Python tensorflow.python.ops.gen_image_ops.adjust_saturation() Examples
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Example #1
Source File: image_ops_impl.py From lambda-packs with MIT License | 5 votes |
def random_saturation(image, lower, upper, seed=None): """Adjust the saturation of an RGB image by a random factor. Equivalent to `adjust_saturation()` but uses a `saturation_factor` randomly picked in the interval `[lower, upper]`. Args: image: RGB image or images. Size of the last dimension must be 3. lower: float. Lower bound for the random saturation factor. upper: float. Upper bound for the random saturation factor. 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: Adjusted image(s), same shape and DType as `image`. Raises: ValueError: if `upper <= lower` or if `lower < 0`. """ if upper <= lower: raise ValueError('upper must be > lower.') if lower < 0: raise ValueError('lower must be non-negative.') # Pick a float in [lower, upper] saturation_factor = random_ops.random_uniform([], lower, upper, seed=seed) return adjust_saturation(image, saturation_factor)
Example #2
Source File: image_ops_impl.py From auto-alt-text-lambda-api with MIT License | 5 votes |
def random_saturation(image, lower, upper, seed=None): """Adjust the saturation of an RGB image by a random factor. Equivalent to `adjust_saturation()` but uses a `saturation_factor` randomly picked in the interval `[lower, upper]`. Args: image: RGB image or images. Size of the last dimension must be 3. lower: float. Lower bound for the random saturation factor. upper: float. Upper bound for the random saturation factor. 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: Adjusted image(s), same shape and DType as `image`. Raises: ValueError: if `upper <= lower` or if `lower < 0`. """ if upper <= lower: raise ValueError('upper must be > lower.') if lower < 0: raise ValueError('lower must be non-negative.') # Pick a float in [lower, upper] saturation_factor = random_ops.random_uniform([], lower, upper, seed=seed) return adjust_saturation(image, saturation_factor)
Example #3
Source File: image_ops_impl.py From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License | 5 votes |
def random_saturation(image, lower, upper, seed=None): """Adjust the saturation of an RGB image by a random factor. Equivalent to `adjust_saturation()` but uses a `saturation_factor` randomly picked in the interval `[lower, upper]`. Args: image: RGB image or images. Size of the last dimension must be 3. lower: float. Lower bound for the random saturation factor. upper: float. Upper bound for the random saturation factor. 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: Adjusted image(s), same shape and DType as `image`. Raises: ValueError: if `upper <= lower` or if `lower < 0`. """ if upper <= lower: raise ValueError('upper must be > lower.') if lower < 0: raise ValueError('lower must be non-negative.') # Pick a float in [lower, upper] saturation_factor = random_ops.random_uniform([], lower, upper, seed=seed) return adjust_saturation(image, saturation_factor)
Example #4
Source File: image_ops_impl.py From keras-lambda with MIT License | 5 votes |
def random_saturation(image, lower, upper, seed=None): """Adjust the saturation of an RGB image by a random factor. Equivalent to `adjust_saturation()` but uses a `saturation_factor` randomly picked in the interval `[lower, upper]`. Args: image: RGB image or images. Size of the last dimension must be 3. lower: float. Lower bound for the random saturation factor. upper: float. Upper bound for the random saturation factor. 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: Adjusted image(s), same shape and DType as `image`. Raises: ValueError: if `upper <= lower` or if `lower < 0`. """ if upper <= lower: raise ValueError('upper must be > lower.') if lower < 0: raise ValueError('lower must be non-negative.') # Pick a float in [lower, upper] saturation_factor = random_ops.random_uniform([], lower, upper, seed=seed) return adjust_saturation(image, saturation_factor)
Example #5
Source File: official_tf_image.py From X-Detector with Apache License 2.0 | 5 votes |
def random_saturation(image, lower, upper, seed=None): """Adjust the saturation of an RGB image by a random factor. Equivalent to `adjust_saturation()` but uses a `saturation_factor` randomly picked in the interval `[lower, upper]`. Args: image: RGB image or images. Size of the last dimension must be 3. lower: float. Lower bound for the random saturation factor. upper: float. Upper bound for the random saturation factor. 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: Adjusted image(s), same shape and DType as `image`. Raises: ValueError: if `upper <= lower` or if `lower < 0`. """ if upper <= lower: raise ValueError('upper must be > lower.') if lower < 0: raise ValueError('lower must be non-negative.') # Pick a float in [lower, upper] saturation_factor = random_ops.random_uniform([], lower, upper, seed=seed) return adjust_saturation(image, saturation_factor)
Example #6
Source File: image_ops_impl.py From lambda-packs with MIT License | 4 votes |
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)
Example #7
Source File: image_ops_impl.py From auto-alt-text-lambda-api with MIT License | 4 votes |
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)
Example #8
Source File: image_ops_impl.py From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License | 4 votes |
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)
Example #9
Source File: image_ops_impl.py From keras-lambda with MIT License | 4 votes |
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)
Example #10
Source File: official_tf_image.py From X-Detector with Apache License 2.0 | 4 votes |
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)