def solve_intrinsics(dir):
os.chdir(dir)
for file in glob.glob("*rgb.png"):
print(file)
input = IntrinsicInput.from_file(file)
solver = IntrinsicSolver(input, IntrinsicParameters())
r, s, decomposition = solver.solve()
name = file.split(".")[0]
image_util.save(dir + "/" + name + "_r.png", r, rescale=True)
image_util.save(dir + "/" + name + "_s.png", s, rescale=True)
def decompose_single_image(image_file, feat_net, rref_net, nystrom_file=None, srgb=True,
save_reflectance_file=None, save_shading_file=None):
"""
Input:
image_file - file path to the input image
feat_net - caffe net for extracting dense local features
rref_net - caffe net for relative reflectance judgment
Output:
Estimated reflectance and shading layers of the input image
"""
if nystrom_file is not None:
W_ns = pickle.load(open(nystrom_file, 'rb'))
else:
W_ns = nystrom_single_image(image_file, feat_net, rref_net)
input = IntrinsicInput.from_file(image_file, image_is_srgb=srgb)
params = IntrinsicParameters()
solver = IntrinsicSolver(input, params, W_ns)
reflectance, shading, decomposition = solver.solve()
if save_reflectance_file is not None:
image_util.save(save_reflectance_file, reflectance, rescale=True, srgb=srgb)
if save_shading_file is not None:
image_util.save(save_shading_file, shading, rescale=True, srgb=srgb)
return reflectance, shading
print("Applying texture to model")
proc_texture = subprocess.Popen([
os.path.join(OMVS_BIN, "TextureMesh"), "scene_dense_mesh_refine.mvs", "-w",
MVS_DIR
])
proc_texture.wait()
print("----------/nINTRINSIC DECOMPOSITION")
in_name = raw_input("Image filename: ")
base, _ = os.path.splitext(in_name)
r_name = base + "-R.png"
s_name = base + "-S.png"
in_img = IntrinsicInput.from_file(os.path.join(INPUT_DIR, in_name))
params = IntrinsicParameters()
params.logging = True
solver = IntrinsicSolver(in_img, params)
reflect, shading, decomp = solver.solve()
image_util.save(r_name,
reflect,
mask_nz=in_img.mask_nz,
rescale=True,
srgb=True)
image_util.save(s_name,
shading,
mask_nz=in_img.mask_nz,
rescale=True,
srgb=True)
mask_filename=mask_filename,
judgements_filename=judgements_filename,
)
print('mask_nnz: %s' % input.mask_nnz)
print('rows * cols: %s' % (input.rows * input.cols))
# load parameters
if parameters_filename:
params = IntrinsicParameters.from_file(parameters_filename)
else:
params = IntrinsicParameters()
params.logging = not args.quiet
# solve
solver = IntrinsicSolver(input, params)
r, s, decomposition = solver.solve()
# save output
image_util.save(r_filename, r, mask_nz=input.mask_nz, rescale=True, srgb=sRGB)
image_util.save(s_filename, s, mask_nz=input.mask_nz, rescale=True, srgb=sRGB)
if args.show_labels:
labels_vis = decomposition.get_labels_visualization()
r_path, r_ext = os.path.splitext(r_filename)
image_util.save('%s_labels%s' % (r_path, r_ext), labels_vis, mask_nz=solver.input.mask_nz, rescale=True)
# compute error
if judgements_filename:
print 'WHDR: %.1f%%' % (input.compute_whdr(r) * 100.0)