tensorflow之读取jpg图像保存为tfrecord再读取

有了jpg读取的经验和tfrecord写入的经验之后，开始尝试把jpg图像写入到tfrecord，另外还想尝试从tfrecord文件读出jpg图像

上示例把jpg的图片的二进制以及长和宽的信息保存进tfrecord

decode_jpeg_data = tf.placeholder(dtype=tf.string)decode_jpeg = tf.image.decode_jpeg(decode_jpeg_data, channels=3)tfrecords_filename = './tfrecords'writer = tf.python_io.TFRecordWriter(tfrecords_filename) # 创建.tfrecord文件，准备写入image_data = tf.gfile.FastGFile("C:/Users/shenwei/Desktop/timg.jpg", 'rb').read()print(type(image_data))with tf.Session() as sess:image = sess.run(decode_jpeg,feed_dict={decode_jpeg_data: image_data})print(image.shape[0])print(image.shape[1])example = tf.train.Example(features=tf.train.Features(feature={'encoded': tf.train.Feature(bytes_list = tf.train.BytesList(value=[image_data])) ,'height': tf.train.Feature(int64_list = tf.train.Int64List(value = [image.shape[0]])),'width': tf.train.Feature(int64_list = tf.train.Int64List(value = [image.shape[1]])),}))writer.write(example.SerializeToString()) writer.close()

********************上一段完整的保存以及读取的示例********************

参考/MOU_IT/article/details/82773839

slim = tf.contrib.slim # 创建TFrecord文件def create_record_file():train_filename = "train.tfrecords"if os.path.exists(train_filename):os.remove(train_filename)# 创建.tfrecord文件，准备写入writer = tf.python_io.TFRecordWriter('./'+train_filename)with tf.Session() as sess:for i in range(10): img_raw = tf.gfile.FastGFile("C:/Users/shenwei/Desktop/test/"+str(i)+".jpg", 'rb').read()decode_data = tf.image.decode_jpeg(img_raw)image_shape= decode_data.eval().shapeexample = tf.train.Example(features=tf.train.Features(feature={'image/encoded':tf.train.Feature(bytes_list=tf.train.BytesList(value=[img_raw])),'image/format':tf.train.Feature(bytes_list = tf.train.BytesList(value=[b'jpg'])),'image/width':tf.train.Feature(int64_list = tf.train.Int64List(value=[image_shape[1]])), 'image/height':tf.train.Feature(int64_list = tf.train.Int64List(value=[image_shape[0]])),'image/label':tf.train.Feature(int64_list = tf.train.Int64List(value=[i])),}))writer.write(example.SerializeToString()) # 序列化保存writer.close()print ("保存tfrecord文件成功。")# 使用Slim的方法从TFrecord文件中读取def read_record_file(): tfrecords_filename = "train.tfrecords" # 将tf.train.Example反序列化成存储之前的格式。由tf完成keys_to_features = {'image/encoded': tf.FixedLenFeature((), tf.string, default_value=''),'image/format': tf.FixedLenFeature((), tf.string, default_value='jpeg'),'image/width': tf.FixedLenFeature((), tf.int64, default_value=0),'image/height': tf.FixedLenFeature((), tf.int64, default_value=0),'image/label': tf.FixedLenFeature((), tf.int64, default_value=0),}# 将反序列化的数据组装成更高级的格式。由slim完成items_to_handlers = {'image': slim.tfexample_decoder.Image(image_key='image/encoded',format_key='image/format',channels=3),'label': slim.tfexample_decoder.Tensor('image/label'),'height': slim.tfexample_decoder.Tensor('image/height'),'width': slim.tfexample_decoder.Tensor('image/width')}# 定义解码器，进行解码decoder = slim.tfexample_decoder.TFExampleDecoder(keys_to_features, items_to_handlers)# 定义dataset，该对象定义了数据集的文件位置，解码方式等元信息dataset = slim.dataset.Dataset(data_sources=tfrecords_filename,reader=tf.TFRecordReader,decoder=decoder,num_samples=10, # 训练数据的总数items_to_descriptions=None,num_classes=10,)#使用provider对象根据dataset信息读取数据provider = slim.dataset_data_provider.DatasetDataProvider(dataset,num_readers=1,common_queue_capacity=20,common_queue_min=1)# 获取数据[image, label,height,width] = provider.get(['image', 'label','height','width']) with tf.Session() as sess:init_op = tf.global_variables_initializer()sess.run(init_op)coord=tf.train.Coordinator()threads= tf.train.start_queue_runners(coord=coord)for i in range(10):img,l,h,w= sess.run([image,label,height,width]) #img = tf.reshape(img, [h,w,3]) print (img.shape) #img=Image.fromarray(img.eval(), 'RGB') # 这里将narray转为Image类，Image转narray：a=np.array(img)#img.save('./'+str(l)+'.jpg') # 保存图片coord.request_stop()coord.join(threads) if __name__ == '__main__':#create_record_file()read_record_file()