识别验证码系列（2）生成tfrecord 成功版 2018-7-31

import tensorflow as tf
import os
import random
import math
import sys
from PIL import Image
import numpy as np

# 验证集数量
_NUM_TEST = 500
# 随机种子
_RANDOM_SEED = 0
# 数据集路径
DATASET_DIR = "/home/mj/test/captcha/image/"
# tfrecord文件存放路径
TFRECORD_DIR = "/home/mj/test/captcha/"


# 判断tfrecord文件是不是存在
def _dataset_exists(dataset_dir):
    for split_name in ['train', 'test']:
        output_filename = os.path.join(dataset_dir, split_name + '.tfrecords')
        if not tf.gfile.Exists(output_filename):
            return False
    return True


# 获取所有的验证码图片
def _get_filenames_and_classes(dataset_dir):
    photo_filenames = []
    for filename in os.listdir(dataset_dir):
        path = os.path.join(dataset_dir, filename)
        photo_filenames.append(path)
    return photo_filenames


def int64_feature(values):
    if not isinstance(values, (tuple, list)):
        values = [values]
    return tf.train.Feature(int64_list=tf.train.Int64List(value=values))


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


def image_to_tfexample(image_data, label0, label1, label2, label3):
    return tf.train.Example(features=tf.train.Features(feature={
        'image': bytes_feature(image_data),
        'label0': int64_feature(label0),
        'label1': int64_feature(label1),
        'label2': int64_feature(label2),
        'label3': int64_feature(label3),
    }))


# 把数据转为tfrecord格式
def _convert_dataset(split_name, filenames, dataset_dir):
    assert split_name in ['train', 'test']

    with tf.Session() as sess:
        # 定义tfrecord文件的路径和名字
        output_filename = os.path.join(TFRECORD_DIR, split_name + '.tfrecords')
        with tf.python_io.TFRecordWriter(output_filename) as tfrecord_writer:
            for i, filename in enumerate(filenames):
                try:
                    # sys.stdout.write('\r>> Converting image %d/%d' % (i+1, len(filenames)))
                    # sys.stdout.flush()

                    # 读取照片
                    image_data = Image.open(filename)
                    # 根据模型的结构resize
                    image_data = image_data.resize((224, 224))
                    # 灰度化
                    image_data = np.array(image_data.convert('L'))
                    image_data = image_data.tobytes()

                    # 获取labels
                    labels = filename.split('/')[-1][0:4]
                    num_labels = []
                    for j in range(4):
                        num_labels.append(int(labels[j]))

                    # 生成protocol数据类型
                    example = image_to_tfexample(image_data, num_labels[0], num_labels[1], num_labels[2], num_labels[3])
                    tfrecord_writer.write(example.SerializeToString())

                except IOError as e:
                    print('could not read: ', filename)
                    print('error: ', e)
                    print('skip it \n')

    # sys.stdout.write('\n')
    # sys.stdout.flush()


if _dataset_exists(TFRECORD_DIR):
    print("wenjiancunzai")
else:
    # 获取所有的图片
    photo_filenames = _get_filenames_and_classes(DATASET_DIR)

    # 把数据切分为训练集和测试机斌打乱
    random.seed(_RANDOM_SEED)
    random.shuffle(photo_filenames)
    training_filenames = photo_filenames[_NUM_TEST:]
    testing_filenames = photo_filenames[:_NUM_TEST]

    # 数据转化
    _convert_dataset('train', training_filenames, DATASET_DIR)
    _convert_dataset('test', testing_filenames, DATASET_DIR)
    print('生成tfrecord文件')