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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) | |