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| # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved | |
| import json | |
| from pathlib import Path | |
| import numpy as np | |
| import torch | |
| from PIL import Image | |
| from panopticapi.utils import rgb2id | |
| from util.box_ops import masks_to_boxes | |
| from .coco import make_coco_transforms | |
| class CocoPanoptic: | |
| def __init__(self, img_folder, ann_folder, ann_file, transforms=None, return_masks=True): | |
| with open(ann_file, 'r') as f: | |
| self.coco = json.load(f) | |
| # sort 'images' field so that they are aligned with 'annotations' | |
| # i.e., in alphabetical order | |
| self.coco['images'] = sorted(self.coco['images'], key=lambda x: x['id']) | |
| # sanity check | |
| if "annotations" in self.coco: | |
| for img, ann in zip(self.coco['images'], self.coco['annotations']): | |
| assert img['file_name'][:-4] == ann['file_name'][:-4] | |
| self.img_folder = img_folder | |
| self.ann_folder = ann_folder | |
| self.ann_file = ann_file | |
| self.transforms = transforms | |
| self.return_masks = return_masks | |
| def __getitem__(self, idx): | |
| ann_info = self.coco['annotations'][idx] if "annotations" in self.coco else self.coco['images'][idx] | |
| img_path = Path(self.img_folder) / ann_info['file_name'].replace('.png', '.jpg') | |
| ann_path = Path(self.ann_folder) / ann_info['file_name'] | |
| img = Image.open(img_path).convert('RGB') | |
| w, h = img.size | |
| if "segments_info" in ann_info: | |
| masks = np.asarray(Image.open(ann_path), dtype=np.uint32) | |
| masks = rgb2id(masks) | |
| ids = np.array([ann['id'] for ann in ann_info['segments_info']]) | |
| masks = masks == ids[:, None, None] | |
| masks = torch.as_tensor(masks, dtype=torch.uint8) | |
| labels = torch.tensor([ann['category_id'] for ann in ann_info['segments_info']], dtype=torch.int64) | |
| target = {} | |
| target['image_id'] = torch.tensor([ann_info['image_id'] if "image_id" in ann_info else ann_info["id"]]) | |
| if self.return_masks: | |
| target['masks'] = masks | |
| target['labels'] = labels | |
| target["boxes"] = masks_to_boxes(masks) | |
| target['size'] = torch.as_tensor([int(h), int(w)]) | |
| target['orig_size'] = torch.as_tensor([int(h), int(w)]) | |
| if "segments_info" in ann_info: | |
| for name in ['iscrowd', 'area']: | |
| target[name] = torch.tensor([ann[name] for ann in ann_info['segments_info']]) | |
| if self.transforms is not None: | |
| img, target = self.transforms(img, target) | |
| return img, target | |
| def __len__(self): | |
| return len(self.coco['images']) | |
| def get_height_and_width(self, idx): | |
| img_info = self.coco['images'][idx] | |
| height = img_info['height'] | |
| width = img_info['width'] | |
| return height, width | |
| def build(image_set, args): | |
| img_folder_root = Path(args.coco_path) | |
| ann_folder_root = Path(args.coco_panoptic_path) | |
| assert img_folder_root.exists(), f'provided COCO path {img_folder_root} does not exist' | |
| assert ann_folder_root.exists(), f'provided COCO path {ann_folder_root} does not exist' | |
| mode = 'panoptic' | |
| PATHS = { | |
| "train": ("train2017", Path("annotations") / f'{mode}_train2017.json'), | |
| "val": ("val2017", Path("annotations") / f'{mode}_val2017.json'), | |
| } | |
| img_folder, ann_file = PATHS[image_set] | |
| img_folder_path = img_folder_root / img_folder | |
| ann_folder = ann_folder_root / f'{mode}_{img_folder}' | |
| ann_file = ann_folder_root / ann_file | |
| dataset = CocoPanoptic(img_folder_path, ann_folder, ann_file, | |
| transforms=make_coco_transforms(image_set), return_masks=args.masks) | |
| return dataset | |