def prepare_raw(path):
# Settings.
data = []
color = []
padding = abs(size_input - size_label) / 2
stride = 21
# Read in image and convert to ycrcb color space.
img = cv.imread(path)
im = cv.cvtColor(img, cv.COLOR_BGR2YCR_CB)
img = im2double(im) # Only use the luminance value.
# Create groundtruth and baseline image.
# im_label = modcrop(img)
# size = im_label.shape
size = img.shape
# im_label = scipy.misc.imresize(im_label, [size[0] * 3, size[1] * 3], interp='bicubic')
img_temp = scipy.misc.imresize(
img, [size[0] * multiplier, size[1] * multiplier], interp='bicubic')
color_temp = scipy.misc.imresize(
im, [size[0] * multiplier, size[1] * multiplier], interp='bicubic')
# img_temp = scipy.ndimage.interpolation.zoom(img, 3.0, prefilter=False)
im_label = img_temp[:, :, 0]
im_color = color_temp[:, :, 1:3]
h = im_label.shape[0]
w = im_label.shape[1]
# Generate subimages.
for x in range(0, h - size_input, stride):
for y in range(0, w - size_input, stride):
subim_input = im_label[x:x + size_input, y:y + size_input]
subim_color = im_color[int(x + padding):int(x + padding +
size_label),
int(y + padding):int(y + padding +
size_label), :]
# subim_label = im_label[int(x + padding) : int(x + padding + size_label), int(y + padding) : int(y + padding + size_label)]
subim_input = subim_input.reshape([size_input, size_input, 1])
subim_color = subim_color.reshape([size_label, size_label, 2])
# subim_label = subim_label.reshape([size_label, size_label, 1])
data.append(subim_input)
color.append(subim_color)
# label.append(subim_label)
data = np.array(data)
color = np.array(color)
# label = np.array(label)
# Write to HDF5 file.
# savepath = os.path.join(os.getcwd(), 'checkpoint/test_raw_image.h5')
# with h5py.File(savepath, 'w') as hf:
# hf.create_dataset('data', data=data)
# hf.create_dataset('color', data=color)
# hf.create_dataset('label', data=label)
return data, color
def prepare_data(path):
# Settings.
data = []
label = []
padding = abs(size_input - size_label) / 2
stride = 21
# Read in image and convert to ycrcb color space.
img = cv.imread(path)
img = cv.cvtColor(img, cv.COLOR_RGB2YCR_CB)
img = im2double(img) # Only use the luminance value.
# Create groundtruth and baseline image.
im_label = modcrop(img, scale=multiplier)
size = im_label.shape
h = size[0]
w = size[1]
im_temp = scipy.misc.imresize(im_label, 1 / multiplier, interp='bicubic')
im_input = scipy.misc.imresize(im_temp, multiplier * 1.0, interp='bicubic')
print('im_temp shape:', im_temp.shape)
print('im_input shape:', im_input.shape)
# Generate subimages.
for x in range(0, h - size_input, stride):
for y in range(0, w - size_input, stride):
subim_input = im_input[x:x + size_input, y:y + size_input]
subim_label = im_label[int(x + padding):int(x + padding +
size_label),
int(y + padding):int(y + padding +
size_label)]
subim_input = subim_input.reshape([size_input, size_input, 1])
subim_label = subim_label.reshape([size_label, size_label, 1])
data.append(subim_input)
label.append(subim_label)
data = np.array(data)
label = np.array(label)
# Write to HDF5 file.
# savepath = os.path.join(os.getcwd(), 'checkpoint/test_image.h5')
# with h5py.File(savepath, 'w') as hf:
# hf.create_dataset('data', data=data)
# hf.create_dataset('label', data=label)
return data, label
def prepare_raw(path): # Settings. data = [] color = [] # Read in image and convert to ycrcb color space. img = cv.imread(path) im = cv.cvtColor(img, cv.COLOR_BGR2YCR_CB) img = im2double(im) # Only use the luminance value. size = img.shape img_temp = scipy.misc.imresize(img, [size[0] * multiplier, size[1] * multiplier], interp='bicubic') color_temp = scipy.misc.imresize(im, [size[0] * multiplier, size[1] * multiplier], interp='bicubic') im_label = img_temp[:, :, 0] im_color = color_temp[:, :, 1:3] data = np.array(im_label).reshape([1, img.shape[0] * multiplier, img.shape[1] * multiplier, 1]) color = np.array(im_color) return data, color