def convert2YUV(input, stream, idx):
streamtmp = input[stream][idx]
streamtmp = rgb2YUV(streamtmp)
input[stream][idx] = streamtmp
return (input)
def encode_decode_lightfield(data,
LF_LR,
LF_HR,
inputs,
outputs,
ColorSpace,
decoder_path='cv'):
# light field size
H = LF_LR.shape[2]
W = LF_LR.shape[3]
H_HR = LF_HR.shape[2]
W_HR = LF_HR.shape[3]
dc = cdf.data_config
# patch step sizes
bs_y = dc['SY']
bs_x = dc['SX']
bs_y_HR = dc['SY_HR']
bs_x_HR = dc['SX_HR']
# patch height/width
ps_y = dc['H']
ps_x = dc['W']
ps_y_HR = dc['H_HR']
ps_x_HR = dc['W_HR']
ps_v = dc['D']
# patches per row/column
by = np.int16((H - ps_y) / bs_y) + 1
bx = np.int16((W - ps_x) / bs_x) + 1
# one complete row per batch
cv_interp = np.zeros([H_HR, W_HR, 3], np.float32)
cv_raw = np.zeros([H_HR, W_HR, 3], np.float32)
mask_sum = np.zeros([H_HR, W_HR], dtype=np.float32)
print('starting LF encoding/decoding [', end='', flush=True)
start = timer()
results_received = 0
for py in range(by):
print('.', end='', flush=True)
stacks_h = np.zeros([bx, ps_v, ps_y, ps_x, 3], np.float32)
stacks_v = np.zeros([bx, ps_v, ps_y, ps_x, 3], np.float32)
cv_in = np.zeros([bx, ps_y_HR, ps_x_HR, 3], np.float32)
for px in range(bx):
# get single patch
patch = cdf.get_patch(LF_LR, py, px)
stacks_v[px, :, :, :, :] = patch['stack_v']
stacks_h[px, :, :, :, :] = patch['stack_h']
cv_in[px, :, :, :] = patch['cv']
if ColorSpace == 'YUV':
stacks_v[px, :, :, :, :] = rgb2YUV(stacks_v[px, :, :, :, :])
stacks_h[px, :, :, :, :] = rgb2YUV(stacks_h[px, :, :, :, :])
cv_in[px, :, :, :] = rgb2YUV(cv_in[px, :, :, :])
elif ColorSpace == 'YCBCR':
stacks_v[px, :, :, :, :] = rgb2YCbCr(stacks_v[px, :, :, :, :])
stacks_h[px, :, :, :, :] = rgb2YCbCr(stacks_h[px, :, :, :, :])
cv_in[px, :, :, :] = rgb2YCbCr(cv_in[px, :, :, :])
elif ColorSpace == 'LAB':
stacks_v[px, :, :, :, :] = rgb2lab(stacks_v[px, :, :, :, :])
stacks_h[px, :, :, :, :] = rgb2lab(stacks_h[px, :, :, :, :])
cv_in[px, :, :, :] = rgb2lab(cv_in[px, :, :, :])
# push complete batch to encoder/decoder pipeline
batch = dict()
batch['stacks_h'] = stacks_h
batch['stacks_v'] = stacks_v
batch['cv'] = cv_in
batch['py'] = py
batch['decoder_path'] = decoder_path
inputs.put(batch)
#
if not outputs.empty():
result = outputs.get()
add_result_to_cv(data, result, cv_interp, cv_raw, mask_sum,
bs_x_HR, bs_y_HR, bx, dc)
results_received += 1
outputs.task_done()
# catch remaining results
while results_received < by:
result = outputs.get()
add_result_to_cv(data, result, cv_interp, cv_raw, mask_sum, bs_x_HR,
bs_y_HR, bx, dc)
results_received += 1
outputs.task_done()
# elapsed time since start of dmap computation
end = timer()
total_time = end - start
print('] done, total time %g seconds.' % total_time)
# evaluate result
mse = 0.0
# compute stats and return result
print('total time ', end - start)
# print('MSE : ', mse)
# code.interact( local=locals() )
return (cv_interp, total_time, mse, mask_sum, cv_raw)
import numpy as np
import matplotlib.pyplot as plt
import numpy as np
from libs.convert_colorspace import rgb2YUV, rgb2YCbCr
from scipy.misc import imread
from skimage.color import rgb2lab, lab2rgb
a = np.zeros((2, 512, 512, 3))
imgx = imread(
'/home/z/PycharmProjects/SR/full_data_512/platonic/input_Cam044.png') / 255
imgy = imread(
'/home/z/PycharmProjects/SR/full_data_512/platonic/input_Cam045.png') / 255
a[0, :, :, :] = imgx
a[1, :, :, :] = imgy
a2 = rgb2lab(a)
img1x = rgb2lab(imgx)
img1y = rgb2lab(imgy)
err1 = np.sum(np.abs(img1x - a2[0, :, :, :]))
err2 = np.sum(np.abs(img1y - a2[1, :, :, :]))
img2 = rgb2YUV(imgx)
err = np.sum(np.abs(img1x - img2))
k = 0
data_file = data_eval_folder + lf_name[0] + "/" + lf_name[1] + "/" + lf_name[2] + "/" + \
lf_name[3] + ".hdf5"
hdf_file = h5py.File(data_file, 'r')
# hard-coded size, just for testing
LF_LR = hdf_file['LF_LR']
LF_HR = hdf_file['LF']
cv_gt = lf_tools.cv(LF_HR)
cv_LR = lf_tools.cv(LF_LR)
data = []
color_space = hp.config['ColorSpace']
if color_space == 'YUV':
cv_gt_orig = rgb2YUV(cv_gt)
cv_LR_orig = rgb2YUV(cv_LR)
decoder_path = 'Y'
result_cv = encode_decode_lightfield(
data,
LF_LR,
LF_HR,
inputs,
outputs,
color_space,
decoder_path=decoder_path,
)
cv_out1 = result_cv[0]
mask = result_cv[3]