{
 "0.jpg59329": {
 "filename": "0.jpg",
 "size": 59329,
 "regions": [{
 "shape_attributes": {
 "name": "rect",
 "x": 412,
 "y": 130,
 "width": 95,
 "height": 104
 },
 "region_attributes": {}
 }, {
 "shape_attributes": {
 "name": "rect",
 "x": 521,
 "y": 82,
 "width": 126,
 "height": 106
 },
 "region_attributes": {}
 }
}

# Ref 1: https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/using_your_own_dataset.md
# Ref 2: https://github.com/datitran/raccoon_dataset/blob/master/generate_tfrecord.py
import json
import glob
from object_detection.utils import dataset_util
import tensorflow as tf
from pathlib import Path
flags = tf.compat.v1.app.flags
flags.DEFINE_string('output_path', '', 'Path to output TFRecord')
FLAGS = flags.FLAGS
def json_to_tf(jsonFile, im):
 with open(im, "rb") as image:
 encoded_image_data = image.read()
 with open(jsonFile) as json_file:
 data = json.load(json_file)
 for key, value in data.items():
 width = 1920
 height = 1080
 filename = value["filename"]
 filename = filename.encode('utf8')
 image_format = b'jpeg'
 xmins = []
 xmaxs = []
 ymins = []
 ymaxs = []
 classes_text = []
 classes = []
 for x in value["regions"]:
 xmins.append(x["shape_attributes"]['x'])
 xmaxs.append(x["shape_attributes"]['width'] + x["shape_attributes"]['x'])
 ymins.append(x["shape_attributes"]['y'])
 ymaxs.append(x["shape_attributes"]['height'] + x["shape_attributes"]['y'])
 classes_text.append("car".encode('utf8'))
 classes.append(1)
 tf_example = tf.train.Example(features=tf.train.Features(feature={
 'image/height': dataset_util.int64_feature(height),
 'image/width': dataset_util.int64_feature(width),
 'image/filename': dataset_util.bytes_feature(filename),
 'image/source_id': dataset_util.bytes_feature(filename),
 'image/encoded': dataset_util.bytes_feature(encoded_image_data),
 'image/format': dataset_util.bytes_feature(image_format),
 'image/object/bbox/xmin': dataset_util.float_list_feature(xmins),
 'image/object/bbox/xmax': dataset_util.float_list_feature(xmaxs),
 'image/object/bbox/ymin': dataset_util.float_list_feature(ymins),
 'image/object/bbox/ymax': dataset_util.float_list_feature(ymaxs),
 'image/object/class/text': dataset_util.bytes_list_feature(classes_text),
 'image/object/class/label': dataset_util.int64_list_feature(classes),
 }))
 writer.write(tf_example.SerializeToString())
writer = tf.compat.v1.python_io.TFRecordWriter("train.record")
for fn in glob.glob("..\\annotation_refined\\*.json"):
 for img in glob.glob("..\\images\\*.jpg"):
 if Path(fn).stem == Path(img).stem:
 tf_example_1 = json_to_tf(fn, img)
writer.close()