detectron2/datasets/prepare_cocofied_lvis.py

#!/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)),
        )