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Source code for mmedit.datasets.pipelines.crop

# Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import numpy as np
from torch.nn.modules.utils import _pair

from ..registry import PIPELINES
from .utils import random_choose_unknown


[docs]@PIPELINES.register_module() class Crop: """Crop data to specific size for training. Args: keys (Sequence[str]): The images to be cropped. crop_size (Tuple[int]): Target spatial size (h, w). random_crop (bool): If set to True, it will random crop image. Otherwise, it will work as center crop. """ def __init__(self, keys, crop_size, random_crop=True): if not mmcv.is_tuple_of(crop_size, int): raise TypeError( 'Elements of crop_size must be int and crop_size must be' f' tuple, but got {type(crop_size[0])} in {type(crop_size)}') self.keys = keys self.crop_size = crop_size self.random_crop = random_crop def _crop(self, data): if not isinstance(data, list): data_list = [data] else: data_list = data crop_bbox_list = [] data_list_ = [] for item in data_list: data_h, data_w = item.shape[:2] crop_h, crop_w = self.crop_size crop_h = min(data_h, crop_h) crop_w = min(data_w, crop_w) if self.random_crop: x_offset = np.random.randint(0, data_w - crop_w + 1) y_offset = np.random.randint(0, data_h - crop_h + 1) else: x_offset = max(0, (data_w - crop_w)) // 2 y_offset = max(0, (data_h - crop_h)) // 2 crop_bbox = [x_offset, y_offset, crop_w, crop_h] item_ = item[y_offset:y_offset + crop_h, x_offset:x_offset + crop_w, ...] crop_bbox_list.append(crop_bbox) data_list_.append(item_) if not isinstance(data, list): return data_list_[0], crop_bbox_list[0] return data_list_, crop_bbox_list def __call__(self, results): """Call function. Args: results (dict): A dict containing the necessary information and data for augmentation. Returns: dict: A dict containing the processed data and information. """ for k in self.keys: data_, crop_bbox = self._crop(results[k]) results[k] = data_ results[k + '_crop_bbox'] = crop_bbox results['crop_size'] = self.crop_size return results def __repr__(self): repr_str = self.__class__.__name__ repr_str += (f'keys={self.keys}, crop_size={self.crop_size}, ' f'random_crop={self.random_crop}') return repr_str
[docs]@PIPELINES.register_module() class CropSequence(Crop): """Crop a sequence to specific size for training. The main difference to 'Crop' is that the region to be cropped is the same for every images in the sequence. Args: keys (Sequence[str]): The images to be cropped. crop_size (Tuple[int]): Target spatial size (h, w). random_crop (bool): If set to True, it will random crop image. Otherwise, it will work as center crop. """ def _crop(self, data): if not isinstance(data, list): raise TypeError(f'Input must be a list, but got {type(data)}.') # determine crop location. Must be the same for all images data_h, data_w = data[0].shape[:2] crop_h, crop_w = self.crop_size crop_h = min(data_h, crop_h) crop_w = min(data_w, crop_w) if self.random_crop: x_offset = np.random.randint(0, data_w - crop_w + 1) y_offset = np.random.randint(0, data_h - crop_h + 1) else: x_offset = max(0, (data_w - crop_w)) // 2 y_offset = max(0, (data_h - crop_h)) // 2 crop_bbox = [x_offset, y_offset, crop_w, crop_h] data_list = [] for item in data: item = item[y_offset:y_offset + crop_h, x_offset:x_offset + crop_w, ...] data_list.append(item) return data_list, crop_bbox
[docs]@PIPELINES.register_module() class FixedCrop: """Crop paired data (at a specific position) to specific size for training. Args: keys (Sequence[str]): The images to be cropped. crop_size (Tuple[int]): Target spatial size (h, w). crop_pos (Tuple[int]): Specific position (x, y). If set to None, random initialize the position to crop paired data batch. """ def __init__(self, keys, crop_size, crop_pos=None): if not mmcv.is_tuple_of(crop_size, int): raise TypeError( 'Elements of crop_size must be int and crop_size must be' f' tuple, but got {type(crop_size[0])} in {type(crop_size)}') if not mmcv.is_tuple_of(crop_pos, int) and (crop_pos is not None): raise TypeError( 'Elements of crop_pos must be int and crop_pos must be' f' tuple or None, but got {type(crop_pos[0])} in ' f'{type(crop_pos)}') self.keys = keys self.crop_size = crop_size self.crop_pos = crop_pos def _crop(self, data, x_offset, y_offset, crop_w, crop_h): crop_bbox = [x_offset, y_offset, crop_w, crop_h] data_ = data[y_offset:y_offset + crop_h, x_offset:x_offset + crop_w, ...] return data_, crop_bbox def __call__(self, results): """Call function. Args: results (dict): A dict containing the necessary information and data for augmentation. Returns: dict: A dict containing the processed data and information. """ if isinstance(results[self.keys[0]], list): data_h, data_w = results[self.keys[0]][0].shape[:2] else: data_h, data_w = results[self.keys[0]].shape[:2] crop_h, crop_w = self.crop_size crop_h = min(data_h, crop_h) crop_w = min(data_w, crop_w) if self.crop_pos is None: x_offset = np.random.randint(0, data_w - crop_w + 1) y_offset = np.random.randint(0, data_h - crop_h + 1) else: x_offset, y_offset = self.crop_pos crop_w = min(data_w - x_offset, crop_w) crop_h = min(data_h - y_offset, crop_h) for k in self.keys: images = results[k] is_list = isinstance(images, list) if not is_list: images = [images] cropped_images = [] crop_bbox = None for image in images: # In fixed crop for paired images, sizes should be the same if (image.shape[0] != data_h or image.shape[1] != data_w): raise ValueError( 'The sizes of paired images should be the same. ' f'Expected ({data_h}, {data_w}), ' f'but got ({image.shape[0]}, ' f'{image.shape[1]}).') data_, crop_bbox = self._crop(image, x_offset, y_offset, crop_w, crop_h) cropped_images.append(data_) results[k + '_crop_bbox'] = crop_bbox if not is_list: cropped_images = cropped_images[0] results[k] = cropped_images results['crop_size'] = self.crop_size results['crop_pos'] = self.crop_pos return results def __repr__(self): repr_str = self.__class__.__name__ repr_str += (f'keys={self.keys}, crop_size={self.crop_size}, ' f'crop_pos={self.crop_pos}') return repr_str
[docs]@PIPELINES.register_module() class PairedRandomCrop: """Paried random crop. It crops a pair of lq and gt images with corresponding locations. It also supports accepting lq list and gt list. Required keys are "scale", "lq", and "gt", added or modified keys are "lq" and "gt". Args: gt_patch_size (int): cropped gt patch size. """ def __init__(self, gt_patch_size): self.gt_patch_size = gt_patch_size def __call__(self, results): """Call function. Args: results (dict): A dict containing the necessary information and data for augmentation. Returns: dict: A dict containing the processed data and information. """ scale = results['scale'] lq_patch_size = self.gt_patch_size // scale lq_is_list = isinstance(results['lq'], list) if not lq_is_list: results['lq'] = [results['lq']] gt_is_list = isinstance(results['gt'], list) if not gt_is_list: results['gt'] = [results['gt']] h_lq, w_lq, _ = results['lq'][0].shape h_gt, w_gt, _ = results['gt'][0].shape 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}). Please check ' f'{results["lq_path"][0]} and {results["gt_path"][0]}.') # randomly choose top and left coordinates for lq patch top = np.random.randint(h_lq - lq_patch_size + 1) left = np.random.randint(w_lq - lq_patch_size + 1) # crop lq patch results['lq'] = [ v[top:top + lq_patch_size, left:left + lq_patch_size, ...] for v in results['lq'] ] # crop corresponding gt patch top_gt, left_gt = int(top * scale), int(left * scale) results['gt'] = [ v[top_gt:top_gt + self.gt_patch_size, left_gt:left_gt + self.gt_patch_size, ...] for v in results['gt'] ] if not lq_is_list: results['lq'] = results['lq'][0] if not gt_is_list: results['gt'] = results['gt'][0] return results def __repr__(self): repr_str = self.__class__.__name__ repr_str += f'(gt_patch_size={self.gt_patch_size})' return repr_str
[docs]@PIPELINES.register_module() class CropAroundCenter: """Randomly crop the images around unknown area in the center 1/4 images. This cropping strategy is adopted in GCA matting. The `unknown area` is the same as `semi-transparent area`. https://arxiv.org/pdf/2001.04069.pdf It retains the center 1/4 images and resizes the images to 'crop_size'. Required keys are "fg", "bg", "trimap" and "alpha", added or modified keys are "crop_bbox", "fg", "bg", "trimap" and "alpha". Args: crop_size (int | tuple): Desired output size. If int, square crop is applied. """ def __init__(self, crop_size): if mmcv.is_tuple_of(crop_size, int): assert len(crop_size) == 2, 'length of crop_size must be 2.' elif not isinstance(crop_size, int): raise TypeError('crop_size must be int or a tuple of int, but got ' f'{type(crop_size)}') self.crop_size = _pair(crop_size) def __call__(self, results): """Call function. Args: results (dict): A dict containing the necessary information and data for augmentation. Returns: dict: A dict containing the processed data and information. """ fg = results['fg'] alpha = results['alpha'] trimap = results['trimap'] bg = results['bg'] h, w = fg.shape[:2] assert bg.shape == fg.shape, (f'shape of bg {bg.shape} should be the ' f'same as fg {fg.shape}.') crop_h, crop_w = self.crop_size # Make sure h >= crop_h, w >= crop_w. If not, rescale imgs rescale_ratio = max(crop_h / h, crop_w / w) if rescale_ratio > 1: new_h = max(int(h * rescale_ratio), crop_h) new_w = max(int(w * rescale_ratio), crop_w) fg = mmcv.imresize(fg, (new_w, new_h), interpolation='nearest') alpha = mmcv.imresize( alpha, (new_w, new_h), interpolation='nearest') trimap = mmcv.imresize( trimap, (new_w, new_h), interpolation='nearest') bg = mmcv.imresize(bg, (new_w, new_h), interpolation='bicubic') h, w = new_h, new_w # resize to 1/4 to ignore small unknown patches small_trimap = mmcv.imresize( trimap, (w // 4, h // 4), interpolation='nearest') # find unknown area in center 1/4 region margin_h, margin_w = crop_h // 2, crop_w // 2 sample_area = small_trimap[margin_h // 4:(h - margin_h) // 4, margin_w // 4:(w - margin_w) // 4] unknown_xs, unknown_ys = np.where(sample_area == 128) unknown_num = len(unknown_xs) if unknown_num < 10: # too few unknown area in the center, crop from the whole image top = np.random.randint(0, h - crop_h + 1) left = np.random.randint(0, w - crop_w + 1) else: idx = np.random.randint(unknown_num) top = unknown_xs[idx] * 4 left = unknown_ys[idx] * 4 bottom = top + crop_h right = left + crop_w results['fg'] = fg[top:bottom, left:right] results['alpha'] = alpha[top:bottom, left:right] results['trimap'] = trimap[top:bottom, left:right] results['bg'] = bg[top:bottom, left:right] results['crop_bbox'] = (left, top, right, bottom) return results def __repr__(self): return self.__class__.__name__ + f'(crop_size={self.crop_size})'
[docs]@PIPELINES.register_module() class CropAroundUnknown: """Crop around unknown area with a randomly selected scale. Randomly select the w and h from a list of (w, h). Required keys are the keys in argument `keys`, added or modified keys are "crop_bbox" and the keys in argument `keys`. This class assumes value of "alpha" ranges from 0 to 255. Args: keys (Sequence[str]): The images to be cropped. It must contain 'alpha'. If unknown_source is set to 'trimap', then it must also contain 'trimap'. crop_sizes (list[int | tuple[int]]): List of (w, h) to be selected. unknown_source (str, optional): Unknown area to select from. It must be 'alpha' or 'tirmap'. Default to 'alpha'. interpolations (str | list[str], optional): Interpolation method of mmcv.imresize. The interpolation operation will be applied when image size is smaller than the crop_size. If given as a list of str, it should have the same length as `keys`. Or if given as a str all the keys will be resized with the same method. Default to 'bilinear'. """ def __init__(self, keys, crop_sizes, unknown_source='alpha', interpolations='bilinear'): if 'alpha' not in keys: raise ValueError(f'"alpha" must be in keys, but got {keys}') self.keys = keys if not isinstance(crop_sizes, list): raise TypeError( f'Crop sizes must be list, but got {type(crop_sizes)}.') self.crop_sizes = [_pair(crop_size) for crop_size in crop_sizes] if not mmcv.is_tuple_of(self.crop_sizes[0], int): raise TypeError('Elements of crop_sizes must be int or tuple of ' f'int, but got {type(self.crop_sizes[0][0])}.') if unknown_source not in ['alpha', 'trimap']: raise ValueError('unknown_source must be "alpha" or "trimap", ' f'but got {unknown_source}') if unknown_source not in keys: # it could only be trimap, since alpha is checked before raise ValueError( 'if unknown_source is "trimap", it must also be set in keys') self.unknown_source = unknown_source if isinstance(interpolations, str): self.interpolations = [interpolations] * len(self.keys) elif mmcv.is_list_of(interpolations, str) and len(interpolations) == len(self.keys): self.interpolations = interpolations else: raise TypeError( 'interpolations must be a str or list of str with ' f'the same length as keys, but got {interpolations}') def __call__(self, results): """Call function. Args: results (dict): A dict containing the necessary information and data for augmentation. Returns: dict: A dict containing the processed data and information. """ h, w = results[self.keys[0]].shape[:2] rand_ind = np.random.randint(len(self.crop_sizes)) crop_h, crop_w = self.crop_sizes[rand_ind] # Make sure h >= crop_h, w >= crop_w. If not, rescale imgs rescale_ratio = max(crop_h / h, crop_w / w) if rescale_ratio > 1: h = max(int(h * rescale_ratio), crop_h) w = max(int(w * rescale_ratio), crop_w) for key, interpolation in zip(self.keys, self.interpolations): results[key] = mmcv.imresize( results[key], (w, h), interpolation=interpolation) # Select the cropping top-left point which is an unknown pixel if self.unknown_source == 'alpha': unknown = (results['alpha'] > 0) & (results['alpha'] < 255) else: unknown = results['trimap'] == 128 top, left = random_choose_unknown(unknown.squeeze(), (crop_h, crop_w)) bottom = top + crop_h right = left + crop_w for key in self.keys: results[key] = results[key][top:bottom, left:right] results['crop_bbox'] = (left, top, right, bottom) return results def __repr__(self): repr_str = self.__class__.__name__ repr_str += (f'(keys={self.keys}, crop_sizes={self.crop_sizes}, ' f"unknown_source='{self.unknown_source}', " f'interpolations={self.interpolations})') return repr_str
[docs]@PIPELINES.register_module() class CropAroundFg: """Crop around the whole foreground in the segmentation mask. Required keys are "seg" and the keys in argument `keys`. Meanwhile, "seg" must be in argument `keys`. Added or modified keys are "crop_bbox" and the keys in argument `keys`. Args: keys (Sequence[str]): The images to be cropped. It must contain 'seg'. bd_ratio_range (tuple, optional): The range of the boundary (bd) ratio to select from. The boundary ratio is the ratio of the boundary to the minimal bbox that contains the whole foreground given by segmentation. Default to (0.1, 0.4). test_mode (bool): Whether use test mode. In test mode, the tight crop area of foreground will be extended to the a square. Default to False. """ def __init__(self, keys, bd_ratio_range=(0.1, 0.4), test_mode=False): if 'seg' not in keys: raise ValueError(f'"seg" must be in keys, but got {keys}') if (not mmcv.is_tuple_of(bd_ratio_range, float) or len(bd_ratio_range) != 2): raise TypeError('bd_ratio_range must be a tuple of 2 int, but got ' f'{bd_ratio_range}') self.keys = keys self.bd_ratio_range = bd_ratio_range self.test_mode = test_mode def __call__(self, results): """Call function. Args: results (dict): A dict containing the necessary information and data for augmentation. Returns: dict: A dict containing the processed data and information. """ seg = results['seg'] height, width = seg.shape[:2] # get foreground bbox fg_coor = np.array(np.where(seg)) top, left = np.amin(fg_coor, axis=1) bottom, right = np.amax(fg_coor, axis=1) # enlarge bbox long_side = np.maximum(bottom - top, right - left) if self.test_mode: bottom = top + long_side right = left + long_side boundary_ratio = np.random.uniform(*self.bd_ratio_range) boundary = int(np.round(boundary_ratio * long_side)) # NOTE: Different from the original repo, we keep track of the four # corners of the bbox (left, top, right, bottom) while the original # repo use (top, left, height, width) to represent bbox. This may # introduce an difference of 1 pixel. top = max(top - boundary, 0) left = max(left - boundary, 0) bottom = min(bottom + boundary, height) right = min(right + boundary, width) for key in self.keys: results[key] = results[key][top:bottom, left:right] results['crop_bbox'] = (left, top, right, bottom) return results
[docs]@PIPELINES.register_module() class ModCrop: """Mod crop gt images, used during testing. Required keys are "scale" and "gt", added or modified keys are "gt". """ def __call__(self, results): """Call function. Args: results (dict): A dict containing the necessary information and data for augmentation. Returns: dict: A dict containing the processed data and information. """ img = results['gt'].copy() scale = results['scale'] 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}.') results['gt'] = img return results
[docs]@PIPELINES.register_module() class CropLike: """Crop/pad the image in the target_key according to the size of image in the reference_key . Args: target_key (str): The key needs to be cropped. reference_key (str | None): The reference key, need its size. Default: None. """ def __init__(self, target_key, reference_key=None): assert reference_key and target_key self.target_key = target_key self.reference_key = reference_key def __call__(self, results): """Call function. Args: results (dict): A dict containing the necessary information and data for augmentation. Require self.target_key and self.reference_key. Returns: dict: A dict containing the processed data and information. Modify self.target_key. """ size = results[self.reference_key].shape old_image = results[self.target_key] old_size = old_image.shape h, w = old_size[:2] new_size = size[:2] + old_size[2:] h_cover, w_cover = min(h, size[0]), min(w, size[1]) format_image = np.zeros(new_size, dtype=old_image.dtype) format_image[:h_cover, :w_cover] = old_image[:h_cover, :w_cover] results[self.target_key] = format_image return results def __repr__(self): return (self.__class__.__name__ + f' target_key={self.target_key}, ' + f'reference_key={self.reference_key}')
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