def __init__(self,
image_dirs,
is_gray=False,
crop_size=64,
random_scale=True,
rotate=True,
fliplr=True,
fliptb=True,
preload=True):
super(SingleImageLoader, self).__init__()
self.image_filenames = []
self.preload = preload
all_files = os.walk(image_dirs)
for path, dir_list, file_list in all_files:
self.image_filenames.extend(
join(path, x) for x in file_list if is_image_file(x))
if self.preload:
self.image_list = []
for file in self.image_filenames:
img = Image.open(file).convert('RGB')
self.image_list.append(img)
self.is_gray = is_gray
self.crop_size = crop_size
self.rotate = rotate
self.fliplr = fliplr
self.fliptb = fliptb
self.random_scale = random_scale
def prepare_data(self, train_path, is_ref_syn):
blended = []
transmission = []
reflection = []
if is_ref_syn:
print('loading synthetic data...')
else:
print('loading real data...')
for dirname in train_path:
train_t_gt = dirname + "/transmission_layer/"
train_r_gt = dirname + "/reflection_layer/"
train_b = dirname + "/blended/"
if is_ref_syn:
r_list = os.listdir(train_r_gt)
for _, _, fnames in sorted(os.walk(train_t_gt)):
for fname in fnames:
if is_ref_syn:
if not fname in r_list:
continue
if is_image_file(fname):
path_transmission = os.path.join(train_t_gt, fname)
transmission_img = cv2.imread(path_transmission)
path_blended = os.path.join(train_b, fname)
blended_img = cv2.imread(path_blended)
path_reflection = os.path.join(train_r_gt, fname)
reflection_img = cv2.imread(path_reflection)
blended.append(blended_img)
transmission.append(transmission_img)
reflection.append(reflection_img)
return blended, transmission, reflection
def __init__(self, dataPath='', loadSize=72, fineSize=64, flip=1):
super(DATASET, self).__init__()
# list all images into a list
self.list = [x for x in listdir(dataPath) if is_image_file(x)]
self.dataPath = dataPath
self.loadSize = loadSize
self.fineSize = fineSize
self.flip = flip
def __init__(self, image_dir, is_gray=False, scale_factor=[2, 3, 4]):
super(BenchmarkLoader, self).__init__()
self.image_filenames = []
all_files = os.walk(image_dir)
for path, dir_list, file_list in all_files:
self.image_filenames.extend(
join(path, x) for x in file_list if is_image_file(x))
# self.image_filenames = [join(image_dir, x) for x in sorted(listdir(image_dir)) if is_image_file(x)]
self.is_gray = is_gray
self.scale_factor = scale_factor
def __init__(self,
dataPath='facades/train',
loadSize=286,
fineSize=256,
flip=1):
super(Facades, self).__init__()
# list all images into a list
self.image_list = [x for x in listdir(dataPath) if is_image_file(x)]
self.dataPath = dataPath
self.loadSize = loadSize
self.fineSize = fineSize
self.flip = flip
def get_test_data(self):
data_path = self.config['data_path'] + self.config['test_path']
files_list = [
join(data_path, x) for x in sorted(listdir(data_path))
if is_image_file(x)
]
img_list = []
# TODO: use scipy.misc when tuning, finally use imresize for matlab-like bicubic performance
for file in files_list:
img = io.imread(file)
if img.shape[2] == 4:
img = color.rgba2rgb(img)
img_ycbcr = color.rgb2ycbcr(img) / 255
img_ycbcr = img_ycbcr.astype('float32')
(rows, cols, channel) = img_ycbcr.shape
img_y, img_cb, img_cr = np.split(img_ycbcr,
indices_or_sections=channel,
axis=2)
size_lr = (int(rows // self.config['upscale']),
int(cols // self.config['upscale']))
# img_y_lr = cv2.resize(img_y.squeeze(), dsize=(int(cols // self.config['upscale']), int(rows // self.config['upscale'])),
# interpolation=cv2.INTER_CUBIC)
# img_y_lr = misc.imresize(img_y.squeeze(), size=size_lr, interp='bicubic', mode='F')
img_y_lr = imresize(img_y.squeeze(), output_shape=size_lr)
img_cb = imresize(img_cb.squeeze(), output_shape=size_lr)
img_cr = imresize(img_cr.squeeze(), output_shape=size_lr)
img = imresize(img, output_shape=size_lr)
# import matplotlib.pyplot as plt
# plt.imshow(img_y_lr, cmap ='gray')
# plt.show()
img_bundle = {
'name': os.path.basename(file),
'origin': img,
'x': img_y_lr,
'y': img_ycbcr,
'cb': img_cb.squeeze(),
'cr': img_cr.squeeze(),
'size': img_ycbcr.shape[0:-1]
}
# img_bundle = {'name': os.path.basename(file), 'x': img_y_lr, 'y': img_y, 'cb': img_cb.squeeze(),
# 'cr': img_cr.squeeze(),
# 'size': size_lr}
img_list.append(img_bundle)
return img_list
def __init__(self, image_dir, is_gray=False, preload=False):
super(SampleLoader, self).__init__()
self.image_filenames = []
self.preload = preload
all_files = os.walk(image_dir)
for path, dir_list, file_list in all_files:
self.image_filenames.extend(
join(path, x) for x in file_list if is_image_file(x))
if self.preload:
self.image_list = []
for file in self.image_filenames:
img = Image.open(file).convert('RGB')
self.image_list.append(img)
self.is_gray = is_gray
