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# Copyright (c) Facebook, Inc. and its affiliates.
import argparse
import json
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": "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 map_name(x):
x = x.replace('_', ' ')
if '(' in x:
x = x[:x.find('(')]
return x.lower().strip()
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--cc_ann', default='datasets/cc3m/train_image_info.json')
parser.add_argument('--out_path', default='datasets/cc3m/train_image_info_tags.json')
parser.add_argument('--keep_images', action='store_true')
parser.add_argument('--allcaps', action='store_true')
parser.add_argument('--cat_path', default='')
parser.add_argument('--convert_caption', action='store_true')
# parser.add_argument('--lvis_ann', default='datasets/lvis/lvis_v1_val.json')
args = parser.parse_args()
# lvis_data = json.load(open(args.lvis_ann, 'r'))
cc_data = json.load(open(args.cc_ann, 'r'))
if args.convert_caption:
num_caps = 0
caps = defaultdict(list)
for x in cc_data['annotations']:
caps[x['image_id']].append(x['caption'])
for x in cc_data['images']:
x['captions'] = caps[x['id']]
num_caps += len(x['captions'])
print('# captions', num_caps)
if args.cat_path != '':
print('Loading', args.cat_path)
cats = json.load(open(args.cat_path))['categories']
if 'synonyms' not in cats[0]:
cocoid2synset = {x['coco_cat_id']: x['synset'] \
for x in COCO_SYNSET_CATEGORIES}
synset2synonyms = {x['synset']: x['synonyms'] \
for x in cc_data['categories']}
for x in cats:
synonyms = synset2synonyms[cocoid2synset[x['id']]]
x['synonyms'] = synonyms
x['frequency'] = 'f'
cc_data['categories'] = cats
id2cat = {x['id']: x for x in cc_data['categories']}
class_count = {x['id']: 0 for x in cc_data['categories']}
class_data = {x['id']: [' ' + map_name(xx) + ' ' for xx in x['synonyms']] \
for x in cc_data['categories']}
num_examples = 5
examples = {x['id']: [] for x in cc_data['categories']}
print('class_data', class_data)
images = []
for i, x in enumerate(cc_data['images']):
if i % 10000 == 0:
print(i, len(cc_data['images']))
if args.allcaps:
caption = (' '.join(x['captions'])).lower()
else:
caption = x['captions'][0].lower()
x['pos_category_ids'] = []
for cat_id, cat_names in class_data.items():
find = False
for c in cat_names:
if c in caption or caption.startswith(c[1:]) \
or caption.endswith(c[:-1]):
find = True
break
if find:
x['pos_category_ids'].append(cat_id)
class_count[cat_id] += 1
if len(examples[cat_id]) < num_examples:
examples[cat_id].append(caption)
if len(x['pos_category_ids']) > 0 or args.keep_images:
images.append(x)
zero_class = []
for cat_id, count in class_count.items():
print(id2cat[cat_id]['name'], count, end=', ')
if count == 0:
zero_class.append(id2cat[cat_id])
print('==')
print('zero class', zero_class)
# for freq in ['r', 'c', 'f']:
# print('#cats', freq, len([x for x in cc_data['categories'] \
# if x['frequency'] == freq] and class_count[x['id']] > 0))
for freq in ['r', 'c', 'f']:
print('#Images', freq, sum([v for k, v in class_count.items() \
if id2cat[k]['frequency'] == freq]))
try:
out_data = {'images': images, 'categories': cc_data['categories'], \
'annotations': []}
for k, v in out_data.items():
print(k, len(v))
if args.keep_images and not args.out_path.endswith('_full.json'):
args.out_path = args.out_path[:-5] + '_full.json'
print('Writing to', args.out_path)
json.dump(out_data, open(args.out_path, 'w'))
except:
pass
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