RPPT Tensor Operations - Geometric Augmentations. More...

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

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

RppStatus	rppt_crop_mirror_normalize_host (RppPtr_t srcPtr, RpptDescPtr srcDescPtr, RppPtr_t dstPtr, RpptDescPtr dstDescPtr, Rpp32f offsetTensor, Rpp32f multiplierTensor, Rpp32u *mirrorTensor, RpptROIPtr roiTensorPtrSrc, RpptRoiType roiType, rppHandle_t rppHandle)
	Crop mirror normalize augmentation on HOST backend for a NCHW/NHWC layout tensor. More...

RppStatus	rppt_crop_mirror_normalize_gpu (RppPtr_t srcPtr, RpptDescPtr srcDescPtr, RppPtr_t dstPtr, RpptDescPtr dstDescPtr, Rpp32f offsetTensor, Rpp32f multiplierTensor, Rpp32u *mirrorTensor, RpptROIPtr roiTensorPtrSrc, RpptRoiType roiType, rppHandle_t rppHandle)
	Crop mirror normalize augmentation on HIP backend for a NCHW/NHWC layout tensor. More...

RppStatus	rppt_warp_affine_host (RppPtr_t srcPtr, RpptDescPtr srcDescPtr, RppPtr_t dstPtr, RpptDescPtr dstDescPtr, Rpp32f *affineTensor, RpptInterpolationType interpolationType, RpptROIPtr roiTensorPtrSrc, RpptRoiType roiType, rppHandle_t rppHandle)
	Warp affine augmentation on HOST backend for a NCHW/NHWC layout tensor. More...

RppStatus	rppt_warp_affine_gpu (RppPtr_t srcPtr, RpptDescPtr srcDescPtr, RppPtr_t dstPtr, RpptDescPtr dstDescPtr, Rpp32f *affineTensor, RpptInterpolationType interpolationType, RpptROIPtr roiTensorPtrSrc, RpptRoiType roiType, rppHandle_t rppHandle)
	Warp affine augmentation on HIP backend for a NCHW/NHWC layout tensor. More...

RppStatus	rppt_flip_host (RppPtr_t srcPtr, RpptDescPtr srcDescPtr, RppPtr_t dstPtr, RpptDescPtr dstDescPtr, Rpp32u horizontalTensor, Rpp32u verticalTensor, RpptROIPtr roiTensorPtrSrc, RpptRoiType roiType, rppHandle_t rppHandle)
	Flip augmentation on HOST backend for a NCHW/NHWC layout tensor. More...

RppStatus	rppt_flip_gpu (RppPtr_t srcPtr, RpptDescPtr srcDescPtr, RppPtr_t dstPtr, RpptDescPtr dstDescPtr, Rpp32u horizontalTensor, Rpp32u verticalTensor, RpptROIPtr roiTensorPtrSrc, RpptRoiType roiType, rppHandle_t rppHandle)
	Flip augmentation on HIP backend for a NCHW/NHWC layout tensor. More...

RppStatus	rppt_resize_host (RppPtr_t srcPtr, RpptDescPtr srcDescPtr, RppPtr_t dstPtr, RpptDescPtr dstDescPtr, RpptImagePatchPtr dstImgSizes, RpptInterpolationType interpolationType, RpptROIPtr roiTensorPtrSrc, RpptRoiType roiType, rppHandle_t rppHandle)
	Resize augmentation on HOST backend for a NCHW/NHWC layout tensor. More...

RppStatus	rppt_resize_gpu (RppPtr_t srcPtr, RpptDescPtr srcDescPtr, RppPtr_t dstPtr, RpptDescPtr dstDescPtr, RpptImagePatchPtr dstImgSizes, RpptInterpolationType interpolationType, RpptROIPtr roiTensorPtrSrc, RpptRoiType roiType, rppHandle_t rppHandle)
	Resize augmentation on HIP backend for a NCHW/NHWC layout tensor. More...

RppStatus	rppt_resize_mirror_normalize_host (RppPtr_t srcPtr, RpptDescPtr srcDescPtr, RppPtr_t dstPtr, RpptDescPtr dstDescPtr, RpptImagePatchPtr dstImgSizes, RpptInterpolationType interpolationType, Rpp32f meanTensor, Rpp32f stdDevTensor, Rpp32u *mirrorTensor, RpptROIPtr roiTensorPtrSrc, RpptRoiType roiType, rppHandle_t rppHandle)
	Resize mirror normalize augmentation on HOST backend for a NCHW/NHWC layout tensor. More...

RppStatus	rppt_resize_mirror_normalize_gpu (RppPtr_t srcPtr, RpptDescPtr srcDescPtr, RppPtr_t dstPtr, RpptDescPtr dstDescPtr, RpptImagePatchPtr dstImgSizes, RpptInterpolationType interpolationType, Rpp32f meanTensor, Rpp32f stdDevTensor, Rpp32u *mirrorTensor, RpptROIPtr roiTensorPtrSrc, RpptRoiType roiType, rppHandle_t rppHandle)
	Resize mirror normalize augmentation on HIP backend for a NCHW/NHWC layout tensor. More...

RppStatus	rppt_resize_crop_mirror_host (RppPtr_t srcPtr, RpptDescPtr srcDescPtr, RppPtr_t dstPtr, RpptDescPtr dstDescPtr, RpptImagePatchPtr dstImgSizes, RpptInterpolationType interpolationType, Rpp32u *mirrorTensor, RpptROIPtr roiTensorPtrSrc, RpptRoiType roiType, rppHandle_t rppHandle)
	Resize crop mirror augmentation on HOST backend for a NCHW/NHWC layout tensor. More...

RppStatus	rppt_resize_crop_mirror_gpu (RppPtr_t srcPtr, RpptDescPtr srcDescPtr, RppPtr_t dstPtr, RpptDescPtr dstDescPtr, RpptImagePatchPtr dstImgSizes, RpptInterpolationType interpolationType, Rpp32u *mirrorTensor, RpptROIPtr roiTensorPtrSrc, RpptRoiType roiType, rppHandle_t rppHandle)
	Resize crop mirror augmentation on HIP backend for a NCHW/NHWC layout tensor. More...

RppStatus	rppt_rotate_host (RppPtr_t srcPtr, RpptDescPtr srcDescPtr, RppPtr_t dstPtr, RpptDescPtr dstDescPtr, Rpp32f *angle, RpptInterpolationType interpolationType, RpptROIPtr roiTensorPtrSrc, RpptRoiType roiType, rppHandle_t rppHandle)
	Rotate augmentation on HOST backend for a NCHW/NHWC layout tensor. More...

RppStatus	rppt_rotate_gpu (RppPtr_t srcPtr, RpptDescPtr srcDescPtr, RppPtr_t dstPtr, RpptDescPtr dstDescPtr, Rpp32f *angle, RpptInterpolationType interpolationType, RpptROIPtr roiTensorPtrSrc, RpptRoiType roiType, rppHandle_t rppHandle)
	Rotate augmentation on HIP backend for a NCHW/NHWC layout tensor. More...

RppStatus	rppt_phase_host (RppPtr_t srcPtr1, RppPtr_t srcPtr2, RpptDescPtr srcDescPtr, RppPtr_t dstPtr, RpptDescPtr dstDescPtr, RpptROIPtr roiTensorPtrSrc, RpptRoiType roiType, rppHandle_t rppHandle)
	Phase augmentation on HOST backend for a NCHW/NHWC layout tensor. More...

RppStatus	rppt_phase_gpu (RppPtr_t srcPtr1, RppPtr_t srcPtr2, RpptDescPtr srcDescPtr, RppPtr_t dstPtr, RpptDescPtr dstDescPtr, RpptROIPtr roiTensorPtrSrc, RpptRoiType roiType, rppHandle_t rppHandle)
	Phase augmentation on HIP backend for a NCHW/NHWC layout tensor. More...

RppStatus	rppt_slice_host (RppPtr_t srcPtr, RpptGenericDescPtr srcGenericDescPtr, RppPtr_t dstPtr, RpptGenericDescPtr dstGenericDescPtr, Rpp32s anchorTensor, Rpp32s shapeTensor, RppPtr_t fillValue, bool enablePadding, Rpp32u *roiTensor, rppHandle_t rppHandle)
	Slice augmentation HOST. More...

RppStatus	rppt_slice_gpu (RppPtr_t srcPtr, RpptGenericDescPtr srcGenericDescPtr, RppPtr_t dstPtr, RpptGenericDescPtr dstGenericDescPtr, Rpp32s anchorTensor, Rpp32s shapeTensor, RppPtr_t fillValue, bool enablePadding, Rpp32u *roiTensor, rppHandle_t rppHandle)
	Slice augmentation GPU. More...

RppStatus	rppt_crop_and_patch_host (RppPtr_t srcPtr1, RppPtr_t srcPtr2, RpptDescPtr srcDescPtr, RppPtr_t dstPtr, RpptDescPtr dstDescPtr, RpptROIPtr roiTensorPtrDst, RpptROIPtr cropRoi, RpptROIPtr patchRoi, RpptRoiType roiType, rppHandle_t rppHandle)
	Crop and Patch augmentation on HOST backend for a NCHW/NHWC layout tensor. More...

RppStatus	rppt_crop_and_patch_gpu (RppPtr_t srcPtr1, RppPtr_t srcPtr2, RpptDescPtr srcDescPtr, RppPtr_t dstPtr, RpptDescPtr dstDescPtr, RpptROIPtr roiTensorPtrDst, RpptROIPtr cropRoi, RpptROIPtr patchRoi, RpptRoiType roiType, rppHandle_t rppHandle)
	Crop and Patch augmentation on HIP backend for a NCHW/NHWC layout tensor. More...

RppStatus	rppt_flip_voxel_host (RppPtr_t srcPtr, RpptGenericDescPtr srcGenericDescPtr, RppPtr_t dstPtr, RpptGenericDescPtr dstGenericDescPtr, Rpp32u horizontalTensor, Rpp32u verticalTensor, Rpp32u *depthTensor, RpptROI3DPtr roiGenericPtrSrc, RpptRoi3DType roiType, rppHandle_t rppHandle)
	Flip voxel augmentation HOST. More...

RppStatus	rppt_flip_voxel_gpu (RppPtr_t srcPtr, RpptGenericDescPtr srcGenericDescPtr, RppPtr_t dstPtr, RpptGenericDescPtr dstGenericDescPtr, Rpp32u horizontalTensor, Rpp32u verticalTensor, Rpp32u *depthTensor, RpptROI3DPtr roiGenericPtrSrc, RpptRoi3DType roiType, rppHandle_t rppHandle)
	Flip voxel augmentation GPU. More...

RppStatus	rppt_remap_host (RppPtr_t srcPtr, RpptDescPtr srcDescPtr, RppPtr_t dstPtr, RpptDescPtr dstDescPtr, Rpp32f rowRemapTable, Rpp32f colRemapTable, RpptDescPtr tableDescPtr, RpptInterpolationType interpolationType, RpptROIPtr roiTensorPtrSrc, RpptRoiType roiType, rppHandle_t rppHandle)
	Remap augmentation on HOST backend for a NCHW/NHWC layout tensor. More...

RppStatus	rppt_remap_gpu (RppPtr_t srcPtr, RpptDescPtr srcDescPtr, RppPtr_t dstPtr, RpptDescPtr dstDescPtr, Rpp32f rowRemapTable, Rpp32f colRemapTable, RpptDescPtr tableDescPtr, RpptInterpolationType interpolationType, RpptROIPtr roiTensorPtrSrc, RpptRoiType roiType, rppHandle_t rppHandle)
	Remap augmentation on HIP backend for a NCHW/NHWC layout tensor. More...

RppStatus	rppt_lens_correction_host (RppPtr_t srcPtr, RpptDescPtr srcDescPtr, RppPtr_t dstPtr, RpptDescPtr dstDescPtr, Rpp32f rowRemapTable, Rpp32f colRemapTable, RpptDescPtr tableDescPtr, Rpp32f cameraMatrixTensor, Rpp32f distortionCoeffsTensor, RpptROIPtr roiTensorPtrSrc, RpptRoiType roiType, rppHandle_t rppHandle)
	Lens correction transformation on HOST backend for a NCHW/NHWC layout tensor. More...

RppStatus	rppt_lens_correction_gpu (RppPtr_t srcPtr, RpptDescPtr srcDescPtr, RppPtr_t dstPtr, RpptDescPtr dstDescPtr, Rpp32f rowRemapTable, Rpp32f colRemapTable, RpptDescPtr tableDescPtr, Rpp32f cameraMatrixTensor, Rpp32f distortionCoeffsTensor, RpptROIPtr roiTensorPtrSrc, RpptRoiType roiType, rppHandle_t rppHandle)
	Lens correction transformation on HIP backend for a NCHW/NHWC layout tensor. More...

RppStatus	rppt_transpose_host (RppPtr_t srcPtr, RpptGenericDescPtr srcGenericDescPtr, RppPtr_t dstPtr, RpptGenericDescPtr dstGenericDescPtr, Rpp32u permTensor, Rpp32u roiTensor, rppHandle_t rppHandle)
	Transpose Generic augmentation on HOST backend. More...

RppStatus	rppt_transpose_gpu (RppPtr_t srcPtr, RpptGenericDescPtr srcGenericDescPtr, RppPtr_t dstPtr, RpptGenericDescPtr dstGenericDescPtr, Rpp32u permTensor, Rpp32u roiTensor, rppHandle_t rppHandle)
	Transpose Generic augmentation on HIP backend. More...

Detailed Description

RPPT Tensor Operations - Geometric Augmentations.

Function Documentation

◆ rppt_crop_and_patch_gpu()

RppStatus rppt_crop_and_patch_gpu	(	RppPtr_t	srcPtr1,
		RppPtr_t	srcPtr2,
		RpptDescPtr	srcDescPtr,
		RppPtr_t	dstPtr,
		RpptDescPtr	dstDescPtr,
		RpptROIPtr	roiTensorPtrDst,
		RpptROIPtr	cropRoi,
		RpptROIPtr	patchRoi,
		RpptRoiType	roiType,
		rppHandle_t	rppHandle
	)

Crop and Patch augmentation on HIP backend for a NCHW/NHWC layout tensor.

The crop and patch augmentation crops a ROI from 1st image and patches the cropped region in 2nd image as per the patch co-ordinates for a batch of RGB(3 channel) / greyscale(1 channel) images with an NHWC/NCHW tensor layout.

srcPtr1 depth ranges - Rpp8u (0 to 255), Rpp16f (0 to 1), Rpp32f (0 to 1), Rpp8s (-128 to 127).
srcPtr2 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	source tensor1 in HIP memory
[in]	srcPtr2	source tensor2 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]	roiTensorPtrSrc	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]	cropRoiTensor	crop co-ordinates 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]	patchRoiTensor	patch co-ordinates 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_crop_and_patch_host()

RppStatus rppt_crop_and_patch_host	(	RppPtr_t	srcPtr1,
		RppPtr_t	srcPtr2,
		RpptDescPtr	srcDescPtr,
		RppPtr_t	dstPtr,
		RpptDescPtr	dstDescPtr,
		RpptROIPtr	roiTensorPtrDst,
		RpptROIPtr	cropRoi,
		RpptROIPtr	patchRoi,
		RpptRoiType	roiType,
		rppHandle_t	rppHandle
	)

Crop and Patch augmentation on HOST backend for a NCHW/NHWC layout tensor.

The crop and patch augmentation crops a ROI from 1st image and patches the cropped region in 2nd image as per the patch co-ordinates for a batch of RGB(3 channel) / greyscale(1 channel) images with an NHWC/NCHW tensor layout.

srcPtr1 depth ranges - Rpp8u (0 to 255), Rpp16f (0 to 1), Rpp32f (0 to 1), Rpp8s (-128 to 127).
srcPtr2 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	source tensor1 in HOST memory
[in]	srcPtr2	source tensor2 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]	roiTensorPtrSrc	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]	cropRoiTensor	crop co-ordinates 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]	patchRoiTensor	patch co-ordinates 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_crop_gpu()

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

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

The crop augmentation crops each image to a given ROI, 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]	roiTensorPtrSrc	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_crop_host()

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

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

The crop augmentation crops each image to a given ROI, 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]	roiTensorPtrSrc	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_crop_mirror_normalize_gpu()

RppStatus rppt_crop_mirror_normalize_gpu	(	RppPtr_t	srcPtr,
		RpptDescPtr	srcDescPtr,
		RppPtr_t	dstPtr,
		RpptDescPtr	dstDescPtr,
		Rpp32f *	offsetTensor,
		Rpp32f *	multiplierTensor,
		Rpp32u *	mirrorTensor,
		RpptROIPtr	roiTensorPtrSrc,
		RpptRoiType	roiType,
		rppHandle_t	rppHandle
	)

Crop mirror normalize augmentation on HIP backend for a NCHW/NHWC layout tensor.

The crop mirror normalize augmentation crops each image to a given ROI, does an optional mirror and/or normalize 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]	offsetTensor	offset values for normalization (1D tensor in pinned/HOST memory, of size batchSize, with offsetTensor[n] <= 0)
[in]	multiplierTensor	multiplier values for normalization (1D tensor in pinned/HOST memory, of size batchSize, with multiplierTensor[n] > 0)
[in]	mirrorTensor	mirror flag values to set mirroring on/off (1D tensor in pinned/HOST memory, of size batchSize, with mirrorTensor[n] = 0/1)
[in]	roiTensorPtrSrc	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_crop_mirror_normalize_host()

RppStatus rppt_crop_mirror_normalize_host	(	RppPtr_t	srcPtr,
		RpptDescPtr	srcDescPtr,
		RppPtr_t	dstPtr,
		RpptDescPtr	dstDescPtr,
		Rpp32f *	offsetTensor,
		Rpp32f *	multiplierTensor,
		Rpp32u *	mirrorTensor,
		RpptROIPtr	roiTensorPtrSrc,
		RpptRoiType	roiType,
		rppHandle_t	rppHandle
	)

Crop mirror normalize augmentation on HOST backend for a NCHW/NHWC layout tensor.

The crop mirror normalize augmentation crops each image to a given ROI, does an optional mirror and/or normalize 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]	offsetTensor	offset values for normalization (1D tensor in HOST memory, of size batchSize, with offsetTensor[n] <= 0)
[in]	multiplierTensor	multiplier values for normalization (1D tensor in HOST memory, of size batchSize, with multiplierTensor[n] > 0)
[in]	mirrorTensor	mirror flag values to set mirroring on/off (1D tensor in HOST memory, of size batchSize, with mirrorTensor[n] = 0/1)
[in]	roiTensorPtrSrc	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_flip_gpu()

RppStatus rppt_flip_gpu	(	RppPtr_t	srcPtr,
		RpptDescPtr	srcDescPtr,
		RppPtr_t	dstPtr,
		RpptDescPtr	dstDescPtr,
		Rpp32u *	horizontalTensor,
		Rpp32u *	verticalTensor,
		RpptROIPtr	roiTensorPtrSrc,
		RpptRoiType	roiType,
		rppHandle_t	rppHandle
	)

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

The flip augmentation performs a mask-controlled horizontal/vertical flip 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]	horizontalTensor	horizontal flag values to set horizontal flip on/off (1D tensor in pinned/HOST memory, of size batchSize, with horizontalTensor[i] = 0/1)
[in]	verticalTensor	vertical flag values to set vertical flip on/off (1D tensor in pinned/HOST memory, of size batchSize, with verticalTensor[i] = 0/1)
[in]	roiTensorPtrSrc	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_flip_host()

RppStatus rppt_flip_host	(	RppPtr_t	srcPtr,
		RpptDescPtr	srcDescPtr,
		RppPtr_t	dstPtr,
		RpptDescPtr	dstDescPtr,
		Rpp32u *	horizontalTensor,
		Rpp32u *	verticalTensor,
		RpptROIPtr	roiTensorPtrSrc,
		RpptRoiType	roiType,
		rppHandle_t	rppHandle
	)

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

The flip augmentation performs a mask-controlled horizontal/vertical flip 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]	horizontalTensor	horizontal flag values to set horizontal flip on/off (1D tensor in HOST memory, of size batchSize, with horizontalTensor[i] = 0/1)
[in]	verticalTensor	vertical flag values to set vertical flip on/off (1D tensor in HOST memory, of size batchSize, with verticalTensor[i] = 0/1)
[in]	roiTensorPtrSrc	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_flip_voxel_gpu()

RppStatus rppt_flip_voxel_gpu	(	RppPtr_t	srcPtr,
		RpptGenericDescPtr	srcGenericDescPtr,
		RppPtr_t	dstPtr,
		RpptGenericDescPtr	dstGenericDescPtr,
		Rpp32u *	horizontalTensor,
		Rpp32u *	verticalTensor,
		Rpp32u *	depthTensor,
		RpptROI3DPtr	roiGenericPtrSrc,
		RpptRoi3DType	roiType,
		rppHandle_t	rppHandle
	)

Flip voxel augmentation GPU.

The flip voxel augmentation performs a mask-controlled horizontal/vertical/depth flip on a generic 4D tensor.
Support added for f32 -> f32 and u8 -> u8 dataypes.

Sample Input

Sample Output

Parameters

[in]	srcPtr	source tensor in HIP memory
[in]	srcGenericDescPtr	source tensor descriptor (Restrictions - numDims = 5, offsetInBytes >= 0, dataType = U8/F32, layout = NCDHW/NDHWC, c = 1/3)
[out]	dstPtr	destination tensor in HIP memory
[in]	dstGenericDescPtr	destination tensor descriptor (Restrictions - numDims = 5, offsetInBytes >= 0, dataType = U8/F32, layout = NCDHW/NDHWC, c = 1/3)
[in]	horizontalTensor	horizontal flag values to set horizontal flip on/off (1D tensor in pinned/HOST memory, of size batchSize, with horizontalTensor[i] = 0/1)
[in]	verticalTensor	vertical flag values to set vertical flip on/off (1D tensor in pinned/HOST memory, of size batchSize, with verticalTensor[i] = 0/1)
[in]	depthTensor	depth flag values to set depth flip on/off (1D tensor in pinned/HOST memory, of size batchSize, with depthTensor[i] = 0/1)
[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 handle created with `rppCreateWithStreamAndBatchSize()`

Returns: A RppStatus enumeration.

Return values

RPP_SUCCESS	Successful completion.
RPP_ERROR*	Unsuccessful completion.

◆ rppt_flip_voxel_host()

RppStatus rppt_flip_voxel_host	(	RppPtr_t	srcPtr,
		RpptGenericDescPtr	srcGenericDescPtr,
		RppPtr_t	dstPtr,
		RpptGenericDescPtr	dstGenericDescPtr,
		Rpp32u *	horizontalTensor,
		Rpp32u *	verticalTensor,
		Rpp32u *	depthTensor,
		RpptROI3DPtr	roiGenericPtrSrc,
		RpptRoi3DType	roiType,
		rppHandle_t	rppHandle
	)

Flip voxel augmentation HOST.

The flip voxel augmentation performs a mask-controlled horizontal/vertical/depth flip on a generic 4D tensor.
Support added for f32 -> f32 and u8 -> u8 dataypes.

Sample Input

Sample Output

Parameters

[in]	srcPtr	source tensor in HOST memory
[in]	srcGenericDescPtr	source tensor descriptor (Restrictions - numDims = 5, offsetInBytes >= 0, dataType = U8/F32, layout = NCDHW/NDHWC, c = 1/3)
[out]	dstPtr	destination tensor in HOST memory
[in]	dstGenericDescPtr	destination tensor descriptor (Restrictions - numDims = 5, offsetInBytes >= 0, dataType = U8/F32, layout = NCDHW/NDHWC, c = 1/3)
[in]	horizontalTensor	horizontal flag values to set horizontal flip on/off (1D tensor in HOST memory, of size batchSize, with horizontalTensor[i] = 0/1)
[in]	verticalTensor	vertical flag values to set vertical flip on/off (1D tensor in HOST memory, of size batchSize, with verticalTensor[i] = 0/1)
[in]	depthTensor	depth flag values to set depth flip on/off (1D tensor in HOST memory, of size batchSize, with depthTensor[i] = 0/1)
[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 HOST handle created with `rppCreateWithBatchSize()`

Returns: A RppStatus enumeration.

Return values

RPP_SUCCESS	Successful completion.
RPP_ERROR*	Unsuccessful completion.

◆ rppt_lens_correction_gpu()

RppStatus rppt_lens_correction_gpu	(	RppPtr_t	srcPtr,
		RpptDescPtr	srcDescPtr,
		RppPtr_t	dstPtr,
		RpptDescPtr	dstDescPtr,
		Rpp32f *	rowRemapTable,
		Rpp32f *	colRemapTable,
		RpptDescPtr	tableDescPtr,
		Rpp32f *	cameraMatrixTensor,
		Rpp32f *	distortionCoeffsTensor,
		RpptROIPtr	roiTensorPtrSrc,
		RpptRoiType	roiType,
		rppHandle_t	rppHandle
	)

Lens correction transformation on HIP backend for a NCHW/NHWC layout tensor.

Performs lens correction transforms on an image to compensate barrel lens distortion 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. Note: Returns a black image if the passed camera matrix has a 0 determinant

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]	rowRemapTable	Rpp32f row numbers in HIP memory for every pixel in the input batch of images (1D tensor of size width * height * batchSize)
[in]	colRemapTable	Rpp32f column numbers in HIP memory for every pixel in the input batch of images (1D tensor of size width * height * batchSize)
[in]	tableDescPtr	table tensor descriptor (Restrictions - numDims = 4, offsetInBytes >= 0, dataType = F32, layout = NHWC, c = 1)
[in]	cameraMatrixTensor	contains camera intrinsic parameters required to compute lens corrected image. (1D tensor of size 9 * batchSize)
[in]	distortionCoeffsTensor	contains distortion coefficients required to compute lens corrected image. (1D tensor of size 8 * batchSize)
[in]	roiTensorPtrSrc	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_lens_correction_host()

RppStatus rppt_lens_correction_host	(	RppPtr_t	srcPtr,
		RpptDescPtr	srcDescPtr,
		RppPtr_t	dstPtr,
		RpptDescPtr	dstDescPtr,
		Rpp32f *	rowRemapTable,
		Rpp32f *	colRemapTable,
		RpptDescPtr	tableDescPtr,
		Rpp32f *	cameraMatrixTensor,
		Rpp32f *	distortionCoeffsTensor,
		RpptROIPtr	roiTensorPtrSrc,
		RpptRoiType	roiType,
		rppHandle_t	rppHandle
	)

Lens correction transformation on HOST backend for a NCHW/NHWC layout tensor.

Performs lens correction transforms on an image to compensate barrel lens distortion 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. Note: Returns a black image if the passed camera matrix has a 0 determinant

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]	rowRemapTable	Rpp32f row numbers in HOST memory for every pixel in the input batch of images (1D tensor of size width * height * batchSize)
[in]	colRemapTable	Rpp32f column numbers in HOST memory for every pixel in the input batch of images (1D tensor of size width * height * batchSize)
[in]	tableDescPtr	table tensor descriptor (Restrictions - numDims = 4, offsetInBytes >= 0, dataType = F32, layout = NHWC, c = 1)
[in]	cameraMatrixTensor	contains camera intrinsic parameters required to compute lens corrected image. (1D tensor of size 9 * batchSize)
[in]	distortionCoeffsTensor	contains distortion coefficients required to compute lens corrected image. (1D tensor of size 8 * batchSize)
[in]	roiTensorPtrSrc	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_phase_gpu()

RppStatus rppt_phase_gpu	(	RppPtr_t	srcPtr1,
		RppPtr_t	srcPtr2,
		RpptDescPtr	srcDescPtr,
		RppPtr_t	dstPtr,
		RpptDescPtr	dstDescPtr,
		RpptROIPtr	roiTensorPtrSrc,
		RpptRoiType	roiType,
		rppHandle_t	rppHandle
	)

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

The phase augmentation computes phase of corresponding pixels 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 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]	roiTensorPtrSrc	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_phase_host()

RppStatus rppt_phase_host	(	RppPtr_t	srcPtr1,
		RppPtr_t	srcPtr2,
		RpptDescPtr	srcDescPtr,
		RppPtr_t	dstPtr,
		RpptDescPtr	dstDescPtr,
		RpptROIPtr	roiTensorPtrSrc,
		RpptRoiType	roiType,
		rppHandle_t	rppHandle
	)

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

The phase augmentation computes phase of corresponding pixels 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 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]	roiTensorPtrSrc	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_remap_gpu()

RppStatus rppt_remap_gpu	(	RppPtr_t	srcPtr,
		RpptDescPtr	srcDescPtr,
		RppPtr_t	dstPtr,
		RpptDescPtr	dstDescPtr,
		Rpp32f *	rowRemapTable,
		Rpp32f *	colRemapTable,
		RpptDescPtr	tableDescPtr,
		RpptInterpolationType	interpolationType,
		RpptROIPtr	roiTensorPtrSrc,
		RpptRoiType	roiType,
		rppHandle_t	rppHandle
	)

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

Performs a remap operation using user specified remap tables for a batch of RGB(3 channel) / greyscale(1 channel) images with an NHWC/NCHW tensor layout. For each image, the output(x,y) = input(mapx(x, y), mapy(x, y)) for every (x,y) in the destination image.

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]	rowRemapTable	Rpp32f row numbers in HIP memory for every pixel in the input batch of images (Restrictions - rois in the rowRemapTable data for each image in batch must match roiTensorSrc)
[in]	colRemapTable	Rpp32f column numbers in HIP memory for every pixel in the input batch of images (Restrictions - rois in the colRemapTable data for each image in batch must match roiTensorSrc)
[in]	tableDescPtr	rowRemapTable and colRemapTable common tensor descriptor (Restrictions - numDims = 4, offsetInBytes >= 0, dataType = F32, layout = NHWC, c = 1)
[in]	interpolationType	Interpolation type used in `RpptInterpolationType` (Restrictions - Supports only NEAREST_NEIGHBOR and BILINEAR)
[in]	roiTensorPtrSrc	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_remap_host()

RppStatus rppt_remap_host	(	RppPtr_t	srcPtr,
		RpptDescPtr	srcDescPtr,
		RppPtr_t	dstPtr,
		RpptDescPtr	dstDescPtr,
		Rpp32f *	rowRemapTable,
		Rpp32f *	colRemapTable,
		RpptDescPtr	tableDescPtr,
		RpptInterpolationType	interpolationType,
		RpptROIPtr	roiTensorPtrSrc,
		RpptRoiType	roiType,
		rppHandle_t	rppHandle
	)

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

Performs a remap operation using user specified remap tables for a batch of RGB(3 channel) / greyscale(1 channel) images with an NHWC/NCHW tensor layout. For each image, the output(x,y) = input(mapx(x, y), mapy(x, y)) for every (x,y) in the destination image.

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]	rowRemapTable	Rpp32f row numbers in HOST memory for every pixel in the input batch of images (Restrictions - rois in the rowRemapTable data for each image in batch must match roiTensorSrc)
[in]	colRemapTable	Rpp32f column numbers in HOST memory for every pixel in the input batch of images (Restrictions - rois in the colRemapTable data for each image in batch must match roiTensorSrc)
[in]	tableDescPtr	rowRemapTable and colRemapTable common tensor descriptor (Restrictions - numDims = 4, offsetInBytes >= 0, dataType = F32, layout = NHWC, c = 1)
[in]	interpolationType	Interpolation type used in `RpptInterpolationType` (Restrictions - Supports only NEAREST_NEIGHBOR and BILINEAR)
[in]	roiTensorPtrSrc	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_resize_crop_mirror_gpu()

RppStatus rppt_resize_crop_mirror_gpu	(	RppPtr_t	srcPtr,
		RpptDescPtr	srcDescPtr,
		RppPtr_t	dstPtr,
		RpptDescPtr	dstDescPtr,
		RpptImagePatchPtr	dstImgSizes,
		RpptInterpolationType	interpolationType,
		Rpp32u *	mirrorTensor,
		RpptROIPtr	roiTensorPtrSrc,
		RpptRoiType	roiType,
		rppHandle_t	rppHandle
	)

Resize crop mirror augmentation on HIP backend for a NCHW/NHWC layout tensor.

The resize crop mirror augmentation performs an image resized crop, with an optional mirror, 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]	dstImgSizes	destination image sizes ( `RpptImagePatchPtr` type pointer to array, in pinned/HOST memory, of size batchSize)
[in]	interpolationType	Interpolation type used in `RpptInterpolationType`
[in]	mirrorTensor	mirror flag value to set mirroring on/off (1D tensor in pinned/HOST memory, of size batchSize, with mirrorTensor[n] = 0/1)
[in]	roiTensorPtrSrc	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_resize_crop_mirror_host()

RppStatus rppt_resize_crop_mirror_host	(	RppPtr_t	srcPtr,
		RpptDescPtr	srcDescPtr,
		RppPtr_t	dstPtr,
		RpptDescPtr	dstDescPtr,
		RpptImagePatchPtr	dstImgSizes,
		RpptInterpolationType	interpolationType,
		Rpp32u *	mirrorTensor,
		RpptROIPtr	roiTensorPtrSrc,
		RpptRoiType	roiType,
		rppHandle_t	rppHandle
	)

Resize crop mirror augmentation on HOST backend for a NCHW/NHWC layout tensor.

The resize crop mirror augmentation performs an image resized crop, with an optional mirror, 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]	dstImgSizes	destination image sizes ( `RpptImagePatchPtr` type pointer to array, in HOST memory, of size batchSize)
[in]	interpolationType	Interpolation type used in `RpptInterpolationType`
[in]	mirrorTensor	mirror flag value to set mirroring on/off (1D tensor in HOST memory, of size batchSize, with mirrorTensor[n] = 0/1)
[in]	roiTensorPtrSrc	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_resize_gpu()

RppStatus rppt_resize_gpu	(	RppPtr_t	srcPtr,
		RpptDescPtr	srcDescPtr,
		RppPtr_t	dstPtr,
		RpptDescPtr	dstDescPtr,
		RpptImagePatchPtr	dstImgSizes,
		RpptInterpolationType	interpolationType,
		RpptROIPtr	roiTensorPtrSrc,
		RpptRoiType	roiType,
		rppHandle_t	rppHandle
	)

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

The resize augmentation performs an image resize 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]	dstImgSizes	destination image sizes ( `RpptImagePatchPtr` type pointer to array, in pinned/HOST memory, of size batchSize)
[in]	interpolationType	Interpolation type used in `RpptInterpolationType`
[in]	roiTensorPtrSrc	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_resize_host()

RppStatus rppt_resize_host	(	RppPtr_t	srcPtr,
		RpptDescPtr	srcDescPtr,
		RppPtr_t	dstPtr,
		RpptDescPtr	dstDescPtr,
		RpptImagePatchPtr	dstImgSizes,
		RpptInterpolationType	interpolationType,
		RpptROIPtr	roiTensorPtrSrc,
		RpptRoiType	roiType,
		rppHandle_t	rppHandle
	)

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

The resize augmentation performs an image resize 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]	dstImgSizes	destination image sizes ( `RpptImagePatchPtr` type pointer to array, in HOST memory, of size batchSize)
[in]	interpolationType	Interpolation type used in `RpptInterpolationType`
[in]	roiTensorPtrSrc	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_resize_mirror_normalize_gpu()

RppStatus rppt_resize_mirror_normalize_gpu	(	RppPtr_t	srcPtr,
		RpptDescPtr	srcDescPtr,
		RppPtr_t	dstPtr,
		RpptDescPtr	dstDescPtr,
		RpptImagePatchPtr	dstImgSizes,
		RpptInterpolationType	interpolationType,
		Rpp32f *	meanTensor,
		Rpp32f *	stdDevTensor,
		Rpp32u *	mirrorTensor,
		RpptROIPtr	roiTensorPtrSrc,
		RpptRoiType	roiType,
		rppHandle_t	rppHandle
	)

Resize mirror normalize augmentation on HIP backend for a NCHW/NHWC layout tensor.

The resize mirror normalize augmentation performs an image resize, an optional mirror and/or normalize, 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]	dstImgSizes	destination image sizes ( `RpptImagePatchPtr` type pointer to array, in HIP memory, of size batchSize)
[in]	interpolationType	Interpolation type used in `RpptInterpolationType`
[in]	meanTensor	mean value for each image in the batch (meanTensor[n] >= 0, 1D tensor in pinned/HOST memory, of size = batchSize for greyscale images, size = batchSize * 3 for RGB images))
[in]	stdDevTensor	standard deviation value for each image in the batch (stdDevTensor[n] >= 0, 1D tensor in pinned/HOST memory, of size = batchSize for greyscale images, size = batchSize * 3 for RGB images)
[in]	mirrorTensor	mirror flag value to set mirroring on/off (1D tensor in pinned/HOST memory, of size batchSize, with mirrorTensor[n] = 0/1)
[in]	roiTensorPtrSrc	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_resize_mirror_normalize_host()

RppStatus rppt_resize_mirror_normalize_host	(	RppPtr_t	srcPtr,
		RpptDescPtr	srcDescPtr,
		RppPtr_t	dstPtr,
		RpptDescPtr	dstDescPtr,
		RpptImagePatchPtr	dstImgSizes,
		RpptInterpolationType	interpolationType,
		Rpp32f *	meanTensor,
		Rpp32f *	stdDevTensor,
		Rpp32u *	mirrorTensor,
		RpptROIPtr	roiTensorPtrSrc,
		RpptRoiType	roiType,
		rppHandle_t	rppHandle
	)

Resize mirror normalize augmentation on HOST backend for a NCHW/NHWC layout tensor.

The resize mirror normalize augmentation performs an image resize, an optional mirror and/or normalize, 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]	dstImgSizes	destination image sizes ( `RpptImagePatchPtr` type pointer to array, in HOST memory, of size batchSize)
[in]	interpolationType	Interpolation type used in `RpptInterpolationType`
[in]	meanTensor	mean value for each image in the batch (meanTensor[n] >= 0, 1D tensor in HOST memory, of size = batchSize for greyscale images, size = batchSize * 3 for RGB images))
[in]	stdDevTensor	standard deviation value for each image in the batch (stdDevTensor[n] >= 0, 1D tensor in HOST memory, of size = batchSize for greyscale images, size = batchSize * 3 for RGB images)
[in]	mirrorTensor	mirror flag value to set mirroring on/off (1D tensor in HOST memory, of size batchSize, with mirrorTensor[n] = 0/1)
[in]	roiTensorPtrSrc	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_rotate_gpu()

RppStatus rppt_rotate_gpu	(	RppPtr_t	srcPtr,
		RpptDescPtr	srcDescPtr,
		RppPtr_t	dstPtr,
		RpptDescPtr	dstDescPtr,
		Rpp32f *	angle,
		RpptInterpolationType	interpolationType,
		RpptROIPtr	roiTensorPtrSrc,
		RpptRoiType	roiType,
		rppHandle_t	rppHandle
	)

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

The rotate augmentation performs a rotate transformations 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]	angle	image rotation angle in degrees - positive deg-anticlockwise/negative deg-clockwise (1D tensor in pinned/HOST memory, of size batchSize)
[in]	interpolationType	Interpolation type used (RpptInterpolationType::XYWH or RpptRoiType::LTRB)
[in]	roiTensorPtrSrc	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_rotate_host()

RppStatus rppt_rotate_host	(	RppPtr_t	srcPtr,
		RpptDescPtr	srcDescPtr,
		RppPtr_t	dstPtr,
		RpptDescPtr	dstDescPtr,
		Rpp32f *	angle,
		RpptInterpolationType	interpolationType,
		RpptROIPtr	roiTensorPtrSrc,
		RpptRoiType	roiType,
		rppHandle_t	rppHandle
	)

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

The rotate augmentation performs a rotate transformations 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]	angle	image rotation angle in degrees - positive deg-anticlockwise/negative deg-clockwise (1D tensor in HOST memory, of size batchSize)
[in]	interpolationType	Interpolation type used (RpptInterpolationType::XYWH or RpptRoiType::LTRB)
[in]	roiTensorPtrSrc	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_slice_gpu()

RppStatus rppt_slice_gpu	(	RppPtr_t	srcPtr,
		RpptGenericDescPtr	srcGenericDescPtr,
		RppPtr_t	dstPtr,
		RpptGenericDescPtr	dstGenericDescPtr,
		Rpp32s *	anchorTensor,
		Rpp32s *	shapeTensor,
		RppPtr_t	fillValue,
		bool	enablePadding,
		Rpp32u *	roiTensor,
		rppHandle_t	rppHandle
	)

Slice augmentation GPU.

This function performs slice augmentation on a generic 4D tensor. Slice augmentation involves selecting a region of interest (ROI) from the source tensor and copying it to the destination tensor. Support added for f32 -> f32 and u8 -> u8 dataypes.

Parameters

[in]	srcPtr	source tensor memory in HIP memory
[in]	srcGenericDescPtr	source tensor descriptor
[out]	dstPtr	destination tensor memory in HIP memory
[in]	dstGenericDescPtr	destination tensor descriptor
[in]	anchorTensor	starting index of the slice for each dimension in input (1D tensor in pinned/HOST memory of size = batchSize * numberOfDimensions)
[in]	shapeTensor	length of the slice for each dimension in input (1D tensor in pinned/HOST memory of size = batchSize * numberOfDimensions)
[in]	fillValue	fill value that is used to fill output if enablePadding is set to true
[in]	enablePadding	boolean flag to specify if padding is enabled or not
[in]	roiTensor	roi data in pinned/HOST memory (1D tensor of size = batchSize * numberOfDimensions * 2)
[in]	rppHandle	RPP HIP handle created with `rppCreateWithStreamAndBatchSize()`

Returns: A RppStatus enumeration.

Return values

RPP_SUCCESS	Successful completion.
RPP_ERROR*	Unsuccessful completion.

◆ rppt_slice_host()

RppStatus rppt_slice_host	(	RppPtr_t	srcPtr,
		RpptGenericDescPtr	srcGenericDescPtr,
		RppPtr_t	dstPtr,
		RpptGenericDescPtr	dstGenericDescPtr,
		Rpp32s *	anchorTensor,
		Rpp32s *	shapeTensor,
		RppPtr_t	fillValue,
		bool	enablePadding,
		Rpp32u *	roiTensor,
		rppHandle_t	rppHandle
	)

Slice augmentation HOST.

This function performs slice augmentation on a generic 4D tensor. Slice augmentation involves selecting a region of interest (ROI) from the source tensor and copying it to the destination tensor. Support added for f32 -> f32 and u8 -> u8 dataypes.

Parameters

[in]	srcPtr	source tensor memory in HOST memory
[in]	srcGenericDescPtr	source tensor descriptor
[out]	dstPtr	destination tensor memory in HOST memory
[in]	dstGenericDescPtr	destination tensor descriptor
[in]	anchorTensor	starting index of the slice for each dimension in input (1D tensor of size = batchSize * numberOfDimensions)
[in]	shapeTensor	length of the slice for each dimension in input (1D tensor of size = batchSize * numberOfDimensions)
[in]	fillValue	fill value that is used to fill output if enablePadding is set to true
[in]	enablePadding	boolean flag to specify if padding is enabled or not
[in]	roiTensor	roi data in HOST memory (1D tensor of size = batchSize * numberOfDimensions * 2)
[in]	rppHandle	RPP HOST handle created with `rppCreateWithBatchSize()`

Returns: A RppStatus enumeration.

Return values

RPP_SUCCESS	Successful completion.
RPP_ERROR*	Unsuccessful completion.

◆ rppt_transpose_gpu()

RppStatus rppt_transpose_gpu	(	RppPtr_t	srcPtr,
		RpptGenericDescPtr	srcGenericDescPtr,
		RppPtr_t	dstPtr,
		RpptGenericDescPtr	dstGenericDescPtr,
		Rpp32u *	permTensor,
		Rpp32u *	roiTensor,
		rppHandle_t	rppHandle
	)

Transpose Generic augmentation on HIP backend.

The transpose augmentation performs an input-permutation based transpose on a generic ND Tensor.

Parameters

[in]	srcPtr	source tensor in HIP memory
[in]	srcGenericDescPtr	source tensor descriptor
[out]	dstPtr	source tensor in HIP memory
[in]	dstGenericDescPtr	destination tensor descriptor
[in]	permTensor	permutation tensor for transpose operation in pinned memory
[in]	roiTensor	ROI data for each element in source tensor (tensor of batchSize * number of dimensions * 2 values)
[in]	rppHandle	RPP HIP handle created with `rppCreateWithStreamAndBatchSize()`

Returns: A RppStatus enumeration.

Return values

RPP_SUCCESS	Successful completion.
RPP_ERROR*	Unsuccessful completion.

◆ rppt_transpose_host()

RppStatus rppt_transpose_host	(	RppPtr_t	srcPtr,
		RpptGenericDescPtr	srcGenericDescPtr,
		RppPtr_t	dstPtr,
		RpptGenericDescPtr	dstGenericDescPtr,
		Rpp32u *	permTensor,
		Rpp32u *	roiTensor,
		rppHandle_t	rppHandle
	)

Transpose Generic augmentation on HOST backend.

The transpose augmentation performs an input-permutation based transpose on a generic ND Tensor.

Parameters

[in]	srcPtr	source tensor in HOST memory
[in]	srcGenericDescPtr	source tensor descriptor
[out]	dstPtr	source tensor in HOST memory
[in]	dstGenericDescPtr	destination tensor descriptor
[in]	permTensor	permutation tensor for transpose operation
[in]	roiTensor	ROI data for each element in source tensor (tensor of batchSize * number of dimensions * 2 values)
[in]	rppHandle	RPP HOST handle created with `rppCreateWithBatchSize()`

Returns: A RppStatus enumeration.

Return values

RPP_SUCCESS	Successful completion.
RPP_ERROR*	Unsuccessful completion.

◆ rppt_warp_affine_gpu()

RppStatus rppt_warp_affine_gpu	(	RppPtr_t	srcPtr,
		RpptDescPtr	srcDescPtr,
		RppPtr_t	dstPtr,
		RpptDescPtr	dstDescPtr,
		Rpp32f *	affineTensor,
		RpptInterpolationType	interpolationType,
		RpptROIPtr	roiTensorPtrSrc,
		RpptRoiType	roiType,
		rppHandle_t	rppHandle
	)

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

The warp affine augmentation performs affine transformations 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]	affineTensor	affine matrix values for transformation calculation (2D tensor in pinned/HOST memory, of size batchSize * 6 for each image in batch)
[in]	interpolationType	Interpolation type used (RpptInterpolationType::XYWH or RpptRoiType::LTRB)
[in]	roiTensorPtrSrc	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_warp_affine_host()

RppStatus rppt_warp_affine_host	(	RppPtr_t	srcPtr,
		RpptDescPtr	srcDescPtr,
		RppPtr_t	dstPtr,
		RpptDescPtr	dstDescPtr,
		Rpp32f *	affineTensor,
		RpptInterpolationType	interpolationType,
		RpptROIPtr	roiTensorPtrSrc,
		RpptRoiType	roiType,
		rppHandle_t	rppHandle
	)

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

The warp affine augmentation performs affine transformations 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]	affineTensor	affine matrix values for transformation calculation (2D tensor in HOST memory, of size batchSize * 6 for each image in batch)
[in]	interpolationType	Interpolation type used (RpptInterpolationType::XYWH or RpptRoiType::LTRB)
[in]	roiTensorPtrSrc	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 - Geometric Augmentations.

RPPT Tensor Operations - Geometric Augmentations.#

Functions

Detailed Description

Function Documentation

◆ rppt_crop_and_patch_gpu()

◆ rppt_crop_and_patch_host()

◆ rppt_crop_gpu()

◆ rppt_crop_host()

◆ rppt_crop_mirror_normalize_gpu()

◆ rppt_crop_mirror_normalize_host()

◆ rppt_flip_gpu()

◆ rppt_flip_host()

◆ rppt_flip_voxel_gpu()

◆ rppt_flip_voxel_host()

◆ rppt_lens_correction_gpu()

◆ rppt_lens_correction_host()

◆ rppt_phase_gpu()

◆ rppt_phase_host()

◆ rppt_remap_gpu()

◆ rppt_remap_host()

◆ rppt_resize_crop_mirror_gpu()

◆ rppt_resize_crop_mirror_host()

◆ rppt_resize_gpu()

◆ rppt_resize_host()

◆ rppt_resize_mirror_normalize_gpu()

◆ rppt_resize_mirror_normalize_host()

◆ rppt_rotate_gpu()

◆ rppt_rotate_host()

◆ rppt_slice_gpu()

◆ rppt_slice_host()

◆ rppt_transpose_gpu()

◆ rppt_transpose_host()

◆ rppt_warp_affine_gpu()

◆ rppt_warp_affine_host()