Tensorflow之构建自己的图片数据集TFrecords

   学习谷歌的深度学习终于有点眉目了，给大家分享我的Tensorflow学习历程。

   tensorflow的官方中文文档比较生涩，数据集一直采用的MNIST二进制数据集。并没有过多讲述怎么构建自己的图片数据集tfrecords。

   先贴我的转化代码将图片文件夹下的图片转存tfrecords的数据集。

#############################################################################################!/usr/bin/python2.7# -*- coding: utf-8 -*-#Author  : zhaoqinghui#Date    : 2016.5.10#Function: image convert to tfrecords #############################################################################################import tensorflow as tfimport numpy as npimport cv2import osimport os.pathfrom PIL import Image#参数设置###############################################################################################train_file = 'train.txt' #训练图片name='train'      #生成train.tfrecordsoutput_directory='./tfrecords'resize_height=32 #存储图片高度resize_width=32 #存储图片宽度###############################################################################################def _int64_feature(value):    return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))def _bytes_feature(value):    return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))def load_file(examples_list_file):    lines = np.genfromtxt(examples_list_file, delimiter=" ", dtype=[('col1', 'S120'), ('col2', 'i8')])    examples = []    labels = []    for example, label in lines:        examples.append(example)        labels.append(label)    return np.asarray(examples), np.asarray(labels), len(lines)def extract_image(filename,  resize_height, resize_width):    image = cv2.imread(filename)    image = cv2.resize(image, (resize_height, resize_width))    b,g,r = cv2.split(image)           rgb_image = cv2.merge([r,g,b])         return rgb_imagedef transform2tfrecord(train_file, name, output_directory, resize_height, resize_width):    if not os.path.exists(output_directory) or os.path.isfile(output_directory):        os.makedirs(output_directory)    _examples, _labels, examples_num = load_file(train_file)    filename = output_directory + "/" + name + '.tfrecords'    writer = tf.python_io.TFRecordWriter(filename)    for i, [example, label] in enumerate(zip(_examples, _labels)):        print('No.%d' % (i))        image = extract_image(example, resize_height, resize_width)        print('shape: %d, %d, %d, label: %d' % (image.shape[0], image.shape[1], image.shape[2], label))        image_raw = image.tostring()        example = tf.train.Example(features=tf.train.Features(feature={            'image_raw': _bytes_feature(image_raw),            'height': _int64_feature(image.shape[0]),            'width': _int64_feature(image.shape[1]),            'depth': _int64_feature(image.shape[2]),            'label': _int64_feature(label)        }))        writer.write(example.SerializeToString())    writer.close()def disp_tfrecords(tfrecord_list_file):    filename_queue = tf.train.string_input_producer([tfrecord_list_file])    reader = tf.TFRecordReader()    _, serialized_example = reader.read(filename_queue)    features = tf.parse_single_example(        serialized_example, features={          'image_raw': tf.FixedLenFeature([], tf.string),          'height': tf.FixedLenFeature([], tf.int64),          'width': tf.FixedLenFeature([], tf.int64),          'depth': tf.FixedLenFeature([], tf.int64),          'label': tf.FixedLenFeature([], tf.int64)      }    )    image = tf.decode_raw(features['image_raw'], tf.uint8)    #print(repr(image))    height = features['height']    width = features['width']    depth = features['depth']    label = tf.cast(features['label'], tf.int32)    init_op = tf.initialize_all_variables()    resultImg=[]    resultLabel=[]    with tf.Session() as sess:        sess.run(init_op)        coord = tf.train.Coordinator()        threads = tf.train.start_queue_runners(sess=sess, coord=coord)        for i in range(21):            image_eval = image.eval()            resultLabel.append(label.eval())            image_eval_reshape = image_eval.reshape([height.eval(), width.eval(), depth.eval()])            resultImg.append(image_eval_reshape)            pilimg = Image.fromarray(np.asarray(image_eval_reshape))            pilimg.show()        coord.request_stop()        coord.join(threads)        sess.close()    return resultImg,resultLabeldef read_tfrecord(filename_queuetemp):    filename_queue = tf.train.string_input_producer([filename_queuetemp])    reader = tf.TFRecordReader()    _, serialized_example = reader.read(filename_queue)    features = tf.parse_single_example(        serialized_example,        features={          'image_raw': tf.FixedLenFeature([], tf.string),          'width': tf.FixedLenFeature([], tf.int64),          'depth': tf.FixedLenFeature([], tf.int64),          'label': tf.FixedLenFeature([], tf.int64)      }    )    image = tf.decode_raw(features['image_raw'], tf.uint8)    # image    tf.reshape(image, [256, 256, 3])    # normalize    image = tf.cast(image, tf.float32) * (1. /255) - 0.5    # label    label = tf.cast(features['label'], tf.int32)    return image, labeldef test():    transform2tfrecord(train_file, name , output_directory,  resize_height, resize_width) #转化函数       img,label=disp_tfrecords(output_directory+'/'+name+'.tfrecords') #显示函数    img,label=read_tfrecord(output_directory+'/'+name+'.tfrecords') #读取函数    print labelif __name__ == '__main__':    test()

这样就可以得到自己专属的数据集.tfrecords了  ，它可以直接用于tensorflow的数据集。