generate_tfrecord_JPG.py

from random import shuffle
import numpy as np
import glob
import tensorflow as tf
import cv2
import sys
import os
import PIL.Image as Image


def encode_utf8_string(text, length, dic, null_char_id=2445):
    char_ids_padded = [null_char_id]*length
    char_ids_unpadded = [null_char_id]*len(text)
    for i in range(len(text)):
        hash_id = dic[text[i]]
        char_ids_padded[i] = hash_id
        char_ids_unpadded[i] = hash_id
    return char_ids_padded, char_ids_unpadded

def _bytes_feature(value):
    return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))

def _int64_feature(value):
    return tf.train.Feature(int64_list=tf.train.Int64List(value=value))

dict={}
with open('kor_datasets.txt', encoding="utf") as dict_file:
    for line in dict_file:
        (key, value) = line.strip().split('\t')
        dict[value] = int(key)
print((dict))

image_path = 'data/*/*.jpg'
addrs_image = glob.glob(image_path)

label_path = 'data/*/*.txt'
addrs_label = glob.glob(label_path)

print(len(addrs_image))
print(len(addrs_label))

tfrecord_writer  = tf.python_io.TFRecordWriter("eval") 
for j in range(0,int(len(addrs_image))):
    

            # 这是写入操作可视化处理
    print('Train data: {}/{}'.format(j,int(len(addrs_image))))
    sys.stdout.flush()

    img = Image.open(addrs_image[j])

    img = img.resize((200, 50), Image.ANTIALIAS)
    np_data = np.array(img)
    image_data = img.tobytes()
    for text in open(addrs_label[j], encoding="utf"):
                 char_ids_padded, char_ids_unpadded = encode_utf8_string(
                            text=text,
                            dic=dict,
                            length=37,
                            null_char_id=2445)



    example = tf.train.Example(features=tf.train.Features(
                        feature={
                            'image/encoded': _bytes_feature(image_data),
                            'image/format': _bytes_feature(b"raw"),
                            'image/width': _int64_feature([np_data.shape[1]]),
                            'image/orig_width': _int64_feature([np_data.shape[1]]),
                            'image/class': _int64_feature(char_ids_padded),
                            'image/unpadded_class': _int64_feature(char_ids_unpadded),
                            'image/text': _bytes_feature(bytes(text, 'utf-8')),
                            # 'height': _int64_feature([crop_data.shape[0]]),
                        }
                    ))
    tfrecord_writer.write(example.SerializeToString())
tfrecord_writer.close()

sys.stdout.flush()