tf创建tfRecord文件

本文主要是介绍tf创建tfRecord文件，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

项目详细请猛戳我的github地址，直接可运行：https://github.com/SamXiaosheng/create-tfRecord

下面是main文件代码和create tfRecord文件：

import tensorflow as tf
from tfRecord import *
import cv2FLAGS = tf.app.flags.FLAGStf.app.flags.DEFINE_string('image_dir', './image/',"""Directory where to write event logs """)def main(_):
    create_tfrecords(FLAGS.image_dir)image_batch,label_batch =read_and_decode('test.tfRecord')with tf.Session() as sess:
        coord = tf.train.Coordinator()threads = tf.train.start_queue_runners(sess=sess, coord=coord)while not coord.should_stop():
            image,label = sess.run([image_batch,label_batch])print(label)cv2.imshow('image',image[0])cv2.waitKey(200)coord.request_stop()coord.join(threads)if __name__ == '__main__':
    tf.app.run()


import tensorflow as tf
import numpy as np
import os
import cv2def read_and_decode(filename):
    filename_queue = tf.train.string_input_producer([filename])reader = tf.TFRecordReader()_, serialized_example = reader.read(filename_queue)features = tf.parse_single_example(serialized_example,features={'label': tf.FixedLenFeature([], tf.int64),'img_raw' : tf.FixedLenFeature([], tf.string),})img = tf.decode_raw(features['img_raw'], tf.uint8)#这里的格式非常重要
    img = tf.reshape(img, [227, 227, 3])#img = tf.cast(img, tf.float32) * (1. / 255) - 0.5
    label = tf.cast(features['label'], tf.uint8)image_batch, label_batch = tf.train.shuffle_batch([img, label],batch_size=1,#这里参数设置目的是每次只读取一个样本
                                                    capacity=1,min_after_dequeue=0)#label_batch = tf.one_hot(label_batch, NUM_CLASSES)
    #label_batch = tf.cast(label_batch, dtype=tf.int64)
    #label_batch = tf.reshape(label_batch, [batch_size, NUM_CLASSES])

    return image_batch, label_batch
#读取某目录路径下的所有文件，返回图片的名称列表
def dirtomdfbatchmsra(dirpath):#读取目录下训练图像和对应的label
    image_ext = 'jpg'
    images = [fn for fn in os.listdir(dirpath) if fn.endswith(image_ext)]#返回dirpath路径下所有后缀jpg文件
    images.sort()#排序的目的有利于样本和标签的对应
    #print(images)
    gt_ext = 'png'
    gt_maps = [fn for fn in os.listdir(dirpath) if fn.endswith(gt_ext)]gt_maps.sort()#print(gt_maps)
    return gt_maps,images#返回gt图和训练image的所有文件名

def create_tfrecords(image_dir):
    writer = tf.python_io.TFRecordWriter("test.tfRecord")image_png,image_jpg = dirtomdfbatchmsra(image_dir)for index, name in enumerate(image_jpg):
            img = cv2.imread(image_dir+name).astype(np.uint8)img = cv2.resize(img,(227,227))#统一大小
            img_raw = img.tobytes()#转换成字节形式
            example = tf.train.Example(features=tf.train.Features(feature={"label": tf.train.Feature(int64_list=tf.train.Int64List(value=[index])),'img_raw': tf.train.Feature(bytes_list=tf.train.BytesList(value=[img_raw]))}))writer.write(example.SerializeToString())for index, name in enumerate(image_png):
            img = cv2.imread(image_dir+name).astype(np.uint8)img = cv2.resize(img,(227, 227))#
            img_raw = img.tobytes()example = tf.train.Example(features=tf.train.Features(feature={"label": tf.train.Feature(int64_list=tf.train.Int64List(value=[index])),'img_raw': tf.train.Feature(bytes_list=tf.train.BytesList(value=[img_raw]))}))writer.write(example.SerializeToString())writer.close()

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