try:
intrinsic_mean_normals = model['intrinsic_mean_normals']
except Exception:
pass
# <codecell>
from pybug.image import MaskedNDImage
from pybug.io import auto_import
from pybug.landmark import labeller, ibug_68_closed_mouth
from landmarks import ibug_68_edge
sfs_index = 2
bej = auto_import('/vol/atlas/databases/alex_images/bej*.ppm')
# Create a 4 channel image where each channel is the greyscale of an image
ground_truth_images = MaskedNDImage(
np.concatenate([im.as_greyscale().pixels for im in bej], axis=2))
intensity_image = bej[sfs_index].as_greyscale()
intensity_image.landmarks = bej[0].landmarks
ground_truth_images.landmarks['PTS'] = bej[0].landmarks['PTS']
labeller([ground_truth_images, intensity_image], 'PTS', ibug_68_closed_mouth)
# labeller([ground_truth_images, intensity_image], 'PTS', ibug_68_edge)
lights = np.array([[0.5, 0.4, 2], [-0.5, 0.4, 2], [-0.5, -0.4, 2],
[0.5, -0.4, 2]])
# <codecell>
from pybug.transform.tps import TPS
from warp import build_similarity_transform
normal_model = model['appearance_model']
reference_frame = model['template']
mean_normals = model['mean_normals']
reference_frame = model['template']
try:
intrinsic_mean_normals = model['intrinsic_mean_normals']
except Exception:
intrinsic_mean_normals = None
# Estimate light direction for image
I = intensity_image.as_vector()
estimate_light = np.dot(pinv2(mean_normals), I)
print estimate_light
# Perform SFS
warped_intensity_image = MaskedNDImage(intensity_image.pixels.copy(),
mask=intensity_image.mask)
initial_estimate_image = warped_intensity_image.from_vector(
mean_normals.copy(), n_channels=3)
mapping_object = build_mapping_object(feature_space,
initial_estimate_image,
intrinsic_mean_normals)
# Normalise the image so that it has unit albedo?
#warped_intensity_image.masked_pixels /= ground_truth_albedo.masked_pixels
#warped_intensity_image.masked_pixels /= np.max(warped_intensity_image.masked_pixels)
reconstructed_normals = sfs(warped_intensity_image,
initial_estimate_image, normal_model,
estimate_light, n_iters=200,
mapping_object=mapping_object)
normals[subject_id][feature_space] = reconstructed_normals
std_angular_error_results = np.zeros([len(yaleb_subjects),
len(feature_spaces)])
# 5 feature spaces + ground truth
normals = dict(zip(yaleb_subjects, [{}, {}, {}, {}, {}, {}, {}, {}, {}, {}]))
for s in normals.values():
s.update(zip(['ground_truth'] + feature_spaces, [None] * (len(feature_spaces) + 1)))
for i, subject_id in enumerate(yaleb_subjects):
print "Running experiment for {0}".format(subject_id)
subject_images = [auto_import(os.path.join(yaleb_path, subject_id[-3:], light[0].format(subject_id)))[0]
for light in image_light_paths]
# Create a 4 channel image where each channel is the greyscale of an image
ground_truth_images = MaskedNDImage(
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(
