Python gen_image_ops.hsv_to_rgb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 hsv_to_rgb [as 别名]
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) 

# 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 hsv_to_rgb [as 别名]
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) 

# 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 hsv_to_rgb [as 别名]
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) 

# 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 hsv_to_rgb [as 别名]
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)

    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)


# TODO(irving): Remove once the C++ RandomCrop op is deprecated.