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 (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, RppBackend executionBackend)
	Gridmask augmentation on HIP/HOST backend for a NCHW/NHWC layout tensor. More...

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

RppStatus	rppt_salt_and_pepper_noise (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, RppBackend executionBackend)
	Salt and pepper noise augmentation on HIP/HOST backend for a NCHW/NHWC layout tensor. More...

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

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

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

RppStatus	rppt_water (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, RppBackend executionBackend)
	Water augmentation on HIP/HOST backend for a NCHW/NHWC layout tensor. More...

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

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

RppStatus	rppt_jitter (RppPtr_t srcPtr, RpptDescPtr srcDescPtr, RppPtr_t dstPtr, RpptDescPtr dstDescPtr, Rpp32u *kernelSizeTensor, Rpp32u seed, RpptROIPtr roiTensorPtrSrc, RpptRoiType roiType, rppHandle_t rppHandle, RppBackend executionBackend)
	Jitter augmentation on HIP/HOST backend for a NCHW/NHWC layout tensor. More...

RppStatus	rppt_gaussian_noise_voxel (RppPtr_t srcPtr, RpptGenericDescPtr srcGenericDescPtr, RppPtr_t dstPtr, RpptGenericDescPtr dstGenericDescPtr, Rpp32f meanTensor, Rpp32f stdDevTensor, Rpp32u seed, RpptROI3DPtr roiGenericPtrSrc, RpptRoi3DType roiType, rppHandle_t rppHandle, RppBackend executionBackend)
	Gaussian noise augmentation on HIP/HOST backend. More...

RppStatus	rppt_erase (RppPtr_t srcPtr, RpptDescPtr srcDescPtr, RppPtr_t dstPtr, RpptDescPtr dstDescPtr, RpptRoiLtrb anchorBoxInfoTensor, RppPtr_t colorsTensor, Rpp32u numBoxesTensor, RpptROIPtr roiTensorPtrSrc, RpptRoiType roiType, rppHandle_t rppHandle, RppBackend executionBackend)
	Erase augmentation on HIP/HOST backend for a NCHW/NHWC layout tensor. More...

RppStatus	rppt_glitch (RppPtr_t srcPtr, RpptDescPtr srcDescPtr, RppPtr_t dstPtr, RpptDescPtr dstDescPtr, RpptChannelOffsets *rgbOffsets, RpptROIPtr roiTensorPtrSrc, RpptRoiType roiType, rppHandle_t rppHandle, RppBackend executionBackend)
	Glitch augmentation on HIP/HOST backend for a NCHW/NHWC layout tensor. More...

RppStatus	rppt_rain (RppPtr_t srcPtr, RpptDescPtr srcDescPtr, RppPtr_t dstPtr, RpptDescPtr dstDescPtr, Rpp32f rainPercentage, Rpp32u rainWidth, Rpp32u rainHeight, Rpp32f slantAngle, Rpp32f *alpha, RpptROIPtr roiTensorPtrSrc, RpptRoiType roiType, rppHandle_t rppHandle, RppBackend executionBackend)
	Rain augmentation on HIP/HOST backend for a NCHW/NHWC layout tensor. More...

RppStatus	rppt_pixelate (RppPtr_t srcPtr, RpptDescPtr srcDescPtr, RppPtr_t dstPtr, RpptDescPtr dstDescPtr, RppPtr_t intermediateScratchBufferPtr, Rpp32f pixelationPercentage, RpptROIPtr roiTensorPtrSrc, RpptRoiType roiType, rppHandle_t rppHandle, RppBackend executionBackend)
	Pixelate augmentation on HIP/HOST backend for a NCHW/NHWC layout tensor. More...

RppStatus	rppt_fog (RppPtr_t srcPtr, RpptDescPtr srcDescPtr, RppPtr_t dstPtr, RpptDescPtr dstDescPtr, Rpp32f intensityFactor, Rpp32f greyFactor, RpptROIPtr roiTensorPtrSrc, RpptRoiType roiType, rppHandle_t rppHandle, RppBackend executionBackend)
	Fog augmentation on HIP/HOST backend for a NCHW/NHWC layout tensor. More...

RppStatus	rppt_posterize (RppPtr_t srcPtr, RpptDescPtr srcDescPtr, RppPtr_t dstPtr, RpptDescPtr dstDescPtr, Rpp8u *posterizeLevelBits, RpptROIPtr roiTensorPtrSrc, RpptRoiType roiType, rppHandle_t rppHandle, RppBackend executionBackend)
	Posterize augmentation on HIP/HOST backend for a NCHW/NHWC layout tensor. More...

RppStatus	rppt_solarize (RppPtr_t srcPtr, RpptDescPtr srcDescPtr, RppPtr_t dstPtr, RpptDescPtr dstDescPtr, Rpp32f *thresholdTensor, RpptROIPtr roiTensorPtrSrc, RpptRoiType roiType, rppHandle_t rppHandle, RppBackend executionBackend)
	Solarize augmentation on HIP/HOST backend for a NCHW/NHWC layout tensor. More...

RppStatus	rppt_snow (RppPtr_t srcPtr, RpptDescPtr srcDescPtr, RppPtr_t dstPtr, RpptDescPtr dstDescPtr, Rpp32f brightnessCoefficient, Rpp32f snowThreshold, Rpp32s *darkMode, RpptROIPtr roiTensorPtrSrc, RpptRoiType roiType, rppHandle_t rppHandle, RppBackend executionBackend)
	Snow augmentation on HIP/HOST backend for a NCHW/NHWC layout tensor. More...

RppStatus	rppt_channel_dropout (RppPtr_t srcPtr, RpptDescPtr srcDescPtr, RppPtr_t dstPtr, RpptDescPtr dstDescPtr, Rpp8u *dropoutTensor, RpptROIPtr roiTensorPtrSrc, RpptRoiType roiType, rppHandle_t rppHandle, RppBackend executionBackend)
	Channel dropout augmentation on HIP/HOST backend for a NCHW/NHWC layout tensor. More...

RppStatus	rppt_cutout_dropout (RppPtr_t srcPtr, RpptDescPtr srcDescPtr, RppPtr_t dstPtr, RpptDescPtr dstDescPtr, RpptRoiLtrb anchorBoxInfoTensor, RppPtr_t colorsTensor, Rpp32u numBoxesTensor, RpptROIPtr roiTensorPtrSrc, RpptRoiType roiType, rppHandle_t rppHandle, RppBackend executionBackend)
	Cutout dropout augmentation on HIP/HOST backend for a NCHW/NHWC layout tensor. More...

RppStatus	rppt_grid_dropout (RppPtr_t srcPtr, RpptDescPtr srcDescPtr, RppPtr_t dstPtr, RpptDescPtr dstDescPtr, RpptRoiLtrb *anchorBoxInfoTensor, Rpp32u boxesInEachImage, Rpp32u maxHoleW, Rpp32u maxHoleH, RpptROIPtr roiTensorPtrSrc, RpptRoiType roiType, rppHandle_t rppHandle, RppBackend executionBackend)
	Grid dropout augmentation on HIP/HOST backend for a NCHW/NHWC layout tensor. More...

RppStatus	rppt_random_erase (RppPtr_t srcPtr, RpptDescPtr srcDescPtr, RppPtr_t dstPtr, RpptDescPtr dstDescPtr, RpptRoiLtrb *anchorBoxInfoTensor, RppPtr_t noiseBuffer, RpptROIPtr roiTensorPtrSrc, RpptRoiType roiType, rppHandle_t rppHandle, RppBackend executionBackend)
	Random Erase augmentation on HIP/HOST backend for a NCHW/NHWC layout tensor. More...

RppStatus	rppt_coarse_dropout (RppPtr_t srcPtr, RpptDescPtr srcDescPtr, RppPtr_t dstPtr, RpptDescPtr dstDescPtr, RpptRoiLtrb anchorBoxInfoTensor, Rpp32u numBoxesTensor, Rpp32u maxBoxesPerImage, RpptROIPtr roiTensorPtrSrc, RpptRoiType roiType, rppHandle_t rppHandle, RppBackend executionBackend)
	Coarse dropout augmentation on HIP/HOST backend for a NCHW/NHWC layout tensor. More...

Detailed Description

RPPT Tensor Operations - Effects Augmentations.

Function Documentation

◆ rppt_channel_dropout()

RppStatus rppt_channel_dropout	(	RppPtr_t	srcPtr,
		RpptDescPtr	srcDescPtr,
		RppPtr_t	dstPtr,
		RpptDescPtr	dstDescPtr,
		Rpp8u *	dropoutTensor,
		RpptROIPtr	roiTensorPtrSrc,
		RpptRoiType	roiType,
		rppHandle_t	rppHandle,
		RppBackend	executionBackend
	)

Channel dropout augmentation on HIP/HOST backend for a NCHW/NHWC layout tensor.

The channel dropout augmentation function erases one or more user defined channel from an image, 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 (for HIP backend) or HOST memory (for HOST backend)
[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 (for HIP backend) or HOST memory (for HOST backend)
[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]	dropoutTensor	channel dropout tensor (1D Rpp8u tensor in pinned / HIP memory (for HIP backend) or HOST memory (for HOST backend), of size batchSize * channels Values must be 0 (Drop) or 1 (Keep) for each channel of each image in batch)
[in]	roiTensorPtrSrc	ROI data in HIP memory (for HIP backend) or HOST memory (for HOST backend), 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/HOST handle created with `rppCreate()`
[in]	executionBackend	backend for execution (RppBackend::RPP_HOST_BACKEND or RppBackend::RPP_HIP_BACKEND)

Returns: A RppStatus enumeration.

Return values

RPP_SUCCESS	Successful completion.
RPP_ERROR*	Unsuccessful completion.

◆ rppt_coarse_dropout()

RppStatus rppt_coarse_dropout	(	RppPtr_t	srcPtr,
		RpptDescPtr	srcDescPtr,
		RppPtr_t	dstPtr,
		RpptDescPtr	dstDescPtr,
		RpptRoiLtrb *	anchorBoxInfoTensor,
		Rpp32u *	numBoxesTensor,
		Rpp32u	maxBoxesPerImage,
		RpptROIPtr	roiTensorPtrSrc,
		RpptRoiType	roiType,
		rppHandle_t	rppHandle,
		RppBackend	executionBackend
	)

Coarse dropout augmentation on HIP/HOST backend for a NCHW/NHWC layout tensor.

This function erases one or more user defined regions from an image, 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 (for HIP backend) or HOST memory (for HOST backend)
[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 (for HIP backend) or HOST memory (for HOST backend)
[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]	anchorBoxInfoTensor	anchorBoxInfo values of type RpptRoiLtrb for each erase-region inside each image in the batch (tensor in pinned / HIP memory (for HIP backend) or HOST memory (for HOST backend)). Restrictions - 0 <= anchorBoxInfo[i] < respective image width/height Erase-region anchor boxes on each image given by the user must not overlap
[in]	numBoxesTensor	number of erase-regions per image, for each image in the batch (1D tensor in pinned / HIP memory (for HIP backend) or HOST memory (for HOST backend)). (numBoxesTensor[n] >= 0)
[in]	maxBoxesPerImage	Maximum number of erase-regions allocated per image in the batch (stride for anchorBoxInfoTensor/colorsTensor)
[in]	roiTensorPtrSrc	ROI data in HIP memory (for HIP backend) or HOST memory (for HOST backend), 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/HOST handle created with `rppCreate()`
[in]	executionBackend	backend for execution (RppBackend::RPP_HOST_BACKEND or RppBackend::RPP_HIP_BACKEND)

Returns: A RppStatus enumeration.

Return values

RPP_SUCCESS	Successful completion.
RPP_ERROR*	Unsuccessful completion.

◆ rppt_cutout_dropout()

RppStatus rppt_cutout_dropout	(	RppPtr_t	srcPtr,
		RpptDescPtr	srcDescPtr,
		RppPtr_t	dstPtr,
		RpptDescPtr	dstDescPtr,
		RpptRoiLtrb *	anchorBoxInfoTensor,
		RppPtr_t	colorsTensor,
		Rpp32u *	numBoxesTensor,
		RpptROIPtr	roiTensorPtrSrc,
		RpptRoiType	roiType,
		rppHandle_t	rppHandle,
		RppBackend	executionBackend
	)

Cutout dropout augmentation on HIP/HOST backend for a NCHW/NHWC layout tensor.

Cutout dropout function erases random regions from an image, 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 (for HIP backend) or HOST memory (for HOST backend)
[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 (for HIP backend) or HOST memory (for HOST backend)
[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]	anchorBoxInfoTensor	precomputed cutout erase regions for the batch in pinned / HIP memory (for HIP backend) or HOST memory (for HOST backend), stored as a flat array of RpptRoiLtrb of size (batchSize * maxBoxesPerImage), where maxBoxesPerImage = max(numBoxesTensor[n])
[in]	colorsTensor	pointer to erase color values for each erase region in HIP memory (for HIP backend) or HOST memory (for HOST backend), laid out identically to anchorBoxInfoTensor, i.e., of size (batchSize * maxBoxesPerImage), with colorsTensor[(n * maxBoxesPerImage) + k]
[in]	numBoxesTensor	number of erase regions per image in the batch in pinned / HIP memory (for HIP backend) or HOST memory (for HOST backend) (1D tensor of size batchSize, Data Type - Rpp32u*)
[in]	roiTensorPtrSrc	ROI data in HIP memory (for HIP backend) or HOST memory (for HOST backend), 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/HOST handle created with `rppCreate()`
[in]	executionBackend	backend for execution (RppBackend::RPP_HOST_BACKEND or RppBackend::RPP_HIP_BACKEND)

Returns: A RppStatus enumeration.

Return values

RPP_SUCCESS	Successful completion.
RPP_ERROR*	Unsuccessful completion.

◆ rppt_erase()

RppStatus rppt_erase	(	RppPtr_t	srcPtr,
		RpptDescPtr	srcDescPtr,
		RppPtr_t	dstPtr,
		RpptDescPtr	dstDescPtr,
		RpptRoiLtrb *	anchorBoxInfoTensor,
		RppPtr_t	colorsTensor,
		Rpp32u *	numBoxesTensor,
		RpptROIPtr	roiTensorPtrSrc,
		RpptRoiType	roiType,
		rppHandle_t	rppHandle,
		RppBackend	executionBackend
	)

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

This function erases one or more user defined regions from an image, 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 (for HIP backend) or HOST memory (for HOST backend)
[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 (for HIP backend) or HOST memory (for HOST backend)
[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]	anchorBoxInfoTensor	anchorBoxInfo values of type RpptRoiLtrb for each erase-region inside each image in the batch (tensor in HIP memory (for HIP backend) or HOST memory (for HOST backend)). Restrictions - 0 <= anchorBoxInfo[i] < respective image width/height Erase-region anchor boxes on each image given by the user must not overlap
[in]	colorsTensor	RGB values to use for each erase-region inside each image in the batch (tensor in HIP memory (for HIP backend) or HOST memory (for HOST backend)). (colors[i] will have range equivalent of srcPtr)
[in]	numBoxesTensor	number of erase-regions per image, for each image in the batch (1D tensor in pinned / HIP memory (for HIP backend) or HOST memory (for HOST backend)). (numBoxesTensor[n] >= 0)
[in]	roiTensorPtrSrc	ROI data in HIP memory (for HIP backend) or HOST memory (for HOST backend), 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/HOST handle created with `rppCreate()`
[in]	executionBackend	backend for execution (RppBackend::RPP_HOST_BACKEND or RppBackend::RPP_HIP_BACKEND)

Returns: A RppStatus enumeration.

Return values

RPP_SUCCESS	Successful completion.
RPP_ERROR*	Unsuccessful completion.

◆ rppt_fog()

RppStatus rppt_fog	(	RppPtr_t	srcPtr,
		RpptDescPtr	srcDescPtr,
		RppPtr_t	dstPtr,
		RpptDescPtr	dstDescPtr,
		Rpp32f *	intensityFactor,
		Rpp32f *	greyFactor,
		RpptROIPtr	roiTensorPtrSrc,
		RpptRoiType	roiType,
		rppHandle_t	rppHandle,
		RppBackend	executionBackend
	)

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

The fog augmentation adds a fog effect for a batch of RGB(3 channel) / greyscale(1 channel) images with an NHWC/NCHW tensor layout.
NOTE: This augmentation gives a more realistic fog output when all images in a batch are of similar / same sizes

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 (for HIP backend) or HOST memory (for HOST backend)
[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 (for HIP backend) or HOST memory (for HOST backend)
[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]	intensityFactor	intensity factor values for fog calculation (1D tensor in pinned / HIP memory (for HIP backend) or HOST memory (for HOST backend), of size batchSize, with 0 <= intensityFactor <= 0.5 for each image in batch)
[in]	greyFactor	gray factor values to introduce grayness in the image for fog calculation (1D tensor in pinned / HIP memory (for HIP backend) or HOST memory (for HOST backend), of size batchSize, with 0 <= greyFactor <= 1 for each image in batch)
[in]	roiTensorPtrSrc	ROI data in HIP memory (for HIP backend) or HOST memory (for HOST backend) 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/HIP handle created with `rppCreate()`
[in]	executionBackend	backend for execution (RppBackend::RPP_HOST_BACKEND or RppBackend::RPP_HIP_BACKEND)

Returns: A RppStatus enumeration.

Return values

RPP_SUCCESS	Successful completion.
RPP_ERROR*	Unsuccessful completion.

◆ rppt_gaussian_noise()

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

Gaussian noise augmentation on HIP/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 HIP memory (for HIP backend) or HOST memory (for HOST backend)
[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 (for HIP backend) or HOST memory (for HOST backend)
[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 / HIP memory (for HIP backend) or HOST memory (for HOST backend), 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 / HIP memory (for HIP backend) or HOST memory (for HOST backend), of size batchSize with stdDevTensor[i] >= 0 for each image in batch)
[in]	seed	A user-defined seed value (single Rpp32u value)
[in]	roiTensorPtrSrc	ROI data in HIP memory (for HIP backend) or HOST memory (for HOST backend), 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/HOST handle created with `rppCreate()`
[in]	executionBackend	backend for execution (RppBackend::RPP_HOST_BACKEND or RppBackend::RPP_HIP_BACKEND)

Returns: A RppStatus enumeration.

Return values

RPP_SUCCESS	Successful completion.
RPP_ERROR*	Unsuccessful completion.

◆ rppt_gaussian_noise_voxel()

RppStatus rppt_gaussian_noise_voxel	(	RppPtr_t	srcPtr,
		RpptGenericDescPtr	srcGenericDescPtr,
		RppPtr_t	dstPtr,
		RpptGenericDescPtr	dstGenericDescPtr,
		Rpp32f *	meanTensor,
		Rpp32f *	stdDevTensor,
		Rpp32u	seed,
		RpptROI3DPtr	roiGenericPtrSrc,
		RpptRoi3DType	roiType,
		rppHandle_t	rppHandle,
		RppBackend	executionBackend
	)

Gaussian noise augmentation on HIP/HOST backend.

This function adds gaussian noise to a batch of 4D tensors. Support added for u8 -> u8, f32 -> f32 datatypes.

Sample Input

Sample Output

Parameters

[in]	srcPtr	source tensor in HIP memory (for HIP backend) or HOST memory (for HOST backend)
[in]	srcGenericDescPtr	source tensor descriptor
[out]	dstPtr	destination tensor in HIP memory (for HIP backend) or HOST memory (for HOST backend)
[in]	dstGenericDescPtr	destination tensor descriptor
[in]	meanTensor	mean values for each input, which are used to compute the generalized Box-Mueller transforms in a gaussian distribution (1D tensor in pinned / HIP memory (for HIP backend) or HOST memory (for HOST backend), 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 / HIP memory (for HIP backend) or HOST memory (for HOST backend), of size batchSize with stdDevTensor[i] >= 0 for each image in batch)
[in]	seed	A user-defined seed value (single Rpp32u value)
[in]	roiGenericPtrSrc	ROI data for each image in source tensor (tensor of batchSize RpptRoiGeneric values)
[in]	roiType	ROI type used (RpptRoi3DType::XYZWHD or RpptRoi3DType::LTFRBB)
[in]	rppHandle	RPP HIP/HOST handle created with `rppCreate()`

Returns: A RppStatus enumeration.

Return values

RPP_SUCCESS	Successful completion.
RPP_ERROR*	Unsuccessful completion.

◆ rppt_glitch()

RppStatus rppt_glitch	(	RppPtr_t	srcPtr,
		RpptDescPtr	srcDescPtr,
		RppPtr_t	dstPtr,
		RpptDescPtr	dstDescPtr,
		RpptChannelOffsets *	rgbOffsets,
		RpptROIPtr	roiTensorPtrSrc,
		RpptRoiType	roiType,
		rppHandle_t	rppHandle,
		RppBackend	executionBackend
	)

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

The glitch augmentation adds a glitch 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 (for HIP backend) or HOST memory (for HOST backend)
[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 (for HIP backend) or HOST memory (for HOST backend)
[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]	rgbOffsets	RGB offset values to use for the glitch augmentation (A single set of 3 Rppi point values that applies to all images in the batch. For each point and for each image in the batch: 0 < point.x < width, 0 < point.y < height)
[in]	roiTensorPtrSrc	ROI data in HIP memory (for HIP backend) or HOST memory (for HOST backend) 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/HOST handle created with `rppCreate()`
[in]	executionBackend	backend for execution (RppBackend::RPP_HOST_BACKEND or RppBackend::RPP_HIP_BACKEND)

Returns: A RppStatus enumeration.

Return values

RPP_SUCCESS	Successful completion.
RPP_ERROR*	Unsuccessful completion.

◆ rppt_grid_dropout()

RppStatus rppt_grid_dropout	(	RppPtr_t	srcPtr,
		RpptDescPtr	srcDescPtr,
		RppPtr_t	dstPtr,
		RpptDescPtr	dstDescPtr,
		RpptRoiLtrb *	anchorBoxInfoTensor,
		Rpp32u	boxesInEachImage,
		Rpp32u	maxHoleW,
		Rpp32u	maxHoleH,
		RpptROIPtr	roiTensorPtrSrc,
		RpptRoiType	roiType,
		rppHandle_t	rppHandle,
		RppBackend	executionBackend
	)

Grid dropout augmentation on HIP/HOST backend for a NCHW/NHWC layout tensor.

Grid dropout function erases grid wise random regions from an image, 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 (for HIP backend) or HOST memory (for HOST backend)
[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 (for HIP backend) or HOST memory (for HOST backend)
[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]	anchorBoxInfoTensor	Precomputed grid erase regions for the batch in pinned / HIP memory (for HIP backend) or HOST memory (for HOST backend), stored as an array of RpptRoiLtrb of size (batchSize * boxesInEachImage)
[in]	boxesInEachImage	Number of grid boxes per image (Data Type - Rpp32u)
[in]	maxHoleW	Maximum hole width across all grid boxes (Data Type - Rpp32u)
[in]	maxHoleH	Maximum hole height across all grid boxes (Data Type - Rpp32u)
[in]	roiTensorPtrSrc	ROI data in HIP memory (for HIP backend) or HOST memory (for HOST backend), 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/HOST handle created with `rppCreate()`
[in]	executionBackend	backend for execution (RppBackend::RPP_HOST_BACKEND or RppBackend::RPP_HIP_BACKEND)

Returns: A RppStatus enumeration.

Return values

RPP_SUCCESS	Successful completion.
RPP_ERROR*	Unsuccessful completion.

◆ rppt_gridmask()

RppStatus rppt_gridmask	(	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,
		RppBackend	executionBackend
	)

Gridmask augmentation on HIP/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 HIP memory (for HIP backend) or HOST memory (for HOST backend)
[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 (for HIP backend) or HOST memory (for HOST backend)
[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]	roiTensorPtrSrc	ROI data in HIP memory (for HIP backend) or HOST memory (for HOST backend), 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/HOST handle created with `rppCreate()`
[in]	executionBackend	backend for execution (RppBackend::RPP_HOST_BACKEND or RppBackend::RPP_HIP_BACKEND)

Returns: A RppStatus enumeration.

Return values

RPP_SUCCESS	Successful completion.
RPP_ERROR*	Unsuccessful completion.

◆ rppt_jitter()

RppStatus rppt_jitter	(	RppPtr_t	srcPtr,
		RpptDescPtr	srcDescPtr,
		RppPtr_t	dstPtr,
		RpptDescPtr	dstDescPtr,
		Rpp32u *	kernelSizeTensor,
		Rpp32u	seed,
		RpptROIPtr	roiTensorPtrSrc,
		RpptRoiType	roiType,
		rppHandle_t	rppHandle,
		RppBackend	executionBackend
	)

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

The jitter augmentation adds a jitter 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 (for HIP backend) or HOST memory (for HOST backend)
[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 (for HIP backend) or HOST memory (for HOST backend)
[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]	kernelSizeTensor	kernelsize value for jitter calculation (1D tensor in pinned / HIP memory (for HIP backend) or HOST memory (for HOST backend), kernelSize = 3/5/7 for optimal use)
[in]	roiTensorPtrSrc	ROI data in HIP memory (for HIP backend) or HOST memory (for HOST backend), 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/HOST handle created with `rppCreate()`
[in]	executionBackend	backend for execution (RppBackend::RPP_HOST_BACKEND or RppBackend::RPP_HIP_BACKEND)

Returns: A RppStatus enumeration.

Return values

RPP_SUCCESS	Successful completion.
RPP_ERROR*	Unsuccessful completion.

◆ rppt_non_linear_blend()

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

Non linear blend augmentation on HIP/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 HIP memory (for HIP backend) or HOST memory (for HOST backend)
[in]	srcPtr2	source2 tensor in HIP memory (for HIP backend) or HOST memory (for HOST backend)
[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 (for HIP backend) or HOST memory (for HOST backend)
[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 / HIP memory (for HIP backend) or HOST memory (for HOST backend), of size batchSize with stdDevTensor[i] >= 0 for each image in batch)
[in]	roiTensorPtrSrc	ROI data in HIP memory (for HIP backend) or HOST memory (for HOST backend), 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/HOST handle created with `rppCreate()`
[in]	executionBackend	backend for execution (RppBackend::RPP_HOST_BACKEND or RppBackend::RPP_HIP_BACKEND)

Returns: A RppStatus enumeration.

Return values

RPP_SUCCESS	Successful completion.
RPP_ERROR*	Unsuccessful completion.

◆ rppt_pixelate()

RppStatus rppt_pixelate	(	RppPtr_t	srcPtr,
		RpptDescPtr	srcDescPtr,
		RppPtr_t	dstPtr,
		RpptDescPtr	dstDescPtr,
		RppPtr_t	intermediateScratchBufferPtr,
		Rpp32f	pixelationPercentage,
		RpptROIPtr	roiTensorPtrSrc,
		RpptRoiType	roiType,
		rppHandle_t	rppHandle,
		RppBackend	executionBackend
	)

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

The pixelate augmentation performs a pixelate transformation 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 (for HIP backend) or HOST memory (for HOST backend)
[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 (for HIP backend) or HOST memory (for HOST backend)
[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]	intermediateScratchBufferPtr	intermediate scratch buffer in HIP memory (for HIP backend) or HOST memory (for HOST backend) (Minimum size = srcDescPtr->n * srcDescPtr->strides.nStride * sizeof(Rpp32f))
[in]	pixelationPercentage	'pixelationPercentage' variable controls how much pixelation is applied to images.(pixelationPercentage value ranges from 0 to 100)
[in]	roiTensorPtrSrc	ROI data in HIP memory (for HIP backend) or HOST memory (for HOST backend), 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/HOST handle created with `rppCreate()`
[in]	executionBackend	backend for execution (RppBackend::RPP_HOST_BACKEND or RppBackend::RPP_HIP_BACKEND)

Returns: A RppStatus enumeration.

Return values

RPP_SUCCESS	Successful completion.
RPP_ERROR*	Unsuccessful completion.

◆ rppt_posterize()

RppStatus rppt_posterize	(	RppPtr_t	srcPtr,
		RpptDescPtr	srcDescPtr,
		RppPtr_t	dstPtr,
		RpptDescPtr	dstDescPtr,
		Rpp8u *	posterizeLevelBits,
		RpptROIPtr	roiTensorPtrSrc,
		RpptRoiType	roiType,
		rppHandle_t	rppHandle,
		RppBackend	executionBackend
	)

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

The posterize augmentation adds a posterize 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 (for HIP backend) or HOST memory (for HOST backend)
[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 (for HIP backend) or HOST memory (for HOST backend)
[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]	posterizeLevelBits	number of bits used to represent the image with posterize Operation (1D tensor in pinned / HIP memory (for HIP backend) or HOST memory (for HOST backend), of size batchSize, with 1 <= posterizeLevelBits <= 8 for each image in batch))
[in]	roiTensorPtrSrc	ROI data in HIP memory (for HIP backend) or HOST memory (for HOST backend) 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/HOST handle created with `rppCreate()`
[in]	executionBackend	backend for execution (RppBackend::RPP_HOST_BACKEND or RppBackend::RPP_HIP_BACKEND)

Returns: A RppStatus enumeration.

Return values

RPP_SUCCESS	Successful completion.
RPP_ERROR*	Unsuccessful completion.

◆ rppt_rain()

RppStatus rppt_rain	(	RppPtr_t	srcPtr,
		RpptDescPtr	srcDescPtr,
		RppPtr_t	dstPtr,
		RpptDescPtr	dstDescPtr,
		Rpp32f	rainPercentage,
		Rpp32u	rainWidth,
		Rpp32u	rainHeight,
		Rpp32f	slantAngle,
		Rpp32f *	alpha,
		RpptROIPtr	roiTensorPtrSrc,
		RpptRoiType	roiType,
		rppHandle_t	rppHandle,
		RppBackend	executionBackend
	)

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

The rain augmentation simulates a rain effect for a batch of RGB (3-channel) / greyscale (1-channel) images with an NHWC/NCHW tensor layout.
NOTE: This augmentation gives a more realistic Rain output when all images in a batch are of similar / same sizes

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

dstPtr depth ranges - Will be the same depth as srcPtr.

Sample Input

Sample Output

Parameters

[in]	srcPtr	source tensor in HIP memory (for HIP backend) or HOST memory (for HOST backend)
[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 (for HIP backend) or HOST memory (for HOST backend)
[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]	rainPercentage	The percentage of the rain effect to be applied (0 <= rainPercentage <= 100)
[in]	rainWidth	Width of the rain drops in pixels. To be tuned by user depending on size of the image.
[in]	rainHeight	Height of the rain drops in pixels. To be tuned by user depending on size of the image.
[in]	slantAngle	Slant angle of the rain drops (positive value for right slant, negative for left slant). A single Rpp32s/f representing the slant of raindrops in degrees. Values range from [-90, 90], where -90 represents extreme left slant, 0 is vertical, and 90 is extreme right slant.
[in]	alpha	An array of alpha blending values to be used for blending the rainLayer and the input image for each image in the batch (1D tensor in pinned / HIP memory (for HIP backend) or HOST memory (for HOST backend), 0 ≤ alpha ≤ 1 for each image in the batch).
[in]	roiTensorPtrSrc	ROI data in HIP memory (for HIP backend) or HOST memory (for HOST backend) 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/HOST handle created with `rppCreateWithBatchSize()`
[in]	executionBackend	backend for execution (RppBackend::RPP_HOST_BACKEND or RppBackend::RPP_HIP_BACKEND)

Returns: A RppStatus enumeration.

Return values

RPP_SUCCESS	Successful completion.
RPP_ERROR*	Unsuccessful completion.

◆ rppt_random_erase()

RppStatus rppt_random_erase	(	RppPtr_t	srcPtr,
		RpptDescPtr	srcDescPtr,
		RppPtr_t	dstPtr,
		RpptDescPtr	dstDescPtr,
		RpptRoiLtrb *	anchorBoxInfoTensor,
		RppPtr_t	noiseBuffer,
		RpptROIPtr	roiTensorPtrSrc,
		RpptRoiType	roiType,
		rppHandle_t	rppHandle,
		RppBackend	executionBackend
	)

Random Erase augmentation on HIP/HOST backend for a NCHW/NHWC layout tensor.

This function erases random regions from an image and fills with random noise, 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 (for HIP backend) or HOST memory (for HOST backend)
[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 (for HIP backend) or HOST memory (for HOST backend)
[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]	anchorBoxInfoTensor	anchorBoxInfo values of type RpptRoiLtrb for each erase-region inside each image in the batch (tensor in pinned / HIP memory (for HIP backend) or HOST memory (for HOST backend)). Restrictions - 0 <= anchorBoxInfo[i] < respective image width/height Erase-region anchor boxes on each image given by the user must not overlap
[in]	noiseBuffer	pre-allocated buffer containing random noise values in pinned / HIP memory (for HIP backend) or HOST memory (for HOST backend) (Buffer size must be 255 * 255 * srcDescPtr->c. Values are accessed spatially (tiled) to fill erased regions)
[in]	roiTensorPtrSrc	ROI data in HIP memory (for HIP backend) or HOST memory (for HOST backend), 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/HOST handle created with `rppCreate()`
[in]	executionBackend	backend for execution (RppBackend::RPP_HOST_BACKEND or RppBackend::RPP_HIP_BACKEND)

Returns: A RppStatus enumeration.

Return values

RPP_SUCCESS	Successful completion.
RPP_ERROR*	Unsuccessful completion.

◆ rppt_ricap()

RppStatus rppt_ricap	(	RppPtr_t	srcPtr,
		RpptDescPtr	srcDescPtr,
		RppPtr_t	dstPtr,
		RpptDescPtr	dstDescPtr,
		Rpp32u *	permutationTensor,
		RpptROIPtr	roiPtrInputCropRegion,
		RpptRoiType	roiType,
		rppHandle_t	rppHandle,
		RppBackend	executionBackend
	)

RICAP (Random Image Crop And Patch) augmentation on HIP/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 HIP memory (for HIP backend) or HOST memory (for HOST backend)
[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 (for HIP backend) or HOST memory (for HOST backend)
[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]	permutationTensor	Array of batchSize permutation sets (2D tensor in HIP memory (for HIP backend) or HOST memory (for HOST backend), 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 (for HIP backend) or HOST memory (for HOST backend), 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/HOST handle created with `rppCreate()`
[in]	executionBackend	backend for execution (RppBackend::RPP_HOST_BACKEND or RppBackend::RPP_HIP_BACKEND)

Returns: A RppStatus enumeration.

Return values

RPP_SUCCESS	Successful completion.
RPP_ERROR*	Unsuccessful completion.

◆ rppt_salt_and_pepper_noise()

RppStatus rppt_salt_and_pepper_noise	(	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,
		RppBackend	executionBackend
	)

Salt and pepper noise augmentation on HIP/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 HIP memory (for HIP backend) or HOST memory (for HOST backend)
[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 (for HIP backend) or HOST memory (for HOST backend)
[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]	noiseProbabilityTensor	noiseProbability values to decide if a destination pixel is a noise-pixel, or equal to source (1D tensor in pinned / HIP memory (for HIP backend) or HOST memory (for HOST backend), of size batchSize with 0 <= noiseProbabilityTensor[i] <= 1 for each image in batch)
[in]	saltProbabilityTensor	saltProbability values to decide if a given destination noise-pixel is salt or pepper (1D tensor in pinned / HIP memory (for HIP backend) or HOST memory (for HOST backend), of size batchSize with 0 <= saltProbabilityTensor[i] <= 1 for each image in batch)
[in]	saltValueTensor	A user-defined salt noise value (1D tensor in pinned / HIP memory (for HIP backend) or HOST memory (for HOST backend), 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 / HIP memory (for HIP backend) or HOST memory (for HOST backend), of size batchSize with 0 <= pepperValueTensor[i] <= 1 for each image in batch)
[in]	seed	A user-defined seed value (single Rpp32u value)
[in]	roiTensorPtrSrc	ROI data in HIP memory (for HIP backend) or HOST memory (for HOST backend), 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/HOST handle created with `rppCreate()`
[in]	executionBackend	backend for execution (RppBackend::RPP_HOST_BACKEND or RppBackend::RPP_HIP_BACKEND)

Returns: A RppStatus enumeration.

Return values

RPP_SUCCESS	Successful completion.
RPP_ERROR*	Unsuccessful completion.

◆ rppt_shot_noise()

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

Shot noise augmentation on HIP/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 HIP memory (for HIP backend) or HOST memory (for HOST backend)
[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 (for HIP backend) or HOST memory (for HOST backend)
[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 / HIP memory (for HIP backend) or HOST memory (for HOST backend), of size batchSize with shotNoiseFactorTensor[i] >= 0 for each image in batch)
[in]	seed	A user-defined seed value (single Rpp32u value)
[in]	roiTensorPtrSrc	ROI data in HIP memory (for HIP backend) or HOST memory (for HOST backend), 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/HOST handle created with `rppCreate()`
[in]	executionBackend	backend for execution (RppBackend::RPP_HOST_BACKEND or RppBackend::RPP_HIP_BACKEND)

Returns: A RppStatus enumeration.

Return values

RPP_SUCCESS	Successful completion.
RPP_ERROR*	Unsuccessful completion.

◆ rppt_snow()

RppStatus rppt_snow	(	RppPtr_t	srcPtr,
		RpptDescPtr	srcDescPtr,
		RppPtr_t	dstPtr,
		RpptDescPtr	dstDescPtr,
		Rpp32f *	brightnessCoefficient,
		Rpp32f *	snowThreshold,
		Rpp32s *	darkMode,
		RpptROIPtr	roiTensorPtrSrc,
		RpptRoiType	roiType,
		rppHandle_t	rppHandle,
		RppBackend	executionBackend
	)

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

The Snow augmentation adds a snowed-in effect on 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 (for HIP backend) or HOST memory (for HOST backend)
[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 (for HIP backend) or HOST memory (for HOST backend)
[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]	brightnessCoefficient	brightness modification parameter for snow calculation (1D tensor in pinned / HIP memory (for HIP backend) or HOST memory (for HOST backend), of size batchSize with brightnessCoefficient[i] in the range (1, 4] for each image in batch)
[in]	snowThreshold	threshold parameter for snow calculation (1D tensor in pinned / HIP memory (for HIP backend) or HOST memory (for HOST backend), of size batchSize with 0 < snowThreshold[i] <= 1 for each image in batch)
[in]	darkMode	darkMode values to set dark mode on/off (1D tensor in pinned / HIP memory (for HIP backend) or HOST memory (for HOST backend), of size batchSize, with darkMode[i] = 0/1)
[in]	roiTensorPtrSrc	ROI data in HIP memory (for HIP backend) or HOST memory (for HOST backend), 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/HOST handle created with `rppCreate()`
[in]	executionBackend	backend for execution (RppBackend::RPP_HOST_BACKEND or RppBackend::RPP_HIP_BACKEND)

Returns: A RppStatus enumeration.

Return values

RPP_SUCCESS	Successful completion.
RPP_ERROR*	Unsuccessful completion.

◆ rppt_solarize()

RppStatus rppt_solarize	(	RppPtr_t	srcPtr,
		RpptDescPtr	srcDescPtr,
		RppPtr_t	dstPtr,
		RpptDescPtr	dstDescPtr,
		Rpp32f *	thresholdTensor,
		RpptROIPtr	roiTensorPtrSrc,
		RpptRoiType	roiType,
		rppHandle_t	rppHandle,
		RppBackend	executionBackend
	)

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

The solarize augmentation inverts pixel values above a specified threshold 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 (for HIP backend) or HOST memory (for HOST backend)
[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 (for HIP backend) or HOST memory (for HOST backend)
[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]	thresholdTensor	threshold values for solarize effect (1D tensor in pinned / HIP memory (for HIP backend) or HOST memory (for HOST backend), of size batchSize, with 0 <= threshold <= 1 for each image in batch)
[in]	roiTensorPtrSrc	ROI data in HIP memory (for HIP backend) or HOST memory (for HOST backend) 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/HOST handle created with `rppCreate()`
[in]	executionBackend	backend for execution (RppBackend::RPP_HOST_BACKEND or RppBackend::RPP_HIP_BACKEND)

Returns: A RppStatus enumeration.

Return values

RPP_SUCCESS	Successful completion.
RPP_ERROR*	Unsuccessful completion.

◆ rppt_spatter()

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

Spatter augmentation on HIP/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 HIP memory (for HIP backend) or HOST memory (for HOST backend)
[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 (for HIP backend) or HOST memory (for HOST backend)
[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]	roiTensorPtrSrc	ROI data in HIP memory (for HIP backend) or HOST memory (for HOST backend), 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 - roiTensorPtrSrc[i].xywhROI.roiWidth <= 1920 and roiTensorPtrSrc[i].xywhROI.roiHeight <= 1080)
[in]	roiType	ROI type used (RpptRoiType::XYWH or RpptRoiType::LTRB)
[in]	rppHandle	RPP HIP/HOST handle created with `rppCreate()`
[in]	executionBackend	backend for execution (RppBackend::RPP_HOST_BACKEND or RppBackend::RPP_HIP_BACKEND)

Returns: A RppStatus enumeration.

Return values

RPP_SUCCESS	Successful completion.
RPP_ERROR*	Unsuccessful completion.

◆ rppt_vignette()

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

Vignette augmentation on HIP/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 HIP memory (for HIP backend) or HOST memory (for HOST backend)
[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 (for HIP backend) or HOST memory (for HOST backend)
[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 in pinned / HIP memory (for HIP backend) or HOST memory (for HOST backend), of size batchSize with 0 < vignetteIntensityTensor[n] for each image in batch)
[in]	roiTensorPtrSrc	ROI data in HIP memory (for HIP backend) or HOST memory (for HOST backend), 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/HOST handle created with `rppCreate()`

Returns: A RppStatus enumeration.

Return values

RPP_SUCCESS	Successful completion.
RPP_ERROR*	Unsuccessful completion.

◆ rppt_water()

RppStatus rppt_water	(	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,
		RppBackend	executionBackend
	)

Water augmentation on HIP/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 HIP memory (for HIP backend) or HOST memory (for HOST backend)
[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 (for HIP backend) or HOST memory (for HOST backend)
[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 / HIP memory (for HIP backend) or HOST memory (for HOST backend), of size batchSize)
[in]	amplitudeYTensor	amplitudeY values for water effect (1D tensor in pinned / HIP memory (for HIP backend) or HOST memory (for HOST backend), of size batchSize)
[in]	frequencyXTensor	frequencyX values for water effect (1D tensor in pinned / HIP memory (for HIP backend) or HOST memory (for HOST backend), of size batchSize)
[in]	frequencyYTensor	frequencyY values for water effect (1D tensor in pinned / HIP memory (for HIP backend) or HOST memory (for HOST backend), of size batchSize)
[in]	phaseXTensor	phaseX values for water effect (1D tensor in pinned / HIP memory (for HIP backend) or HOST memory (for HOST backend), of size batchSize)
[in]	phaseYTensor	phaseY values for water effect (1D tensor in pinned / HIP memory (for HIP backend) or HOST memory (for HOST backend), of size batchSize)
[in]	roiTensorPtrSrc	ROI data in HIP memory (for HIP backend) or HOST memory (for HOST backend), 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/HOST handle created with `rppCreate()`
[in]	executionBackend	backend for execution (RppBackend::RPP_HOST_BACKEND or RppBackend::RPP_HIP_BACKEND)

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_channel_dropout()

◆ rppt_coarse_dropout()

◆ rppt_cutout_dropout()

◆ rppt_erase()

◆ rppt_fog()

◆ rppt_gaussian_noise()

◆ rppt_gaussian_noise_voxel()

◆ rppt_glitch()

◆ rppt_grid_dropout()

◆ rppt_gridmask()

◆ rppt_jitter()

◆ rppt_non_linear_blend()

◆ rppt_pixelate()

◆ rppt_posterize()

◆ rppt_rain()

◆ rppt_random_erase()

◆ rppt_ricap()

◆ rppt_salt_and_pepper_noise()

◆ rppt_shot_noise()

◆ rppt_snow()

◆ rppt_solarize()

◆ rppt_spatter()

◆ rppt_vignette()

◆ rppt_water()