prepro_reference_json.py

# coding: utf-8
"""
Preprocess a raw json dataset into hdf5/json files for use in data_loader.lua

Input: json file that has the form
[{ file_path: 'path/img.jpg', captions: ['a caption', ...] }, ...]
example element in this list would look like
{'captions': [u'A man with a red helmet on a small moped on a dirt road. ', u'Man riding a motor bike on a dirt road on the countryside.', u'A man riding on the back of a motorcycle.', u'A dirt path with a young person on a motor bike rests to the foreground of a verdant area with a bridge and a background of cloud-wreathed mountains. ', u'A man in a red shirt and a red hat is on a motorcycle on a hill side.'], 'file_path': u'val2014/COCO_val2014_000000391895.jpg', 'id': 391895}

This script reads this json, does some basic preprocessing on the captions
(e.g. lowercase, etc.), creates a special UNK token, and encodes everything to arrays

Output: a json file and an hdf5 file
The hdf5 file contains several fields:
/images is (N,3,256,256) uint8 array of raw image data in RGB format
/labels is (M,max_length) uint32 array of encoded labels, zero padded
/label_start_ix and /label_end_ix are (N,) uint32 arrays of pointers to the 
  first and last indices (in range 1..M) of labels for each image
/label_length stores the length of the sequence for each of the M sequences

The json file has a dict that contains:
- an 'ix_to_word' field storing the vocab in form {ix:'word'}, where ix is 1-indexed
- an 'images' field that is a list holding auxiliary information for each image, 
  such as in particular the 'split' it was assigned to.
"""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os
import json
import argparse
import sys
import hashlib
from random import shuffle, seed


def main(params):

    imgs = json.load(open(params['input_json'], 'r'))  ## ['images']  ## [0]
    # tmp = []
    # for k in imgs.keys():
    #     for img in imgs[k]:
    #         img['filename'] = img['image_id']  # k+'/'+img['image_id']
    #         img['image_id'] = int(
    #             int(hashlib.sha256(img['image_id']).hexdigest(), 16) % sys.maxint)
    #         tmp.append(img)
    # imgs = tmp

    # create output json file
    out = {u'info': {u'description': u'This is stable 1.0 version of the 2014 MS COCO dataset.', u'url': u'http://mscoco.org', u'version': u'1.0', u'year': 2014, u'contributor': u'Microsoft COCO group', u'date_created': u'2015-01-27 09:11:52.357475'}, u'licenses': [{u'url': u'http://creativecommons.org/licenses/by-nc-sa/2.0/', u'id': 1, u'name': u'Attribution-NonCommercial-ShareAlike License'}, {u'url': u'http://creativecommons.org/licenses/by-nc/2.0/', u'id': 2, u'name': u'Attribution-NonCommercial License'}, {u'url': u'http://creativecommons.org/licenses/by-nc-nd/2.0/', u'id': 3, u'name': u'Attribution-NonCommercial-NoDerivs License'}, {u'url': u'http://creativecommons.org/licenses/by/2.0/', u'id': 4, u'name': u'Attribution License'}, {u'url': u'http://creativecommons.org/licenses/by-sa/2.0/', u'id': 5, u'name': u'Attribution-ShareAlike License'}, {u'url': u'http://creativecommons.org/licenses/by-nd/2.0/', u'id': 6, u'name': u'Attribution-NoDerivs License'}, {u'url': u'http://flickr.com/commons/usage/', u'id': 7, u'name': u'No known copyright restrictions'}, {u'url': u'http://www.usa.gov/copyright.shtml', u'id': 8, u'name': u'United States Government Work'}], u'type': u'captions'}
    out.update({'images': [], 'annotations': []})

    cnt = 0
    empty_cnt = 0
    for i, img in enumerate(imgs):
        if img['split'] == 'train':
            continue
        out['images'].append(
            {u'id': img.get('cocoid', img['id'])})
        for j, s in enumerate(img['captions']):
            if len(s) == 0:
                continue
            #s = ' '.join(s)   ['tokens']
            out['annotations'].append(
                {'image_id': out['images'][-1]['id'], 'caption': s, 'id': cnt})
            cnt += 1

    json.dump(out, open(params['output_json'], 'w'))
    print('wrote ', params['output_json'])


if __name__ == "__main__":

    parser = argparse.ArgumentParser()

    # input json
    parser.add_argument('--input_json', default='data/cityscapes/2. city_new_traffic_sign/cityscapes_paragraph_trainval_traffic_sign.json',
                        help='input json file to process into hdf5')
    parser.add_argument('--output_json', default='data/cityscapes/2. city_new_traffic_sign/cityscapes_paragraph_trainval_traffic_sign_captions4eval.json',
                        help='output json file')

    args = parser.parse_args()
    params = vars(args)  # convert to ordinary dict
    print('parsed input parameters:')
    print(json.dumps(params, indent=2))
    main(params)