Python parsing_ops.decode_raw方法代码示例

# 需要导入模块: from tensorflow.python.ops import parsing_ops [as 别名]
# 或者: from tensorflow.python.ops.parsing_ops import decode_raw [as 别名]
def __init__(self,
               image_key=None,
               format_key=None,
               shape=None,
               channels=3,
               dtype=dtypes.uint8,
               repeated=False):
    """Initializes the image.

    Args:
      image_key: the name of the TF-Example feature in which the encoded image
        is stored.
      format_key: the name of the TF-Example feature in which the image format
        is stored.
      shape: the output shape of the image as 1-D `Tensor`
        [height, width, channels]. If provided, the image is reshaped
        accordingly. If left as None, no reshaping is done. A shape should
        be supplied only if all the stored images have the same shape.
      channels: the number of channels in the image.
      dtype: images will be decoded at this bit depth. Different formats
        support different bit depths.
          See tf.image.decode_image,
              tf.decode_raw,
      repeated: if False, decodes a single image. If True, decodes a
        variable number of image strings from a 1D tensor of strings.
    """
    if not image_key:
      image_key = 'image/encoded'
    if not format_key:
      format_key = 'image/format'

    super(Image, self).__init__([image_key, format_key])
    self._image_key = image_key
    self._format_key = format_key
    self._shape = shape
    self._channels = channels
    self._dtype = dtype
    self._repeated = repeated 

# 需要导入模块: from tensorflow.python.ops import parsing_ops [as 别名]
# 或者: from tensorflow.python.ops.parsing_ops import decode_raw [as 别名]
def _decode(self, image_buffer, image_format):
    """Decodes the image buffer.

    Args:
      image_buffer: The tensor representing the encoded image tensor.
      image_format: The image format for the image in `image_buffer`. If image
        format is `raw`, all images are expected to be in this format, otherwise
        this op can decode a mix of `jpg` and `png` formats.

    Returns:
      A tensor that represents decoded image of self._shape, or
      (?, ?, self._channels) if self._shape is not specified.
    """
    def decode_image():
      """Decodes a png or jpg based on the headers."""
      return image_ops.decode_image(image_buffer, self._channels)

    def decode_raw():
      """Decodes a raw image."""
      return parsing_ops.decode_raw(image_buffer, out_type=self._dtype)

    pred_fn_pairs = {
        math_ops.logical_or(
            math_ops.equal(image_format, 'raw'),
            math_ops.equal(image_format, 'RAW')): decode_raw,
    }
    image = control_flow_ops.case(
        pred_fn_pairs, default=decode_image, exclusive=True)

    image.set_shape([None, None, self._channels])
    if self._shape is not None:
      image = array_ops.reshape(image, self._shape)

    return image 

# 需要导入模块: from tensorflow.python.ops import parsing_ops [as 别名]
# 或者: from tensorflow.python.ops.parsing_ops import decode_raw [as 别名]
def _decode(self, image_buffer, image_format):
    """Decodes the image buffer.

    Args:
      image_buffer: T tensor representing the encoded image tensor.
      image_format: The image format for the image in `image_buffer`.

    Returns:
      A decoder image.
    """
    def decode_png():
      return image_ops.decode_png(image_buffer, self._channels)
    def decode_raw():
      return parsing_ops.decode_raw(image_buffer, dtypes.uint8)
    def decode_jpg():
      return image_ops.decode_jpeg(image_buffer, self._channels)

    image = control_flow_ops.case({
        math_ops.logical_or(math_ops.equal(image_format, 'png'),
                            math_ops.equal(image_format, 'PNG')): decode_png,
        math_ops.logical_or(math_ops.equal(image_format, 'raw'),
                            math_ops.equal(image_format, 'RAW')): decode_raw,
    }, default=decode_jpg, exclusive=True)

    image.set_shape([None, None, self._channels])
    if self._shape is not None:
      image = array_ops.reshape(image, self._shape)

    return image 

# 需要导入模块: from tensorflow.python.ops import parsing_ops [as 别名]
# 或者: from tensorflow.python.ops.parsing_ops import decode_raw [as 别名]
def _decode(self, image_buffer, image_format):
    """Decodes the image buffer.

    Args:
      image_buffer: The tensor representing the encoded image tensor.
      image_format: The image format for the image in `image_buffer`.

    Returns:
      A tensor that represents decoded image of self._shape, or
      (?, ?, self._channels) if self._shape is not specified.
    """

    def decode_png():
      return image_ops.decode_png(image_buffer, self._channels)

    def decode_raw():
      return parsing_ops.decode_raw(image_buffer, dtypes.uint8)

    def decode_jpg():
      return image_ops.decode_jpeg(image_buffer, self._channels)

    # For RGBA images JPEG is not a valid decoder option.
    if self._channels > 3:
      pred_fn_pairs = {
          math_ops.logical_or(
              math_ops.equal(image_format, 'raw'),
              math_ops.equal(image_format, 'RAW')): decode_raw,
      }
      default_decoder = decode_png
    else:
      pred_fn_pairs = {
          math_ops.logical_or(
              math_ops.equal(image_format, 'png'),
              math_ops.equal(image_format, 'PNG')): decode_png,
          math_ops.logical_or(
              math_ops.equal(image_format, 'raw'),
              math_ops.equal(image_format, 'RAW')): decode_raw,
      }
      default_decoder = decode_jpg

    image = control_flow_ops.case(
        pred_fn_pairs, default=default_decoder, exclusive=True)

    image.set_shape([None, None, self._channels])
    if self._shape is not None:
      image = array_ops.reshape(image, self._shape)

    return image 

# 需要导入模块: from tensorflow.python.ops import parsing_ops [as 别名]
# 或者: from tensorflow.python.ops.parsing_ops import decode_raw [as 别名]
def __init__(self,
               image_key=None,
               format_key=None,
               shape=None,
               channels=3,
               dtype=dtypes.uint8,
               repeated=False,
               dct_method=''):
    """Initializes the image.

    Args:
      image_key: the name of the TF-Example feature in which the encoded image
        is stored.
      format_key: the name of the TF-Example feature in which the image format
        is stored.
      shape: the output shape of the image as 1-D `Tensor`
        [height, width, channels]. If provided, the image is reshaped
        accordingly. If left as None, no reshaping is done. A shape should
        be supplied only if all the stored images have the same shape.
      channels: the number of channels in the image.
      dtype: images will be decoded at this bit depth. Different formats
        support different bit depths.
          See tf.image.decode_image,
              tf.io.decode_raw,
      repeated: if False, decodes a single image. If True, decodes a
        variable number of image strings from a 1D tensor of strings.
      dct_method: An optional string. Defaults to empty string. It only takes
        effect when image format is jpeg, used to specify a hint about the
        algorithm used for jpeg decompression. Currently valid values
        are ['INTEGER_FAST', 'INTEGER_ACCURATE']. The hint may be ignored, for
        example, the jpeg library does not have that specific option.
    """
    if not image_key:
      image_key = 'image/encoded'
    if not format_key:
      format_key = 'image/format'

    super(Image, self).__init__([image_key, format_key])
    self._image_key = image_key
    self._format_key = format_key
    self._shape = shape
    self._channels = channels
    self._dtype = dtype
    self._repeated = repeated
    self._dct_method = dct_method 

# 需要导入模块: from tensorflow.python.ops import parsing_ops [as 别名]
# 或者: from tensorflow.python.ops.parsing_ops import decode_raw [as 别名]
def _decode(self, image_buffer, image_format):
    """Decodes the image buffer.

    Args:
      image_buffer: The tensor representing the encoded image tensor.
      image_format: The image format for the image in `image_buffer`. If image
        format is `raw`, all images are expected to be in this format, otherwise
        this op can decode a mix of `jpg` and `png` formats.

    Returns:
      A tensor that represents decoded image of self._shape, or
      (?, ?, self._channels) if self._shape is not specified.
    """

    def decode_image():
      """Decodes a image based on the headers."""
      return math_ops.cast(
          image_ops.decode_image(image_buffer, channels=self._channels),
          self._dtype)

    def decode_jpeg():
      """Decodes a jpeg image with specified '_dct_method'."""
      return math_ops.cast(
          image_ops.decode_jpeg(
              image_buffer,
              channels=self._channels,
              dct_method=self._dct_method), self._dtype)

    def check_jpeg():
      """Checks if an image is jpeg."""
      # For jpeg, we directly use image_ops.decode_jpeg rather than decode_image
      # in order to feed the jpeg specify parameter 'dct_method'.
      return control_flow_ops.cond(
          image_ops.is_jpeg(image_buffer),
          decode_jpeg,
          decode_image,
          name='cond_jpeg')

    def decode_raw():
      """Decodes a raw image."""
      return parsing_ops.decode_raw(image_buffer, out_type=self._dtype)

    pred_fn_pairs = [(math_ops.logical_or(
        math_ops.equal(image_format, 'raw'),
        math_ops.equal(image_format, 'RAW')), decode_raw)]

    image = control_flow_ops.case(
        pred_fn_pairs, default=check_jpeg, exclusive=True)

    image.set_shape([None, None, self._channels])
    if self._shape is not None:
      image = array_ops.reshape(image, self._shape)

    return image