285 lines
8.7 KiB
Python
285 lines
8.7 KiB
Python
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# ------------------------------------------------------------------------
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# Deformable DETR
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# Copyright (c) 2020 SenseTime. All Rights Reserved.
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# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
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# ------------------------------------------------------------------------
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# Modified from DETR (https://github.com/facebookresearch/detr)
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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
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# ------------------------------------------------------------------------
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"""
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Transforms and data augmentation for both image + bbox.
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"""
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import random
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import PIL
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import torch
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import torchvision.transforms as T
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import torchvision.transforms.functional as F
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from util.box_ops import box_xyxy_to_cxcywh
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from util.misc import interpolate
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def crop(image, target, region):
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cropped_image = F.crop(image, *region)
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target = target.copy()
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i, j, h, w = region
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# should we do something wrt the original size?
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target["size"] = torch.tensor([h, w])
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fields = ["labels", "area", "iscrowd"]
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if "boxes" in target:
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boxes = target["boxes"]
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max_size = torch.as_tensor([w, h], dtype=torch.float32)
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cropped_boxes = boxes - torch.as_tensor([j, i, j, i])
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cropped_boxes = torch.min(cropped_boxes.reshape(-1, 2, 2), max_size)
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cropped_boxes = cropped_boxes.clamp(min=0)
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area = (cropped_boxes[:, 1, :] - cropped_boxes[:, 0, :]).prod(dim=1)
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target["boxes"] = cropped_boxes.reshape(-1, 4)
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target["area"] = area
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fields.append("boxes")
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if "masks" in target:
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# FIXME should we update the area here if there are no boxes?
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target['masks'] = target['masks'][:, i:i + h, j:j + w]
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fields.append("masks")
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# remove elements for which the boxes or masks that have zero area
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if "boxes" in target or "masks" in target:
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# favor boxes selection when defining which elements to keep
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# this is compatible with previous implementation
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if "boxes" in target:
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cropped_boxes = target['boxes'].reshape(-1, 2, 2)
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keep = torch.all(cropped_boxes[:, 1, :] > cropped_boxes[:, 0, :], dim=1)
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else:
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keep = target['masks'].flatten(1).any(1)
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for field in fields:
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target[field] = target[field][keep]
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return cropped_image, target
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def hflip(image, target):
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flipped_image = F.hflip(image)
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w, h = image.size
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target = target.copy()
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if "boxes" in target:
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boxes = target["boxes"]
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boxes = boxes[:, [2, 1, 0, 3]] * torch.as_tensor([-1, 1, -1, 1]) + torch.as_tensor([w, 0, w, 0])
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target["boxes"] = boxes
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if "masks" in target:
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target['masks'] = target['masks'].flip(-1)
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return flipped_image, target
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def resize(image, target, size, max_size=None):
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# size can be min_size (scalar) or (w, h) tuple
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def get_size_with_aspect_ratio(image_size, size, max_size=None):
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w, h = image_size
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if max_size is not None:
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min_original_size = float(min((w, h)))
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max_original_size = float(max((w, h)))
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if max_original_size / min_original_size * size > max_size:
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size = int(round(max_size * min_original_size / max_original_size))
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if (w <= h and w == size) or (h <= w and h == size):
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return (h, w)
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if w < h:
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ow = size
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oh = int(size * h / w)
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else:
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oh = size
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ow = int(size * w / h)
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return (oh, ow)
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def get_size(image_size, size, max_size=None):
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if isinstance(size, (list, tuple)):
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return size[::-1]
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else:
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return get_size_with_aspect_ratio(image_size, size, max_size)
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size = get_size(image.size, size, max_size)
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rescaled_image = F.resize(image, size)
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if target is None:
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return rescaled_image, None
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ratios = tuple(float(s) / float(s_orig) for s, s_orig in zip(rescaled_image.size, image.size))
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ratio_width, ratio_height = ratios
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target = target.copy()
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if "boxes" in target:
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boxes = target["boxes"]
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scaled_boxes = boxes * torch.as_tensor([ratio_width, ratio_height, ratio_width, ratio_height])
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target["boxes"] = scaled_boxes
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if "area" in target:
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area = target["area"]
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scaled_area = area * (ratio_width * ratio_height)
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target["area"] = scaled_area
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h, w = size
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target["size"] = torch.tensor([h, w])
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if "masks" in target:
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target['masks'] = interpolate(
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target['masks'][:, None].float(), size, mode="nearest")[:, 0] > 0.5
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return rescaled_image, target
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def pad(image, target, padding):
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# assumes that we only pad on the bottom right corners
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padded_image = F.pad(image, (0, 0, padding[0], padding[1]))
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if target is None:
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return padded_image, None
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target = target.copy()
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# should we do something wrt the original size?
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target["size"] = torch.tensor(padded_image[::-1])
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if "masks" in target:
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target['masks'] = torch.nn.functional.pad(target['masks'], (0, padding[0], 0, padding[1]))
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return padded_image, target
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class RandomCrop(object):
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def __init__(self, size):
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self.size = size
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def __call__(self, img, target):
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region = T.RandomCrop.get_params(img, self.size)
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return crop(img, target, region)
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class RandomSizeCrop(object):
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def __init__(self, min_size: int, max_size: int):
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self.min_size = min_size
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self.max_size = max_size
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def __call__(self, img: PIL.Image.Image, target: dict):
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w = random.randint(self.min_size, min(img.width, self.max_size))
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h = random.randint(self.min_size, min(img.height, self.max_size))
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region = T.RandomCrop.get_params(img, [h, w])
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return crop(img, target, region)
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class CenterCrop(object):
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def __init__(self, size):
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self.size = size
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def __call__(self, img, target):
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image_width, image_height = img.size
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crop_height, crop_width = self.size
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crop_top = int(round((image_height - crop_height) / 2.))
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crop_left = int(round((image_width - crop_width) / 2.))
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return crop(img, target, (crop_top, crop_left, crop_height, crop_width))
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class RandomHorizontalFlip(object):
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def __init__(self, p=0.5):
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self.p = p
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def __call__(self, img, target):
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if random.random() < self.p:
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return hflip(img, target)
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return img, target
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class RandomResize(object):
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def __init__(self, sizes, max_size=None):
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assert isinstance(sizes, (list, tuple))
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self.sizes = sizes
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self.max_size = max_size
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def __call__(self, img, target=None):
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size = random.choice(self.sizes)
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return resize(img, target, size, self.max_size)
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class RandomPad(object):
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def __init__(self, max_pad):
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self.max_pad = max_pad
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def __call__(self, img, target):
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pad_x = random.randint(0, self.max_pad)
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pad_y = random.randint(0, self.max_pad)
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return pad(img, target, (pad_x, pad_y))
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class RandomSelect(object):
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"""
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Randomly selects between transforms1 and transforms2,
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with probability p for transforms1 and (1 - p) for transforms2
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"""
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def __init__(self, transforms1, transforms2, p=0.5):
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self.transforms1 = transforms1
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self.transforms2 = transforms2
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self.p = p
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def __call__(self, img, target):
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if random.random() < self.p:
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return self.transforms1(img, target)
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return self.transforms2(img, target)
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class ToTensor(object):
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def __call__(self, img, target):
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return F.to_tensor(img), target
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class RandomErasing(object):
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def __init__(self, *args, **kwargs):
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self.eraser = T.RandomErasing(*args, **kwargs)
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def __call__(self, img, target):
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return self.eraser(img), target
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class Normalize(object):
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def __init__(self, mean, std):
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self.mean = mean
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self.std = std
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def __call__(self, image, target=None):
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image = F.normalize(image, mean=self.mean, std=self.std)
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if target is None:
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return image, None
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target = target.copy()
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h, w = image.shape[-2:]
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if "boxes" in target:
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boxes = target["boxes"]
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boxes = box_xyxy_to_cxcywh(boxes)
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boxes = boxes / torch.tensor([w, h, w, h], dtype=torch.float32)
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target["boxes"] = boxes
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return image, target
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class Compose(object):
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def __init__(self, transforms):
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self.transforms = transforms
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def __call__(self, image, target):
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for t in self.transforms:
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image, target = t(image, target)
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return image, target
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def __repr__(self):
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format_string = self.__class__.__name__ + "("
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for t in self.transforms:
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format_string += "\n"
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format_string += " {0}".format(t)
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format_string += "\n)"
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return format_string
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