import inspect import math import random import mmcv import numpy as np from ..builder import PIPELINES try: import albumentations from albumentations import Compose except ImportError: albumentations = None Compose = None @PIPELINES.register_module() class RandomCrop(object): """Crop the given Image at a random location. Args: size (sequence or int): Desired output size of the crop. If size is an int instead of sequence like (h, w), a square crop (size, size) is made. padding (int or sequence, optional): Optional padding on each border of the image. If a sequence of length 4 is provided, it is used to pad left, top, right, bottom borders respectively. If a sequence of length 2 is provided, it is used to pad left/right, top/bottom borders, respectively. Default: None, which means no padding. pad_if_needed (boolean): It will pad the image if smaller than the desired size to avoid raising an exception. Since cropping is done after padding, the padding seems to be done at a random offset. Default: False. pad_val (Number | Sequence[Number]): Pixel pad_val value for constant fill. If a tuple of length 3, it is used to pad_val R, G, B channels respectively. Default: 0. padding_mode (str): Type of padding. Should be: constant, edge, reflect or symmetric. Default: constant. -constant: Pads with a constant value, this value is specified with pad_val. -edge: pads with the last value at the edge of the image. -reflect: Pads with reflection of image without repeating the last value on the edge. For example, padding [1, 2, 3, 4] with 2 elements on both sides in reflect mode will result in [3, 2, 1, 2, 3, 4, 3, 2]. -symmetric: Pads with reflection of image repeating the last value on the edge. For example, padding [1, 2, 3, 4] with 2 elements on both sides in symmetric mode will result in [2, 1, 1, 2, 3, 4, 4, 3]. """ def __init__(self, size, padding=None, pad_if_needed=False, pad_val=0, padding_mode='constant'): if isinstance(size, (tuple, list)): self.size = size else: self.size = (size, size) # check padding mode assert padding_mode in ['constant', 'edge', 'reflect', 'symmetric'] self.padding = padding self.pad_if_needed = pad_if_needed self.pad_val = pad_val self.padding_mode = padding_mode @staticmethod def get_params(img, output_size): """Get parameters for ``crop`` for a random crop. Args: img (ndarray): Image to be cropped. output_size (tuple): Expected output size of the crop. Returns: tuple: Params (xmin, ymin, target_height, target_width) to be passed to ``crop`` for random crop. """ height = img.shape[0] width = img.shape[1] target_height, target_width = output_size if width == target_width and height == target_height: return 0, 0, height, width xmin = random.randint(0, height - target_height) ymin = random.randint(0, width - target_width) return xmin, ymin, target_height, target_width def __call__(self, results): """ Args: img (ndarray): Image to be cropped. """ for key in results.get('img_fields', ['img']): img = results[key] if self.padding is not None: img = mmcv.impad( img, padding=self.padding, pad_val=self.pad_val) # pad the height if needed if self.pad_if_needed and img.shape[0] < self.size[0]: img = mmcv.impad( img, padding=(0, self.size[0] - img.shape[0], 0, self.size[0] - img.shape[0]), pad_val=self.pad_val, padding_mode=self.padding_mode) # pad the width if needed if self.pad_if_needed and img.shape[1] < self.size[1]: img = mmcv.impad( img, padding=(self.size[1] - img.shape[1], 0, self.size[1] - img.shape[1], 0), pad_val=self.pad_val, padding_mode=self.padding_mode) xmin, ymin, height, width = self.get_params(img, self.size) results[key] = mmcv.imcrop( img, np.array([ymin, xmin, ymin + width - 1, xmin + height - 1])) return results def __repr__(self): return (self.__class__.__name__ + f'(size={self.size}, padding={self.padding})') @PIPELINES.register_module() class RandomResizedCrop(object): """Crop the given image to random size and aspect ratio. A crop of random size (default: of 0.08 to 1.0) of the original size and a random aspect ratio (default: of 3/4 to 4/3) of the original aspect ratio is made. This crop is finally resized to given size. Args: size (sequence or int): Desired output size of the crop. If size is an int instead of sequence like (h, w), a square crop (size, size) is made. scale (tuple): Range of the random size of the cropped image compared to the original image. Default: (0.08, 1.0). ratio (tuple): Range of the random aspect ratio of the cropped image compared to the original image. Default: (3. / 4., 4. / 3.). interpolation (str): Interpolation method, accepted values are 'nearest', 'bilinear', 'bicubic', 'area', 'lanczos'. Default: 'bilinear'. backend (str): The image resize backend type, accpeted values are `cv2` and `pillow`. Default: `cv2`. """ def __init__(self, size, scale=(0.08, 1.0), ratio=(3. / 4., 4. / 3.), interpolation='bilinear', backend='cv2'): if isinstance(size, (tuple, list)): self.size = size else: self.size = (size, size) if (scale[0] > scale[1]) or (ratio[0] > ratio[1]): raise ValueError('range should be of kind (min, max). ' f'But received {scale}') if backend not in ['cv2', 'pillow']: raise ValueError(f'backend: {backend} is not supported for resize.' 'Supported backends are "cv2", "pillow"') self.interpolation = interpolation self.scale = scale self.ratio = ratio self.backend = backend @staticmethod def get_params(img, scale, ratio): """Get parameters for ``crop`` for a random sized crop. Args: img (ndarray): Image to be cropped. scale (tuple): Range of the random size of the cropped image compared to the original image size. ratio (tuple): Range of the random aspect ratio of the cropped image compared to the original image area. Returns: tuple: Params (xmin, ymin, target_height, target_width) to be passed to ``crop`` for a random sized crop. """ height = img.shape[0] width = img.shape[1] area = height * width for _ in range(10): target_area = random.uniform(*scale) * area log_ratio = (math.log(ratio[0]), math.log(ratio[1])) aspect_ratio = math.exp(random.uniform(*log_ratio)) target_width = int(round(math.sqrt(target_area * aspect_ratio))) target_height = int(round(math.sqrt(target_area / aspect_ratio))) if 0 < target_width <= width and 0 < target_height <= height: xmin = random.randint(0, height - target_height) ymin = random.randint(0, width - target_width) return xmin, ymin, target_height, target_width # Fallback to central crop in_ratio = float(width) / float(height) if in_ratio < min(ratio): target_width = width target_height = int(round(target_width / min(ratio))) elif in_ratio > max(ratio): target_height = height target_width = int(round(target_height * max(ratio))) else: # whole image target_width = width target_height = height xmin = (height - target_height) // 2 ymin = (width - target_width) // 2 return xmin, ymin, target_height, target_width def __call__(self, results): """ Args: img (ndarray): Image to be cropped and resized. Returns: ndarray: Randomly cropped and resized image. """ for key in results.get('img_fields', ['img']): img = results[key] xmin, ymin, target_height, target_width = self.get_params( img, self.scale, self.ratio) img = mmcv.imcrop( img, np.array([ ymin, xmin, ymin + target_width - 1, xmin + target_height - 1 ])) results[key] = mmcv.imresize( img, tuple(self.size[::-1]), interpolation=self.interpolation, backend=self.backend) return results def __repr__(self): format_string = self.__class__.__name__ + f'(size={self.size}' format_string += f', scale={tuple(round(s, 4) for s in self.scale)}' format_string += f', ratio={tuple(round(r, 4) for r in self.ratio)}' format_string += f', interpolation={self.interpolation})' return format_string @PIPELINES.register_module() class RandomGrayscale(object): """Randomly convert image to grayscale with a probability of gray_prob. Args: gray_prob (float): Probability that image should be converted to grayscale. Default: 0.1. Returns: ndarray: Grayscale version of the input image with probability gray_prob and unchanged with probability (1-gray_prob). - If input image is 1 channel: grayscale version is 1 channel. - If input image is 3 channel: grayscale version is 3 channel with r == g == b. """ def __init__(self, gray_prob=0.1): self.gray_prob = gray_prob def __call__(self, results): """ Args: img (ndarray): Image to be converted to grayscale. Returns: ndarray: Randomly grayscaled image. """ for key in results.get('img_fields', ['img']): img = results[key] num_output_channels = img.shape[2] if random.random() < self.gray_prob: if num_output_channels > 1: img = mmcv.rgb2gray(img)[:, :, None] results[key] = np.dstack( [img for _ in range(num_output_channels)]) return results results[key] = img return results def __repr__(self): return self.__class__.__name__ + f'(gray_prob={self.gray_prob})' @PIPELINES.register_module() class RandomFlip(object): """Flip the image randomly. Flip the image randomly based on flip probaility and flip direction. Args: flip_prob (float): probability of the image being flipped. Default: 0.5 direction (str): The flipping direction. Options are 'horizontal' and 'vertical'. Default: 'horizontal'. """ def __init__(self, flip_prob=0.5, direction='horizontal'): assert 0 <= flip_prob <= 1 assert direction in ['horizontal', 'vertical'] self.flip_prob = flip_prob self.direction = direction def __call__(self, results): """Call function to flip image. Args: results (dict): Result dict from loading pipeline. Returns: dict: Flipped results, 'flip', 'flip_direction' keys are added into result dict. """ flip = True if np.random.rand() < self.flip_prob else False results['flip'] = flip results['flip_direction'] = self.direction if results['flip']: # flip image for key in results.get('img_fields', ['img']): results[key] = mmcv.imflip( results[key], direction=results['flip_direction']) return results def __repr__(self): return self.__class__.__name__ + f'(flip_prob={self.flip_prob})' @PIPELINES.register_module() class Resize(object): """Resize images. Args: size (int | tuple): Images scales for resizing (h, w). When size is int, the default behavior is to resize an image to (size, size). When size is tuple and the second value is -1, the short edge of an image is resized to its first value. For example, when size is 224, the image is resized to 224x224. When size is (224, -1), the short side is resized to 224 and the other side is computed based on the short side, maintaining the aspect ratio. interpolation (str): Interpolation method, accepted values are "nearest", "bilinear", "bicubic", "area", "lanczos". More details can be found in `mmcv.image.geometric`. backend (str): The image resize backend type, accpeted values are `cv2` and `pillow`. Default: `cv2`. """ def __init__(self, size, interpolation='bilinear', backend='cv2'): assert isinstance(size, int) or (isinstance(size, tuple) and len(size) == 2) self.resize_w_short_side = False if isinstance(size, int): assert size > 0 size = (size, size) else: assert size[0] > 0 and (size[1] > 0 or size[1] == -1) if size[1] == -1: self.resize_w_short_side = True assert interpolation in ('nearest', 'bilinear', 'bicubic', 'area', 'lanczos') if backend not in ['cv2', 'pillow']: raise ValueError(f'backend: {backend} is not supported for resize.' 'Supported backends are "cv2", "pillow"') self.size = size self.interpolation = interpolation self.backend = backend def _resize_img(self, results): for key in results.get('img_fields', ['img']): img = results[key] ignore_resize = False if self.resize_w_short_side: h, w = img.shape[:2] short_side = self.size[0] if (w <= h and w == short_side) or (h <= w and h == short_side): ignore_resize = True else: if w < h: width = short_side height = int(short_side * h / w) else: height = short_side width = int(short_side * w / h) else: height, width = self.size if not ignore_resize: img = mmcv.imresize( img, size=(width, height), interpolation=self.interpolation, return_scale=False, backend=self.backend) results[key] = img results['img_shape'] = img.shape def __call__(self, results): self._resize_img(results) return results def __repr__(self): repr_str = self.__class__.__name__ repr_str += f'(size={self.size}, ' repr_str += f'interpolation={self.interpolation})' return repr_str @PIPELINES.register_module() class CenterCrop(object): """Center crop the image. Args: crop_size (int | tuple): Expected size after cropping, (h, w). Notes: If the image is smaller than the crop size, return the original image """ def __init__(self, crop_size): assert isinstance(crop_size, int) or (isinstance(crop_size, tuple) and len(crop_size) == 2) if isinstance(crop_size, int): crop_size = (crop_size, crop_size) assert crop_size[0] > 0 and crop_size[1] > 0 self.crop_size = crop_size def __call__(self, results): crop_height, crop_width = self.crop_size[0], self.crop_size[1] for key in results.get('img_fields', ['img']): img = results[key] # img.shape has length 2 for grayscale, length 3 for color img_height, img_width = img.shape[:2] y1 = max(0, int(round((img_height - crop_height) / 2.))) x1 = max(0, int(round((img_width - crop_width) / 2.))) y2 = min(img_height, y1 + crop_height) - 1 x2 = min(img_width, x1 + crop_width) - 1 # crop the image img = mmcv.imcrop(img, bboxes=np.array([x1, y1, x2, y2])) img_shape = img.shape results[key] = img results['img_shape'] = img_shape return results def __repr__(self): return self.__class__.__name__ + f'(crop_size={self.crop_size})' @PIPELINES.register_module() class Normalize(object): """Normalize the image. Args: mean (sequence): Mean values of 3 channels. std (sequence): Std values of 3 channels. to_rgb (bool): Whether to convert the image from BGR to RGB, default is true. """ def __init__(self, mean, std, to_rgb=True): self.mean = np.array(mean, dtype=np.float32) self.std = np.array(std, dtype=np.float32) self.to_rgb = to_rgb def __call__(self, results): for key in results.get('img_fields', ['img']): results[key] = mmcv.imnormalize(results[key], self.mean, self.std, self.to_rgb) results['img_norm_cfg'] = dict( mean=self.mean, std=self.std, to_rgb=self.to_rgb) return results def __repr__(self): repr_str = self.__class__.__name__ repr_str += f'(mean={list(self.mean)}, ' repr_str += f'std={list(self.std)}, ' repr_str += f'to_rgb={self.to_rgb})' return repr_str @PIPELINES.register_module() class Albu(object): """Albumentation augmentation. Adds custom transformations from Albumentations library. Please, visit `https://albumentations.readthedocs.io` to get more information. An example of ``transforms`` is as followed: .. code-block:: [ dict( type='ShiftScaleRotate', shift_limit=0.0625, scale_limit=0.0, rotate_limit=0, interpolation=1, p=0.5), dict( type='RandomBrightnessContrast', brightness_limit=[0.1, 0.3], contrast_limit=[0.1, 0.3], p=0.2), dict(type='ChannelShuffle', p=0.1), dict( type='OneOf', transforms=[ dict(type='Blur', blur_limit=3, p=1.0), dict(type='MedianBlur', blur_limit=3, p=1.0) ], p=0.1), ] Args: transforms (list[dict]): A list of albu transformations keymap (dict): Contains {'input key':'albumentation-style key'} """ def __init__(self, transforms, keymap=None, update_pad_shape=False): if Compose is None: raise RuntimeError('albumentations is not installed') self.transforms = transforms self.filter_lost_elements = False self.update_pad_shape = update_pad_shape self.aug = Compose([self.albu_builder(t) for t in self.transforms]) if not keymap: self.keymap_to_albu = { 'img': 'image', } else: self.keymap_to_albu = keymap self.keymap_back = {v: k for k, v in self.keymap_to_albu.items()} def albu_builder(self, cfg): """Import a module from albumentations. It inherits some of :func:`build_from_cfg` logic. Args: cfg (dict): Config dict. It should at least contain the key "type". Returns: obj: The constructed object. """ assert isinstance(cfg, dict) and 'type' in cfg args = cfg.copy() obj_type = args.pop('type') if mmcv.is_str(obj_type): if albumentations is None: raise RuntimeError('albumentations is not installed') obj_cls = getattr(albumentations, obj_type) elif inspect.isclass(obj_type): obj_cls = obj_type else: raise TypeError( f'type must be a str or valid type, but got {type(obj_type)}') if 'transforms' in args: args['transforms'] = [ self.albu_builder(transform) for transform in args['transforms'] ] return obj_cls(**args) @staticmethod def mapper(d, keymap): """Dictionary mapper. Renames keys according to keymap provided. Args: d (dict): old dict keymap (dict): {'old_key':'new_key'} Returns: dict: new dict. """ updated_dict = {} for k, v in zip(d.keys(), d.values()): new_k = keymap.get(k, k) updated_dict[new_k] = d[k] return updated_dict def __call__(self, results): # dict to albumentations format results = self.mapper(results, self.keymap_to_albu) results = self.aug(**results) if 'gt_labels' in results: if isinstance(results['gt_labels'], list): results['gt_labels'] = np.array(results['gt_labels']) results['gt_labels'] = results['gt_labels'].astype(np.int64) # back to the original format results = self.mapper(results, self.keymap_back) # update final shape if self.update_pad_shape: results['pad_shape'] = results['img'].shape return results def __repr__(self): repr_str = self.__class__.__name__ + f'(transforms={self.transforms})' return repr_str