# Copyright (c) Facebook, Inc. and its affiliates. import json import os from detectron2.data import DatasetCatalog, MetadataCatalog from detectron2.utils.file_io import PathManager from detectron2.data.datasets.coco import load_sem_seg from . import openseg_classes ADE20K_150_CATEGORIES = openseg_classes.get_ade20k_categories_with_prompt_eng() ADE20k_COLORS = [k["color"] for k in ADE20K_150_CATEGORIES] MetadataCatalog.get("openvocab_ade20k_sem_seg_train").set( stuff_colors=ADE20k_COLORS[:], ) MetadataCatalog.get("openvocab_ade20k_sem_seg_val").set( stuff_colors=ADE20k_COLORS[:], ) def load_ade20k_panoptic_json(json_file, image_dir, gt_dir, semseg_dir, meta): """ Args: image_dir (str): path to the raw dataset. e.g., "~/coco/train2017". gt_dir (str): path to the raw annotations. e.g., "~/coco/panoptic_train2017". json_file (str): path to the json file. e.g., "~/coco/annotations/panoptic_train2017.json". Returns: list[dict]: a list of dicts in Detectron2 standard format. (See `Using Custom Datasets `_ ) """ def _convert_category_id(segment_info, meta): if segment_info["category_id"] in meta["thing_dataset_id_to_contiguous_id"]: segment_info["category_id"] = meta["thing_dataset_id_to_contiguous_id"][ segment_info["category_id"] ] segment_info["isthing"] = True else: segment_info["category_id"] = meta["stuff_dataset_id_to_contiguous_id"][ segment_info["category_id"] ] segment_info["isthing"] = False return segment_info with PathManager.open(json_file) as f: json_info = json.load(f) ret = [] for ann in json_info["annotations"]: image_id = ann["image_id"] # TODO: currently we assume image and label has the same filename but # different extension, and images have extension ".jpg" for COCO. Need # to make image extension a user-provided argument if we extend this # function to support other COCO-like datasets. image_file = os.path.join(image_dir, os.path.splitext(ann["file_name"])[0] + ".jpg") label_file = os.path.join(gt_dir, ann["file_name"]) sem_label_file = os.path.join(semseg_dir, ann["file_name"]) segments_info = [_convert_category_id(x, meta) for x in ann["segments_info"]] ret.append( { "file_name": image_file, "image_id": image_id, "pan_seg_file_name": label_file, "sem_seg_file_name": sem_label_file, "segments_info": segments_info, } ) assert len(ret), f"No images found in {image_dir}!" assert PathManager.isfile(ret[0]["file_name"]), ret[0]["file_name"] assert PathManager.isfile(ret[0]["pan_seg_file_name"]), ret[0]["pan_seg_file_name"] assert PathManager.isfile(ret[0]["sem_seg_file_name"]), ret[0]["sem_seg_file_name"] return ret def register_ade20k_panoptic( name, metadata, image_root, panoptic_root, semantic_root, panoptic_json, instances_json=None ): """ Register a "standard" version of ADE20k panoptic segmentation dataset named `name`. The dictionaries in this registered dataset follows detectron2's standard format. Hence it's called "standard". Args: name (str): the name that identifies a dataset, e.g. "ade20k_panoptic_train" metadata (dict): extra metadata associated with this dataset. image_root (str): directory which contains all the images panoptic_root (str): directory which contains panoptic annotation images in COCO format panoptic_json (str): path to the json panoptic annotation file in COCO format sem_seg_root (none): not used, to be consistent with `register_coco_panoptic_separated`. instances_json (str): path to the json instance annotation file """ panoptic_name = name DatasetCatalog.register( panoptic_name, lambda: load_ade20k_panoptic_json( panoptic_json, image_root, panoptic_root, semantic_root, metadata ), ) MetadataCatalog.get(panoptic_name).set( panoptic_root=panoptic_root, image_root=image_root, panoptic_json=panoptic_json, json_file=instances_json, evaluator_type="ade20k_panoptic_seg", ignore_label=255, label_divisor=1000, **metadata, ) _PREDEFINED_SPLITS_ADE20K_PANOPTIC = { "openvocab_ade20k_panoptic_train": ( "ADEChallengeData2016/images/training", "ADEChallengeData2016/ade20k_panoptic_train", "ADEChallengeData2016/ade20k_panoptic_train.json", "ADEChallengeData2016/annotations_detectron2/training", "ADEChallengeData2016/ade20k_instance_train.json", ), "openvocab_ade20k_panoptic_val": ( "ADEChallengeData2016/images/validation", "ADEChallengeData2016/ade20k_panoptic_val", "ADEChallengeData2016/ade20k_panoptic_val.json", "ADEChallengeData2016/annotations_detectron2/validation", "ADEChallengeData2016/ade20k_instance_val.json", ), } def get_metadata(): meta = {} # The following metadata maps contiguous id from [0, #thing categories + # #stuff categories) to their names and colors. We have to replica of the # same name and color under "thing_*" and "stuff_*" because the current # visualization function in D2 handles thing and class classes differently # due to some heuristic used in Panoptic FPN. We keep the same naming to # enable reusing existing visualization functions. thing_classes = [k["name"] for k in ADE20K_150_CATEGORIES if k["isthing"] == 1] thing_colors = [k["color"] for k in ADE20K_150_CATEGORIES if k["isthing"] == 1] stuff_classes = [k["name"] for k in ADE20K_150_CATEGORIES] stuff_colors = [k["color"] for k in ADE20K_150_CATEGORIES] meta["thing_classes"] = thing_classes meta["thing_colors"] = thing_colors meta["stuff_classes"] = stuff_classes meta["stuff_colors"] = stuff_colors # Convert category id for training: # category id: like semantic segmentation, it is the class id for each # pixel. Since there are some classes not used in evaluation, the category # id is not always contiguous and thus we have two set of category ids: # - original category id: category id in the original dataset, mainly # used for evaluation. # - contiguous category id: [0, #classes), in order to train the linear # softmax classifier. thing_dataset_id_to_contiguous_id = {} stuff_dataset_id_to_contiguous_id = {} for i, cat in enumerate(ADE20K_150_CATEGORIES): if cat["isthing"]: thing_dataset_id_to_contiguous_id[cat["id"]] = i # else: # stuff_dataset_id_to_contiguous_id[cat["id"]] = i # in order to use sem_seg evaluator stuff_dataset_id_to_contiguous_id[cat["id"]] = i meta["thing_dataset_id_to_contiguous_id"] = thing_dataset_id_to_contiguous_id meta["stuff_dataset_id_to_contiguous_id"] = stuff_dataset_id_to_contiguous_id return meta def register_all_ade20k_panoptic(root): metadata = get_metadata() for ( prefix, (image_root, panoptic_root, panoptic_json, semantic_root, instance_json), ) in _PREDEFINED_SPLITS_ADE20K_PANOPTIC.items(): # The "standard" version of COCO panoptic segmentation dataset, # e.g. used by Panoptic-DeepLab register_ade20k_panoptic( prefix, metadata, os.path.join(root, image_root), os.path.join(root, panoptic_root), os.path.join(root, semantic_root), os.path.join(root, panoptic_json), os.path.join(root, instance_json), ) def register_all_ade20k_semantic(root): root = os.path.join(root, "ADEChallengeData2016") for name, dirname in [("train", "training"), ("val", "validation")]: image_dir = os.path.join(root, "images", dirname) gt_dir = os.path.join(root, "annotations_detectron2", dirname) name = f"openvocab_ade20k_sem_seg_{name}" DatasetCatalog.register( name, lambda x=image_dir, y=gt_dir: load_sem_seg(y, x, gt_ext="png", image_ext="jpg") ) MetadataCatalog.get(name).set( stuff_classes=[x["name"] for x in ADE20K_150_CATEGORIES], image_root=image_dir, sem_seg_root=gt_dir, evaluator_type="sem_seg", ignore_label=255, ) _root = os.getenv("DETECTRON2_DATASETS", "datasets") register_all_ade20k_panoptic(_root) register_all_ade20k_semantic(_root)