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| # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved | ||
| import contextlib | ||
| import io | ||
| import logging | ||
| import os | ||
| from fvcore.common.timer import Timer | ||
| from fvcore.common.file_io import PathManager | ||
|
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| from detectron2.structures import BoxMode | ||
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| from detectron2.data import DatasetCatalog, MetadataCatalog | ||
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| """ | ||
| This file contains functions to parse COCO-format text annotations into dicts in "Detectron2 format". | ||
| """ | ||
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| logger = logging.getLogger(__name__) | ||
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| __all__ = ["load_text_json", "register_text_instances"] | ||
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| def register_text_instances(name, metadata, json_file, image_root): | ||
| """ | ||
| Register a dataset in json annotation format for text detection and recognition. | ||
| Args: | ||
| name (str): a name that identifies the dataset, e.g. "lvis_v0.5_train". | ||
| metadata (dict): extra metadata associated with this dataset. It can be an empty dict. | ||
| json_file (str): path to the json instance annotation file. | ||
| image_root (str or path-like): directory which contains all the images. | ||
| """ | ||
| DatasetCatalog.register(name, lambda: load_text_json(json_file, image_root, name)) | ||
| MetadataCatalog.get(name).set( | ||
| json_file=json_file, image_root=image_root, evaluator_type="text", **metadata | ||
| ) | ||
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| def load_text_json(json_file, image_root, dataset_name=None, extra_annotation_keys=None): | ||
| """ | ||
| Load a json file with totaltext annotation format. | ||
| Currently supports text detection and recognition. | ||
| Args: | ||
| json_file (str): full path to the json file in totaltext annotation format. | ||
| image_root (str or path-like): the directory where the images in this json file exists. | ||
| dataset_name (str): the name of the dataset (e.g., coco_2017_train). | ||
| If provided, this function will also put "thing_classes" into | ||
| the metadata associated with this dataset. | ||
| extra_annotation_keys (list[str]): list of per-annotation keys that should also be | ||
| loaded into the dataset dict (besides "iscrowd", "bbox", "keypoints", | ||
| "category_id", "segmentation"). The values for these keys will be returned as-is. | ||
| For example, the densepose annotations are loaded in this way. | ||
| Returns: | ||
| list[dict]: a list of dicts in Detectron2 standard dataset dicts format. (See | ||
| `Using Custom Datasets </tutorials/datasets.html>`_ ) | ||
| Notes: | ||
| 1. This function does not read the image files. | ||
| The results do not have the "image" field. | ||
| """ | ||
| from pycocotools.coco import COCO | ||
|
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||
| timer = Timer() | ||
| json_file = PathManager.get_local_path(json_file) | ||
| with contextlib.redirect_stdout(io.StringIO()): | ||
| coco_api = COCO(json_file) | ||
| if timer.seconds() > 1: | ||
| logger.info("Loading {} takes {:.2f} seconds.".format(json_file, timer.seconds())) | ||
|
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| id_map = None | ||
| if dataset_name is not None: | ||
| meta = MetadataCatalog.get(dataset_name) | ||
| cat_ids = sorted(coco_api.getCatIds()) | ||
| cats = coco_api.loadCats(cat_ids) | ||
| # The categories in a custom json file may not be sorted. | ||
| thing_classes = [c["name"] for c in sorted(cats, key=lambda x: x["id"])] | ||
| meta.thing_classes = thing_classes | ||
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| # In COCO, certain category ids are artificially removed, | ||
| # and by convention they are always ignored. | ||
| # We deal with COCO's id issue and translate | ||
| # the category ids to contiguous ids in [0, 80). | ||
|
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| # It works by looking at the "categories" field in the json, therefore | ||
| # if users' own json also have incontiguous ids, we'll | ||
| # apply this mapping as well but print a warning. | ||
| if not (min(cat_ids) == 1 and max(cat_ids) == len(cat_ids)): | ||
| if "coco" not in dataset_name: | ||
| logger.warning( | ||
| """ | ||
| Category ids in annotations are not in [1, #categories]! We'll apply a mapping for you. | ||
| """ | ||
| ) | ||
| id_map = {v: i for i, v in enumerate(cat_ids)} | ||
| meta.thing_dataset_id_to_contiguous_id = id_map | ||
|
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||
| # sort indices for reproducible results | ||
| img_ids = sorted(coco_api.imgs.keys()) | ||
| # imgs is a list of dicts, each looks something like: | ||
| # {'license': 4, | ||
| # 'url': 'http://farm6.staticflickr.com/5454/9413846304_881d5e5c3b_z.jpg', | ||
| # 'file_name': 'COCO_val2014_000000001268.jpg', | ||
| # 'height': 427, | ||
| # 'width': 640, | ||
| # 'date_captured': '2013-11-17 05:57:24', | ||
| # 'id': 1268} | ||
| imgs = coco_api.loadImgs(img_ids) | ||
| # anns is a list[list[dict]], where each dict is an annotation | ||
| # record for an object. The inner list enumerates the objects in an image | ||
| # and the outer list enumerates over images. Example of anns[0]: | ||
| # [{'segmentation': [[192.81, | ||
| # 247.09, | ||
| # ... | ||
| # 219.03, | ||
| # 249.06]], | ||
| # 'area': 1035.749, | ||
| # 'rec': [84, 72, ... 96], | ||
| # 'bezier_pts': [169.0, 425.0, ..., ] | ||
| # 'iscrowd': 0, | ||
| # 'image_id': 1268, | ||
| # 'bbox': [192.81, 224.8, 74.73, 33.43], | ||
| # 'category_id': 16, | ||
| # 'id': 42986}, | ||
| # ...] | ||
| anns = [coco_api.imgToAnns[img_id] for img_id in img_ids] | ||
|
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| if "minival" not in json_file: | ||
| # The popular valminusminival & minival annotations for COCO2014 contain this bug. | ||
| # However the ratio of buggy annotations there is tiny and does not affect accuracy. | ||
| # Therefore we explicitly white-list them. | ||
| 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 | ||
| ) | ||
|
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| imgs_anns = list(zip(imgs, anns)) | ||
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| logger.info("Loaded {} images in COCO format from {}".format(len(imgs_anns), json_file)) | ||
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| dataset_dicts = [] | ||
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| ann_keys = ["iscrowd", "bbox", "rec", "category_id"] + (extra_annotation_keys or []) | ||
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| num_instances_without_valid_segmentation = 0 | ||
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| for (img_dict, anno_dict_list) in imgs_anns: | ||
| record = {} | ||
| record["file_name"] = os.path.join(image_root, img_dict["file_name"]) | ||
| record["height"] = img_dict["height"] | ||
| record["width"] = img_dict["width"] | ||
| image_id = record["image_id"] = img_dict["id"] | ||
|
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| objs = [] | ||
| for anno in anno_dict_list: | ||
| # Check that the image_id in this annotation is the same as | ||
| # the image_id we're looking at. | ||
| # This fails only when the data parsing logic or the annotation file is buggy. | ||
|
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| # The original COCO valminusminival2014 & minival2014 annotation files | ||
| # actually contains bugs that, together with certain ways of using COCO API, | ||
| # can trigger this assertion. | ||
| assert anno["image_id"] == image_id | ||
|
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| assert anno.get("ignore", 0) == 0, '"ignore" in COCO json file is not supported.' | ||
|
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| obj = {key: anno[key] for key in ann_keys if key in anno} | ||
|
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| segm = anno.get("segmentation", None) | ||
| if segm: # either list[list[float]] or dict(RLE) | ||
| if not isinstance(segm, dict): | ||
| # filter out invalid polygons (< 3 points) | ||
| segm = [poly for poly in segm if len(poly) % 2 == 0 and len(poly) >= 6] | ||
| if len(segm) == 0: | ||
| num_instances_without_valid_segmentation += 1 | ||
| continue # ignore this instance | ||
| obj["segmentation"] = segm | ||
|
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| bezierpts = anno.get("bezier_pts", None) | ||
| # Bezier Points are the control points for BezierAlign Text recognition (BAText) | ||
| if bezierpts: # list[float] | ||
| obj["beziers"] = bezierpts | ||
|
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| text = anno.get("rec", None) | ||
| if text: | ||
| obj["text"] = text | ||
|
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| obj["bbox_mode"] = BoxMode.XYWH_ABS | ||
| if id_map: | ||
| obj["category_id"] = id_map[obj["category_id"]] | ||
| objs.append(obj) | ||
| record["annotations"] = objs | ||
| dataset_dicts.append(record) | ||
|
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| if num_instances_without_valid_segmentation > 0: | ||
| logger.warning( | ||
| "Filtered out {} instances without valid segmentation. " | ||
| "There might be issues in your dataset generation process.".format( | ||
| num_instances_without_valid_segmentation | ||
| ) | ||
| ) | ||
| return dataset_dicts |
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