From 446becbce02ef8c33f79e159a86b7e0287deb0ba Mon Sep 17 00:00:00 2001
From: Olegs NIKISINS <onikisins@italix03.idiap.ch>
Date: Fri, 13 Oct 2017 16:18:09 +0200
Subject: [PATCH] Added unit tests for classes used in the IQM-GMM pad
algorithm
---
bob/pad/face/test/test.py | 120 ++++++++++++++++++++++++++++++++++++++
1 file changed, 120 insertions(+)
diff --git a/bob/pad/face/test/test.py b/bob/pad/face/test/test.py
index 160fbdc9..06dcf820 100644
--- a/bob/pad/face/test/test.py
+++ b/bob/pad/face/test/test.py
@@ -29,8 +29,12 @@ from ..extractor import FrameDiffFeatures
from ..extractor import VideoLBPHistogram
+from ..extractor import VideoQualityMeasure
+
from ..algorithm import VideoSvmPadAlgorithm
+from ..algorithm import VideoGmmPadAlgorithm
+
import random
#==============================================================================
@@ -149,6 +153,43 @@ def test_video_face_crop():
assert np.sum(faces[0][1]) == 429158
assert np.sum(faces[-1][1]) == 429158
+ #==========================================================================
+ # test another configuration of the VideoFaceCrop preprocessor:
+
+ CROPPED_IMAGE_SIZE = (64, 64) # The size of the resulting face
+ CROPPED_POSITIONS = {'topleft' : (0,0) , 'bottomright' : CROPPED_IMAGE_SIZE}
+ FIXED_POSITIONS = None
+ MASK_SIGMA = None # The sigma for random values areas outside image
+ MASK_NEIGHBORS = 5 # The number of neighbors to consider while extrapolating
+ MASK_SEED = None # The seed for generating random values during extrapolation
+ CHECK_FACE_SIZE_FLAG = True # Check the size of the face
+ MIN_FACE_SIZE = 50
+ USE_LOCAL_CROPPER_FLAG = True # Use the local face cropping class (identical to Ivana's paper)
+ RGB_OUTPUT_FLAG = True # Return RGB cropped face using local cropper
+ DETECT_FACES_FLAG = True # find annotations locally replacing the database annotations
+
+ preprocessor = VideoFaceCrop(cropped_image_size = CROPPED_IMAGE_SIZE,
+ cropped_positions = CROPPED_POSITIONS,
+ fixed_positions = FIXED_POSITIONS,
+ mask_sigma = MASK_SIGMA,
+ mask_neighbors = MASK_NEIGHBORS,
+ mask_seed = None,
+ check_face_size_flag = CHECK_FACE_SIZE_FLAG,
+ min_face_size = MIN_FACE_SIZE,
+ use_local_cropper_flag = USE_LOCAL_CROPPER_FLAG,
+ rgb_output_flag = RGB_OUTPUT_FLAG,
+ detect_faces_flag = DETECT_FACES_FLAG)
+
+ 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]) == 1253048
+ assert np.sum(faces[-1][1]) == 1253048
+
#==============================================================================
def test_frame_difference():
@@ -261,6 +302,34 @@ def test_video_lbp_histogram():
assert (lbp_histograms[0][1][-1] - 0.031737773152965658) < 0.000001
+#==============================================================================
+def test_video_quality_measure():
+ """
+ Test VideoQualityMeasure extractor.
+ """
+
+ 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, 2)
+
+ GALBALLY=True
+ MSU=True
+ DTYPE=None
+
+ extractor = VideoQualityMeasure(galbally=GALBALLY,
+ msu=MSU,
+ dtype=DTYPE)
+
+ features = extractor(video)
+
+ assert len(features) == 2
+ assert len(features[0][1]) == 139
+ 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):
"""
@@ -350,6 +419,57 @@ def test_video_svm_pad_algorithm():
assert precision > 0.99
+#==============================================================================
+def test_video_gmm_pad_algorithm():
+ """
+ Test the VideoGmmPadAlgorithm algorithm.
+ """
+
+ random.seed(7)
+
+ N = 1000
+ 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)
+
+ N_COMPONENTS = 1
+ RANDOM_STATE = 3
+ FRAME_LEVEL_SCORES_FLAG = True
+
+ algorithm = VideoGmmPadAlgorithm(n_components = N_COMPONENTS,
+ random_state = RANDOM_STATE,
+ frame_level_scores_flag = FRAME_LEVEL_SCORES_FLAG)
+
+ # training_features[0] - training features for the REAL class.
+ real_array_converted = algorithm.convert_list_of_frame_cont_to_array(real) # output is array
+
+ assert (real_array == real_array_converted).all()
+
+ # Train the GMM machine and get normalizers:
+ machine, features_mean, features_std = algorithm.train_gmm(real = real_array_converted,
+ n_components = algorithm.n_components,
+ random_state = algorithm.random_state)
+
+ algorithm.machine = machine
+
+ algorithm.features_mean = features_mean
+
+ algorithm.features_std = features_std
+
+ scores_real = algorithm.project(real_array_converted)
+
+ scores_attack = algorithm.project(attack_array)
+
+ assert (np.min(scores_real) + 7.9423798970985917) < 0.000001
+ assert (np.max(scores_real) + 1.8380480068281055) < 0.000001
+ assert (np.min(scores_attack) + 38.831260843070098) < 0.000001
+ assert (np.max(scores_attack) + 5.3633030621521272) < 0.000001
--
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