From e684c59d359939b9cc4b85d7482f8bc4ba62494d Mon Sep 17 00:00:00 2001
From: Olegs NIKISINS <onikisins@italix03.idiap.ch>
Date: Thu, 13 Jul 2017 16:34:46 +0200
Subject: [PATCH] Added the unit tests for the VideoSvmPadAlgorithm algorithm
---
.../face/algorithm/VideoSvmPadAlgorithm.py | 29 +++++---
bob/pad/face/test/test.py | 70 +++++++++++++++++--
2 files changed, 83 insertions(+), 16 deletions(-)
diff --git a/bob/pad/face/algorithm/VideoSvmPadAlgorithm.py b/bob/pad/face/algorithm/VideoSvmPadAlgorithm.py
index 110c0786..c8d55779 100644
--- a/bob/pad/face/algorithm/VideoSvmPadAlgorithm.py
+++ b/bob/pad/face/algorithm/VideoSvmPadAlgorithm.py
@@ -446,7 +446,8 @@ class VideoSvmPadAlgorithm(Algorithm):
machine_type = 'C_SVC', kernel_type = 'RBF',
trainer_grid_search_params = { 'cost': [2**p for p in range(-5, 16, 2)], 'gamma': [2**p for p in range(-15, 4, 2)]},
mean_std_norm_flag = False,
- projector_file = ""):
+ projector_file = "",
+ save_debug_data_flag = True):
"""
First, this function tunes the hyper-parameters of the SVM classifier using
grid search on the sub-sets of training data. Train and cross-validation
@@ -487,6 +488,10 @@ class VideoSvmPadAlgorithm(Algorithm):
be save in this path. This file contains information, which might be
usefull for debugging.
+ ``save_debug_data_flag`` : :py:class:`bool`
+ Save the data, which might be usefull for debugging if ``True``.
+ Default: ``True``.
+
**Returns:**
``machine`` : object
@@ -544,16 +549,18 @@ class VideoSvmPadAlgorithm(Algorithm):
setattr(trainer, key, selected_params[key]) # set the params of trainer
# Save the data, which is usefull for debugging.
- debug_file = os.path.join( os.path.split(projector_file)[0], "debug_data.hdf5" )
- debug_dict = {}
- debug_dict['precisions_train'] = precisions_train
- debug_dict['precisions_cv'] = precisions_cv
- debug_dict['cost'] = selected_params['cost']
- debug_dict['gamma'] = selected_params['gamma']
- f = bob.io.base.HDF5File(debug_file, 'w') # open hdf5 file to save the debug data
- for key in debug_dict.keys():
- f.set(key, debug_dict[key])
- del f
+ if save_debug_data_flag:
+
+ debug_file = os.path.join( os.path.split(projector_file)[0], "debug_data.hdf5" )
+ debug_dict = {}
+ debug_dict['precisions_train'] = precisions_train
+ debug_dict['precisions_cv'] = precisions_cv
+ debug_dict['cost'] = selected_params['cost']
+ debug_dict['gamma'] = selected_params['gamma']
+ f = bob.io.base.HDF5File(debug_file, 'w') # open hdf5 file to save the debug data
+ for key in debug_dict.keys():
+ f.set(key, debug_dict[key])
+ del f
# training_features[0] - training features for the REAL class.
real = self.convert_list_of_frame_cont_to_array(training_features[0]) # output is array
diff --git a/bob/pad/face/test/test.py b/bob/pad/face/test/test.py
index 54f5e302..160fbdc9 100644
--- a/bob/pad/face/test/test.py
+++ b/bob/pad/face/test/test.py
@@ -29,6 +29,10 @@ from ..extractor import FrameDiffFeatures
from ..extractor import VideoLBPHistogram
+from ..algorithm import VideoSvmPadAlgorithm
+
+import random
+
#==============================================================================
def test_lbp_histogram():
lbp = LBPHistogram()
@@ -257,37 +261,93 @@ def test_video_lbp_histogram():
assert (lbp_histograms[0][1][-1] - 0.031737773152965658) < 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_video_svm_pad_algorithm():
+ """
+ Test the VideoSvmPadAlgorithm algorithm.
+ """
+ random.seed(7)
+ N = 20000
+ mu = 1
+ sigma = 1
+ real_array = np.transpose( np.vstack([[random.gauss(mu, sigma) for _ in range(N)], [random.gauss(mu, sigma) for _ in range(N)]]) )
+ mu = 5
+ sigma = 1
+ attack_array = np.transpose( np.vstack([[random.gauss(mu, sigma) for _ in range(N)], [random.gauss(mu, sigma) for _ in range(N)]]) )
+ real = convert_array_to_list_of_frame_cont(real_array)
+ attack = convert_array_to_list_of_frame_cont(attack_array)
+ training_features = [real, attack]
+ MACHINE_TYPE = 'C_SVC'
+ KERNEL_TYPE = 'RBF'
+ N_SAMPLES = 1000
+ TRAINER_GRID_SEARCH_PARAMS = {'cost': [1], 'gamma': [0.5, 1]}
+ MEAN_STD_NORM_FLAG = True # enable mean-std normalization
+ FRAME_LEVEL_SCORES_FLAG = True # one score per frame(!) in this case
+ algorithm = VideoSvmPadAlgorithm(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)
+ machine = algorithm.train_svm(training_features = training_features,
+ n_samples = algorithm.n_samples,
+ machine_type = algorithm.machine_type,
+ kernel_type = algorithm.kernel_type,
+ trainer_grid_search_params = algorithm.trainer_grid_search_params,
+ mean_std_norm_flag = algorithm.mean_std_norm_flag,
+ projector_file = "",
+ save_debug_data_flag = False)
+ assert machine.n_support_vectors == [148, 150]
+ assert machine.gamma == 0.5
+ real_sample = algorithm.convert_frame_cont_to_array(real[0])
+ prob = machine.predict_class_and_probabilities( real_sample )[1]
+ assert prob[0,0] > prob[0,1]
+ precision = algorithm.comp_prediction_precision(machine, real_array, attack_array)
-
-
-
-
-
+ assert precision > 0.99
--
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