ground_truth_normals.view_new(channel=0)
# reconstructed_normals_horn.view_new(channel=0)
# <codecell>
temp_texture = np.concatenate([warped_intensity_image.pixels] * 3, axis=2)
# <codecell>
# <codecell>
from surface_reconstruction import frankotchellappa
# If we use gradient fields then we don't need to negate the x-axis
recovered_depth = frankotchellappa(-worthington.pixels[:, :, 0], worthington.pixels[:, :, 1])
recovered_depth_image = DepthImage((recovered_depth - np.min(recovered_depth)))
recovered_depth_image.view_new(mode="mesh")
# <codecell>
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)) / 1.3, texture=RGBImage(temp_texture)
)
ground_truth_depth_image.view_new(mode="mesh")
# <codecell>
# <codecell>
from pybug.image import DepthImage, RGBImage
ground_truth_normals.view_new(channel=0)
# reconstructed_normals_horn.view_new(channel=0)
# <codecell>
temp_texture = np.concatenate([warped_intensity_image.pixels] * 3, axis=2)
# <codecell>
# <codecell>
from surface_reconstruction import frankotchellappa
# If we use gradient fields then we don't need to negate the x-axis
recovered_depth = frankotchellappa(-worthington.pixels[:, :, 0],
worthington.pixels[:, :, 1])
recovered_depth_image = DepthImage((recovered_depth - np.min(recovered_depth)))
recovered_depth_image.view_new(mode='mesh')
# <codecell>
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)) / 1.3,
texture=RGBImage(temp_texture))
ground_truth_depth_image.view_new(mode='mesh')
# <codecell>
# <codecell>
# 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)
normals[subject_id]['ground_truth'] = ground_truth_normals
# TODO: save images
#ground_truth_depth_image.view(mode='mesh')
#save_result_images(subject_id, 'all', 'groundtruth')
for k, feature_space in enumerate(feature_spaces):
print "Running {0} for {1}".format(feature_space, subject_id)
model_path = '/vol/atlas/pts08/cvpr/frgc_spring2003_sfs_tps_{0}_{1}.pkl'.format(subject_id, feature_space)
with open(model_path, 'rb') as f:
model = cPickle.load(f)
normal_model = model['appearance_model']
# 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
#ground_truth_depth_image.view(mode='mesh')
#save_result_images(subject_id, 'all', 'groundtruth')
for k, feature_space in enumerate(feature_spaces):
print "Running {0} for {1}".format(feature_space, subject_id)
model_path = '/vol/atlas/pts08/cvpr/frgc_spring2003_sfs_tps_{0}_{1}.pkl'.format(subject_id, feature_space)
with open(model_path, 'rb') as f:
model = cPickle.load(f)
