Load a json file with COCO's instances annotation format. Currently supports instance detection, instance segmentation, and person keypoints annotations. Args: json_file (str): full path to the json file in COCO instances annotation format. image_root (str or path-l
(json_file, image_root, dataset_name=None, extra_annotation_keys=None)
| 26 | |
| 27 | |
| 28 | def load_crowdhuman_json(json_file, image_root, dataset_name=None, extra_annotation_keys=None): |
| 29 | """ |
| 30 | Load a json file with COCO's instances annotation format. |
| 31 | Currently supports instance detection, instance segmentation, |
| 32 | and person keypoints annotations. |
| 33 | |
| 34 | Args: |
| 35 | json_file (str): full path to the json file in COCO instances annotation format. |
| 36 | image_root (str or path-like): the directory where the images in this json file exists. |
| 37 | dataset_name (str): the name of the dataset (e.g., coco_2017_train). |
| 38 | If provided, this function will also put "thing_classes" into |
| 39 | the metadata associated with this dataset. |
| 40 | extra_annotation_keys (list[str]): list of per-annotation keys that should also be |
| 41 | loaded into the dataset dict (besides "iscrowd", "bbox", "keypoints", |
| 42 | "category_id", "segmentation"). The values for these keys will be returned as-is. |
| 43 | For example, the densepose annotations are loaded in this way. |
| 44 | |
| 45 | Returns: |
| 46 | list[dict]: a list of dicts in Detectron2 standard dataset dicts format. (See |
| 47 | `Using Custom Datasets </tutorials/datasets.html>`_ ) |
| 48 | |
| 49 | Notes: |
| 50 | 1. This function does not read the image files. |
| 51 | The results do not have the "image" field. |
| 52 | """ |
| 53 | from pycocotools.coco import COCO |
| 54 | |
| 55 | timer = Timer() |
| 56 | json_file = PathManager.get_local_path(json_file) |
| 57 | with contextlib.redirect_stdout(io.StringIO()): |
| 58 | coco_api = COCO(json_file) |
| 59 | if timer.seconds() > 1: |
| 60 | logger.info("Loading {} takes {:.2f} seconds.".format(json_file, timer.seconds())) |
| 61 | |
| 62 | id_map = None |
| 63 | if dataset_name is not None: |
| 64 | meta = MetadataCatalog.get(dataset_name) |
| 65 | cat_ids = sorted(coco_api.getCatIds()) |
| 66 | cats = coco_api.loadCats(cat_ids) |
| 67 | # The categories in a custom json file may not be sorted. |
| 68 | thing_classes = [c["name"] for c in sorted(cats, key=lambda x: x["id"])] |
| 69 | meta.thing_classes = thing_classes |
| 70 | |
| 71 | # In COCO, certain category ids are artificially removed, |
| 72 | # and by convention they are always ignored. |
| 73 | # We deal with COCO's id issue and translate |
| 74 | # the category ids to contiguous ids in [0, 80). |
| 75 | |
| 76 | # It works by looking at the "categories" field in the json, therefore |
| 77 | # if users' own json also have incontiguous ids, we'll |
| 78 | # apply this mapping as well but print a warning. |
| 79 | if not (min(cat_ids) == 1 and max(cat_ids) == len(cat_ids)): |
| 80 | if "coco" not in dataset_name: |
| 81 | logger.warning( |
| 82 | """ |
| 83 | Category ids in annotations are not in [1, #categories]! We'll apply a mapping for you. |
| 84 | """ |
| 85 | ) |
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