def get_patch(LF, cv, disp, by, bx):
patch = dict()
# compute actual coordinates
y = by * data_config['SY']
x = bx * data_config['SX']
py = data_config['H']
px = data_config['W']
# extract data
# (stack_h, stack_v) = lf_tools.epi_stacks( LF, y, x, py, px )
(stack_v, stack_h) = lf_tools.epi_stacks(LF, y, x, py, px)
# make sure the direction of the view shift is the first spatial dimension
stack_h = np.transpose(stack_h, (0, 2, 1, 3))
patch['stack_v'] = stack_v
patch['stack_h'] = stack_h
patch['cv'] = cv[y:y + py, x:x + px]
if disp is not None:
depth = disp[y:y + py, x:x + px]
depth = np.minimum(data_config['dmax'], depth)
depth = np.maximum(data_config['dmin'], depth)
patch['depth'] = depth
return patch
def get_patch( LF, by, bx, lf_scale ): patch = dict() # compute actual coordinates # if lf_scale == 's2': # y = by * hp.sy_s2 # x = bx * hp.sx_s2 # py = hp.H_s2 # px = hp.W_s2 # # if lf_scale == 's4': # y = by * hp.sy_HR # x = bx * hp.sx_HR # py = hp.H_s4 # px = hp.W_s4 y = by * hp.sy x = bx * hp.sx py = hp.H px = hp.W # extract data (stack_h, stack_v) = lf_tools.epi_stacks( LF, y, x, py, px ) # make sure the direction of the view shift is the first spatial dimension stack_h = np.transpose( stack_h, (0, 2, 1, 3) ) patch[ 'stack_v' ] = stack_v patch[ 'stack_h' ] = stack_h return patch
def get_patch(LF_LR, by, bx):
patch = dict()
# compute actual coordinates
y = by * data_config['SY']
x = bx * data_config['SX']
py = data_config['H']
px = data_config['W']
# extract data
# (stack_h, stack_v) = lf_tools.epi_stacks( LF, y, x, py, px )
(stack_v, stack_h) = lf_tools.epi_stacks(LF_LR, y, x, py, px)
# make sure the direction of the view shift is the first spatial dimension
stack_h = np.transpose(stack_h, (0, 2, 1, 3))
patch['stack_v'] = stack_v
patch['stack_h'] = stack_h
patch['cv'] = np.zeros((data_config['H_HR'], data_config['W_HR'], 3))
return patch
def get_patch( LF_LR, by, bx ): patch = dict() # compute actual coordinates y = by * hp.sy x = bx * hp.sx py = hp.H px = hp.W # extract data # (stack_h, stack_v) = lf_tools.epi_stacks( LF, y, x, py, px ) (stack_v, stack_h) = lf_tools.epi_stacks( LF_LR, y, x, py, px ) # make sure the direction of the view shift is the first spatial dimension stack_h = np.transpose( stack_h, (0, 2, 1, 3) ) patch[ 'stack_v' ] = stack_v patch[ 'stack_h' ] = stack_h patch['cv'] = np.zeros((hp.H_HR,hp.W_HR,hp.C) ) return patch
# 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
# extract data
(stack_v, stack_h) = lf_tools.epi_stacks(LF, y, x, py, px)
# make sure the direction of the view shift is the first spatial dimension
stack_h = np.transpose(stack_h, (0, 2, 1, 3))
cv = cv_gt[y:y + py, x:x + px]
#code.interact( local=locals() )
# write to respective HDF5 datasets
dset_v.resize(index + 1, 4)
dset_v[:, :, :, :, index] = stack_v
dset_h.resize(index + 1, 4)
dset_h[:, :, :, :, index] = stack_h
dset_cv.resize(index + 1, 3)
cx_HR = np.int32((LF.shape[3] - px) / sx) + 1
cy_HR = np.int32((LF.shape[2] - py) / sy) + 1
for by in np.arange(0, cy_LR):
sys.stdout.write('.')
sys.stdout.flush()
for bx in np.arange(0, cx_LR):
x_LR = bx * sx_LR
y_LR = by * sx_LR
x = bx * sx
y = by * sx
# extract data
(stack_v,
stack_h) = lf_tools.epi_stacks(LF_LR, y_LR, x_LR, py_LR,
px_LR)
# make sure the direction of the view shift is the first spatial dimension
stack_h = np.transpose(stack_h, (0, 2, 1, 3))
cv = cv_gt[y:y + py, x:x + px]
# plt.imshow(cv)
# plt.axis('off')
# plt.show()
# code.interact( local=locals() )
# write to respective HDF5 datasets
dset_v.resize(index + 1, 4)
dset_v[:, :, :, :, index] = stack_v
dset_h.resize(index + 1, 4)
dset_h[:, :, :, :, index] = stack_h
# 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
# extract data
(stack_v, stack_h) = lf_tools.epi_stacks(LF, y, x, py, px)
# make sure the direction of the view shift is the first spatial dimension
stack_h = np.transpose(stack_h, (0, 2, 1, 3))
(diffuse_stack_v,
diffuse_stack_h) = lf_tools.epi_stacks(LF_diffuse, y, x, py, px)
diffuse_stack_h = np.transpose(diffuse_stack_h, (0, 2, 1, 3))
(specular_stack_v,
specular_stack_h) = lf_tools.epi_stacks(LF_specular, y, x, py, px)
specular_stack_h = np.transpose(specular_stack_h, (0, 2, 1, 3))
depth = disp_gt[y:y + py, x:x + px]
cv = cv_gt[y:y + py, x:x + px]
diffuse = cv_diffuse[y:y + py, x:x + px]
specular = cv_specular[y:y + py, x:x + px]
# 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
# extract data
(stack_v, stack_h) = lf_tools.epi_stacks(LF, y, x, py, px)
# make sure the direction of the view shift is the first spatial dimension
stack_h = np.transpose(stack_h, (0, 2, 1, 3))
(stack_v_albedo,
stack_h_albedo) = lf_tools.epi_stacks(LF_albedo, y, x, py, px)
# make sure the direction of the view shift is the first spatial dimension
stack_h_albedo = np.transpose(stack_h_albedo, (0, 2, 1, 3))
(stack_v_sh, stack_h_sh) = lf_tools.epi_stacks(LF_sh, y, x, py, px)
# make sure the direction of the view shift is the first spatial dimension
stack_h_sh = np.transpose(stack_h_sh, (0, 2, 1, 3))
(stack_v_specular,
stack_h_specular) = lf_tools.epi_stacks(LF_specular, y, x, py, px)
stack_h_specular = np.transpose(stack_h_specular, (0, 2, 1, 3))
# block count
cx_HR = np.int32((LF_temp.shape[3] - px) / sx) + 1
cy_HR = np.int32((LF_temp.shape[2] - py) / sy) + 1
for by in np.arange(0, cy_HR):
sys.stdout.write('.')
sys.stdout.flush()
for bx in np.arange(0, cx_HR):
x = bx * sx
y = by * sx
# extract data
(stack_h_HR,
stack_v_HR) = lf_tools.epi_stacks(LF_temp, y, x, py, px)
# make sure the direction of the view shift is the first spatial dimension
stack_h_HR = np.transpose(stack_h_HR, (0, 2, 1, 3))
# write to respective HDF5 datasets
# dset_v_HR.resize(index + 1, 4)
# dset_v_HR[:, :, :, :, index] = stack_v_HR
#
# dset_h_HR.resize(index + 1, 4)
# dset_h_HR[:, :, :, :, index] = stack_h_HR
# next patch
index = index + 1
elif folder == 'K_H_O':
data_folders = os.listdir(data_source + folder + '/')
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
x_LR_s4 = bx * sx_LR_s4
y_LR_s4 = by * sx_LR_s4
x = bx * sx
y = by * sx
# extract data
# scale 4
(stack_h_LR_s4,
stack_v_LR_s4) = lf_tools.epi_stacks(LF_LR_s4, y_LR_s4, x_LR_s4,
py_LR_s4, px_LR_s4)
# make sure the direction of the view shift is the first spatial dimension
stack_h_LR_s4 = np.transpose(stack_h_LR_s4, (0, 2, 1, 3))
# write to respective HDF5 datasets
dset_v_LR_s4.resize(index + 1, 4)
dset_v_LR_s4[:, :, :, :, index] = stack_v_LR_s4
dset_h_LR_s4.resize(index + 1, 4)
dset_h_LR_s4[:, :, :, :, index] = stack_h_LR_s4
# scale 2
(stack_h_LR_s2,
stack_v_LR_s2) = lf_tools.epi_stacks(LF_LR_s2, y_LR_s2, x_LR_s2,
py_LR_s2, px_LR_s2)
# make sure the direction of the view shift is the first spatial dimension
# stack_h = np.transpose(stack_h, (0, 2, 1, 3))
#
data_source = "/home/mz/PyCharm/Data/testData_s4/super_resolution/HCI/not seen/lf_test_HCI_"
# data = sc.loadmat(data_path)
name_file = '.hdf5'
data_folders = []
data_folders.append('buddha')
# data_folders.append('buddha2')
# data_folders.append('horses')
# data_folders.append('medieval')
# data_folders.append('monasRoom')
# data_folders.append('papillon')
# data_folders.append('stillLife')
for i in range(0, len(data_folders)):
data_path = data_source + data_folders[i] + name_file
f = h5py.File(data_path, 'r')
LF = f['LF'].value
LF_LR = f['LF_LR'].value
(stack_h, stack_v) = lf_tools.epi_stacks(LF, 0, 0, LF.shape[2],
LF.shape[3])
(stack_h2, stack_v2) = lf_tools.epi_stacks(LF_LR, 0, 0, LF_LR.shape[2],
LF_LR.shape[3])
# stack_h = np.transpose(stack_h, (0, 2, 1, 3))
# for k in range(0, 9):
# plt.figure(k)
# plt.imshow(stack_h[k, :, :, :])
k = 0
