全部存入一个TFrecords文件，然后读取并显示第一张。

不多写了，直接贴代码。

from PIL import Imageimport numpy as npimport matplotlib.pyplot as pltimport tensorflow as tfIMAGE_PATH = 'test/'tfrecord_file = IMAGE_PATH + 'test.tfrecord'writer = tf.python_io.TFRecordWriter(tfrecord_file)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 get_image_binary(filename): """ You can read in the image using tensorflow too, but it's a drag since you have to create graphs. It's much easier using Pillow and NumPy """ image = Image.open(filename) image = np.asarray(image, np.uint8) shape = np.array(image.shape, np.int32) return shape, image.tobytes() # convert image to raw data bytes in the array.def write_to_tfrecord(label, shape, binary_image, tfrecord_file): """ This example is to write a sample to TFRecord file. If you want to write more samples, just use a loop. """ # write label, shape, and image content to the TFRecord file example = tf.train.Example(features=tf.train.Features(feature={ 'label': _int64_feature(label), 'h': _int64_feature(shape[0]), 'w': _int64_feature(shape[1]), 'c': _int64_feature(shape[2]), 'image': _bytes_feature(binary_image) })) writer.write(example.SerializeToString())def write_tfrecord(label, image_file, tfrecord_file): shape, binary_image = get_image_binary(image_file) write_to_tfrecord(label, shape, binary_image, tfrecord_file) # print(shape)def main(): # assume the image has the label Chihuahua, which corresponds to class number 1 label = [1,2] image_files = [IMAGE_PATH + 'a.jpg', IMAGE_PATH + 'b.jpg'] for i in range(2): write_tfrecord(label[i], image_files[i], tfrecord_file) writer.close() batch_size = 2 filename_queue = tf.train.string_input_producer([tfrecord_file]) reader = tf.TFRecordReader() _, serialized_example = reader.read(filename_queue) img_features = tf.parse_single_example( serialized_example, features={ 'label': tf.FixedLenFeature([], tf.int64), 'h': tf.FixedLenFeature([], tf.int64), 'w': tf.FixedLenFeature([], tf.int64), 'c': tf.FixedLenFeature([], tf.int64), 'image': tf.FixedLenFeature([], tf.string), }) h = tf.cast(img_features['h'], tf.int32) w = tf.cast(img_features['w'], tf.int32) c = tf.cast(img_features['c'], tf.int32) image = tf.decode_raw(img_features['image'], tf.uint8) image = tf.reshape(image, [h, w, c]) label = tf.cast(img_features['label'],tf.int32) label = tf.reshape(label, [1]) # image = tf.image.resize_images(image, (500,500)) #image, label = tf.train.batch([image, label], batch_size= batch_size) with tf.Session() as sess: coord = tf.train.Coordinator() threads = tf.train.start_queue_runners(coord=coord) image, label=sess.run([image, label]) coord.request_stop() coord.join(threads) print(label) plt.figure() plt.imshow(image) plt.show()if __name__ == '__main__': main()

全部存入一个TFrecords文件，然后按照batch_size读取，注意需要将图片变成一样大才能按照batch_size读取。

from PIL import Imageimport numpy as npimport matplotlib.pyplot as pltimport tensorflow as tfIMAGE_PATH = 'test/'tfrecord_file = IMAGE_PATH + 'test.tfrecord'writer = tf.python_io.TFRecordWriter(tfrecord_file)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 get_image_binary(filename): """ You can read in the image using tensorflow too, but it's a drag since you have to create graphs. It's much easier using Pillow and NumPy """ image = Image.open(filename) image = np.asarray(image, np.uint8) shape = np.array(image.shape, np.int32) return shape, image.tobytes() # convert image to raw data bytes in the array.def write_to_tfrecord(label, shape, binary_image, tfrecord_file): """ This example is to write a sample to TFRecord file. If you want to write more samples, just use a loop. """ # write label, shape, and image content to the TFRecord file example = tf.train.Example(features=tf.train.Features(feature={ 'label': _int64_feature(label), 'h': _int64_feature(shape[0]), 'w': _int64_feature(shape[1]), 'c': _int64_feature(shape[2]), 'image': _bytes_feature(binary_image) })) writer.write(example.SerializeToString())def write_tfrecord(label, image_file, tfrecord_file): shape, binary_image = get_image_binary(image_file) write_to_tfrecord(label, shape, binary_image, tfrecord_file) # print(shape)def main(): # assume the image has the label Chihuahua, which corresponds to class number 1 label = [1,2] image_files = [IMAGE_PATH + 'a.jpg', IMAGE_PATH + 'b.jpg'] for i in range(2): write_tfrecord(label[i], image_files[i], tfrecord_file) writer.close() batch_size = 2 filename_queue = tf.train.string_input_producer([tfrecord_file]) reader = tf.TFRecordReader() _, serialized_example = reader.read(filename_queue) img_features = tf.parse_single_example( serialized_example, features={ 'label': tf.FixedLenFeature([], tf.int64), 'h': tf.FixedLenFeature([], tf.int64), 'w': tf.FixedLenFeature([], tf.int64), 'c': tf.FixedLenFeature([], tf.int64), 'image': tf.FixedLenFeature([], tf.string), }) h = tf.cast(img_features['h'], tf.int32) w = tf.cast(img_features['w'], tf.int32) c = tf.cast(img_features['c'], tf.int32) image = tf.decode_raw(img_features['image'], tf.uint8) image = tf.reshape(image, [h, w, c]) label = tf.cast(img_features['label'],tf.int32) label = tf.reshape(label, [1]) image = tf.image.resize_images(image, (224,224)) image = tf.reshape(image, [224, 224, 3]) image, label = tf.train.batch([image, label], batch_size= batch_size) with tf.Session() as sess: coord = tf.train.Coordinator() threads = tf.train.start_queue_runners(coord=coord) image, label=sess.run([image, label]) coord.request_stop() coord.join(threads) print(image.shape) print(label) plt.figure() plt.imshow(image[0,:,:,0]) plt.show() plt.figure() plt.imshow(image[0,:,:,1]) plt.show() image1 = image[0,:,:,:] print(image1.shape) print(image1.dtype) im = Image.fromarray(np.uint8(image1)) #参考numpy和图片的互转：http://blog.csdn.net/zywvvd/article/details/72810360 im.show()if __name__ == '__main__': main()

输出是

(2, 224, 224, 3)[[1] [2]]第一张图片的三种显示（略）

封装成函数：

# -*- coding: utf-8 -*-"""Created on Fri Sep 8 14:38:15 2017@author: wayne"""'''本文参考了以下代码，在多个不同大小图片存取方面做了重新开发：https://github.com/chiphuyen/stanford-tensorflow-tutorials/blob/master/examples/09_tfrecord_example.pyhttp://blog.csdn.net/hjxu2016/article/details/76165559https://stackoverflow.com/questions/41921746/tensorflow-varlenfeature-vs-fixedlenfeaturehttps://github.com/tensorflow/tensorflow/issues/10492后续：-存入多个TFrecords文件的例子见http://blog.csdn.net/xierhacker/article/details/72357651-如何作shuffle和数据增强string_input_producer (需要理解tf的数据流，标签队列的工作方式等等)http://blog.csdn.net/liuchonge/article/details/73649251'''from PIL import Imageimport numpy as npimport matplotlib.pyplot as pltimport tensorflow as tfIMAGE_PATH = 'test/'tfrecord_file = IMAGE_PATH + 'test.tfrecord'writer = tf.python_io.TFRecordWriter(tfrecord_file)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 get_image_binary(filename): """ You can read in the image using tensorflow too, but it's a drag since you have to create graphs. It's much easier using Pillow and NumPy """ image = Image.open(filename) image = np.asarray(image, np.uint8) shape = np.array(image.shape, np.int32) return shape, image.tobytes() # convert image to raw data bytes in the array.def write_to_tfrecord(label, shape, binary_image, tfrecord_file): """ This example is to write a sample to TFRecord file. If you want to write more samples, just use a loop. """ # write label, shape, and image content to the TFRecord file example = tf.train.Example(features=tf.train.Features(feature={ 'label': _int64_feature(label), 'h': _int64_feature(shape[0]), 'w': _int64_feature(shape[1]), 'c': _int64_feature(shape[2]), 'image': _bytes_feature(binary_image) })) writer.write(example.SerializeToString())def write_tfrecord(label, image_file, tfrecord_file): shape, binary_image = get_image_binary(image_file) write_to_tfrecord(label, shape, binary_image, tfrecord_file)def read_and_decode(tfrecords_file, batch_size): '''''read and decode tfrecord file, generate (image, label) batches Args: tfrecords_file: the directory of tfrecord file batch_size: number of images in each batch Returns: image: 4D tensor - [batch_size, width, height, channel] label: 1D tensor - [batch_size] ''' # make an input queue from the tfrecord file filename_queue = tf.train.string_input_producer([tfrecord_file]) reader = tf.TFRecordReader() _, serialized_example = reader.read(filename_queue) img_features = tf.parse_single_example( serialized_example, features={ 'label': tf.FixedLenFeature([], tf.int64), 'h': tf.FixedLenFeature([], tf.int64), 'w': tf.FixedLenFeature([], tf.int64), 'c': tf.FixedLenFeature([], tf.int64), 'image': tf.FixedLenFeature([], tf.string), }) h = tf.cast(img_features['h'], tf.int32) w = tf.cast(img_features['w'], tf.int32) c = tf.cast(img_features['c'], tf.int32) image = tf.decode_raw(img_features['image'], tf.uint8) image = tf.reshape(image, [h, w, c]) label = tf.cast(img_features['label'],tf.int32) label = tf.reshape(label, [1]) ########################################################## # you can put data augmentation here # distorted_image = tf.random_crop(images, [530, 530, img_channel])# distorted_image = tf.image.random_flip_left_right(distorted_image)# distorted_image = tf.image.random_brightness(distorted_image, max_delta=63)# distorted_image = tf.image.random_contrast(distorted_image, lower=0.2, upper=1.8)# distorted_image = tf.image.resize_images(distorted_image, (imagesize,imagesize))# float_image = tf.image.per_image_standardization(distorted_image) image = tf.image.resize_images(image, (224,224)) image = tf.reshape(image, [224, 224, 3]) #image, label = tf.train.batch([image, label], batch_size= batch_size) image_batch, label_batch = tf.train.batch([image, label], batch_size= batch_size, num_threads= 64, capacity = 2000) return image_batch, tf.reshape(label_batch, [batch_size]) def read_tfrecord2(tfrecord_file, batch_size): train_batch, train_label_batch = read_and_decode(tfrecord_file, batch_size) with tf.Session() as sess: coord = tf.train.Coordinator() threads = tf.train.start_queue_runners(coord=coord) train_batch, train_label_batch = sess.run([train_batch, train_label_batch]) coord.request_stop() coord.join(threads) return train_batch, train_label_batchdef main(): # assume the image has the label Chihuahua, which corresponds to class number 1 label = [1,2] image_files = [IMAGE_PATH + 'a.jpg', IMAGE_PATH + 'b.jpg'] for i in range(2): write_tfrecord(label[i], image_files[i], tfrecord_file) writer.close() batch_size = 2 # read_tfrecord(tfrecord_file) # 读取一个图 train_batch, train_label_batch = read_tfrecord2(tfrecord_file, batch_size) print(train_batch.shape) print(train_label_batch) plt.figure() plt.imshow(train_batch[0,:,:,0]) plt.show() plt.figure() plt.imshow(train_batch[0,:,:,1]) plt.show() train_batch1 = train_batch[0,:,:,:] print(train_batch.shape) print(train_batch1.dtype) im = Image.fromarray(np.uint8(train_batch1)) #参考numpy和图片的互转：http://blog.csdn.net/zywvvd/article/details/72810360 im.show()if __name__ == '__main__': main()

以上这篇TensorFLow 不同大小图片的TFrecords存取实例就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持。