TensorFLow如何实现不同大小图片的TFrecords存取

这篇文章主要介绍了TensorFLow如何实现不同大小图片的TFrecords存取，具有一定借鉴价值，感兴趣的朋友可以参考下，希望大家阅读完这篇文章之后大有收获，下面让小编带着大家一起了解一下。

全部存入一个TFrecords文件，然后读取并显示第一张。

示例：

from PIL import Image import numpy as np import matplotlib.pyplot as plt import tensorflow as tf IMAGE_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 Image import numpy as np import matplotlib.pyplot as plt import tensorflow as tf IMAGE_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.py http://blog.csdn.net/hjxu2016/article/details/76165559 https://stackoverflow.com/questions/41921746/tensorflow-varlenfeature-vs-fixedlenfeature https://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 Image import numpy as np import matplotlib.pyplot as plt import tensorflow as tf IMAGE_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_batch 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   # 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_batch2 = train_batch[0,:,:,:]   print(train_batch.shape)   print(train_batch2.dtype)   im = Image.fromarray(np.uint8(train_batch2)) #参考numpy和图片的互转：http://blog.csdn.net/zywvvd/article/details/72810360   im.show() if __name__ == '__main__':   main()

感谢你能够认真阅读完这篇文章，希望小编分享的“TensorFLow如何实现不同大小图片的TFrecords存取”这篇文章对大家有帮助，同时也希望大家多多支持亿速云，关注亿速云行业资讯频道，更多相关知识等着你来学习!