>
- -1.9288620352745056e-01 2.8958499431610107e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 105 -6.6721157054416835e-05</internalNodes>
- <leafValues>
- 3.0290710926055908e-01 -1.9854369759559631e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 106 2.6281431317329407e-01</internalNodes>
- <leafValues>
- -2.3293940722942352e-01 2.3692460358142853e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 107 -2.3569669574499130e-02</internalNodes>
- <leafValues>
- 1.9401040673255920e-01 -2.8484618663787842e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 108 -3.9120172150433064e-03</internalNodes>
- <leafValues>
- 5.5378979444503784e-01 -9.5665678381919861e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 109 5.0788799853762612e-05</internalNodes>
- <leafValues>
- -2.3912659287452698e-01 2.1799489855766296e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 110 -7.8732017427682877e-03</internalNodes>
- <leafValues>
- 4.0697428584098816e-01 -1.2768040597438812e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 111 -1.6778609715402126e-03</internalNodes>
- <leafValues>
- -5.7744657993316650e-01 9.7324788570404053e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 112 -2.6832430739887059e-04</internalNodes>
- <leafValues>
- 2.9021880030632019e-01 -1.6831269860267639e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 113 7.8687182394787669e-05</internalNodes>
- <leafValues>
- -1.9551570713520050e-01 2.7720969915390015e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 114 1.2953500263392925e-02</internalNodes>
- <leafValues>
- -9.6838317811489105e-02 4.0323871374130249e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 115 -1.3043959625065327e-02</internalNodes>
- <leafValues>
- 4.7198569774627686e-01 -8.9287549257278442e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 116 3.0261781066656113e-03</internalNodes>
- <leafValues>
- -1.3623380661010742e-01 3.0686271190643311e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 117 -6.0438038781285286e-03</internalNodes>
- <leafValues>
- -7.7954101562500000e-01 5.7316310703754425e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 118 -2.2507249377667904e-03</internalNodes>
- <leafValues>
- 3.0877059698104858e-01 -1.5006309747695923e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 119 1.5826810151338577e-02</internalNodes>
- <leafValues>
- 6.4551889896392822e-02 -7.2455567121505737e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 120 6.5864507632795721e-05</internalNodes>
- <leafValues>
- -1.7598840594291687e-01 2.3210389912128448e-01</leafValues></_></weakClassifiers></_>
- <_>
- <maxWeakCount>36</maxWeakCount>
- <stageThreshold>-1.2257250547409058e+00</stageThreshold>
- <weakClassifiers>
- <_>
- <internalNodes>
- 0 -1 121 -2.7854869142174721e-02</internalNodes>
- <leafValues>
- 4.5518448948860168e-01 -1.8099910020828247e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 122 1.2895040214061737e-01</internalNodes>
- <leafValues>
- -5.2565532922744751e-01 1.6188900172710419e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 123 2.4403180927038193e-02</internalNodes>
- <leafValues>
- -1.4974960684776306e-01 4.2357379198074341e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 124 -2.4458570405840874e-03</internalNodes>
- <leafValues>
- 3.2948669791221619e-01 -1.7447690665721893e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 125 -3.5336529836058617e-03</internalNodes>
- <leafValues>
- 4.7426640987396240e-01 -7.3618359863758087e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 126 5.1358150813030079e-05</internalNodes>
- <leafValues>
- -3.0421930551528931e-01 1.5633270144462585e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 127 -1.6225680708885193e-02</internalNodes>
- <leafValues>
- 2.3002180457115173e-01 -2.0359820127487183e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 128 -4.6007009223103523e-03</internalNodes>
- <leafValues>
- 4.0459269285202026e-01 -1.3485440611839294e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 129 -2.1928999572992325e-02</internalNodes>
- <leafValues>
- -6.8724489212036133e-01 8.0684266984462738e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 130 -2.8971210122108459e-03</internalNodes>
- <leafValues>
- -6.9619607925415039e-01 4.8545219004154205e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 131 -4.4074649922549725e-03</internalNodes>
- <leafValues>
- 2.5166261196136475e-01 -1.6236649453639984e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 132 2.8437169268727303e-02</internalNodes>
- <leafValues>
- 6.0394261032342911e-02 -6.6744458675384521e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 133 8.3212882280349731e-02</internalNodes>
- <leafValues>
- 6.4357921481132507e-02 -5.3626042604446411e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 134 -1.2419329956173897e-02</internalNodes>
- <leafValues>
- -7.0816862583160400e-01 5.7526610791683197e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 135 -4.6992599964141846e-03</internalNodes>
- <leafValues>
- 5.1254332065582275e-01 -8.7350800633430481e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 136 -7.8025809489190578e-04</internalNodes>
- <leafValues>
- 2.6687660813331604e-01 -1.7961509525775909e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 137 -1.9724339246749878e-02</internalNodes>
- <leafValues>
- -6.7563730478286743e-01 7.2941906750202179e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 138 1.0269250487908721e-03</internalNodes>
- <leafValues>
- 5.3919319063425064e-02 -5.5540180206298828e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 139 -2.5957189500331879e-02</internalNodes>
- <leafValues>
- 5.6362527608871460e-01 -7.1898393332958221e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 140 -1.2552699772641063e-03</internalNodes>
- <leafValues>
- -5.0346630811691284e-01 8.9691452682018280e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 141 -4.9970578402280807e-02</internalNodes>
- <leafValues>
- 1.7685119807720184e-01 -2.2301959991455078e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 142 -2.9899610672146082e-03</internalNodes>
- <leafValues>
- 3.9122420549392700e-01 -1.0149750113487244e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 143 4.8546842299401760e-03</internalNodes>
- <leafValues>
- -1.1770179867744446e-01 4.2190939188003540e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 144 1.0448860120959580e-04</internalNodes>
- <leafValues>
- -1.7333979904651642e-01 2.2344440221786499e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 145 5.9689260524464771e-05</internalNodes>
- <leafValues>
- -2.3409630358219147e-01 1.6558240354061127e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 146 -1.3423919677734375e-02</internalNodes>
- <leafValues>
- 4.3023818731307983e-01 -9.9723652005195618e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 147 2.2581999655812979e-03</internalNodes>
- <leafValues>
- 7.2720989584922791e-02 -5.7501018047332764e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 148 -1.2546280398964882e-02</internalNodes>
- <leafValues>
- 3.6184579133987427e-01 -1.1457010358572006e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 149 -2.8705769218504429e-03</internalNodes>
- <leafValues>
- 2.8210538625717163e-01 -1.2367550283670425e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 150 1.9785640761256218e-02</internalNodes>
- <leafValues>
- 4.7876749187707901e-02 -8.0666238069534302e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 151 4.7588930465281010e-03</internalNodes>
- <leafValues>
- -1.0925389826297760e-01 3.3746978640556335e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 152 -6.9974269717931747e-03</internalNodes>
- <leafValues>
- -8.0295938253402710e-01 4.5706700533628464e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 153 -1.3033480383455753e-02</internalNodes>
- <leafValues>
- 1.8680439889431000e-01 -1.7688910663127899e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 154 -1.3742579612880945e-03</internalNodes>
- <leafValues>
- 2.7725479006767273e-01 -1.2809009850025177e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 155 2.7657810132950544e-03</internalNodes>
- <leafValues>
- 9.0758942067623138e-02 -4.2594739794731140e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 156 2.8941841446794569e-04</internalNodes>
- <leafValues>
- -3.8816329836845398e-01 8.9267797768115997e-02</leafValues></_></weakClassifiers></_>
- <_>
- <maxWeakCount>47</maxWeakCount>
- <stageThreshold>-1.2863140106201172e+00</stageThreshold>
- <weakClassifiers>
- <_>
- <internalNodes>
- 0 -1 157 -1.4469229616224766e-02</internalNodes>
- <leafValues>
- 3.7507829070091248e-01 -2.4928289651870728e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 158 -1.3317629694938660e-01</internalNodes>
- <leafValues>
- 3.0166378617286682e-01 -2.2414070367813110e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 159 -1.0132160037755966e-02</internalNodes>
- <leafValues>
- 3.6985591053962708e-01 -1.7850010097026825e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 160 -7.8511182218790054e-03</internalNodes>
- <leafValues>
- 4.6086761355400085e-01 -1.2931390106678009e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 161 -1.4295839704573154e-02</internalNodes>
- <leafValues>
- 4.4841429591178894e-01 -1.0226240009069443e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 162 -5.9606940485537052e-03</internalNodes>
- <leafValues>
- 2.7927988767623901e-01 -1.5323829650878906e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 163 1.0932769626379013e-02</internalNodes>
- <leafValues>
- -1.5141740441322327e-01 3.9889648556709290e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 164 5.0430990086169913e-05</internalNodes>
- <leafValues>
- -2.2681570053100586e-01 2.1644389629364014e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 165 -5.8431681245565414e-03</internalNodes>
- <leafValues>
- 4.5420148968696594e-01 -1.2587159872055054e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 166 -2.2346209734678268e-02</internalNodes>
- <leafValues>
- -6.2690192461013794e-01 8.2403123378753662e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 167 -4.8836669884622097e-03</internalNodes>
- <leafValues>
- 2.6359251141548157e-01 -1.4686630666255951e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 168 7.5506002758629620e-05</internalNodes>
- <leafValues>
- -2.4507020413875580e-01 1.6678880155086517e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 169 -4.9026997294276953e-04</internalNodes>
- <leafValues>
- -4.2649960517883301e-01 8.9973561465740204e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 170 1.4861579984426498e-03</internalNodes>
- <leafValues>
- -1.2040250003337860e-01 3.0097651481628418e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 171 -1.1988339945673943e-02</internalNodes>
- <leafValues>
- 2.7852478623390198e-01 -1.2244340032339096e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 172 1.0502239689230919e-02</internalNodes>
- <leafValues>
- 4.0452759712934494e-02 -7.4050408601760864e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 173 -3.0963009223341942e-02</internalNodes>
- <leafValues>
- -6.2842690944671631e-01 4.8013761639595032e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 174 1.1414520442485809e-02</internalNodes>
- <leafValues>
- 3.9405211806297302e-02 -7.1674120426177979e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 175 -1.2337000109255314e-02</internalNodes>
- <leafValues>
- 1.9941329956054688e-01 -1.9274300336837769e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 176 -5.9942267835140228e-03</internalNodes>
- <leafValues>
- 5.1318162679672241e-01 -6.1658058315515518e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 177 -1.1923230485990644e-03</internalNodes>
- <leafValues>
- -7.2605299949645996e-01 5.0652720034122467e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 178 -7.4582789093255997e-03</internalNodes>
- <leafValues>
- 2.9603078961372375e-01 -1.1754789948463440e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 179 2.7877509128302336e-03</internalNodes>
- <leafValues>
- 4.5068711042404175e-02 -6.9535410404205322e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 180 -2.2503209766000509e-04</internalNodes>
- <leafValues>
- 2.0047250390052795e-01 -1.5775249898433685e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 181 -5.0367889925837517e-03</internalNodes>
- <leafValues>
- 2.9299819469451904e-01 -1.1700499802827835e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 182 7.4742160737514496e-02</internalNodes>
- <leafValues>
- -1.1392319947481155e-01 3.0256620049476624e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 183 2.0255519077181816e-02</internalNodes>
- <leafValues>
- -1.0515890270471573e-01 4.0670460462570190e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 184 4.4214509427547455e-02</internalNodes>
- <leafValues>
- -2.7631640434265137e-01 1.2363869696855545e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 185 -8.7259558495134115e-04</internalNodes>
- <leafValues>
- 2.4355030059814453e-01 -1.3300949335098267e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 186 -2.4453739169985056e-03</internalNodes>
- <leafValues>
- -5.3866171836853027e-01 6.2510646879673004e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 187 8.2725353422574699e-05</internalNodes>
- <leafValues>
- -2.0772209763526917e-01 1.6270439326763153e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 188 -3.6627110093832016e-02</internalNodes>
- <leafValues>
- 3.6568409204483032e-01 -9.0330280363559723e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 189 3.0996399000287056e-03</internalNodes>
- <leafValues>
- -1.3183020055294037e-01 2.5354298949241638e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 190 -2.4709280114620924e-03</internalNodes>
- <leafValues>
- -5.6853497028350830e-01 5.3505431860685349e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 191 -1.4114670455455780e-02</internalNodes>
- <leafValues>
- -4.8599010705947876e-01 5.8485250920057297e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 192 8.4537261864170432e-04</internalNodes>
- <leafValues>
- -8.0093637108802795e-02 4.0265649557113647e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 193 -7.1098632179200649e-03</internalNodes>
- <leafValues>
- 4.4703239202499390e-01 -6.2947437167167664e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 194 -1.9125960767269135e-02</internalNodes>
- <leafValues>
- -6.6422867774963379e-01 4.9822770059108734e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 195 -5.0773010589182377e-03</internalNodes>
- <leafValues>
- 1.7379400134086609e-01 -1.6850599646568298e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 196 -2.9198289848864079e-03</internalNodes>
- <leafValues>
- -6.0110282897949219e-01 5.7427939027547836e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 197 -2.4902150034904480e-02</internalNodes>
- <leafValues>
- 2.3397980630397797e-01 -1.1818459630012512e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 198 2.0147779956459999e-02</internalNodes>
- <leafValues>
- -8.9459821581840515e-02 3.6024400591850281e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 199 1.7597640398889780e-03</internalNodes>
- <leafValues>
- 4.9458440393209457e-02 -6.3102620840072632e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 200 1.3812039978802204e-03</internalNodes>
- <leafValues>
- -1.5218059718608856e-01 1.8971739709377289e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 201 -1.0904540307819843e-02</internalNodes>
- <leafValues>
- -5.8097380399703979e-01 4.4862728565931320e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 202 7.5157178798690438e-05</internalNodes>
- <leafValues>
- -1.3777349889278412e-01 1.9543160498142242e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 203 3.8649770431220531e-03</internalNodes>
- <leafValues>
- -1.0302229970693588e-01 2.5374969840049744e-01</leafValues></_></weakClassifiers></_>
- <_>
- <maxWeakCount>48</maxWeakCount>
- <stageThreshold>-1.1189440488815308e+00</stageThreshold>
- <weakClassifiers>
- <_>
- <internalNodes>
- 0 -1 204 -1.0215889662504196e-01</internalNodes>
- <leafValues>
- 4.1681259870529175e-01 -1.6655629873275757e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 205 -5.1939819008111954e-02</internalNodes>
- <leafValues>
- 3.3023950457572937e-01 -2.0715710520744324e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 206 -4.2717780917882919e-02</internalNodes>
- <leafValues>
- 2.6093730330467224e-01 -1.6013890504837036e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 207 4.3890418601222336e-04</internalNodes>
- <leafValues>
- -3.4750530123710632e-01 1.3918919861316681e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 208 2.4264389649033546e-02</internalNodes>
- <leafValues>
- -4.2552059888839722e-01 1.3578380644321442e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 209 -2.3820599541068077e-02</internalNodes>
- <leafValues>
- 3.1749808788299561e-01 -1.6652040183544159e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 210 -7.0518180727958679e-03</internalNodes>
- <leafValues>
- 3.0947178602218628e-01 -1.3338300585746765e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 211 -6.8517157342284918e-04</internalNodes>
- <leafValues>
- -6.0082262754440308e-01 8.7747000157833099e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 212 5.3705149330198765e-03</internalNodes>
- <leafValues>
- -1.2311449646949768e-01 3.8333550095558167e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 213 -1.3403539545834064e-02</internalNodes>
- <leafValues>
- 3.3877369761466980e-01 -1.0140489786863327e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 214 -6.6856360062956810e-03</internalNodes>
- <leafValues>
- -6.1193597316741943e-01 4.7740221023559570e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 215 -4.2887418530881405e-03</internalNodes>
- <leafValues>
- 2.5275790691375732e-01 -1.4434510469436646e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 216 -1.0876749642193317e-02</internalNodes>
- <leafValues>
- 5.4775732755661011e-01 -5.9455480426549911e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 217 3.7882640026509762e-04</internalNodes>
- <leafValues>
- 8.3410300314426422e-02 -4.4226369261741638e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 218 -2.4550149682909250e-03</internalNodes>
- <leafValues>
- 2.3330999910831451e-01 -1.3964480161666870e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 219 1.2721839593723416e-03</internalNodes>
- <leafValues>
- 6.0480289161205292e-02 -4.9456089735031128e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 220 -4.8933159559965134e-03</internalNodes>
- <leafValues>
- -6.6833269596099854e-01 4.6218499541282654e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 221 2.6449989527463913e-02</internalNodes>
- <leafValues>
- -7.3235362768173218e-02 4.4425961375236511e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 222 -3.3706070389598608e-03</internalNodes>
- <leafValues>
- -4.2464339733123779e-01 6.8676561117172241e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 223 -2.9559480026364326e-03</internalNodes>
- <leafValues>
- 1.6218039393424988e-01 -1.8222999572753906e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 224 3.0619909986853600e-02</internalNodes>
- <leafValues>
- -5.8643341064453125e-02 5.3263628482818604e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 225 -9.5765907317399979e-03</internalNodes>
- <leafValues>
- -6.0562682151794434e-01 5.3345989435911179e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 226 6.6372493165545166e-05</internalNodes>
- <leafValues>
- -1.6680839657783508e-01 1.9284160435199738e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 227 5.0975950434803963e-03</internalNodes>
- <leafValues>
- 4.4119510799646378e-02 -5.7458841800689697e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 228 3.7112718564458191e-04</internalNodes>
- <leafValues>
- -1.1086399853229523e-01 2.3105390369892120e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 229 -8.6607588455080986e-03</internalNodes>
- <leafValues>
- 4.0456289052963257e-01 -6.2446091324090958e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 230 8.7489158613607287e-04</internalNodes>
- <leafValues>
- 6.4875148236751556e-02 -4.4871041178703308e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 231 1.1120870476588607e-03</internalNodes>
- <leafValues>
- -9.3861460685729980e-02 3.0453911423683167e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 232 -2.3837819695472717e-02</internalNodes>
- <leafValues>
- -5.8887428045272827e-01 4.6659421175718307e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 233 2.2272899514064193e-04</internalNodes>
- <leafValues>
- -1.4898599684238434e-01 1.7701950669288635e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 234 2.4467470124363899e-02</internalNodes>
- <leafValues>
- -5.5789601057767868e-02 4.9208301305770874e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 235 -1.4239320158958435e-01</internalNodes>
- <leafValues>
- 1.5192000567913055e-01 -1.8778899312019348e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 236 -2.0123120397329330e-02</internalNodes>
- <leafValues>
- 2.1780100464820862e-01 -1.2081900238990784e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 237 1.1513679783092812e-04</internalNodes>
- <leafValues>
- -1.6856589913368225e-01 1.6451929509639740e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 238 -2.7556740678846836e-03</internalNodes>
- <leafValues>
- -6.9442039728164673e-01 3.9449468255043030e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 239 -7.5843912782147527e-05</internalNodes>
- <leafValues>
- 1.8941369652748108e-01 -1.5183840692043304e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 240 -7.0697711780667305e-03</internalNodes>
- <leafValues>
- 4.7064599394798279e-01 -5.7927619665861130e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 241 -3.7393178790807724e-02</internalNodes>
- <leafValues>
- -7.5892448425292969e-01 3.4116048365831375e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 242 -1.5995610505342484e-02</internalNodes>
- <leafValues>
- 3.0670469999313354e-01 -8.7525576353073120e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 243 -3.1183990649878979e-03</internalNodes>
- <leafValues>
- 2.6195371150970459e-01 -9.1214887797832489e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 244 1.0651360498741269e-03</internalNodes>
- <leafValues>
- -1.7427560687065125e-01 1.5277640521526337e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 245 -1.6029420075938106e-03</internalNodes>
- <leafValues>
- 3.5612630844116211e-01 -7.6629996299743652e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 246 4.3619908392429352e-03</internalNodes>
- <leafValues>
- 4.9356970936059952e-02 -5.9228771924972534e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 247 -1.0779909789562225e-02</internalNodes>
- <leafValues>
- -6.3922178745269775e-01 3.3204540610313416e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 248 -4.3590869754552841e-03</internalNodes>
- <leafValues>
- 1.6107389330863953e-01 -1.5221320092678070e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 249 7.4596069753170013e-03</internalNodes>
- <leafValues>
- 3.3172961324453354e-02 -7.5007742643356323e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 250 8.1385448575019836e-03</internalNodes>
- <leafValues>
- 2.6325279846787453e-02 -7.1731162071228027e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 251 -3.3338490873575211e-02</internalNodes>
- <leafValues>
- 3.3536610007286072e-01 -7.0803590118885040e-02</leafValues></_></weakClassifiers></_>
- <_>
- <maxWeakCount>55</maxWeakCount>
- <stageThreshold>-1.1418989896774292e+00</stageThreshold>
- <weakClassifiers>
- <_>
- <internalNodes>
- 0 -1 252 1.9553979858756065e-02</internalNodes>
- <leafValues>
- -1.0439720004796982e-01 5.3128951787948608e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 253 2.2122919559478760e-02</internalNodes>
- <leafValues>
- -2.4747270345687866e-01 2.0847250521183014e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 254 -4.1829389519989491e-03</internalNodes>
- <leafValues>
- 3.8289439678192139e-01 -1.4711579680442810e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 255 -8.6381728760898113e-04</internalNodes>
- <leafValues>
- -6.2632888555526733e-01 1.1993259936571121e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 256 7.9958612332120538e-04</internalNodes>
- <leafValues>
- 9.2573471367359161e-02 -5.5168831348419189e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 257 9.1527570039033890e-03</internalNodes>
- <leafValues>
- -7.2929807007312775e-02 5.5512511730194092e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 258 -3.9388681761920452e-03</internalNodes>
- <leafValues>
- 2.0196039974689484e-01 -2.0912039279937744e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 259 1.4613410166930407e-04</internalNodes>
- <leafValues>
- -2.7861818671226501e-01 1.3817410171031952e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 260 -3.1691689509898424e-03</internalNodes>
- <leafValues>
- 3.6685898900032043e-01 -7.6308242976665497e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 261 -2.2189389914274216e-02</internalNodes>
- <leafValues>
- 3.9096599817276001e-01 -1.0971540212631226e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 262 -7.4523608200252056e-03</internalNodes>
- <leafValues>
- 1.2838590145111084e-01 -2.4159869551658630e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 263 7.7997002517804503e-04</internalNodes>
- <leafValues>
- 7.1978069841861725e-02 -4.3976500630378723e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 264 -4.6783639118075371e-03</internalNodes>
- <leafValues>
- 2.1569849550724030e-01 -1.4205920696258545e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 265 -1.5188639983534813e-02</internalNodes>
- <leafValues>
- 3.6458781361579895e-01 -8.2675926387310028e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 266 5.0619798712432384e-03</internalNodes>
- <leafValues>
- -3.4380409121513367e-01 9.2068232595920563e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 267 -1.7351920250803232e-03</internalNodes>
- <leafValues>
- -6.1725497245788574e-01 4.9214478582143784e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 268 -1.2423450127243996e-02</internalNodes>
- <leafValues>
- -5.8558952808380127e-01 4.6112600713968277e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 269 -1.3031429611146450e-02</internalNodes>
- <leafValues>
- -5.9710788726806641e-01 4.0672458708286285e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 270 -1.2369629694148898e-03</internalNodes>
- <leafValues>
- -6.8334168195724487e-01 3.3156178891658783e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 271 6.1022108420729637e-03</internalNodes>
- <leafValues>
- -9.4729237258434296e-02 3.0102241039276123e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 272 6.6952849738299847e-04</internalNodes>
- <leafValues>
- 8.1816866993904114e-02 -3.5196030139923096e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 273 -1.7970580374822021e-03</internalNodes>
- <leafValues>
- 2.3718979954719543e-01 -1.1768709868192673e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 274 -7.1074528386816382e-04</internalNodes>
- <leafValues>
- -4.4763788580894470e-01 5.7682480663061142e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 275 -5.9126471169292927e-03</internalNodes>
- <leafValues>
- 4.3425410985946655e-01 -6.6868573427200317e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 276 -3.3132149837911129e-03</internalNodes>
- <leafValues>
- 1.8150010704994202e-01 -1.4180320501327515e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 277 -6.0814660042524338e-02</internalNodes>
- <leafValues>
- 4.7221711277961731e-01 -6.1410639435052872e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 278 -9.6714183688163757e-02</internalNodes>
- <leafValues>
- 2.7683168649673462e-01 -9.4490036368370056e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 279 3.9073550142347813e-03</internalNodes>
- <leafValues>
- -1.2278530001640320e-01 2.1057400107383728e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 280 -9.0431869029998779e-03</internalNodes>
- <leafValues>
- 3.5641568899154663e-01 -7.7806226909160614e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 281 -4.8800031654536724e-03</internalNodes>
- <leafValues>
- -4.1034790873527527e-01 6.9694377481937408e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 282 -4.3547428213059902e-03</internalNodes>
- <leafValues>
- -7.3017889261245728e-01 3.6655150353908539e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 283 -9.6500627696514130e-03</internalNodes>
- <leafValues>
- 5.5181127786636353e-01 -5.3168080747127533e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 284 -1.7397310584783554e-02</internalNodes>
- <leafValues>
- -5.7084232568740845e-01 5.0214089453220367e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 285 -6.8304329179227352e-03</internalNodes>
- <leafValues>
- -4.6180281043052673e-01 5.0202690064907074e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 286 3.3255619928240776e-04</internalNodes>
- <leafValues>
- -9.5362730324268341e-02 2.5983759760856628e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 287 -2.3100529797375202e-03</internalNodes>
- <leafValues>
- 2.2872470319271088e-01 -1.0533530265092850e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 288 -7.5426651164889336e-03</internalNodes>
- <leafValues>
- -5.6990510225296021e-01 4.8863459378480911e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 289 -5.2723060362040997e-03</internalNodes>
- <leafValues>
- 3.5145181417465210e-01 -8.2390107214450836e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 290 -4.8578968271613121e-03</internalNodes>
- <leafValues>
- -6.0417622327804565e-01 4.4539440423250198e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 291 1.5867310576140881e-03</internalNodes>
- <leafValues>
- -1.0340909659862518e-01 2.3282019793987274e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 292 -4.7427811659872532e-03</internalNodes>
- <leafValues>
- 2.8490281105041504e-01 -9.8090499639511108e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 293 -1.3515240279957652e-03</internalNodes>
- <leafValues>
- 2.3096430301666260e-01 -1.1361840367317200e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 294 2.2526069078594446e-03</internalNodes>
- <leafValues>
- 6.4478322863578796e-02 -4.2205891013145447e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 295 -3.8038659840822220e-04</internalNodes>
- <leafValues>
- -3.8076201081275940e-01 6.0043290257453918e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 296 4.9043921753764153e-03</internalNodes>
- <leafValues>
- -7.6104998588562012e-02 3.3232170343399048e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 297 -9.0969670563936234e-03</internalNodes>
- <leafValues>
- 1.4287790656089783e-01 -1.6887800395488739e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 298 -6.9317929446697235e-03</internalNodes>
- <leafValues>
- 2.7255409955978394e-01 -9.2879563570022583e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 299 1.1471060570329428e-03</internalNodes>
- <leafValues>
- -1.5273059904575348e-01 1.9702400267124176e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 300 -3.7662889808416367e-02</internalNodes>
- <leafValues>
- -5.9320437908172607e-01 4.0738601237535477e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 301 -6.8165571428835392e-03</internalNodes>
- <leafValues>
- 2.5494089722633362e-01 -9.4081960618495941e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 302 6.6205562325194478e-04</internalNodes>
- <leafValues>
- 4.6795718371868134e-02 -4.8454371094703674e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 303 -4.2202551849186420e-03</internalNodes>
- <leafValues>
- 2.4682149291038513e-01 -9.4673976302146912e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 304 -6.8986512720584869e-02</internalNodes>
- <leafValues>
- -6.6514801979064941e-01 3.5926390439271927e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 305 6.1707608401775360e-03</internalNodes>
- <leafValues>
- 2.5833319872617722e-02 -7.2686272859573364e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 306 1.0536249727010727e-02</internalNodes>
- <leafValues>
- -8.1828996539115906e-02 2.9760798811912537e-01</leafValues></_></weakClassifiers></_>
- <_>
- <maxWeakCount>32</maxWeakCount>
- <stageThreshold>-1.1255199909210205e+00</stageThreshold>
- <weakClassifiers>
- <_>
- <internalNodes>
- 0 -1 307 -6.2758728861808777e-02</internalNodes>
- <leafValues>
- 2.7899080514907837e-01 -2.9656109213829041e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 308 3.4516479354351759e-03</internalNodes>
- <leafValues>
- -3.4635880589485168e-01 2.0903840661048889e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 309 -7.8699486330151558e-03</internalNodes>
- <leafValues>
- 2.4144889414310455e-01 -1.9205570220947266e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 310 -3.4624869003891945e-03</internalNodes>
- <leafValues>
- -5.9151780605316162e-01 1.2486449629068375e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 311 -9.4818761572241783e-03</internalNodes>
- <leafValues>
- 1.8391540646553040e-01 -2.4858260154724121e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 312 2.3226840130519122e-04</internalNodes>
- <leafValues>
- -3.3047258853912354e-01 1.0999260097742081e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 313 1.8101120367646217e-03</internalNodes>
- <leafValues>
- 9.8744012415409088e-02 -4.9634781479835510e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 314 -5.4422430694103241e-03</internalNodes>
- <leafValues>
- 2.9344418644905090e-01 -1.3094750046730042e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 315 7.4148122221231461e-03</internalNodes>
- <leafValues>
- -1.4762699604034424e-01 3.3277168869972229e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 316 -1.5565140172839165e-02</internalNodes>
- <leafValues>
- -6.8404901027679443e-01 9.9872693419456482e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 317 2.8720520436763763e-02</internalNodes>
- <leafValues>
- -1.4833280444145203e-01 3.0902579426765442e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 318 9.6687392215244472e-05</internalNodes>
- <leafValues>
- -1.7431040108203888e-01 2.1402959525585175e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 319 5.2371058613061905e-02</internalNodes>
- <leafValues>
- -7.0156857371330261e-02 4.9222990870475769e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 320 -8.6485691368579865e-02</internalNodes>
- <leafValues>
- 5.0757247209548950e-01 -7.5294211506843567e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 321 -4.2169868946075439e-02</internalNodes>
- <leafValues>
- 4.5680961012840271e-01 -9.0219900012016296e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 322 4.5369830331765115e-05</internalNodes>
- <leafValues>
- -2.6538279652595520e-01 1.6189539432525635e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 323 5.2918000146746635e-03</internalNodes>
- <leafValues>
- 7.4890151619911194e-02 -5.4054671525955200e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 324 -7.5511651812121272e-04</internalNodes>
- <leafValues>
- -4.9261990189552307e-01 5.8723948895931244e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 325 7.5108138844370842e-05</internalNodes>
- <leafValues>
- -2.1432100236415863e-01 1.4077760279178619e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 326 4.9981209449470043e-03</internalNodes>
- <leafValues>
- -9.0547338128089905e-02 3.5716068744659424e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 327 -1.4929979806765914e-03</internalNodes>
- <leafValues>
- 2.5623458623886108e-01 -1.4229069650173187e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 328 2.7239411137998104e-03</internalNodes>
- <leafValues>
- -1.5649250149726868e-01 2.1088710427284241e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 329 2.2218320518732071e-03</internalNodes>
- <leafValues>
- -1.5072989463806152e-01 2.6801869273185730e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 330 -7.3993072146549821e-04</internalNodes>
- <leafValues>
- 2.9546990990638733e-01 -1.0692390054464340e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 331 2.0113459322601557e-03</internalNodes>
- <leafValues>
- 5.0614349544048309e-02 -7.1683371067047119e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 332 1.1452870443463326e-02</internalNodes>
- <leafValues>
- -1.2719069421291351e-01 2.4152779579162598e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 333 -1.0782170575112104e-03</internalNodes>
- <leafValues>
- 2.4813009798526764e-01 -1.3461199402809143e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 334 3.3417691010981798e-03</internalNodes>
- <leafValues>
- 5.3578309714794159e-02 -5.2274167537689209e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 335 6.9398651248775423e-05</internalNodes>
- <leafValues>
- -2.1698740124702454e-01 1.2812179327011108e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 336 -4.0982551872730255e-03</internalNodes>
- <leafValues>
- 2.4401889741420746e-01 -1.1570589989423752e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 337 -1.6289720078930259e-03</internalNodes>
- <leafValues>
- 2.8261470794677734e-01 -1.0659469664096832e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 338 1.3984859921038151e-02</internalNodes>
- <leafValues>
- 4.2715899646282196e-02 -7.3646312952041626e-01</leafValues></_></weakClassifiers></_>
- <_>
- <maxWeakCount>30</maxWeakCount>
- <stageThreshold>-1.1729990243911743e+00</stageThreshold>
- <weakClassifiers>
- <_>
- <internalNodes>
- 0 -1 339 1.6416519880294800e-01</internalNodes>
- <leafValues>
- -4.8960301280021667e-01 1.7607709765434265e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 340 8.3413062384352088e-04</internalNodes>
- <leafValues>
- -2.8220430016517639e-01 2.4199579656124115e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 341 -1.7193210078403354e-03</internalNodes>
- <leafValues>
- -7.1485888957977295e-01 8.6162216961383820e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 342 -1.5654950402677059e-03</internalNodes>
- <leafValues>
- -7.2972381114959717e-01 9.4070672988891602e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 343 1.9124479731544852e-03</internalNodes>
- <leafValues>
- -3.1187158823013306e-01 1.8143390119075775e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 344 -1.3512369990348816e-01</internalNodes>
- <leafValues>
- 2.9577299952507019e-01 -2.2179250419139862e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 345 -4.0300549007952213e-03</internalNodes>
- <leafValues>
- -6.6595137119293213e-01 8.5431016981601715e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 346 -2.8640460222959518e-03</internalNodes>
- <leafValues>
- -6.2086361646652222e-01 5.3106021136045456e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 347 -1.4065420255064964e-03</internalNodes>
- <leafValues>
- 2.2346289455890656e-01 -2.0211009681224823e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 348 -3.5820449702441692e-03</internalNodes>
- <leafValues>
- -5.4030400514602661e-01 6.8213619291782379e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 349 4.1544470936059952e-02</internalNodes>
- <leafValues>
- -6.5215840935707092e-02 6.2109231948852539e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 350 -9.1709550470113754e-03</internalNodes>
- <leafValues>
- -7.5553297996520996e-01 5.2640449255704880e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 351 6.1552738770842552e-03</internalNodes>
- <leafValues>
- 9.0939402580261230e-02 -4.4246131181716919e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 352 -1.0043520014733076e-03</internalNodes>
- <leafValues>
- 2.4292330443859100e-01 -1.8669790029525757e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 353 1.1519829742610455e-02</internalNodes>
- <leafValues>
- -1.1763150244951248e-01 3.6723458766937256e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 354 -8.9040733873844147e-03</internalNodes>
- <leafValues>
- -4.8931330442428589e-01 1.0897020250558853e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 355 5.3973670583218336e-04</internalNodes>
- <leafValues>
- -2.1850399672985077e-01 1.8489989638328552e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 356 1.3727260520681739e-03</internalNodes>
- <leafValues>
- -1.5072910487651825e-01 2.9173129796981812e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 357 -1.0807390324771404e-02</internalNodes>
- <leafValues>
- 4.2897450923919678e-01 -1.0280139744281769e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 358 1.2670770520344377e-03</internalNodes>
- <leafValues>
- 7.4192158877849579e-02 -6.4208251237869263e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 359 2.2991129662841558e-03</internalNodes>
- <leafValues>
- 4.7100279480218887e-02 -7.2335231304168701e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 360 2.7187510859221220e-03</internalNodes>
- <leafValues>
- -1.7086869478225708e-01 2.3513509333133698e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 361 -6.6619180142879486e-03</internalNodes>
- <leafValues>
- -7.8975427150726318e-01 4.5084670186042786e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 362 -4.8266649246215820e-02</internalNodes>
- <leafValues>
- -6.9579917192459106e-01 4.1976079344749451e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 363 1.5214690007269382e-02</internalNodes>
- <leafValues>
- -1.0818280279636383e-01 3.6460620164871216e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 364 -6.0080131515860558e-03</internalNodes>
- <leafValues>
- 3.0970990657806396e-01 -1.1359210312366486e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 365 6.6127157770097256e-03</internalNodes>
- <leafValues>
- 8.0665342509746552e-02 -4.6658530831336975e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 366 -7.9607013612985611e-03</internalNodes>
- <leafValues>
- -8.7201941013336182e-01 3.6774590611457825e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 367 3.8847199175506830e-03</internalNodes>
- <leafValues>
- -1.1666289716959000e-01 3.3070269227027893e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 368 -1.0988810099661350e-03</internalNodes>
- <leafValues>
- 2.3872570693492889e-01 -1.7656759917736053e-01</leafValues></_></weakClassifiers></_>
- <_>
- <maxWeakCount>44</maxWeakCount>
- <stageThreshold>-1.0368299484252930e+00</stageThreshold>
- <weakClassifiers>
- <_>
- <internalNodes>
- 0 -1 369 3.5903379321098328e-03</internalNodes>
- <leafValues>
- -2.3688079416751862e-01 2.4631640315055847e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 370 6.4815930090844631e-03</internalNodes>
- <leafValues>
- -3.1373620033264160e-01 1.8675759434700012e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 371 7.3048402555286884e-05</internalNodes>
- <leafValues>
- -2.7644351124763489e-01 1.6496239602565765e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 372 -3.8514640182256699e-03</internalNodes>
- <leafValues>
- -5.6014508008956909e-01 1.1294739693403244e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 373 3.8588210009038448e-03</internalNodes>
- <leafValues>
- 3.9848998188972473e-02 -5.8071857690811157e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 374 -2.4651220068335533e-02</internalNodes>
- <leafValues>
- 1.6755010187625885e-01 -2.5343671441078186e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 375 4.7245521098375320e-02</internalNodes>
- <leafValues>
- -1.0662080347537994e-01 3.9451980590820312e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 376 6.5964651294052601e-03</internalNodes>
- <leafValues>
- -1.7744250595569611e-01 2.7280190587043762e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 377 -1.3177490327507257e-03</internalNodes>
- <leafValues>
- -5.4272651672363281e-01 4.8606589436531067e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 378 -5.0261709839105606e-03</internalNodes>
- <leafValues>
- 2.4394249916076660e-01 -1.3143649697303772e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 379 3.4632768947631121e-03</internalNodes>
- <leafValues>
- 6.9049343466758728e-02 -7.0336240530014038e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 380 2.1692588925361633e-03</internalNodes>
- <leafValues>
- -1.3289460539817810e-01 2.2098529338836670e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 381 2.9395870864391327e-02</internalNodes>
- <leafValues>
- -2.8530520200729370e-01 1.3543990254402161e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 382 -9.6181448316201568e-04</internalNodes>
- <leafValues>
- -5.8041381835937500e-01 3.7450648844242096e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 383 -1.0820999741554260e-01</internalNodes>
- <leafValues>
- 3.9467281103134155e-01 -7.8655943274497986e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 384 -1.8024869263172150e-02</internalNodes>
- <leafValues>
- 2.7355629205703735e-01 -1.3415299355983734e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 385 6.2509840354323387e-03</internalNodes>
- <leafValues>
- 2.3388059809803963e-02 -8.0088591575622559e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 386 -1.6088379779830575e-03</internalNodes>
- <leafValues>
- -5.6762522459030151e-01 4.1215669363737106e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 387 7.7564752427861094e-04</internalNodes>
- <leafValues>
- -1.4891269803047180e-01 1.9086180627346039e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 388 8.7122338300105184e-05</internalNodes>
- <leafValues>
- -1.5557530522346497e-01 1.9428220391273499e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 389 -2.0755320787429810e-02</internalNodes>
- <leafValues>
- -6.3006532192230225e-01 3.6134380847215652e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 390 -6.2931738793849945e-03</internalNodes>
- <leafValues>
- 2.5609248876571655e-01 -1.0588269680738449e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 391 1.0844149626791477e-02</internalNodes>
- <leafValues>
- -1.0124850273132324e-01 3.0322128534317017e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 392 -6.3752777350600809e-05</internalNodes>
- <leafValues>
- 1.9111579656600952e-01 -1.3849230110645294e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 393 6.6480963141657412e-05</internalNodes>
- <leafValues>
- -1.5205250680446625e-01 2.1706309914588928e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 394 1.3560829684138298e-03</internalNodes>
- <leafValues>
- 4.9431789666414261e-02 -6.4279842376708984e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 395 -9.0662558795884252e-04</internalNodes>
- <leafValues>
- 1.7982010543346405e-01 -1.4044609665870667e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 396 1.0473709553480148e-03</internalNodes>
- <leafValues>
- -1.0933549702167511e-01 2.4265940487384796e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 397 -1.0243969736620784e-03</internalNodes>
- <leafValues>
- 2.7162680029869080e-01 -1.1820919811725616e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 398 -1.2024149764329195e-03</internalNodes>
- <leafValues>
- -7.0151102542877197e-01 3.9489898830652237e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 399 7.6911649666726589e-03</internalNodes>
- <leafValues>
- -9.2218913137912750e-02 3.1046289205551147e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 400 -1.3966549932956696e-01</internalNodes>
- <leafValues>
- 6.8979388475418091e-01 -3.9706118404865265e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 401 2.1276050247251987e-03</internalNodes>
- <leafValues>
- 9.7277611494064331e-02 -2.8841799497604370e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 402 -2.7594310231506824e-03</internalNodes>
- <leafValues>
- 2.4168670177459717e-01 -1.1277820169925690e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 403 5.2236132323741913e-03</internalNodes>
- <leafValues>
- -1.1430279910564423e-01 2.4256780743598938e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 404 -1.2590440455824137e-03</internalNodes>
- <leafValues>
- -5.9679388999938965e-01 4.7663960605859756e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 405 -3.7192099262028933e-03</internalNodes>
- <leafValues>
- -4.6414130926132202e-01 5.2847690880298615e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 406 5.9696151874959469e-03</internalNodes>
- <leafValues>
- -7.3244288563728333e-02 3.8743090629577637e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 407 -5.1776720210909843e-03</internalNodes>
- <leafValues>
- -7.4193227291107178e-01 4.0496710687875748e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 408 5.0035100430250168e-03</internalNodes>
- <leafValues>
- -1.3888800144195557e-01 1.8767620623111725e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 409 -5.2013457752764225e-04</internalNodes>
- <leafValues>
- -5.4940617084503174e-01 4.9417849630117416e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 410 5.3168768063187599e-03</internalNodes>
- <leafValues>
- -8.2482978701591492e-02 3.1740561127662659e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 411 -1.4774589799344540e-02</internalNodes>
- <leafValues>
- 2.0816099643707275e-01 -1.2115559726953506e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 412 -4.1416451334953308e-02</internalNodes>
- <leafValues>
- -8.2437807321548462e-01 3.3329188823699951e-02</leafValues></_></weakClassifiers></_>
- <_>
- <maxWeakCount>53</maxWeakCount>
- <stageThreshold>-1.0492420196533203e+00</stageThreshold>
- <weakClassifiers>
- <_>
- <internalNodes>
- 0 -1 413 9.0962520334869623e-04</internalNodes>
- <leafValues>
- 8.4579966962337494e-02 -5.6118410825729370e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 414 -5.6139789521694183e-02</internalNodes>
- <leafValues>
- 1.5341749787330627e-01 -2.6967319846153259e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 415 1.0292009683325887e-03</internalNodes>
- <leafValues>
- -2.0489980280399323e-01 2.0153179764747620e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 416 2.8783010784536600e-03</internalNodes>
- <leafValues>
- -1.7351140081882477e-01 2.1297949552536011e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 417 -7.4144392274320126e-03</internalNodes>
- <leafValues>
- -5.9624868631362915e-01 4.7077950090169907e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 418 -1.4831849839538336e-03</internalNodes>
- <leafValues>
- 1.9024610519409180e-01 -1.5986390411853790e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 419 4.5968941412866116e-03</internalNodes>
- <leafValues>
- 3.1447131186723709e-02 -6.8694341182708740e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 420 2.4255330208688974e-03</internalNodes>
- <leafValues>
- -2.3609359562397003e-01 1.1036109924316406e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 421 -8.4950566291809082e-02</internalNodes>
- <leafValues>
- 2.3107160627841949e-01 -1.3776530325412750e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 422 -5.0145681016147137e-03</internalNodes>
- <leafValues>
- 3.8676109910011292e-01 -5.6217379868030548e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 423 -2.1482061129063368e-03</internalNodes>
- <leafValues>
- 1.8191599845886230e-01 -1.7615699768066406e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 424 -1.0396770201623440e-02</internalNodes>
- <leafValues>
- -7.5351381301879883e-01 2.4091970175504684e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 425 -1.3466750271618366e-02</internalNodes>
- <leafValues>
- -7.2118860483169556e-01 3.4949369728565216e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 426 -8.4435477852821350e-02</internalNodes>
- <leafValues>
- -3.3792638778686523e-01 7.1113817393779755e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 427 2.4771490134298801e-03</internalNodes>
- <leafValues>
- -1.1765109747648239e-01 2.2541989386081696e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 428 1.5828050673007965e-02</internalNodes>
- <leafValues>
- -6.9536216557025909e-02 3.1395369768142700e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 429 6.4916983246803284e-02</internalNodes>
- <leafValues>
- -7.5043588876724243e-02 4.0677338838577271e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 430 2.9652469675056636e-04</internalNodes>
- <leafValues>
- 7.3953360319137573e-02 -3.4544008970260620e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 431 1.3129520229995251e-03</internalNodes>
- <leafValues>
- -1.6909439861774445e-01 1.5258370339870453e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 432 -5.8032129891216755e-03</internalNodes>
- <leafValues>
- 3.5260149836540222e-01 -8.3444066345691681e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 433 -1.4791679382324219e-01</internalNodes>
- <leafValues>
- 4.3004658818244934e-01 -5.7309929281473160e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 434 -1.6584150493144989e-02</internalNodes>
- <leafValues>
- 2.3432689905166626e-01 -1.0907640308141708e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 435 3.0183270573616028e-03</internalNodes>
- <leafValues>
- -1.3600939512252808e-01 2.6409289240837097e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 436 -3.6471918225288391e-02</internalNodes>
- <leafValues>
- -6.2809741497039795e-01 4.3545108288526535e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 437 -7.3119226726703346e-05</internalNodes>
- <leafValues>
- 1.6470630466938019e-01 -1.6463780403137207e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 438 -3.6719450727105141e-03</internalNodes>
- <leafValues>
- -4.7421360015869141e-01 4.8586919903755188e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 439 -4.0151178836822510e-03</internalNodes>
- <leafValues>
- 1.8222180008888245e-01 -1.4097510278224945e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 440 1.9948020577430725e-02</internalNodes>
- <leafValues>
- -6.9787658751010895e-02 3.6707460880279541e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 441 7.6699437340721488e-04</internalNodes>
- <leafValues>
- 5.5729299783706665e-02 -4.4585430622100830e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 442 -1.1806039838120341e-03</internalNodes>
- <leafValues>
- -4.6876621246337891e-01 4.8902221024036407e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 443 1.5847349539399147e-02</internalNodes>
- <leafValues>
- -1.2120209634304047e-01 2.0566530525684357e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 444 -1.1985700111836195e-03</internalNodes>
- <leafValues>
- 2.0262099802494049e-01 -1.2823820114135742e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 445 -1.0964959859848022e-01</internalNodes>
- <leafValues>
- -8.6619192361831665e-01 3.0351849272847176e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 446 -9.2532606795430183e-03</internalNodes>
- <leafValues>
- 2.9343119263648987e-01 -8.5361950099468231e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 447 1.4686530455946922e-02</internalNodes>
- <leafValues>
- 3.2798621803522110e-02 -7.7556562423706055e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 448 -1.3514430029317737e-03</internalNodes>
- <leafValues>
- 2.4426999688148499e-01 -1.1503250151872635e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 449 -4.3728090822696686e-03</internalNodes>
- <leafValues>
- 2.1687670052051544e-01 -1.3984480500221252e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 450 3.4263390116393566e-03</internalNodes>
- <leafValues>
- 4.5614220201969147e-02 -5.4567712545394897e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 451 -3.8404068909585476e-03</internalNodes>
- <leafValues>
- 1.4949500560760498e-01 -1.5062509477138519e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 452 3.7988980766385794e-03</internalNodes>
- <leafValues>
- -8.7301626801490784e-02 2.5481531023979187e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 453 -2.0094281062483788e-03</internalNodes>
- <leafValues>
- 1.7259070277214050e-01 -1.4288470149040222e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 454 -2.4370709434151649e-03</internalNodes>
- <leafValues>
- 2.6848098635673523e-01 -8.1898219883441925e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 455 1.0485399980098009e-03</internalNodes>
- <leafValues>
- 4.6113260090351105e-02 -4.7243279218673706e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 456 1.7460780218243599e-03</internalNodes>
- <leafValues>
- -1.1030430346727371e-01 2.0379729568958282e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 457 5.8608627878129482e-03</internalNodes>
- <leafValues>
- -1.5619659423828125e-01 1.5927439928054810e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 458 -2.7724979445338249e-02</internalNodes>
- <leafValues>
- 1.1349119991064072e-01 -2.1885140240192413e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 459 4.7080639749765396e-02</internalNodes>
- <leafValues>
- -4.1688729077577591e-02 5.3630048036575317e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 460 -7.9283770173788071e-03</internalNodes>
- <leafValues>
- -5.3595131635665894e-01 4.4237509369850159e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 461 -1.2880540452897549e-02</internalNodes>
- <leafValues>
- 2.3237949609756470e-01 -1.0246250033378601e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 462 2.3604769259691238e-02</internalNodes>
- <leafValues>
- -8.8291436433792114e-02 3.0561059713363647e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 463 1.5902200713753700e-02</internalNodes>
- <leafValues>
- -1.2238109856843948e-01 1.7849120497703552e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 464 7.9939495772123337e-03</internalNodes>
- <leafValues>
- -8.3729006350040436e-02 3.2319590449333191e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 465 5.7100867852568626e-03</internalNodes>
- <leafValues>
- 3.8479208946228027e-02 -6.8138152360916138e-01</leafValues></_></weakClassifiers></_>
- <_>
- <maxWeakCount>51</maxWeakCount>
- <stageThreshold>-1.1122100353240967e+00</stageThreshold>
- <weakClassifiers>
- <_>
- <internalNodes>
- 0 -1 466 2.2480720654129982e-03</internalNodes>
- <leafValues>
- -1.6416870057582855e-01 4.1648530960083008e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 467 4.5813550241291523e-03</internalNodes>
- <leafValues>
- -1.2465959787368774e-01 4.0385121107101440e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 468 -1.6073239967226982e-03</internalNodes>
- <leafValues>
- 2.6082459092140198e-01 -2.0282520353794098e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 469 2.5205370038747787e-03</internalNodes>
- <leafValues>
- -1.0557229816913605e-01 3.6669111251831055e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 470 2.4119189474731684e-03</internalNodes>
- <leafValues>
- -1.3877600431442261e-01 2.9959911108016968e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 471 5.7156179100275040e-03</internalNodes>
- <leafValues>
- -7.7683463692665100e-02 4.8481920361518860e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 472 3.1093840952962637e-03</internalNodes>
- <leafValues>
- -1.1229000240564346e-01 2.9215508699417114e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 473 -8.6836628615856171e-02</internalNodes>
- <leafValues>
- -3.6779600381851196e-01 7.2597242891788483e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 474 5.2652182057499886e-03</internalNodes>
- <leafValues>
- -1.0890290141105652e-01 3.1791260838508606e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 475 -1.9913529977202415e-02</internalNodes>
- <leafValues>
- -5.3373438119888306e-01 7.0585712790489197e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 476 3.8297839928418398e-03</internalNodes>
- <leafValues>
- -1.3575910031795502e-01 2.2788879275321960e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 477 1.0431859642267227e-02</internalNodes>
- <leafValues>
- 8.8797912001609802e-02 -4.7958970069885254e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 478 -2.0040439441800117e-02</internalNodes>
- <leafValues>
- 1.5745539963245392e-01 -1.7771570384502411e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 479 -5.2967290394008160e-03</internalNodes>
- <leafValues>
- -6.8434917926788330e-01 3.5671461373567581e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 480 -2.1624139044433832e-03</internalNodes>
- <leafValues>
- 2.8318038582801819e-01 -9.8511278629302979e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 481 -3.5464888787828386e-04</internalNodes>
- <leafValues>
- -3.7077340483665466e-01 8.0932952463626862e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 482 -1.8152060511056334e-04</internalNodes>
- <leafValues>
- -3.2207030057907104e-01 7.7551059424877167e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 483 -2.7563021285459399e-04</internalNodes>
- <leafValues>
- -3.2441279292106628e-01 8.7949477136135101e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 484 6.3823810778558254e-03</internalNodes>
- <leafValues>
- -8.8924713432788849e-02 3.1727218627929688e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 485 1.1150909587740898e-02</internalNodes>
- <leafValues>
- 7.1019843220710754e-02 -4.0494039654731750e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 486 -1.0593760525807738e-03</internalNodes>
- <leafValues>
- 2.6050668954849243e-01 -1.1765640228986740e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 487 2.3906480055302382e-03</internalNodes>
- <leafValues>
- -8.4388621151447296e-02 3.1230551004409790e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 488 -1.1000749655067921e-02</internalNodes>
- <leafValues>
- 1.9152249395847321e-01 -1.5210020542144775e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 489 -2.4643228971399367e-04</internalNodes>
- <leafValues>
- -3.1765159964561462e-01 8.6582258343696594e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 490 2.3053269833326340e-02</internalNodes>
- <leafValues>
- -1.0089760273694992e-01 2.5769290328025818e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 491 -2.2135660983622074e-03</internalNodes>
- <leafValues>
- 4.5689210295677185e-01 -5.2404791116714478e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 492 -9.7139709396287799e-04</internalNodes>
- <leafValues>
- -3.5518380999565125e-01 8.0094382166862488e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 493 1.5676229959353805e-03</internalNodes>
- <leafValues>
- 1.0091420263051987e-01 -2.1603040397167206e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 494 7.5460801599547267e-04</internalNodes>
- <leafValues>
- 5.7896178215742111e-02 -4.0461111068725586e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 495 -2.0698970183730125e-02</internalNodes>
- <leafValues>
- 3.1543630361557007e-01 -8.0713048577308655e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 496 -2.0619940012693405e-02</internalNodes>
- <leafValues>
- 2.7181661128997803e-01 -7.6358616352081299e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 497 2.1611129865050316e-02</internalNodes>
- <leafValues>
- 3.9493449032306671e-02 -5.9429651498794556e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 498 6.5676742233335972e-03</internalNodes>
- <leafValues>
- -9.8353669047355652e-02 2.3649279773235321e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 499 -8.8434796780347824e-03</internalNodes>
- <leafValues>
- -5.2523428201675415e-01 4.3099921196699142e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 500 -9.4260741025209427e-03</internalNodes>
- <leafValues>
- 2.4665130674839020e-01 -9.4130717217922211e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 501 -1.9830230157822371e-03</internalNodes>
- <leafValues>
- 2.6743701100349426e-01 -9.0069316327571869e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 502 -1.7358399927616119e-03</internalNodes>
- <leafValues>
- 1.5940019488334656e-01 -1.5789410471916199e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 503 -1.3513869605958462e-02</internalNodes>
- <leafValues>
- 4.0792331099510193e-01 -6.4223118126392365e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 504 -1.9394010305404663e-02</internalNodes>
- <leafValues>
- 1.8015649914741516e-01 -1.3731400668621063e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 505 -3.2684770412743092e-03</internalNodes>
- <leafValues>
- 2.9080390930175781e-01 -8.0161906778812408e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 506 4.1773589327931404e-04</internalNodes>
- <leafValues>
- -2.1412980556488037e-01 1.1273439973592758e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 507 -7.6351119205355644e-03</internalNodes>
- <leafValues>
- -4.5365959405899048e-01 5.4625060409307480e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 508 -8.3652976900339127e-03</internalNodes>
- <leafValues>
- 2.6472920179367065e-01 -9.4334110617637634e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 509 2.7768449857831001e-02</internalNodes>
- <leafValues>
- -1.0136710107326508e-01 2.0743979513645172e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 510 -5.4891228675842285e-02</internalNodes>
- <leafValues>
- 2.8840309381484985e-01 -7.5312040746212006e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 511 2.5793339591473341e-03</internalNodes>
- <leafValues>
- -1.1088529974222183e-01 2.1724960207939148e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 512 6.6196516854688525e-05</internalNodes>
- <leafValues>
- -1.8872100114822388e-01 1.4440689980983734e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 513 5.0907251425087452e-03</internalNodes>
- <leafValues>
- -7.7601231634616852e-02 2.9398378729820251e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 514 -1.0444259643554688e-01</internalNodes>
- <leafValues>
- 2.0133109390735626e-01 -1.0903970152139664e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 515 -6.7273090826347470e-04</internalNodes>
- <leafValues>
- 1.7945900559425354e-01 -1.2023670226335526e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 516 3.2412849832326174e-03</internalNodes>
- <leafValues>
- 4.0688131004571915e-02 -5.4600572586059570e-01</leafValues></_></weakClassifiers></_>
- <_>
- <maxWeakCount>44</maxWeakCount>
- <stageThreshold>-1.2529590129852295e+00</stageThreshold>
- <weakClassifiers>
- <_>
- <internalNodes>
- 0 -1 517 5.2965320646762848e-03</internalNodes>
- <leafValues>
- -1.2154529988765717e-01 6.4420372247695923e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 518 -2.5326260365545750e-03</internalNodes>
- <leafValues>
- 5.1233220100402832e-01 -1.1108259856700897e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 519 -2.9183230362832546e-03</internalNodes>
- <leafValues>
- -5.0615429878234863e-01 1.1501979827880859e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 520 -2.3692339658737183e-02</internalNodes>
- <leafValues>
- 3.7167280912399292e-01 -1.4672680199146271e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 521 2.0177470520138741e-02</internalNodes>
- <leafValues>
- -1.7388840019702911e-01 4.7759491205215454e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 522 -2.1723210811614990e-02</internalNodes>
- <leafValues>
- -4.3880090117454529e-01 1.3576899468898773e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 523 2.8369780629873276e-03</internalNodes>
- <leafValues>
- -1.2512069940567017e-01 4.6789029240608215e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 524 2.7148420922458172e-03</internalNodes>
- <leafValues>
- -8.8018856942653656e-02 3.6866518855094910e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 525 3.2625689636915922e-03</internalNodes>
- <leafValues>
- -8.5335306823253632e-02 5.1644730567932129e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 526 -3.5618850961327553e-03</internalNodes>
- <leafValues>
- -4.4503930211067200e-01 9.1738171875476837e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 527 1.9227749435231090e-03</internalNodes>
- <leafValues>
- -1.1077310144901276e-01 3.9416998624801636e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 528 -3.5111969918943942e-04</internalNodes>
- <leafValues>
- -3.7775701284408569e-01 1.2166170030832291e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 529 1.9121779769193381e-04</internalNodes>
- <leafValues>
- 7.4816018342971802e-02 -4.0767100453376770e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 530 -2.6525629800744355e-04</internalNodes>
- <leafValues>
- -3.3151718974113464e-01 1.1291120201349258e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 531 2.0086700096726418e-02</internalNodes>
- <leafValues>
- -6.1598118394613266e-02 5.6128817796707153e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 532 3.6783248186111450e-02</internalNodes>
- <leafValues>
- -6.0251388698816299e-02 5.2192491292953491e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 533 1.3941619545221329e-03</internalNodes>
- <leafValues>
- -3.5503050684928894e-01 1.0863020271062851e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 534 -1.5181669965386391e-02</internalNodes>
- <leafValues>
- 2.2739650309085846e-01 -1.6252990067005157e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 535 4.6796840615570545e-03</internalNodes>
- <leafValues>
- -5.7535041123628616e-02 4.8124238848686218e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 536 -1.7988319450523704e-04</internalNodes>
- <leafValues>
- -3.0587670207023621e-01 1.0868159681558609e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 537 -3.5850999411195517e-03</internalNodes>
- <leafValues>
- 3.8596940040588379e-01 -9.2194072902202606e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 538 1.0793360415846109e-03</internalNodes>
- <leafValues>
- -1.1190389841794968e-01 3.1125208735466003e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 539 7.3285802500322461e-05</internalNodes>
- <leafValues>
- -2.0239910483360291e-01 1.5586680173873901e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 540 1.3678739964962006e-01</internalNodes>
- <leafValues>
- -2.1672859787940979e-01 1.4420390129089355e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 541 -1.1729259975254536e-02</internalNodes>
- <leafValues>
- 4.3503770232200623e-01 -7.4886530637741089e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 542 3.9230841211974621e-03</internalNodes>
- <leafValues>
- -5.0289329141378403e-02 5.8831161260604858e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 543 -2.9819121118634939e-04</internalNodes>
- <leafValues>
- -3.8232401013374329e-01 9.2451132833957672e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 544 -4.7992770560085773e-03</internalNodes>
- <leafValues>
- 4.8488789796829224e-01 -7.3136523365974426e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 545 -3.0155890271998942e-04</internalNodes>
- <leafValues>
- -3.5757359862327576e-01 1.0581880062818527e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 546 1.0390769690275192e-02</internalNodes>
- <leafValues>
- 5.2920468151569366e-02 -5.7249659299850464e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 547 -9.4488041941076517e-04</internalNodes>
- <leafValues>
- 4.4966828823089600e-01 -8.3075523376464844e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 548 1.2651870492845774e-03</internalNodes>
- <leafValues>
- -9.6695438027381897e-02 3.1302270293235779e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 549 1.7094539478421211e-02</internalNodes>
- <leafValues>
- -8.1248976290225983e-02 3.6113831400871277e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 550 2.5973359588533640e-03</internalNodes>
- <leafValues>
- -1.1338350176811218e-01 2.2233949601650238e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 551 1.4527440071105957e-03</internalNodes>
- <leafValues>
- 6.9750443100929260e-02 -3.6720710992813110e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 552 4.7638658434152603e-03</internalNodes>
- <leafValues>
- -6.5788961946964264e-02 3.8328540325164795e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 553 -6.2501081265509129e-03</internalNodes>
- <leafValues>
- -7.0754468441009521e-01 3.8350198417901993e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 554 -3.1765329185873270e-03</internalNodes>
- <leafValues>
- 1.3755400478839874e-01 -2.3240029811859131e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 555 3.2191169448196888e-03</internalNodes>
- <leafValues>
- -1.2935450673103333e-01 2.2737880051136017e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 556 -5.6365579366683960e-03</internalNodes>
- <leafValues>
- 3.8067150115966797e-01 -6.7246839404106140e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 557 -2.3844049428589642e-04</internalNodes>
- <leafValues>
- -3.1122380495071411e-01 8.3838358521461487e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 558 -4.1017560288310051e-03</internalNodes>
- <leafValues>
- 2.6067280769348145e-01 -1.0449740290641785e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 559 1.3336989795789123e-03</internalNodes>
- <leafValues>
- -5.8250140398740768e-02 4.7682440280914307e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 560 -1.2090239906683564e-03</internalNodes>
- <leafValues>
- 1.4834509789943695e-01 -1.7329469323158264e-01</leafValues></_></weakClassifiers></_>
- <_>
- <maxWeakCount>72</maxWeakCount>
- <stageThreshold>-1.1188739538192749e+00</stageThreshold>
- <weakClassifiers>
- <_>
- <internalNodes>
- 0 -1 561 -3.1760931015014648e-03</internalNodes>
- <leafValues>
- 3.3333331346511841e-01 -1.6642349958419800e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 562 2.4858079850673676e-02</internalNodes>
- <leafValues>
- -7.2728872299194336e-02 5.6674581766128540e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 563 -7.7597280032932758e-03</internalNodes>
- <leafValues>
- 4.6258568763732910e-01 -9.3112178146839142e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 564 7.8239021822810173e-03</internalNodes>
- <leafValues>
- -2.7414610981941223e-01 1.3243049383163452e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 565 -1.0948839597404003e-02</internalNodes>
- <leafValues>
- 2.2345480322837830e-01 -1.4965449273586273e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 566 -3.4349008928984404e-03</internalNodes>
- <leafValues>
- 3.8724988698959351e-01 -6.6121727228164673e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 567 -3.1156290322542191e-02</internalNodes>
- <leafValues>
- 2.4078279733657837e-01 -1.1406909674406052e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 568 1.1100519914180040e-03</internalNodes>
- <leafValues>
- -2.8207978606224060e-01 1.3275429606437683e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 569 3.1762740109115839e-03</internalNodes>
- <leafValues>
- 3.4585930407047272e-02 -5.1374310255050659e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 570 -2.7977459132671356e-02</internalNodes>
- <leafValues>
- 2.3926779627799988e-01 -1.3255919516086578e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 571 -2.3097939789295197e-02</internalNodes>
- <leafValues>
- 3.9019620418548584e-01 -7.8478008508682251e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 572 -3.9731930010020733e-03</internalNodes>
- <leafValues>
- 3.0691069364547729e-01 -7.0601403713226318e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 573 3.0335749033838511e-03</internalNodes>
- <leafValues>
- -1.4002190530300140e-01 1.9134859740734100e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 574 -1.0844370350241661e-02</internalNodes>
- <leafValues>
- 1.6548730432987213e-01 -1.5657779574394226e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 575 -1.8150510266423225e-02</internalNodes>
- <leafValues>
- -6.3243591785430908e-01 3.9561819285154343e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 576 7.1052298881113529e-04</internalNodes>
- <leafValues>
- -1.8515570461750031e-01 1.3408809900283813e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 577 1.0893340222537518e-02</internalNodes>
- <leafValues>
- -2.6730230078101158e-02 6.0971802473068237e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 578 -2.8780900174751878e-04</internalNodes>
- <leafValues>
- -3.0065140128135681e-01 7.3171459138393402e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 579 -3.5855069290846586e-03</internalNodes>
- <leafValues>
- 2.6217609643936157e-01 -7.9714097082614899e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 580 -1.9759280607104301e-02</internalNodes>
- <leafValues>
- -5.9039229154586792e-01 4.0698971599340439e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 581 -1.0845210403203964e-02</internalNodes>
- <leafValues>
- 1.6364559531211853e-01 -1.2586060166358948e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 582 -4.3183090165257454e-03</internalNodes>
- <leafValues>
- -5.7474881410598755e-01 3.7644311785697937e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 583 1.4913700288161635e-03</internalNodes>
- <leafValues>
- 6.0913469642400742e-02 -3.0222928524017334e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 584 1.5675699338316917e-02</internalNodes>
- <leafValues>
- -7.3145911097526550e-02 2.9379451274871826e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 585 -1.1033560149371624e-02</internalNodes>
- <leafValues>
- 3.9318808913230896e-01 -4.7084320336580276e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 586 8.8555756956338882e-03</internalNodes>
- <leafValues>
- 3.7601381540298462e-02 -4.9108490347862244e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 587 -8.9665671112015843e-04</internalNodes>
- <leafValues>
- 1.7952020466327667e-01 -1.1086239665746689e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 588 -3.0592409893870354e-03</internalNodes>
- <leafValues>
- -4.4429460167884827e-01 5.1005430519580841e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 589 6.3201179727911949e-03</internalNodes>
- <leafValues>
- -5.2841089665889740e-02 3.7197101116180420e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 590 2.0682830363512039e-02</internalNodes>
- <leafValues>
- 5.7667169719934464e-02 -3.6901599168777466e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 591 9.9822662770748138e-02</internalNodes>
- <leafValues>
- -3.7377018481492996e-02 5.8165591955184937e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 592 -6.5854229032993317e-03</internalNodes>
- <leafValues>
- 2.8509441018104553e-01 -6.0978069901466370e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 593 -6.0900300741195679e-02</internalNodes>
- <leafValues>
- -5.1031768321990967e-01 3.7787400186061859e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 594 -2.9991709161549807e-03</internalNodes>
- <leafValues>
- -4.7943010926246643e-01 3.8833890110254288e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 595 -9.8906438797712326e-03</internalNodes>
- <leafValues>
- 4.0609079599380493e-01 -4.7869648784399033e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 596 -8.2688927650451660e-02</internalNodes>
- <leafValues>
- -7.0671182870864868e-01 2.7487749233841896e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 597 5.0060399807989597e-03</internalNodes>
- <leafValues>
- 2.8208440169692039e-02 -5.2909690141677856e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 598 6.1695030890405178e-03</internalNodes>
- <leafValues>
- -5.4554861038923264e-02 3.2837980985641479e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 599 -3.3914761152118444e-03</internalNodes>
- <leafValues>
- 9.2117667198181152e-02 -2.1637110412120819e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 600 -2.6131230406463146e-03</internalNodes>
- <leafValues>
- 1.3651019334793091e-01 -1.3781130313873291e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 601 8.0490659456700087e-04</internalNodes>
- <leafValues>
- -6.8637110292911530e-02 3.3581069111824036e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 602 -3.8106508553028107e-02</internalNodes>
- <leafValues>
- 2.9445430636405945e-01 -6.8239226937294006e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 603 7.2450799052603543e-05</internalNodes>
- <leafValues>
- -1.6750130057334900e-01 1.2178230285644531e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 604 1.5837959945201874e-03</internalNodes>
- <leafValues>
- -9.2042848467826843e-02 2.1348990499973297e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 605 1.2924340553581715e-03</internalNodes>
- <leafValues>
- 6.2917232513427734e-02 -3.6174508929252625e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 606 9.9146775901317596e-03</internalNodes>
- <leafValues>
- 1.9534060731530190e-02 -8.1015038490295410e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 607 -1.7086310544982553e-03</internalNodes>
- <leafValues>
- 2.5525239109992981e-01 -6.8229459226131439e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 608 2.1844399161636829e-03</internalNodes>
- <leafValues>
- 2.3314049467444420e-02 -8.4296780824661255e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 609 -3.4244330599904060e-03</internalNodes>
- <leafValues>
- 2.7213689684867859e-01 -7.6395228505134583e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 610 2.7591470279730856e-04</internalNodes>
- <leafValues>
- -1.0742840170860291e-01 2.2888970375061035e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 611 -6.0005177510902286e-04</internalNodes>
- <leafValues>
- -2.9854211211204529e-01 6.3479736447334290e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 612 -2.5001438916660845e-04</internalNodes>
- <leafValues>
- -2.7178969979286194e-01 6.9615006446838379e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 613 6.8751391954720020e-03</internalNodes>
- <leafValues>
- -5.7185899466276169e-02 3.6695951223373413e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 614 1.2761900201439857e-02</internalNodes>
- <leafValues>
- 6.7955687642097473e-02 -2.8534150123596191e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 615 -1.4752789866179228e-03</internalNodes>
- <leafValues>
- 2.0680660009384155e-01 -1.0059390217065811e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 616 1.2138819694519043e-01</internalNodes>
- <leafValues>
- -9.7126796841621399e-02 1.9789619743824005e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 617 -5.0081279128789902e-02</internalNodes>
- <leafValues>
- 2.8417178988456726e-01 -6.7879997193813324e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 618 3.1454950571060181e-02</internalNodes>
- <leafValues>
- -8.9468672871589661e-02 2.1298420429229736e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 619 1.8878319533541799e-03</internalNodes>
- <leafValues>
- -1.1656440049409866e-01 1.6663520038127899e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 620 -5.7211960665881634e-03</internalNodes>
- <leafValues>
- 2.3702140152454376e-01 -9.0776607394218445e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 621 -1.8076719425152987e-04</internalNodes>
- <leafValues>
- 1.7951929569244385e-01 -1.0793480277061462e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 622 -1.9761849939823151e-01</internalNodes>
- <leafValues>
- 4.5674291253089905e-01 -4.0480159223079681e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 623 -2.3846809926908463e-04</internalNodes>
- <leafValues>
- -2.3733009397983551e-01 7.5922161340713501e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 624 2.1540730085689574e-04</internalNodes>
- <leafValues>
- 8.1688016653060913e-02 -2.8685030341148376e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 625 1.0163090191781521e-02</internalNodes>
- <leafValues>
- -4.1250020265579224e-02 4.8038348555564880e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 626 -7.2184870950877666e-03</internalNodes>
- <leafValues>
- 1.7458580434322357e-01 -1.0146500170230865e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 627 2.4263170361518860e-01</internalNodes>
- <leafValues>
- 5.3426481783390045e-02 -3.2318529486656189e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 628 6.9304101634770632e-04</internalNodes>
- <leafValues>
- -1.1499179899692535e-01 1.4793939888477325e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 629 3.5475199110805988e-03</internalNodes>
- <leafValues>
- -3.9424978196620941e-02 5.3126180171966553e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 630 2.1403690334409475e-04</internalNodes>
- <leafValues>
- 6.9753833115100861e-02 -2.7319580316543579e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 631 -5.7119462871924043e-04</internalNodes>
- <leafValues>
- 3.4369900822639465e-01 -5.7699009776115417e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 632 -6.6290069371461868e-03</internalNodes>
- <leafValues>
- 1.1758489906787872e-01 -1.5020139515399933e-01</leafValues></_></weakClassifiers></_>
- <_>
- <maxWeakCount>66</maxWeakCount>
- <stageThreshold>-1.0888810157775879e+00</stageThreshold>
- <weakClassifiers>
- <_>
- <internalNodes>
- 0 -1 633 -2.6513449847698212e-02</internalNodes>
- <leafValues>
- 2.0568640530109406e-01 -2.6473900675773621e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 634 9.7727458924055099e-03</internalNodes>
- <leafValues>
- -1.1192840337753296e-01 3.2570549845695496e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 635 3.2290350645780563e-02</internalNodes>
- <leafValues>
- -9.8574757575988770e-02 3.1779170036315918e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 636 -2.8103240765631199e-03</internalNodes>
- <leafValues>
- 1.5213899314403534e-01 -1.9686409831047058e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 637 -1.0991429910063744e-02</internalNodes>
- <leafValues>
- 5.1407659053802490e-01 -4.3707210570573807e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 638 6.3133831135928631e-03</internalNodes>
- <leafValues>
- -9.2781022191047668e-02 3.4702470898628235e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 639 8.7105982005596161e-02</internalNodes>
- <leafValues>
- 3.0053649097681046e-02 -8.2814818620681763e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 640 1.1799359926953912e-03</internalNodes>
- <leafValues>
- -1.2928420305252075e-01 2.0646120607852936e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 641 -9.3056890182197094e-04</internalNodes>
- <leafValues>
- -5.0021439790725708e-01 9.3666993081569672e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 642 -1.3687170110642910e-02</internalNodes>
- <leafValues>
- -7.9358148574829102e-01 -6.6733639687299728e-03</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 643 -7.5917452573776245e-02</internalNodes>
- <leafValues>
- 3.0469641089439392e-01 -7.9655893146991730e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 644 -2.8559709899127483e-03</internalNodes>
- <leafValues>
- 2.0961460471153259e-01 -1.2732550501823425e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 645 -4.0231510065495968e-03</internalNodes>
- <leafValues>
- -6.5817278623580933e-01 5.0683639943599701e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 646 1.7558040097355843e-02</internalNodes>
- <leafValues>
- -8.5382692515850067e-02 3.6174559593200684e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 647 2.1988239139318466e-02</internalNodes>
- <leafValues>
- 6.2943696975708008e-02 -7.0896339416503906e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 648 -2.8599589131772518e-03</internalNodes>
- <leafValues>
- 1.4683780074119568e-01 -1.6465979814529419e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 649 -1.0030849836766720e-02</internalNodes>
- <leafValues>
- 4.9579939246177673e-01 -2.7188340201973915e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 650 -6.9560329429805279e-03</internalNodes>
- <leafValues>
- 2.7977779507637024e-01 -7.7953331172466278e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 651 -3.8356808945536613e-03</internalNodes>
- <leafValues>
- -5.8163982629776001e-01 3.5739939659833908e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 652 -3.2647319603711367e-03</internalNodes>
- <leafValues>
- -4.9945080280303955e-01 4.6986490488052368e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 653 -7.8412350267171860e-03</internalNodes>
- <leafValues>
- 3.4532830119132996e-01 -6.8810403347015381e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 654 -8.1718113506212831e-05</internalNodes>
- <leafValues>
- 1.5041710436344147e-01 -1.4146679639816284e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 655 -3.2448628917336464e-03</internalNodes>
- <leafValues>
- 2.2724510729312897e-01 -9.2860206961631775e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 656 -7.8561151167377830e-04</internalNodes>
- <leafValues>
- -4.4319018721580505e-01 5.7812441140413284e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 657 -6.2474247533828020e-04</internalNodes>
- <leafValues>
- 1.3952389359474182e-01 -1.4668719470500946e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 658 -3.2942948746494949e-04</internalNodes>
- <leafValues>
- -2.9901570081710815e-01 7.6066739857196808e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 659 1.2605739757418633e-03</internalNodes>
- <leafValues>
- -1.6125600039958954e-01 1.3953800499439240e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 660 -5.1667019724845886e-02</internalNodes>
- <leafValues>
- -5.3142839670181274e-01 4.0719520300626755e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 661 -1.5285619534552097e-02</internalNodes>
- <leafValues>
- -7.8206378221511841e-01 2.7183769270777702e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 662 6.9029822945594788e-02</internalNodes>
- <leafValues>
- -3.6427021026611328e-02 7.1102517843246460e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 663 1.4522749697789550e-03</internalNodes>
- <leafValues>
- -9.6890516579151154e-02 2.1668420732021332e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 664 -2.4765590205788612e-03</internalNodes>
- <leafValues>
- 1.1645310372114182e-01 -1.8227979540824890e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 665 -1.5134819550439715e-03</internalNodes>
- <leafValues>
- 1.7863979935646057e-01 -1.2214969843626022e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 666 -1.5099470037966967e-03</internalNodes>
- <leafValues>
- 1.8086239695549011e-01 -1.1446069926023483e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 667 -6.7054620012640953e-03</internalNodes>
- <leafValues>
- 2.5106599926948547e-01 -9.1871462762355804e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 668 -1.4075200073421001e-02</internalNodes>
- <leafValues>
- 1.3707509636878967e-01 -1.7333500087261200e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 669 -2.2400720044970512e-03</internalNodes>
- <leafValues>
- 4.0092980861663818e-01 -4.7576878219842911e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 670 1.9782369956374168e-02</internalNodes>
- <leafValues>
- -1.9040350615978241e-01 1.4923410117626190e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 671 2.6002870872616768e-03</internalNodes>
- <leafValues>
- 4.6971768140792847e-02 -4.3307659029960632e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 672 -5.3445628145709634e-04</internalNodes>
- <leafValues>
- -4.3744230270385742e-01 4.1520189493894577e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 673 -1.7466509714722633e-02</internalNodes>
- <leafValues>
- 6.5818172693252563e-01 -3.4447491168975830e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 674 -2.0425589755177498e-03</internalNodes>
- <leafValues>
- 3.9657929539680481e-01 -4.4052429497241974e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 675 2.6661779265850782e-03</internalNodes>
- <leafValues>
- 5.8770958334207535e-02 -3.2806369662284851e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 676 -5.5982369929552078e-02</internalNodes>
- <leafValues>
- -5.1735472679138184e-01 3.5791840404272079e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 677 -1.5066330088302493e-03</internalNodes>
- <leafValues>
- 1.5123869478702545e-01 -1.2520180642604828e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 678 -1.1472369544208050e-02</internalNodes>
- <leafValues>
- -6.2930530309677124e-01 3.4704331308603287e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 679 2.3409629240632057e-02</internalNodes>
- <leafValues>
- -5.8063350617885590e-02 3.8668221235275269e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 680 -2.3243729956448078e-03</internalNodes>
- <leafValues>
- 1.8754099309444427e-01 -9.8394669592380524e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 681 -2.9039299115538597e-02</internalNodes>
- <leafValues>
- -5.4486900568008423e-01 4.0926340967416763e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 682 -1.4474649913609028e-02</internalNodes>
- <leafValues>
- -6.7248392105102539e-01 2.3128850385546684e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 683 -5.2086091600358486e-03</internalNodes>
- <leafValues>
- -4.3271440267562866e-01 4.3780650943517685e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 684 4.9382899887859821e-03</internalNodes>
- <leafValues>
- -1.0878620296716690e-01 1.9342589378356934e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 685 -4.3193930760025978e-03</internalNodes>
- <leafValues>
- 2.4080930650234222e-01 -1.0380800068378448e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 686 2.3705669445917010e-04</internalNodes>
- <leafValues>
- -8.7349072098731995e-02 2.0466239750385284e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 687 4.7858079778961837e-04</internalNodes>
- <leafValues>
- 4.5624580234289169e-02 -3.8854670524597168e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 688 -8.5342838428914547e-04</internalNodes>
- <leafValues>
- -5.5077940225601196e-01 3.5825889557600021e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 689 5.4772121075075120e-05</internalNodes>
- <leafValues>
- -1.1225239932537079e-01 1.7503519356250763e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 690 -3.8445889949798584e-03</internalNodes>
- <leafValues>
- 2.4526700377464294e-01 -8.1132568418979645e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 691 -4.0128458291292191e-02</internalNodes>
- <leafValues>
- -6.3122707605361938e-01 2.6972670108079910e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 692 -1.7886360001284629e-04</internalNodes>
- <leafValues>
- 1.9855099916458130e-01 -1.0333680361509323e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 693 1.7668239888735116e-04</internalNodes>
- <leafValues>
- -9.1359011828899384e-02 1.9848720729351044e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 694 7.2763383388519287e-02</internalNodes>
- <leafValues>
- 5.0075579434633255e-02 -3.3852630853652954e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 695 1.0181630030274391e-02</internalNodes>
- <leafValues>
- -9.3229979276657104e-02 2.0059590041637421e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 696 2.4409969337284565e-03</internalNodes>
- <leafValues>
- 6.4636632800102234e-02 -2.6921740174293518e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 697 -3.6227488890290260e-03</internalNodes>
- <leafValues>
- 1.3169890642166138e-01 -1.2514840066432953e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 698 -1.3635610230267048e-03</internalNodes>
- <leafValues>
- 1.6350460052490234e-01 -1.0665939748287201e-01</leafValues></_></weakClassifiers></_>
- <_>
- <maxWeakCount>69</maxWeakCount>
- <stageThreshold>-1.0408929586410522e+00</stageThreshold>
- <weakClassifiers>
- <_>
- <internalNodes>
- 0 -1 699 -9.6991164609789848e-03</internalNodes>
- <leafValues>
- 6.1125320196151733e-01 -6.6225312650203705e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 700 -9.6426531672477722e-03</internalNodes>
- <leafValues>
- -1. 2.7699959464371204e-03</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 701 -9.6381865441799164e-03</internalNodes>
- <leafValues>
- 1. -2.9904270195402205e-04</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 702 -4.2553939856588840e-03</internalNodes>
- <leafValues>
- 2.8464388847351074e-01 -1.5540120005607605e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 703 -9.6223521977663040e-03</internalNodes>
- <leafValues>
- -1. 4.3999180197715759e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 704 -9.1231241822242737e-03</internalNodes>
- <leafValues>
- 8.6869341135025024e-01 -2.7267890982329845e-03</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 705 -8.6240433156490326e-03</internalNodes>
- <leafValues>
- 4.5352488756179810e-01 -8.6071379482746124e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 706 -8.9324144646525383e-03</internalNodes>
- <leafValues>
- 1.3375559449195862e-01 -2.6012519001960754e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 707 -1.4207810163497925e-02</internalNodes>
- <leafValues>
- 3.2077640295028687e-01 -9.7226411104202271e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 708 2.5911010801792145e-02</internalNodes>
- <leafValues>
- -1.2964080274105072e-01 2.6218649744987488e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 709 2.0531509653665125e-04</internalNodes>
- <leafValues>
- -1.2404280155897141e-01 2.1062959730625153e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 710 -5.4795680625829846e-05</internalNodes>
- <leafValues>
- 1.1974299699068069e-01 -2.3201279342174530e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 711 6.8555199541151524e-03</internalNodes>
- <leafValues>
- -6.3276126980781555e-02 4.1044250130653381e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 712 -1.2253040447831154e-02</internalNodes>
- <leafValues>
- 5.4883331060409546e-01 -3.9731100201606750e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 713 -3.9058770053088665e-03</internalNodes>
- <leafValues>
- 2.4190980195999146e-01 -9.7096011042594910e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 714 2.7560980524867773e-03</internalNodes>
- <leafValues>
- -1.2569679319858551e-01 1.9456650316715240e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 715 -7.7662160620093346e-03</internalNodes>
- <leafValues>
- 2.9765701293945312e-01 -9.6818156540393829e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 716 3.8997188676148653e-04</internalNodes>
- <leafValues>
- 6.2188401818275452e-02 -4.2040899395942688e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 717 3.3579880837351084e-03</internalNodes>
- <leafValues>
- 4.7498140484094620e-02 -6.3216882944107056e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 718 -1.6745539382100105e-02</internalNodes>
- <leafValues>
- 7.1098130941390991e-01 -3.9157349616289139e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 719 -6.5409899689257145e-03</internalNodes>
- <leafValues>
- -3.5043171048164368e-01 7.0616953074932098e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 720 3.0016340315341949e-04</internalNodes>
- <leafValues>
- 9.1902457177639008e-02 -2.4618670344352722e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 721 1.4918990433216095e-02</internalNodes>
- <leafValues>
- -5.1909450441598892e-02 5.6636041402816772e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 722 4.8153079114854336e-04</internalNodes>
- <leafValues>
- 6.4659558236598969e-02 -3.6590608954429626e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 723 -3.0211321427486837e-04</internalNodes>
- <leafValues>
- 1.7926569283008575e-01 -1.1410660296678543e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 724 3.8521419628523290e-04</internalNodes>
- <leafValues>
- 1.0345619916915894e-01 -2.0072460174560547e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 725 8.0837132409214973e-03</internalNodes>
- <leafValues>
- -6.6073462367057800e-02 3.0284249782562256e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 726 -2.2804969921708107e-02</internalNodes>
- <leafValues>
- 5.2962350845336914e-01 -4.0118999779224396e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 727 1.9440450705587864e-04</internalNodes>
- <leafValues>
- 8.1854820251464844e-02 -2.4663360416889191e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 728 -1.2848090380430222e-02</internalNodes>
- <leafValues>
- -3.4973311424255371e-01 5.6916229426860809e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 729 -1.0937290498986840e-03</internalNodes>
- <leafValues>
- 2.3368680477142334e-01 -9.1604806482791901e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 730 1.0032650316134095e-03</internalNodes>
- <leafValues>
- 1.1852180212736130e-01 -1.8469190597534180e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 731 -4.4688429683446884e-02</internalNodes>
- <leafValues>
- -6.4362460374832153e-01 3.0363269150257111e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 732 8.1657543778419495e-03</internalNodes>
- <leafValues>
- 4.3674658983945847e-02 -4.3002089858055115e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 733 -1.1717810295522213e-02</internalNodes>
- <leafValues>
- 4.1781479120254517e-01 -4.8233699053525925e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 734 8.4277130663394928e-02</internalNodes>
- <leafValues>
- 5.3461279720067978e-02 -3.7952190637588501e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 735 1.4211839996278286e-02</internalNodes>
- <leafValues>
- 4.4900938868522644e-02 -4.2981499433517456e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 736 1.5028340276330709e-03</internalNodes>
- <leafValues>
- 8.2227639853954315e-02 -2.4706399440765381e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 737 1.0003579780459404e-02</internalNodes>
- <leafValues>
- -5.7221669703722000e-02 3.4609371423721313e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 738 -9.0706320479512215e-03</internalNodes>
- <leafValues>
- 4.5058089494705200e-01 -4.2795319110155106e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 739 -3.3141620224341750e-04</internalNodes>
- <leafValues>
- 1.8336910009384155e-01 -1.0759949684143066e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 740 1.9723279774188995e-01</internalNodes>
- <leafValues>
- -3.0363829806447029e-02 6.6423428058624268e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 741 -7.1258801035583019e-03</internalNodes>
- <leafValues>
- -8.9225047826766968e-01 2.5669990107417107e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 742 8.6921341717243195e-03</internalNodes>
- <leafValues>
- -7.0764370262622833e-02 2.8210529685020447e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 743 8.9262127876281738e-03</internalNodes>
- <leafValues>
- 7.1078233420848846e-02 -3.0232560634613037e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 744 5.7286009192466736e-02</internalNodes>
- <leafValues>
- 5.0974130630493164e-02 -3.9196950197219849e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 745 3.7920880131423473e-03</internalNodes>
- <leafValues>
- 3.3841941505670547e-02 -5.1016288995742798e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 746 -1.4508679741993546e-03</internalNodes>
- <leafValues>
- 3.0879148840904236e-01 -6.3845083117485046e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 747 9.8390132188796997e-04</internalNodes>
- <leafValues>
- -1.3029569387435913e-01 1.4604410529136658e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 748 -1.7221809830516577e-03</internalNodes>
- <leafValues>
- 2.9157009720802307e-01 -6.8549558520317078e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 749 1.0948250070214272e-02</internalNodes>
- <leafValues>
- 3.4351408481597900e-02 -4.7702258825302124e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 750 -1.7176309484057128e-05</internalNodes>
- <leafValues>
- 1.6055269539356232e-01 -1.1690840125083923e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 751 -5.4884208366274834e-03</internalNodes>
- <leafValues>
- -4.3415889143943787e-01 4.6106241643428802e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 752 -3.0975250992923975e-03</internalNodes>
- <leafValues>
- 3.7943339347839355e-01 -5.6860551238059998e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 753 6.4182081259787083e-03</internalNodes>
- <leafValues>
- -1.5858210623264313e-01 1.2335419654846191e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 754 1.1831239797174931e-02</internalNodes>
- <leafValues>
- -4.0929291397333145e-02 4.5878958702087402e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 755 1.3540499843657017e-02</internalNodes>
- <leafValues>
- -5.3725559264421463e-02 3.5056120157241821e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 756 -2.5932150892913342e-03</internalNodes>
- <leafValues>
- 1.1010520160198212e-01 -1.6752210259437561e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 757 1.6856270376592875e-03</internalNodes>
- <leafValues>
- 6.6574357450008392e-02 -3.0835020542144775e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 758 2.6524690911173820e-03</internalNodes>
- <leafValues>
- 6.6318482160568237e-02 -2.7861338853836060e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 759 -7.7341729775071144e-03</internalNodes>
- <leafValues>
- 1.9718359410762787e-01 -1.0782919824123383e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 760 5.0944271497428417e-03</internalNodes>
- <leafValues>
- 8.5337489843368530e-02 -2.4847009778022766e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 761 -2.9162371065467596e-03</internalNodes>
- <leafValues>
- -4.7476351261138916e-01 3.3566489815711975e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 762 3.0121419113129377e-03</internalNodes>
- <leafValues>
- -4.7575380653142929e-02 4.2586800456047058e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 763 3.1694869976490736e-03</internalNodes>
- <leafValues>
- -1.0519450157880783e-01 1.7163459956645966e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 764 2.2327560186386108e-01</internalNodes>
- <leafValues>
- -1.4370209537446499e-02 9.2483651638031006e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 765 -9.5585048198699951e-02</internalNodes>
- <leafValues>
- -7.4206638336181641e-01 2.7818970382213593e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 766 3.4773729566950351e-05</internalNodes>
- <leafValues>
- -1.2765780091285706e-01 1.2926669418811798e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 767 7.2459770308341831e-05</internalNodes>
- <leafValues>
- -1.6518579423427582e-01 1.0036809742450714e-01</leafValues></_></weakClassifiers></_>
- <_>
- <maxWeakCount>59</maxWeakCount>
- <stageThreshold>-1.0566600561141968e+00</stageThreshold>
- <weakClassifiers>
- <_>
- <internalNodes>
- 0 -1 768 -6.5778270363807678e-03</internalNodes>
- <leafValues>
- 3.3815258741378784e-01 -1.5281909704208374e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 769 -1.0922809597104788e-03</internalNodes>
- <leafValues>
- 2.2282369434833527e-01 -1.9308499991893768e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 770 -2.9759589582681656e-02</internalNodes>
- <leafValues>
- 2.5959870219230652e-01 -1.5409409999847412e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 771 -1.3147540390491486e-02</internalNodes>
- <leafValues>
- 1.9033810496330261e-01 -1.6543999314308167e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 772 -1.4396329643204808e-03</internalNodes>
- <leafValues>
- 2.0071710646152496e-01 -1.2338940054178238e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 773 -3.5928250290453434e-03</internalNodes>
- <leafValues>
- 2.3985520005226135e-01 -1.2922149896621704e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 774 -1.5314699849113822e-03</internalNodes>
- <leafValues>
- -4.9014899134635925e-01 1.0275030136108398e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 775 -6.2372139655053616e-03</internalNodes>
- <leafValues>
- 3.1214639544487000e-01 -1.1405629664659500e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 776 -3.3364649862051010e-02</internalNodes>
- <leafValues>
- -4.9520879983901978e-01 5.1328450441360474e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 777 -2.2827699780464172e-02</internalNodes>
- <leafValues>
- 3.2558828592300415e-01 -6.5089307725429535e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 778 -8.6199097335338593e-02</internalNodes>
- <leafValues>
- -6.7646330595016479e-01 2.6985699310898781e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 779 -2.1065981127321720e-03</internalNodes>
- <leafValues>
- 2.2452430427074432e-01 -1.2610229849815369e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 780 3.9120148867368698e-02</internalNodes>
- <leafValues>
- 1.1329399794340134e-01 -2.6860630512237549e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 781 3.5082739777863026e-03</internalNodes>
- <leafValues>
- -1.1359959840774536e-01 2.5649771094322205e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 782 5.9289898490533233e-04</internalNodes>
- <leafValues>
- -1.4942969381809235e-01 1.6409839689731598e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 783 7.1766850305721164e-04</internalNodes>
- <leafValues>
- 9.9905692040920258e-02 -2.1967969834804535e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 784 -2.1803600713610649e-02</internalNodes>
- <leafValues>
- -3.1711721420288086e-01 8.2889586687088013e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 785 -3.2962779514491558e-03</internalNodes>
- <leafValues>
- -3.8048729300498962e-01 6.0819379985332489e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 786 2.4196270387619734e-03</internalNodes>
- <leafValues>
- -9.6013016998767853e-02 2.8540581464767456e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 787 -4.4187481398694217e-04</internalNodes>
- <leafValues>
- 2.2127939760684967e-01 -9.7434908151626587e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 788 3.4523929934948683e-03</internalNodes>
- <leafValues>
- 3.7553120404481888e-02 -5.7969051599502563e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 789 -2.1834600716829300e-02</internalNodes>
- <leafValues>
- 2.9562139511108398e-01 -8.0048300325870514e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 790 -2.1309500152710825e-04</internalNodes>
- <leafValues>
- 2.2814509272575378e-01 -1.0114189982414246e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 791 -1.6166249988600612e-03</internalNodes>
- <leafValues>
- -5.0541198253631592e-01 4.4764541089534760e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 792 7.5959609821438789e-03</internalNodes>
- <leafValues>
- 4.5986540615558624e-02 -4.1197681427001953e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 793 3.8601809646934271e-03</internalNodes>
- <leafValues>
- -8.6563169956207275e-02 2.4809999763965607e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 794 6.0622231103479862e-03</internalNodes>
- <leafValues>
- -7.5557373464107513e-02 2.8433260321617126e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 795 -1.7097420059144497e-03</internalNodes>
- <leafValues>
- -3.5295820236206055e-01 5.8410499244928360e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 796 1.6515579074621201e-02</internalNodes>
- <leafValues>
- -8.0486953258514404e-02 2.3537430167198181e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 797 4.8465100117027760e-03</internalNodes>
- <leafValues>
- 4.1895218193531036e-02 -4.8443049192428589e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 798 -3.1167170032858849e-02</internalNodes>
- <leafValues>
- 1.9192309677600861e-01 -1.0268159955739975e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 799 6.1892281519249082e-04</internalNodes>
- <leafValues>
- -2.1085770428180695e-01 9.3886926770210266e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 800 1.1946310289204121e-02</internalNodes>
- <leafValues>
- 3.9096169173717499e-02 -6.2248629331588745e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 801 -7.5677200220525265e-03</internalNodes>
- <leafValues>
- 1.5936839580535889e-01 -1.2250780314207077e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 802 -5.3747411817312241e-02</internalNodes>
- <leafValues>
- -5.5622178316116333e-01 4.1190009564161301e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 803 1.5513530001044273e-02</internalNodes>
- <leafValues>
- -3.9826881140470505e-02 6.2400728464126587e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 804 1.5246650436893106e-03</internalNodes>
- <leafValues>
- 7.0138677954673767e-02 -3.0789071321487427e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 805 -4.8315100139006972e-04</internalNodes>
- <leafValues>
- 1.7887659370899200e-01 -1.0958620160818100e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 806 2.7374739293009043e-03</internalNodes>
- <leafValues>
- 2.7478590607643127e-02 -8.8489568233489990e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 807 -6.5787717700004578e-02</internalNodes>
- <leafValues>
- -4.6432140469551086e-01 3.5037148743867874e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 808 1.2409730115905404e-03</internalNodes>
- <leafValues>
- -9.6479237079620361e-02 2.8779220581054688e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 809 8.1398809561505914e-04</internalNodes>
- <leafValues>
- 1.1511719971895218e-01 -1.6766160726547241e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 810 2.3901820182800293e-02</internalNodes>
- <leafValues>
- -3.2603189349174500e-02 6.0017347335815430e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 811 2.7556600049138069e-02</internalNodes>
- <leafValues>
- -6.6137343645095825e-02 2.9994478821754456e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 812 -3.8070970913395286e-04</internalNodes>
- <leafValues>
- -3.3881181478500366e-01 6.4450770616531372e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 813 -1.3335429830476642e-03</internalNodes>
- <leafValues>
- 1.4588660001754761e-01 -1.3217620551586151e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 814 -9.3507990241050720e-03</internalNodes>
- <leafValues>
- -5.1177829504013062e-01 3.4969471395015717e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 815 7.6215229928493500e-03</internalNodes>
- <leafValues>
- 2.3249529302120209e-02 -6.9619411230087280e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 816 -5.3407860832521692e-05</internalNodes>
- <leafValues>
- 2.3727379739284515e-01 -8.6910709738731384e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 817 -1.5332329785451293e-03</internalNodes>
- <leafValues>
- 1.9228410720825195e-01 -1.0422399640083313e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 818 4.3135890737175941e-03</internalNodes>
- <leafValues>
- -9.6219547092914581e-02 2.5601211190223694e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 819 -2.3042880638968199e-04</internalNodes>
- <leafValues>
- -3.1564751267433167e-01 5.8838598430156708e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 820 -7.8411828726530075e-03</internalNodes>
- <leafValues>
- -6.6340929269790649e-01 2.4500999599695206e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 821 1.7103740572929382e-01</internalNodes>
- <leafValues>
- 3.3831499516963959e-02 -4.5615941286087036e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 822 -1.6011140542104840e-03</internalNodes>
- <leafValues>
- 2.1574890613555908e-01 -8.3622530102729797e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 823 -1.0535780340433121e-02</internalNodes>
- <leafValues>
- 2.4552319943904877e-01 -8.2384489476680756e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 824 -5.8351638726890087e-03</internalNodes>
- <leafValues>
- -4.7807329893112183e-01 4.4086221605539322e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 825 -1.8706109374761581e-02</internalNodes>
- <leafValues>
- -6.0024029016494751e-01 2.1410040557384491e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 826 -9.3307439237833023e-04</internalNodes>
- <leafValues>
- 2.4323590099811554e-01 -7.4165716767311096e-02</leafValues></_></weakClassifiers></_>
- <_>
- <maxWeakCount>88</maxWeakCount>
- <stageThreshold>-9.7693431377410889e-01</stageThreshold>
- <weakClassifiers>
- <_>
- <internalNodes>
- 0 -1 827 1.0646229609847069e-02</internalNodes>
- <leafValues>
- -1.3861389458179474e-01 2.6494070887565613e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 828 3.5298269242048264e-02</internalNodes>
- <leafValues>
- -7.5821727514266968e-02 3.9021068811416626e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 829 7.5638387352228165e-04</internalNodes>
- <leafValues>
- -9.5521442592144012e-02 2.9061999917030334e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 830 9.2497706413269043e-02</internalNodes>
- <leafValues>
- -2.7704238891601562e-01 7.9474702477455139e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 831 -2.9340879991650581e-03</internalNodes>
- <leafValues>
- 2.2989539802074432e-01 -7.8550010919570923e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 832 -8.6535848677158356e-02</internalNodes>
- <leafValues>
- 4.7744810581207275e-01 -6.8231220357120037e-03</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 833 5.4699288739357144e-05</internalNodes>
- <leafValues>
- -2.2642609477043152e-01 8.8192112743854523e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 834 -3.6592520773410797e-02</internalNodes>
- <leafValues>
- 2.7353870868682861e-01 -9.8606742918491364e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 835 2.6469118893146515e-03</internalNodes>
- <leafValues>
- -4.4083978980779648e-02 3.1445288658142090e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 836 -4.4271810911595821e-03</internalNodes>
- <leafValues>
- 2.3822729289531708e-01 -8.6784273386001587e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 837 -5.1882481202483177e-03</internalNodes>
- <leafValues>
- 1.5042769908905029e-01 -1.2672109901905060e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 838 4.5530400238931179e-03</internalNodes>
- <leafValues>
- -5.5945020169019699e-02 3.6501631140708923e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 839 1.4562410302460194e-02</internalNodes>
- <leafValues>
- 3.6397770047187805e-02 -5.3559190034866333e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 840 6.8677567469421774e-05</internalNodes>
- <leafValues>
- -1.7479629814624786e-01 1.1068709939718246e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 841 -5.9744901955127716e-03</internalNodes>
- <leafValues>
- 3.1077870726585388e-01 -6.6530227661132812e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 842 -5.8691250160336494e-03</internalNodes>
- <leafValues>
- -3.1901490688323975e-01 6.3931830227375031e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 843 -1.1140310205519199e-02</internalNodes>
- <leafValues>
- 2.4364790320396423e-01 -8.0935180187225342e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 844 -5.8643531054258347e-02</internalNodes>
- <leafValues>
- -7.6083260774612427e-01 3.0809629708528519e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 845 -4.6097282320261002e-03</internalNodes>
- <leafValues>
- -4.5315021276473999e-01 2.9879059642553329e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 846 -9.3032103031873703e-03</internalNodes>
- <leafValues>
- 1.4513379335403442e-01 -1.1033169925212860e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 847 1.3253629440441728e-03</internalNodes>
- <leafValues>
- -9.7698956727981567e-02 1.9646440446376801e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 848 4.9800761044025421e-03</internalNodes>
- <leafValues>
- 3.3648081123828888e-02 -3.9792209863662720e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 849 -7.6542161405086517e-03</internalNodes>
- <leafValues>
- 9.0841993689537048e-02 -1.5967549383640289e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 850 -3.8920590281486511e-01</internalNodes>
- <leafValues>
- -6.6571092605590820e-01 1.9028829410672188e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 851 -1.0019669681787491e-01</internalNodes>
- <leafValues>
- -5.7559269666671753e-01 2.4282779544591904e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 852 7.3541211895644665e-04</internalNodes>
- <leafValues>
- 8.7919801473617554e-02 -1.6195340454578400e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 853 -3.4802639856934547e-03</internalNodes>
- <leafValues>
- 2.6064491271972656e-01 -6.0200810432434082e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 854 8.4000425413250923e-03</internalNodes>
- <leafValues>
- -1.0979729890823364e-01 1.5707309544086456e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 855 2.3786011151969433e-03</internalNodes>
- <leafValues>
- 3.6058239638805389e-02 -4.7277191281318665e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 856 7.3831682093441486e-03</internalNodes>
- <leafValues>
- -3.5756360739469528e-02 4.9498590826988220e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 857 3.2115620560944080e-03</internalNodes>
- <leafValues>
- -1.0125560313463211e-01 1.5747989714145660e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 858 -7.8209668397903442e-02</internalNodes>
- <leafValues>
- -7.6627081632614136e-01 2.2965829819440842e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 859 5.3303989261621609e-05</internalNodes>
- <leafValues>
- -1.3414350152015686e-01 1.1114919930696487e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 860 -9.6419155597686768e-03</internalNodes>
- <leafValues>
- 2.5068029761314392e-01 -6.6608138382434845e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 861 -7.1092672646045685e-02</internalNodes>
- <leafValues>
- -4.0056818723678589e-01 4.0297791361808777e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 862 3.5171560011804104e-04</internalNodes>
- <leafValues>
- 4.1861180216073990e-02 -3.2961198687553406e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 863 -3.3458150574006140e-04</internalNodes>
- <leafValues>
- -2.6029831171035767e-01 6.7892737686634064e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 864 -4.1451421566307545e-03</internalNodes>
- <leafValues>
- 2.3967699706554413e-01 -7.2093337774276733e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 865 3.1754500232636929e-03</internalNodes>
- <leafValues>
- -7.1235269308090210e-02 2.4128450453281403e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 866 -5.5184490047395229e-03</internalNodes>
- <leafValues>
- 5.0320237874984741e-01 -2.9686680063605309e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 867 -3.0242869979701936e-04</internalNodes>
- <leafValues>
- 2.4879050254821777e-01 -5.6758578866720200e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 868 -1.3125919504091144e-03</internalNodes>
- <leafValues>
- 3.1747800111770630e-01 -4.1845861822366714e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 869 -2.7123570907860994e-04</internalNodes>
- <leafValues>
- -2.7042070031166077e-01 5.6828990578651428e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 870 -7.3241777718067169e-03</internalNodes>
- <leafValues>
- 2.7556678652763367e-01 -5.4252970963716507e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 871 -1.6851710155606270e-02</internalNodes>
- <leafValues>
- -3.4852910041809082e-01 4.5368999242782593e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 872 2.9902100563049316e-02</internalNodes>
- <leafValues>
- 3.1621079891920090e-02 -4.3114370107650757e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 873 2.8902660124003887e-03</internalNodes>
- <leafValues>
- 3.8029961287975311e-02 -3.7027099728584290e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 874 -1.9242949783802032e-03</internalNodes>
- <leafValues>
- 2.4800279736518860e-01 -5.9333298355340958e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 875 4.9354149959981441e-03</internalNodes>
- <leafValues>
- -8.3068400621414185e-02 2.2043809294700623e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 876 8.2075603306293488e-02</internalNodes>
- <leafValues>
- -1.9413439556956291e-02 6.9089287519454956e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 877 -2.4699489586055279e-04</internalNodes>
- <leafValues>
- -2.4660569429397583e-01 6.4776450395584106e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 878 -1.8365769647061825e-03</internalNodes>
- <leafValues>
- 2.8836160898208618e-01 -5.3390458226203918e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 879 -4.9553811550140381e-03</internalNodes>
- <leafValues>
- 1.2740829586982727e-01 -1.2559419870376587e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 880 -8.3086621016263962e-03</internalNodes>
- <leafValues>
- 2.3478110134601593e-01 -7.1676492691040039e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 881 -1.0879919677972794e-01</internalNodes>
- <leafValues>
- -2.5992238521575928e-01 5.8689739555120468e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 882 -9.6786450594663620e-03</internalNodes>
- <leafValues>
- -7.0720428228378296e-01 1.8749259412288666e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 883 -2.7136830613017082e-02</internalNodes>
- <leafValues>
- -5.8384227752685547e-01 2.1684130653738976e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 884 -6.5389778465032578e-03</internalNodes>
- <leafValues>
- -5.9748911857604980e-01 2.1480310708284378e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 885 -1.2095630168914795e-02</internalNodes>
- <leafValues>
- 1.3269039988517761e-01 -9.9722720682621002e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 886 -1.6776099801063538e-01</internalNodes>
- <leafValues>
- -5.6655067205429077e-01 3.2123088836669922e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 887 -1.3262550346553326e-02</internalNodes>
- <leafValues>
- 1.1495590209960938e-01 -1.1738389730453491e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 888 7.6744519174098969e-02</internalNodes>
- <leafValues>
- -3.1413231045007706e-02 5.9935492277145386e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 889 5.0785229541361332e-03</internalNodes>
- <leafValues>
- -5.2911940962076187e-02 2.3342399299144745e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 890 3.1800279393792152e-03</internalNodes>
- <leafValues>
- -7.7734388411045074e-02 1.7652909457683563e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 891 -1.7729829996824265e-03</internalNodes>
- <leafValues>
- 1.9591629505157471e-01 -7.9752199351787567e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 892 -4.8560940194875002e-04</internalNodes>
- <leafValues>
- -2.8800371289253235e-01 4.9047119915485382e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 893 3.6554320831783116e-04</internalNodes>
- <leafValues>
- 6.7922897636890411e-02 -2.2499430179595947e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 894 -2.6938671362586319e-04</internalNodes>
- <leafValues>
- 1.6582170128822327e-01 -8.9744098484516144e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 895 7.8684233129024506e-02</internalNodes>
- <leafValues>
- 2.6081679388880730e-02 -5.5693739652633667e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 896 -7.3774810880422592e-04</internalNodes>
- <leafValues>
- 1.4036870002746582e-01 -1.1800300329923630e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 897 2.3957829922437668e-02</internalNodes>
- <leafValues>
- 3.0470740050077438e-02 -4.6159979701042175e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 898 -1.6239080578088760e-03</internalNodes>
- <leafValues>
- 2.6327079534530640e-01 -5.6765370070934296e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 899 -9.0819748584181070e-04</internalNodes>
- <leafValues>
- 1.5462459623813629e-01 -1.1087069660425186e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 900 3.9806248969398439e-04</internalNodes>
- <leafValues>
- 5.5630370974540710e-02 -2.8331959247589111e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 901 2.0506449509412050e-03</internalNodes>
- <leafValues>
- -9.1604836285114288e-02 1.7585539817810059e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 902 2.6742549613118172e-02</internalNodes>
- <leafValues>
- 6.2003031373023987e-02 -2.4487000703811646e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 903 -2.1497008856385946e-03</internalNodes>
- <leafValues>
- 2.9449298977851868e-01 -5.3218148648738861e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 904 5.6671658530831337e-03</internalNodes>
- <leafValues>
- -6.4298242330551147e-02 2.4905680119991302e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 905 6.8317902332637459e-05</internalNodes>
- <leafValues>
- -1.6819630563259125e-01 9.6548579633235931e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 906 1.7600439605303109e-04</internalNodes>
- <leafValues>
- 6.5308012068271637e-02 -2.4267880618572235e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 907 4.1861608624458313e-03</internalNodes>
- <leafValues>
- -9.7988583147525787e-02 1.8052889406681061e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 908 -2.1808340679854155e-03</internalNodes>
- <leafValues>
- 1.9231270253658295e-01 -9.4123929738998413e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 909 2.1730400621891022e-02</internalNodes>
- <leafValues>
- 3.5578511655330658e-02 -4.5088538527488708e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 910 -1.4780269935727119e-02</internalNodes>
- <leafValues>
- -4.3927010893821716e-01 3.1735591590404510e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 911 -3.6145891062915325e-03</internalNodes>
- <leafValues>
- 1.9811479747295380e-01 -7.7701419591903687e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 912 1.8892709631472826e-03</internalNodes>
- <leafValues>
- 1.9962439313530922e-02 -7.2041720151901245e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 913 -1.3822480104863644e-03</internalNodes>
- <leafValues>
- 9.8466947674751282e-02 -1.4881080389022827e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 914 -3.9505911991000175e-03</internalNodes>
- <leafValues>
- 1.1593230068683624e-01 -1.2791970372200012e-01</leafValues></_></weakClassifiers></_>
- <_>
- <maxWeakCount>58</maxWeakCount>
- <stageThreshold>-1.0129359960556030e+00</stageThreshold>
- <weakClassifiers>
- <_>
- <internalNodes>
- 0 -1 915 -1.9395539537072182e-02</internalNodes>
- <leafValues>
- 4.7474750876426697e-01 -1.1721090227365494e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 916 1.3118919916450977e-02</internalNodes>
- <leafValues>
- -2.5552129745483398e-01 1.6378800570964813e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 917 -5.1606801571324468e-04</internalNodes>
- <leafValues>
- 1.9452619552612305e-01 -1.7448890209197998e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 918 -1.3184159994125366e-02</internalNodes>
- <leafValues>
- 4.4181451201438904e-01 -9.0048752725124359e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 919 3.4657081123441458e-03</internalNodes>
- <leafValues>
- -1.3477090001106262e-01 1.8056340515613556e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 920 6.2980200164020061e-03</internalNodes>
- <leafValues>
- -5.4164979606866837e-02 3.6033380031585693e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 921 1.6879989998415112e-03</internalNodes>
- <leafValues>
- -1.9997949898242950e-01 1.2021599709987640e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 922 3.6039709812030196e-04</internalNodes>
- <leafValues>
- 1.0524140298366547e-01 -2.4116060137748718e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 923 -1.5276849735528231e-03</internalNodes>
- <leafValues>
- 2.8135529160499573e-01 -6.8964816629886627e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 924 3.5033570602536201e-03</internalNodes>
- <leafValues>
- -8.2519583404064178e-02 4.0713590383529663e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 925 -4.7337161377072334e-03</internalNodes>
- <leafValues>
- 1.9727009534835815e-01 -1.1710140109062195e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 926 -1.1557149700820446e-02</internalNodes>
- <leafValues>
- -5.6061112880706787e-01 6.8170957267284393e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 927 -2.7445720508694649e-02</internalNodes>
- <leafValues>
- 4.9718621373176575e-01 -6.2380149960517883e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 928 -5.2825778722763062e-02</internalNodes>
- <leafValues>
- 1.6921220719814301e-01 -1.3093550503253937e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 929 -2.9849699139595032e-01</internalNodes>
- <leafValues>
- -6.4649671316146851e-01 4.0076818317174911e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 930 -2.6307269581593573e-04</internalNodes>
- <leafValues>
- 2.5127941370010376e-01 -8.9494839310646057e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 931 2.3261709429789335e-04</internalNodes>
- <leafValues>
- -8.6843989789485931e-02 2.3831979930400848e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 932 2.3631360090803355e-04</internalNodes>
- <leafValues>
- 1.1554460227489471e-01 -1.8936349451541901e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 933 2.0742209162563086e-03</internalNodes>
- <leafValues>
- -4.8594851046800613e-02 5.7485991716384888e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 934 -7.0308889262378216e-03</internalNodes>
- <leafValues>
- -5.4120808839797974e-01 4.8743750900030136e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 935 8.2652270793914795e-03</internalNodes>
- <leafValues>
- 2.6494519785046577e-02 -6.1728459596633911e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 936 2.0042760297656059e-04</internalNodes>
- <leafValues>
- -1.1768630146980286e-01 1.6333860158920288e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 937 1.6470040427520871e-03</internalNodes>
- <leafValues>
- -5.9954918920993805e-02 3.5179701447486877e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 938 -3.5642538568936288e-04</internalNodes>
- <leafValues>
- -3.4420299530029297e-01 6.4948253333568573e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 939 -3.0935870483517647e-02</internalNodes>
- <leafValues>
- 1.9979700446128845e-01 -9.7693696618080139e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 940 -6.3578772824257612e-04</internalNodes>
- <leafValues>
- -3.1481391191482544e-01 5.9425041079521179e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 941 -1.1862180195748806e-02</internalNodes>
- <leafValues>
- 2.0043690502643585e-01 -8.9447543025016785e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 942 7.1508930996060371e-03</internalNodes>
- <leafValues>
- -3.9006061851978302e-02 5.3327161073684692e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 943 -2.0059191156178713e-03</internalNodes>
- <leafValues>
- -2.8469720482826233e-01 7.0723608136177063e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 944 3.6412389017641544e-03</internalNodes>
- <leafValues>
- -1.0660319775342941e-01 2.4944800138473511e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 945 -1.3467429578304291e-01</internalNodes>
- <leafValues>
- 4.9910080432891846e-01 -4.0332220494747162e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 946 -2.2547659464180470e-03</internalNodes>
- <leafValues>
- 1.6851690411567688e-01 -1.1119280010461807e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 947 4.3842289596796036e-03</internalNodes>
- <leafValues>
- 8.6139492690563202e-02 -2.7431771159172058e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 948 -7.3361168615520000e-03</internalNodes>
- <leafValues>
- 2.4875210225582123e-01 -9.5919162034988403e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 949 6.4666912658140063e-04</internalNodes>
- <leafValues>
- 6.7431576550006866e-02 -3.3754080533981323e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 950 2.2983769304119051e-04</internalNodes>
- <leafValues>
- -8.3903051912784576e-02 2.4584099650382996e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 951 6.7039071582257748e-03</internalNodes>
- <leafValues>
- 2.9079329222440720e-02 -6.9055938720703125e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 952 5.0734888645820320e-05</internalNodes>
- <leafValues>
- -1.5696719288825989e-01 1.1965429782867432e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 953 -2.0335559546947479e-01</internalNodes>
- <leafValues>
- -6.9506347179412842e-01 2.7507519349455833e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 954 9.4939414411783218e-03</internalNodes>
- <leafValues>
- -8.7449371814727783e-02 2.3968330025672913e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 955 -2.4055240210145712e-03</internalNodes>
- <leafValues>
- 2.1150960028171539e-01 -1.3148930668830872e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 956 -1.1342419747961685e-04</internalNodes>
- <leafValues>
- 1.5233789384365082e-01 -1.2725900113582611e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 957 1.4992210082709789e-02</internalNodes>
- <leafValues>
- -3.4127969294786453e-02 5.0624072551727295e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 958 7.4068200774490833e-04</internalNodes>
- <leafValues>
- 4.8764750361442566e-02 -4.0225321054458618e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 959 -4.2459447868168354e-03</internalNodes>
- <leafValues>
- 2.1554760634899139e-01 -8.7126992642879486e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 960 6.8655109498649836e-04</internalNodes>
- <leafValues>
- -7.5418718159198761e-02 2.6405909657478333e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 961 -1.6751460731029510e-02</internalNodes>
- <leafValues>
- -6.7729032039642334e-01 3.2918728888034821e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 962 -2.6301678735762835e-04</internalNodes>
- <leafValues>
- 2.2725869715213776e-01 -9.0534873306751251e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 963 4.3398610432632267e-04</internalNodes>
- <leafValues>
- 5.5894378572702408e-02 -3.5592669248580933e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 964 -2.0150149241089821e-02</internalNodes>
- <leafValues>
- 1.9162760674953461e-01 -9.4929970800876617e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 965 -1.4452129602432251e-02</internalNodes>
- <leafValues>
- -6.8510341644287109e-01 2.5422170758247375e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 966 -2.1149739623069763e-02</internalNodes>
- <leafValues>
- 3.7533190846443176e-01 -5.1496580243110657e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 967 2.1137770265340805e-02</internalNodes>
- <leafValues>
- 2.9083080589771271e-02 -8.9430367946624756e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 968 1.1524349683895707e-03</internalNodes>
- <leafValues>
- -6.9694936275482178e-02 2.7299800515174866e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 969 -1.9070580310653895e-04</internalNodes>
- <leafValues>
- 1.8228119611740112e-01 -9.8367072641849518e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 970 -3.6349631845951080e-02</internalNodes>
- <leafValues>
- -8.3693099021911621e-01 2.5055760517716408e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 971 -9.0632075443863869e-03</internalNodes>
- <leafValues>
- 4.1463500261306763e-01 -5.4413449019193649e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 972 -2.0535490475594997e-03</internalNodes>
- <leafValues>
- -1.9750310480594635e-01 1.0506899654865265e-01</leafValues></_></weakClassifiers></_>
- <_>
- <maxWeakCount>93</maxWeakCount>
- <stageThreshold>-9.7747492790222168e-01</stageThreshold>
- <weakClassifiers>
- <_>
- <internalNodes>
- 0 -1 973 -2.2717019543051720e-02</internalNodes>
- <leafValues>
- 2.4288550019264221e-01 -1.4745520055294037e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 974 2.5505950674414635e-02</internalNodes>
- <leafValues>
- -2.8551739454269409e-01 1.0837209969758987e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 975 -2.6640091091394424e-03</internalNodes>
- <leafValues>
- 2.9275730252265930e-01 -1.0372710227966309e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 976 -3.8115289062261581e-03</internalNodes>
- <leafValues>
- 2.1426899731159210e-01 -1.3811139762401581e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 977 -1.6732690855860710e-02</internalNodes>
- <leafValues>
- 2.6550260186195374e-01 -4.3911330401897430e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 978 4.9277010839432478e-04</internalNodes>
- <leafValues>
- 2.1104559302330017e-02 -4.2971360683441162e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 979 -3.6691110581159592e-02</internalNodes>
- <leafValues>
- 5.3992420434951782e-01 -4.3648801743984222e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 980 1.2615970335900784e-03</internalNodes>
- <leafValues>
- -1.2933869659900665e-01 1.6638770699501038e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 981 -8.4106856957077980e-03</internalNodes>
- <leafValues>
- -9.4698411226272583e-01 2.1465849131345749e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 982 6.4902722835540771e-02</internalNodes>
- <leafValues>
- -7.1727760136127472e-02 2.6613479852676392e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 983 3.0305000022053719e-02</internalNodes>
- <leafValues>
- -8.2782492041587830e-02 2.7694320678710938e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 984 2.5875340215861797e-03</internalNodes>
- <leafValues>
- -1.2966169416904449e-01 1.7756630480289459e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 985 -7.0240451022982597e-03</internalNodes>
- <leafValues>
- -6.4243179559707642e-01 3.9943210780620575e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 986 -1.0099769569933414e-03</internalNodes>
- <leafValues>
- 1.4176610112190247e-01 -1.1659970134496689e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 987 -4.1179071558872238e-05</internalNodes>
- <leafValues>
- 1.5687669813632965e-01 -1.1127340048551559e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 988 -4.7293151146732271e-04</internalNodes>
- <leafValues>
- -3.3554559946060181e-01 4.5977730304002762e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 989 -1.7178079579025507e-03</internalNodes>
- <leafValues>
- 1.6952909529209137e-01 -1.0578069835901260e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 990 -1.3333169743418694e-02</internalNodes>
- <leafValues>
- -5.8257812261581421e-01 3.0978430062532425e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 991 -1.8783430568873882e-03</internalNodes>
- <leafValues>
- 1.4266879856586456e-01 -1.1131259799003601e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 992 -6.5765981562435627e-03</internalNodes>
- <leafValues>
- 2.7561360597610474e-01 -5.3100328892469406e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 993 -7.7210381277836859e-05</internalNodes>
- <leafValues>
- 1.3240240514278412e-01 -1.1167799681425095e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 994 2.1968539804220200e-02</internalNodes>
- <leafValues>
- -2.6968160644173622e-02 5.0067168474197388e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 995 -2.7445750311017036e-02</internalNodes>
- <leafValues>
- -2.4086740612983704e-01 6.0478270053863525e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 996 7.8305849456228316e-05</internalNodes>
- <leafValues>
- -1.3334889709949493e-01 1.0123469680547714e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 997 7.0190683007240295e-02</internalNodes>
- <leafValues>
- -5.4863780736923218e-02 2.4809940159320831e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 998 -7.1902133524417877e-02</internalNodes>
- <leafValues>
- -3.7846690416336060e-01 4.2210999876260757e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 999 -1.0780979692935944e-01</internalNodes>
- <leafValues>
- -3.7486588954925537e-01 4.2833440005779266e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 1000 1.4364200178533792e-03</internalNodes>
- <leafValues>
- 8.0476358532905579e-02 -1.7263789474964142e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 1001 6.8289190530776978e-02</internalNodes>
- <leafValues>
- -3.5595789551734924e-02 4.0761318802833557e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 1002 -6.8037179298698902e-03</internalNodes>
- <leafValues>
- 1.9233790040016174e-01 -8.2368023693561554e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 1003 -5.6193489581346512e-04</internalNodes>
- <leafValues>
- 1.3057120144367218e-01 -1.4355149865150452e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 1004 -5.8276649564504623e-02</internalNodes>
- <leafValues>
- -3.0125439167022705e-01 5.2819650620222092e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 1005 -6.1205718666315079e-03</internalNodes>
- <leafValues>
- 2.2043900191783905e-01 -7.5691752135753632e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 1006 -1.3594309799373150e-02</internalNodes>
- <leafValues>
- -3.9049360156059265e-01 4.1857108473777771e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 1007 1.3626200379803777e-03</internalNodes>
- <leafValues>
- -9.5363423228263855e-02 1.4970320463180542e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 1008 -1.5074219845701009e-04</internalNodes>
- <leafValues>
- -2.3945580422878265e-01 6.4798332750797272e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 1009 -7.7414259314537048e-02</internalNodes>
- <leafValues>
- 5.5941981077194214e-01 -2.4516880512237549e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 1010 9.2117872554808855e-04</internalNodes>
- <leafValues>
- 5.4928861558437347e-02 -2.7934810519218445e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 1011 1.0250780032947659e-03</internalNodes>
- <leafValues>
- -6.2167309224605560e-02 2.4976369738578796e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 1012 -8.1174750812351704e-04</internalNodes>
- <leafValues>
- 2.3437939584255219e-01 -6.5725810825824738e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 1013 8.3431020379066467e-02</internalNodes>
- <leafValues>
- 5.0954800099134445e-02 -3.1020981073379517e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 1014 -9.2014456167817116e-03</internalNodes>
- <leafValues>
- -3.9242538809776306e-01 3.2926950603723526e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 1015 -2.9086650465615094e-04</internalNodes>
- <leafValues>
- -3.1039750576019287e-01 4.9711819738149643e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 1016 7.7576898038387299e-03</internalNodes>
- <leafValues>
- -4.4040750712156296e-02 3.6431351304054260e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 1017 -1.2466090172529221e-01</internalNodes>
- <leafValues>
- -8.1957077980041504e-01 1.9150640815496445e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 1018 1.3242550194263458e-02</internalNodes>
- <leafValues>
- 3.8988839834928513e-02 -3.3230680227279663e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 1019 -6.6770128905773163e-03</internalNodes>
- <leafValues>
- -3.5790139436721802e-01 4.0460210293531418e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 1020 -2.7479929849505424e-03</internalNodes>
- <leafValues>
- 2.5253900885581970e-01 -5.6427821516990662e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 1021 8.2659651525318623e-04</internalNodes>
- <leafValues>
- -7.1988657116889954e-02 2.2780479490756989e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 1022 -5.0153400748968124e-02</internalNodes>
- <leafValues>
- -6.3036471605300903e-01 2.7462050318717957e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 1023 7.4203149415552616e-03</internalNodes>
- <leafValues>
- -6.6610716283321381e-02 2.7787339687347412e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 1024 -6.7951780511066318e-04</internalNodes>
- <leafValues>
- -3.6327061057090759e-01 4.2795430868864059e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 1025 -1.9305750029161572e-03</internalNodes>
- <leafValues>
- 1.4196230471134186e-01 -1.0759980231523514e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 1026 -3.8132671033963561e-04</internalNodes>
- <leafValues>
- 2.1591760218143463e-01 -7.0202663540840149e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 1027 -7.0990346372127533e-02</internalNodes>
- <leafValues>
- 4.5266601443290710e-01 -4.0750481188297272e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 1028 -5.3368080407381058e-02</internalNodes>
- <leafValues>
- -6.7674058675765991e-01 1.9288340583443642e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 1029 -2.0064849406480789e-02</internalNodes>
- <leafValues>
- -4.3365430831909180e-01 3.1853288412094116e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 1030 1.1976360110566020e-03</internalNodes>
- <leafValues>
- -2.6559870690107346e-02 5.0797182321548462e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 1031 -2.2697300300933421e-04</internalNodes>
- <leafValues>
- 1.8012599647045135e-01 -8.3606548607349396e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 1032 1.5262699685990810e-02</internalNodes>
- <leafValues>
- -2.0238929986953735e-01 6.7422017455101013e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 1033 -2.0811769366264343e-01</internalNodes>
- <leafValues>
- 6.6943860054016113e-01 -2.2452110424637794e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 1034 1.5514369588345289e-03</internalNodes>
- <leafValues>
- -7.5121842324733734e-02 1.7326919734477997e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 1035 -5.2924010902643204e-02</internalNodes>
- <leafValues>
- 2.4992519617080688e-01 -6.2879167497158051e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 1036 -2.1648850291967392e-02</internalNodes>
- <leafValues>
- -2.9194280505180359e-01 5.2614491432905197e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 1037 -2.2905069636180997e-04</internalNodes>
- <leafValues>
- -2.2117300331592560e-01 6.3168339431285858e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 1038 5.0170070608146489e-05</internalNodes>
- <leafValues>
- -1.1510709673166275e-01 1.1611440032720566e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 1039 -1.6416069411206990e-04</internalNodes>
- <leafValues>
- 1.5871520340442657e-01 -8.2600601017475128e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 1040 -1.2003289535641670e-02</internalNodes>
- <leafValues>
- 1.2218090146780014e-01 -1.1229699850082397e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 1041 -1.7784100025892258e-02</internalNodes>
- <leafValues>
- -3.5072788596153259e-01 3.1341921538114548e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 1042 -6.3457582145929337e-03</internalNodes>
- <leafValues>
- 1.3078069686889648e-01 -1.0574410110712051e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 1043 -7.9523242311552167e-04</internalNodes>
- <leafValues>
- 1.7204670608043671e-01 -8.6001992225646973e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 1044 -3.1029590172693133e-04</internalNodes>
- <leafValues>
- -2.8433170914649963e-01 5.1817119121551514e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 1045 -1.7053710296750069e-02</internalNodes>
- <leafValues>
- 3.9242428541183472e-01 -4.0143270045518875e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 1046 4.6504959464073181e-03</internalNodes>
- <leafValues>
- -3.1837560236454010e-02 4.1237699985504150e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 1047 -1.0358760133385658e-02</internalNodes>
- <leafValues>
- -5.6993198394775391e-01 2.9248379170894623e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 1048 -2.2196240723133087e-02</internalNodes>
- <leafValues>
- -4.5605289936065674e-01 2.6285989210009575e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 1049 -7.0536029525101185e-03</internalNodes>
- <leafValues>
- 1.5998320281505585e-01 -9.1594859957695007e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 1050 -5.7094299700111151e-04</internalNodes>
- <leafValues>
- -1.4076329767704010e-01 1.0287419706583023e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 1051 -2.2152599412947893e-03</internalNodes>
- <leafValues>
- 1.6593599319458008e-01 -8.5273988544940948e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 1052 -2.8084890916943550e-02</internalNodes>
- <leafValues>
- 2.7022340893745422e-01 -5.5873811244964600e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 1053 2.1515151020139456e-03</internalNodes>
- <leafValues>
- 4.2472891509532928e-02 -3.2005849480628967e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 1054 -2.9733829433098435e-04</internalNodes>
- <leafValues>
- 1.6177169978618622e-01 -8.5115589201450348e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 1055 -1.6694780439138412e-02</internalNodes>
- <leafValues>
- -4.2858770489692688e-01 3.0541609972715378e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 1056 1.1982990056276321e-01</internalNodes>
- <leafValues>
- -1.6277290880680084e-02 7.9846781492233276e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 1057 -3.5499420482665300e-04</internalNodes>
- <leafValues>
- 1.5935939550399780e-01 -8.3272881805896759e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 1058 -1.8226269632577896e-02</internalNodes>
- <leafValues>
- 1.9527280330657959e-01 -7.3939889669418335e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 1059 -4.0238600922748446e-04</internalNodes>
- <leafValues>
- 7.9101808369159698e-02 -2.0806129276752472e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 1060 4.0892060496844351e-04</internalNodes>
- <leafValues>
- 1.0036630183458328e-01 -1.5128210186958313e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 1061 9.5368112670257688e-04</internalNodes>
- <leafValues>
- -7.3011666536331177e-02 2.1752020716667175e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 1062 4.3081799149513245e-01</internalNodes>
- <leafValues>
- -2.7450699359178543e-02 5.7061582803726196e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 1063 5.3564831614494324e-04</internalNodes>
- <leafValues>
- 1.1587540060281754e-01 -1.2790560722351074e-01</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 1064 2.4430730263702571e-05</internalNodes>
- <leafValues>
- -1.6816629469394684e-01 8.0449983477592468e-02</leafValues></_>
- <_>
- <internalNodes>
- 0 -1 1065 -5.5345650762319565e-02</internalNodes>
- <leafValues>
- 4.5338949561119080e-01 -3.1222779303789139e-02</leafValues></_></weakClassifiers></_></stages>
- <features>
- <_>
- <rects>
- <_>
- 0 8 20 12 -1.</_>
- <_>
- 0 14 20 6 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 9 1 4 15 -1.</_>
- <_>
- 9 6 4 5 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 10 9 2 -1.</_>
- <_>
- 9 10 3 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 7 0 10 9 -1.</_>
- <_>
- 7 3 10 3 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 12 2 2 18 -1.</_>
- <_>
- 12 8 2 6 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 8 6 8 6 -1.</_>
- <_>
- 8 9 8 3 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 2 0 17 18 -1.</_>
- <_>
- 2 6 17 6 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 10 10 1 8 -1.</_>
- <_>
- 10 14 1 4 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 7 10 9 2 -1.</_>
- <_>
- 10 10 3 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 5 1 6 6 -1.</_>
- <_>
- 5 3 6 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 3 1 15 9 -1.</_>
- <_>
- 3 4 15 3 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 3 9 6 -1.</_>
- <_>
- 6 5 9 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 8 17 6 3 -1.</_>
- <_>
- 10 17 2 3 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 9 10 9 1 -1.</_>
- <_>
- 12 10 3 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 1 7 6 11 -1.</_>
- <_>
- 3 7 2 11 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 9 18 3 1 -1.</_>
- <_>
- 10 18 1 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 16 16 1 2 -1.</_>
- <_>
- 16 17 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 9 17 6 3 -1.</_>
- <_>
- 11 17 2 3 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 8 0 5 18 -1.</_>
- <_>
- 8 6 5 6 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 7 9 7 -1.</_>
- <_>
- 9 7 3 7 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 14 6 6 10 -1.</_>
- <_>
- 16 6 2 10 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 9 8 9 5 -1.</_>
- <_>
- 12 8 3 5 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 3 7 9 6 -1.</_>
- <_>
- 6 7 3 6 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 1 7 6 6 -1.</_>
- <_>
- 3 7 2 6 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 16 0 4 18 -1.</_>
- <_>
- 16 6 4 6 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 17 3 3 -1.</_>
- <_>
- 0 18 3 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 16 0 2 1 -1.</_>
- <_>
- 17 0 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 8 20 12 -1.</_>
- <_>
- 0 14 20 6 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 6 9 8 -1.</_>
- <_>
- 9 6 3 8 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 5 3 12 9 -1.</_>
- <_>
- 5 6 12 3 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 4 16 1 2 -1.</_>
- <_>
- 4 17 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 18 10 2 1 -1.</_>
- <_>
- 19 10 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 9 8 6 5 -1.</_>
- <_>
- 11 8 2 5 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 0 2 1 -1.</_>
- <_>
- 1 0 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 8 6 6 -1.</_>
- <_>
- 8 8 2 6 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 11 7 6 7 -1.</_>
- <_>
- 13 7 2 7 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 19 14 1 2 -1.</_>
- <_>
- 19 15 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 17 1 2 -1.</_>
- <_>
- 6 18 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 14 7 2 7 -1.</_>
- <_>
- 15 7 1 7 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 8 2 4 -1.</_>
- <_>
- 7 8 1 4 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 5 8 12 6 -1.</_>
- <_>
- 5 10 12 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 2 17 1 3 -1.</_>
- <_>
- 2 18 1 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 7 3 6 -1.</_>
- <_>
- 7 7 1 6 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 7 9 12 -1.</_>
- <_>
- 9 7 3 12 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 2 11 12 -1.</_>
- <_>
- 6 6 11 4 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 1 12 5 8 -1.</_>
- <_>
- 1 16 5 4 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 14 7 6 7 -1.</_>
- <_>
- 16 7 2 7 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 10 8 6 6 -1.</_>
- <_>
- 12 8 2 6 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 16 18 4 2 -1.</_>
- <_>
- 16 19 4 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 18 17 2 3 -1.</_>
- <_>
- 18 18 2 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 9 7 3 7 -1.</_>
- <_>
- 10 7 1 7 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 5 6 6 8 -1.</_>
- <_>
- 7 6 2 8 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 2 6 6 11 -1.</_>
- <_>
- 4 6 2 11 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 8 10 12 8 -1.</_>
- <_>
- 8 14 12 4 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 7 17 6 3 -1.</_>
- <_>
- 9 17 2 3 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 10 9 3 3 -1.</_>
- <_>
- 11 9 1 3 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 8 8 3 6 -1.</_>
- <_>
- 9 8 1 6 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 7 0 6 5 -1.</_>
- <_>
- 9 0 2 5 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 17 1 3 -1.</_>
- <_>
- 6 18 1 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 18 4 2 -1.</_>
- <_>
- 0 19 4 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 4 1 11 9 -1.</_>
- <_>
- 4 4 11 3 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 3 1 14 9 -1.</_>
- <_>
- 3 4 14 3 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 9 6 4 -1.</_>
- <_>
- 2 9 2 4 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 18 13 1 2 -1.</_>
- <_>
- 18 14 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 13 5 3 11 -1.</_>
- <_>
- 14 5 1 11 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 18 8 2 -1.</_>
- <_>
- 0 18 4 1 2.</_>
- <_>
- 4 19 4 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 5 8 12 5 -1.</_>
- <_>
- 9 8 4 5 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 4 7 11 10 -1.</_>
- <_>
- 4 12 11 5 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 14 9 6 4 -1.</_>
- <_>
- 16 9 2 4 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 7 6 8 -1.</_>
- <_>
- 3 7 3 8 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 16 3 3 -1.</_>
- <_>
- 0 17 3 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 7 11 12 1 -1.</_>
- <_>
- 11 11 4 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 4 8 9 4 -1.</_>
- <_>
- 7 8 3 4 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 5 16 6 4 -1.</_>
- <_>
- 7 16 2 4 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 18 17 1 3 -1.</_>
- <_>
- 18 18 1 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 18 17 1 3 -1.</_>
- <_>
- 18 18 1 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 4 9 4 10 -1.</_>
- <_>
- 4 9 2 5 2.</_>
- <_>
- 6 14 2 5 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 4 8 6 4 -1.</_>
- <_>
- 6 8 2 4 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 10 2 2 18 -1.</_>
- <_>
- 10 8 2 6 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 5 8 6 -1.</_>
- <_>
- 0 5 4 3 2.</_>
- <_>
- 4 8 4 3 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 0 6 5 -1.</_>
- <_>
- 8 0 2 5 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 18 0 2 14 -1.</_>
- <_>
- 18 7 2 7 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 8 18 4 2 -1.</_>
- <_>
- 10 18 2 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 1 17 6 3 -1.</_>
- <_>
- 1 18 6 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 11 8 3 5 -1.</_>
- <_>
- 12 8 1 5 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 11 8 3 4 -1.</_>
- <_>
- 12 8 1 4 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 11 0 6 5 -1.</_>
- <_>
- 13 0 2 5 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 1 7 6 7 -1.</_>
- <_>
- 3 7 2 7 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 13 1 3 -1.</_>
- <_>
- 0 14 1 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 3 2 9 6 -1.</_>
- <_>
- 3 4 9 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 8 6 9 2 -1.</_>
- <_>
- 8 7 9 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 14 3 6 -1.</_>
- <_>
- 0 16 3 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 1 11 6 4 -1.</_>
- <_>
- 3 11 2 4 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 9 9 3 -1.</_>
- <_>
- 9 9 3 3 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 0 9 6 -1.</_>
- <_>
- 6 2 9 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 8 5 6 6 -1.</_>
- <_>
- 8 7 6 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 1 12 2 1 -1.</_>
- <_>
- 2 12 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 10 10 6 2 -1.</_>
- <_>
- 12 10 2 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 13 8 6 6 -1.</_>
- <_>
- 15 8 2 6 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 16 6 4 -1.</_>
- <_>
- 8 16 2 4 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 8 0 9 9 -1.</_>
- <_>
- 8 3 9 3 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 18 17 1 3 -1.</_>
- <_>
- 18 18 1 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 18 17 1 3 -1.</_>
- <_>
- 18 18 1 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 7 10 3 3 -1.</_>
- <_>
- 8 10 1 3 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 9 14 2 2 -1.</_>
- <_>
- 9 14 1 1 2.</_>
- <_>
- 10 15 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 9 14 2 2 -1.</_>
- <_>
- 9 14 1 1 2.</_>
- <_>
- 10 15 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 8 19 12 -1.</_>
- <_>
- 0 14 19 6 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 7 6 9 14 -1.</_>
- <_>
- 10 6 3 14 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 13 8 3 4 -1.</_>
- <_>
- 14 8 1 4 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 4 17 1 3 -1.</_>
- <_>
- 4 18 1 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 4 9 6 3 -1.</_>
- <_>
- 6 9 2 3 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 2 18 5 2 -1.</_>
- <_>
- 2 19 5 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 7 8 2 2 -1.</_>
- <_>
- 7 8 1 1 2.</_>
- <_>
- 8 9 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 7 8 2 2 -1.</_>
- <_>
- 7 8 1 1 2.</_>
- <_>
- 8 9 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 5 10 13 2 -1.</_>
- <_>
- 5 11 13 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 10 8 1 9 -1.</_>
- <_>
- 10 11 1 3 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 15 8 2 12 -1.</_>
- <_>
- 15 8 1 6 2.</_>
- <_>
- 16 14 1 6 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 4 0 3 5 -1.</_>
- <_>
- 5 0 1 5 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 12 6 3 7 -1.</_>
- <_>
- 13 6 1 7 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 7 16 6 4 -1.</_>
- <_>
- 9 16 2 4 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 9 16 2 1 -1.</_>
- <_>
- 10 16 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 10 9 2 -1.</_>
- <_>
- 9 10 3 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 6 15 14 -1.</_>
- <_>
- 0 13 15 7 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 9 1 5 6 -1.</_>
- <_>
- 9 3 5 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 3 9 3 4 -1.</_>
- <_>
- 4 9 1 4 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 5 7 3 6 -1.</_>
- <_>
- 6 7 1 6 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 17 16 1 2 -1.</_>
- <_>
- 17 17 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 9 8 6 12 -1.</_>
- <_>
- 11 8 2 12 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 10 6 1 -1.</_>
- <_>
- 8 10 2 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 7 17 9 3 -1.</_>
- <_>
- 10 17 3 3 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 14 18 6 2 -1.</_>
- <_>
- 14 19 6 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 9 5 3 14 -1.</_>
- <_>
- 10 5 1 14 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 8 16 9 4 -1.</_>
- <_>
- 11 16 3 4 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 0 4 14 -1.</_>
- <_>
- 0 7 4 7 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 8 1 6 3 -1.</_>
- <_>
- 10 1 2 3 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 8 3 4 -1.</_>
- <_>
- 7 8 1 4 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 4 8 3 4 -1.</_>
- <_>
- 5 8 1 4 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 5 1 6 5 -1.</_>
- <_>
- 7 1 2 5 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 1 18 1 2 -1.</_>
- <_>
- 1 19 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 7 0 6 6 -1.</_>
- <_>
- 7 2 6 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 18 4 2 -1.</_>
- <_>
- 0 19 4 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 12 3 8 12 -1.</_>
- <_>
- 12 7 8 4 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 12 9 3 4 -1.</_>
- <_>
- 13 9 1 4 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 12 8 3 5 -1.</_>
- <_>
- 13 8 1 5 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 16 0 2 1 -1.</_>
- <_>
- 17 0 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 5 17 1 3 -1.</_>
- <_>
- 5 18 1 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 10 2 3 6 -1.</_>
- <_>
- 10 4 3 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 4 17 2 3 -1.</_>
- <_>
- 4 18 2 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 12 7 1 9 -1.</_>
- <_>
- 12 10 1 3 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 7 6 3 9 -1.</_>
- <_>
- 8 6 1 9 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 17 13 3 6 -1.</_>
- <_>
- 17 15 3 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 7 7 3 8 -1.</_>
- <_>
- 8 7 1 8 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 5 0 3 5 -1.</_>
- <_>
- 6 0 1 5 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 4 6 9 8 -1.</_>
- <_>
- 7 6 3 8 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 2 9 3 3 -1.</_>
- <_>
- 3 9 1 3 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 16 18 4 2 -1.</_>
- <_>
- 16 19 4 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 17 10 3 10 -1.</_>
- <_>
- 17 15 3 5 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 8 9 6 4 -1.</_>
- <_>
- 10 9 2 4 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 5 2 10 12 -1.</_>
- <_>
- 5 6 10 4 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 9 6 3 -1.</_>
- <_>
- 8 9 2 3 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 11 7 3 7 -1.</_>
- <_>
- 12 7 1 7 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 12 8 6 4 -1.</_>
- <_>
- 14 8 2 4 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 14 8 6 5 -1.</_>
- <_>
- 16 8 2 5 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 12 12 2 4 -1.</_>
- <_>
- 12 14 2 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 3 15 1 2 -1.</_>
- <_>
- 3 16 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 12 7 3 4 -1.</_>
- <_>
- 13 7 1 4 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 10 0 6 6 -1.</_>
- <_>
- 12 0 2 6 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 10 6 3 8 -1.</_>
- <_>
- 11 6 1 8 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 16 17 1 2 -1.</_>
- <_>
- 16 18 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 16 16 1 3 -1.</_>
- <_>
- 16 17 1 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 11 11 1 2 -1.</_>
- <_>
- 11 12 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 3 7 6 9 -1.</_>
- <_>
- 5 7 2 9 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 4 18 9 1 -1.</_>
- <_>
- 7 18 3 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 11 4 9 -1.</_>
- <_>
- 0 14 4 3 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 9 17 6 3 -1.</_>
- <_>
- 11 17 2 3 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 7 8 6 12 -1.</_>
- <_>
- 9 8 2 12 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 8 3 4 -1.</_>
- <_>
- 7 8 1 4 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 3 17 1 3 -1.</_>
- <_>
- 3 18 1 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 11 9 6 4 -1.</_>
- <_>
- 13 9 2 4 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 1 3 2 -1.</_>
- <_>
- 7 1 1 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 1 0 2 1 -1.</_>
- <_>
- 2 0 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 1 0 2 14 -1.</_>
- <_>
- 1 0 1 7 2.</_>
- <_>
- 2 7 1 7 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 5 5 11 8 -1.</_>
- <_>
- 5 9 11 4 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 9 3 5 6 -1.</_>
- <_>
- 9 5 5 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 7 9 5 10 -1.</_>
- <_>
- 7 14 5 5 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 15 10 2 2 -1.</_>
- <_>
- 16 10 1 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 18 8 2 -1.</_>
- <_>
- 0 19 8 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 7 17 1 3 -1.</_>
- <_>
- 7 18 1 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 7 2 11 6 -1.</_>
- <_>
- 7 4 11 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 8 3 9 3 -1.</_>
- <_>
- 8 4 9 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 9 2 2 -1.</_>
- <_>
- 0 10 2 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 5 3 6 -1.</_>
- <_>
- 0 7 3 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 7 2 2 -1.</_>
- <_>
- 6 7 1 1 2.</_>
- <_>
- 7 8 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 7 6 3 6 -1.</_>
- <_>
- 8 6 1 6 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 12 1 6 4 -1.</_>
- <_>
- 14 1 2 4 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 9 11 6 8 -1.</_>
- <_>
- 11 11 2 8 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 17 15 3 3 -1.</_>
- <_>
- 17 16 3 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 6 3 9 -1.</_>
- <_>
- 6 9 3 3 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 5 8 6 -1.</_>
- <_>
- 0 5 4 3 2.</_>
- <_>
- 4 8 4 3 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 6 1 3 -1.</_>
- <_>
- 0 7 1 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 17 0 2 6 -1.</_>
- <_>
- 18 0 1 6 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 10 17 6 3 -1.</_>
- <_>
- 12 17 2 3 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 13 15 2 2 -1.</_>
- <_>
- 13 15 1 1 2.</_>
- <_>
- 14 16 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 4 0 12 3 -1.</_>
- <_>
- 4 1 12 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 5 3 10 9 -1.</_>
- <_>
- 5 6 10 3 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 7 7 9 7 -1.</_>
- <_>
- 10 7 3 7 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 5 8 9 6 -1.</_>
- <_>
- 8 8 3 6 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 16 6 2 -1.</_>
- <_>
- 0 17 6 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 12 6 7 14 -1.</_>
- <_>
- 12 13 7 7 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 13 7 6 8 -1.</_>
- <_>
- 15 7 2 8 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 2 10 6 3 -1.</_>
- <_>
- 4 10 2 3 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 18 17 1 3 -1.</_>
- <_>
- 18 18 1 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 7 1 6 2 -1.</_>
- <_>
- 7 2 6 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 0 6 4 -1.</_>
- <_>
- 6 2 6 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 8 18 6 2 -1.</_>
- <_>
- 10 18 2 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 7 6 5 2 -1.</_>
- <_>
- 7 7 5 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 7 3 6 -1.</_>
- <_>
- 7 7 1 6 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 18 18 2 2 -1.</_>
- <_>
- 18 18 1 1 2.</_>
- <_>
- 19 19 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 16 8 3 7 -1.</_>
- <_>
- 17 8 1 7 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 16 2 3 -1.</_>
- <_>
- 0 17 2 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 5 19 6 1 -1.</_>
- <_>
- 7 19 2 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 9 5 6 6 -1.</_>
- <_>
- 9 7 6 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 10 2 4 -1.</_>
- <_>
- 0 12 2 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 9 4 3 -1.</_>
- <_>
- 2 9 2 3 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 1 10 6 9 -1.</_>
- <_>
- 3 10 2 9 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 9 0 6 2 -1.</_>
- <_>
- 11 0 2 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 14 1 2 1 -1.</_>
- <_>
- 15 1 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 8 1 4 -1.</_>
- <_>
- 0 10 1 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 15 6 2 2 -1.</_>
- <_>
- 15 6 1 1 2.</_>
- <_>
- 16 7 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 7 5 3 6 -1.</_>
- <_>
- 8 5 1 6 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 19 17 1 3 -1.</_>
- <_>
- 19 18 1 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 7 10 3 1 -1.</_>
- <_>
- 8 10 1 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 12 1 6 6 -1.</_>
- <_>
- 14 1 2 6 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 15 5 2 1 -1.</_>
- <_>
- 16 5 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 8 2 7 4 -1.</_>
- <_>
- 8 4 7 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 4 0 14 15 -1.</_>
- <_>
- 4 5 14 5 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 7 8 6 6 -1.</_>
- <_>
- 9 8 2 6 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 11 17 1 3 -1.</_>
- <_>
- 11 18 1 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 12 16 2 4 -1.</_>
- <_>
- 12 16 1 2 2.</_>
- <_>
- 13 18 1 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 10 13 2 1 -1.</_>
- <_>
- 11 13 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 11 8 3 3 -1.</_>
- <_>
- 12 8 1 3 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 2 0 6 8 -1.</_>
- <_>
- 4 0 2 8 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 3 5 6 6 -1.</_>
- <_>
- 3 5 3 3 2.</_>
- <_>
- 6 8 3 3 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 10 8 3 3 -1.</_>
- <_>
- 11 8 1 3 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 5 17 4 2 -1.</_>
- <_>
- 5 18 4 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 8 16 5 2 -1.</_>
- <_>
- 8 17 5 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 4 3 3 -1.</_>
- <_>
- 0 5 3 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 3 6 2 -1.</_>
- <_>
- 8 3 2 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 4 4 9 3 -1.</_>
- <_>
- 7 4 3 3 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 13 1 4 -1.</_>
- <_>
- 0 15 1 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 17 8 3 -1.</_>
- <_>
- 0 18 8 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 1 11 6 -1.</_>
- <_>
- 6 3 11 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 4 10 6 2 -1.</_>
- <_>
- 6 10 2 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 10 8 1 12 -1.</_>
- <_>
- 10 14 1 6 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 5 8 3 4 -1.</_>
- <_>
- 6 8 1 4 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 17 1 3 -1.</_>
- <_>
- 0 18 1 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 17 1 3 -1.</_>
- <_>
- 0 18 1 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 13 8 3 4 -1.</_>
- <_>
- 14 8 1 4 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 1 5 5 4 -1.</_>
- <_>
- 1 7 5 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 18 14 1 2 -1.</_>
- <_>
- 18 15 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 13 8 2 4 -1.</_>
- <_>
- 14 8 1 4 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 10 6 6 8 -1.</_>
- <_>
- 12 6 2 8 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 8 6 6 10 -1.</_>
- <_>
- 10 6 2 10 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 17 16 1 3 -1.</_>
- <_>
- 17 17 1 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 1 7 2 10 -1.</_>
- <_>
- 2 7 1 10 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 5 9 6 3 -1.</_>
- <_>
- 7 9 2 3 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 8 5 12 -1.</_>
- <_>
- 0 14 5 6 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 11 1 3 -1.</_>
- <_>
- 0 12 1 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 16 6 4 -1.</_>
- <_>
- 8 16 2 4 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 6 2 6 -1.</_>
- <_>
- 0 8 2 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 11 18 2 1 -1.</_>
- <_>
- 12 18 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 5 1 9 2 -1.</_>
- <_>
- 5 2 9 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 0 1 2 -1.</_>
- <_>
- 0 1 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 15 9 3 3 -1.</_>
- <_>
- 16 9 1 3 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 18 16 1 3 -1.</_>
- <_>
- 18 17 1 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 11 10 6 1 -1.</_>
- <_>
- 13 10 2 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 1 3 4 4 -1.</_>
- <_>
- 3 3 2 4 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 11 2 1 18 -1.</_>
- <_>
- 11 8 1 6 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 9 1 5 12 -1.</_>
- <_>
- 9 5 5 4 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 12 0 8 1 -1.</_>
- <_>
- 16 0 4 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 8 6 3 10 -1.</_>
- <_>
- 9 6 1 10 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 19 2 1 6 -1.</_>
- <_>
- 19 4 1 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 18 6 2 2 -1.</_>
- <_>
- 18 7 2 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 7 7 3 4 -1.</_>
- <_>
- 8 7 1 4 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 5 0 6 5 -1.</_>
- <_>
- 7 0 2 5 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 3 7 3 -1.</_>
- <_>
- 0 4 7 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 1 6 2 1 -1.</_>
- <_>
- 2 6 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 4 8 2 10 -1.</_>
- <_>
- 4 8 1 5 2.</_>
- <_>
- 5 13 1 5 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 2 18 18 2 -1.</_>
- <_>
- 2 18 9 1 2.</_>
- <_>
- 11 19 9 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 2 7 4 4 -1.</_>
- <_>
- 2 7 2 2 2.</_>
- <_>
- 4 9 2 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 17 3 3 4 -1.</_>
- <_>
- 18 3 1 4 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 16 9 2 8 -1.</_>
- <_>
- 16 9 1 4 2.</_>
- <_>
- 17 13 1 4 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 15 7 1 6 -1.</_>
- <_>
- 15 9 1 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 14 2 2 2 -1.</_>
- <_>
- 14 3 2 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 17 0 2 3 -1.</_>
- <_>
- 17 1 2 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 16 18 2 2 -1.</_>
- <_>
- 16 18 1 1 2.</_>
- <_>
- 17 19 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 10 4 4 3 -1.</_>
- <_>
- 10 5 4 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 2 8 6 -1.</_>
- <_>
- 4 2 4 6 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 7 14 6 6 -1.</_>
- <_>
- 7 16 6 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 11 15 2 2 -1.</_>
- <_>
- 11 16 2 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 7 1 9 4 -1.</_>
- <_>
- 10 1 3 4 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 9 7 3 7 -1.</_>
- <_>
- 10 7 1 7 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 17 2 2 -1.</_>
- <_>
- 6 17 1 1 2.</_>
- <_>
- 7 18 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 4 6 3 9 -1.</_>
- <_>
- 5 6 1 9 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 10 19 10 -1.</_>
- <_>
- 0 15 19 5 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 5 17 6 1 -1.</_>
- <_>
- 7 17 2 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 12 6 3 -1.</_>
- <_>
- 3 12 3 3 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 2 5 18 5 -1.</_>
- <_>
- 8 5 6 5 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 1 15 6 4 -1.</_>
- <_>
- 1 17 6 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 14 10 6 6 -1.</_>
- <_>
- 16 10 2 6 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 14 4 3 -1.</_>
- <_>
- 0 15 4 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 1 7 6 11 -1.</_>
- <_>
- 3 7 2 11 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 13 17 7 2 -1.</_>
- <_>
- 13 18 7 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 14 2 3 -1.</_>
- <_>
- 0 15 2 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 0 6 2 -1.</_>
- <_>
- 3 0 3 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 1 6 3 -1.</_>
- <_>
- 3 1 3 3 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 8 2 6 -1.</_>
- <_>
- 0 10 2 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 1 2 6 14 -1.</_>
- <_>
- 1 2 3 7 2.</_>
- <_>
- 4 9 3 7 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 17 5 2 2 -1.</_>
- <_>
- 17 5 1 1 2.</_>
- <_>
- 18 6 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 11 10 9 4 -1.</_>
- <_>
- 14 10 3 4 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 2 9 12 4 -1.</_>
- <_>
- 6 9 4 4 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 7 10 12 2 -1.</_>
- <_>
- 11 10 4 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 2 13 1 2 -1.</_>
- <_>
- 2 14 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 16 7 4 3 -1.</_>
- <_>
- 16 8 4 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 19 16 1 3 -1.</_>
- <_>
- 19 17 1 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 18 11 1 2 -1.</_>
- <_>
- 18 12 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 12 7 8 2 -1.</_>
- <_>
- 12 7 4 1 2.</_>
- <_>
- 16 8 4 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 14 9 2 4 -1.</_>
- <_>
- 15 9 1 4 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 14 2 6 4 -1.</_>
- <_>
- 14 2 3 2 2.</_>
- <_>
- 17 4 3 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 14 0 6 1 -1.</_>
- <_>
- 17 0 3 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 3 12 2 1 -1.</_>
- <_>
- 4 12 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 17 2 3 1 -1.</_>
- <_>
- 18 2 1 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 1 16 18 2 -1.</_>
- <_>
- 7 16 6 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 2 19 8 1 -1.</_>
- <_>
- 6 19 4 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 1 17 4 3 -1.</_>
- <_>
- 1 18 4 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 19 13 1 2 -1.</_>
- <_>
- 19 14 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 9 16 10 4 -1.</_>
- <_>
- 9 16 5 2 2.</_>
- <_>
- 14 18 5 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 12 9 2 4 -1.</_>
- <_>
- 12 9 1 2 2.</_>
- <_>
- 13 11 1 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 19 11 1 9 -1.</_>
- <_>
- 19 14 1 3 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 6 14 14 -1.</_>
- <_>
- 6 13 14 7 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 2 17 4 2 -1.</_>
- <_>
- 2 18 4 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 2 1 3 -1.</_>
- <_>
- 0 3 1 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 12 1 3 -1.</_>
- <_>
- 0 13 1 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 15 15 4 4 -1.</_>
- <_>
- 15 17 4 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 2 5 18 7 -1.</_>
- <_>
- 8 5 6 7 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 1 16 5 3 -1.</_>
- <_>
- 1 17 5 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 4 2 3 -1.</_>
- <_>
- 0 5 2 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 6 2 6 -1.</_>
- <_>
- 1 6 1 6 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 16 14 4 3 -1.</_>
- <_>
- 16 15 4 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 0 10 6 -1.</_>
- <_>
- 0 0 5 3 2.</_>
- <_>
- 5 3 5 3 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 2 2 3 6 -1.</_>
- <_>
- 3 2 1 6 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 2 0 3 10 -1.</_>
- <_>
- 3 0 1 10 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 5 5 2 2 -1.</_>
- <_>
- 5 6 2 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 12 6 4 4 -1.</_>
- <_>
- 12 8 4 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 13 5 7 3 -1.</_>
- <_>
- 13 6 7 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 10 13 1 2 -1.</_>
- <_>
- 10 14 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 16 16 4 2 -1.</_>
- <_>
- 18 16 2 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 16 12 4 7 -1.</_>
- <_>
- 18 12 2 7 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 16 17 1 3 -1.</_>
- <_>
- 16 18 1 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 19 9 1 3 -1.</_>
- <_>
- 19 10 1 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 18 7 2 6 -1.</_>
- <_>
- 19 7 1 6 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 8 1 3 4 -1.</_>
- <_>
- 9 1 1 4 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 14 0 6 9 -1.</_>
- <_>
- 16 0 2 9 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 4 2 10 2 -1.</_>
- <_>
- 9 2 5 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 2 12 8 4 -1.</_>
- <_>
- 2 12 4 2 2.</_>
- <_>
- 6 14 4 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 4 7 3 -1.</_>
- <_>
- 0 5 7 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 14 14 3 3 -1.</_>
- <_>
- 15 14 1 3 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 3 4 3 -1.</_>
- <_>
- 2 3 2 3 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 1 0 2 7 -1.</_>
- <_>
- 2 0 1 7 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 15 16 4 4 -1.</_>
- <_>
- 15 18 4 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 5 8 12 4 -1.</_>
- <_>
- 5 10 12 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 3 17 1 2 -1.</_>
- <_>
- 3 18 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 1 3 4 -1.</_>
- <_>
- 7 1 1 4 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 2 3 4 -1.</_>
- <_>
- 7 2 1 4 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 8 9 12 -1.</_>
- <_>
- 9 8 3 12 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 8 1 8 6 -1.</_>
- <_>
- 8 3 8 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 14 2 6 3 -1.</_>
- <_>
- 17 2 3 3 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 6 1 3 -1.</_>
- <_>
- 0 7 1 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 10 0 10 2 -1.</_>
- <_>
- 15 0 5 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 11 0 3 2 -1.</_>
- <_>
- 12 0 1 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 3 19 10 1 -1.</_>
- <_>
- 8 19 5 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 4 7 16 -1.</_>
- <_>
- 0 12 7 8 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 2 16 1 3 -1.</_>
- <_>
- 2 17 1 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 7 8 12 6 -1.</_>
- <_>
- 11 8 4 6 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 14 9 6 7 -1.</_>
- <_>
- 16 9 2 7 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 12 17 6 1 -1.</_>
- <_>
- 14 17 2 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 16 1 3 1 -1.</_>
- <_>
- 17 1 1 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 17 8 2 -1.</_>
- <_>
- 0 17 4 1 2.</_>
- <_>
- 4 18 4 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 17 0 2 1 -1.</_>
- <_>
- 18 0 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 4 15 6 5 -1.</_>
- <_>
- 6 15 2 5 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 7 2 8 2 -1.</_>
- <_>
- 7 3 8 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 4 1 8 4 -1.</_>
- <_>
- 4 3 8 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 5 19 2 1 -1.</_>
- <_>
- 6 19 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 5 19 2 1 -1.</_>
- <_>
- 6 19 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 16 17 1 3 -1.</_>
- <_>
- 16 18 1 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 11 2 3 -1.</_>
- <_>
- 1 11 1 3 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 19 4 1 -1.</_>
- <_>
- 2 19 2 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 18 4 2 -1.</_>
- <_>
- 2 18 2 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 2 17 1 3 -1.</_>
- <_>
- 2 18 1 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 5 7 11 2 -1.</_>
- <_>
- 5 8 11 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 9 2 4 10 -1.</_>
- <_>
- 9 7 4 5 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 2 4 3 -1.</_>
- <_>
- 0 3 4 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 10 19 10 1 -1.</_>
- <_>
- 15 19 5 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 11 17 8 3 -1.</_>
- <_>
- 15 17 4 3 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 8 19 3 1 -1.</_>
- <_>
- 9 19 1 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 14 0 3 4 -1.</_>
- <_>
- 15 0 1 4 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 10 6 4 3 -1.</_>
- <_>
- 10 7 4 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 8 3 2 -1.</_>
- <_>
- 0 9 3 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 7 12 3 6 -1.</_>
- <_>
- 7 14 3 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 1 18 1 2 -1.</_>
- <_>
- 1 19 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 12 4 4 -1.</_>
- <_>
- 2 12 2 4 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 1 8 6 7 -1.</_>
- <_>
- 3 8 2 7 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 8 4 5 -1.</_>
- <_>
- 2 8 2 5 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 19 16 1 3 -1.</_>
- <_>
- 19 17 1 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 1 5 18 6 -1.</_>
- <_>
- 7 5 6 6 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 2 15 4 2 -1.</_>
- <_>
- 2 16 4 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 18 6 2 11 -1.</_>
- <_>
- 19 6 1 11 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 12 2 6 -1.</_>
- <_>
- 0 14 2 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 12 5 3 2 -1.</_>
- <_>
- 12 6 3 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 1 3 2 3 -1.</_>
- <_>
- 1 4 2 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 16 14 4 4 -1.</_>
- <_>
- 16 16 4 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 8 12 5 -1.</_>
- <_>
- 10 8 4 5 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 13 7 2 7 -1.</_>
- <_>
- 14 7 1 7 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 1 8 2 6 -1.</_>
- <_>
- 2 8 1 6 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 15 0 3 7 -1.</_>
- <_>
- 16 0 1 7 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 4 2 6 2 -1.</_>
- <_>
- 6 2 2 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 9 20 9 -1.</_>
- <_>
- 0 12 20 3 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 10 14 2 2 -1.</_>
- <_>
- 10 15 2 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 5 10 4 -1.</_>
- <_>
- 6 7 10 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 1 5 9 -1.</_>
- <_>
- 6 4 5 3 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 16 18 2 2 -1.</_>
- <_>
- 16 18 1 1 2.</_>
- <_>
- 17 19 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 14 2 4 -1.</_>
- <_>
- 0 16 2 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 10 8 2 5 -1.</_>
- <_>
- 11 8 1 5 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 3 7 12 7 -1.</_>
- <_>
- 7 7 4 7 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 0 6 6 -1.</_>
- <_>
- 3 0 3 6 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 1 0 4 4 -1.</_>
- <_>
- 3 0 2 4 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 0 6 8 -1.</_>
- <_>
- 2 0 2 8 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 0 2 1 -1.</_>
- <_>
- 1 0 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 0 3 3 -1.</_>
- <_>
- 0 1 3 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 5 4 2 4 -1.</_>
- <_>
- 5 6 2 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 2 10 9 1 -1.</_>
- <_>
- 5 10 3 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 1 17 1 3 -1.</_>
- <_>
- 1 18 1 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 17 2 3 -1.</_>
- <_>
- 0 18 2 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 15 16 3 -1.</_>
- <_>
- 8 15 8 3 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 5 4 1 -1.</_>
- <_>
- 2 5 2 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 1 0 6 20 -1.</_>
- <_>
- 3 0 2 20 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 2 5 4 6 -1.</_>
- <_>
- 2 5 2 3 2.</_>
- <_>
- 4 8 2 3 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 9 16 6 3 -1.</_>
- <_>
- 11 16 2 3 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 11 17 6 1 -1.</_>
- <_>
- 14 17 3 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 3 17 15 2 -1.</_>
- <_>
- 8 17 5 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 18 0 2 3 -1.</_>
- <_>
- 18 1 2 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 13 1 7 4 -1.</_>
- <_>
- 13 3 7 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 13 6 4 4 -1.</_>
- <_>
- 13 6 2 2 2.</_>
- <_>
- 15 8 2 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 17 6 3 4 -1.</_>
- <_>
- 17 8 3 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 14 9 2 2 -1.</_>
- <_>
- 15 9 1 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 17 17 1 3 -1.</_>
- <_>
- 17 18 1 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 3 19 8 1 -1.</_>
- <_>
- 7 19 4 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 9 3 6 -1.</_>
- <_>
- 0 12 3 3 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 4 7 15 5 -1.</_>
- <_>
- 9 7 5 5 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 9 9 5 -1.</_>
- <_>
- 9 9 3 5 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 8 1 6 2 -1.</_>
- <_>
- 10 1 2 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 4 0 12 2 -1.</_>
- <_>
- 10 0 6 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 7 0 10 3 -1.</_>
- <_>
- 12 0 5 3 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 5 0 9 6 -1.</_>
- <_>
- 5 2 9 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 8 3 6 4 -1.</_>
- <_>
- 8 5 6 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 17 4 2 3 -1.</_>
- <_>
- 17 5 2 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 5 2 4 3 -1.</_>
- <_>
- 5 3 4 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 5 9 2 6 -1.</_>
- <_>
- 6 9 1 6 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 14 10 2 6 -1.</_>
- <_>
- 15 10 1 6 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 7 4 3 3 -1.</_>
- <_>
- 7 5 3 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 12 4 8 2 -1.</_>
- <_>
- 12 4 4 1 2.</_>
- <_>
- 16 5 4 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 15 8 1 6 -1.</_>
- <_>
- 15 10 1 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 4 17 11 3 -1.</_>
- <_>
- 4 18 11 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 3 0 16 20 -1.</_>
- <_>
- 3 10 16 10 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 12 4 4 6 -1.</_>
- <_>
- 12 6 4 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 11 0 6 6 -1.</_>
- <_>
- 13 0 2 6 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 13 1 6 4 -1.</_>
- <_>
- 13 1 3 2 2.</_>
- <_>
- 16 3 3 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 11 0 6 4 -1.</_>
- <_>
- 13 0 2 4 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 8 6 6 9 -1.</_>
- <_>
- 10 6 2 9 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 7 0 3 4 -1.</_>
- <_>
- 8 0 1 4 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 17 14 2 -1.</_>
- <_>
- 0 17 7 1 2.</_>
- <_>
- 7 18 7 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 18 2 2 -1.</_>
- <_>
- 6 18 1 1 2.</_>
- <_>
- 7 19 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 18 17 1 3 -1.</_>
- <_>
- 18 18 1 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 17 18 2 2 -1.</_>
- <_>
- 17 18 1 1 2.</_>
- <_>
- 18 19 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 5 7 1 9 -1.</_>
- <_>
- 5 10 1 3 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 5 3 6 4 -1.</_>
- <_>
- 7 3 2 4 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 1 9 6 2 -1.</_>
- <_>
- 1 9 3 1 2.</_>
- <_>
- 4 10 3 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 9 2 3 -1.</_>
- <_>
- 7 9 1 3 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 8 6 12 -1.</_>
- <_>
- 8 8 2 12 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 4 18 2 2 -1.</_>
- <_>
- 4 18 1 1 2.</_>
- <_>
- 5 19 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 9 1 6 6 -1.</_>
- <_>
- 9 3 6 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 17 6 2 -1.</_>
- <_>
- 6 18 6 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 3 18 16 2 -1.</_>
- <_>
- 3 19 16 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 3 0 3 11 -1.</_>
- <_>
- 4 0 1 11 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 13 18 3 1 -1.</_>
- <_>
- 14 18 1 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 0 9 6 -1.</_>
- <_>
- 6 2 9 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 1 2 12 4 -1.</_>
- <_>
- 1 2 6 2 2.</_>
- <_>
- 7 4 6 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 3 3 6 4 -1.</_>
- <_>
- 5 3 2 4 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 12 0 8 1 -1.</_>
- <_>
- 16 0 4 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 9 0 6 2 -1.</_>
- <_>
- 11 0 2 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 3 3 12 1 -1.</_>
- <_>
- 9 3 6 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 2 7 6 2 -1.</_>
- <_>
- 2 7 3 1 2.</_>
- <_>
- 5 8 3 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 8 4 6 -1.</_>
- <_>
- 0 10 4 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 9 6 3 7 -1.</_>
- <_>
- 10 6 1 7 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 9 6 6 13 -1.</_>
- <_>
- 11 6 2 13 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 11 12 6 1 -1.</_>
- <_>
- 13 12 2 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 18 9 2 6 -1.</_>
- <_>
- 18 12 2 3 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 17 2 3 9 -1.</_>
- <_>
- 18 2 1 9 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 13 8 4 6 -1.</_>
- <_>
- 13 8 2 3 2.</_>
- <_>
- 15 11 2 3 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 4 2 12 6 -1.</_>
- <_>
- 10 2 6 6 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 4 14 16 6 -1.</_>
- <_>
- 12 14 8 6 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 19 10 1 -1.</_>
- <_>
- 11 19 5 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 17 1 3 -1.</_>
- <_>
- 6 18 1 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 4 14 10 3 -1.</_>
- <_>
- 4 15 10 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 0 12 12 -1.</_>
- <_>
- 6 4 12 4 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 5 7 4 2 -1.</_>
- <_>
- 5 7 2 1 2.</_>
- <_>
- 7 8 2 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 17 5 3 2 -1.</_>
- <_>
- 18 5 1 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 8 13 6 3 -1.</_>
- <_>
- 8 14 6 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 8 13 5 3 -1.</_>
- <_>
- 8 14 5 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 13 2 1 18 -1.</_>
- <_>
- 13 11 1 9 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 10 9 2 -1.</_>
- <_>
- 9 10 3 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 11 0 7 4 -1.</_>
- <_>
- 11 2 7 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 1 0 6 8 -1.</_>
- <_>
- 3 0 2 8 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 9 15 3 3 -1.</_>
- <_>
- 9 16 3 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 9 17 9 3 -1.</_>
- <_>
- 9 18 9 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 12 12 3 3 -1.</_>
- <_>
- 12 13 3 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 4 1 3 5 -1.</_>
- <_>
- 5 1 1 5 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 10 14 2 3 -1.</_>
- <_>
- 10 15 2 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 18 17 2 2 -1.</_>
- <_>
- 18 17 1 1 2.</_>
- <_>
- 19 18 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 18 18 2 2 -1.</_>
- <_>
- 18 18 1 1 2.</_>
- <_>
- 19 19 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 18 18 2 2 -1.</_>
- <_>
- 18 18 1 1 2.</_>
- <_>
- 19 19 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 4 10 9 1 -1.</_>
- <_>
- 7 10 3 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 3 9 6 5 -1.</_>
- <_>
- 5 9 2 5 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 18 8 1 12 -1.</_>
- <_>
- 18 14 1 6 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 2 8 6 -1.</_>
- <_>
- 0 2 4 3 2.</_>
- <_>
- 4 5 4 3 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 9 4 3 3 -1.</_>
- <_>
- 9 5 3 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 3 18 2 2 -1.</_>
- <_>
- 3 18 1 1 2.</_>
- <_>
- 4 19 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 4 4 3 -1.</_>
- <_>
- 6 5 4 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 16 7 4 2 -1.</_>
- <_>
- 16 7 2 1 2.</_>
- <_>
- 18 8 2 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 5 17 1 3 -1.</_>
- <_>
- 5 18 1 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 2 0 15 20 -1.</_>
- <_>
- 2 10 15 10 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 8 11 6 4 -1.</_>
- <_>
- 8 11 3 2 2.</_>
- <_>
- 11 13 3 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 8 16 4 3 -1.</_>
- <_>
- 8 17 4 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 8 18 2 2 -1.</_>
- <_>
- 8 18 1 1 2.</_>
- <_>
- 9 19 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 2 16 13 3 -1.</_>
- <_>
- 2 17 13 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 16 16 2 2 -1.</_>
- <_>
- 16 16 1 1 2.</_>
- <_>
- 17 17 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 8 1 6 3 -1.</_>
- <_>
- 10 1 2 3 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 16 7 2 2 -1.</_>
- <_>
- 16 7 1 1 2.</_>
- <_>
- 17 8 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 14 7 4 2 -1.</_>
- <_>
- 14 7 2 1 2.</_>
- <_>
- 16 8 2 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 4 0 14 1 -1.</_>
- <_>
- 11 0 7 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 10 4 8 2 -1.</_>
- <_>
- 10 4 4 1 2.</_>
- <_>
- 14 5 4 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 8 2 3 2 -1.</_>
- <_>
- 9 2 1 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 12 11 6 3 -1.</_>
- <_>
- 12 12 6 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 1 5 1 4 -1.</_>
- <_>
- 1 7 1 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 1 1 1 18 -1.</_>
- <_>
- 1 7 1 6 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 11 13 3 2 -1.</_>
- <_>
- 11 14 3 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 1 12 2 -1.</_>
- <_>
- 0 1 6 1 2.</_>
- <_>
- 6 2 6 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 10 18 2 2 -1.</_>
- <_>
- 10 18 1 1 2.</_>
- <_>
- 11 19 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 4 5 4 4 -1.</_>
- <_>
- 4 5 2 2 2.</_>
- <_>
- 6 7 2 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 7 1 3 -1.</_>
- <_>
- 6 8 1 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 14 10 6 2 -1.</_>
- <_>
- 16 10 2 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 16 8 3 6 -1.</_>
- <_>
- 17 8 1 6 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 4 10 6 2 -1.</_>
- <_>
- 6 10 2 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 5 3 7 -1.</_>
- <_>
- 7 5 1 7 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 13 6 6 -1.</_>
- <_>
- 0 16 6 3 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 12 5 1 9 -1.</_>
- <_>
- 12 8 1 3 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 5 9 3 3 -1.</_>
- <_>
- 6 9 1 3 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 7 5 6 13 -1.</_>
- <_>
- 9 5 2 13 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 19 8 1 10 -1.</_>
- <_>
- 19 13 1 5 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 11 18 6 1 -1.</_>
- <_>
- 13 18 2 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 9 7 6 12 -1.</_>
- <_>
- 11 7 2 12 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 12 7 6 6 -1.</_>
- <_>
- 14 7 2 6 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 15 8 3 4 -1.</_>
- <_>
- 16 8 1 4 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 11 4 2 -1.</_>
- <_>
- 6 12 4 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 1 6 6 8 -1.</_>
- <_>
- 3 6 2 8 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 11 15 6 5 -1.</_>
- <_>
- 13 15 2 5 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 15 17 4 2 -1.</_>
- <_>
- 15 18 4 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 13 11 6 1 -1.</_>
- <_>
- 15 11 2 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 5 18 2 2 -1.</_>
- <_>
- 5 18 1 1 2.</_>
- <_>
- 6 19 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 4 8 4 4 -1.</_>
- <_>
- 4 8 2 2 2.</_>
- <_>
- 6 10 2 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 11 7 9 3 -1.</_>
- <_>
- 11 8 9 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 3 10 4 -1.</_>
- <_>
- 0 3 5 2 2.</_>
- <_>
- 5 5 5 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 7 18 6 1 -1.</_>
- <_>
- 9 18 2 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 8 3 3 -1.</_>
- <_>
- 0 9 3 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 0 6 8 -1.</_>
- <_>
- 0 0 3 4 2.</_>
- <_>
- 3 4 3 4 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 7 6 3 8 -1.</_>
- <_>
- 8 6 1 8 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 13 7 7 3 -1.</_>
- <_>
- 13 8 7 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 3 3 2 2 -1.</_>
- <_>
- 3 4 2 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 3 3 3 -1.</_>
- <_>
- 0 4 3 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 9 3 5 2 -1.</_>
- <_>
- 9 4 5 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 5 9 4 -1.</_>
- <_>
- 9 5 3 4 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 3 10 12 3 -1.</_>
- <_>
- 7 10 4 3 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 8 7 3 6 -1.</_>
- <_>
- 9 7 1 6 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 5 5 6 5 -1.</_>
- <_>
- 8 5 3 5 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 5 2 3 -1.</_>
- <_>
- 0 6 2 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 9 7 3 4 -1.</_>
- <_>
- 10 7 1 4 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 1 0 6 15 -1.</_>
- <_>
- 3 0 2 15 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 15 1 3 5 -1.</_>
- <_>
- 16 1 1 5 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 9 2 3 10 -1.</_>
- <_>
- 10 2 1 10 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 8 8 6 12 -1.</_>
- <_>
- 10 8 2 12 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 16 4 3 4 -1.</_>
- <_>
- 16 6 3 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 16 7 2 2 -1.</_>
- <_>
- 16 7 1 1 2.</_>
- <_>
- 17 8 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 13 0 6 9 -1.</_>
- <_>
- 13 3 6 3 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 7 17 1 3 -1.</_>
- <_>
- 7 18 1 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 12 1 4 2 -1.</_>
- <_>
- 12 2 4 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 17 3 1 3 -1.</_>
- <_>
- 17 4 1 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 16 9 3 -1.</_>
- <_>
- 0 17 9 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 3 6 2 4 -1.</_>
- <_>
- 3 6 1 2 2.</_>
- <_>
- 4 8 1 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 13 18 3 1 -1.</_>
- <_>
- 14 18 1 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 18 4 2 -1.</_>
- <_>
- 2 18 2 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 1 19 2 1 -1.</_>
- <_>
- 2 19 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 18 4 2 -1.</_>
- <_>
- 0 19 4 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 2 17 1 3 -1.</_>
- <_>
- 2 18 1 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 4 8 3 5 -1.</_>
- <_>
- 5 8 1 5 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 2 1 6 7 -1.</_>
- <_>
- 4 1 2 7 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 3 6 2 8 -1.</_>
- <_>
- 3 6 1 4 2.</_>
- <_>
- 4 10 1 4 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 4 5 11 10 -1.</_>
- <_>
- 4 10 11 5 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 13 20 2 -1.</_>
- <_>
- 10 13 10 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 1 13 16 3 -1.</_>
- <_>
- 9 13 8 3 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 16 4 4 4 -1.</_>
- <_>
- 16 4 2 2 2.</_>
- <_>
- 18 6 2 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 16 0 4 12 -1.</_>
- <_>
- 16 0 2 6 2.</_>
- <_>
- 18 6 2 6 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 14 15 3 1 -1.</_>
- <_>
- 15 15 1 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 3 4 12 10 -1.</_>
- <_>
- 3 9 12 5 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 9 18 2 2 -1.</_>
- <_>
- 9 18 1 1 2.</_>
- <_>
- 10 19 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 9 18 2 2 -1.</_>
- <_>
- 9 18 1 1 2.</_>
- <_>
- 10 19 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 13 4 2 14 -1.</_>
- <_>
- 13 4 1 7 2.</_>
- <_>
- 14 11 1 7 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 4 2 6 4 -1.</_>
- <_>
- 7 2 3 4 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 0 18 20 -1.</_>
- <_>
- 0 0 9 10 2.</_>
- <_>
- 9 10 9 10 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 15 11 1 2 -1.</_>
- <_>
- 15 12 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 16 10 2 4 -1.</_>
- <_>
- 16 10 1 2 2.</_>
- <_>
- 17 12 1 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 18 17 2 2 -1.</_>
- <_>
- 18 17 1 1 2.</_>
- <_>
- 19 18 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 9 17 1 2 -1.</_>
- <_>
- 9 18 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 8 4 9 6 -1.</_>
- <_>
- 11 4 3 6 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 9 9 10 -1.</_>
- <_>
- 9 9 3 10 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 5 0 5 4 -1.</_>
- <_>
- 5 2 5 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 5 7 11 4 -1.</_>
- <_>
- 5 9 11 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 2 4 2 14 -1.</_>
- <_>
- 3 4 1 14 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 8 6 3 5 -1.</_>
- <_>
- 9 6 1 5 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 8 4 3 9 -1.</_>
- <_>
- 9 4 1 9 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 8 20 6 -1.</_>
- <_>
- 0 10 20 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 14 16 6 1 -1.</_>
- <_>
- 17 16 3 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 17 18 2 2 -1.</_>
- <_>
- 17 19 2 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 8 17 6 3 -1.</_>
- <_>
- 10 17 2 3 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 4 1 9 15 -1.</_>
- <_>
- 7 1 3 15 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 11 5 3 12 -1.</_>
- <_>
- 12 5 1 12 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 15 4 3 -1.</_>
- <_>
- 0 16 4 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 0 15 1 -1.</_>
- <_>
- 5 0 5 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 0 6 4 -1.</_>
- <_>
- 8 0 2 4 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 2 0 9 3 -1.</_>
- <_>
- 5 0 3 3 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 13 6 3 7 -1.</_>
- <_>
- 14 6 1 7 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 7 6 4 2 -1.</_>
- <_>
- 7 7 4 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 18 6 1 -1.</_>
- <_>
- 8 18 2 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 18 6 2 2 -1.</_>
- <_>
- 18 7 2 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 4 7 3 -1.</_>
- <_>
- 6 5 7 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 12 7 3 1 -1.</_>
- <_>
- 13 7 1 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 15 1 2 10 -1.</_>
- <_>
- 15 1 1 5 2.</_>
- <_>
- 16 6 1 5 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 18 2 2 -1.</_>
- <_>
- 0 19 2 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 19 4 1 8 -1.</_>
- <_>
- 19 8 1 4 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 1 17 1 3 -1.</_>
- <_>
- 1 18 1 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 15 6 4 -1.</_>
- <_>
- 0 15 3 2 2.</_>
- <_>
- 3 17 3 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 19 0 1 18 -1.</_>
- <_>
- 19 6 1 6 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 10 2 6 2 -1.</_>
- <_>
- 12 2 2 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 2 8 12 2 -1.</_>
- <_>
- 6 8 4 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 16 0 4 1 -1.</_>
- <_>
- 18 0 2 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 8 4 2 6 -1.</_>
- <_>
- 8 7 2 3 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 14 5 2 10 -1.</_>
- <_>
- 15 5 1 10 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 13 4 2 2 -1.</_>
- <_>
- 13 5 2 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 11 1 3 6 -1.</_>
- <_>
- 11 3 3 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 9 12 2 -1.</_>
- <_>
- 10 9 4 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 9 16 4 2 -1.</_>
- <_>
- 9 17 4 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 5 14 15 4 -1.</_>
- <_>
- 5 16 15 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 18 16 2 2 -1.</_>
- <_>
- 18 17 2 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 16 18 2 2 -1.</_>
- <_>
- 16 18 1 1 2.</_>
- <_>
- 17 19 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 4 3 8 -1.</_>
- <_>
- 7 4 1 8 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 5 9 3 1 -1.</_>
- <_>
- 6 9 1 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 8 1 6 -1.</_>
- <_>
- 0 10 1 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 11 2 9 6 -1.</_>
- <_>
- 14 2 3 6 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 12 2 6 4 -1.</_>
- <_>
- 14 2 2 4 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 1 7 2 4 -1.</_>
- <_>
- 1 9 2 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 13 1 6 4 -1.</_>
- <_>
- 13 3 6 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 4 10 2 10 -1.</_>
- <_>
- 4 10 1 5 2.</_>
- <_>
- 5 15 1 5 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 2 16 9 3 -1.</_>
- <_>
- 5 16 3 3 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 1 2 3 9 -1.</_>
- <_>
- 2 2 1 9 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 19 7 1 4 -1.</_>
- <_>
- 19 9 1 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 14 11 6 8 -1.</_>
- <_>
- 14 11 3 4 2.</_>
- <_>
- 17 15 3 4 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 15 12 4 6 -1.</_>
- <_>
- 15 12 2 3 2.</_>
- <_>
- 17 15 2 3 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 16 15 2 2 -1.</_>
- <_>
- 16 15 1 1 2.</_>
- <_>
- 17 16 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 17 16 2 2 -1.</_>
- <_>
- 17 16 1 1 2.</_>
- <_>
- 18 17 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 17 16 2 2 -1.</_>
- <_>
- 17 16 1 1 2.</_>
- <_>
- 18 17 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 2 3 2 2 -1.</_>
- <_>
- 2 3 1 1 2.</_>
- <_>
- 3 4 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 10 10 3 3 -1.</_>
- <_>
- 11 10 1 3 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 5 9 7 8 -1.</_>
- <_>
- 5 13 7 4 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 7 16 2 2 -1.</_>
- <_>
- 7 16 1 1 2.</_>
- <_>
- 8 17 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 7 16 2 2 -1.</_>
- <_>
- 7 16 1 1 2.</_>
- <_>
- 8 17 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 9 8 10 3 -1.</_>
- <_>
- 14 8 5 3 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 7 4 8 -1.</_>
- <_>
- 6 7 2 4 2.</_>
- <_>
- 8 11 2 4 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 1 6 4 3 -1.</_>
- <_>
- 1 7 4 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 10 6 10 -1.</_>
- <_>
- 8 10 2 10 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 4 6 3 6 -1.</_>
- <_>
- 5 6 1 6 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 3 10 4 4 -1.</_>
- <_>
- 3 10 2 2 2.</_>
- <_>
- 5 12 2 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 3 10 4 4 -1.</_>
- <_>
- 3 10 2 2 2.</_>
- <_>
- 5 12 2 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 3 10 4 4 -1.</_>
- <_>
- 3 10 2 2 2.</_>
- <_>
- 5 12 2 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 14 8 2 6 -1.</_>
- <_>
- 15 8 1 6 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 3 10 4 4 -1.</_>
- <_>
- 3 10 2 2 2.</_>
- <_>
- 5 12 2 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 3 10 4 4 -1.</_>
- <_>
- 3 10 2 2 2.</_>
- <_>
- 5 12 2 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 12 4 3 9 -1.</_>
- <_>
- 13 4 1 9 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 12 3 1 12 -1.</_>
- <_>
- 12 7 1 4 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 2 0 18 1 -1.</_>
- <_>
- 8 0 6 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 10 0 10 6 -1.</_>
- <_>
- 10 0 5 3 2.</_>
- <_>
- 15 3 5 3 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 18 16 2 2 -1.</_>
- <_>
- 18 17 2 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 3 5 4 2 -1.</_>
- <_>
- 3 5 2 1 2.</_>
- <_>
- 5 6 2 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 11 8 3 3 -1.</_>
- <_>
- 12 8 1 3 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 11 7 3 5 -1.</_>
- <_>
- 12 7 1 5 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 3 19 15 1 -1.</_>
- <_>
- 8 19 5 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 8 13 3 2 -1.</_>
- <_>
- 8 14 3 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 2 12 8 4 -1.</_>
- <_>
- 2 12 4 2 2.</_>
- <_>
- 6 14 4 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 16 16 2 2 -1.</_>
- <_>
- 16 16 1 1 2.</_>
- <_>
- 17 17 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 7 0 3 2 -1.</_>
- <_>
- 8 0 1 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 7 2 5 -1.</_>
- <_>
- 7 7 1 5 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 18 0 2 17 -1.</_>
- <_>
- 19 0 1 17 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 16 16 1 3 -1.</_>
- <_>
- 16 17 1 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 14 8 3 7 -1.</_>
- <_>
- 15 8 1 7 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 10 17 2 2 -1.</_>
- <_>
- 10 17 1 1 2.</_>
- <_>
- 11 18 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 4 9 1 3 -1.</_>
- <_>
- 4 10 1 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 18 10 2 3 -1.</_>
- <_>
- 18 11 2 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 12 1 3 10 -1.</_>
- <_>
- 13 1 1 10 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 8 12 9 1 -1.</_>
- <_>
- 11 12 3 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 5 18 2 2 -1.</_>
- <_>
- 5 18 1 1 2.</_>
- <_>
- 6 19 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 19 6 1 9 -1.</_>
- <_>
- 19 9 1 3 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 4 7 2 4 -1.</_>
- <_>
- 4 7 1 2 2.</_>
- <_>
- 5 9 1 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 1 4 6 14 -1.</_>
- <_>
- 3 4 2 14 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 10 5 9 3 -1.</_>
- <_>
- 13 5 3 3 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 18 7 2 6 -1.</_>
- <_>
- 18 9 2 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 5 6 2 7 -1.</_>
- <_>
- 6 6 1 7 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 10 4 6 8 -1.</_>
- <_>
- 13 4 3 8 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 8 2 9 -1.</_>
- <_>
- 0 11 2 3 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 7 5 3 -1.</_>
- <_>
- 0 8 5 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 8 1 7 2 -1.</_>
- <_>
- 8 2 7 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 7 5 3 5 -1.</_>
- <_>
- 8 5 1 5 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 19 2 1 2 -1.</_>
- <_>
- 19 3 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 7 10 11 -1.</_>
- <_>
- 11 7 5 11 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 9 19 6 1 -1.</_>
- <_>
- 11 19 2 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 3 0 12 1 -1.</_>
- <_>
- 7 0 4 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 4 1 6 5 -1.</_>
- <_>
- 6 1 2 5 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 12 12 6 -1.</_>
- <_>
- 10 12 4 6 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 16 13 2 3 -1.</_>
- <_>
- 16 14 2 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 7 14 4 2 -1.</_>
- <_>
- 7 15 4 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 7 14 2 2 -1.</_>
- <_>
- 7 15 2 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 3 10 2 4 -1.</_>
- <_>
- 3 10 1 2 2.</_>
- <_>
- 4 12 1 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 3 2 6 -1.</_>
- <_>
- 0 5 2 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 1 10 2 2 -1.</_>
- <_>
- 1 10 1 1 2.</_>
- <_>
- 2 11 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 16 4 4 3 -1.</_>
- <_>
- 16 5 4 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 5 10 2 4 -1.</_>
- <_>
- 5 10 1 2 2.</_>
- <_>
- 6 12 1 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 5 11 13 2 -1.</_>
- <_>
- 5 12 13 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 10 2 3 11 -1.</_>
- <_>
- 11 2 1 11 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 10 2 4 4 -1.</_>
- <_>
- 10 4 4 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 8 8 6 2 -1.</_>
- <_>
- 10 8 2 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 11 2 3 3 -1.</_>
- <_>
- 12 2 1 3 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 18 14 2 -1.</_>
- <_>
- 6 18 7 1 2.</_>
- <_>
- 13 19 7 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 17 7 1 12 -1.</_>
- <_>
- 17 11 1 4 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 10 5 10 3 -1.</_>
- <_>
- 10 6 10 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 1 3 3 -1.</_>
- <_>
- 7 1 1 3 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 13 8 3 1 -1.</_>
- <_>
- 14 8 1 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 10 14 2 6 -1.</_>
- <_>
- 10 16 2 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 4 1 12 14 -1.</_>
- <_>
- 8 1 4 14 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 14 1 6 14 -1.</_>
- <_>
- 16 1 2 14 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 3 16 2 2 -1.</_>
- <_>
- 3 16 1 1 2.</_>
- <_>
- 4 17 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 16 2 2 -1.</_>
- <_>
- 0 17 2 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 15 6 4 6 -1.</_>
- <_>
- 15 6 2 3 2.</_>
- <_>
- 17 9 2 3 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 12 5 2 2 -1.</_>
- <_>
- 12 6 2 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 7 6 6 13 -1.</_>
- <_>
- 9 6 2 13 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 1 9 6 5 -1.</_>
- <_>
- 3 9 2 5 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 5 3 4 -1.</_>
- <_>
- 0 7 3 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 4 1 16 2 -1.</_>
- <_>
- 4 1 8 1 2.</_>
- <_>
- 12 2 8 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 1 18 4 2 -1.</_>
- <_>
- 1 18 2 1 2.</_>
- <_>
- 3 19 2 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 7 7 3 4 -1.</_>
- <_>
- 8 7 1 4 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 3 4 9 3 -1.</_>
- <_>
- 6 4 3 3 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 4 6 6 10 -1.</_>
- <_>
- 6 6 2 10 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 9 0 8 10 -1.</_>
- <_>
- 13 0 4 10 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 8 0 8 1 -1.</_>
- <_>
- 12 0 4 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 2 8 16 -1.</_>
- <_>
- 6 2 4 8 2.</_>
- <_>
- 10 10 4 8 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 14 10 2 10 -1.</_>
- <_>
- 14 10 1 5 2.</_>
- <_>
- 15 15 1 5 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 12 11 1 2 -1.</_>
- <_>
- 12 12 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 16 0 3 8 -1.</_>
- <_>
- 17 0 1 8 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 14 0 6 10 -1.</_>
- <_>
- 17 0 3 10 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 16 0 3 5 -1.</_>
- <_>
- 17 0 1 5 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 4 5 11 2 -1.</_>
- <_>
- 4 6 11 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 1 0 2 1 -1.</_>
- <_>
- 2 0 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 0 2 3 -1.</_>
- <_>
- 0 1 2 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 11 6 6 11 -1.</_>
- <_>
- 13 6 2 11 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 14 0 3 1 -1.</_>
- <_>
- 15 0 1 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 19 7 1 2 -1.</_>
- <_>
- 19 8 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 17 0 3 9 -1.</_>
- <_>
- 18 0 1 9 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 12 7 3 4 -1.</_>
- <_>
- 13 7 1 4 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 1 14 2 -1.</_>
- <_>
- 0 1 7 1 2.</_>
- <_>
- 7 2 7 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 3 1 3 2 -1.</_>
- <_>
- 4 1 1 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 4 0 15 2 -1.</_>
- <_>
- 9 0 5 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 10 2 6 1 -1.</_>
- <_>
- 12 2 2 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 9 4 6 11 -1.</_>
- <_>
- 11 4 2 11 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 2 16 2 4 -1.</_>
- <_>
- 2 18 2 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 17 6 3 -1.</_>
- <_>
- 8 17 2 3 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 7 9 6 2 -1.</_>
- <_>
- 9 9 2 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 8 9 2 -1.</_>
- <_>
- 9 8 3 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 6 2 10 -1.</_>
- <_>
- 6 6 1 5 2.</_>
- <_>
- 7 11 1 5 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 11 2 3 -1.</_>
- <_>
- 0 12 2 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 11 15 4 1 -1.</_>
- <_>
- 13 15 2 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 17 1 2 -1.</_>
- <_>
- 6 18 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 0 6 20 -1.</_>
- <_>
- 2 0 2 20 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 3 10 2 2 -1.</_>
- <_>
- 4 10 1 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 4 7 3 5 -1.</_>
- <_>
- 5 7 1 5 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 3 12 6 2 -1.</_>
- <_>
- 5 12 2 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 15 7 4 -1.</_>
- <_>
- 6 17 7 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 17 16 2 2 -1.</_>
- <_>
- 17 16 1 1 2.</_>
- <_>
- 18 17 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 15 1 3 16 -1.</_>
- <_>
- 16 1 1 16 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 16 6 3 -1.</_>
- <_>
- 8 16 2 3 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 15 14 3 2 -1.</_>
- <_>
- 15 15 3 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 12 16 1 2 -1.</_>
- <_>
- 12 17 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 2 4 4 -1.</_>
- <_>
- 0 2 2 2 2.</_>
- <_>
- 2 4 2 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 1 1 6 4 -1.</_>
- <_>
- 1 1 3 2 2.</_>
- <_>
- 4 3 3 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 1 18 1 2 -1.</_>
- <_>
- 1 19 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 4 7 2 3 -1.</_>
- <_>
- 4 8 2 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 1 0 9 14 -1.</_>
- <_>
- 1 7 9 7 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 4 9 2 6 -1.</_>
- <_>
- 4 9 1 3 2.</_>
- <_>
- 5 12 1 3 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 3 9 4 3 -1.</_>
- <_>
- 5 9 2 3 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 9 2 4 -1.</_>
- <_>
- 0 11 2 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 16 6 3 10 -1.</_>
- <_>
- 17 6 1 10 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 16 11 2 1 -1.</_>
- <_>
- 17 11 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 5 7 4 4 -1.</_>
- <_>
- 5 9 4 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 10 11 9 2 -1.</_>
- <_>
- 13 11 3 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 15 10 2 2 -1.</_>
- <_>
- 15 10 1 1 2.</_>
- <_>
- 16 11 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 10 6 6 14 -1.</_>
- <_>
- 10 13 6 7 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 14 7 3 5 -1.</_>
- <_>
- 15 7 1 5 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 11 12 3 -1.</_>
- <_>
- 10 11 4 3 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 17 16 1 2 -1.</_>
- <_>
- 17 17 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 8 5 5 4 -1.</_>
- <_>
- 8 7 5 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 11 6 4 2 -1.</_>
- <_>
- 11 7 4 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 3 4 8 2 -1.</_>
- <_>
- 3 4 4 1 2.</_>
- <_>
- 7 5 4 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 8 6 6 -1.</_>
- <_>
- 2 8 2 6 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 7 4 6 2 -1.</_>
- <_>
- 7 5 6 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 7 3 6 3 -1.</_>
- <_>
- 9 3 2 3 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 2 17 3 3 -1.</_>
- <_>
- 2 18 3 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 3 10 6 1 -1.</_>
- <_>
- 5 10 2 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 7 2 6 2 -1.</_>
- <_>
- 9 2 2 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 4 11 9 1 -1.</_>
- <_>
- 7 11 3 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 7 7 11 12 -1.</_>
- <_>
- 7 13 11 6 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 3 2 3 4 -1.</_>
- <_>
- 4 2 1 4 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 9 7 9 3 -1.</_>
- <_>
- 12 7 3 3 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 15 11 2 6 -1.</_>
- <_>
- 15 11 1 3 2.</_>
- <_>
- 16 14 1 3 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 5 5 3 -1.</_>
- <_>
- 0 6 5 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 8 1 6 12 -1.</_>
- <_>
- 10 1 2 12 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 3 7 15 13 -1.</_>
- <_>
- 8 7 5 13 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 9 9 9 -1.</_>
- <_>
- 0 12 9 3 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 16 0 3 8 -1.</_>
- <_>
- 17 0 1 8 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 16 2 4 2 -1.</_>
- <_>
- 18 2 2 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 13 0 6 5 -1.</_>
- <_>
- 16 0 3 5 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 15 1 3 2 -1.</_>
- <_>
- 16 1 1 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 11 8 3 2 -1.</_>
- <_>
- 12 8 1 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 1 8 2 12 -1.</_>
- <_>
- 1 8 1 6 2.</_>
- <_>
- 2 14 1 6 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 1 6 12 -1.</_>
- <_>
- 2 1 2 12 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 19 17 1 3 -1.</_>
- <_>
- 19 18 1 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 11 3 3 10 -1.</_>
- <_>
- 12 3 1 10 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 8 1 9 8 -1.</_>
- <_>
- 11 1 3 8 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 18 16 2 2 -1.</_>
- <_>
- 18 16 1 1 2.</_>
- <_>
- 19 17 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 18 16 2 2 -1.</_>
- <_>
- 18 16 1 1 2.</_>
- <_>
- 19 17 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 13 2 6 -1.</_>
- <_>
- 6 15 2 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 9 14 2 2 -1.</_>
- <_>
- 9 15 2 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 14 10 2 4 -1.</_>
- <_>
- 14 10 1 2 2.</_>
- <_>
- 15 12 1 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 15 2 2 -1.</_>
- <_>
- 0 15 1 1 2.</_>
- <_>
- 1 16 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 7 2 2 -1.</_>
- <_>
- 6 7 1 1 2.</_>
- <_>
- 7 8 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 11 18 2 2 -1.</_>
- <_>
- 11 18 1 1 2.</_>
- <_>
- 12 19 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 0 6 4 -1.</_>
- <_>
- 0 0 3 2 2.</_>
- <_>
- 3 2 3 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 4 1 6 6 -1.</_>
- <_>
- 6 1 2 6 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 15 13 5 4 -1.</_>
- <_>
- 15 15 5 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 7 17 6 1 -1.</_>
- <_>
- 9 17 2 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 16 19 4 1 -1.</_>
- <_>
- 18 19 2 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 16 16 4 4 -1.</_>
- <_>
- 18 16 2 4 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 7 8 9 4 -1.</_>
- <_>
- 10 8 3 4 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 16 18 2 2 -1.</_>
- <_>
- 16 18 1 1 2.</_>
- <_>
- 17 19 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 2 9 2 4 -1.</_>
- <_>
- 2 9 1 2 2.</_>
- <_>
- 3 11 1 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 3 8 4 -1.</_>
- <_>
- 0 3 4 2 2.</_>
- <_>
- 4 5 4 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 1 8 1 -1.</_>
- <_>
- 4 1 4 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 5 8 9 -1.</_>
- <_>
- 4 5 4 9 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 7 18 6 2 -1.</_>
- <_>
- 9 18 2 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 4 1 12 -1.</_>
- <_>
- 0 8 1 4 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 19 13 1 6 -1.</_>
- <_>
- 19 15 1 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 2 8 6 8 -1.</_>
- <_>
- 4 8 2 8 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 0 9 17 -1.</_>
- <_>
- 3 0 3 17 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 7 9 6 8 -1.</_>
- <_>
- 9 9 2 8 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 5 10 9 4 -1.</_>
- <_>
- 8 10 3 4 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 5 0 8 3 -1.</_>
- <_>
- 5 1 8 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 16 6 4 4 -1.</_>
- <_>
- 16 6 2 2 2.</_>
- <_>
- 18 8 2 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 17 4 2 8 -1.</_>
- <_>
- 17 4 1 4 2.</_>
- <_>
- 18 8 1 4 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 2 16 1 3 -1.</_>
- <_>
- 2 17 1 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 2 16 1 3 -1.</_>
- <_>
- 2 17 1 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 11 0 1 3 -1.</_>
- <_>
- 11 1 1 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 11 2 9 7 -1.</_>
- <_>
- 14 2 3 7 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 10 2 3 6 -1.</_>
- <_>
- 11 2 1 6 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 5 9 15 2 -1.</_>
- <_>
- 5 10 15 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 8 16 6 2 -1.</_>
- <_>
- 8 17 6 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 9 16 10 2 -1.</_>
- <_>
- 9 16 5 1 2.</_>
- <_>
- 14 17 5 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 9 17 2 2 -1.</_>
- <_>
- 9 17 1 1 2.</_>
- <_>
- 10 18 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 10 15 6 4 -1.</_>
- <_>
- 10 15 3 2 2.</_>
- <_>
- 13 17 3 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 4 5 15 12 -1.</_>
- <_>
- 9 5 5 12 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 11 13 2 3 -1.</_>
- <_>
- 11 14 2 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 8 13 7 3 -1.</_>
- <_>
- 8 14 7 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 1 12 1 2 -1.</_>
- <_>
- 1 13 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 16 18 2 2 -1.</_>
- <_>
- 16 18 1 1 2.</_>
- <_>
- 17 19 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 1 19 18 1 -1.</_>
- <_>
- 7 19 6 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 1 17 6 1 -1.</_>
- <_>
- 4 17 3 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 1 3 1 12 -1.</_>
- <_>
- 1 9 1 6 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 9 3 6 -1.</_>
- <_>
- 0 11 3 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 5 4 3 10 -1.</_>
- <_>
- 6 4 1 10 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 17 2 1 -1.</_>
- <_>
- 7 17 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 1 0 6 12 -1.</_>
- <_>
- 3 0 2 12 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 4 7 9 2 -1.</_>
- <_>
- 7 7 3 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 11 9 1 -1.</_>
- <_>
- 9 11 3 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 17 10 2 10 -1.</_>
- <_>
- 17 15 2 5 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 4 10 2 10 -1.</_>
- <_>
- 4 10 1 5 2.</_>
- <_>
- 5 15 1 5 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 12 3 3 12 -1.</_>
- <_>
- 13 3 1 12 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 15 3 4 6 -1.</_>
- <_>
- 15 3 2 3 2.</_>
- <_>
- 17 6 2 3 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 12 8 3 3 -1.</_>
- <_>
- 13 8 1 3 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 4 14 2 4 -1.</_>
- <_>
- 4 16 2 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 16 1 3 -1.</_>
- <_>
- 6 17 1 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 1 1 2 3 -1.</_>
- <_>
- 2 1 1 3 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 2 4 1 -1.</_>
- <_>
- 2 2 2 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 8 17 12 3 -1.</_>
- <_>
- 12 17 4 3 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 9 16 6 4 -1.</_>
- <_>
- 11 16 2 4 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 4 6 3 6 -1.</_>
- <_>
- 4 9 3 3 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 2 12 9 -1.</_>
- <_>
- 6 5 12 3 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 0 14 20 -1.</_>
- <_>
- 6 0 7 10 2.</_>
- <_>
- 13 10 7 10 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 15 16 2 2 -1.</_>
- <_>
- 15 16 1 1 2.</_>
- <_>
- 16 17 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 15 16 2 2 -1.</_>
- <_>
- 15 16 1 1 2.</_>
- <_>
- 16 17 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 19 8 1 3 -1.</_>
- <_>
- 19 9 1 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 13 4 1 2 -1.</_>
- <_>
- 13 5 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 4 4 2 -1.</_>
- <_>
- 0 5 4 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 19 5 1 6 -1.</_>
- <_>
- 19 7 1 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 16 0 2 1 -1.</_>
- <_>
- 17 0 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 13 1 1 3 -1.</_>
- <_>
- 13 2 1 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 17 17 1 3 -1.</_>
- <_>
- 17 18 1 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 5 4 8 8 -1.</_>
- <_>
- 5 4 4 4 2.</_>
- <_>
- 9 8 4 4 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 1 2 2 2 -1.</_>
- <_>
- 1 2 1 1 2.</_>
- <_>
- 2 3 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 0 8 6 -1.</_>
- <_>
- 0 0 4 3 2.</_>
- <_>
- 4 3 4 3 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 3 4 2 -1.</_>
- <_>
- 6 4 4 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 1 0 3 3 -1.</_>
- <_>
- 1 1 3 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 1 7 2 -1.</_>
- <_>
- 6 2 7 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 2 6 12 6 -1.</_>
- <_>
- 6 6 4 6 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 1 16 9 2 -1.</_>
- <_>
- 4 16 3 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 7 15 6 4 -1.</_>
- <_>
- 9 15 2 4 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 15 12 1 -1.</_>
- <_>
- 12 15 6 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 17 17 1 3 -1.</_>
- <_>
- 17 18 1 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 17 15 2 2 -1.</_>
- <_>
- 17 15 1 1 2.</_>
- <_>
- 18 16 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 3 13 3 3 -1.</_>
- <_>
- 3 14 3 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 10 17 1 3 -1.</_>
- <_>
- 10 18 1 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 4 0 14 8 -1.</_>
- <_>
- 11 0 7 8 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 2 0 12 2 -1.</_>
- <_>
- 6 0 4 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 2 0 4 3 -1.</_>
- <_>
- 4 0 2 3 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 13 1 1 2 -1.</_>
- <_>
- 13 2 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 7 5 3 6 -1.</_>
- <_>
- 8 5 1 6 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 18 2 2 2 -1.</_>
- <_>
- 18 2 1 1 2.</_>
- <_>
- 19 3 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 15 1 2 14 -1.</_>
- <_>
- 16 1 1 14 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 15 6 2 2 -1.</_>
- <_>
- 15 6 1 1 2.</_>
- <_>
- 16 7 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 3 1 6 3 -1.</_>
- <_>
- 5 1 2 3 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 7 16 2 2 -1.</_>
- <_>
- 7 16 1 1 2.</_>
- <_>
- 8 17 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 5 17 2 2 -1.</_>
- <_>
- 5 17 1 1 2.</_>
- <_>
- 6 18 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 9 10 6 10 -1.</_>
- <_>
- 11 10 2 10 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 10 17 6 3 -1.</_>
- <_>
- 12 17 2 3 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 14 5 2 10 -1.</_>
- <_>
- 14 10 2 5 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 11 12 6 2 -1.</_>
- <_>
- 11 13 6 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 8 1 1 3 -1.</_>
- <_>
- 8 2 1 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 12 15 2 2 -1.</_>
- <_>
- 12 15 1 1 2.</_>
- <_>
- 13 16 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 8 6 4 -1.</_>
- <_>
- 6 8 3 2 2.</_>
- <_>
- 9 10 3 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 7 5 3 5 -1.</_>
- <_>
- 8 5 1 5 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 5 7 3 -1.</_>
- <_>
- 0 6 7 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 7 9 6 6 -1.</_>
- <_>
- 9 9 2 6 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 5 7 8 8 -1.</_>
- <_>
- 5 11 8 4 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 4 9 2 6 -1.</_>
- <_>
- 4 9 1 3 2.</_>
- <_>
- 5 12 1 3 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 10 11 6 1 -1.</_>
- <_>
- 12 11 2 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 13 6 6 11 -1.</_>
- <_>
- 15 6 2 11 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 8 17 2 2 -1.</_>
- <_>
- 8 17 1 1 2.</_>
- <_>
- 9 18 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 4 12 12 1 -1.</_>
- <_>
- 8 12 4 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 11 17 3 2 -1.</_>
- <_>
- 11 18 3 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 8 17 6 1 -1.</_>
- <_>
- 10 17 2 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 4 1 14 6 -1.</_>
- <_>
- 4 3 14 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 14 2 2 12 -1.</_>
- <_>
- 14 8 2 6 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 12 13 3 2 -1.</_>
- <_>
- 12 14 3 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 1 6 1 -1.</_>
- <_>
- 8 1 2 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 10 6 6 1 -1.</_>
- <_>
- 12 6 2 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 3 19 2 1 -1.</_>
- <_>
- 4 19 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 18 16 2 2 -1.</_>
- <_>
- 18 16 1 1 2.</_>
- <_>
- 19 17 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 16 11 3 7 -1.</_>
- <_>
- 17 11 1 7 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 19 5 1 6 -1.</_>
- <_>
- 19 8 1 3 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 9 8 4 3 -1.</_>
- <_>
- 9 9 4 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 16 8 4 4 -1.</_>
- <_>
- 16 8 2 2 2.</_>
- <_>
- 18 10 2 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 2 8 2 2 -1.</_>
- <_>
- 2 8 1 1 2.</_>
- <_>
- 3 9 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 3 5 6 4 -1.</_>
- <_>
- 3 5 3 2 2.</_>
- <_>
- 6 7 3 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 2 3 8 16 -1.</_>
- <_>
- 2 3 4 8 2.</_>
- <_>
- 6 11 4 8 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 17 17 1 3 -1.</_>
- <_>
- 17 18 1 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 7 2 8 11 -1.</_>
- <_>
- 11 2 4 11 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 13 3 6 14 -1.</_>
- <_>
- 16 3 3 14 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 9 18 2 -1.</_>
- <_>
- 6 9 6 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 10 14 3 -1.</_>
- <_>
- 6 11 14 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 10 9 9 3 -1.</_>
- <_>
- 13 9 3 3 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 3 5 4 6 -1.</_>
- <_>
- 3 5 2 3 2.</_>
- <_>
- 5 8 2 3 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 3 7 3 7 -1.</_>
- <_>
- 4 7 1 7 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 2 8 11 6 -1.</_>
- <_>
- 2 10 11 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 8 9 6 3 -1.</_>
- <_>
- 8 10 6 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 3 3 3 11 -1.</_>
- <_>
- 4 3 1 11 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 19 6 1 -1.</_>
- <_>
- 3 19 3 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 18 18 1 2 -1.</_>
- <_>
- 18 19 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 8 0 12 6 -1.</_>
- <_>
- 8 0 6 3 2.</_>
- <_>
- 14 3 6 3 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 19 5 1 3 -1.</_>
- <_>
- 19 6 1 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 5 8 2 1 -1.</_>
- <_>
- 6 8 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 13 11 2 1 -1.</_>
- <_>
- 14 11 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 3 6 15 13 -1.</_>
- <_>
- 8 6 5 13 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 4 3 6 2 -1.</_>
- <_>
- 6 3 2 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 18 1 2 -1.</_>
- <_>
- 0 19 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 7 8 2 6 -1.</_>
- <_>
- 8 8 1 6 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 3 0 6 19 -1.</_>
- <_>
- 5 0 2 19 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 3 1 6 5 -1.</_>
- <_>
- 5 1 2 5 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 17 14 3 6 -1.</_>
- <_>
- 17 16 3 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 17 13 2 6 -1.</_>
- <_>
- 18 13 1 6 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 17 18 2 2 -1.</_>
- <_>
- 18 18 1 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 11 14 9 4 -1.</_>
- <_>
- 14 14 3 4 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 15 8 4 6 -1.</_>
- <_>
- 15 8 2 3 2.</_>
- <_>
- 17 11 2 3 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 1 16 1 3 -1.</_>
- <_>
- 1 17 1 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 7 0 3 14 -1.</_>
- <_>
- 8 0 1 14 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 12 0 2 1 -1.</_>
- <_>
- 13 0 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 7 9 6 5 -1.</_>
- <_>
- 10 9 3 5 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 15 5 4 9 -1.</_>
- <_>
- 17 5 2 9 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 11 0 6 6 -1.</_>
- <_>
- 13 0 2 6 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 16 15 2 2 -1.</_>
- <_>
- 16 15 1 1 2.</_>
- <_>
- 17 16 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 16 15 2 2 -1.</_>
- <_>
- 16 15 1 1 2.</_>
- <_>
- 17 16 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 13 2 2 18 -1.</_>
- <_>
- 13 11 2 9 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 8 4 8 10 -1.</_>
- <_>
- 8 9 8 5 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 8 3 2 3 -1.</_>
- <_>
- 8 4 2 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 11 1 6 9 -1.</_>
- <_>
- 11 4 6 3 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 15 4 5 6 -1.</_>
- <_>
- 15 6 5 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 12 18 2 2 -1.</_>
- <_>
- 12 18 1 1 2.</_>
- <_>
- 13 19 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 1 17 1 3 -1.</_>
- <_>
- 1 18 1 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 12 19 2 1 -1.</_>
- <_>
- 13 19 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 8 10 6 6 -1.</_>
- <_>
- 10 10 2 6 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 14 2 6 5 -1.</_>
- <_>
- 16 2 2 5 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 9 5 2 6 -1.</_>
- <_>
- 9 7 2 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 1 15 2 2 -1.</_>
- <_>
- 2 15 1 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 18 17 1 3 -1.</_>
- <_>
- 18 18 1 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 10 14 4 6 -1.</_>
- <_>
- 10 16 4 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 9 7 3 2 -1.</_>
- <_>
- 10 7 1 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 9 6 2 -1.</_>
- <_>
- 6 9 3 1 2.</_>
- <_>
- 9 10 3 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 2 1 12 -1.</_>
- <_>
- 0 6 1 4 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 4 0 15 1 -1.</_>
- <_>
- 9 0 5 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 9 0 8 2 -1.</_>
- <_>
- 9 0 4 1 2.</_>
- <_>
- 13 1 4 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 12 2 8 1 -1.</_>
- <_>
- 16 2 4 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 7 1 10 6 -1.</_>
- <_>
- 7 3 10 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 18 6 2 3 -1.</_>
- <_>
- 18 7 2 1 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 4 12 2 2 -1.</_>
- <_>
- 4 12 1 1 2.</_>
- <_>
- 5 13 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 6 6 2 -1.</_>
- <_>
- 8 6 2 2 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 0 9 9 6 -1.</_>
- <_>
- 3 9 3 6 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 17 18 2 2 -1.</_>
- <_>
- 18 18 1 2 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 11 2 6 16 -1.</_>
- <_>
- 13 2 2 16 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 2 4 15 13 -1.</_>
- <_>
- 7 4 5 13 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 16 2 3 10 -1.</_>
- <_>
- 17 2 1 10 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 6 10 2 1 -1.</_>
- <_>
- 7 10 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 1 1 18 16 -1.</_>
- <_>
- 10 1 9 16 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 14 4 3 15 -1.</_>
- <_>
- 15 4 1 15 3.</_></rects></_>
- <_>
- <rects>
- <_>
- 19 13 1 2 -1.</_>
- <_>
- 19 14 1 1 2.</_></rects></_>
- <_>
- <rects>
- <_>
- 2 6 5 8 -1.</_>
- <_>
- 2 10 5 4 2.</_></rects></_></features></cascade>
-</opencv_storage>
-
diff --git a/bob/pad/face/config/replay_attack.py b/bob/pad/face/config/replay_attack.py
index befbc148..9ced9d6d 100644
--- a/bob/pad/face/config/replay_attack.py
+++ b/bob/pad/face/config/replay_attack.py
@@ -1,4 +1,3 @@
-#!/usr/bin/env python
"""`Replayattack`_ is a database for face PAD experiments.
The Replay-Attack Database for face spoofing consists of 1300 video clips of photo and video attack attempts to 50 clients,
@@ -10,49 +9,14 @@ the link.
.. include:: links.rst
"""
-
from bob.pad.face.database import ReplayPadDatabase
-
-# Directory where the data files are stored.
-# This directory is given in the .bob_bio_databases.txt file located in your home directory
-ORIGINAL_DIRECTORY = "[YOUR_REPLAY_ATTACK_DIRECTORY]"
-"""Value of ``~/.bob_bio_databases.txt`` for this database"""
-
-ORIGINAL_EXTENSION = ".mov" # extension of the data files
-
-database = ReplayPadDatabase(
- protocol='grandtest',
- original_directory=ORIGINAL_DIRECTORY,
- original_extension=ORIGINAL_EXTENSION,
- training_depends_on_protocol=True,
+from bob.pad.base.pipelines.vanilla_pad import DatabaseConnector
+from bob.extension import rc
+
+database = DatabaseConnector(
+ ReplayPadDatabase(
+ protocol="grandtest",
+ original_directory=rc.get("bob.db.replay.directory"),
+ original_extension=".mov",
+ )
)
-"""The :py:class:`bob.pad.base.database.PadDatabase` derivative with Replayattack
-database settings
-
-.. warning::
-
- This class only provides a programmatic interface to load data in an orderly
- manner, respecting usage protocols. It does **not** contain the raw
- data files. You should procure those yourself.
-
-Notice that ``original_directory`` is set to ``[YOUR_MIFS_DATABASE_DIRECTORY]``.
-You must make sure to create ``${HOME}/.bob_bio_databases.txt`` setting this
-value to the place where you actually installed the Replayattack Database, as
-explained in the section :ref:`bob.pad.face.baselines`.
-"""
-
-protocol = 'grandtest'
-"""The default protocol to use for reproducing the baselines.
-
-You may modify this at runtime by specifying the option ``--protocol`` on the
-command-line of ``spoof.py`` or using the keyword ``protocol`` on a
-configuration file that is loaded **after** this configuration resource.
-"""
-
-groups = ["train", "dev", "eval"]
-"""The default groups to use for reproducing the baselines.
-
-You may modify this at runtime by specifying the option ``--groups`` on the
-command-line of ``spoof.py`` or using the keyword ``groups`` on a
-configuration file that is loaded **after** this configuration resource.
-"""
diff --git a/bob/pad/face/config/replay_mobile.py b/bob/pad/face/config/replay_mobile.py
index 94e61ca6..3c9942f8 100644
--- a/bob/pad/face/config/replay_mobile.py
+++ b/bob/pad/face/config/replay_mobile.py
@@ -1,4 +1,3 @@
-#!/usr/bin/env python
"""`Replay-Mobile`_ is a database for face PAD experiments.
The Replay-Mobile Database for face spoofing consists of 1030 video clips of photo and video attack attempts to 40 clients,
@@ -13,49 +12,14 @@ the link.
.. include:: links.rst
"""
-
from bob.pad.face.database import ReplayMobilePadDatabase
-
-# Directory where the data files are stored.
-# This directory is given in the .bob_bio_databases.txt file located in your home directory
-ORIGINAL_DIRECTORY = "[YOUR_REPLAY_MOBILE_DIRECTORY]"
-"""Value of ``~/.bob_bio_databases.txt`` for this database"""
-
-ORIGINAL_EXTENSION = ".mov" # extension of the data files
-
-database = ReplayMobilePadDatabase(
- protocol='grandtest',
- original_directory=ORIGINAL_DIRECTORY,
- original_extension=ORIGINAL_EXTENSION,
- training_depends_on_protocol=True,
+from bob.pad.base.pipelines.vanilla_pad import DatabaseConnector
+from bob.extension import rc
+
+database = DatabaseConnector(
+ ReplayMobilePadDatabase(
+ protocol="grandtest",
+ original_directory=rc.get("bob.db.replaymobile.directory"),
+ original_extension=".mov",
+ )
)
-"""The :py:class:`bob.pad.base.database.PadDatabase` derivative with Replay-Mobile
-database settings.
-
-.. warning::
-
- This class only provides a programmatic interface to load data in an orderly
- manner, respecting usage protocols. It does **not** contain the raw
- data files. You should procure those yourself.
-
-Notice that ``original_directory`` is set to ``[YOUR_REPLAY_MOBILE_DIRECTORY]``.
-You must make sure to create ``${HOME}/.bob_bio_databases.txt`` setting this
-value to the place where you actually installed the Replay-Mobile Database, as
-explained in the section :ref:`bob.pad.face.baselines`.
-"""
-
-protocol = 'grandtest'
-"""The default protocol to use for reproducing the baselines.
-
-You may modify this at runtime by specifying the option ``--protocol`` on the
-command-line of ``spoof.py`` or using the keyword ``protocol`` on a
-configuration file that is loaded **after** this configuration resource.
-"""
-
-groups = ["train", "dev", "eval"]
-"""The default groups to use for reproducing the baselines.
-
-You may modify this at runtime by specifying the option ``--groups`` on the
-command-line of ``spoof.py`` or using the keyword ``groups`` on a
-configuration file that is loaded **after** this configuration resource.
-"""
diff --git a/bob/pad/face/config/svm_frames.py b/bob/pad/face/config/svm_frames.py
new file mode 100644
index 00000000..32a95d43
--- /dev/null
+++ b/bob/pad/face/config/svm_frames.py
@@ -0,0 +1,32 @@
+import bob.pipelines as mario
+from bob.pad.face.transformer import VideoToFrames
+from sklearn.model_selection import GridSearchCV
+from sklearn.pipeline import make_pipeline
+from sklearn.svm import SVC
+
+preprocessor = globals().get("preprocessor")
+extractor = globals().get("extractor")
+
+# Classifier #
+frame_cont_to_array = VideoToFrames()
+
+param_grid = [
+ {
+ "C": [2 ** P for P in range(-3, 14, 2)],
+ "gamma": [2 ** P for P in range(-15, 0, 2)],
+ "kernel": ["rbf"],
+ },
+]
+
+classifier = GridSearchCV(SVC(), param_grid=param_grid, cv=3)
+classifier = mario.wrap(
+ ["sample", "checkpoint"],
+ classifier,
+ fit_extra_arguments=[("y", "is_bonafide")],
+ model_path="temp/svm.pkl",
+)
+
+
+# Pipeline #
+frames_classifier = make_pipeline(frame_cont_to_array, classifier)
+pipeline = make_pipeline(preprocessor, extractor, frames_classifier)
diff --git a/bob/pad/face/config/vanilla_pad/replay_attack.py b/bob/pad/face/config/vanilla_pad/replay_attack.py
deleted file mode 100644
index 5a9d6ebe..00000000
--- a/bob/pad/face/config/vanilla_pad/replay_attack.py
+++ /dev/null
@@ -1,11 +0,0 @@
-from bob.pad.face.database import ReplayPadDatabase
-from bob.pad.base.pipelines.vanilla_pad import DatabaseConnector
-from bob.extension import rc
-
-database = DatabaseConnector(
- ReplayPadDatabase(
- protocol="grandtest",
- original_directory=rc.get("bob.db.replay.directory"),
- original_extension=".mov",
- )
-)
diff --git a/bob/pad/face/extractor/FrameDiffFeatures.py b/bob/pad/face/extractor/FrameDiffFeatures.py
deleted file mode 100644
index 7180b73a..00000000
--- a/bob/pad/face/extractor/FrameDiffFeatures.py
+++ /dev/null
@@ -1,318 +0,0 @@
-#!/usr/bin/env python2
-# -*- coding: utf-8 -*-
-"""
-Created on Wed Jun 14 10:13:21 2017
-
-@author: Olegs Nikisins
-"""
-
-#==============================================================================
-# Import what is needed here:
-
-from bob.bio.base.extractor import Extractor
-
-import numpy as np
-
-import sys
-
-import bob.bio.video
-
-#==============================================================================
-# Main body:
-
-
-class FrameDiffFeatures(Extractor):
- """
- This class is designed to extract features describing frame differences.
-
- The class allows to compute the following features in the window of the
- length defined by ``window_size`` argument:
-
- 1. The minimum value observed on the cluster
- 2. The maximum value observed on the cluster
- 3. The mean value observed
- 4. The standard deviation on the cluster (unbiased estimator)
- 5. The DC ratio (D) as defined by:
-
- .. math::
-
- D(N) = (\sum_{i=1}^N{|FFT_i|}) / (|FFT_0|)
-
- **Parameters:**
-
- ``window_size`` : :py:class:`int`
- The size of the window to use for feature computation.
-
- ``overlap`` : :py:class:`int`
- Determines the window overlapping; this number has to be between
- 0 (no overlapping) and 'window-size'-1. Default: 0.
- """
-
- def __init__(self, window_size, overlap=0):
-
- Extractor.__init__(self, window_size=window_size, overlap=overlap)
-
- self.window_size = window_size
- self.overlap = overlap
-
- #==========================================================================
- def dcratio(self, arr):
- """
- Calculates the DC ratio as defined by the following formula:
-
- .. math::
-
- D(N) = (\sum_{i=1}^N{|FFT_i|}) / (|FFT_0|)
-
- **Parameters:**
-
- ``arr`` : 1D :py:class:`numpy.ndarray`
- A 1D array containg frame differences.
-
- **Returns:**
-
- ``dcratio`` : :py:class:`float`
- Calculated DC ratio.
- """
-
- if arr.shape[0] <= 1:
- return 0.
-
- res = np.fft.fft(arr.astype('complex128'))
- res = np.absolute(res) # absolute value
-
- if res[0] == 0:
- s = sum(res[1:])
- if s > 0:
- return sys.float_info.max
- elif s < 0:
- return -sys.float_info.max
- else:
- return 0
-
- dcratio = sum(res[1:]) / res[0]
-
- return dcratio
-
- #==========================================================================
- def remove_nan_rows(self, data):
- """
- This function removes rows of nan's from the input array. If the input
- array contains nan's only, then an array of ones of the size
- (1 x n_features) is returned.
-
- **Parameters:**
-
- ``data`` : 2D :py:class:`numpy.ndarray`
- An input array of features. Rows - samples, columns - features.
-
- **Returns:**
-
- ``ret_arr`` : 2D :py:class:`numpy.ndarray`
- Array of features without nan samples. Rows - samples, columns - features.
- """
-
- d = np.vstack(data)
-
- ret_arr = d[~np.isnan(d.sum(axis=1)), :]
-
- if ret_arr.shape[0] == 0: # if array is empty, return array of ones
-
- ret_arr = np.ones((1, ret_arr.shape[1]))
-
- return ret_arr
-
- #==========================================================================
- def cluster_5quantities(self, arr, window_size, overlap):
- """
- Calculates the clustered values as described at the paper: Counter-
- Measures to Photo Attacks in Face Recognition: a public database and a
- baseline, Anjos & Marcel, IJCB'11.
-
- This script will output a number of clustered observations containing the 5
- described quantities for windows of a configurable size (N):
-
- 1. The minimum value observed on the cluster
- 2. The maximum value observed on the cluster
- 3. The mean value observed
- 4. The standard deviation on the cluster (unbiased estimator)
- 5. The DC ratio (D) as defined by:
-
- .. math::
-
- D(N) = (\sum_{i=1}^N{|FFT_i|}) / (|FFT_0|)
-
- .. note::
-
- We always ignore the first entry from the input array as, by
- definition, it is always zero.
-
- **Parameters:**
-
- ``arr`` : 1D :py:class:`numpy.ndarray`
- A 1D array containg frame differences.
-
- ``window_size`` : :py:class:`int`
- The size of the window to use for feature computation.
-
- ``overlap`` : :py:class:`int`
- Determines the window overlapping; this number has to be between
- 0 (no overlapping) and 'window-size'-1.
-
- **Returns:**
-
- ``retval`` : 2D :py:class:`numpy.ndarray`
- Array of features without nan samples. Rows - samples, columns - features.
- Here sample corresponds to features computed from the particular
- window of the length ``window_size``.
- """
-
- retval = np.ndarray((arr.shape[0], 5), dtype='float64')
- retval[:] = np.NaN
-
- for k in range(0, arr.shape[0] - window_size + 1,
- window_size - overlap):
-
- obs = arr[k:k + window_size].copy()
-
- # replace NaN values by set mean so they don't disturb calculations
- # much
- ok = obs[~np.isnan(obs)]
-
- obs[np.isnan(obs)] = ok.mean()
-
- retval[k + window_size - 1] = \
- (obs.min(), obs.max(), obs.mean(), obs.std(ddof=1), self.dcratio(obs))
-
- retval = self.remove_nan_rows(retval) # clean-up nan's in the array
-
- return retval
-
- #==========================================================================
- def convert_arr_to_frame_cont(self, data):
- """
- This function converts an array of samples into a FrameContainer, where
- each frame stores features of a particular sample.
-
- **Parameters:**
-
- ``data`` : 2D :py:class:`numpy.ndarray`
- An input array of features of the size
- (Nr. of samples X Nr. of features).
-
- **Returns:**
-
- ``frames`` : FrameContainer
- Resulting FrameContainer, where each frame stores features of
- a particular sample.
- """
-
- frames = bob.bio.video.FrameContainer(
- ) # initialize the FrameContainer
-
- for idx, sample in enumerate(data):
-
- frames.add(idx, sample)
-
- return frames
-
- #==========================================================================
- def comp_features(self, data, window_size, overlap):
- """
- This function computes features for frame differences in the facial and
- non-facial regions.
-
- **Parameters:**
-
- ``data`` : 2D :py:class:`numpy.ndarray`
- An input array of frame differences in facial and non-facial regions.
- The first column contains frame differences of facial regions.
- The second column contains frame differences of non-facial/background regions.
-
- ``window_size`` : :py:class:`int`
- The size of the window to use for feature computation.
-
- ``overlap`` : :py:class:`int`
- Determines the window overlapping; this number has to be between
- 0 (no overlapping) and 'window-size'-1. Default: 0.
-
- **Returns:**
-
- ``frames`` : FrameContainer
- Features describing frame differences, stored in the FrameContainer.
- """
-
- d_face = self.cluster_5quantities(data[:, 0], window_size, overlap)
-
- d_bg = self.cluster_5quantities(data[:, 1], window_size, overlap)
-
- min_len = min(len(d_face), len(d_bg))
-
- features = np.hstack((d_face[:min_len], d_bg[:min_len]))
-
- frames = self.convert_arr_to_frame_cont(features)
-
- return frames
-
- #==========================================================================
- def __call__(self, data):
- """
- This function computes features for frame differences in the facial and
- non-facial regions.
-
- **Parameters:**
-
- ``data`` : 2D :py:class:`numpy.ndarray`
- An input array of frame differences in facial and non-facial regions.
- The first column contains frame differences of facial regions.
- The second column contains frame differences of non-facial/background regions.
-
- **Returns:**
-
- ``frames`` : FrameContainer
- Features describing frame differences, stored in the FrameContainer.
- """
-
- frames = self.comp_features(data, self.window_size, self.overlap)
-
- return frames
-
- #==========================================================================
- def write_feature(self, frames, file_name):
- """
- Writes the given data (that has been generated using the __call__ function of this class) to file.
- This method overwrites the write_data() method of the Extractor class.
-
- **Parameters:**
-
- ``frames`` :
- Data returned by the __call__ method of the class.
-
- ``file_name`` : :py:class:`str`
- Name of the file.
- """
-
- bob.bio.video.extractor.Wrapper(Extractor()).write_feature(
- frames, file_name)
-
- #==========================================================================
- def read_feature(self, file_name):
- """
- Reads the preprocessed data from file.
- This method overwrites the read_data() method of the Extractor class.
-
- **Parameters:**
-
- ``file_name`` : :py:class:`str`
- Name of the file.
-
- **Returns:**
-
- ``frames`` : :py:class:`bob.bio.video.FrameContainer`
- Frames stored in the frame container.
- """
-
- frames = bob.bio.video.extractor.Wrapper(
- Extractor()).read_feature(file_name)
-
- return frames
diff --git a/bob/pad/face/extractor/ImageQualityMeasure.py b/bob/pad/face/extractor/ImageQualityMeasure.py
index f14c33da..6cf33472 100644
--- a/bob/pad/face/extractor/ImageQualityMeasure.py
+++ b/bob/pad/face/extractor/ImageQualityMeasure.py
@@ -1,18 +1,15 @@
-#!/usr/bin/env python2
-# -*- coding: utf-8 -*-
+from __future__ import division
-#==============================================================================
-# Import what is needed here:
+import logging
-from __future__ import division
+import numpy as np
from bob.ip.qualitymeasure import galbally_iqm_features as iqm
from bob.ip.qualitymeasure import msu_iqa_features as iqa
-import logging
-import numpy as np
from sklearn.preprocessing import FunctionTransformer
logger = logging.getLogger(__name__)
+
def iqm_features(images, galbally=True, msu=True, dtype=None):
if not (galbally or msu):
raise ValueError("At least galbally or msu needs to be True.")
@@ -33,8 +30,7 @@ def iqm_features(images, galbally=True, msu=True, dtype=None):
except Exception:
- logger.error(
- "Failed to extract galbally features.", exc_info=True)
+ logger.error("Failed to extract galbally features.", exc_info=True)
features = np.zeros((18,))
@@ -59,94 +55,7 @@ def iqm_features(images, galbally=True, msu=True, dtype=None):
return np.array(all_features)
+
def ImageQualityMeasure(galbally=True, msu=True, dtype=None, **kwargs):
kw_args = dict(galbally=galbally, msu=msu, dtype=dtype)
return FunctionTransformer(iqm_features, validate=False, kw_args=kw_args)
-
-# class ImageQualityMeasure(Extractor):
-# """
-# This class is designed to extract Image Quality Measures given input RGB
-# image. For further documentation and description of features,
-# see "bob.ip.qualitymeasure".
-
-# **Parameters:**
-
-# ``galbally`` : :py:class:`bool`
-# If ``True``, galbally features will be added to the features.
-# Default: ``True``.
-
-# ``msu`` : :py:class:`bool`
-# If ``True``, MSU features will be added to the features.
-# Default: ``True``.
-
-# ``dtype`` : np.dtype
-# The data type of the resulting feature vector.
-# Default: ``None``.
-# """
-
-# #==========================================================================
-# def __init__(self, galbally=True, msu=True, dtype=None, **kwargs):
-
-# Extractor.__init__(
-# self, galbally=galbally, msu=msu, dtype=dtype, **kwargs)
-
-# self.dtype = dtype
-# self.galbally = galbally
-# self.msu = msu
-
-# #==========================================================================
-# def __call__(self, data):
-# """
-# Compute Image Quality Measures given input RGB image.
-
-# **Parameters:**
-
-# ``data`` : 3D :py:class:`np.ndarray`
-# Input RGB image of the dimensionality (3, Row, Col), as returned
-# by Bob image loading routines.
-
-# **Returns:**
-
-# ``features`` : 1D :py:class:`np.ndarray`
-# Feature vector containing Image Quality Measures.
-# """
-
-# assert isinstance(data, np.ndarray)
-# assert self.galbally or self.msu
-
-# features = []
-
-# if self.galbally:
-
-# try:
-
-# gf_set = iqm.compute_quality_features(data)
-# gf_set = np.nan_to_num(gf_set)
-# features = np.hstack((features, gf_set))
-
-# except Exception as e:
-
-# logger.error(
-# "Failed to extract galbally features.", exc_info=e)
-
-# return None
-
-# if self.msu:
-
-# try:
-
-# msuf_set = iqa.compute_msu_iqa_features(data)
-# msuf_set = np.nan_to_num(msuf_set)
-# features = np.hstack((features, msuf_set))
-
-# except Exception as e:
-
-# logger.error("Failed to extract MSU features.", exc_info=e)
-
-# return None
-
-# if self.dtype is not None:
-
-# features = features.astype(self.dtype)
-
-# return features
diff --git a/bob/pad/face/extractor/LBPHistogram.py b/bob/pad/face/extractor/LBPHistogram.py
index d77a1498..b690a0c7 100644
--- a/bob/pad/face/extractor/LBPHistogram.py
+++ b/bob/pad/face/extractor/LBPHistogram.py
@@ -1,11 +1,12 @@
from __future__ import division
-from bob.bio.base.extractor import Extractor
-import bob.bio.video
-import bob.ip.base
+
+from bob.ip.base import LBP, histogram
import numpy as np
+from sklearn.base import BaseEstimator
+from sklearn.base import TransformerMixin
-class LBPHistogram(Extractor):
+class LBPHistogram(TransformerMixin, BaseEstimator):
"""Calculates a normalized LBP histogram over an image.
These features are implemented based on [CAM12]_.
@@ -36,93 +37,94 @@ class LBPHistogram(Extractor):
dtype : numpy.dtype
If a ``dtype`` is specified in the contructor, it is assured that the
resulting features have that dtype.
- lbp : bob.ip.base.LBP
+ lbp : LBP
The LPB extractor object.
"""
- def __init__(self,
- lbptype='uniform',
- elbptype='regular',
- rad=1,
- neighbors=8,
- circ=False,
- dtype=None,
- n_hor=1,
- n_vert=1):
-
- super(LBPHistogram, self).__init__(
- lbptype=lbptype,
- elbptype=elbptype,
- rad=rad,
- neighbors=neighbors,
- circ=circ,
- dtype=dtype,
- n_hor=n_hor,
- n_vert=n_vert)
+ def __init__(
+ self,
+ lbptype="uniform",
+ elbptype="regular",
+ rad=1,
+ neighbors=8,
+ circ=False,
+ dtype=None,
+ n_hor=1,
+ n_vert=1,
+ **kwargs,
+ ):
+
+ super().__init__(**kwargs)
elbps = {
- 'regular': 'regular',
- 'transitional': 'trainsitional',
- 'direction_coded': 'direction-coded',
- 'modified': 'regular'
+ "regular": "regular",
+ "transitional": "trainsitional",
+ "direction_coded": "direction-coded",
+ "modified": "regular",
}
- if elbptype == 'modified':
+ if elbptype == "modified":
mct = True
else:
mct = False
- if lbptype == 'uniform':
+ if lbptype == "uniform":
if neighbors == 16:
- lbp = bob.ip.base.LBP(
+ lbp = LBP(
neighbors=16,
uniform=True,
circular=circ,
radius=rad,
to_average=mct,
- elbp_type=elbps[elbptype])
+ elbp_type=elbps[elbptype],
+ )
else: # we assume neighbors==8 in this case
- lbp = bob.ip.base.LBP(
+ lbp = LBP(
neighbors=8,
uniform=True,
circular=circ,
radius=rad,
to_average=mct,
- elbp_type=elbps[elbptype])
- elif lbptype == 'riu2':
+ elbp_type=elbps[elbptype],
+ )
+ elif lbptype == "riu2":
if neighbors == 16:
- lbp = bob.ip.base.LBP(
+ lbp = LBP(
neighbors=16,
uniform=True,
rotation_invariant=True,
radius=rad,
circular=circ,
to_average=mct,
- elbp_type=elbps[elbptype])
+ elbp_type=elbps[elbptype],
+ )
else: # we assume neighbors==8 in this case
- lbp = bob.ip.base.LBP(
+ lbp = LBP(
neighbors=8,
uniform=True,
rotation_invariant=True,
radius=rad,
circular=circ,
to_average=mct,
- elbp_type=elbps[elbptype])
+ elbp_type=elbps[elbptype],
+ )
else: # regular LBP
if neighbors == 16:
- lbp = bob.ip.base.LBP(
+ lbp = LBP(
neighbors=16,
circular=circ,
radius=rad,
to_average=mct,
- elbp_type=elbps[elbptype])
+ elbp_type=elbps[elbptype],
+ )
else: # we assume neighbors==8 in this case
- lbp = bob.ip.base.LBP(
+ lbp = LBP(
neighbors=8,
circular=circ,
radius=rad,
to_average=mct,
- elbp_type=elbps[elbptype])
+ elbp_type=elbps[elbptype],
+ )
self.dtype = dtype
self.lbp = lbp
@@ -151,16 +153,17 @@ class LBPHistogram(Extractor):
assert isinstance(data, np.ndarray)
# allocating the image with lbp codes
- lbpimage = np.ndarray(self.lbp.lbp_shape(data), 'uint16')
+ lbpimage = np.ndarray(self.lbp.lbp_shape(data), "uint16")
self.lbp(data, lbpimage) # calculating the lbp image
- hist = bob.ip.base.histogram(lbpimage, (0, self.lbp.max_label - 1),
- self.lbp.max_label)
+ hist = histogram(
+ lbpimage, (0, self.lbp.max_label - 1), self.lbp.max_label
+ )
hist = hist / sum(hist) # histogram normalization
if self.dtype is not None:
hist = hist.astype(self.dtype)
return hist
- def __call__(self, data):
+ def transform_one_image(self, data):
"""
Extracts spatially-enhanced LBP/MCT histograms from a gray-scale image.
@@ -182,7 +185,11 @@ class LBPHistogram(Extractor):
col_max = int(data.shape[1] / self.n_hor) * self.n_hor
data = data[:row_max, :col_max]
- blocks = [sub_block for block in np.hsplit(data, self.n_hor) for sub_block in np.vsplit(block, self.n_vert)]
+ blocks = [
+ sub_block
+ for block in np.hsplit(data, self.n_hor)
+ for sub_block in np.vsplit(block, self.n_vert)
+ ]
hists = [self.comp_block_histogram(block) for block in blocks]
@@ -191,3 +198,12 @@ class LBPHistogram(Extractor):
hist = hist / len(blocks) # histogram normalization
return hist
+
+ def transform(self, images):
+ return [self.transform_one_image(img) for img in images]
+
+ def _more_tags(self):
+ return {"stateless": True, "requires_fit": False}
+
+ def fit(self, X, y=None):
+ return self
diff --git a/bob/pad/face/extractor/__init__.py b/bob/pad/face/extractor/__init__.py
index a7b3d4ca..2878cf78 100644
--- a/bob/pad/face/extractor/__init__.py
+++ b/bob/pad/face/extractor/__init__.py
@@ -1,6 +1,5 @@
from .LBPHistogram import LBPHistogram
from .ImageQualityMeasure import ImageQualityMeasure
-from .FrameDiffFeatures import FrameDiffFeatures
def __appropriate__(*args):
@@ -24,6 +23,5 @@ def __appropriate__(*args):
__appropriate__(
LBPHistogram,
ImageQualityMeasure,
- FrameDiffFeatures,
)
__all__ = [_ for _ in dir() if not _.startswith('_')]
diff --git a/bob/pad/face/preprocessor/FrameDifference.py b/bob/pad/face/preprocessor/FrameDifference.py
deleted file mode 100644
index 8519c69d..00000000
--- a/bob/pad/face/preprocessor/FrameDifference.py
+++ /dev/null
@@ -1,433 +0,0 @@
-#!/usr/bin/env python2
-# -*- coding: utf-8 -*-
-"""
-Created on Fri May 12 14:14:23 2017
-
-@author: Olegs Nikisins
-"""
-
-#==============================================================================
-# Import what is needed here:
-
-from bob.bio.base.preprocessor import Preprocessor
-
-import numpy as np
-
-import bob.bio.video
-
-import bob.ip.base
-
-import bob.ip.color
-
-import bob.ip.facedetect
-
-import logging
-
-#==============================================================================
-# Main body:
-
-logger = logging.getLogger(__name__)
-
-
-class FrameDifference(Preprocessor):
- """
- This class is designed to compute frame differences for both facial and
- background regions. The constraint of minimal size of the face can be
- applied to input video selecting only the frames overcoming the threshold.
- This behavior is controlled by ``check_face_size_flag`` and ``min_face_size``
- arguments of the class.
- It is also possible to compute the frame differences for a limited number
- of frames specifying the ``number_of_frames`` parameter.
-
- **Parameters:**
-
- ``number_of_frames`` : :py:class:`int`
- The number of frames to extract the frame differences from.
- If ``None``, all frames of the input video are used. Default: ``None``.
-
- ``min_face_size`` : :py:class:`int`
- The minimal size of the face in pixels. Only valid when ``check_face_size_flag``
- is set to True. Default: 50.
- """
-
- def __init__(self,
- number_of_frames=None,
- min_face_size=50,
- **kwargs):
-
- super(FrameDifference, self).__init__(
- number_of_frames=number_of_frames,
- min_face_size=min_face_size,
- **kwargs)
-
- self.number_of_frames = number_of_frames
- self.min_face_size = min_face_size
-
- #==========================================================================
- def eval_face_differences(self, previous, current, annotations):
- """
- Evaluates the normalized frame difference on the face region.
-
- If bounding_box is None or invalid, returns 0.
-
- **Parameters:**
-
- ``previous`` : 2D :py:class:`numpy.ndarray`
- Previous frame as a gray-scaled image
-
- ``current`` : 2D :py:class:`numpy.ndarray`
- The current frame as a gray-scaled image
-
- ``annotations`` : :py:class:`dict`
- A dictionary containing annotations of the face bounding box.
- Dictionary must be as follows ``{'topleft': (row, col), 'bottomright': (row, col)}``.
-
- **Returns:**
-
- ``face`` : :py:class:`float`
- A size normalized integral difference of facial regions in two input
- images.
- """
-
- prev = previous[annotations['topleft'][0]:annotations['bottomright'][
- 0], annotations['topleft'][1]:annotations['bottomright'][1]]
-
- curr = current[annotations['topleft'][0]:annotations['bottomright'][0],
- annotations['topleft'][1]:annotations['bottomright'][1]]
-
- face_diff = abs(curr.astype('int32') - prev.astype('int32'))
-
- face = face_diff.sum()
-
- face /= float(face_diff.size)
-
- return face
-
- #==========================================================================
- def eval_background_differences(self,
- previous,
- current,
- annotations,
- border=None):
- """
- Evaluates the normalized frame difference on the background.
-
- If bounding_box is None or invalid, returns 0.
-
- **Parameters:**
-
- ``previous`` : 2D :py:class:`numpy.ndarray`
- Previous frame as a gray-scaled image
-
- ``current`` : 2D :py:class:`numpy.ndarray`
- The current frame as a gray-scaled image
-
- ``annotations`` : :py:class:`dict`
- A dictionary containing annotations of the face bounding box.
- Dictionary must be as follows ``{'topleft': (row, col), 'bottomright': (row, col)}``.
-
- ``border`` : :py:class:`int`
- The border size to consider. If set to ``None``, consider all image from the
- face location up to the end. Default: ``None``.
-
- **Returns:**
-
- ``bg`` : :py:class:`float`
- A size normalized integral difference of non-facial regions in two input
- images.
- """
-
- height = annotations['bottomright'][0] - annotations['topleft'][0]
- width = annotations['bottomright'][1] - annotations['topleft'][1]
-
- full_diff = abs(current.astype('int32') - previous.astype('int32'))
-
- if border is None:
- full = full_diff.sum()
- full_size = full_diff.size
-
- else:
-
- y1 = annotations['topleft'][0] - border
- if y1 < 0:
- y1 = 0
- x1 = annotations['topleft'][1] - border
- if x1 < 0:
- x1 = 0
- y2 = y1 + height + (2 * border)
- if y2 > full_diff.shape[0]:
- y2 = full_diff.shape[0]
- x2 = x1 + width + (2 * border)
- if x2 > full_diff.shape[1]:
- x2 = full_diff.shape[1]
- full = full_diff[y1:y2, x1:x2].sum()
- full_size = full_diff[y1:y2, x1:x2].size
-
- face_diff = full_diff[annotations['topleft'][0]:(
- annotations['topleft'][0] + height), annotations['topleft'][1]:(
- annotations['topleft'][1] + width)]
-
- # calculates the differences in the face and background areas
- face = face_diff.sum()
- bg = full - face
-
- normalization = float(full_size - face_diff.size)
- if normalization < 1: # prevents zero division
- bg = 0.0
- else:
- bg /= float(full_size - face_diff.size)
-
- return bg
-
- #==========================================================================
- def check_face_size(self, frame_container, annotations, min_face_size):
- """
- Return the FrameContainer containing the frames with faces of the
- size overcoming the specified threshold. The annotations for the selected
- frames are also returned.
-
- **Parameters:**
-
- ``frame_container`` : FrameContainer
- Video data stored in the FrameContainer, see ``bob.bio.video.utils.FrameContainer``
- for further details.
-
- ``annotations`` : :py:class:`dict`
- A dictionary containing the annotations for each frame in the video.
- Dictionary structure: ``annotations = {'1': frame1_dict, '2': frame1_dict, ...}``.
- Where ``frameN_dict = {'topleft': (row, col), 'bottomright': (row, col)}``
- is the dictionary defining the coordinates of the face bounding box in frame N.
-
- ``min_face_size`` : :py:class:`int`
- The minimal size of the face in pixels.
-
- **Returns:**
-
- ``selected_frames`` : FrameContainer
- Selected frames stored in the FrameContainer.
-
- ``selected_annotations`` : :py:class:`dict`
- A dictionary containing the annotations for selected frames.
- Dictionary structure: ``annotations = {'1': frame1_dict, '2': frame1_dict, ...}``.
- Where ``frameN_dict = {'topleft': (row, col), 'bottomright': (row, col)}``
- is the dictionary defining the coordinates of the face bounding box in frame N.
- """
-
- selected_frames = bob.bio.video.FrameContainer(
- ) # initialize the FrameContainer
-
- selected_annotations = {}
-
- selected_frame_idx = 0
-
- for idx in range(0, len(annotations)): # idx - frame index
-
- # annotations for particular frame
- frame_annotations = annotations[str(idx)]
-
- if not frame_annotations:
- continue
-
- # Estimate bottomright and topleft if they are not available:
- if 'topleft' not in frame_annotations:
- bbx = bob.ip.facedetect.bounding_box_from_annotation(
- **frame_annotations)
- frame_annotations['topleft'] = bbx.topleft
- frame_annotations['bottomright'] = bbx.bottomright
-
- # size of current face
- face_size = np.min(
- np.array(frame_annotations['bottomright']) -
- np.array(frame_annotations['topleft']))
-
- if face_size >= min_face_size: # check if face size is above the threshold
-
- selected_frame = frame_container[idx][1] # get current frame
-
- selected_frames.add(
- selected_frame_idx,
- selected_frame) # add current frame to FrameContainer
-
- selected_annotations[str(selected_frame_idx)] = annotations[
- str(idx)]
-
- selected_frame_idx = selected_frame_idx + 1
-
- return selected_frames, selected_annotations
-
- #==========================================================================
- def comp_face_bg_diff(self, frames, annotations, number_of_frames=None):
- """
- This function computes the frame differences for both facial and background
- regions. These parameters are computed for ``number_of_frames`` frames
- in the input FrameContainer.
-
- **Parameters:**
-
- ``frames`` : FrameContainer
- RGB video data stored in the FrameContainer, see ``bob.bio.video.utils.FrameContainer``
- for further details.
-
- ``annotations`` : :py:class:`dict`
- A dictionary containing the annotations for each frame in the video.
- Dictionary structure: ``annotations = {'1': frame1_dict, '2': frame1_dict, ...}``.
- Where ``frameN_dict = {'topleft': (row, col), 'bottomright': (row, col)}``
- is the dictionary defining the coordinates of the face bounding box in frame N.
-
- ``number_of_frames`` : :py:class:`int`
- The number of frames to use in processing. If ``None``, all frames of the
- input video are used. Default: ``None``.
-
- **Returns:**
-
- ``diff`` : 2D :py:class:`numpy.ndarray`
- An array of the size ``(number_of_frames - 1) x 2``.
- The first column contains frame differences of facial regions.
- The second column contains frame differences of non-facial/background regions.
- """
-
- # Compute the number of frames to process:
- if number_of_frames is not None:
- number_of_frames = np.min([len(frames), number_of_frames])
- else:
- number_of_frames = len(frames)
-
- previous = frames[0][1] # the first frame in the video
-
- if len(previous.shape) == 3: # if RGB convert to gray-scale
- previous = bob.ip.color.rgb_to_gray(previous)
-
- diff = []
-
- for k in range(1, number_of_frames):
-
- current = frames[k][1]
-
- if len(current.shape) == 3: # if RGB convert to gray-scale
- current = bob.ip.color.rgb_to_gray(current)
-
- face_diff = self.eval_face_differences(previous, current,
- annotations[str(k)])
- bg_diff = self.eval_background_differences(
- previous, current, annotations[str(k)], None)
-
- diff.append((face_diff, bg_diff))
-
- # swap buffers: current <=> previous
- tmp = previous
- previous = current
- current = tmp
-
- if not diff: # if list is empty
-
- diff = [(np.NaN, np.NaN)]
-
- diff = np.vstack(diff)
-
- return diff
-
- #==========================================================================
- def select_annotated_frames(self, frames, annotations):
- """
- Select only annotated frames in the input FrameContainer ``frames``.
-
- **Parameters:**
-
- ``frames`` : FrameContainer
- Video data stored in the FrameContainer, see ``bob.bio.video.utils.FrameContainer``
- for further details.
-
- ``annotations`` : :py:class:`dict`
- A dictionary containing the annotations for each frame in the video.
- Dictionary structure: ``annotations = {'1': frame1_dict, '2': frame1_dict, ...}``.
- Where ``frameN_dict = {'topleft': (row, col), 'bottomright': (row, col)}``
- is the dictionary defining the coordinates of the face bounding box in frame N.
-
- **Returns:**
-
- ``cleaned_frame_container`` : FrameContainer
- FrameContainer containing the annotated frames only.
-
- ``cleaned_annotations`` : :py:class:`dict`
- A dictionary containing the annotations for each frame in the output video.
- Dictionary structure: ``annotations = {'1': frame1_dict, '2': frame1_dict, ...}``.
- Where ``frameN_dict = {'topleft': (row, col), 'bottomright': (row, col)}``
- is the dictionary defining the coordinates of the face bounding box in frame N.
- """
-
- annotated_frames = np.sort([
- np.int(item) for item in annotations.keys()
- ]) # annotated frame numbers
-
- available_frames = range(
- 0, len(frames)) # frame numbers in the input video
-
- valid_frames = list(
- set(annotated_frames).intersection(
- available_frames)) # valid and annotated frames
-
- cleaned_frame_container = bob.bio.video.FrameContainer(
- ) # initialize the FrameContainer
-
- cleaned_annotations = {}
-
- for idx, valid_frame_num in enumerate(valid_frames):
- # valid_frame_num - is the number of the original frame having annotations
-
- cleaned_annotations[str(idx)] = annotations[str(
- valid_frame_num)] # correct the frame numbers
-
- selected_frame = frames[valid_frame_num][1] # get current frame
-
- cleaned_frame_container.add(
- idx, selected_frame) # add current frame to FrameContainer
-
- return cleaned_frame_container, cleaned_annotations
-
- #==========================================================================
- def __call__(self, frames, annotations):
- """
- This method calls the ``comp_face_bg_diff`` function of this class
- computing the frame differences for both facial and background regions.
- The frame differences are computed for selected frames, which are returned
- by ``check_face_size`` function of this class.
-
- **Parameters:**
-
- ``frames`` : FrameContainer
- RGB video data stored in the FrameContainer, see ``bob.bio.video.utils.FrameContainer``
- for further details.
-
- ``annotations`` : :py:class:`dict`
- A dictionary containing the annotations for each frame in the video.
- Dictionary structure: ``annotations = {'1': frame1_dict, '2': frame1_dict, ...}``.
- Where ``frameN_dict = {'topleft': (row, col), 'bottomright': (row, col)}``
- is the dictionary defining the coordinates of the face bounding box in frame N.
-
- **Returns:**
-
- ``diff`` : 2D :py:class:`numpy.ndarray`
- An array of the size ``(number_of_frames - 1) x 2``.
- The first column contains frame differences of facial regions.
- The second column contains frame differences of non-facial/background regions.
- """
-
- if len(frames) != len(annotations): # if some annotations are missing
-
- # Select only annotated frames:
- frames, annotations = self.select_annotated_frames(
- frames, annotations)
-
- selected_frames, selected_annotations = self.check_face_size(
- frames, annotations, self.min_face_size)
-
- if not len(selected_annotations):
- logger.warn("None of the annotations are valid.")
- return None
-
- diff = self.comp_face_bg_diff(
- frames=selected_frames,
- annotations=selected_annotations,
- number_of_frames=self.number_of_frames)
-
- return diff
diff --git a/bob/pad/face/preprocessor/__init__.py b/bob/pad/face/preprocessor/__init__.py
index 2f104e1c..f1248dbf 100644
--- a/bob/pad/face/preprocessor/__init__.py
+++ b/bob/pad/face/preprocessor/__init__.py
@@ -1,4 +1,3 @@
-from .FrameDifference import FrameDifference
from .Patch import ImagePatches, VideoPatches
@@ -21,7 +20,6 @@ def __appropriate__(*args):
__appropriate__(
- FrameDifference,
ImagePatches,
VideoPatches,
)
diff --git a/bob/pad/face/test/test.py b/bob/pad/face/test/test.py
index a2adc93d..4ea4bc13 100644
--- a/bob/pad/face/test/test.py
+++ b/bob/pad/face/test/test.py
@@ -18,24 +18,12 @@ from bob.ip.color import rgb_to_gray
from ..extractor import LBPHistogram
-from ..preprocessor import FaceCropAlign
-from ..preprocessor import FrameDifference
-
-from ..extractor import FrameDiffFeatures
from ..extractor import LBPHistogram
from ..extractor import ImageQualityMeasure
-from ..preprocessor import LiPulseExtraction
-from ..preprocessor import Chrom
-from ..preprocessor import PPGSecure as PPGPreprocessor
-from ..preprocessor import SSR
-
-from ..extractor import LTSS
-from ..extractor import LiSpectralFeatures
-from ..extractor import PPGSecure as PPGExtractor
@@ -47,411 +35,19 @@ from bob.bio.video.utils import FrameSelector
from ..preprocessor import BlockPatch
-from bob.pad.face.config.preprocessor.face_feature_crop_quality_check import face_feature_0_128x128_crop_rgb
-
-from bob.pad.face.utils.patch_utils import reshape_flat_patches
-
-from bob.pad.face.config.preprocessor.video_face_crop_align_block_patch import video_face_crop_align_bw_ir_d_channels_3x128x128 as mc_preprocessor
-
-
-def test_detect_face_landmarks_in_image_mtcnn():
-
- img = load(datafile('testimage.jpg', 'bob.bio.face.test'))
- assert len(img) == 3
- annotations = detect_face_landmarks_in_image(
- img, method='mtcnn')
- assert len(annotations['landmarks']) == 68
- assert len(annotations['leye']) == 2
- assert len(annotations['reye']) == 2
- assert len(annotations['topleft']) == 2
- assert len(annotations['bottomright']) == 2
- #assert len(annotations['leye']) == (176, 220)
-
-def test_detect_face_landmarks_in_image_dlib():
-
- img = load(datafile('testimage.jpg', 'bob.bio.face.test'))
- assert len(img) == 3
- annotations = detect_face_landmarks_in_image(
- img, method='dlib')
- assert len(annotations['landmarks']) == 68
- assert len(annotations['leye']) == 2
- assert len(annotations['reye']) == 2
- assert len(annotations['topleft']) == 2
- assert len(annotations['bottomright']) == 2
-
- #assert len(annotations['leye']) == (176, 220)
-
-
-#==============================================================================
def test_lbp_histogram():
lbp = LBPHistogram()
img = load(datafile('testimage.jpg', 'bob.bio.face.test'))
img = rgb_to_gray(img)
- features = lbp(img)
+ features = lbp.transform([img])[0]
reference = load(datafile('lbp.hdf5', 'bob.pad.face.test'))
assert np.allclose(features, reference)
-#==============================================================================
-def test_face_crop_align():
- """
- Test FaceCropAlign preprocessor, which is designed to crop faces in the images.
- """
-
- image = load(datafile('test_image.png', 'bob.pad.face.test'))
- annotations = {'topleft': (95, 155), 'bottomright': (215, 265)}
-
- preprocessor = FaceCropAlign(face_size=64, rgb_output_flag=False, use_face_alignment=False)
- face = preprocessor(image, annotations)
-
- assert face.shape == (64, 64)
- assert np.sum(face) == 429158
-
- preprocessor = FaceCropAlign(face_size=64, rgb_output_flag=True, use_face_alignment=False)
- face = preprocessor(image, annotations)
-
- assert face.shape == (3, 64, 64)
- assert np.sum(face) == 1215525
-
-
-#==============================================================================
-def convert_image_to_video_data(image, annotations, n_frames):
- """
- Convert input image to video and image annotations to frame annotations.
-
- **Parameters:**
-
- ``image`` : 2D or 3D :py:class:`numpy.ndarray`
- Input image (RGB or gray-scale).
-
- ``annotations`` : :py:class:`dict`
- A dictionary containing annotations of the face bounding box.
- Dictionary must be as follows ``{'topleft': (row, col), 'bottomright': (row, col)}``
-
- ``n_frames`` : :py:class:`int`
- Number of frames in the output video
-
- **Returns:**
-
- ``frame_container`` : FrameContainer
- Video data stored in the FrameContainer, see ``bob.bio.video.utils.FrameContainer``
- for further details.
-
- ``video_annotations`` : :py:class:`dict`
- A dictionary containing the annotations for each frame in the video.
- Dictionary structure: ``annotations = {'1': frame1_dict, '2': frame1_dict, ...}``.
- Where ``frameN_dict = {'topleft': (row, col), 'bottomright': (row, col)}``
- is the dictionary defining the coordinates of the face bounding box in frame N.
- """
-
- frame_container = bob.bio.video.FrameContainer(
- ) # initialize the FrameContainer
-
- video_annotations = {}
-
- for idx, fn in enumerate(range(0, n_frames)):
-
- frame_container.add(idx, image) # add current frame to FrameContainer
-
- video_annotations[str(idx)] = annotations
-
- return frame_container, video_annotations
-
-
-#==============================================================================
-def test_video_face_crop():
- """
- Test FaceCropAlign preprocessor with Wrapper, which is designed to crop faces in the video.
- """
-
- FACE_SIZE = 64 # The size of the resulting face
- RGB_OUTPUT_FLAG = False # Gray-scale output
- USE_FACE_ALIGNMENT = False # use annotations
- MAX_IMAGE_SIZE = None # no limiting here
- FACE_DETECTION_METHOD = None # use annotations
- MIN_FACE_SIZE = 50 # skip small faces
-
- image_preprocessor = FaceCropAlign(face_size = FACE_SIZE,
- rgb_output_flag = RGB_OUTPUT_FLAG,
- use_face_alignment = USE_FACE_ALIGNMENT,
- max_image_size = MAX_IMAGE_SIZE,
- face_detection_method = FACE_DETECTION_METHOD,
- min_face_size = MIN_FACE_SIZE)
-
- preprocessor = Wrapper(image_preprocessor)
-
- image = load(datafile('test_image.png', 'bob.pad.face.test'))
- annotations = {'topleft': (95, 155), 'bottomright': (215, 265)}
-
- video, annotations = convert_image_to_video_data(image, annotations, 20)
-
- faces = preprocessor(frames=video, annotations=annotations)
-
- assert len(faces) == 20
- assert faces[0][1].shape == (64, 64)
- assert faces[-1][1].shape == (64, 64)
- assert np.sum(faces[0][1]) == 429158
- assert np.sum(faces[-1][1]) == 429158
-
- #==========================================================================
- # test another configuration of the preprocessor:
-
- FACE_SIZE = 64 # The size of the resulting face
- RGB_OUTPUT_FLAG = True # Gray-scale output
- USE_FACE_ALIGNMENT = False # use annotations
- MAX_IMAGE_SIZE = None # no limiting here
- FACE_DETECTION_METHOD = "dlib" # use annotations
- MIN_FACE_SIZE = 50 # skip small faces
-
- image_preprocessor = FaceCropAlign(face_size = FACE_SIZE,
- rgb_output_flag = RGB_OUTPUT_FLAG,
- use_face_alignment = USE_FACE_ALIGNMENT,
- max_image_size = MAX_IMAGE_SIZE,
- face_detection_method = FACE_DETECTION_METHOD,
- min_face_size = MIN_FACE_SIZE)
-
- preprocessor = Wrapper(image_preprocessor)
-
- video, _ = convert_image_to_video_data(image, annotations, 3)
-
- faces = preprocessor(frames=video, annotations=annotations)
-
- assert len(faces) == 3
- assert faces[0][1].shape == (3, 64, 64)
- assert faces[-1][1].shape == (3, 64, 64)
- assert np.sum(faces[0][1]) == 1238664
- assert np.sum(faces[-1][1]) == 1238664
-
-# =============================================================================
-def test_video_face_crop_align_block_patch():
- """
- Test VideoFaceCropAlignBlockPatch preprocessor.
- """
-
- # =========================================================================
- # prepare the test data:
-
- image = load(datafile('test_image.png', 'bob.pad.face.test'))
-
- annotations = None
-
- video, annotations = convert_image_to_video_data(image, annotations, 2)
-
- mc_video = {}
- mc_video["color_1"] = video
- mc_video["color_2"] = video
- mc_video["color_3"] = video
-
- # =========================================================================
- # Initialize the VideoFaceCropAlignBlockPatch.
-
- # names of the channels to process:
- _channel_names = ['color_1', 'color_2', 'color_3']
-
- # dictionary containing preprocessors for all channels:
- _preprocessors = {}
-
- """
- All channels are color, so preprocessors for all of them are identical.
- """
- FACE_SIZE = 128 # The size of the resulting face
- RGB_OUTPUT_FLAG = False # BW output
- USE_FACE_ALIGNMENT = True # use annotations
- MAX_IMAGE_SIZE = None # no limiting here
- FACE_DETECTION_METHOD = "mtcnn" # use ANNOTATIONS
- MIN_FACE_SIZE = 50 # skip small faces
-
- _image_preprocessor = FaceCropAlign(face_size = FACE_SIZE,
- rgb_output_flag = RGB_OUTPUT_FLAG,
- use_face_alignment = USE_FACE_ALIGNMENT,
- max_image_size = MAX_IMAGE_SIZE,
- face_detection_method = FACE_DETECTION_METHOD,
- min_face_size = MIN_FACE_SIZE)
-
- _frame_selector = FrameSelector(selection_style = "all")
-
- _preprocessor_rgb = Wrapper(preprocessor = _image_preprocessor,
- frame_selector = _frame_selector)
-
- _preprocessors[_channel_names[0]] = _preprocessor_rgb
- _preprocessors[_channel_names[1]] = _preprocessor_rgb
- _preprocessors[_channel_names[2]] = _preprocessor_rgb
-
- """
- The instance of the BlockPatch preprocessor.
- """
-
- PATCH_SIZE = 64
- STEP = 32
- _block_patch = BlockPatch(patch_size = PATCH_SIZE,
- step = STEP,
- use_annotations_flag = False)
- preprocessor = VideoFaceCropAlignBlockPatch(preprocessors = _preprocessors,
- channel_names = _channel_names,
- return_multi_channel_flag = True,
- block_patch_preprocessor = _block_patch)
-
- # =========================================================================
- # pre-process the data and assert the result:
-
- data_preprocessed = preprocessor(frames = mc_video, annotations = annotations)
-
- assert len(data_preprocessed) == 2
- assert data_preprocessed[0][1].shape == (3, 128, 128)
- assert data_preprocessed[1][1].shape == (3, 128, 128)
-
- preprocessor.return_multi_channel_flag = False # now extract patches
-
- data_preprocessed = preprocessor(frames = mc_video, annotations = annotations)
-
- assert len(data_preprocessed) == 2
- assert data_preprocessed[0][1].shape == (9, 12288)
- assert data_preprocessed[1][1].shape == (9, 12288)
-
-
-# =============================================================================
-def test_preproc_with_quality_check():
- """
- Test _Preprocessor cropping the face and checking the quality of the image
- applying eye detection, and asserting if they are in the expected positions.
- """
-
- # =========================================================================
- # prepare the test data:
- image = load(datafile('test_image.png', 'bob.pad.face.test'))
-
- annotations = None
-
- video, annotations = convert_image_to_video_data(image, annotations, 2)
-
- # =========================================================================
- # test the preprocessor:
- data_preprocessed = face_feature_0_128x128_crop_rgb(video)
-
- assert data_preprocessed is None
-
-
-# =============================================================================
-def test_multi_channel_preprocessing():
- """
- Test video_face_crop_align_bw_ir_d_channels_3x128x128 preprocessor.
- """
-
- # =========================================================================
- # prepare the test data:
-
- image = load(datafile('test_image.png', 'bob.pad.face.test'))
-
- # annotations must be known for this preprocessor, so compute them:
- annotations = detect_face_landmarks_in_image(image, method="mtcnn")
-
- video_color, annotations = convert_image_to_video_data(image, annotations, 2)
-
- video_bw, _ = convert_image_to_video_data(image[0], annotations, 2)
-
- mc_video = {}
- mc_video["color"] = video_color
- mc_video["infrared"] = video_bw
- mc_video["depth"] = video_bw
-
- # =========================================================================
- # test the preprocessor:
-
- data_preprocessed = mc_preprocessor(mc_video, annotations)
-
- assert len(data_preprocessed) == 2
- assert data_preprocessed[0][1].shape == (3, 128, 128)
-
- # chanenls are preprocessed differently, thus this should apply:
- assert np.any(data_preprocessed[0][1][0] != data_preprocessed[0][1][1])
- assert np.any(data_preprocessed[0][1][0] != data_preprocessed[0][1][2])
-
-
-# =============================================================================
-def test_reshape_flat_patches():
- """
- Test reshape_flat_patches function.
- """
-
- image = load(datafile('test_image.png', 'bob.pad.face.test'))
-
- patch1 = image[0,0:10,0:10]
- patch2 = image[1,0:10,0:10]
-
- patches = np.stack([patch1.flatten(), patch2.flatten()])
- patches_3d = reshape_flat_patches(patches, (10, 10))
-
- assert np.all(patch1 == patches_3d[0])
- assert np.all(patch2 == patches_3d[1])
-
- # =========================================================================
- patch1 = image[:,0:10,0:10]
- patch2 = image[:,1:11,1:11]
-
- patches = np.stack([patch1.flatten(), patch2.flatten()])
- patches_3d = reshape_flat_patches(patches, (3, 10, 10))
-
- assert np.all(patch1 == patches_3d[0])
- assert np.all(patch2 == patches_3d[1])
-
-
-#==============================================================================
-def test_frame_difference():
- """
- Test FrameDifference preprocessor computing frame differences for both
- facial and non-facial/background regions.
- """
-
- image = load(datafile('test_image.png', 'bob.pad.face.test'))
- annotations = {'topleft': (95, 155), 'bottomright': (215, 265)}
-
- n_frames = 20
-
- video, annotations = convert_image_to_video_data(image, annotations,
- n_frames)
-
- NUMBER_OF_FRAMES = None # process all frames
- CHECK_FACE_SIZE_FLAG = True # Check size of the face
- MIN_FACE_SIZE = 50 # Minimal size of the face to consider
-
- preprocessor = FrameDifference(
- number_of_frames=NUMBER_OF_FRAMES,
- check_face_size_flag=CHECK_FACE_SIZE_FLAG,
- min_face_size=MIN_FACE_SIZE)
-
- diff = preprocessor(frames=video, annotations=annotations)
-
- assert diff.shape == (n_frames - 1, 2)
- assert (diff == 0).all()
-
-
-#==============================================================================
-def test_frame_diff_features():
- """
- Test FrameDiffFeatures extractor computing 10 features given frame differences.
- """
-
- WINDOW_SIZE = 20
- OVERLAP = 0
-
- extractor = FrameDiffFeatures(window_size=WINDOW_SIZE, overlap=OVERLAP)
-
- data = np.transpose(np.vstack([range(0, 100), range(0, 100)]))
-
- features = extractor(data)
-
- assert len(features) == 5
- assert len(features[0][1]) == 10
- assert len(features[-1][1]) == 10
- assert (features[0][1][0:5] == features[0][1][5:]).all()
- assert (np.sum(features[0][1]) - 73.015116873109207) < 0.000001
-
-
-#==============================================================================
def test_video_lbp_histogram():
"""
Test LBPHistogram with Wrapper extractor.
@@ -532,131 +128,3 @@ def test_video_quality_measure():
assert (features[0][1] == features[-1][1]).all()
assert (features[0][1][0] - 2.7748559659812599e-05) < 0.000001
assert (features[0][1][-1] - 0.16410418866596271) < 0.000001
-
-
-#==============================================================================
-def convert_array_to_list_of_frame_cont(data):
- """
- Convert an input 2D array to a list of FrameContainers.
-
- **Parameters:**
-
- ``data`` : 2D :py:class:`numpy.ndarray`
- Input data array of the dimensionality (N_samples X N_features ).
-
- **Returns:**
-
- ``frame_container_list`` : [FrameContainer]
- A list of FrameContainers, see ``bob.bio.video.utils.FrameContainer``
- for further details. Each frame container contains one feature vector.
- """
-
- frame_container_list = []
-
- for idx, vec in enumerate(data):
-
- frame_container = bob.bio.video.FrameContainer(
- ) # initialize the FrameContainer
-
- frame_container.add(0, vec)
-
- frame_container_list.append(
- frame_container) # add current frame to FrameContainer
-
- return frame_container_list
-
-
-def test_preprocessor_LiPulseExtraction():
- """ Test the pulse extraction using Li's ICPR 2016 algorithm.
- """
-
- image = load(datafile('test_image.png', 'bob.pad.face.test'))
- annotations = {'topleft': (95, 155), 'bottomright': (215, 265)}
- video, annotations = convert_image_to_video_data(image, annotations, 100)
-
- preprocessor = LiPulseExtraction(debug=False)
- pulse = preprocessor(video, annotations)
- assert pulse.shape == (100, 3)
-
-
-def test_preprocessor_Chrom():
- """ Test the pulse extraction using CHROM algorithm.
- """
-
- image = load(datafile('test_image.png', 'bob.pad.face.test'))
- annotations = {'topleft': (95, 155), 'bottomright': (215, 265)}
- video, annotations = convert_image_to_video_data(image, annotations, 100)
-
- preprocessor = Chrom(debug=False)
- pulse = preprocessor(video, annotations)
- assert pulse.shape[0] == 100
-
-
-def test_preprocessor_PPGSecure():
- """ Test the pulse extraction using PPGSecure algorithm.
- """
-
- image = load(datafile('test_image.png', 'bob.pad.face.test'))
- annotations = {'topleft': (456, 212), 'bottomright': (770, 500)}
- video, annotations = convert_image_to_video_data(image, annotations, 100)
-
- preprocessor = PPGPreprocessor(debug=False)
- pulse = preprocessor(video, annotations)
- assert pulse.shape == (100, 5)
-
-
-def test_preprocessor_SSR():
- """ Test the pulse extraction using SSR algorithm.
- """
-
- image = load(datafile('test_image.png', 'bob.pad.face.test'))
- annotations = {'topleft': (95, 155), 'bottomright': (215, 265)}
- video, annotations = convert_image_to_video_data(image, annotations, 100)
-
- preprocessor = SSR(debug=False)
- pulse = preprocessor(video, annotations)
- assert pulse.shape[0] == 100
-
-
-def test_extractor_LTSS():
- """ Test Long Term Spectrum Statistics (LTSS) Feature Extractor
- """
-
- # "pulse" in 3 color channels
- data = np.random.random((200, 3))
-
- extractor = LTSS(concat=True)
- features = extractor(data)
- # n = number of FFT coefficients (default is 64)
- # (n/2 + 1) * 2 (mean and std) * 3 (colors channels)
- assert features.shape[0] == 33*2*3
-
- extractor = LTSS(concat=False)
- features = extractor(data)
- # only one "channel" is considered
- assert features.shape[0] == 33*2
-
-
-def test_extractor_LiSpectralFeatures():
- """ Test Li's ICPR 2016 Spectral Feature Extractor
- """
-
- # "pulse" in 3 color channels
- data = np.random.random((200, 3))
-
- extractor = LiSpectralFeatures()
- features = extractor(data)
- assert features.shape[0] == 6
-
-
-def test_extractor_PPGSecure():
- """ Test PPGSecure Spectral Feature Extractor
- """
- # 5 "pulses"
- data = np.random.random((200, 5))
-
- extractor = PPGExtractor()
- features = extractor(data)
- # n = number of FFT coefficients (default is 32)
- # 5 (pulse signals) * (n/2 + 1)
- assert features.shape[0] == 5*17
--
GitLab