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

def random_hue(image, max_delta, seed=None):
  """Adjust the hue of an RGB image by a random factor.

  Equivalent to `adjust_hue()` but uses a `delta` randomly
  picked in the interval `[-max_delta, max_delta]`.

  `max_delta` must be in the interval `[0, 0.5]`.

  Args:
    image: RGB image or images. Size of the last dimension must be 3.
    max_delta: float.  Maximum value for the random delta.
    seed: An operation-specific seed. It will be used in conjunction
      with the graph-level seed to determine the real seeds that will be
      used in this operation. Please see the documentation of
      set_random_seed for its interaction with the graph-level random seed.

  Returns:
    3-D float tensor of shape `[height, width, channels]`.

  Raises:
    ValueError: if `max_delta` is invalid.
  """
  if max_delta > 0.5:
    raise ValueError('max_delta must be <= 0.5.')

  if max_delta < 0:
    raise ValueError('max_delta must be non-negative.')

  delta = random_ops.random_uniform([], -max_delta, max_delta, seed=seed)
  return adjust_hue(image, delta) 

def random_hue(image, max_delta, seed=None):
  """Adjust the hue of an RGB image by a random factor.

  Equivalent to `adjust_hue()` but uses a `delta` randomly
  picked in the interval `[-max_delta, max_delta]`.

  `max_delta` must be in the interval `[0, 0.5]`.

  Args:
    image: RGB image or images. Size of the last dimension must be 3.
    max_delta: float.  Maximum value for the random delta.
    seed: An operation-specific seed. It will be used in conjunction
      with the graph-level seed to determine the real seeds that will be
      used in this operation. Please see the documentation of
      set_random_seed for its interaction with the graph-level random seed.

  Returns:
    3-D float tensor of shape `[height, width, channels]`.

  Raises:
    ValueError: if `max_delta` is invalid.
  """
  if max_delta > 0.5:
    raise ValueError('max_delta must be <= 0.5.')

  if max_delta < 0:
    raise ValueError('max_delta must be non-negative.')

  delta = random_ops.random_uniform([], -max_delta, max_delta, seed=seed)
  return adjust_hue(image, delta) 

def random_hue(image, max_delta, seed=None):
  """Adjust the hue of an RGB image by a random factor.

  Equivalent to `adjust_hue()` but uses a `delta` randomly
  picked in the interval `[-max_delta, max_delta]`.

  `max_delta` must be in the interval `[0, 0.5]`.

  Args:
    image: RGB image or images. Size of the last dimension must be 3.
    max_delta: float.  Maximum value for the random delta.
    seed: An operation-specific seed. It will be used in conjunction
      with the graph-level seed to determine the real seeds that will be
      used in this operation. Please see the documentation of
      set_random_seed for its interaction with the graph-level random seed.

  Returns:
    3-D float tensor of shape `[height, width, channels]`.

  Raises:
    ValueError: if `max_delta` is invalid.
  """
  if max_delta > 0.5:
    raise ValueError('max_delta must be <= 0.5.')

  if max_delta < 0:
    raise ValueError('max_delta must be non-negative.')

  delta = random_ops.random_uniform([], -max_delta, max_delta, seed=seed)
  return adjust_hue(image, delta) 

def random_hue(image, max_delta, seed=None):
  """Adjust the hue of an RGB image by a random factor.

  Equivalent to `adjust_hue()` but uses a `delta` randomly
  picked in the interval `[-max_delta, max_delta]`.

  `max_delta` must be in the interval `[0, 0.5]`.

  Args:
    image: RGB image or images. Size of the last dimension must be 3.
    max_delta: float.  Maximum value for the random delta.
    seed: An operation-specific seed. It will be used in conjunction
      with the graph-level seed to determine the real seeds that will be
      used in this operation. Please see the documentation of
      set_random_seed for its interaction with the graph-level random seed.

  Returns:
    3-D float tensor of shape `[height, width, channels]`.

  Raises:
    ValueError: if `max_delta` is invalid.
  """
  if max_delta > 0.5:
    raise ValueError('max_delta must be <= 0.5.')

  if max_delta < 0:
    raise ValueError('max_delta must be non-negative.')

  delta = random_ops.random_uniform([], -max_delta, max_delta, seed=seed)
  return adjust_hue(image, delta) 

def random_hue(image, max_delta, seed=None):
  """Adjust the hue of an RGB image by a random factor.

  Equivalent to `adjust_hue()` but uses a `delta` randomly
  picked in the interval `[-max_delta, max_delta]`.

  `max_delta` must be in the interval `[0, 0.5]`.

  Args:
    image: RGB image or images. Size of the last dimension must be 3.
    max_delta: float.  Maximum value for the random delta.
    seed: An operation-specific seed. It will be used in conjunction
      with the graph-level seed to determine the real seeds that will be
      used in this operation. Please see the documentation of
      set_random_seed for its interaction with the graph-level random seed.

  Returns:
    3-D float tensor of shape `[height, width, channels]`.

  Raises:
    ValueError: if `max_delta` is invalid.
  """
  if max_delta > 0.5:
    raise ValueError('max_delta must be <= 0.5.')

  if max_delta < 0:
    raise ValueError('max_delta must be non-negative.')

  delta = random_ops.random_uniform([], -max_delta, max_delta, seed=seed)
  return adjust_hue(image, delta) 

def random_hue(image, max_delta, seed=None):
  """Adjust the hue of an RGB image by a random factor.

  Equivalent to `adjust_hue()` but uses a `delta` randomly
  picked in the interval `[-max_delta, max_delta]`.

  `max_delta` must be in the interval `[0, 0.5]`.

  Args:
    image: RGB image or images. Size of the last dimension must be 3.
    max_delta: float.  Maximum value for the random delta.
    seed: An operation-specific seed. It will be used in conjunction
      with the graph-level seed to determine the real seeds that will be
      used in this operation. Please see the documentation of
      set_random_seed for its interaction with the graph-level random seed.

  Returns:
    3-D float tensor of shape `[height, width, channels]`.

  Raises:
    ValueError: if `max_delta` is invalid.
  """
  if max_delta > 0.5:
    raise ValueError('max_delta must be <= 0.5.')

  if max_delta < 0:
    raise ValueError('max_delta must be non-negative.')

  delta = random_ops.random_uniform([], -max_delta, max_delta, seed=seed)
  return adjust_hue(image, delta) 

def adjust_hue(image, delta, name=None):
  """Adjust hue of an RGB image.

  This is a convenience method that converts an RGB image to float
  representation, converts it to HSV, add an offset to the hue channel, converts
  back to RGB and then back to the original data type. If several adjustments
  are chained it is advisable to minimize the number of redundant conversions.

  `image` is an RGB image.  The image hue is adjusted by converting the
  image to HSV and rotating the hue channel (H) by
  `delta`.  The image is then converted back to RGB.

  `delta` must be in the interval `[-1, 1]`.

  Args:
    image: RGB image or images. Size of the last dimension must be 3.
    delta: float.  How much to add to the hue channel.
    name: A name for this operation (optional).

  Returns:
    Adjusted image(s), same shape and DType as `image`.
  """
  with ops.name_scope(name, 'adjust_hue', [image]) as name:
    image = ops.convert_to_tensor(image, name='image')
    # Remember original dtype to so we can convert back if needed
    orig_dtype = image.dtype
    flt_image = convert_image_dtype(image, dtypes.float32)

    # TODO(zhengxq): we will switch to the fused version after we add a GPU
    # kernel for that.
    fused = os.environ.get('TF_ADJUST_HUE_FUSED', '')
    fused = fused.lower() in ('true', 't', '1')

    if not fused:
      hsv = gen_image_ops.rgb_to_hsv(flt_image)

      hue = array_ops.slice(hsv, [0, 0, 0], [-1, -1, 1])
      saturation = array_ops.slice(hsv, [0, 0, 1], [-1, -1, 1])
      value = array_ops.slice(hsv, [0, 0, 2], [-1, -1, 1])

      # Note that we add 2*pi to guarantee that the resulting hue is a positive
      # floating point number since delta is [-0.5, 0.5].
      hue = math_ops.mod(hue + (delta + 1.), 1.)

      hsv_altered = array_ops.concat([hue, saturation, value], 2)
      rgb_altered = gen_image_ops.hsv_to_rgb(hsv_altered)
    else:
      rgb_altered = gen_image_ops.adjust_hue(flt_image, delta)

    return convert_image_dtype(rgb_altered, orig_dtype) 

def adjust_hue(image, delta, name=None):
  """Adjust hue of an RGB image.

  This is a convenience method that converts an RGB image to float
  representation, converts it to HSV, add an offset to the hue channel, converts
  back to RGB and then back to the original data type. If several adjustments
  are chained it is advisable to minimize the number of redundant conversions.

  `image` is an RGB image.  The image hue is adjusted by converting the
  image to HSV and rotating the hue channel (H) by
  `delta`.  The image is then converted back to RGB.

  `delta` must be in the interval `[-1, 1]`.

  Args:
    image: RGB image or images. Size of the last dimension must be 3.
    delta: float.  How much to add to the hue channel.
    name: A name for this operation (optional).

  Returns:
    Adjusted image(s), same shape and DType as `image`.
  """
  with ops.name_scope(name, 'adjust_hue', [image]) as name:
    image = ops.convert_to_tensor(image, name='image')
    # Remember original dtype to so we can convert back if needed
    orig_dtype = image.dtype
    flt_image = convert_image_dtype(image, dtypes.float32)

    # TODO(zhengxq): we will switch to the fused version after we add a GPU
    # kernel for that.
    fused = os.environ.get('TF_ADJUST_HUE_FUSED', '')
    fused = fused.lower() in ('true', 't', '1')

    if not fused:
      hsv = gen_image_ops.rgb_to_hsv(flt_image)

      hue = array_ops.slice(hsv, [0, 0, 0], [-1, -1, 1])
      saturation = array_ops.slice(hsv, [0, 0, 1], [-1, -1, 1])
      value = array_ops.slice(hsv, [0, 0, 2], [-1, -1, 1])

      # Note that we add 2*pi to guarantee that the resulting hue is a positive
      # floating point number since delta is [-0.5, 0.5].
      hue = math_ops.mod(hue + (delta + 1.), 1.)

      hsv_altered = array_ops.concat([hue, saturation, value], 2)
      rgb_altered = gen_image_ops.hsv_to_rgb(hsv_altered)
    else:
      rgb_altered = gen_image_ops.adjust_hue(flt_image, delta)

    return convert_image_dtype(rgb_altered, orig_dtype) 

def adjust_hue(image, delta, name=None):
  """Adjust hue of an RGB image.

  This is a convenience method that converts an RGB image to float
  representation, converts it to HSV, add an offset to the hue channel, converts
  back to RGB and then back to the original data type. If several adjustments
  are chained it is advisable to minimize the number of redundant conversions.

  `image` is an RGB image.  The image hue is adjusted by converting the
  image to HSV and rotating the hue channel (H) by
  `delta`.  The image is then converted back to RGB.

  `delta` must be in the interval `[-1, 1]`.

  Args:
    image: RGB image or images. Size of the last dimension must be 3.
    delta: float.  How much to add to the hue channel.
    name: A name for this operation (optional).

  Returns:
    Adjusted image(s), same shape and DType as `image`.
  """
  with ops.name_scope(name, 'adjust_hue', [image]) as name:
    image = ops.convert_to_tensor(image, name='image')
    # Remember original dtype to so we can convert back if needed
    orig_dtype = image.dtype
    flt_image = convert_image_dtype(image, dtypes.float32)

    # TODO(zhengxq): we will switch to the fused version after we add a GPU
    # kernel for that.
    fused = os.environ.get('TF_ADJUST_HUE_FUSED', '')
    fused = fused.lower() in ('true', 't', '1')

    if not fused:
      hsv = gen_image_ops.rgb_to_hsv(flt_image)

      hue = array_ops.slice(hsv, [0, 0, 0], [-1, -1, 1])
      saturation = array_ops.slice(hsv, [0, 0, 1], [-1, -1, 1])
      value = array_ops.slice(hsv, [0, 0, 2], [-1, -1, 1])

      # Note that we add 2*pi to guarantee that the resulting hue is a positive
      # floating point number since delta is [-0.5, 0.5].
      hue = math_ops.mod(hue + (delta + 1.), 1.)

      hsv_altered = array_ops.concat(2, [hue, saturation, value])
      rgb_altered = gen_image_ops.hsv_to_rgb(hsv_altered)
    else:
      rgb_altered = gen_image_ops.adjust_hue(flt_image, delta)

    return convert_image_dtype(rgb_altered, orig_dtype) 

def adjust_hue(image, delta, name=None):
  """Adjust hue of an RGB image.

  This is a convenience method that converts an RGB image to float
  representation, converts it to HSV, add an offset to the hue channel, converts
  back to RGB and then back to the original data type. If several adjustments
  are chained it is advisable to minimize the number of redundant conversions.

  `image` is an RGB image.  The image hue is adjusted by converting the
  image to HSV and rotating the hue channel (H) by
  `delta`.  The image is then converted back to RGB.

  `delta` must be in the interval `[-1, 1]`.

  Args:
    image: RGB image or images. Size of the last dimension must be 3.
    delta: float.  How much to add to the hue channel.
    name: A name for this operation (optional).

  Returns:
    Adjusted image(s), same shape and DType as `image`.
  """
  with ops.name_scope(name, 'adjust_hue', [image]) as name:
    image = ops.convert_to_tensor(image, name='image')
    # Remember original dtype to so we can convert back if needed
    orig_dtype = image.dtype
    flt_image = convert_image_dtype(image, dtypes.float32)

    # TODO(zhengxq): we will switch to the fused version after we add a GPU
    # kernel for that.
    fused = os.environ.get('TF_ADJUST_HUE_FUSED', '')
    fused = fused.lower() in ('true', 't', '1')

    if not fused:
      hsv = gen_image_ops.rgb_to_hsv(flt_image)

      hue = array_ops.slice(hsv, [0, 0, 0], [-1, -1, 1])
      saturation = array_ops.slice(hsv, [0, 0, 1], [-1, -1, 1])
      value = array_ops.slice(hsv, [0, 0, 2], [-1, -1, 1])

      # Note that we add 2*pi to guarantee that the resulting hue is a positive
      # floating point number since delta is [-0.5, 0.5].
      hue = math_ops.mod(hue + (delta + 1.), 1.)

      hsv_altered = array_ops.concat([hue, saturation, value], 2)
      rgb_altered = gen_image_ops.hsv_to_rgb(hsv_altered)
    else:
      rgb_altered = gen_image_ops.adjust_hue(flt_image, delta)

    return convert_image_dtype(rgb_altered, orig_dtype) 

def adjust_hue(image, delta, name=None):
  """Adjust hue of an RGB image.

  This is a convenience method that converts an RGB image to float
  representation, converts it to HSV, add an offset to the hue channel, converts
  back to RGB and then back to the original data type. If several adjustments
  are chained it is advisable to minimize the number of redundant conversions.

  `image` is an RGB image.  The image hue is adjusted by converting the
  image to HSV and rotating the hue channel (H) by
  `delta`.  The image is then converted back to RGB.

  `delta` must be in the interval `[-1, 1]`.

  Args:
    image: RGB image or images. Size of the last dimension must be 3.
    delta: float.  How much to add to the hue channel.
    name: A name for this operation (optional).

  Returns:
    Adjusted image(s), same shape and DType as `image`.
  """
  with ops.name_scope(name, 'adjust_hue', [image]) as name:
    image = ops.convert_to_tensor(image, name='image')
    # Remember original dtype to so we can convert back if needed
    orig_dtype = image.dtype
    flt_image = convert_image_dtype(image, dtypes.float32)

    # TODO(zhengxq): we will switch to the fused version after we add a GPU
    # kernel for that.
    fused = os.environ.get('TF_ADJUST_HUE_FUSED', '')
    fused = fused.lower() in ('true', 't', '1')

    if not fused:
      hsv = gen_image_ops.rgb_to_hsv(flt_image)

      hue = array_ops.slice(hsv, [0, 0, 0], [-1, -1, 1])
      saturation = array_ops.slice(hsv, [0, 0, 1], [-1, -1, 1])
      value = array_ops.slice(hsv, [0, 0, 2], [-1, -1, 1])

      # Note that we add 2*pi to guarantee that the resulting hue is a positive
      # floating point number since delta is [-0.5, 0.5].
      hue = math_ops.mod(hue + (delta + 1.), 1.)

      hsv_altered = array_ops.concat([hue, saturation, value], 2)
      rgb_altered = gen_image_ops.hsv_to_rgb(hsv_altered)
    else:
      rgb_altered = gen_image_ops.adjust_hue(flt_image, delta)

    return convert_image_dtype(rgb_altered, orig_dtype) 

def adjust_hue(image, delta, name=None):
  """Adjust hue of an RGB image.

  This is a convenience method that converts an RGB image to float
  representation, converts it to HSV, add an offset to the hue channel, converts
  back to RGB and then back to the original data type. If several adjustments
  are chained it is advisable to minimize the number of redundant conversions.

  `image` is an RGB image.  The image hue is adjusted by converting the
  image to HSV and rotating the hue channel (H) by
  `delta`.  The image is then converted back to RGB.

  `delta` must be in the interval `[-1, 1]`.

  Args:
    image: RGB image or images. Size of the last dimension must be 3.
    delta: float.  How much to add to the hue channel.
    name: A name for this operation (optional).

  Returns:
    Adjusted image(s), same shape and DType as `image`.
  """
  with ops.name_scope(name, 'adjust_hue', [image]) as name:
    image = ops.convert_to_tensor(image, name='image')
    # Remember original dtype to so we can convert back if needed
    orig_dtype = image.dtype
    flt_image = convert_image_dtype(image, dtypes.float32)

    # TODO(zhengxq): we will switch to the fused version after we add a GPU
    # kernel for that.
    fused = os.environ.get('TF_ADJUST_HUE_FUSED', '')
    fused = fused.lower() in ('true', 't', '1')

    if not fused:
      hsv = gen_image_ops.rgb_to_hsv(flt_image)

      hue = array_ops.slice(hsv, [0, 0, 0], [-1, -1, 1])
      saturation = array_ops.slice(hsv, [0, 0, 1], [-1, -1, 1])
      value = array_ops.slice(hsv, [0, 0, 2], [-1, -1, 1])

      # Note that we add 2*pi to guarantee that the resulting hue is a positive
      # floating point number since delta is [-0.5, 0.5].
      hue = math_ops.mod(hue + (delta + 1.), 1.)

      hsv_altered = array_ops.concat([hue, saturation, value], 2)
      rgb_altered = gen_image_ops.hsv_to_rgb(hsv_altered)
    else:
      rgb_altered = gen_image_ops.adjust_hue(flt_image, delta)

    return convert_image_dtype(rgb_altered, orig_dtype)