Python tensorflow.python.ops.gen_image_ops.decode_png() Examples

The following are 4 code examples of tensorflow.python.ops.gen_image_ops.decode_png(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module tensorflow.python.ops.gen_image_ops , or try the search function .
Example #1
Source File: datasets.py    From self-supervision with BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up 
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
Example #2
Source File: image_ops_impl.py    From auto-alt-text-lambda-api with MIT License 4 votes vote down vote up 
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')
Example #3
Source File: datasets.py    From self-supervision with BSD 3-Clause "New" or "Revised" License 4 votes vote down vote up 
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')
Example #4
Source File: image_ops_impl.py    From keras-lambda with MIT License 4 votes vote down vote up 
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')