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')
def convert_to_hdf5(data_folder, tgt=None):
if tgt is None:
tgt = os.path.join(data_folder, "scene.h5")
scene = dict()
scene["LF"] = file_io.read_lightfield(data_folder)
params = file_io.read_parameters(data_folder)
if params["category"] != "test":
scene["disp_highres"] = file_io.read_disparity(data_folder, highres=True)
scene["disp_lowres"] = file_io.read_disparity(data_folder, highres=False)
scene["depth_highres"] = file_io.read_depth(data_folder, highres=True)
scene["depth_lowres"] = file_io.read_depth(data_folder, highres=False)
file_io.write_hdf5(scene, tgt)
def visualize_scene(data_folder):
params = file_io.read_parameters(data_folder)
if params["category"] == "test":
print("Test scenes with hidden ground truth are not visualized.")
return
light_field = file_io.read_lightfield(data_folder)
disp_map_highres = file_io.read_disparity(data_folder, highres=True)
disp_map_lowres = file_io.read_disparity(data_folder, highres=False)
depth_map_lowres = file_io.read_depth(data_folder, highres=False)
rows, cols = 1, 4
cb_shrink = 0.7
fig = plt.figure(figsize=(20, 4))
plt.suptitle("%s:%s (%s)" %
(params["category"].title(), params["scene"].title(),
"x".join(str(i) for i in list(np.shape(light_field)))))
plt.subplot(rows, cols, 1)
plt.title("Center View")
plt.imshow(light_field[4, 4, :, :, :])
plt.subplot(rows, cols, 2)
plt.title("Depth Map (%dx%d)" % np.shape(depth_map_lowres))
cc = plt.imshow(depth_map_lowres, cmap=cm.viridis, interpolation="none")
plt.colorbar(cc, shrink=cb_shrink)
plt.subplot(rows, cols, 3)
plt.title("Disparity Map (%dx%d)" % np.shape(disp_map_lowres))
cc = plt.imshow(disp_map_lowres, cmap=cm.viridis, interpolation="none")
plt.colorbar(cc, shrink=cb_shrink)
plt.subplot(rows, cols, 4)
plt.title("Disparity Map (%dx%d)" % np.shape(disp_map_highres))
cc = plt.imshow(disp_map_highres, cmap=cm.viridis, interpolation="none")
plt.colorbar(cc, shrink=cb_shrink)
fig_name = os.path.join(data_folder, "scene.png")
plt.savefig(fig_name, dpi=200, bbox_inches='tight')
fig.clf()
plt.close(fig)
# data_folders.append('platonic')
# data_folders.append('rosemary')
# data_folders.append('sideboard')
# data_folders.append('table')
# data_folders.append('tomb')
# data_folders.append('town')
# data_folders.append('vinyl')
cv = np.zeros([2, 512, 512, 3])
dispgt = np.zeros([2, 512, 512])
n = 0
for lf_name in data_folders:
data_folder = os.path.join(data_source, lf_name)
LF = file_io.read_lightfield(data_folder)
LF = LF.astype(np.float32)
disp = file_io.read_disparity(data_folder)
disp_gt = np.array(disp[0])
disp_gt = np.flip(disp_gt, 0)
dispgt[n, :, :] = disp_gt
cv[n, :, :, :] = lf_tools.cv(LF)
n = n + 1
stack_h, stack_v = refocus_cross(cv, dispgt, 9)
# for k in range(0,9):
# plt.figure(k)
# plt.imshow(stack_h[k, :, :, :])
# maxshape=(nviews, py, px, 3, None))
# dset_HS = np.random.normal(0, 0.1, [py_LR_s4, px_LR_s4, h_dim])
# dset_HS = np.ones([py_LR_s4, px_LR_s4, h_dim])/10
index = 0
idx_folder = 0
for folder in data_folders_all:
if folder == 'Benchmark':
data_folders = os.listdir(data_source + folder + '/')
for lf_name in data_folders:
data_folder = os.path.join(data_source, lf_name)
print("now %i / %i" % (idx_folder + 1, len(data_folders)))
idx_folder = idx_folder + 1
data_path = data_source + folder + '/' + lf_name
LF_temp = file_io.read_lightfield(data_path)
LF_temp = LF_temp.astype(np.float32)
# write out one individual light field
# block count
cx_HR = np.int32((LF_temp.shape[3] - px_LR_s4) / sx_LR_s4) + 1
cy_HR = np.int32((LF_temp.shape[2] - py_LR_s4) / sy_LR_s4) + 1
for by in np.arange(0, cy_HR):
sys.stdout.write('.')
sys.stdout.flush()
for bx in np.arange(0, cx_HR):
x_LR_s2 = bx * sx_LR_s2
y_LR_s2 = by * sx_LR_s2
