From db78cba9d9e2148eba5df52e7236a13d9ca439b1 Mon Sep 17 00:00:00 2001
From: Olegs NIKISINS <onikisins@italix03.idiap.ch>
Date: Thu, 13 Jul 2017 09:09:17 +0200
Subject: [PATCH] Added unit tests for remaining preprocessors: VideoFaceCrop,
FrameDifference
---
bob/pad/face/test/test.py | 156 +++++++++++++++++++++++++++++++++++++-
1 file changed, 155 insertions(+), 1 deletion(-)
diff --git a/bob/pad/face/test/test.py b/bob/pad/face/test/test.py
index 7ec3460f..4ed747bf 100644
--- a/bob/pad/face/test/test.py
+++ b/bob/pad/face/test/test.py
@@ -13,12 +13,18 @@ from bob.io.base import load
import bob.io.image # for image loading functionality
+import bob.bio.video
+
from bob.ip.color import rgb_to_gray
from ..extractor import LBPHistogram
from ..preprocessor import ImageFaceCrop
+from ..preprocessor import VideoFaceCrop
+
+from ..preprocessor import FrameDifference
+
#==============================================================================
def test_lbp_histogram():
lbp = LBPHistogram()
@@ -48,4 +54,152 @@ def test_image_face_crop():
face = preprocessor(image, annotations)
assert face.shape == (3, 64, 64)
- assert np.sum(face) == 1215525
\ No newline at end of file
+ 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 VideoFaceCrop preprocessor, which is designed to crop faces in the video.
+ """
+
+ image = load(datafile('test_image.png', 'bob.pad.face.test'))
+ annotations = {'topleft': (95, 155), 'bottomright': (215, 265)}
+
+ 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 # Minimal possible size of the face
+ USE_LOCAL_CROPPER_FLAG = True # Use the local face cropping class (identical to Ivana's paper)
+ COLOR_CHANNEL = 'gray' # Convert image to gray-scale format
+
+ 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 = MASK_SEED,
+ check_face_size_flag = CHECK_FACE_SIZE_FLAG,
+ min_face_size = MIN_FACE_SIZE,
+ use_local_cropper_flag = USE_LOCAL_CROPPER_FLAG,
+ color_channel = COLOR_CHANNEL)
+
+ 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
+
+
+#==============================================================================
+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()
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--
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