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| import cv2 | |
| import random | |
| import torch | |
| def mod_crop(img, scale): | |
| """Mod crop images, used during testing. | |
| Args: | |
| img (ndarray): Input image. | |
| scale (int): Scale factor. | |
| Returns: | |
| ndarray: Result image. | |
| """ | |
| img = img.copy() | |
| if img.ndim in (2, 3): | |
| h, w = img.shape[0], img.shape[1] | |
| h_remainder, w_remainder = h % scale, w % scale | |
| img = img[:h - h_remainder, :w - w_remainder, ...] | |
| else: | |
| raise ValueError(f'Wrong img ndim: {img.ndim}.') | |
| return img | |
| def paired_random_crop(img_gts, img_lqs, gt_patch_size, scale, gt_path=None): | |
| """Paired random crop. Support Numpy array and Tensor inputs. | |
| It crops lists of lq and gt images with corresponding locations. | |
| Args: | |
| img_gts (list[ndarray] | ndarray | list[Tensor] | Tensor): GT images. Note that all images | |
| should have the same shape. If the input is an ndarray, it will | |
| be transformed to a list containing itself. | |
| img_lqs (list[ndarray] | ndarray): LQ images. Note that all images | |
| should have the same shape. If the input is an ndarray, it will | |
| be transformed to a list containing itself. | |
| gt_patch_size (int): GT patch size. | |
| scale (int): Scale factor. | |
| gt_path (str): Path to ground-truth. Default: None. | |
| Returns: | |
| list[ndarray] | ndarray: GT images and LQ images. If returned results | |
| only have one element, just return ndarray. | |
| """ | |
| if not isinstance(img_gts, list): | |
| img_gts = [img_gts] | |
| if not isinstance(img_lqs, list): | |
| img_lqs = [img_lqs] | |
| # determine input type: Numpy array or Tensor | |
| input_type = 'Tensor' if torch.is_tensor(img_gts[0]) else 'Numpy' | |
| if input_type == 'Tensor': | |
| h_lq, w_lq = img_lqs[0].size()[-2:] | |
| h_gt, w_gt = img_gts[0].size()[-2:] | |
| else: | |
| h_lq, w_lq = img_lqs[0].shape[0:2] | |
| h_gt, w_gt = img_gts[0].shape[0:2] | |
| lq_patch_size = gt_patch_size // scale | |
| if h_gt != h_lq * scale or w_gt != w_lq * scale: | |
| raise ValueError(f'Scale mismatches. GT ({h_gt}, {w_gt}) is not {scale}x ', | |
| f'multiplication of LQ ({h_lq}, {w_lq}).') | |
| if h_lq < lq_patch_size or w_lq < lq_patch_size: | |
| raise ValueError(f'LQ ({h_lq}, {w_lq}) is smaller than patch size ' | |
| f'({lq_patch_size}, {lq_patch_size}). ' | |
| f'Please remove {gt_path}.') | |
| # randomly choose top and left coordinates for lq patch | |
| top = random.randint(0, h_lq - lq_patch_size) | |
| left = random.randint(0, w_lq - lq_patch_size) | |
| # crop lq patch | |
| if input_type == 'Tensor': | |
| img_lqs = [v[:, :, top:top + lq_patch_size, left:left + lq_patch_size] for v in img_lqs] | |
| else: | |
| img_lqs = [v[top:top + lq_patch_size, left:left + lq_patch_size, ...] for v in img_lqs] | |
| # crop corresponding gt patch | |
| top_gt, left_gt = int(top * scale), int(left * scale) | |
| if input_type == 'Tensor': | |
| img_gts = [v[:, :, top_gt:top_gt + gt_patch_size, left_gt:left_gt + gt_patch_size] for v in img_gts] | |
| else: | |
| img_gts = [v[top_gt:top_gt + gt_patch_size, left_gt:left_gt + gt_patch_size, ...] for v in img_gts] | |
| if len(img_gts) == 1: | |
| img_gts = img_gts[0] | |
| if len(img_lqs) == 1: | |
| img_lqs = img_lqs[0] | |
| return img_gts, img_lqs | |
| def augment(imgs, hflip=True, rotation=True, flows=None, return_status=False): | |
| """Augment: horizontal flips OR rotate (0, 90, 180, 270 degrees). | |
| We use vertical flip and transpose for rotation implementation. | |
| All the images in the list use the same augmentation. | |
| Args: | |
| imgs (list[ndarray] | ndarray): Images to be augmented. If the input | |
| is an ndarray, it will be transformed to a list. | |
| hflip (bool): Horizontal flip. Default: True. | |
| rotation (bool): Ratotation. Default: True. | |
| flows (list[ndarray]: Flows to be augmented. If the input is an | |
| ndarray, it will be transformed to a list. | |
| Dimension is (h, w, 2). Default: None. | |
| return_status (bool): Return the status of flip and rotation. | |
| Default: False. | |
| Returns: | |
| list[ndarray] | ndarray: Augmented images and flows. If returned | |
| results only have one element, just return ndarray. | |
| """ | |
| hflip = hflip and random.random() < 0.5 | |
| vflip = rotation and random.random() < 0.5 | |
| rot90 = rotation and random.random() < 0.5 | |
| def _augment(img): | |
| if hflip: # horizontal | |
| cv2.flip(img, 1, img) | |
| if vflip: # vertical | |
| cv2.flip(img, 0, img) | |
| if rot90: | |
| img = img.transpose(1, 0, 2) | |
| return img | |
| def _augment_flow(flow): | |
| if hflip: # horizontal | |
| cv2.flip(flow, 1, flow) | |
| flow[:, :, 0] *= -1 | |
| if vflip: # vertical | |
| cv2.flip(flow, 0, flow) | |
| flow[:, :, 1] *= -1 | |
| if rot90: | |
| flow = flow.transpose(1, 0, 2) | |
| flow = flow[:, :, [1, 0]] | |
| return flow | |
| if not isinstance(imgs, list): | |
| imgs = [imgs] | |
| imgs = [_augment(img) for img in imgs] | |
| if len(imgs) == 1: | |
| imgs = imgs[0] | |
| if flows is not None: | |
| if not isinstance(flows, list): | |
| flows = [flows] | |
| flows = [_augment_flow(flow) for flow in flows] | |
| if len(flows) == 1: | |
| flows = flows[0] | |
| return imgs, flows | |
| else: | |
| if return_status: | |
| return imgs, (hflip, vflip, rot90) | |
| else: | |
| return imgs | |
| def img_rotate(img, angle, center=None, scale=1.0): | |
| """Rotate image. | |
| Args: | |
| img (ndarray): Image to be rotated. | |
| angle (float): Rotation angle in degrees. Positive values mean | |
| counter-clockwise rotation. | |
| center (tuple[int]): Rotation center. If the center is None, | |
| initialize it as the center of the image. Default: None. | |
| scale (float): Isotropic scale factor. Default: 1.0. | |
| """ | |
| (h, w) = img.shape[:2] | |
| if center is None: | |
| center = (w // 2, h // 2) | |
| matrix = cv2.getRotationMatrix2D(center, angle, scale) | |
| rotated_img = cv2.warpAffine(img, matrix, (w, h)) | |
| return rotated_img | |