Python gen_image_ops.rgb_to_hsv函数代码示例

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_v2([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_saturation(image, saturation_factor, name=None):
  """Adjust saturation 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 saturation 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 saturation is adjusted by converting the
  image to HSV and multiplying the saturation (S) channel by
  `saturation_factor` and clipping. The image is then converted back to RGB.

  Args:
    image: RGB image or images. Size of the last dimension must be 3.
    saturation_factor: float. Factor to multiply the saturation by.
    name: A name for this operation (optional).

  Returns:
    Adjusted image(s), same shape and DType as `image`.
  """
  with ops.name_scope(name, 'adjust_saturation', [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_SATURATION_FUSED', '')
    fused = fused.lower() in ('true', 't', '1')

    if fused:
      return convert_image_dtype(
          gen_image_ops.adjust_saturation(flt_image, saturation_factor),
          orig_dtype)

    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])

    saturation *= saturation_factor
    saturation = clip_ops.clip_by_value(saturation, 0.0, 1.0)

    hsv_altered = array_ops.concat([hue, saturation, value], 2)
    rgb_altered = gen_image_ops.hsv_to_rgb(hsv_altered)

    return convert_image_dtype(rgb_altered, orig_dtype)

def adjust_saturation(image, saturation_factor, name=None):
    with ops.op_scope([image], name, 'adjust_saturation') as name:
        # Remember original dtype to so we can convert back if needed
        orig_dtype = image.dtype
        flt_image = tf.image.convert_image_dtype(image, tf.float32)

        hsv = gen_image_ops.rgb_to_hsv(flt_image)

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

        saturation *= saturation_factor
        saturation = clip_ops.clip_by_value(saturation, 0.0, 1.0)

        hsv_altered = tf.concat(3, [hue, saturation, value])
        rgb_altered = gen_image_ops.hsv_to_rgb(hsv_altered)

        return tf.image.convert_image_dtype(rgb_altered, orig_dtype)

def adjust_hue(image, delta, name=None):
    with ops.op_scope([image], name, 'adjust_hue') as name:
        # Remember original dtype to so we can convert back if needed
        orig_dtype = image.dtype
        flt_image = tf.image.convert_image_dtype(image, tf.float32)

        hsv = gen_image_ops.rgb_to_hsv(flt_image)

        hue = tf.slice(hsv, [0, 0, 0, 0], [-1, -1, -1, 1])
        saturation = tf.slice(hsv, [0, 0, 0, 1], [-1, -1, -1, 1])
        value = tf.slice(hsv, [0, 0, 0, 2], [-1, -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 = tf.concat(3, [hue, saturation, value])
        rgb_altered = gen_image_ops.hsv_to_rgb(hsv_altered)

        return tf.image.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.op_scope([image], name, 'adjust_hue') as name:
    # Remember original dtype to so we can convert back if needed
    orig_dtype = image.dtype
    flt_image = convert_image_dtype(image, dtypes.float32)

    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)

    return convert_image_dtype(rgb_altered, orig_dtype)

def adjust_saturation(image, saturation_factor, name=None):
  """Adjust saturation 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 saturation 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 saturation is adjusted by converting the
  image to HSV and multiplying the saturation (S) channel by
  `saturation_factor` and clipping. The image is then converted back to RGB.

  Args:
    image: RGB image or images. Size of the last dimension must be 3.
    saturation_factor: float. Factor to multiply the saturation by.
    name: A name for this operation (optional).

  Returns:
    Adjusted image(s), same shape and DType as `image`.
  """
  with ops.op_scope([image], name, 'adjust_saturation') 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)

    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])

    saturation *= saturation_factor
    saturation = clip_ops.clip_by_value(saturation, 0.0, 1.0)

    hsv_altered = array_ops.concat(2, [hue, saturation, value])
    rgb_altered = gen_image_ops.hsv_to_rgb(hsv_altered)

    return convert_image_dtype(rgb_altered, orig_dtype)