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# Copyright (c) Facebook, Inc. and its affiliates.
# Modified by Xingyi Zhou from https://github.com/facebookresearch/detectron2/blob/master/detectron2/data/datasets/coco.py
import copy
import io
import logging
import contextlib
import os
import datetime
import json
import numpy as np
from PIL import Image
from fvcore.common.timer import Timer
from fvcore.common.file_io import PathManager, file_lock
from detectron2.structures import BoxMode, PolygonMasks, Boxes
from detectron2.data import DatasetCatalog, MetadataCatalog
logger = logging.getLogger(__name__)
"""
This file contains functions to register a COCO-format dataset to the DatasetCatalog.
"""
__all__ = ["register_coco_instances", "register_coco_panoptic_separated"]
def register_oid_instances(name, metadata, json_file, image_root):
"""
"""
# 1. register a function which returns dicts
DatasetCatalog.register(name, lambda: load_coco_json_mem_efficient(
json_file, image_root, name))
# 2. Optionally, add metadata about this dataset,
# since they might be useful in evaluation, visualization or logging
MetadataCatalog.get(name).set(
json_file=json_file, image_root=image_root, evaluator_type="oid", **metadata
)
def load_coco_json_mem_efficient(json_file, image_root, dataset_name=None, extra_annotation_keys=None):
"""
Actually not mem efficient
"""
from pycocotools.coco import COCO
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()))
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
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
# sort indices for reproducible results
img_ids = sorted(coco_api.imgs.keys())
imgs = coco_api.loadImgs(img_ids)
logger.info("Loaded {} images in COCO format from {}".format(len(imgs), json_file))
dataset_dicts = []
ann_keys = ["iscrowd", "bbox", "category_id"] + (extra_annotation_keys or [])
for img_dict in imgs:
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"]
anno_dict_list = coco_api.imgToAnns[image_id]
if 'neg_category_ids' in img_dict:
record['neg_category_ids'] = \
[id_map[x] for x in img_dict['neg_category_ids']]
objs = []
for anno in anno_dict_list:
assert anno["image_id"] == image_id
assert anno.get("ignore", 0) == 0
obj = {key: anno[key] for key in ann_keys if key in anno}
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
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)
del coco_api
return dataset_dicts |