带Json标注的数据集转成TFRecord（TT100K数据集）

问题描述:

手上有TT100K数据集，图片标注信息是json文件（作者用Qt开发的标注软件标注），但是想用Tensorflow Object Detection API来训练，之前做的Demo都是xml格式的标注，那么，如何将TT100K转成想要的TFRecord呢？

参考上一篇博客（VOC数据集转换成TFRecord文件）：https://blog.csdn.net/m0_37970224/article/details/89305787

做下改动~
主要区别就是之前是读取xml文件的内容，现在改成读取json数据里面的内容~
上代码：tt100k_to_tfrecord.py

# coding=utf-8
import os
import sys
import random
import tensorflow as tf
import json
from PIL import Image

# DIRECTORY_IMAGES = './train/'
DIRECTORY_IMAGES = './test/'
RANDOM_SEED = 4242
SAMPLES_PER_FILES = 1600


def int64_feature(values):
    """Returns a TF-Feature of int64s.
    Args:
    values: A scalar or list of values.
    Returns:
    a TF-Feature.
    """
    if not isinstance(values, (tuple, list)):
        values = [values]
    return tf.train.Feature(int64_list=tf.train.Int64List(value=values))


def float_feature(value):
    """Wrapper for inserting float features into Example proto.
    """
    if not isinstance(value, list):
        value = [value]
    return tf.train.Feature(float_list=tf.train.FloatList(value=value))


def bytes_feature(value):
    """Wrapper for inserting bytes features into Example proto.
    """
    if not isinstance(value, list):
        value = [value]
    return tf.train.Feature(bytes_list=tf.train.BytesList(value=value))


def _process_image(directory, name):
    # Read the image file.
    filename = os.path.join(directory, DIRECTORY_IMAGES, name + '.jpg')
    image_data = tf.gfile.FastGFile(filename, 'rb').read()

    # Read the json annotation file.
    filedir = directory + "/annotations.json"
    annos = json.loads(open(filedir).read())
    annos['imgs'][name]
    # shape
    with Image.open(filename) as img:
        shape = [img.height, img.width, 3]

    # 获取每个object的信息
    bboxes = []
    labels = []
    labels_text = []
    for obj in annos['imgs'][name]['objects']:
        label = obj['category']
        labels.append(annos['types'].index(label) + 1)
        labels_text.append(label.encode('utf8'))

        bbox = obj['bbox']
        bboxes.append((float(bbox['ymin']) / shape[0],
                       float(bbox['xmin']) / shape[1],
                       float(bbox['ymax']) / shape[0],
                       float(bbox['xmax']) / shape[1]
                       ))
    return image_data, shape, bboxes, labels, labels_text


def _convert_to_example(image_data, labels, labels_text, bboxes, shape):
    xmin = []
    ymin = []
    xmax = []
    ymax = []
    for b in bboxes:
        assert len(b) == 4
        # pylint: disable=expression-not-assigned
        [l.append(point) for l, point in zip([ymin, xmin, ymax, xmax], b)]
        # pylint: enable=expression-not-assigned

    image_format = b'JPEG'
    example = tf.train.Example(features=tf.train.Features(feature={
        'image/height': int64_feature(shape[0]),
        'image/width': int64_feature(shape[1]),
        'image/channels': int64_feature(shape[2]),
        'image/shape': int64_feature(shape),
        'image/object/bbox/xmin': float_feature(xmin),
        'image/object/bbox/xmax': float_feature(xmax),
        'image/object/bbox/ymin': float_feature(ymin),
        'image/object/bbox/ymax': float_feature(ymax),
        'image/object/class/label': int64_feature(labels),
        'image/object/class/text': bytes_feature(labels_text),
        'image/format': bytes_feature(image_format),
        'image/encoded': bytes_feature(image_data)}))
    return example


def _add_to_tfrecord(dataset_dir, name, tfrecord_writer):
    image_data, shape, bboxes, labels, labels_text = \
        _process_image(dataset_dir, name)
    print(shape, bboxes, labels, labels_text)
    example = _convert_to_example(image_data,
                                  labels,
                                  labels_text,
                                  bboxes,
                                  shape)
    tfrecord_writer.write(example.SerializeToString())


def run(tt100k_root, split, output_dir, shuffling=False):
    # 如果output_dir不存在则创建
    if not tf.gfile.Exists(output_dir):
        tf.gfile.MakeDirs(output_dir)
    # TT100K/data/train/ids.txt
    # 中存放有所有221个类别的训练样本名称，共6105个
    split_file_path = os.path.join(tt100k_root, split, 'ids.txt')
    print('>> ', split_file_path)
    with open(split_file_path) as f:
        filenames = f.readlines()
    # shuffling == Ture时，打乱顺序
    if shuffling:
        random.seed(RANDOM_SEED)
        random.shuffle(filenames)
    # Process dataset files.
    i = 0
    fidx = 0
    while i < len(filenames):
        # Open new TFRecord file.
        tf_filename = '%s/%s_%03d.tfrecord' % (output_dir, 'test', fidx)
        with tf.python_io.TFRecordWriter(tf_filename) as tfrecord_writer:
            j = 0
            while i < len(filenames) and j < SAMPLES_PER_FILES:
                sys.stdout.write('\r>> Converting image %d/%d' % (i + 1, len(filenames)))
                sys.stdout.flush()
                filename = filenames[i].strip()
                _add_to_tfrecord(tt100k_root, filename, tfrecord_writer)
                i += 1
                j += 1
            fidx += 1
    print('\n>> Finished converting the TT100K %s dataset!' % (split))


if __name__ == '__main__':
    run('E:\data\TT100K\data', 'test', './data/tt100k/test')

模仿着改，因你数据而异，也能够转成tfrecord文件~