tensorflow object detection api) tfrecord create code

tfrecord_create.py

import tensorflow as tf
import numpy as np
import base64
import csv
import os
from PIL import Image
from utils import dataset_util

"""
csv format
xmin, ymin, xmax, ymax, class

https://github.com/tensorflow/models/blob/master/object_detection/g3doc/using_your_own_dataset.md
"""

class_dict = {
    1: b'bird', # List of class map Text with byte
    2: b'tiger'
}

def create_tf_example(img_name, img_path, bbox_data):
    im = Image.open(img_path)
    height = im.height # Image height
    width = im.width # Image width

    filename = str.encode(img_name) # Filename of the image. Empty if image is not from file

    img_file = open(img_path, 'rb')
    encoded_image_data = base64.b64encode(img_file.read()) # Encoded image bytes

    image_format = str.encode(img_name.split('.')[1]) # b'jpeg' or b'png'

    xmins = [bbox_data[0] / width] # List of normalized left x coordinates in bounding box (1 per box)
    xmaxs = [bbox_data[2] / width] # List of normalized right x coordinates in bounding box
             # (1 per box)
    ymins = [bbox_data[1] / height] # List of normalized top y coordinates in bounding box (1 per box)
    ymaxs = [bbox_data[3] / height] # List of normalized bottom y coordinates in bounding box
             # (1 per box)
    classes = [bbox_data[4]] # List of integer class id of bounding box (1 per box)
    classes_text = [class_dict[bbox_data[4]]] # List of string class name of bounding box (1 per box) !!csv data!!

    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),
    }))
    
    return tf_example


def main(_):
    writer = tf.python_io.TFRecordWriter('./tfrecord/data.tfrecords')
    
    # TODO(user): Write code to read in your dataset to examples variable
    data_path = './mydata'
    images = os.listdir(data_path)

    label_csv = './bbox.csv'
    csv_reader = csv.reader(open(label_csv, 'r'))

    for data, label in zip(images, csv_reader):
        label = [int(l) for l in label]
        img_path = data_path + '/' + data
        tf_example = create_tf_example(data, img_path, label)
        writer.write(tf_example.SerializeToString())
        
    # for example in examples:
  
    # tf_example = create_tf_example(1)
    # writer.write(tf_example.SerializeToString())

    writer.close()


if __name__ == '__main__':
    tf.app.run()

If I use encoded_image_data = base64.b64encode(img_file.read()) # Encoded image bytes to encode the image, I will get an error

tensorflow.python.framework.errors_impl.InvalidArgumentError: assertion failed: [Unable to decode bytes as JPEG, PNG, GIF, or BMP]
[[Node: case/If_0/decode_image/cond_jpeg/cond_png/cond_gif/Assert_1/Assert = Assert[T=[DT_STRING], summarize=3, _device="/job:localhost/replica:0/task:0/device:CPU:0"](case/If_0/decode_image/cond_jpeg/cond_png/cond_gif/is_bmp, case/If_0/decode_image/cond_jpeg/cond_png/cond_gif/Assert_1/Asse
rt/data_0)]]

when I try to run train.py.

But the following code works for me:

with tf.gfile.GFile(filename, 'rb') as fid:
    encoded_jpg = bytes(fid.read())

Thanks to https://stackoverflow.com/questions/46687348/decoding-tfrecord-with-tfslim.