460 lines
17 KiB
Python
460 lines
17 KiB
Python
import math
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import random
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import mmcv
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import numpy as np
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from ..builder import PIPELINES
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@PIPELINES.register_module()
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class RandomCrop(object):
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"""Crop the given Image at a random location.
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Args:
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size (sequence or int): Desired output size of the crop. If size is an
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int instead of sequence like (h, w), a square crop (size, size) is
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made.
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padding (int or sequence, optional): Optional padding on each border
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of the image. If a sequence of length 4 is provided, it is used to
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pad left, top, right, bottom borders respectively. If a sequence
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of length 2 is provided, it is used to pad left/right, top/bottom
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borders, respectively. Default: None, which means no padding.
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pad_if_needed (boolean): It will pad the image if smaller than the
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desired size to avoid raising an exception. Since cropping is done
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after padding, the padding seems to be done at a random offset.
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Default: False.
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pad_val (Number | Sequence[Number]): Pixel pad_val value for constant
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fill. If a tuple of length 3, it is used to pad_val R, G, B
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channels respectively. Default: 0.
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padding_mode (str): Type of padding. Should be: constant, edge,
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reflect or symmetric. Default: constant.
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-constant: Pads with a constant value, this value is specified
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with pad_val.
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-edge: pads with the last value at the edge of the image.
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-reflect: Pads with reflection of image without repeating the
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last value on the edge. For example, padding [1, 2, 3, 4]
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with 2 elements on both sides in reflect mode will result
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in [3, 2, 1, 2, 3, 4, 3, 2].
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-symmetric: Pads with reflection of image repeating the last
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value on the edge. For example, padding [1, 2, 3, 4] with
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2 elements on both sides in symmetric mode will result in
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[2, 1, 1, 2, 3, 4, 4, 3].
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"""
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def __init__(self,
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size,
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padding=None,
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pad_if_needed=False,
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pad_val=0,
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padding_mode='constant'):
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if isinstance(size, (tuple, list)):
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self.size = size
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else:
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self.size = (size, size)
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# check padding mode
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assert padding_mode in ['constant', 'edge', 'reflect', 'symmetric']
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self.padding = padding
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self.pad_if_needed = pad_if_needed
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self.pad_val = pad_val
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self.padding_mode = padding_mode
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@staticmethod
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def get_params(img, output_size):
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"""Get parameters for ``crop`` for a random crop.
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Args:
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img (ndarray): Image to be cropped.
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output_size (tuple): Expected output size of the crop.
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Returns:
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tuple: Params (xmin, ymin, target_height, target_width) to be
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passed to ``crop`` for random crop.
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"""
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height = img.shape[0]
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width = img.shape[1]
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target_height, target_width = output_size
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if width == target_width and height == target_height:
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return 0, 0, height, width
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xmin = random.randint(0, height - target_height)
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ymin = random.randint(0, width - target_width)
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return xmin, ymin, target_height, target_width
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def __call__(self, results):
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"""
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Args:
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img (ndarray): Image to be cropped.
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"""
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for key in results.get('img_fields', ['img']):
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img = results[key]
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if self.padding is not None:
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img = mmcv.impad(
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img, padding=self.padding, pad_val=self.pad_val)
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# pad the height if needed
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if self.pad_if_needed and img.shape[0] < self.size[0]:
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img = mmcv.impad(
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img,
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padding=(0, self.size[0] - img.shape[0], 0,
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self.size[0] - img.shape[0]),
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pad_val=self.pad_val,
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padding_mode=self.padding_mode)
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# pad the width if needed
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if self.pad_if_needed and img.shape[1] < self.size[1]:
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img = mmcv.impad(
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img,
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padding=(self.size[1] - img.shape[1], 0,
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self.size[1] - img.shape[1], 0),
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pad_val=self.pad_val,
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padding_mode=self.padding_mode)
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xmin, ymin, height, width = self.get_params(img, self.size)
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results[key] = mmcv.imcrop(
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img,
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np.array([ymin, xmin, ymin + width - 1, xmin + height - 1]))
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return results
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def __repr__(self):
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return (self.__class__.__name__ +
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f'(size={self.size}, padding={self.padding})')
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@PIPELINES.register_module()
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class RandomResizedCrop(object):
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"""Crop the given image to random size and aspect ratio.
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A crop of random size (default: of 0.08 to 1.0) of the original size and a
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random aspect ratio (default: of 3/4 to 4/3) of the original aspect ratio
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is made. This crop is finally resized to given size.
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Args:
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size (sequence or int): Desired output size of the crop. If size is an
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int instead of sequence like (h, w), a square crop (size, size) is
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made.
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scale (tuple): Range of the random size of the cropped image compared
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to the original image. Default: (0.08, 1.0).
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ratio (tuple): Range of the random aspect ratio of the cropped image
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compared to the original image. Default: (3. / 4., 4. / 3.).
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interpolation (str): Interpolation method, accepted values are
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'nearest', 'bilinear', 'bicubic', 'area', 'lanczos'. Default:
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'bilinear'.
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backend (str): The image resize backend type, accpeted values are
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`cv2` and `pillow`. Default: `cv2`.
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"""
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def __init__(self,
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size,
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scale=(0.08, 1.0),
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ratio=(3. / 4., 4. / 3.),
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interpolation='bilinear',
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backend='cv2'):
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if isinstance(size, (tuple, list)):
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self.size = size
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else:
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self.size = (size, size)
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if (scale[0] > scale[1]) or (ratio[0] > ratio[1]):
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raise ValueError("range should be of kind (min, max). "
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f"But received {scale}")
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if backend not in ['cv2', 'pillow']:
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raise ValueError(f'backend: {backend} is not supported for resize.'
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'Supported backends are "cv2", "pillow"')
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self.interpolation = interpolation
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self.scale = scale
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self.ratio = ratio
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self.backend = backend
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@staticmethod
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def get_params(img, scale, ratio):
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"""Get parameters for ``crop`` for a random sized crop.
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Args:
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img (ndarray): Image to be cropped.
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scale (tuple): Range of the random size of the cropped image
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compared to the original image size.
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ratio (tuple): Range of the random aspect ratio of the cropped
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image compared to the original image area.
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Returns:
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tuple: Params (xmin, ymin, target_height, target_width) to be
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passed to ``crop`` for a random sized crop.
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"""
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height = img.shape[0]
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width = img.shape[1]
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area = height * width
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for _ in range(10):
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target_area = random.uniform(*scale) * area
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log_ratio = (math.log(ratio[0]), math.log(ratio[1]))
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aspect_ratio = math.exp(random.uniform(*log_ratio))
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target_width = int(round(math.sqrt(target_area * aspect_ratio)))
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target_height = int(round(math.sqrt(target_area / aspect_ratio)))
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if 0 < target_width <= width and 0 < target_height <= height:
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xmin = random.randint(0, height - target_height)
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ymin = random.randint(0, width - target_width)
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return xmin, ymin, target_height, target_width
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# Fallback to central crop
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in_ratio = float(width) / float(height)
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if in_ratio < min(ratio):
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target_width = width
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target_height = int(round(target_width / min(ratio)))
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elif in_ratio > max(ratio):
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target_height = height
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target_width = int(round(target_height * max(ratio)))
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else: # whole image
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target_width = width
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target_height = height
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xmin = (height - target_height) // 2
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ymin = (width - target_width) // 2
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return xmin, ymin, target_height, target_width
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def __call__(self, results):
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"""
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Args:
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img (ndarray): Image to be cropped and resized.
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Returns:
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ndarray: Randomly cropped and resized image.
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"""
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for key in results.get('img_fields', ['img']):
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img = results[key]
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xmin, ymin, target_height, target_width = self.get_params(
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img, self.scale, self.ratio)
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img = mmcv.imcrop(
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img,
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np.array([
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ymin, xmin, ymin + target_width - 1,
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xmin + target_height - 1
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]))
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results[key] = mmcv.imresize(
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img,
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tuple(self.size[::-1]),
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interpolation=self.interpolation,
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backend=self.backend)
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return results
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def __repr__(self):
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format_string = self.__class__.__name__ + f'(size={self.size}'
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format_string += f', scale={tuple(round(s, 4) for s in self.scale)}'
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format_string += f', ratio={tuple(round(r, 4) for r in self.ratio)}'
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format_string += f', interpolation={self.interpolation})'
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return format_string
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@PIPELINES.register_module()
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class RandomGrayscale(object):
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"""Randomly convert image to grayscale with a probability of gray_prob.
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Args:
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gray_prob (float): Probability that image should be converted to
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grayscale. Default: 0.1.
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Returns:
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ndarray: Grayscale version of the input image with probability
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gray_prob and unchanged with probability (1-gray_prob).
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- If input image is 1 channel: grayscale version is 1 channel.
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- If input image is 3 channel: grayscale version is 3 channel
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with r == g == b.
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"""
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def __init__(self, gray_prob=0.1):
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self.gray_prob = gray_prob
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def __call__(self, results):
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"""
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Args:
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img (ndarray): Image to be converted to grayscale.
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Returns:
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ndarray: Randomly grayscaled image.
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"""
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for key in results.get('img_fields', ['img']):
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img = results[key]
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num_output_channels = img.shape[2]
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if random.random() < self.gray_prob:
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if num_output_channels > 1:
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img = mmcv.rgb2gray(img)[:, :, None]
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results[key] = np.dstack(
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[img for _ in range(num_output_channels)])
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return results
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results[key] = img
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return results
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def __repr__(self):
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return self.__class__.__name__ + f'(gray_prob={self.gray_prob})'
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@PIPELINES.register_module()
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class RandomFlip(object):
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"""Flip the image randomly.
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Flip the image randomly based on flip probaility and flip direction.
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Args:
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flip_prob (float): probability of the image being flipped. Default: 0.5
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direction (str, optional): The flipping direction. Options are
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'horizontal' and 'vertical'. Default: 'horizontal'.
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"""
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def __init__(self, flip_prob=0.5, direction='horizontal'):
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assert 0 <= flip_prob <= 1
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assert direction in ['horizontal', 'vertical']
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self.flip_prob = flip_prob
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self.direction = direction
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def __call__(self, results):
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"""Call function to flip image.
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Args:
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results (dict): Result dict from loading pipeline.
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Returns:
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dict: Flipped results, 'flip', 'flip_direction' keys are added into
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result dict.
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"""
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flip = True if np.random.rand() < self.flip_prob else False
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results['flip'] = flip
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results['flip_direction'] = self.direction
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if results['flip']:
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# flip image
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for key in results.get('img_fields', ['img']):
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results[key] = mmcv.imflip(
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results[key], direction=results['flip_direction'])
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return results
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def __repr__(self):
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return self.__class__.__name__ + f'(flip_prob={self.flip_prob})'
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@PIPELINES.register_module()
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class Resize(object):
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"""Resize images.
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Args:
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size (int | tuple): Images scales for resizing (h, w).
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interpolation (str): Interpolation method, accepted values are
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"nearest", "bilinear", "bicubic", "area", "lanczos".
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More details can be found in `mmcv.image.geometric`.
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backend (str): The image resize backend type, accpeted values are
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`cv2` and `pillow`. Default: `cv2`.
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"""
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def __init__(self, size, interpolation='bilinear', backend='cv2'):
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assert isinstance(size, int) or (isinstance(size, tuple)
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and len(size) == 2)
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if isinstance(size, int):
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size = (size, size)
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assert size[0] > 0 and size[1] > 0
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assert interpolation in ("nearest", "bilinear", "bicubic", "area",
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"lanczos")
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if backend not in ['cv2', 'pillow']:
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raise ValueError(f'backend: {backend} is not supported for resize.'
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'Supported backends are "cv2", "pillow"')
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self.height = size[0]
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self.width = size[1]
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self.size = size
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self.interpolation = interpolation
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self.backend = backend
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def _resize_img(self, results):
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for key in results.get('img_fields', ['img']):
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img = mmcv.imresize(
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results[key],
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size=(self.width, self.height),
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interpolation=self.interpolation,
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return_scale=False,
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backend=self.backend)
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results[key] = img
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results['img_shape'] = img.shape
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def __call__(self, results):
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self._resize_img(results)
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return results
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def __repr__(self):
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repr_str = self.__class__.__name__
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repr_str += f'(size={self.size}, '
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repr_str += f'interpolation={self.interpolation})'
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return repr_str
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@PIPELINES.register_module()
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class CenterCrop(object):
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"""Center crop the image.
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Args:
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crop_size (int | tuple): Expected size after cropping, (h, w).
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Notes:
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If the image is smaller than the crop size, return the original image
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"""
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def __init__(self, crop_size):
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assert isinstance(crop_size, int) or (isinstance(crop_size, tuple)
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and len(crop_size) == 2)
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if isinstance(crop_size, int):
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crop_size = (crop_size, crop_size)
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assert crop_size[0] > 0 and crop_size[1] > 0
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self.crop_size = crop_size
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def __call__(self, results):
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crop_height, crop_width = self.crop_size[0], self.crop_size[1]
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for key in results.get('img_fields', ['img']):
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img = results[key]
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img_height, img_width, _ = img.shape
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y1 = max(0, int(round((img_height - crop_height) / 2.)))
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x1 = max(0, int(round((img_width - crop_width) / 2.)))
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y2 = min(img_height, y1 + crop_height) - 1
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x2 = min(img_width, x1 + crop_width) - 1
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# crop the image
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img = mmcv.imcrop(img, bboxes=np.array([x1, y1, x2, y2]))
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img_shape = img.shape
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results[key] = img
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results['img_shape'] = img_shape
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return results
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def __repr__(self):
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return self.__class__.__name__ + f'(crop_size={self.crop_size})'
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@PIPELINES.register_module()
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class Normalize(object):
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"""Normalize the image.
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Args:
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mean (sequence): Mean values of 3 channels.
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std (sequence): Std values of 3 channels.
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to_rgb (bool): Whether to convert the image from BGR to RGB,
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default is true.
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"""
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def __init__(self, mean, std, to_rgb=True):
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self.mean = np.array(mean, dtype=np.float32)
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self.std = np.array(std, dtype=np.float32)
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self.to_rgb = to_rgb
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def __call__(self, results):
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for key in results.get('img_fields', ['img']):
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results[key] = mmcv.imnormalize(results[key], self.mean, self.std,
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self.to_rgb)
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results['img_norm_cfg'] = dict(
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mean=self.mean, std=self.std, to_rgb=self.to_rgb)
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return results
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def __repr__(self):
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repr_str = self.__class__.__name__
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repr_str += f'(mean={list(self.mean)}, '
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repr_str += f'std={list(self.std)}, '
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repr_str += f'to_rgb={self.to_rgb})'
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return repr_str
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