def create_hdf5_container(path_i1, path_o, lf_name):
px = 48
py = 48
nviews = 9
sx = 16
sy = 16
file = h5py.File(path_o + '/' + lf_name + '.hdf5', 'w')
# read diffuse color
LF = file_io.read_lightfield(path_i1)
LF = LF.astype(np.float32) # / 255.0
cv_gt = lf_tools.cv(LF)
lf_tools.save_image(path_o + '/' + lf_name, cv_gt)
# maybe we need those, probably not.
param_dict = file_io.read_parameters(path_i1)
dset_blocks = []
# block count: write out one individual light field
cx = np.int32((LF.shape[3] - px) / sx) + 1
cy = np.int32((LF.shape[2] - py) / sy) + 1
for i, j in itertools.product(np.arange(0, nviews), np.arange(0, nviews)):
dset_blocks.append(
file.create_dataset('views%d%d' % (i, j), (cy, cx, 3, px, py),
chunks=(1, 1, 3, px, py),
maxshape=(None, None, 3, px, py)))
# lists indexed in 2D
dset_blocks = [
dset_blocks[x:x + nviews] for x in range(0, len(dset_blocks), nviews)
]
sys.stdout.write(lf_name + ': ')
for bx in np.arange(0, cx):
sys.stdout.write('.')
sys.stdout.flush()
for by in np.arange(0, cy):
x = bx * sx
y = by * sx
# extract data
for i, j in itertools.product(np.arange(0, nviews),
np.arange(0, nviews)):
dset_blocks[i][j][bx, by, :, :, :] = np.transpose(
np.array(LF[i, j, x:x + px, y:y + py, :]),
(-1, 0, 1)).reshape(3, px, py)
sys.stdout.write(' Done.\n')
#
# loop over all datasets, write out each dataset in patches
# to feed to autoencoder in random order
#
index = 0
for lf_name in data_folders:
data_file = os.path.join(data_source, lf_name)
# input LF
mat_content = h5py.File(data_file, 'r')
LF = mat_content['LF'].value
LF = np.transpose(LF, (4, 3, 2, 1, 0))
cv_gt = lf_tools.cv(LF)
lf_tools.save_image(training_data_dir + 'input' + lf_name, cv_gt)
# write out one individual light field
# block count
cx = np.int32((LF.shape[3] - px) / sx) + 1
cy = np.int32((LF.shape[2] - py) / sy) + 1
for by in np.arange(0, cy):
sys.stdout.write('.')
sys.stdout.flush()
for bx in np.arange(0, cx):
x = bx * sx
y = by * sx
nan_LF_sh = np.sum(np.isnan(LF_sh) == True)
nan_LF_specular = np.sum(np.isnan(LF_specular) == True)
nan_disp = np.sum(np.isnan(disp_gt) == True)
inf_LF = np.sum(np.isinf(LF) == True)
inf_LF_albedo = np.sum(np.isinf(LF_albedo) == True)
inf_LF_sh = np.sum(np.isinf(LF_sh) == True)
inf_LF_specular = np.sum(np.isinf(LF_specular) == True)
inf_disp = np.sum(np.isinf(disp_gt) == True)
naninf_sum = nan_disp + nan_LF + nan_LF_albedo + nan_LF_sh + nan_LF_specular + inf_disp + inf_LF + inf_LF_albedo + inf_LF_sh + inf_LF_specular
if naninf_sum > 0:
print('inf_nan' + lf_name)
lf_name = lf_name + '_inf_nan'
lf_tools.save_image(data_source + '\\' + 'input_' + lf_name,
lf_tools.cv(LF))
lf_tools.save_image(data_source + '\\' + 'albedo_' + lf_name,
lf_tools.cv(LF_albedo))
lf_tools.save_image(
data_source + '\\' + 'sh_' + lf_name + '_' + np.str(np.amax(LF_sh)),
lf_tools.cv(LF_sh))
lf_tools.save_image(
data_source + '\\' + 'specular_' + lf_name + '_' +
np.str(np.amax(LF_specular)), lf_tools.cv(LF_specular))
# write out one individual light field
# block count
cx = np.int32((LF.shape[3] - px) / sx) + 1
cy = np.int32((LF.shape[2] - py) / sy) + 1
for by in np.arange(0, cy):