深度学习之二进制文件读取案例

import tensorflow as tf
import os


class Cifar():

    def __init__(self):

        # 设置图像大小
        self.height = 32
        self.width = 32
        self.channel = 3

        # 设置图像字节数
        self.image = self.height * self.width * self.channel
        self.label = 1
        self.sample = self.image + self.label


    def read_binary(self):
        """
        读取二进制文件
        :return:
        """
        # 1、构造文件名队列
        filename_list = os.listdir("./cifar-10-batches-bin")
        # print("filename_list:\n", filename_list)
        file_list = [os.path.join("./cifar-10-batches-bin/", i) for i in filename_list if i[-3:]=="bin"]
        # print("file_list:\n", file_list)
        file_queue = tf.train.string_input_producer(file_list)

        # 2、读取与解码
        # 读取
        reader = tf.FixedLengthRecordReader(self.sample)
        # key文件名 value样本
        key, value = reader.read(file_queue)

        # 解码
        image_decoded = tf.decode_raw(value, tf.uint8)
        print("image_decoded:\n", image_decoded)

        # 切片操作
        label = tf.slice(image_decoded, [0], [self.label])
        image = tf.slice(image_decoded, [self.label], [self.image])
        print("label:\n", label)
        print("image:\n", image)

        # 调整图像的形状
        image_reshaped = tf.reshape(image, [self.channel, self.height, self.width])
        print("image_reshaped:\n", image_reshaped)

        # 三维数组的转置
        image_transposed = tf.transpose(image_reshaped, [1, 2, 0])
        print("image_transposed:\n", image_transposed)

        # 3、构造批处理队列
        image_batch, label_batch = tf.train.batch([image_transposed, label], batch_size=100, num_threads=2, capacity=100)

        # 开启会话
        with tf.Session() as sess:

            # 开启线程
            coord = tf.train.Coordinator()
            threads = tf.train.start_queue_runners(sess=sess, coord=coord)

            label_value, image_value = sess.run([label_batch, image_batch])
            print("label_value:\n", label_value)
            print("image:\n", image_value)

            coord.request_stop()
            coord.join(threads)

        return image_value, label_value

    def write_to_tfrecords(self, image_batch, label_batch):
        """
        将样本的特征值和目标值一起写入tfrecords文件
        :param image:
        :param label:
        :return:
        """
        with tf.python_io.TFRecordWriter("cifar10.tfrecords") as writer:
            # 循环构造example对象，并序列化写入文件
            for i in range(100):
                image = image_batch[i].tostring()
                label = label_batch[i][0]
                # print("tfrecords_image:\n", image)
                # print("tfrecords_label:\n", label)
                example = tf.train.Example(features=tf.train.Features(feature={
                    "image": tf.train.Feature(bytes_list=tf.train.BytesList(value=[image])),
                    "label": tf.train.Feature(int64_list=tf.train.Int64List(value=[label])),
                }))
                # example.SerializeToString()
                # 将序列化后的example写入文件
                writer.write(example.SerializeToString())

        return None

    def read_tfrecords(self):
        """
        读取TFRecords文件
        :return:
        """
        # 1、构造文件名队列
        file_queue = tf.train.string_input_producer(["cifar10.tfrecords"])

        # 2、读取与解码
        # 读取
        reader = tf.TFRecordReader()
        key, value = reader.read(file_queue)

        # 解析example
        feature = tf.parse_single_example(value, features={
            "image": tf.FixedLenFeature([], tf.string),
            "label": tf.FixedLenFeature([], tf.int64)
        })
        image = feature["image"]
        label = feature["label"]
        print("read_tf_image:\n", image)
        print("read_tf_label:\n", label)

        # 解码
        image_decoded = tf.decode_raw(image, tf.uint8)
        print("image_decoded:\n", image_decoded)
        # 图像形状调整
        image_reshaped = tf.reshape(image_decoded, [self.height, self.width, self.channel])
        print("image_reshaped:\n", image_reshaped)

        # 3、构造批处理队列
        image_batch, label_batch = tf.train.batch([image_reshaped, label], batch_size=100, num_threads=2, capacity=100)
        print("image_batch:\n", image_batch)
        print("label_batch:\n", label_batch)

        # 开启会话
        with tf.Session() as sess:

            # 开启线程
            coord = tf.train.Coordinator()
            threads = tf.train.start_queue_runners(sess=sess, coord=coord)

            image_value, label_value = sess.run([image_batch, label_batch])
            print("image_value:\n", image_value)
            print("label_value:\n", label_value)

            # 回收资源
            coord.request_stop()
            coord.join(threads)

        return None

if __name__ == "__main__":
    cifar = Cifar()
    # image_value, label_value = cifar.read_binary()
    # cifar.write_to_tfrecords(image_value, label_value)
    cifar.read_tfrecords()