#!/usr/bin/env python3 # -*- coding: utf-8 -*- # Copyright (c) Facebook, Inc. and its affiliates. import copy import json import os from collections import defaultdict # This mapping is extracted from the official LVIS mapping: # https://github.com/lvis-dataset/lvis-api/blob/master/data/coco_to_synset.json COCO_SYNSET_CATEGORIES = [ {"synset": "person.n.01", "coco_cat_id": 1}, {"synset": "bicycle.n.01", "coco_cat_id": 2}, {"synset": "car.n.01", "coco_cat_id": 3}, {"synset": "motorcycle.n.01", "coco_cat_id": 4}, {"synset": "airplane.n.01", "coco_cat_id": 5}, {"synset": "bus.n.01", "coco_cat_id": 6}, {"synset": "train.n.01", "coco_cat_id": 7}, {"synset": "truck.n.01", "coco_cat_id": 8}, {"synset": "boat.n.01", "coco_cat_id": 9}, {"synset": "traffic_light.n.01", "coco_cat_id": 10}, {"synset": "fireplug.n.01", "coco_cat_id": 11}, {"synset": "stop_sign.n.01", "coco_cat_id": 13}, {"synset": "parking_meter.n.01", "coco_cat_id": 14}, {"synset": "bench.n.01", "coco_cat_id": 15}, {"synset": "bird.n.01", "coco_cat_id": 16}, {"synset": "cat.n.01", "coco_cat_id": 17}, {"synset": "dog.n.01", "coco_cat_id": 18}, {"synset": "horse.n.01", "coco_cat_id": 19}, {"synset": "sheep.n.01", "coco_cat_id": 20}, {"synset": "beef.n.01", "coco_cat_id": 21}, {"synset": "elephant.n.01", "coco_cat_id": 22}, {"synset": "bear.n.01", "coco_cat_id": 23}, {"synset": "zebra.n.01", "coco_cat_id": 24}, {"synset": "giraffe.n.01", "coco_cat_id": 25}, {"synset": "backpack.n.01", "coco_cat_id": 27}, {"synset": "umbrella.n.01", "coco_cat_id": 28}, {"synset": "bag.n.04", "coco_cat_id": 31}, {"synset": "necktie.n.01", "coco_cat_id": 32}, {"synset": "bag.n.06", "coco_cat_id": 33}, {"synset": "frisbee.n.01", "coco_cat_id": 34}, {"synset": "ski.n.01", "coco_cat_id": 35}, {"synset": "snowboard.n.01", "coco_cat_id": 36}, {"synset": "ball.n.06", "coco_cat_id": 37}, {"synset": "kite.n.03", "coco_cat_id": 38}, {"synset": "baseball_bat.n.01", "coco_cat_id": 39}, {"synset": "baseball_glove.n.01", "coco_cat_id": 40}, {"synset": "skateboard.n.01", "coco_cat_id": 41}, {"synset": "surfboard.n.01", "coco_cat_id": 42}, {"synset": "tennis_racket.n.01", "coco_cat_id": 43}, {"synset": "bottle.n.01", "coco_cat_id": 44}, {"synset": "wineglass.n.01", "coco_cat_id": 46}, {"synset": "cup.n.01", "coco_cat_id": 47}, {"synset": "fork.n.01", "coco_cat_id": 48}, {"synset": "knife.n.01", "coco_cat_id": 49}, {"synset": "spoon.n.01", "coco_cat_id": 50}, {"synset": "bowl.n.03", "coco_cat_id": 51}, {"synset": "banana.n.02", "coco_cat_id": 52}, {"synset": "apple.n.01", "coco_cat_id": 53}, {"synset": "sandwich.n.01", "coco_cat_id": 54}, {"synset": "orange.n.01", "coco_cat_id": 55}, {"synset": "broccoli.n.01", "coco_cat_id": 56}, {"synset": "carrot.n.01", "coco_cat_id": 57}, {"synset": "frank.n.02", "coco_cat_id": 58}, {"synset": "pizza.n.01", "coco_cat_id": 59}, {"synset": "doughnut.n.02", "coco_cat_id": 60}, {"synset": "cake.n.03", "coco_cat_id": 61}, {"synset": "chair.n.01", "coco_cat_id": 62}, {"synset": "sofa.n.01", "coco_cat_id": 63}, {"synset": "pot.n.04", "coco_cat_id": 64}, {"synset": "bed.n.01", "coco_cat_id": 65}, {"synset": "dining_table.n.01", "coco_cat_id": 67}, {"synset": "toilet.n.02", "coco_cat_id": 70}, {"synset": "television_receiver.n.01", "coco_cat_id": 72}, {"synset": "laptop.n.01", "coco_cat_id": 73}, {"synset": "mouse.n.04", "coco_cat_id": 74}, {"synset": "remote_control.n.01", "coco_cat_id": 75}, {"synset": "computer_keyboard.n.01", "coco_cat_id": 76}, {"synset": "cellular_telephone.n.01", "coco_cat_id": 77}, {"synset": "microwave.n.02", "coco_cat_id": 78}, {"synset": "oven.n.01", "coco_cat_id": 79}, {"synset": "toaster.n.02", "coco_cat_id": 80}, {"synset": "sink.n.01", "coco_cat_id": 81}, {"synset": "electric_refrigerator.n.01", "coco_cat_id": 82}, {"synset": "book.n.01", "coco_cat_id": 84}, {"synset": "clock.n.01", "coco_cat_id": 85}, {"synset": "vase.n.01", "coco_cat_id": 86}, {"synset": "scissors.n.01", "coco_cat_id": 87}, {"synset": "teddy.n.01", "coco_cat_id": 88}, {"synset": "hand_blower.n.01", "coco_cat_id": 89}, {"synset": "toothbrush.n.01", "coco_cat_id": 90}, ] def cocofy_lvis(input_filename, output_filename): """ Filter LVIS instance segmentation annotations to remove all categories that are not included in COCO. The new json files can be used to evaluate COCO AP using `lvis-api`. The category ids in the output json are the incontiguous COCO dataset ids. Args: input_filename (str): path to the LVIS json file. output_filename (str): path to the COCOfied json file. """ with open(input_filename, "r") as f: lvis_json = json.load(f) lvis_annos = lvis_json.pop("annotations") cocofied_lvis = copy.deepcopy(lvis_json) lvis_json["annotations"] = lvis_annos # Mapping from lvis cat id to coco cat id via synset lvis_cat_id_to_synset = {cat["id"]: cat["synset"] for cat in lvis_json["categories"]} synset_to_coco_cat_id = {x["synset"]: x["coco_cat_id"] for x in COCO_SYNSET_CATEGORIES} # Synsets that we will keep in the dataset synsets_to_keep = set(synset_to_coco_cat_id.keys()) coco_cat_id_with_instances = defaultdict(int) new_annos = [] ann_id = 1 for ann in lvis_annos: lvis_cat_id = ann["category_id"] synset = lvis_cat_id_to_synset[lvis_cat_id] if synset not in synsets_to_keep: continue coco_cat_id = synset_to_coco_cat_id[synset] new_ann = copy.deepcopy(ann) new_ann["category_id"] = coco_cat_id new_ann["id"] = ann_id ann_id += 1 new_annos.append(new_ann) coco_cat_id_with_instances[coco_cat_id] += 1 cocofied_lvis["annotations"] = new_annos for image in cocofied_lvis["images"]: for key in ["not_exhaustive_category_ids", "neg_category_ids"]: new_category_list = [] for lvis_cat_id in image[key]: synset = lvis_cat_id_to_synset[lvis_cat_id] if synset not in synsets_to_keep: continue coco_cat_id = synset_to_coco_cat_id[synset] new_category_list.append(coco_cat_id) coco_cat_id_with_instances[coco_cat_id] += 1 image[key] = new_category_list coco_cat_id_with_instances = set(coco_cat_id_with_instances.keys()) new_categories = [] for cat in lvis_json["categories"]: synset = cat["synset"] if synset not in synsets_to_keep: continue coco_cat_id = synset_to_coco_cat_id[synset] if coco_cat_id not in coco_cat_id_with_instances: continue new_cat = copy.deepcopy(cat) new_cat["id"] = coco_cat_id new_categories.append(new_cat) cocofied_lvis["categories"] = new_categories with open(output_filename, "w") as f: json.dump(cocofied_lvis, f) print("{} is COCOfied and stored in {}.".format(input_filename, output_filename)) if __name__ == "__main__": dataset_dir = os.path.join(os.getenv("DETECTRON2_DATASETS", "datasets"), "lvis") for s in ["lvis_v0.5_train", "lvis_v0.5_val"]: print("Start COCOfing {}.".format(s)) cocofy_lvis( os.path.join(dataset_dir, "{}.json".format(s)), os.path.join(dataset_dir, "{}_cocofied.json".format(s)), )