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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], # use bbox area
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]:
# noinspection PyProtectedMember
return self.image_descriptions[image_id]._asdict()
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