from photometric_stereo import photometric_stereo as ps ground_truth_normals, ground_truth_albedo = ps(warped_ground_truth_image, lights) # <codecell> from pybug.image import MaskedNDImage from scipy.ndimage.filters import gaussian_filter, median_filter from geometric_sfs import geometric_sfs as sfs, worthington_hancock_sfs from mapping import AEP, PGA, Spherical, ImageMapper, IdentityMapper # mapping_object = ImageMapper(PGA(intrinsic_mean_normals.as_vector(keep_channels=True))) mapping_object = ImageMapper(Spherical()) warped_intensity_image = MaskedNDImage(warped_intensity_image.pixels.copy(), mask=warped_intensity_image.mask) mean_normals_image = warped_intensity_image.from_vector(mean_normals, n_channels=3) # 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) worthington = worthington_hancock_sfs(warped_intensity_image, mean_normals_image, estimate_light, n_iters=40) # <codecell> # from geometric_sfs import horn_brooks # reconstructed_normals_horn = horn_brooks(warped_intensity_image, mean_normals, normal_model, estimate_light, n_iters=100, c_lambda=10, mapping_object=mapping_object) # <codecell> from pybug.visualize.viewmayavi import MayaviVectorViewer3d from pybug.image import DepthImage, RGBImage
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
#reconstructed_depth = frankotchellappa(
with open(model_path, 'rb') as f:
model = cPickle.load(f)
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
# 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 = \
# ((warped_intensity_image.masked_pixels - np.nanmin(warped_intensity_image.masked_pixels)) /
# (np.nanmax(warped_intensity_image.masked_pixels) - np.nanmin(warped_intensity_image.masked_pixels)))
reconstructed_normals = sfs(warped_intensity_image,
initial_estimate_image,
normal_model,
sfs_light, n_iters=50,
mapping_object=mapping_object)
normals[subject_id][feature_space] = reconstructed_normals
ground_truth_normals, ground_truth_albedo = ps(warped_ground_truth_image,
lights)
# <codecell>
from pybug.image import MaskedNDImage
from scipy.ndimage.filters import gaussian_filter, median_filter
from geometric_sfs import geometric_sfs as sfs, worthington_hancock_sfs
from mapping import AEP, PGA, Spherical, ImageMapper, IdentityMapper
# mapping_object = ImageMapper(PGA(intrinsic_mean_normals.as_vector(keep_channels=True)))
mapping_object = ImageMapper(Spherical())
warped_intensity_image = MaskedNDImage(warped_intensity_image.pixels.copy(),
mask=warped_intensity_image.mask)
mean_normals_image = warped_intensity_image.from_vector(mean_normals,
n_channels=3)
# 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)
worthington = worthington_hancock_sfs(warped_intensity_image,
mean_normals_image,
estimate_light,
n_iters=40)
# <codecell>
# from geometric_sfs import horn_brooks
# reconstructed_normals_horn = horn_brooks(warped_intensity_image, mean_normals, normal_model, estimate_light, n_iters=100, c_lambda=10, mapping_object=mapping_object)
# <codecell>
