def __getitem__(self, index):
img_path = self.images_path[index]
target_ = cv2.imread(img_path)
input_ = imresize(target_, scalar_scale=1 / self.sr_factor)
subim_in, subim_tar = get_patch(input_, target_, self.patch_size,
self.sr_factor)
if not self.rgb:
subim_in = utils.rgb2ycbcr(subim_in)
subim_tar = utils.rgb2ycbcr(subim_tar)
subim_in = np.expand_dims(subim_in[:, :, 0], 2)
subim_tar = np.expand_dims(subim_tar[:, :, 0], 2)
if self.input_up:
subim_bic = imresize(subim_in, scalar_scale=self.sr_factor)
subim_in = utils.np2tensor(subim_in, self.rgb_range)
subim_tar = utils.np2tensor(subim_tar, self.rgb_range)
subim_bic = utils.np2tensor(subim_bic, self.rgb_range)
return {
'input': subim_in,
'target': subim_tar,
'input_up': subim_bic
}
subim_in = utils.np2tensor(subim_in, self.rgb_range)
subim_tar = utils.np2tensor(subim_tar, self.rgb_range)
return {'input': subim_in, 'target': subim_tar}
def __getitem__(self, index):
path = self.test_path[index]
images = {}
images.update({'name': path['save']})
input_ = cv2.imread(path['input'])
input_ = cv2.cvtColor(input_, cv2.COLOR_BGR2RGB)
target_ = cv2.imread(path['target'])
target_ = utils.modcrop(target_, self.sr_factor)
target_ = cv2.cvtColor(target_, cv2.COLOR_BGR2RGB)
if not self.rgb:
input_out = np.copy(input_)
input_out = utils.np2tensor(input_out, self.rgb_range)
# print(input_out)
input_ = utils.rgb2ycbcr(input_)
input_cbcr = input_[:, :, 1:]
input_ = np.expand_dims(input_[:, :, 0], 2)
input_cbcr = utils.np2tensor(input_cbcr, self.rgb_range)
images.update({'input_cbcr': input_cbcr, 'input_rgb': input_out})
if self.target_down:
target_down = imresize(target_, scalar_scale=1 / self.sr_factor)
target_down = utils.np2tensor(target_down, self.rgb_range)
images.update({'target_down': target_down})
input_ = utils.np2tensor(input_, self.rgb_range)
target_ = utils.np2tensor(target_, self.rgb_range)
images.update({'input': input_, 'target': target_})
return images
def read_avgblur(self, filename, skip=True):
print('Average Blurring')
cap = cv2.VideoCapture(filename)
nframe = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
print(nframe)
if skip:
if nframe // self.depth >= 2:
skip_frames = nframe % self.depth + self.depth
else:
skip_frames = nframe % self.depth
else:
skip_frames = nframe % self.depth
start = skip_frames//2
end = nframe-skip_frames//2
if skip_frames % 2 == 1:
end -= 1
framelst = []
for i in range(start, end):
cap.set(cv2.CAP_PROP_POS_FRAMES, i)
ret, frame = cap.read()
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
frame = self.dsampleWithAvg(frame, self.ksize)
frame = imresize(frame, output_shape=(120, 160))
framelst.append(frame)
cap.release()
print(np.array(framelst).shape)
print(nframe-skip_frames)
return np.split(np.array(framelst), (nframe-skip_frames) // self.depth, axis=0)
def get_subimages(self, frame):
subimage_lst = []
for x in range(self.ksize):
for y in range(self.ksize):
mask = np.fromfunction(lambda i, j, k: (i%self.ksize==x) & (j%self.ksize==y), frame.shape, dtype=int)
subimage = frame[mask].reshape(frame.shape[0]//self.ksize, frame.shape[1]//self.ksize, -1)
subimage = imresize(subimage, output_shape=(120, 160))
subimage_lst.append(subimage)
return subimage_lst
def __getitem__(self, index):
img_path = self.images_path[index]
target_ = cv2.imread(img_path)
input_ = imresize(target_, scalar_scale=1 / self.sr_factor)
if not self.rgb:
input_ = utils.rgb2ycbcr(input_)
target_ = utils.rgb2ycbcr(target_)
if self.input_up:
input_bic = imresize(input_, scalar_scale=self.sr_factor).round()
input_ = utils.np2tensor(input_, self.rgb_range)
target_ = utils.np2tensor(target_, self.rgb_range)
input_bic_ = utils.np2tensor(input_bic, self.rgb_range)
return {'input': input_, 'target': target_, 'input_up': input_bic_}
input_ = utils.np2tensor(input_, self.rgb_range)
target_ = utils.np2tensor(target_, self.rgb_range)
return {'input': input_, 'target': target_}
def __getitem__(self, index):
path = self.train_path[index]
images = {}
if self.npy_reader:
input_ = np.load(path['input'], allow_pickle=False)
target_ = np.load(path['target'], allow_pickle=False)
target_ = utils.modcrop(target_, self.sr_factor)
else:
input_ = cv2.imread(path['input'])
input_ = cv2.cvtColor(input_, cv2.COLOR_BGR2RGB)
target_ = cv2.imread(path['target'])
target_ = utils.modcrop(target_, self.sr_factor)
target_ = cv2.cvtColor(target_, cv2.COLOR_BGR2RGB)
# for i in range(10):
# subim_in, subim_tar = get_patch(input_, target_, self.patch_size, self.sr_factor)
# win_mean = ndimage.uniform_filter(subim_in[:, :, 0], (5, 5))
# win_sqr_mean = ndimage.uniform_filter(subim_in[:, :, 0]**2, (5, 5))
# win_var = win_sqr_mean - win_mean**2
#
# if np.sum(win_var) / (win_var.shape[0]*win_var.shape[1]) > 30:
# break
subim_in, subim_tar = get_patch(input_, target_, self.patch_size,
self.sr_factor)
if not self.rgb:
subim_in = utils.rgb2ycbcr(subim_in)
subim_tar = utils.rgb2ycbcr(subim_tar)
subim_in = np.expand_dims(subim_in[:, :, 0], 2)
subim_tar = np.expand_dims(subim_tar[:, :, 0], 2)
if self.target_down:
subim_target_down = imresize(subim_tar,
scalar_scale=1 / self.sr_factor)
subim_target_down = utils.np2tensor(subim_target_down,
self.rgb_range)
images.update({'target_down': subim_target_down})
subim_in = utils.np2tensor(subim_in, self.rgb_range)
subim_tar = utils.np2tensor(subim_tar, self.rgb_range)
images.update({'input': subim_in, 'target': subim_tar})
return images
def __getitem__(self, index):
input_ = np.load(self.input_path[index])
target_ = np.load(self.target_path[index])
if not self.rgb:
input_ = utils.rgb2ycbcr(input_)
target_ = utils.rgb2ycbcr(target_)
if self.input_up:
input_bic = imresize(input_, scalar_scale=self.sr_factor).round()
input_ = utils.np2tensor(input_, self.rgb_range)
target_ = utils.np2tensor(target_, self.rgb_range)
input_bic_ = utils.np2tensor(input_bic, self.rgb_range)
return {'input': input_, 'target': target_, 'input_up': input_bic_}
input_ = utils.np2tensor(input_, self.rgb_range)
target_ = utils.np2tensor(target_, self.rgb_range)
return {'input': input_, 'target': target_}
def preprocess_py(path, scale=4):
"""
Preprocess single image file
(1) Read original image as YCbCr format (and grayscale as default)
(2) Normalize
(3) Apply image file with bicubic interpolation
Args:
path: file path of desired file
input_: image applied bicubic interpolation (low-resolution)
label_: image with original resolution (high-resolution)
"""
im_uint8 = imread(path)
im_double = im_uint8 / 255.
input_ = modcrop(im_double, scale)
label_ = imresize(input_, scalar_scale=1. / scale)
return label_
def read(self, filename):
print('No Blurring')
cap = cv2.VideoCapture(filename)
nframe = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
if nframe > self.depth:
indices = self.indices_augment(nframe, self.depth, 20)
else:
indices = [x for x in range(nframe)
] + [nframe - 1] * (self.depth - nframe)
framelst = []
for i in indices:
cap.set(cv2.CAP_PROP_POS_FRAMES, i)
ret, frame = cap.read()
print(frame.shape)
#print(frame[:,:,0])
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
frame = imresize(frame, output_shape=(120, 160))
framelst.append(frame)
cap.release()
clip_lst = np.split(np.array(framelst),
len(framelst) // self.depth,
axis=0)
return clip_lst
def save_image2npy(path, save_path, sr_fator):
images = make_dataset(path)
input_save_path = os.path.join(save_path, 'x%d' % (sr_fator))
target_save_path = os.path.join(save_path, 'origin')
mkdir(input_save_path)
mkdir(target_save_path)
for img_name in images:
print(img_name)
_, name = os.path.split(img_name)
name, _ = name.split('.')
target_name = '%s.npy' % name
input_name = '%s.npy' % name
input_save_name = os.path.join(input_save_path, input_name)
target_save_name = os.path.join(target_save_path, target_name)
img = cv2.imread(img_name)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img_down = imresize(img, scalar_scale=1/sr_fator)
print(img.shape, img_down.shape)
np.save(target_save_name, img)
np.save(input_save_name, img_down)
import cv2
import os
from matlab_imresize import imresize
from utils import modcrop, mkdir
origin_path = '/media/luo/data/data/super-resolution/VISTA/origin'
path = '/media/luo/data/data/super-resolution/VISTA'
#
Q_list = [5, 10, 20, 30, 40, 50]
sr_factor = [2, 3, 4]
for root, _, names in os.walk(origin_path):
for name in names:
target_name = os.path.join(origin_path, name)
img_tar = cv2.imread(target_name)
# img_tar = cv2.cvtColor(img_tar, cv2.cvtColor())
for i in sr_factor:
img_tar_ = modcrop(img_tar, i)
sr_path = os.path.join(path, 'x{}'.format(i))
img_down = imresize(img_tar_, scalar_scale=1 / i)
for Q in Q_list:
webp_path = os.path.join(sr_path, 'webp{}'.format(Q))
if not os.path.exists(webp_path):
mkdir(webp_path)
save_name = name[:-4] + '.webp'
save = os.path.join(webp_path, save_name)
cv2.imwrite(save, img_down, [cv2.IMWRITE_WEBP_QUALITY, Q])
def __call__(self, img):
dsize = (int(np.shape(img)[1] / self.scale_factor),
int(np.shape(img)[0] / self.scale_factor))
return matlab_imresize.imresize(img, output_shape=dsize)
