| import json |
| from itertools import chain |
| from pathlib import Path |
| from typing import Iterable, Dict, List, Callable, Any |
| from collections import defaultdict |
|
|
| from tqdm import tqdm |
|
|
| from taming.data.annotated_objects_dataset import AnnotatedObjectsDataset |
| from taming.data.helper_types import Annotation, ImageDescription, Category |
|
|
| COCO_PATH_STRUCTURE = { |
| 'train': { |
| 'top_level': '', |
| 'instances_annotations': 'annotations/instances_train2017.json', |
| 'stuff_annotations': 'annotations/stuff_train2017.json', |
| 'files': 'train2017' |
| }, |
| 'validation': { |
| 'top_level': '', |
| 'instances_annotations': 'annotations/instances_val2017.json', |
| 'stuff_annotations': 'annotations/stuff_val2017.json', |
| 'files': 'val2017' |
| } |
| } |
|
|
|
|
| def load_image_descriptions(description_json: List[Dict]) -> Dict[str, ImageDescription]: |
| return { |
| str(img['id']): ImageDescription( |
| id=img['id'], |
| license=img.get('license'), |
| file_name=img['file_name'], |
| coco_url=img['coco_url'], |
| original_size=(img['width'], img['height']), |
| date_captured=img.get('date_captured'), |
| flickr_url=img.get('flickr_url') |
| ) |
| for img in description_json |
| } |
|
|
|
|
| def load_categories(category_json: Iterable) -> Dict[str, Category]: |
| return {str(cat['id']): Category(id=str(cat['id']), super_category=cat['supercategory'], name=cat['name']) |
| for cat in category_json if cat['name'] != 'other'} |
|
|
|
|
| def load_annotations(annotations_json: List[Dict], image_descriptions: Dict[str, ImageDescription], |
| category_no_for_id: Callable[[str], int], split: str) -> Dict[str, List[Annotation]]: |
| annotations = defaultdict(list) |
| total = sum(len(a) for a in annotations_json) |
| for ann in tqdm(chain(*annotations_json), f'Loading {split} annotations', total=total): |
| image_id = str(ann['image_id']) |
| if image_id not in image_descriptions: |
| raise ValueError(f'image_id [{image_id}] has no image description.') |
| category_id = ann['category_id'] |
| try: |
| category_no = category_no_for_id(str(category_id)) |
| except KeyError: |
| continue |
|
|
| width, height = image_descriptions[image_id].original_size |
| bbox = (ann['bbox'][0] / width, ann['bbox'][1] / height, ann['bbox'][2] / width, ann['bbox'][3] / height) |
|
|
| annotations[image_id].append( |
| Annotation( |
| id=ann['id'], |
| area=bbox[2]*bbox[3], |
| is_group_of=ann['iscrowd'], |
| image_id=ann['image_id'], |
| bbox=bbox, |
| category_id=str(category_id), |
| category_no=category_no |
| ) |
| ) |
| return dict(annotations) |
|
|
|
|
| class AnnotatedObjectsCoco(AnnotatedObjectsDataset): |
| def __init__(self, use_things: bool = True, use_stuff: bool = True, **kwargs): |
| """ |
| @param data_path: is the path to the following folder structure: |
| coco/ |
| ├── annotations |
| │ ├── instances_train2017.json |
| │ ├── instances_val2017.json |
| │ ├── stuff_train2017.json |
| │ └── stuff_val2017.json |
| ├── train2017 |
| │ ├── 000000000009.jpg |
| │ ├── 000000000025.jpg |
| │ └── ... |
| ├── val2017 |
| │ ├── 000000000139.jpg |
| │ ├── 000000000285.jpg |
| │ └── ... |
| @param: split: one of 'train' or 'validation' |
| @param: desired image size (give square images) |
| """ |
| super().__init__(**kwargs) |
| self.use_things = use_things |
| self.use_stuff = use_stuff |
|
|
| with open(self.paths['instances_annotations']) as f: |
| inst_data_json = json.load(f) |
| with open(self.paths['stuff_annotations']) as f: |
| stuff_data_json = json.load(f) |
|
|
| category_jsons = [] |
| annotation_jsons = [] |
| if self.use_things: |
| category_jsons.append(inst_data_json['categories']) |
| annotation_jsons.append(inst_data_json['annotations']) |
| if self.use_stuff: |
| category_jsons.append(stuff_data_json['categories']) |
| annotation_jsons.append(stuff_data_json['annotations']) |
|
|
| self.categories = load_categories(chain(*category_jsons)) |
| self.filter_categories() |
| self.setup_category_id_and_number() |
|
|
| self.image_descriptions = load_image_descriptions(inst_data_json['images']) |
| annotations = load_annotations(annotation_jsons, self.image_descriptions, self.get_category_number, self.split) |
| self.annotations = self.filter_object_number(annotations, self.min_object_area, |
| self.min_objects_per_image, self.max_objects_per_image) |
| self.image_ids = list(self.annotations.keys()) |
| self.clean_up_annotations_and_image_descriptions() |
|
|
| def get_path_structure(self) -> Dict[str, str]: |
| if self.split not in COCO_PATH_STRUCTURE: |
| raise ValueError(f'Split [{self.split} does not exist for COCO data.]') |
| return COCO_PATH_STRUCTURE[self.split] |
|
|
| def get_image_path(self, image_id: str) -> Path: |
| return self.paths['files'].joinpath(self.image_descriptions[str(image_id)].file_name) |
|
|
| def get_image_description(self, image_id: str) -> Dict[str, Any]: |
| |
| return self.image_descriptions[image_id]._asdict() |
|
|
