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import random
import torch.utils.data as data
import utils.utils_image as util
import cv2
class DatasetJPEG(data.Dataset):
def __init__(self, opt):
super(DatasetJPEG, self).__init__()
print('Dataset: JPEG compression artifact reduction (deblocking) with quality factor. Only dataroot_H is needed.')
self.opt = opt
self.n_channels = opt['n_channels'] if opt['n_channels'] else 3
self.patch_size = self.opt['H_size'] if opt['H_size'] else 128
self.quality_factor = opt['quality_factor'] if opt['quality_factor'] else 40
self.quality_factor_test = opt['quality_factor_test'] if opt['quality_factor_test'] else 40
self.is_color = opt['is_color'] if opt['is_color'] else False
# -------------------------------------
# get the path of H, return None if input is None
# -------------------------------------
self.paths_H = util.get_image_paths(opt['dataroot_H'])
def __getitem__(self, index):
if self.opt['phase'] == 'train':
# -------------------------------------
# get H image
# -------------------------------------
H_path = self.paths_H[index]
img_H = util.imread_uint(H_path, 3)
L_path = H_path
H, W = img_H.shape[:2]
self.patch_size_plus = self.patch_size + 8
# ---------------------------------
# randomly crop a large patch
# ---------------------------------
rnd_h = random.randint(0, max(0, H - self.patch_size_plus))
rnd_w = random.randint(0, max(0, W - self.patch_size_plus))
patch_H = img_H[rnd_h:rnd_h + self.patch_size_plus, rnd_w:rnd_w + self.patch_size_plus, ...]
# ---------------------------------
# augmentation - flip, rotate
# ---------------------------------
mode = random.randint(0, 7)
patch_H = util.augment_img(patch_H, mode=mode)
# ---------------------------------
# HWC to CHW, numpy(uint) to tensor
# ---------------------------------
img_L = patch_H.copy()
# ---------------------------------
# set quality factor
# ---------------------------------
quality_factor = self.quality_factor
if self.is_color: # color image
img_H = img_L.copy()
img_L = cv2.cvtColor(img_L, cv2.COLOR_RGB2BGR)
result, encimg = cv2.imencode('.jpg', img_L, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor])
img_L = cv2.imdecode(encimg, 1)
img_L = cv2.cvtColor(img_L, cv2.COLOR_BGR2RGB)
else:
if random.random() > 0.5:
img_L = util.rgb2ycbcr(img_L)
else:
img_L = cv2.cvtColor(img_L, cv2.COLOR_RGB2GRAY)
img_H = img_L.copy()
result, encimg = cv2.imencode('.jpg', img_L, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor])
img_L = cv2.imdecode(encimg, 0)
# ---------------------------------
# randomly crop a patch
# ---------------------------------
H, W = img_H.shape[:2]
if random.random() > 0.5:
rnd_h = random.randint(0, max(0, H - self.patch_size))
rnd_w = random.randint(0, max(0, W - self.patch_size))
else:
rnd_h = 0
rnd_w = 0
img_H = img_H[rnd_h:rnd_h + self.patch_size, rnd_w:rnd_w + self.patch_size]
img_L = img_L[rnd_h:rnd_h + self.patch_size, rnd_w:rnd_w + self.patch_size]
else:
H_path = self.paths_H[index]
L_path = H_path
# ---------------------------------
# set quality factor
# ---------------------------------
quality_factor = self.quality_factor_test
if self.is_color: # color JPEG image deblocking
img_H = util.imread_uint(H_path, 3)
img_L = img_H.copy()
img_L = cv2.cvtColor(img_L, cv2.COLOR_RGB2BGR)
result, encimg = cv2.imencode('.jpg', img_L, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor])
img_L = cv2.imdecode(encimg, 1)
img_L = cv2.cvtColor(img_L, cv2.COLOR_BGR2RGB)
else:
img_H = cv2.imread(H_path, cv2.IMREAD_UNCHANGED)
is_to_ycbcr = True if img_L.ndim == 3 else False
if is_to_ycbcr:
img_H = cv2.cvtColor(img_H, cv2.COLOR_BGR2RGB)
img_H = util.rgb2ycbcr(img_H)
result, encimg = cv2.imencode('.jpg', img_H, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor])
img_L = cv2.imdecode(encimg, 0)
img_L, img_H = util.uint2tensor3(img_L), util.uint2tensor3(img_H)
return {'L': img_L, 'H': img_H, 'L_path': L_path, 'H_path': H_path}
def __len__(self):
return len(self.paths_H)
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