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| # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved | |
| """ | |
| Transforms and data augmentation for both image + bbox. | |
| """ | |
| import os | |
| import random | |
| import PIL | |
| import torch | |
| import torchvision.transforms as T | |
| import torchvision.transforms.functional as F | |
| from GroundingDINO.groundingdino.util.box_ops import box_xyxy_to_cxcywh | |
| from GroundingDINO.groundingdino.util.misc import interpolate | |
| def crop(image, target, region): | |
| cropped_image = F.crop(image, *region) | |
| target = target.copy() | |
| i, j, h, w = region | |
| # should we do something wrt the original size? | |
| target["size"] = torch.tensor([h, w]) | |
| fields = ["labels", "area", "iscrowd", "positive_map"] | |
| if "boxes" in target: | |
| boxes = target["boxes"] | |
| max_size = torch.as_tensor([w, h], dtype=torch.float32) | |
| cropped_boxes = boxes - torch.as_tensor([j, i, j, i]) | |
| cropped_boxes = torch.min(cropped_boxes.reshape(-1, 2, 2), max_size) | |
| cropped_boxes = cropped_boxes.clamp(min=0) | |
| area = (cropped_boxes[:, 1, :] - cropped_boxes[:, 0, :]).prod(dim=1) | |
| target["boxes"] = cropped_boxes.reshape(-1, 4) | |
| target["area"] = area | |
| fields.append("boxes") | |
| if "masks" in target: | |
| # FIXME should we update the area here if there are no boxes? | |
| target["masks"] = target["masks"][:, i : i + h, j : j + w] | |
| fields.append("masks") | |
| # remove elements for which the boxes or masks that have zero area | |
| if "boxes" in target or "masks" in target: | |
| # favor boxes selection when defining which elements to keep | |
| # this is compatible with previous implementation | |
| if "boxes" in target: | |
| cropped_boxes = target["boxes"].reshape(-1, 2, 2) | |
| keep = torch.all(cropped_boxes[:, 1, :] > cropped_boxes[:, 0, :], dim=1) | |
| else: | |
| keep = target["masks"].flatten(1).any(1) | |
| for field in fields: | |
| if field in target: | |
| target[field] = target[field][keep] | |
| if os.environ.get("IPDB_SHILONG_DEBUG", None) == "INFO": | |
| # for debug and visualization only. | |
| if "strings_positive" in target: | |
| target["strings_positive"] = [ | |
| _i for _i, _j in zip(target["strings_positive"], keep) if _j | |
| ] | |
| return cropped_image, target | |
| def hflip(image, target): | |
| flipped_image = F.hflip(image) | |
| w, h = image.size | |
| target = target.copy() | |
| if "boxes" in target: | |
| boxes = target["boxes"] | |
| boxes = boxes[:, [2, 1, 0, 3]] * torch.as_tensor([-1, 1, -1, 1]) + torch.as_tensor( | |
| [w, 0, w, 0] | |
| ) | |
| target["boxes"] = boxes | |
| if "masks" in target: | |
| target["masks"] = target["masks"].flip(-1) | |
| return flipped_image, target | |
| def resize(image, target, size, max_size=None): | |
| # size can be min_size (scalar) or (w, h) tuple | |
| def get_size_with_aspect_ratio(image_size, size, max_size=None): | |
| w, h = image_size | |
| if max_size is not None: | |
| min_original_size = float(min((w, h))) | |
| max_original_size = float(max((w, h))) | |
| if max_original_size / min_original_size * size > max_size: | |
| size = int(round(max_size * min_original_size / max_original_size)) | |
| if (w <= h and w == size) or (h <= w and h == size): | |
| return (h, w) | |
| if w < h: | |
| ow = size | |
| oh = int(size * h / w) | |
| else: | |
| oh = size | |
| ow = int(size * w / h) | |
| return (oh, ow) | |
| def get_size(image_size, size, max_size=None): | |
| if isinstance(size, (list, tuple)): | |
| return size[::-1] | |
| else: | |
| return get_size_with_aspect_ratio(image_size, size, max_size) | |
| size = get_size(image.size, size, max_size) | |
| rescaled_image = F.resize(image, size) | |
| if target is None: | |
| return rescaled_image, None | |
| ratios = tuple(float(s) / float(s_orig) for s, s_orig in zip(rescaled_image.size, image.size)) | |
| ratio_width, ratio_height = ratios | |
| target = target.copy() | |
| if "boxes" in target: | |
| boxes = target["boxes"] | |
| scaled_boxes = boxes * torch.as_tensor( | |
| [ratio_width, ratio_height, ratio_width, ratio_height] | |
| ) | |
| target["boxes"] = scaled_boxes | |
| if "area" in target: | |
| area = target["area"] | |
| scaled_area = area * (ratio_width * ratio_height) | |
| target["area"] = scaled_area | |
| h, w = size | |
| target["size"] = torch.tensor([h, w]) | |
| if "masks" in target: | |
| target["masks"] = ( | |
| interpolate(target["masks"][:, None].float(), size, mode="nearest")[:, 0] > 0.5 | |
| ) | |
| return rescaled_image, target | |
| def pad(image, target, padding): | |
| # assumes that we only pad on the bottom right corners | |
| padded_image = F.pad(image, (0, 0, padding[0], padding[1])) | |
| if target is None: | |
| return padded_image, None | |
| target = target.copy() | |
| # should we do something wrt the original size? | |
| target["size"] = torch.tensor(padded_image.size[::-1]) | |
| if "masks" in target: | |
| target["masks"] = torch.nn.functional.pad(target["masks"], (0, padding[0], 0, padding[1])) | |
| return padded_image, target | |
| class ResizeDebug(object): | |
| def __init__(self, size): | |
| self.size = size | |
| def __call__(self, img, target): | |
| return resize(img, target, self.size) | |
| class RandomCrop(object): | |
| def __init__(self, size): | |
| self.size = size | |
| def __call__(self, img, target): | |
| region = T.RandomCrop.get_params(img, self.size) | |
| return crop(img, target, region) | |
| class RandomSizeCrop(object): | |
| def __init__(self, min_size: int, max_size: int, respect_boxes: bool = False): | |
| # respect_boxes: True to keep all boxes | |
| # False to tolerence box filter | |
| self.min_size = min_size | |
| self.max_size = max_size | |
| self.respect_boxes = respect_boxes | |
| def __call__(self, img: PIL.Image.Image, target: dict): | |
| init_boxes = len(target["boxes"]) | |
| max_patience = 10 | |
| for i in range(max_patience): | |
| w = random.randint(self.min_size, min(img.width, self.max_size)) | |
| h = random.randint(self.min_size, min(img.height, self.max_size)) | |
| region = T.RandomCrop.get_params(img, [h, w]) | |
| result_img, result_target = crop(img, target, region) | |
| if ( | |
| not self.respect_boxes | |
| or len(result_target["boxes"]) == init_boxes | |
| or i == max_patience - 1 | |
| ): | |
| return result_img, result_target | |
| return result_img, result_target | |
| class CenterCrop(object): | |
| def __init__(self, size): | |
| self.size = size | |
| def __call__(self, img, target): | |
| image_width, image_height = img.size | |
| crop_height, crop_width = self.size | |
| crop_top = int(round((image_height - crop_height) / 2.0)) | |
| crop_left = int(round((image_width - crop_width) / 2.0)) | |
| return crop(img, target, (crop_top, crop_left, crop_height, crop_width)) | |
| class RandomHorizontalFlip(object): | |
| def __init__(self, p=0.5): | |
| self.p = p | |
| def __call__(self, img, target): | |
| if random.random() < self.p: | |
| return hflip(img, target) | |
| return img, target | |
| class RandomResize(object): | |
| def __init__(self, sizes, max_size=None): | |
| assert isinstance(sizes, (list, tuple)) | |
| self.sizes = sizes | |
| self.max_size = max_size | |
| def __call__(self, img, target=None): | |
| size = random.choice(self.sizes) | |
| return resize(img, target, size, self.max_size) | |
| class RandomPad(object): | |
| def __init__(self, max_pad): | |
| self.max_pad = max_pad | |
| def __call__(self, img, target): | |
| pad_x = random.randint(0, self.max_pad) | |
| pad_y = random.randint(0, self.max_pad) | |
| return pad(img, target, (pad_x, pad_y)) | |
| class RandomSelect(object): | |
| """ | |
| Randomly selects between transforms1 and transforms2, | |
| with probability p for transforms1 and (1 - p) for transforms2 | |
| """ | |
| def __init__(self, transforms1, transforms2, p=0.5): | |
| self.transforms1 = transforms1 | |
| self.transforms2 = transforms2 | |
| self.p = p | |
| def __call__(self, img, target): | |
| if random.random() < self.p: | |
| return self.transforms1(img, target) | |
| return self.transforms2(img, target) | |
| class ToTensor(object): | |
| def __call__(self, img, target): | |
| return F.to_tensor(img), target | |
| class RandomErasing(object): | |
| def __init__(self, *args, **kwargs): | |
| self.eraser = T.RandomErasing(*args, **kwargs) | |
| def __call__(self, img, target): | |
| return self.eraser(img), target | |
| class Normalize(object): | |
| def __init__(self, mean, std): | |
| self.mean = mean | |
| self.std = std | |
| def __call__(self, image, target=None): | |
| image = F.normalize(image, mean=self.mean, std=self.std) | |
| if target is None: | |
| return image, None | |
| target = target.copy() | |
| h, w = image.shape[-2:] | |
| if "boxes" in target: | |
| boxes = target["boxes"] | |
| boxes = box_xyxy_to_cxcywh(boxes) | |
| boxes = boxes / torch.tensor([w, h, w, h], dtype=torch.float32) | |
| target["boxes"] = boxes | |
| return image, target | |
| class Compose(object): | |
| def __init__(self, transforms): | |
| self.transforms = transforms | |
| def __call__(self, image, target): | |
| for t in self.transforms: | |
| image, target = t(image, target) | |
| return image, target | |
| def __repr__(self): | |
| format_string = self.__class__.__name__ + "(" | |
| for t in self.transforms: | |
| format_string += "\n" | |
| format_string += " {0}".format(t) | |
| format_string += "\n)" | |
| return format_string | |