无法将张量流图像保存到文件中

Question

我需要将图像调整到一定大小并将其保存到文件中,所以我选择了tf.image.resize_image_with_crop_or_pad函数:

import tensorflow as tf

image_decoded = tf.image.decode_jpeg(tf.read_file('1.jpg'), channels=3)
cropped       = tf.image.resize_image_with_crop_or_pad(image_decoded, 200, 200)
tf.write_file('2.jpg', cropped)

错误失败:

Traceback (most recent call last):
  File "/home/test/anaconda3/lib/python3.5/site-packages/tensorflow/python/framework/op_def_library.py", line 490, in apply_op
    preferred_dtype=default_dtype)
  File "/home/test/anaconda3/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 669, in convert_to_tensor
    ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
  File "/home/test/anaconda3/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 583, in _TensorTensorConversionFunction
    % (dtype.name, t.dtype.name, str(t)))
ValueError: Tensor conversion requested dtype string for Tensor with dtype uint8: 'Tensor("control_dependency_3:0", shape=(200, 200, 3), dtype=uint8)'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "train.py", line 15, in <module>
    tf.write_file('2.jpg', cropped)
  File "/home/test/anaconda3/lib/python3.5/site-packages/tensorflow/python/ops/gen_io_ops.py", line 694, in write_file
    contents=contents, name=name)
  File "/home/test/anaconda3/lib/python3.5/site-packages/tensorflow/python/framework/op_def_library.py", line 508, in apply_op
    (prefix, dtypes.as_dtype(input_arg.type).name))
TypeError: Input 'contents' of 'WriteFile' Op has type uint8 that does not match expected type of string.

我尝试使用tf.as_string()将Tensor转换为字符串,但崩溃了:

TypeError: DataType uint8 for attr 'T' not in list of allowed values: int32, int64, complex64, float32, float64, bool, int8

我在Linux Mint上使用Tensorflow v0.12.0-rc0

Answer 1

首先需要将图像从张量编码为jpeg然后保存.此外,您应该执行会话来评估您的代码:

import tensorflow as tf

image_decoded = tf.image.decode_jpeg(tf.read_file('1.jpg'), channels=3)
cropped       = tf.image.resize_image_with_crop_or_pad(image_decoded, 200, 200)
enc = tf.image.encode_jpeg(cropped)
fname = tf.constant('2.jpg')
fwrite = tf.write_file(fname, enc)

sess = tf.Session()
result = sess.run(fwrite)