Python gen_image_ops.decode_png方法代码示例

# 需要导入模块: from tensorflow.python.ops import gen_image_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_image_ops import decode_png [as 别名]
def _voc_seg_load_file(path, epochs=None, shuffle=True, seed=0):

    PASCAL_ROOT = os.environ['VOC_DIR']
    filename_queue = tf.train.string_input_producer([path],
            num_epochs=epochs, shuffle=shuffle, seed=seed)

    reader = tf.TextLineReader()
    key, value = reader.read(filename_queue)
    image_path, seg_path = tf.decode_csv(value, record_defaults=[[''], ['']], field_delim=' ')

    image_abspath = PASCAL_ROOT + image_path
    seg_abspath = PASCAL_ROOT + seg_path

    image_content = tf.read_file(image_abspath)
    image = decode_image(image_content, channels=3)
    image.set_shape([None, None, 3])

    imgshape = tf.shape(image)[:2]
    imgname = image_path

    seg_content = tf.read_file(seg_abspath)
    seg = tf.cast(tf.image.decode_png(seg_content, channels=1), tf.int32)
    return image, seg, imgshape, imgname 

# 需要导入模块: from tensorflow.python.ops import gen_image_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_image_ops import decode_png [as 别名]
def decode_image(contents, channels=None, name=None):
  """Convenience function for `decode_gif`, `decode_jpeg`, and `decode_png`.
  Detects whether an image is a GIF, JPEG, or PNG, and performs the appropriate 
  operation to convert the input bytes `string` into a `Tensor` of type `uint8`.

  Note: `decode_gif` returns a 4-D array `[num_frames, height, width, 3]`, as 
  opposed to `decode_jpeg` and `decode_png`, which return 3-D arrays 
  `[height, width, num_channels]`. Make sure to take this into account when 
  constructing your graph if you are intermixing GIF files with JPEG and/or PNG 
  files.

  Args:
    contents: 0-D `string`. The encoded image bytes.
    channels: An optional `int`. Defaults to `0`. Number of color channels for 
      the decoded image.
    name: A name for the operation (optional)
    
  Returns:
    `Tensor` with type `uint8` with shape `[height, width, num_channels]` for 
      JPEG and PNG images and shape `[num_frames, height, width, 3]` for GIF 
      images.
  """
  with ops.name_scope(name, 'decode_image') as scope:
    if channels not in (None, 0, 1, 3):
      raise ValueError('channels must be in (None, 0, 1, 3)')
    substr = string_ops.substr(contents, 0, 4)

    def _gif():
      # Create assert op to check that bytes are GIF decodable
      is_gif = math_ops.equal(substr, b'\x47\x49\x46\x38', name='is_gif')
      decode_msg = 'Unable to decode bytes as JPEG, PNG, or GIF'
      assert_decode = control_flow_ops.Assert(is_gif, [decode_msg])
      # Create assert to make sure that channels is not set to 1
      # Already checked above that channels is in (None, 0, 1, 3)
      gif_channels = 0 if channels is None else channels
      good_channels = math_ops.not_equal(gif_channels, 1, name='check_channels')
      channels_msg = 'Channels must be in (None, 0, 3) when decoding GIF images'
      assert_channels = control_flow_ops.Assert(good_channels, [channels_msg])
      with ops.control_dependencies([assert_decode, assert_channels]):
        return gen_image_ops.decode_gif(contents)

    def _png():
      return gen_image_ops.decode_png(contents, channels)

    def check_png():
      is_png = math_ops.equal(substr, b'\211PNG', name='is_png')
      return control_flow_ops.cond(is_png, _png, _gif, name='cond_png')

    def _jpeg():
      return gen_image_ops.decode_jpeg(contents, channels)

    is_jpeg = math_ops.equal(substr, b'\xff\xd8\xff\xe0', name='is_jpeg')
    return control_flow_ops.cond(is_jpeg, _jpeg, check_png, name='cond_jpeg') 

# 需要导入模块: from tensorflow.python.ops import gen_image_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_image_ops import decode_png [as 别名]
def decode_image(contents, channels=None, name=None):
  """Convenience function for `decode_gif`, `decode_jpeg`, and `decode_png`.
  Detects whether an image is a GIF, JPEG, or PNG, and performs the appropriate
  operation to convert the input bytes `string` into a `Tensor` of type `uint8`.

  Note: `decode_gif` returns a 4-D array `[num_frames, height, width, 3]`, as
  opposed to `decode_jpeg` and `decode_png`, which return 3-D arrays
  `[height, width, num_channels]`. Make sure to take this into account when
  constructing your graph if you are intermixing GIF files with JPEG and/or PNG
  files.

  Args:
    contents: 0-D `string`. The encoded image bytes.
    channels: An optional `int`. Defaults to `0`. Number of color channels for
      the decoded image.
    name: A name for the operation (optional)

  Returns:
    `Tensor` with type `uint8` with shape `[height, width, num_channels]` for
      JPEG and PNG images and shape `[num_frames, height, width, 3]` for GIF
      images.
  """
  with ops.name_scope(name, 'decode_image') as scope:
    if channels not in (None, 0, 1, 3):
      raise ValueError('channels must be in (None, 0, 1, 3)')
    substr = tf.substr(contents, 0, 4)

    def _gif():
      # Create assert op to check that bytes are GIF decodable
      is_gif = tf.equal(substr, b'\x47\x49\x46\x38', name='is_gif')
      decode_msg = 'Unable to decode bytes as JPEG, PNG, or GIF'
      assert_decode = control_flow_ops.Assert(is_gif, [decode_msg])
      # Create assert to make sure that channels is not set to 1
      # Already checked above that channels is in (None, 0, 1, 3)
      gif_channels = 0 if channels is None else channels
      good_channels = tf.not_equal(gif_channels, 1, name='check_channels')
      channels_msg = 'Channels must be in (None, 0, 3) when decoding GIF images'
      assert_channels = control_flow_ops.Assert(good_channels, [channels_msg])
      with ops.control_dependencies([assert_decode, assert_channels]):
        return gen_image_ops.decode_gif(contents)

    def _png():
      return gen_image_ops.decode_png(contents, channels)

    def check_png():
      is_png = tf.equal(substr, b'\211PNG', name='is_png')
      return control_flow_ops.cond(is_png, _png, _gif, name='cond_png')

    def _jpeg():
      return gen_image_ops.decode_jpeg(contents, channels)

    is_jpeg = tf.logical_or(tf.equal(substr, b'\xff\xd8\xff\xe0', name='is_jpeg0'),
                           tf.equal(substr, b'\xff\xd8\xff\xe1', name='is_jpeg0'))

    return control_flow_ops.cond(is_jpeg, _jpeg, check_png, name='cond_jpeg')