Source code for

# Copyright (c) Facebook, Inc. and its affiliates.
import logging
import os
from fvcore.common.timer import Timer

from import DatasetCatalog, MetadataCatalog
from detectron2.structures import BoxMode
from detectron2.utils.file_io import PathManager

from .builtin_meta import _get_coco_instances_meta
from .lvis_v0_5_categories import LVIS_CATEGORIES as LVIS_V0_5_CATEGORIES
from .lvis_v1_categories import LVIS_CATEGORIES as LVIS_V1_CATEGORIES

This file contains functions to parse LVIS-format annotations into dicts in the
"Detectron2 format".

logger = logging.getLogger(__name__)

__all__ = ["load_lvis_json", "register_lvis_instances", "get_lvis_instances_meta"]

[docs]def register_lvis_instances(name, metadata, json_file, image_root): """ Register a dataset in LVIS's json annotation format for instance detection and segmentation. 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_lvis_json(json_file, image_root, name)) MetadataCatalog.get(name).set( json_file=json_file, image_root=image_root, evaluator_type="lvis", **metadata )
[docs]def load_lvis_json(json_file, image_root, dataset_name=None, extra_annotation_keys=None): """ Load a json file in LVIS's annotation format. Args: json_file (str): full path to the LVIS json annotation file. image_root (str): the directory where the images in this json file exists. dataset_name (str): the name of the dataset (e.g., "lvis_v0.5_train"). If provided, this function will 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 "bbox", "bbox_mode", "category_id", "segmentation"). The values for these keys will be returned as-is. Returns: list[dict]: a list of dicts in Detectron2 standard 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 lvis import LVIS json_file = PathManager.get_local_path(json_file) timer = Timer() lvis_api = LVIS(json_file) if timer.seconds() > 1:"Loading {} takes {:.2f} seconds.".format(json_file, timer.seconds())) if dataset_name is not None: meta = get_lvis_instances_meta(dataset_name) MetadataCatalog.get(dataset_name).set(**meta) # sort indices for reproducible results img_ids = sorted(lvis_api.imgs.keys()) # imgs is a list of dicts, each looks something like: # {'license': 4, # 'url': '', # 'file_name': 'COCO_val2014_000000001268.jpg', # 'height': 427, # 'width': 640, # 'date_captured': '2013-11-17 05:57:24', # 'id': 1268} imgs = lvis_api.load_imgs(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, # 'image_id': 1268, # 'bbox': [192.81, 224.8, 74.73, 33.43], # 'category_id': 16, # 'id': 42986}, # ...] anns = [lvis_api.img_ann_map[img_id] for img_id in img_ids] # Sanity check that each annotation has a unique id 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))"Loaded {} images in the LVIS format from {}".format(len(imgs_anns), json_file)) if extra_annotation_keys: "The following extra annotation keys will be loaded: {} ".format(extra_annotation_keys) ) else: extra_annotation_keys = [] def get_file_name(img_root, img_dict): # Determine the path including the split folder ("train2017", "val2017", "test2017") from # the coco_url field. Example: # 'coco_url': '' split_folder, file_name = img_dict["coco_url"].split("/")[-2:] return os.path.join(img_root + split_folder, file_name) dataset_dicts = [] for (img_dict, anno_dict_list) in imgs_anns: record = {} record["file_name"] = get_file_name(image_root, img_dict) record["height"] = img_dict["height"] record["width"] = img_dict["width"] record["not_exhaustive_category_ids"] = img_dict.get("not_exhaustive_category_ids", []) record["neg_category_ids"] = img_dict.get("neg_category_ids", []) image_id = record["image_id"] = img_dict["id"] 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. assert anno["image_id"] == image_id obj = {"bbox": anno["bbox"], "bbox_mode": BoxMode.XYWH_ABS} # LVIS data loader can be used to load COCO dataset categories. In this case `meta` # variable will have a field with COCO-specific category mapping. if dataset_name is not None and "thing_dataset_id_to_contiguous_id" in meta: obj["category_id"] = meta["thing_dataset_id_to_contiguous_id"][anno["category_id"]] else: obj["category_id"] = anno["category_id"] - 1 # Convert 1-indexed to 0-indexed segm = anno["segmentation"] # list[list[float]] # filter out invalid polygons (< 3 points) valid_segm = [poly for poly in segm if len(poly) % 2 == 0 and len(poly) >= 6] assert len(segm) == len( valid_segm ), "Annotation contains an invalid polygon with < 3 points" assert len(segm) > 0 obj["segmentation"] = segm for extra_ann_key in extra_annotation_keys: obj[extra_ann_key] = anno[extra_ann_key] objs.append(obj) record["annotations"] = objs dataset_dicts.append(record) return dataset_dicts
[docs]def get_lvis_instances_meta(dataset_name): """ Load LVIS metadata. Args: dataset_name (str): LVIS dataset name without the split name (e.g., "lvis_v0.5"). Returns: dict: LVIS metadata with keys: thing_classes """ if "cocofied" in dataset_name: return _get_coco_instances_meta() if "v0.5" in dataset_name: return _get_lvis_instances_meta_v0_5() elif "v1" in dataset_name: return _get_lvis_instances_meta_v1() raise ValueError("No built-in metadata for dataset {}".format(dataset_name))
def _get_lvis_instances_meta_v0_5(): assert len(LVIS_V0_5_CATEGORIES) == 1230 cat_ids = [k["id"] for k in LVIS_V0_5_CATEGORIES] assert min(cat_ids) == 1 and max(cat_ids) == len( cat_ids ), "Category ids are not in [1, #categories], as expected" # Ensure that the category list is sorted by id lvis_categories = sorted(LVIS_V0_5_CATEGORIES, key=lambda x: x["id"]) thing_classes = [k["synonyms"][0] for k in lvis_categories] meta = {"thing_classes": thing_classes} return meta def _get_lvis_instances_meta_v1(): assert len(LVIS_V1_CATEGORIES) == 1203 cat_ids = [k["id"] for k in LVIS_V1_CATEGORIES] assert min(cat_ids) == 1 and max(cat_ids) == len( cat_ids ), "Category ids are not in [1, #categories], as expected" # Ensure that the category list is sorted by id lvis_categories = sorted(LVIS_V1_CATEGORIES, key=lambda x: x["id"]) thing_classes = [k["synonyms"][0] for k in lvis_categories] meta = {"thing_classes": thing_classes} return meta if __name__ == "__main__": """ Test the LVIS json dataset loader. Usage: python -m \ path/to/json path/to/image_root dataset_name vis_limit """ import sys import numpy as np from detectron2.utils.logger import setup_logger from PIL import Image import # noqa # add pre-defined metadata from detectron2.utils.visualizer import Visualizer logger = setup_logger(name=__name__) meta = MetadataCatalog.get(sys.argv[3]) dicts = load_lvis_json(sys.argv[1], sys.argv[2], sys.argv[3])"Done loading {} samples.".format(len(dicts))) dirname = "lvis-data-vis" os.makedirs(dirname, exist_ok=True) for d in dicts[: int(sys.argv[4])]: img = np.array(["file_name"])) visualizer = Visualizer(img, metadata=meta) vis = visualizer.draw_dataset_dict(d) fpath = os.path.join(dirname, os.path.basename(d["file_name"]))