# Copyright (c) Facebook, Inc. and its affiliates. from collections import defaultdict import torch import sys import json import numpy as np from detectron2.structures import Boxes, pairwise_iou COCO_PATH = 'datasets/coco/annotations/instances_train2017.json' IMG_PATH = 'datasets/coco/train2017/' LVIS_PATH = 'datasets/lvis/lvis_v1_train.json' NO_SEG = False if NO_SEG: SAVE_PATH = 'datasets/lvis/lvis_v1_train+coco_box.json' else: SAVE_PATH = 'datasets/lvis/lvis_v1_train+coco_mask.json' THRESH = 0.7 DEBUG = False # 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": "sausage.n.01", "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 get_bbox(ann): bbox = ann['bbox'] return [bbox[0], bbox[1], bbox[0] + bbox[2], bbox[1] + bbox[3]] if __name__ == '__main__': file_name_key = 'file_name' if 'v0.5' in LVIS_PATH else 'coco_url' coco_data = json.load(open(COCO_PATH, 'r')) lvis_data = json.load(open(LVIS_PATH, 'r')) coco_cats = coco_data['categories'] lvis_cats = lvis_data['categories'] num_find = 0 num_not_find = 0 num_twice = 0 coco2lviscats = {} synset2lvisid = {x['synset']: x['id'] for x in lvis_cats} # cocoid2synset = {x['coco_cat_id']: x['synset'] for x in COCO_SYNSET_CATEGORIES} coco2lviscats = {x['coco_cat_id']: synset2lvisid[x['synset']] \ for x in COCO_SYNSET_CATEGORIES if x['synset'] in synset2lvisid} print(len(coco2lviscats)) lvis_file2id = {x[file_name_key][-16:]: x['id'] for x in lvis_data['images']} lvis_id2img = {x['id']: x for x in lvis_data['images']} lvis_catid2name = {x['id']: x['name'] for x in lvis_data['categories']} coco_file2anns = {} coco_id2img = {x['id']: x for x in coco_data['images']} coco_img2anns = defaultdict(list) for ann in coco_data['annotations']: coco_img = coco_id2img[ann['image_id']] file_name = coco_img['file_name'][-16:] if ann['category_id'] in coco2lviscats and \ file_name in lvis_file2id: lvis_image_id = lvis_file2id[file_name] lvis_image = lvis_id2img[lvis_image_id] lvis_cat_id = coco2lviscats[ann['category_id']] if lvis_cat_id in lvis_image['neg_category_ids']: continue if DEBUG: import cv2 img_path = IMG_PATH + file_name img = cv2.imread(img_path) print(lvis_catid2name[lvis_cat_id]) print('neg', [lvis_catid2name[x] for x in lvis_image['neg_category_ids']]) cv2.imshow('img', img) cv2.waitKey() ann['category_id'] = lvis_cat_id ann['image_id'] = lvis_image_id coco_img2anns[file_name].append(ann) lvis_img2anns = defaultdict(list) for ann in lvis_data['annotations']: lvis_img = lvis_id2img[ann['image_id']] file_name = lvis_img[file_name_key][-16:] lvis_img2anns[file_name].append(ann) ann_id_count = 0 anns = [] for file_name in lvis_img2anns: coco_anns = coco_img2anns[file_name] lvis_anns = lvis_img2anns[file_name] ious = pairwise_iou( Boxes(torch.tensor([get_bbox(x) for x in coco_anns])), Boxes(torch.tensor([get_bbox(x) for x in lvis_anns])) ) for ann in lvis_anns: ann_id_count = ann_id_count + 1 ann['id'] = ann_id_count anns.append(ann) for i, ann in enumerate(coco_anns): if len(ious[i]) == 0 or ious[i].max() < THRESH: ann_id_count = ann_id_count + 1 ann['id'] = ann_id_count anns.append(ann) else: duplicated = False for j in range(len(ious[i])): if ious[i, j] >= THRESH and \ coco_anns[i]['category_id'] == lvis_anns[j]['category_id']: duplicated = True if not duplicated: ann_id_count = ann_id_count + 1 ann['id'] = ann_id_count anns.append(ann) if NO_SEG: for ann in anns: del ann['segmentation'] lvis_data['annotations'] = anns print('# Images', len(lvis_data['images'])) print('# Anns', len(lvis_data['annotations'])) json.dump(lvis_data, open(SAVE_PATH, 'w'))