RPPT Tensor Operations - Effects Augmentations.

RPPT Tensor Operations - Effects Augmentations.#

RPP: RPPT Tensor Operations - Effects Augmentations.

RPPT Tensor Operations - Effects Augmentations. More...

Functions
RppStatus	rppt_gridmask_host (RppPtr_t srcPtr, RpptDescPtr srcDescPtr, RppPtr_t dstPtr, RpptDescPtr dstDescPtr, Rpp32u tileWidth, Rpp32f gridRatio, Rpp32f gridAngle, RpptUintVector2D translateVector, RpptROIPtr roiTensorPtrSrc, RpptRoiType roiType, rppHandle_t rppHandle)
	Gridmask augmentation on HOST backend for a NCHW/NHWC layout tensor. More...

RppStatus	rppt_gridmask_gpu (RppPtr_t srcPtr, RpptDescPtr srcDescPtr, RppPtr_t dstPtr, RpptDescPtr dstDescPtr, Rpp32u tileWidth, Rpp32f gridRatio, Rpp32f gridAngle, RpptUintVector2D translateVector, RpptROIPtr roiTensorPtrSrc, RpptRoiType roiType, rppHandle_t rppHandle)
	Gridmask augmentation on HIP backend for a NCHW/NHWC layout tensor. More...

RppStatus	rppt_spatter_host (RppPtr_t srcPtr, RpptDescPtr srcDescPtr, RppPtr_t dstPtr, RpptDescPtr dstDescPtr, RpptRGB spatterColor, RpptROIPtr roiTensorPtrSrc, RpptRoiType roiType, rppHandle_t rppHandle)
	Spatter augmentation on HOST backend for a NCHW/NHWC layout tensor. More...

RppStatus	rppt_spatter_gpu (RppPtr_t srcPtr, RpptDescPtr srcDescPtr, RppPtr_t dstPtr, RpptDescPtr dstDescPtr, RpptRGB spatterColor, RpptROIPtr roiTensorPtrSrc, RpptRoiType roiType, rppHandle_t rppHandle)
	Spatter augmentation on HIP backend for a NCHW/NHWC layout tensor. More...

RppStatus	rppt_salt_and_pepper_noise_host (RppPtr_t srcPtr, RpptDescPtr srcDescPtr, RppPtr_t dstPtr, RpptDescPtr dstDescPtr, Rpp32f noiseProbabilityTensor, Rpp32f saltProbabilityTensor, Rpp32f saltValueTensor, Rpp32f pepperValueTensor, Rpp32u seed, RpptROIPtr roiTensorPtrSrc, RpptRoiType roiType, rppHandle_t rppHandle)
	Salt and pepper noise augmentation on HOST backend for a NCHW/NHWC layout tensor. More...

RppStatus	rppt_salt_and_pepper_noise_gpu (RppPtr_t srcPtr, RpptDescPtr srcDescPtr, RppPtr_t dstPtr, RpptDescPtr dstDescPtr, Rpp32f noiseProbabilityTensor, Rpp32f saltProbabilityTensor, Rpp32f saltValueTensor, Rpp32f pepperValueTensor, Rpp32u seed, RpptROIPtr roiTensorPtrSrc, RpptRoiType roiType, rppHandle_t rppHandle)
	Salt and pepper noise augmentation on HIP backend for a NCHW/NHWC layout tensor. More...

RppStatus	rppt_shot_noise_host (RppPtr_t srcPtr, RpptDescPtr srcDescPtr, RppPtr_t dstPtr, RpptDescPtr dstDescPtr, Rpp32f *shotNoiseFactorTensor, Rpp32u seed, RpptROIPtr roiTensorPtrSrc, RpptRoiType roiType, rppHandle_t rppHandle)
	Shot noise augmentation on HOST backend for a NCHW/NHWC layout tensor. More...

RppStatus	rppt_shot_noise_gpu (RppPtr_t srcPtr, RpptDescPtr srcDescPtr, RppPtr_t dstPtr, RpptDescPtr dstDescPtr, Rpp32f *shotNoiseFactorTensor, Rpp32u seed, RpptROIPtr roiTensorPtrSrc, RpptRoiType roiType, rppHandle_t rppHandle)
	Shot noise augmentation on HIP backend for a NCHW/NHWC layout tensor. More...

RppStatus	rppt_gaussian_noise_host (RppPtr_t srcPtr, RpptDescPtr srcDescPtr, RppPtr_t dstPtr, RpptDescPtr dstDescPtr, Rpp32f meanTensor, Rpp32f stdDevTensor, Rpp32u seed, RpptROIPtr roiTensorPtrSrc, RpptRoiType roiType, rppHandle_t rppHandle)
	Gaussian noise augmentation on HOST backend for a NCHW/NHWC layout tensor. More...

RppStatus	rppt_gaussian_noise_gpu (RppPtr_t srcPtr, RpptDescPtr srcDescPtr, RppPtr_t dstPtr, RpptDescPtr dstDescPtr, Rpp32f meanTensor, Rpp32f stdDevTensor, Rpp32u seed, RpptROIPtr roiTensorPtrSrc, RpptRoiType roiType, rppHandle_t rppHandle)
	Gaussian noise augmentation on HIP backend for a NCHW/NHWC layout tensor. More...

RppStatus	rppt_non_linear_blend_host (RppPtr_t srcPtr1, RppPtr_t srcPtr2, RpptDescPtr srcDescPtr, RppPtr_t dstPtr, RpptDescPtr dstDescPtr, Rpp32f *stdDevTensor, RpptROIPtr roiTensorPtrSrc, RpptRoiType roiType, rppHandle_t rppHandle)
	Non linear blend augmentation on HOST backend for a NCHW/NHWC layout tensor. More...

RppStatus	rppt_non_linear_blend_gpu (RppPtr_t srcPtr1, RppPtr_t srcPtr2, RpptDescPtr srcDescPtr, RppPtr_t dstPtr, RpptDescPtr dstDescPtr, Rpp32f *stdDevTensor, RpptROIPtr roiTensorPtrSrc, RpptRoiType roiType, rppHandle_t rppHandle)
	Non linear blend augmentation on HIP backend for a NCHW/NHWC layout tensor. More...

RppStatus	rppt_water_host (RppPtr_t srcPtr, RpptDescPtr srcDescPtr, RppPtr_t dstPtr, RpptDescPtr dstDescPtr, Rpp32f amplitudeXTensor, Rpp32f amplitudeYTensor, Rpp32f frequencyXTensor, Rpp32f frequencyYTensor, Rpp32f phaseXTensor, Rpp32f phaseYTensor, RpptROIPtr roiTensorPtrSrc, RpptRoiType roiType, rppHandle_t rppHandle)
	Water augmentation on HOST backend for a NCHW/NHWC layout tensor. More...

RppStatus	rppt_water_gpu (RppPtr_t srcPtr, RpptDescPtr srcDescPtr, RppPtr_t dstPtr, RpptDescPtr dstDescPtr, Rpp32f amplitudeXTensor, Rpp32f amplitudeYTensor, Rpp32f frequencyXTensor, Rpp32f frequencyYTensor, Rpp32f phaseXTensor, Rpp32f phaseYTensor, RpptROIPtr roiTensorPtrSrc, RpptRoiType roiType, rppHandle_t rppHandle)
	Water augmentation on HIP backend for a NCHW/NHWC layout tensor. More...

RppStatus	rppt_ricap_host (RppPtr_t srcPtr, RpptDescPtr srcDescPtr, RppPtr_t dstPtr, RpptDescPtr dstDescPtr, Rpp32u *permutedIndicesTensor, RpptROIPtr roiPtrInputCropRegion, RpptRoiType roiType, rppHandle_t rppHandle)
	RICAP (Random Image Crop And Patch) augmentation on HOST backend for a NCHW/NHWC layout tensor. More...

RppStatus	rppt_ricap_gpu (RppPtr_t srcPtr, RpptDescPtr srcDescPtr, RppPtr_t dstPtr, RpptDescPtr dstDescPtr, Rpp32u *permutedIndicesTensor, RpptROIPtr roiPtrInputCropRegion, RpptRoiType roiType, rppHandle_t rppHandle)
	RICAP (Random Image Crop And Patch) augmentation on HIP backend for a NCHW/NHWC layout tensor. More...

RppStatus	rppt_vignette_host (RppPtr_t srcPtr, RpptDescPtr srcDescPtr, RppPtr_t dstPtr, RpptDescPtr dstDescPtr, Rpp32f *vignetteIntensityTensor, RpptROIPtr roiTensorPtrSrc, RpptRoiType roiType, rppHandle_t rppHandle)
	Vignette augmentation on HOST backend for a NCHW/NHWC layout tensor. More...

RppStatus	rppt_vignette_gpu (RppPtr_t srcPtr, RpptDescPtr srcDescPtr, RppPtr_t dstPtr, RpptDescPtr dstDescPtr, Rpp32f *vignetteIntensityTensor, RpptROIPtr roiTensorPtrSrc, RpptRoiType roiType, rppHandle_t rppHandle)
	Vignette augmentation on HIP backend for a NCHW/NHWC layout tensor. More...

Detailed Description

RPPT Tensor Operations - Effects Augmentations.

Function Documentation

◆ rppt_gaussian_noise_gpu()

RppStatus rppt_gaussian_noise_gpu	(	RppPtr_t	srcPtr,
		RpptDescPtr	srcDescPtr,
		RppPtr_t	dstPtr,
		RpptDescPtr	dstDescPtr,
		Rpp32f *	meanTensor,
		Rpp32f *	stdDevTensor,
		Rpp32u	seed,
		RpptROIPtr	roiTensorPtrSrc,
		RpptRoiType	roiType,
		rppHandle_t	rppHandle
	)

Gaussian noise augmentation on HIP backend for a NCHW/NHWC layout tensor.

The gaussian noise augmentation adds Gaussian noise based on user defined means and standard deviations, for a batch of RGB(3 channel) / greyscale(1 channel) images with an NHWC/NCHW tensor layout.

srcPtr depth ranges - Rpp8u (0 to 255), Rpp16f (0 to 1), Rpp32f (0 to 1), Rpp8s (-128 to 127).

dstPtr depth ranges - Will be same depth as srcPtr.

Sample Input

Sample Output

Parameters

[in]	srcPtr	source tensor in HIP memory
[in]	srcDescPtr	source tensor descriptor (Restrictions - numDims = 4, offsetInBytes >= 0, dataType = U8/F16/F32/I8, layout = NCHW/NHWC, c = 1/3)
[out]	dstPtr	destination tensor in HIP memory
[in]	dstDescPtr	destination tensor descriptor (Restrictions - numDims = 4, offsetInBytes >= 0, dataType = U8/F16/F32/I8, layout = NCHW/NHWC, c = same as that of srcDescPtr)
[in]	meanTensor	mean values for each image, which are used to compute the generalized Box-Mueller transforms in a gaussian distribution (1D tensor in pinned/HOST memory, of size batchSize with meanTensor[i] >= 0 for each image in batch)
[in]	stdDevTensor	stdDev values for each image, which are used to compute the generalized Box-Mueller transforms in a gaussian distribution (1D tensor in pinned/HOST memory, of size batchSize with stdDevTensor[i] >= 0 for each image in batch)
[in]	seed	A user-defined seed value (single Rpp32u value)
[in]	roiTensorSrc	ROI data in HIP memory, for each image in source tensor (2D tensor of size batchSize * 4, in either format - XYWH(xy.x, xy.y, roiWidth, roiHeight) or LTRB(lt.x, lt.y, rb.x, rb.y))
[in]	roiType	ROI type used (RpptRoiType::XYWH or RpptRoiType::LTRB)
[in]	rppHandle	RPP HIP handle created with `rppCreateWithStreamAndBatchSize()`

Returns: A RppStatus enumeration.

Return values

RPP_SUCCESS	Successful completion.
RPP_ERROR*	Unsuccessful completion.

◆ rppt_gaussian_noise_host()

RppStatus rppt_gaussian_noise_host	(	RppPtr_t	srcPtr,
		RpptDescPtr	srcDescPtr,
		RppPtr_t	dstPtr,
		RpptDescPtr	dstDescPtr,
		Rpp32f *	meanTensor,
		Rpp32f *	stdDevTensor,
		Rpp32u	seed,
		RpptROIPtr	roiTensorPtrSrc,
		RpptRoiType	roiType,
		rppHandle_t	rppHandle
	)

Gaussian noise augmentation on HOST backend for a NCHW/NHWC layout tensor.

The gaussian noise augmentation adds Gaussian noise based on user defined means and standard deviations, for a batch of RGB(3 channel) / greyscale(1 channel) images with an NHWC/NCHW tensor layout.

srcPtr depth ranges - Rpp8u (0 to 255), Rpp16f (0 to 1), Rpp32f (0 to 1), Rpp8s (-128 to 127).

dstPtr depth ranges - Will be same depth as srcPtr.

Sample Input

Sample Output

Parameters

[in]	srcPtr	source tensor in HOST memory
[in]	srcDescPtr	source tensor descriptor (Restrictions - numDims = 4, offsetInBytes >= 0, dataType = U8/F16/F32/I8, layout = NCHW/NHWC, c = 1/3)
[out]	dstPtr	destination tensor in HOST memory
[in]	dstDescPtr	destination tensor descriptor (Restrictions - numDims = 4, offsetInBytes >= 0, dataType = U8/F16/F32/I8, layout = NCHW/NHWC, c = same as that of srcDescPtr)
[in]	meanTensor	mean values for each image, which are used to compute the generalized Box-Mueller transforms in a gaussian distribution (1D tensor in HOST memory, of size batchSize with meanTensor[i] >= 0 for each image in batch)
[in]	stdDevTensor	stdDev values for each image, which are used to compute the generalized Box-Mueller transforms in a gaussian distribution (1D tensor in HOST memory, of size batchSize with stdDevTensor[i] >= 0 for each image in batch)
[in]	seed	A user-defined seed value (single Rpp32u value)
[in]	roiTensorSrc	ROI data in HOST memory, for each image in source tensor (2D tensor of size batchSize * 4, in either format - XYWH(xy.x, xy.y, roiWidth, roiHeight) or LTRB(lt.x, lt.y, rb.x, rb.y))
[in]	roiType	ROI type used (RpptRoiType::XYWH or RpptRoiType::LTRB)
[in]	rppHandle	RPP HOST handle created with `rppCreateWithBatchSize()`

Returns: A RppStatus enumeration.

Return values

RPP_SUCCESS	Successful completion.
RPP_ERROR*	Unsuccessful completion.

◆ rppt_gridmask_gpu()

RppStatus rppt_gridmask_gpu	(	RppPtr_t	srcPtr,
		RpptDescPtr	srcDescPtr,
		RppPtr_t	dstPtr,
		RpptDescPtr	dstDescPtr,
		Rpp32u	tileWidth,
		Rpp32f	gridRatio,
		Rpp32f	gridAngle,
		RpptUintVector2D	translateVector,
		RpptROIPtr	roiTensorPtrSrc,
		RpptRoiType	roiType,
		rppHandle_t	rppHandle
	)

Gridmask augmentation on HIP backend for a NCHW/NHWC layout tensor.

The gridmask augmentation runs as per https://arxiv.org/abs/2001.04086 for a batch of RGB(3 channel) / greyscale(1 channel) images with an NHWC/NCHW tensor layout.

srcPtr depth ranges - Rpp8u (0 to 255), Rpp16f (0 to 1), Rpp32f (0 to 1), Rpp8s (-128 to 127).

dstPtr depth ranges - Will be same depth as srcPtr.

Sample Input

Sample Output

Parameters

[in]	srcPtr	source tensor in HIP memory
[in]	srcDescPtr	source tensor descriptor (Restrictions - numDims = 4, offsetInBytes >= 0, dataType = U8/F16/F32/I8, layout = NCHW/NHWC, c = 1/3)
[out]	dstPtr	destination tensor in HIP memory
[in]	dstDescPtr	destination tensor descriptor (Restrictions - numDims = 4, offsetInBytes >= 0, dataType = U8/F16/F32/I8, layout = NCHW/NHWC, c = same as that of srcDescPtr)
[in]	tileWidth	tileWidth value for gridmask calculation = width of black square + width of spacing until next black square on grid (a single Rpp32u number with tileWidth <= min(srcDescPtr->w, srcDescPtr->h) that applies to all images in the batch)
[in]	gridRatio	gridRatio value for gridmask calculation = black square width / tileWidth (a single Rpp32f number with 0 <= gridRatio <= 1 that applies to all images in the batch)
[in]	gridAngle	gridAngle value for gridmask calculation = grid rotation angle in radians (a single Rpp32f number that applies to all images in the batch)
[in]	translateVector	translateVector for gridmask calculation = grid X and Y translation lengths in pixels (a single RpptUintVector2D x,y value pair that applies to all images in the batch)
[in]	roiTensorSrc	ROI data in HIP memory, for each image in source tensor (2D tensor of size batchSize * 4, in either format - XYWH(xy.x, xy.y, roiWidth, roiHeight) or LTRB(lt.x, lt.y, rb.x, rb.y))
[in]	roiType	ROI type used (RpptRoiType::XYWH or RpptRoiType::LTRB)
[in]	rppHandle	RPP HIP handle created with `rppCreateWithStreamAndBatchSize()`

Returns: A RppStatus enumeration.

Return values

RPP_SUCCESS	Successful completion.
RPP_ERROR*	Unsuccessful completion.

◆ rppt_gridmask_host()

RppStatus rppt_gridmask_host	(	RppPtr_t	srcPtr,
		RpptDescPtr	srcDescPtr,
		RppPtr_t	dstPtr,
		RpptDescPtr	dstDescPtr,
		Rpp32u	tileWidth,
		Rpp32f	gridRatio,
		Rpp32f	gridAngle,
		RpptUintVector2D	translateVector,
		RpptROIPtr	roiTensorPtrSrc,
		RpptRoiType	roiType,
		rppHandle_t	rppHandle
	)

Gridmask augmentation on HOST backend for a NCHW/NHWC layout tensor.

The gridmask augmentation runs as per https://arxiv.org/abs/2001.04086 for a batch of RGB(3 channel) / greyscale(1 channel) images with an NHWC/NCHW tensor layout.

srcPtr depth ranges - Rpp8u (0 to 255), Rpp16f (0 to 1), Rpp32f (0 to 1), Rpp8s (-128 to 127).

dstPtr depth ranges - Will be same depth as srcPtr.

Sample Input

Sample Output

Parameters

[in]	srcPtr	source tensor in HOST memory
[in]	srcDescPtr	source tensor descriptor (Restrictions - numDims = 4, offsetInBytes >= 0, dataType = U8/F16/F32/I8, layout = NCHW/NHWC, c = 1/3)
[out]	dstPtr	destination tensor in HOST memory
[in]	dstDescPtr	destination tensor descriptor (Restrictions - numDims = 4, offsetInBytes >= 0, dataType = U8/F16/F32/I8, layout = NCHW/NHWC, c = same as that of srcDescPtr)
[in]	tileWidth	tileWidth value for gridmask calculation = width of black square + width of spacing until next black square on grid (a single Rpp32u number with tileWidth <= min(srcDescPtr->w, srcDescPtr->h) that applies to all images in the batch)
[in]	gridRatio	gridRatio value for gridmask calculation = black square width / tileWidth (a single Rpp32f number with 0 <= gridRatio <= 1 that applies to all images in the batch)
[in]	gridAngle	gridAngle value for gridmask calculation = grid rotation angle in radians (a single Rpp32f number that applies to all images in the batch)
[in]	translateVector	translateVector for gridmask calculation = grid X and Y translation lengths in pixels (a single RpptUintVector2D x,y value pair that applies to all images in the batch)
[in]	roiTensorSrc	ROI data in HOST memory, for each image in source tensor (2D tensor of size batchSize * 4, in either format - XYWH(xy.x, xy.y, roiWidth, roiHeight) or LTRB(lt.x, lt.y, rb.x, rb.y))
[in]	roiType	ROI type used (RpptRoiType::XYWH or RpptRoiType::LTRB)
[in]	rppHandle	RPP HOST handle created with `rppCreateWithBatchSize()`

Returns: A RppStatus enumeration.

Return values

RPP_SUCCESS	Successful completion.
RPP_ERROR*	Unsuccessful completion.

◆ rppt_non_linear_blend_gpu()

RppStatus rppt_non_linear_blend_gpu	(	RppPtr_t	srcPtr1,
		RppPtr_t	srcPtr2,
		RpptDescPtr	srcDescPtr,
		RppPtr_t	dstPtr,
		RpptDescPtr	dstDescPtr,
		Rpp32f *	stdDevTensor,
		RpptROIPtr	roiTensorPtrSrc,
		RpptRoiType	roiType,
		rppHandle_t	rppHandle
	)

Non linear blend augmentation on HIP backend for a NCHW/NHWC layout tensor.

The non linear blend augmentation adds standard deviation based non-linear alpha-blending, between two sets of batches of RGB(3 channel) / greyscale(1 channel) images with an NHWC/NCHW tensor layout.

srcPtr depth ranges - Rpp8u (0 to 255), Rpp16f (0 to 1), Rpp32f (0 to 1), Rpp8s (-128 to 127).

dstPtr depth ranges - Will be same depth as srcPtr.

Sample Input1

Sample Input2

Sample Output

Parameters

[in]	srcPtr1	source1 tensor in HIP memory
[in]	srcPtr2	source2 tensor in HIP memory
[in]	srcDescPtr	source tensor descriptor (Restrictions - numDims = 4, offsetInBytes >= 0, dataType = U8/F16/F32/I8, layout = NCHW/NHWC, c = 1/3)
[out]	dstPtr	destination tensor in HIP memory
[in]	dstDescPtr	destination tensor descriptor (Restrictions - numDims = 4, offsetInBytes >= 0, dataType = U8/F16/F32/I8, layout = NCHW/NHWC, c = same as that of srcDescPtr)
[in]	stdDevTensor	stdDev values for each image, which are used to compute the generalized Box-Mueller transforms in a gaussian distribution (1D tensor in pinned/HOST memory, of size batchSize with stdDevTensor[i] >= 0 for each image in batch)
[in]	roiTensorSrc	ROI data in HIP memory, for each image in source tensor (2D tensor of size batchSize * 4, in either format - XYWH(xy.x, xy.y, roiWidth, roiHeight) or LTRB(lt.x, lt.y, rb.x, rb.y))
[in]	roiType	ROI type used (RpptRoiType::XYWH or RpptRoiType::LTRB)
[in]	rppHandle	RPP HIP handle created with `rppCreateWithStreamAndBatchSize()`

Returns: A RppStatus enumeration.

Return values

RPP_SUCCESS	Successful completion.
RPP_ERROR*	Unsuccessful completion.

◆ rppt_non_linear_blend_host()

RppStatus rppt_non_linear_blend_host	(	RppPtr_t	srcPtr1,
		RppPtr_t	srcPtr2,
		RpptDescPtr	srcDescPtr,
		RppPtr_t	dstPtr,
		RpptDescPtr	dstDescPtr,
		Rpp32f *	stdDevTensor,
		RpptROIPtr	roiTensorPtrSrc,
		RpptRoiType	roiType,
		rppHandle_t	rppHandle
	)

Non linear blend augmentation on HOST backend for a NCHW/NHWC layout tensor.

The non linear blend augmentation adds standard deviation based non-linear alpha-blending, between two sets of batches of RGB(3 channel) / greyscale(1 channel) images with an NHWC/NCHW tensor layout.

srcPtr depth ranges - Rpp8u (0 to 255), Rpp16f (0 to 1), Rpp32f (0 to 1), Rpp8s (-128 to 127).

dstPtr depth ranges - Will be same depth as srcPtr.

Sample Input1

Sample Input2

Sample Output

Parameters

[in]	srcPtr1	source1 tensor in HOST memory
[in]	srcPtr2	source2 tensor in HOST memory
[in]	srcDescPtr	source tensor descriptor (Restrictions - numDims = 4, offsetInBytes >= 0, dataType = U8/F16/F32/I8, layout = NCHW/NHWC, c = 1/3)
[out]	dstPtr	destination tensor in HOST memory
[in]	dstDescPtr	destination tensor descriptor (Restrictions - numDims = 4, offsetInBytes >= 0, dataType = U8/F16/F32/I8, layout = NCHW/NHWC, c = same as that of srcDescPtr)
[in]	stdDevTensor	stdDev values for each image, which are used to compute the generalized Box-Mueller transforms in a gaussian distribution (1D tensor in HOST memory, of size batchSize with stdDevTensor[i] >= 0 for each image in batch)
[in]	roiTensorSrc	ROI data in HOST memory, for each image in source tensor (2D tensor of size batchSize * 4, in either format - XYWH(xy.x, xy.y, roiWidth, roiHeight) or LTRB(lt.x, lt.y, rb.x, rb.y))
[in]	roiType	ROI type used (RpptRoiType::XYWH or RpptRoiType::LTRB)
[in]	rppHandle	RPP HOST handle created with `rppCreateWithBatchSize()`

Returns: A RppStatus enumeration.

Return values

RPP_SUCCESS	Successful completion.
RPP_ERROR*	Unsuccessful completion.

◆ rppt_ricap_gpu()

RppStatus rppt_ricap_gpu	(	RppPtr_t	srcPtr,
		RpptDescPtr	srcDescPtr,
		RppPtr_t	dstPtr,
		RpptDescPtr	dstDescPtr,
		Rpp32u *	permutedIndicesTensor,
		RpptROIPtr	roiPtrInputCropRegion,
		RpptRoiType	roiType,
		rppHandle_t	rppHandle
	)

RICAP (Random Image Crop And Patch) augmentation on HIP backend for a NCHW/NHWC layout tensor.

The RICAP (Random Image Crop And Patch) augmentation runs as per https://arxiv.org/abs/1811.09030 for a batch of RGB(3 channel) / greyscale(1 channel) images with an NHWC/NCHW tensor layout.
The RICAP augmentation requires dimensions of input images to be the same across entire batch.

srcPtr depth ranges - Rpp8u (0 to 255), Rpp16f (0 to 1), Rpp32f (0 to 1), Rpp8s (-128 to 127).

dstPtr depth ranges - Will be same depth as srcPtr.

Sample Input1

Sample Input2

Sample Input3

Sample Output

Parameters

[in]	srcPtr	source tensor in HIP memory
[in]	srcDescPtr	source tensor descriptor (Restrictions - numDims = 4, offsetInBytes >= 0, dataType = U8/F16/F32/I8, layout = NCHW/NHWC, c = 1/3)
[out]	dstPtr	destination tensor in HIP memory
[in]	dstDescPtr	destination tensor descriptor (Restrictions - numDims = 4, offsetInBytes >= 0, dataType = U8/F16/F32/I8, layout = NCHW/NHWC, c = same as that of srcDescPtr)
[in]	permutedIndicesTensor	Array of batchSize permutation sets (2D tensor in pinned/HOST memory, of batchSize * 4. Each set of 4 permutations contains Rpp32u image indices for each region in the respective RICAP-output-image in the batch)
[in]	roiPtrInputCropRegion	Array of 4 ROIs (2D tensor in HIP memory, of size 4 * 4-elements per ROI, in either format - XYWH(xy.x, xy.y, roiWidth, roiHeight) or LTRB(lt.x, lt.y, rb.x, rb.y))
[in]	roiType	ROI type used (RpptRoiType::XYWH or RpptRoiType::LTRB)
[in]	rppHandle	RPP HIP handle created with `rppCreateWithStreamAndBatchSize()`

Returns: A RppStatus enumeration.

Return values

RPP_SUCCESS	Successful completion.
RPP_ERROR*	Unsuccessful completion.

◆ rppt_ricap_host()

RppStatus rppt_ricap_host	(	RppPtr_t	srcPtr,
		RpptDescPtr	srcDescPtr,
		RppPtr_t	dstPtr,
		RpptDescPtr	dstDescPtr,
		Rpp32u *	permutedIndicesTensor,
		RpptROIPtr	roiPtrInputCropRegion,
		RpptRoiType	roiType,
		rppHandle_t	rppHandle
	)

RICAP (Random Image Crop And Patch) augmentation on HOST backend for a NCHW/NHWC layout tensor.

The RICAP (Random Image Crop And Patch) augmentation runs as per https://arxiv.org/abs/1811.09030 for a batch of RGB(3 channel) / greyscale(1 channel) images with an NHWC/NCHW tensor layout.
The RICAP augmentation requires dimensions of input images to be the same across entire batch.

srcPtr depth ranges - Rpp8u (0 to 255), Rpp16f (0 to 1), Rpp32f (0 to 1), Rpp8s (-128 to 127).

dstPtr depth ranges - Will be same depth as srcPtr.

Sample Input1

Sample Input2

Sample Input3

Sample Output

Parameters

[in]	srcPtr	source tensor in HOST memory
[in]	srcDescPtr	source tensor descriptor (Restrictions - numDims = 4, offsetInBytes >= 0, dataType = U8/F16/F32/I8, layout = NCHW/NHWC, c = 1/3)
[out]	dstPtr	destination tensor in HOST memory
[in]	dstDescPtr	destination tensor descriptor (Restrictions - numDims = 4, offsetInBytes >= 0, dataType = U8/F16/F32/I8, layout = NCHW/NHWC, c = same as that of srcDescPtr)
[in]	permutedIndicesTensor	Array of batchSize permutation sets (2D tensor in HOST memory, of batchSize * 4. Each set of 4 permutations contains Rpp32u image indices for each region in the respective RICAP-output-image in the batch)
[in]	roiPtrInputCropRegion	Array of 4 ROIs (2D tensor in HOST memory, of size 4 * 4-elements per ROI, in either format - XYWH(xy.x, xy.y, roiWidth, roiHeight) or LTRB(lt.x, lt.y, rb.x, rb.y))
[in]	roiType	ROI type used (RpptRoiType::XYWH or RpptRoiType::LTRB)
[in]	rppHandle	RPP HOST handle created with `rppCreateWithBatchSize()`

Returns: A RppStatus enumeration.

Return values

RPP_SUCCESS	Successful completion.
RPP_ERROR*	Unsuccessful completion.

◆ rppt_salt_and_pepper_noise_gpu()

RppStatus rppt_salt_and_pepper_noise_gpu	(	RppPtr_t	srcPtr,
		RpptDescPtr	srcDescPtr,
		RppPtr_t	dstPtr,
		RpptDescPtr	dstDescPtr,
		Rpp32f *	noiseProbabilityTensor,
		Rpp32f *	saltProbabilityTensor,
		Rpp32f *	saltValueTensor,
		Rpp32f *	pepperValueTensor,
		Rpp32u	seed,
		RpptROIPtr	roiTensorPtrSrc,
		RpptRoiType	roiType,
		rppHandle_t	rppHandle
	)

Salt and pepper noise augmentation on HIP backend for a NCHW/NHWC layout tensor.

The salt and pepper noise augmentation adds SnP noise based on user defined noise/salt probabilities, and user defined salt/pepper values for a batch of RGB(3 channel) / greyscale(1 channel) images with an NHWC/NCHW tensor layout.

srcPtr depth ranges - Rpp8u (0 to 255), Rpp16f (0 to 1), Rpp32f (0 to 1), Rpp8s (-128 to 127).

dstPtr depth ranges - Will be same depth as srcPtr.

Sample Input

Sample Output

Parameters

[in]	srcPtr	source tensor in HIP memory
[in]	srcDescPtr	source tensor descriptor (Restrictions - numDims = 4, offsetInBytes >= 0, dataType = U8/F16/F32/I8, layout = NCHW/NHWC, c = 1/3)
[out]	dstPtr	destination tensor in HIP memory
[in]	dstDescPtr	destination tensor descriptor (Restrictions - numDims = 4, offsetInBytes >= 0, dataType = U8/F16/F32/I8, layout = NCHW/NHWC, c = same as that of srcDescPtr)
[in]	noiseProbailityTensor	noiseProbaility values to decide if a destination pixel is a noise-pixel, or equal to source (1D tensor in pinned/HOST memory, of size batchSize with 0 <= noiseProbailityTensor[i] <= 1 for each image in batch)
[in]	saltProbailityTensor	saltProbaility values to decide if a given destination noise-pixel is salt or pepper (1D tensor in pinned/HOST memory, of size batchSize with 0 <= saltProbailityTensor[i] <= 1 for each image in batch)
[in]	saltValueTensor	A user-defined salt noise value (1D tensor in pinned/HOST memory, of size batchSize with 0 <= saltValueTensor[i] <= 1 for each image in batch)
[in]	pepperValueTensor	A user-defined pepper noise value (1D tensor in pinned/HOST memory, of size batchSize with 0 <= pepperValueTensor[i] <= 1 for each image in batch)
[in]	seed	A user-defined seed value (single Rpp32u value)
[in]	roiTensorSrc	ROI data in HIP memory, for each image in source tensor (2D tensor of size batchSize * 4, in either format - XYWH(xy.x, xy.y, roiWidth, roiHeight) or LTRB(lt.x, lt.y, rb.x, rb.y))
[in]	roiType	ROI type used (RpptRoiType::XYWH or RpptRoiType::LTRB)
[in]	rppHandle	RPP HIP handle created with `rppCreateWithStreamAndBatchSize()`

Returns: A RppStatus enumeration.

Return values

RPP_SUCCESS	Successful completion.
RPP_ERROR*	Unsuccessful completion.

◆ rppt_salt_and_pepper_noise_host()

RppStatus rppt_salt_and_pepper_noise_host	(	RppPtr_t	srcPtr,
		RpptDescPtr	srcDescPtr,
		RppPtr_t	dstPtr,
		RpptDescPtr	dstDescPtr,
		Rpp32f *	noiseProbabilityTensor,
		Rpp32f *	saltProbabilityTensor,
		Rpp32f *	saltValueTensor,
		Rpp32f *	pepperValueTensor,
		Rpp32u	seed,
		RpptROIPtr	roiTensorPtrSrc,
		RpptRoiType	roiType,
		rppHandle_t	rppHandle
	)

Salt and pepper noise augmentation on HOST backend for a NCHW/NHWC layout tensor.

The salt and pepper noise augmentation adds SnP noise based on user defined noise/salt probabilities, and user defined salt/pepper values for a batch of RGB(3 channel) / greyscale(1 channel) images with an NHWC/NCHW tensor layout.

srcPtr depth ranges - Rpp8u (0 to 255), Rpp16f (0 to 1), Rpp32f (0 to 1), Rpp8s (-128 to 127).

dstPtr depth ranges - Will be same depth as srcPtr.

Sample Input

Sample Output

Parameters

[in]	srcPtr	source tensor in HOST memory
[in]	srcDescPtr	source tensor descriptor (Restrictions - numDims = 4, offsetInBytes >= 0, dataType = U8/F16/F32/I8, layout = NCHW/NHWC, c = 1/3)
[out]	dstPtr	destination tensor in HOST memory
[in]	dstDescPtr	destination tensor descriptor (Restrictions - numDims = 4, offsetInBytes >= 0, dataType = U8/F16/F32/I8, layout = NCHW/NHWC, c = same as that of srcDescPtr)
[in]	noiseProbailityTensor	noiseProbaility values to decide if a destination pixel is a noise-pixel, or equal to source (1D tensor in HOST memory, of size batchSize with 0 <= noiseProbailityTensor[i] <= 1 for each image in batch)
[in]	saltProbailityTensor	saltProbaility values to decide if a given destination noise-pixel is salt or pepper (1D tensor in HOST memory, of size batchSize with 0 <= saltProbailityTensor[i] <= 1 for each image in batch)
[in]	saltValueTensor	A user-defined salt noise value (1D tensor in HOST memory, of size batchSize with 0 <= saltValueTensor[i] <= 1 for each image in batch)
[in]	pepperValueTensor	A user-defined pepper noise value (1D tensor in HOST memory, of size batchSize with 0 <= pepperValueTensor[i] <= 1 for each image in batch)
[in]	seed	A user-defined seed value (single Rpp32u value)
[in]	roiTensorSrc	ROI data in HOST memory, for each image in source tensor (2D tensor of size batchSize * 4, in either format - XYWH(xy.x, xy.y, roiWidth, roiHeight) or LTRB(lt.x, lt.y, rb.x, rb.y))
[in]	roiType	ROI type used (RpptRoiType::XYWH or RpptRoiType::LTRB)
[in]	rppHandle	RPP HOST handle created with `rppCreateWithBatchSize()`

Returns: A RppStatus enumeration.

Return values

RPP_SUCCESS	Successful completion.
RPP_ERROR*	Unsuccessful completion.

◆ rppt_shot_noise_gpu()

RppStatus rppt_shot_noise_gpu	(	RppPtr_t	srcPtr,
		RpptDescPtr	srcDescPtr,
		RppPtr_t	dstPtr,
		RpptDescPtr	dstDescPtr,
		Rpp32f *	shotNoiseFactorTensor,
		Rpp32u	seed,
		RpptROIPtr	roiTensorPtrSrc,
		RpptRoiType	roiType,
		rppHandle_t	rppHandle
	)

Shot noise augmentation on HIP backend for a NCHW/NHWC layout tensor.

The shot noise augmentation adds Poisson/shot noise based on a user defined shotNoiseFactor, for a batch of RGB(3 channel) / greyscale(1 channel) images with an NHWC/NCHW tensor layout.

srcPtr depth ranges - Rpp8u (0 to 255), Rpp16f (0 to 1), Rpp32f (0 to 1), Rpp8s (-128 to 127).

dstPtr depth ranges - Will be same depth as srcPtr.

Sample Input

Sample Output

Parameters

[in]	srcPtr	source tensor in HIP memory
[in]	srcDescPtr	source tensor descriptor (Restrictions - numDims = 4, offsetInBytes >= 0, dataType = U8/F16/F32/I8, layout = NCHW/NHWC, c = 1/3)
[out]	dstPtr	destination tensor in HIP memory
[in]	dstDescPtr	destination tensor descriptor (Restrictions - numDims = 4, offsetInBytes >= 0, dataType = U8/F16/F32/I8, layout = NCHW/NHWC, c = same as that of srcDescPtr)
[in]	shotNoiseFactorTensor	shotNoiseFactor values for each image, which are used to compute the lambda values in a poisson distribution (1D tensor in pinned/HOST memory, of size batchSize with shotNoiseFactorTensor[i] >= 0 for each image in batch)
[in]	seed	A user-defined seed value (single Rpp32u value)
[in]	roiTensorSrc	ROI data in HIP memory, for each image in source tensor (2D tensor of size batchSize * 4, in either format - XYWH(xy.x, xy.y, roiWidth, roiHeight) or LTRB(lt.x, lt.y, rb.x, rb.y))
[in]	roiType	ROI type used (RpptRoiType::XYWH or RpptRoiType::LTRB)
[in]	rppHandle	RPP HIP handle created with `rppCreateWithStreamAndBatchSize()`

Returns: A RppStatus enumeration.

Return values

RPP_SUCCESS	Successful completion.
RPP_ERROR*	Unsuccessful completion.

◆ rppt_shot_noise_host()

RppStatus rppt_shot_noise_host	(	RppPtr_t	srcPtr,
		RpptDescPtr	srcDescPtr,
		RppPtr_t	dstPtr,
		RpptDescPtr	dstDescPtr,
		Rpp32f *	shotNoiseFactorTensor,
		Rpp32u	seed,
		RpptROIPtr	roiTensorPtrSrc,
		RpptRoiType	roiType,
		rppHandle_t	rppHandle
	)

Shot noise augmentation on HOST backend for a NCHW/NHWC layout tensor.

The shot noise augmentation adds Poisson/shot noise based on a user defined shotNoiseFactor, for a batch of RGB(3 channel) / greyscale(1 channel) images with an NHWC/NCHW tensor layout.

srcPtr depth ranges - Rpp8u (0 to 255), Rpp16f (0 to 1), Rpp32f (0 to 1), Rpp8s (-128 to 127).

dstPtr depth ranges - Will be same depth as srcPtr.

Sample Input

Sample Output

Parameters

[in]	srcPtr	source tensor in HOST memory
[in]	srcDescPtr	source tensor descriptor (Restrictions - numDims = 4, offsetInBytes >= 0, dataType = U8/F16/F32/I8, layout = NCHW/NHWC, c = 1/3)
[out]	dstPtr	destination tensor in HOST memory
[in]	dstDescPtr	destination tensor descriptor (Restrictions - numDims = 4, offsetInBytes >= 0, dataType = U8/F16/F32/I8, layout = NCHW/NHWC, c = same as that of srcDescPtr)
[in]	shotNoiseFactorTensor	shotNoiseFactor values for each image, which are used to compute the lambda values in a poisson distribution (1D tensor in HOST memory, of size batchSize with shotNoiseFactorTensor[i] >= 0 for each image in batch)
[in]	seed	A user-defined seed value (single Rpp32u value)
[in]	roiTensorSrc	ROI data in HOST memory, for each image in source tensor (2D tensor of size batchSize * 4, in either format - XYWH(xy.x, xy.y, roiWidth, roiHeight) or LTRB(lt.x, lt.y, rb.x, rb.y))
[in]	roiType	ROI type used (RpptRoiType::XYWH or RpptRoiType::LTRB)
[in]	rppHandle	RPP HOST handle created with `rppCreateWithBatchSize()`

Returns: A RppStatus enumeration.

Return values

RPP_SUCCESS	Successful completion.
RPP_ERROR*	Unsuccessful completion.

◆ rppt_spatter_gpu()

RppStatus rppt_spatter_gpu	(	RppPtr_t	srcPtr,
		RpptDescPtr	srcDescPtr,
		RppPtr_t	dstPtr,
		RpptDescPtr	dstDescPtr,
		RpptRGB	spatterColor,
		RpptROIPtr	roiTensorPtrSrc,
		RpptRoiType	roiType,
		rppHandle_t	rppHandle
	)

Spatter augmentation on HIP backend for a NCHW/NHWC layout tensor.

The spatter augmentation adds random spatter of a user-defined color, for a batch of RGB(3 channel) / greyscale(1 channel) images with an NHWC/NCHW tensor layout.

srcPtr depth ranges - Rpp8u (0 to 255), Rpp16f (0 to 1), Rpp32f (0 to 1), Rpp8s (-128 to 127).

dstPtr depth ranges - Will be same depth as srcPtr.

Sample Input

Sample Output - Mud Spatter

Sample Output - Ink Spatter

Sample Output - Blood Spatter

Parameters

[in]	srcPtr	source tensor in HIP memory
[in]	srcDescPtr	source tensor descriptor (Restrictions - numDims = 4, offsetInBytes >= 0, dataType = U8/F16/F32/I8, layout = NCHW/NHWC, c = 1/3)
[out]	dstPtr	destination tensor in HIP memory
[in]	dstDescPtr	destination tensor descriptor (Restrictions - numDims = 4, offsetInBytes >= 0, dataType = U8/F16/F32/I8, layout = NCHW/NHWC, c = same as that of srcDescPtr)
[in]	spatterColor	RGB values to use for the spatter augmentation (A single set of 3 Rpp8u values as RpptRGB that applies to all images in the batch)
[in]	roiTensorSrc	ROI data in HIP memory, for each image in source tensor (2D tensor of size batchSize * 4, in either format - XYWH(xy.x, xy.y, roiWidth, roiHeight) or LTRB(lt.x, lt.y, rb.x, rb.y)) \| (Restrictions - roiTensorSrc[i].xywhROI.roiWidth <= 1920 and roiTensorSrc[i].xywhROI.roiHeight <= 1080)
[in]	roiType	ROI type used (RpptRoiType::XYWH or RpptRoiType::LTRB)
[in]	rppHandle	RPP HIP handle created with `rppCreateWithStreamAndBatchSize()`

Returns: A RppStatus enumeration.

Return values

RPP_SUCCESS	Successful completion.
RPP_ERROR*	Unsuccessful completion.

◆ rppt_spatter_host()

RppStatus rppt_spatter_host	(	RppPtr_t	srcPtr,
		RpptDescPtr	srcDescPtr,
		RppPtr_t	dstPtr,
		RpptDescPtr	dstDescPtr,
		RpptRGB	spatterColor,
		RpptROIPtr	roiTensorPtrSrc,
		RpptRoiType	roiType,
		rppHandle_t	rppHandle
	)

Spatter augmentation on HOST backend for a NCHW/NHWC layout tensor.

The spatter augmentation adds random spatter of a user-defined color, for a batch of RGB(3 channel) / greyscale(1 channel) images with an NHWC/NCHW tensor layout.

srcPtr depth ranges - Rpp8u (0 to 255), Rpp16f (0 to 1), Rpp32f (0 to 1), Rpp8s (-128 to 127).

dstPtr depth ranges - Will be same depth as srcPtr.

Sample Input

Sample Output - Mud Spatter

Sample Output - Ink Spatter

Sample Output - Blood Spatter

Parameters

[in]	srcPtr	source tensor in HOST memory
[in]	srcDescPtr	source tensor descriptor (Restrictions - numDims = 4, offsetInBytes >= 0, dataType = U8/F16/F32/I8, layout = NCHW/NHWC, c = 1/3)
[out]	dstPtr	destination tensor in HOST memory
[in]	dstDescPtr	destination tensor descriptor (Restrictions - numDims = 4, offsetInBytes >= 0, dataType = U8/F16/F32/I8, layout = NCHW/NHWC, c = same as that of srcDescPtr)
[in]	spatterColor	RGB values to use for the spatter augmentation (A single set of 3 Rpp8u values as RpptRGB that applies to all images in the batch)
[in]	roiTensorSrc	ROI data in HOST memory, for each image in source tensor (2D tensor of size batchSize * 4, in either format - XYWH(xy.x, xy.y, roiWidth, roiHeight) or LTRB(lt.x, lt.y, rb.x, rb.y)) \| (Restrictions - roiTensorSrc[i].xywhROI.roiWidth <= 1920 and roiTensorSrc[i].xywhROI.roiHeight <= 1080)
[in]	roiType	ROI type used (RpptRoiType::XYWH or RpptRoiType::LTRB)
[in]	rppHandle	RPP HOST handle created with `rppCreateWithBatchSize()`

Returns: A RppStatus enumeration.

Return values

RPP_SUCCESS	Successful completion.
RPP_ERROR*	Unsuccessful completion.

◆ rppt_vignette_gpu()

RppStatus rppt_vignette_gpu	(	RppPtr_t	srcPtr,
		RpptDescPtr	srcDescPtr,
		RppPtr_t	dstPtr,
		RpptDescPtr	dstDescPtr,
		Rpp32f *	vignetteIntensityTensor,
		RpptROIPtr	roiTensorPtrSrc,
		RpptRoiType	roiType,
		rppHandle_t	rppHandle
	)

Vignette augmentation on HIP backend for a NCHW/NHWC layout tensor.

The vignette augmentation adds a vignette effect for a batch of RGB(3 channel) / greyscale(1 channel) images with an NHWC/NCHW tensor layout.

srcPtr depth ranges - Rpp8u (0 to 255), Rpp16f (0 to 1), Rpp32f (0 to 1), Rpp8s (-128 to 127).

dstPtr depth ranges - Will be same depth as srcPtr.

Sample Input

Sample Output

Parameters

[in]	srcPtr	source tensor in HIP memory
[in]	srcDescPtr	source tensor descriptor (Restrictions - numDims = 4, offsetInBytes >= 0, dataType = U8/F16/F32/I8, layout = NCHW/NHWC, c = 1/3)
[out]	dstPtr	destination tensor in HIP memory
[in]	dstDescPtr	destination tensor descriptor (Restrictions - numDims = 4, offsetInBytes >= 0, dataType = U8/F16/F32/I8, layout = NCHW/NHWC, c = same as that of srcDescPtr)
[in]	vignetteIntensityTensor	intensity values to quantify vignette effect (1D tensor of size batchSize with 0 < vignetteIntensityTensor[n] for each image in batch)
[in]	roiTensorSrc	ROI data in HIP memory, for each image in source tensor (2D tensor of size batchSize * 4, in either format - XYWH(xy.x, xy.y, roiWidth, roiHeight) or LTRB(lt.x, lt.y, rb.x, rb.y))
[in]	roiType	ROI type used (RpptRoiType::XYWH or RpptRoiType::LTRB)
[in]	rppHandle	RPP HIP handle created with `rppCreateWithStreamAndBatchSize()`

Returns: A RppStatus enumeration.

Return values

RPP_SUCCESS	Successful completion.
RPP_ERROR*	Unsuccessful completion.

◆ rppt_vignette_host()

RppStatus rppt_vignette_host	(	RppPtr_t	srcPtr,
		RpptDescPtr	srcDescPtr,
		RppPtr_t	dstPtr,
		RpptDescPtr	dstDescPtr,
		Rpp32f *	vignetteIntensityTensor,
		RpptROIPtr	roiTensorPtrSrc,
		RpptRoiType	roiType,
		rppHandle_t	rppHandle
	)

Vignette augmentation on HOST backend for a NCHW/NHWC layout tensor.

The Vignette augmentation adds a vignette effect for a batch of RGB(3 channel) / greyscale(1 channel) images with an NHWC/NCHW tensor layout.

srcPtr depth ranges - Rpp8u (0 to 255), Rpp16f (0 to 1), Rpp32f (0 to 1), Rpp8s (-128 to 127).

dstPtr depth ranges - Will be same depth as srcPtr.

Sample Input

Sample Output

Parameters

[in]	srcPtr	source tensor in HOST memory
[in]	srcDescPtr	source tensor descriptor (Restrictions - numDims = 4, offsetInBytes >= 0, dataType = U8/F16/F32/I8, layout = NCHW/NHWC, c = 1/3)
[out]	dstPtr	destination tensor in HOST memory
[in]	dstDescPtr	destination tensor descriptor (Restrictions - numDims = 4, offsetInBytes >= 0, dataType = U8/F16/F32/I8, layout = NCHW/NHWC, c = same as that of srcDescPtr)
[in]	vignetteIntensityTensor	intensity values to quantify vignette effect (1D tensor of size batchSize with 0 < vignetteIntensityTensor[n] for each image in batch)
[in]	roiTensorSrc	ROI data in HOST memory, for each image in source tensor (2D tensor of size batchSize * 4, in either format - XYWH(xy.x, xy.y, roiWidth, roiHeight) or LTRB(lt.x, lt.y, rb.x, rb.y))
[in]	roiType	ROI type used (RpptRoiType::XYWH or RpptRoiType::LTRB)
[in]	rppHandle	RPP HOST handle created with `rppCreateWithBatchSize()`

Returns: A RppStatus enumeration.

Return values

RPP_SUCCESS	Successful completion.
RPP_ERROR*	Unsuccessful completion.

◆ rppt_water_gpu()

RppStatus rppt_water_gpu	(	RppPtr_t	srcPtr,
		RpptDescPtr	srcDescPtr,
		RppPtr_t	dstPtr,
		RpptDescPtr	dstDescPtr,
		Rpp32f *	amplitudeXTensor,
		Rpp32f *	amplitudeYTensor,
		Rpp32f *	frequencyXTensor,
		Rpp32f *	frequencyYTensor,
		Rpp32f *	phaseXTensor,
		Rpp32f *	phaseYTensor,
		RpptROIPtr	roiTensorPtrSrc,
		RpptRoiType	roiType,
		rppHandle_t	rppHandle
	)

Water augmentation on HIP backend for a NCHW/NHWC layout tensor.

The water augmentation adds a water effect for a batch of RGB(3 channel) / greyscale(1 channel) images with an NHWC/NCHW tensor layout.

srcPtr depth ranges - Rpp8u (0 to 255), Rpp16f (0 to 1), Rpp32f (0 to 1), Rpp8s (-128 to 127).

dstPtr depth ranges - Will be same depth as srcPtr.

Sample Input

Sample Output

Parameters

[in]	srcPtr	source tensor in HIP memory
[in]	srcDescPtr	source tensor descriptor (Restrictions - numDims = 4, offsetInBytes >= 0, dataType = U8/F16/F32/I8, layout = NCHW/NHWC, c = 1/3)
[out]	dstPtr	destination tensor in HIP memory
[in]	dstDescPtr	destination tensor descriptor (Restrictions - numDims = 4, offsetInBytes >= 0, dataType = U8/F16/F32/I8, layout = NCHW/NHWC, c = same as that of srcDescPtr)
[in]	amplitudeXTensor	amplitudeX values for water effect (1D tensor in pinned/HOST memory, of size batchSize)
[in]	amplitudeYTensor	amplitudeY values for water effect (1D tensor in pinned/HOST memory, of size batchSize)
[in]	freqXTensor	freqX values for water effect (1D tensor in pinned/HOST memory, of size batchSize)
[in]	freqYTensor	freqY values for water effect (1D tensor in pinned/HOST memory, of size batchSize)
[in]	phaseXTensor	amplitudeY values for water effect (1D tensor in pinned/HOST memory, of size batchSize)
[in]	phaseYTensor	amplitudeY values for water effect (1D tensor in pinned/HOST memory, of size batchSize)
[in]	roiTensorSrc	ROI data in HIP memory, for each image in source tensor (2D tensor of size batchSize * 4, in either format - XYWH(xy.x, xy.y, roiWidth, roiHeight) or LTRB(lt.x, lt.y, rb.x, rb.y))
[in]	roiType	ROI type used (RpptRoiType::XYWH or RpptRoiType::LTRB)
[in]	rppHandle	RPP HIP handle created with `rppCreateWithStreamAndBatchSize()`

Returns: A RppStatus enumeration.

Return values

RPP_SUCCESS	Successful completion.
RPP_ERROR*	Unsuccessful completion.

◆ rppt_water_host()

RppStatus rppt_water_host	(	RppPtr_t	srcPtr,
		RpptDescPtr	srcDescPtr,
		RppPtr_t	dstPtr,
		RpptDescPtr	dstDescPtr,
		Rpp32f *	amplitudeXTensor,
		Rpp32f *	amplitudeYTensor,
		Rpp32f *	frequencyXTensor,
		Rpp32f *	frequencyYTensor,
		Rpp32f *	phaseXTensor,
		Rpp32f *	phaseYTensor,
		RpptROIPtr	roiTensorPtrSrc,
		RpptRoiType	roiType,
		rppHandle_t	rppHandle
	)

Water augmentation on HOST backend for a NCHW/NHWC layout tensor.

The water augmentation adds a water effect for a batch of RGB(3 channel) / greyscale(1 channel) images with an NHWC/NCHW tensor layout.

srcPtr depth ranges - Rpp8u (0 to 255), Rpp16f (0 to 1), Rpp32f (0 to 1), Rpp8s (-128 to 127).

dstPtr depth ranges - Will be same depth as srcPtr.

Sample Input

Sample Output

Parameters

[in]	srcPtr	source tensor in HOST memory
[in]	srcDescPtr	source tensor descriptor (Restrictions - numDims = 4, offsetInBytes >= 0, dataType = U8/F16/F32/I8, layout = NCHW/NHWC, c = 1/3)
[out]	dstPtr	destination tensor in HOST memory
[in]	dstDescPtr	destination tensor descriptor (Restrictions - numDims = 4, offsetInBytes >= 0, dataType = U8/F16/F32/I8, layout = NCHW/NHWC, c = same as that of srcDescPtr)
[in]	amplitudeXTensor	amplitudeX values for water effect (1D tensor in HOST memory, of size batchSize)
[in]	amplitudeYTensor	amplitudeY values for water effect (1D tensor in HOST memory, of size batchSize)
[in]	freqXTensor	freqX values for water effect (1D tensor in HOST memory, of size batchSize)
[in]	freqYTensor	freqY values for water effect (1D tensor in HOST memory, of size batchSize)
[in]	phaseXTensor	amplitudeY values for water effect (1D tensor in HOST memory, of size batchSize)
[in]	phaseYTensor	amplitudeY values for water effect (1D tensor in HOST memory, of size batchSize)
[in]	roiTensorSrc	ROI data in HOST memory, for each image in source tensor (2D tensor of size batchSize * 4, in either format - XYWH(xy.x, xy.y, roiWidth, roiHeight) or LTRB(lt.x, lt.y, rb.x, rb.y))
[in]	roiType	ROI type used (RpptRoiType::XYWH or RpptRoiType::LTRB)
[in]	rppHandle	RPP HOST handle created with `rppCreateWithBatchSize()`

Returns: A RppStatus enumeration.

Return values

RPP_SUCCESS	Successful completion.
RPP_ERROR*	Unsuccessful completion.

RPPT Tensor Operations - Effects Augmentations.

RPPT Tensor Operations - Effects Augmentations.#

Functions

Detailed Description

Function Documentation

◆ rppt_gaussian_noise_gpu()

◆ rppt_gaussian_noise_host()

◆ rppt_gridmask_gpu()

◆ rppt_gridmask_host()

◆ rppt_non_linear_blend_gpu()

◆ rppt_non_linear_blend_host()

◆ rppt_ricap_gpu()

◆ rppt_ricap_host()

◆ rppt_salt_and_pepper_noise_gpu()

◆ rppt_salt_and_pepper_noise_host()

◆ rppt_shot_noise_gpu()

◆ rppt_shot_noise_host()

◆ rppt_spatter_gpu()

◆ rppt_spatter_host()

◆ rppt_vignette_gpu()

◆ rppt_vignette_host()

◆ rppt_water_gpu()

◆ rppt_water_host()