for im in subject_images], axis=2))
# Choose the third image as the reconstruction candidate
intensity_image = subject_images[sfs_index].as_greyscale()
# The first image is the only landmarked one
intensity_image.landmarks = subject_images[0].landmarks
# Pass landmarks to all ground truth images
ground_truth_images.landmarks['PTS'] = intensity_image.landmarks['PTS']
# Label with correct labels
labeller([ground_truth_images, intensity_image],
'PTS', ibug_68_closed_mouth)
# Constrain to mask
ground_truth_images.constrain_mask_to_landmarks(
group='ibug_68_closed_mouth', label='all')
intensity_image.constrain_mask_to_landmarks(
group='ibug_68_closed_mouth', label='all')
intensity_image.crop_to_landmarks(group='ibug_68_closed_mouth',
label='all', boundary=2)
ground_truth_images.crop_to_landmarks(group='ibug_68_closed_mouth',
label='all', boundary=2)
temp_texture = subject_images[sfs_index]
# Perform Photometric Stereo
ground_truth_normals, ground_truth_albedo = ps(ground_truth_images, lights)
ground_truth_depth = frankotchellappa(-ground_truth_normals.pixels[:, :, 0],
ground_truth_normals.pixels[:, :, 1])
ground_truth_depth_image = DepthImage((ground_truth_depth - np.min(ground_truth_depth)) / 2,
texture=temp_texture)
np.concatenate([im.pixels
for im in subject_images], axis=2))
# Choose the first image as the reconstruction candidate
# (frontal illumination)
intensity_image = deepcopy(subject_images[0])
# Pass landmarks to all ground truth images
ground_truth_images.landmarks['PTS'] = intensity_image.landmarks['PTS']
# Label with correct labels
labeller([ground_truth_images, intensity_image],
'PTS', ibug_68_closed_mouth)
# Constrain to mask
ground_truth_images.constrain_mask_to_landmarks(
group='ibug_68_closed_mouth', label='all')
intensity_image.constrain_mask_to_landmarks(
group='ibug_68_closed_mouth', label='all')
temp_texture = RGBImage(np.dstack([subject_images[0].pixels] * 3))
# Perform Photometric Stereo
ground_truth_normals, ground_truth_albedo = ps(ground_truth_images, lights)
ground_truth_depth = frankotchellappa(ground_truth_normals.pixels[:, :, 0],
ground_truth_normals.pixels[:, :, 1])
ground_truth_depth_image = DepthImage((ground_truth_depth - np.min(ground_truth_depth)),
texture=temp_texture,
mask=intensity_image.mask)
normals[subject_id]['ground_truth'] = ground_truth_normals
# TODO: save images
