Spaces:
Running
Running
| import logging | |
| import os | |
| from fvcore.common.timer import Timer | |
| from detectron2.structures import BoxMode | |
| from fvcore.common.file_io import PathManager | |
| from detectron2.data import DatasetCatalog, MetadataCatalog | |
| from lvis import LVIS | |
| logger = logging.getLogger(__name__) | |
| __all__ = ["load_o365_json", "register_o365_instances"] | |
| def register_o365_instances(name, metadata, json_file, image_root): | |
| DatasetCatalog.register(name, lambda: load_o365_json( | |
| json_file, image_root, name)) | |
| MetadataCatalog.get(name).set( | |
| json_file=json_file, image_root=image_root, | |
| evaluator_type="lvis", **metadata | |
| ) | |
| def get_o365_meta(): | |
| categories = [{'supercategory': 'object', 'id': 1, 'name': 'object'}] | |
| o365_categories = sorted(categories, key=lambda x: x["id"]) | |
| thing_classes = [k["name"] for k in o365_categories] | |
| meta = {"thing_classes": thing_classes} | |
| return meta | |
| def load_o365_json(json_file, image_root, dataset_name=None): | |
| ''' | |
| Load Object365 class name text for object description for GRiT | |
| ''' | |
| json_file = PathManager.get_local_path(json_file) | |
| timer = Timer() | |
| lvis_api = LVIS(json_file) | |
| if timer.seconds() > 1: | |
| logger.info("Loading {} takes {:.2f} seconds.".format( | |
| json_file, timer.seconds())) | |
| class_names = {} | |
| sort_cat = sorted(lvis_api.dataset['categories'], key=lambda x: x['id']) | |
| for x in sort_cat: | |
| if '/' in x['name']: | |
| text = '' | |
| for xx in x['name'].split('/'): | |
| text += xx | |
| text += ' ' | |
| text = text[:-1] | |
| else: | |
| text = x['name'] | |
| class_names[x['id']] = text | |
| img_ids = sorted(lvis_api.imgs.keys()) | |
| imgs = lvis_api.load_imgs(img_ids) | |
| anns = [lvis_api.img_ann_map[img_id] for img_id in img_ids] | |
| ann_ids = [ann["id"] for anns_per_image in anns for ann in anns_per_image] | |
| assert len(set(ann_ids)) == len(ann_ids), \ | |
| "Annotation ids in '{}' are not unique".format(json_file) | |
| imgs_anns = list(zip(imgs, anns)) | |
| logger.info("Loaded {} images in the LVIS v1 format from {}".format( | |
| len(imgs_anns), json_file)) | |
| dataset_dicts = [] | |
| for (img_dict, anno_dict_list) in imgs_anns: | |
| record = {} | |
| if "file_name" in img_dict: | |
| file_name = img_dict["file_name"] | |
| record["file_name"] = os.path.join(image_root, file_name) | |
| record["height"] = int(img_dict["height"]) | |
| record["width"] = int(img_dict["width"]) | |
| image_id = record["image_id"] = img_dict["id"] | |
| objs = [] | |
| for anno in anno_dict_list: | |
| assert anno["image_id"] == image_id | |
| if anno.get('iscrowd', 0) > 0: | |
| continue | |
| obj = {"bbox": anno["bbox"], "bbox_mode": BoxMode.XYWH_ABS} | |
| obj["category_id"] = 0 | |
| obj["object_description"] = class_names[anno['category_id']] | |
| objs.append(obj) | |
| record["annotations"] = objs | |
| if len(record["annotations"]) == 0: | |
| continue | |
| record["task"] = "ObjectDet" | |
| dataset_dicts.append(record) | |
| return dataset_dicts | |
| _CUSTOM_SPLITS_LVIS = { | |
| "object365_train": ("object365/images/train/", "object365/annotations/train_v1.json"), | |
| } | |
| for key, (image_root, json_file) in _CUSTOM_SPLITS_LVIS.items(): | |
| register_o365_instances( | |
| key, | |
| get_o365_meta(), | |
| os.path.join("datasets", json_file) if "://" not in json_file else json_file, | |
| os.path.join("datasets", image_root), | |
| ) |