RPPT Tensor Operations - Geometric Augmentations.

RPPT Tensor Operations - Geometric Augmentations.#

RPP: RPPT Tensor Operations - Geometric Augmentations.

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 cropRoiTensor, RpptROIPtr patchRoiTensor, 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 cropRoiTensor, RpptROIPtr patchRoiTensor, 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...

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

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

RppStatus	rppt_concat_host (RppPtr_t srcPtr1, RppPtr_t srcPtr2, RpptGenericDescPtr srcPtr1GenericDescPtr, RpptGenericDescPtr srcPtr2GenericDescPtr, RppPtr_t dstPtr, RpptGenericDescPtr dstGenericDescPtr, Rpp32u axisMask, Rpp32u srcPtr1roiTensor, Rpp32u srcPtr2roiTensor, rppHandle_t rppHandle)
	Concat Generic augmentation on HOST backend. More...

RppStatus	rppt_concat_gpu (RppPtr_t srcPtr, RppPtr_t srcPtr2, RpptGenericDescPtr srcPtr1GenericDescPtr, RpptGenericDescPtr srcPtr2GenericDescPtr, RppPtr_t dstPtr, RpptGenericDescPtr dstGenericDescPtr, Rpp32u axis, Rpp32u srcPtr1roiTensor, Rpp32u srcPtr2roiTensor, rppHandle_t rppHandle)
	Concat Generic augmentation on HIP backend. More...

RppStatus	rppt_jpeg_compression_distortion_host (RppPtr_t srcPtr, RpptDescPtr srcDescPtr, RppPtr_t dstPtr, RpptDescPtr dstDescPtr, Rpp32s *qualityTensor, RpptROIPtr roiTensorPtrSrc, RpptRoiType roiType, rppHandle_t rppHandle)
	JPEG Compression Distortion on HOST backend. More...

RppStatus	rppt_jpeg_compression_distortion_gpu (RppPtr_t srcPtr, RpptDescPtr srcDescPtr, RppPtr_t dstPtr, RpptDescPtr dstDescPtr, RpptROIPtr roiTensorPtrSrc, RpptRoiType roiType, rppHandle_t rppHandle)
	JPEG Compression Distortion on HIP backend. More...

Detailed Description

RPPT Tensor Operations - Geometric Augmentations.

Function Documentation

◆ rppt_concat_gpu()

RppStatus rppt_concat_gpu	(	RppPtr_t	srcPtr,
		RppPtr_t	srcPtr2,
		RpptGenericDescPtr	srcPtr1GenericDescPtr,
		RpptGenericDescPtr	srcPtr2GenericDescPtr,
		RppPtr_t	dstPtr,
		RpptGenericDescPtr	dstGenericDescPtr,
		Rpp32u	axis,
		Rpp32u *	srcPtr1roiTensor,
		Rpp32u *	srcPtr2roiTensor,
		rppHandle_t	rppHandle
	)

Concat Generic augmentation on HIP backend.

Concatenates two 2D, 3D or ND tensors in HIP memory along a specified axis. It is optimized for 2D and 3D tensors, ensuring that all dimensions except the concatenation axis must match.

Parameters

[in]	srcPtr1	source tensor memory in HIP memory
[in]	srcPtr2	source tensor memory in HIP memory
[in]	srcPtr1GenericDescPtr	source tensor descriptor for the input tensor srcPtr1
[in]	srcPtr2GenericDescPtr	source tensor descriptor for the input tensor srcPtr2
[out]	dstPtr	destination tensor memory in HOST memory
[in]	dstGenericDescPtr	destination tensor descriptor
[in]	axis	axis along which concat needs to be done
[in]	srcPtr1roiTensor	values to represent dimensions of input tensor srcPtr1
[in]	srcPtr2roiTensor	values to represent dimensions of input tensor srcPtr2
[in]	rppHandle	RPP HIP handle created with `rppCreateWithStreamAndBatchSize()`

Returns: A RppStatus enumeration.

Return values

RPP_SUCCESS	Successful completion.
RPP_ERROR*	Unsuccessful completion.

◆ rppt_concat_host()

RppStatus rppt_concat_host	(	RppPtr_t	srcPtr1,
		RppPtr_t	srcPtr2,
		RpptGenericDescPtr	srcPtr1GenericDescPtr,
		RpptGenericDescPtr	srcPtr2GenericDescPtr,
		RppPtr_t	dstPtr,
		RpptGenericDescPtr	dstGenericDescPtr,
		Rpp32u	axisMask,
		Rpp32u *	srcPtr1roiTensor,
		Rpp32u *	srcPtr2roiTensor,
		rppHandle_t	rppHandle
	)

Concat Generic augmentation on HOST backend.

Concatenates two 2D, 3D or ND tensors in HOST memory along a specified axis. It is optimized for 2D and 3D tensors, ensuring that all dimensions except the concatenation axis must match.

Parameters

[in]	srcPtr1	source tensor memory in HOST memory
[in]	srcPtr2	source tensor memory in HOST memory
[in]	srcPtr1GenericDescPtr	source tensor descriptor for the input tensor srcPtr1
[in]	srcPtr2GenericDescPtr	source tensor descriptor for the input tensor srcPtr2
[out]	dstPtr	destination tensor memory in HOST memory
[in]	dstGenericDescPtr	destination tensor descriptor
[in]	axisMask	axis along which concat needs to be done
[in]	srcPtr1roiTensor	values to represent dimensions of input tensor srcPtr1
[in]	srcPtr2roiTensor	values to represent dimensions of input tensor srcPtr2
[in]	rppHandle	RPP HOST handle created with `rppCreateWithBatchSize()`

Returns: A RppStatus enumeration.

Return values

RPP_SUCCESS	Successful completion.
RPP_ERROR*	Unsuccessful completion.

◆ 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	cropRoiTensor,
		RpptROIPtr	patchRoiTensor,
		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]	roiTensorPtrDst	ROI data in HIP memory, for each image in destination 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 `rppCreate()`

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	cropRoiTensor,
		RpptROIPtr	patchRoiTensor,
		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]	roiTensorPtrDst	ROI data in HOST memory, for each image in destination 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 `rppCreate()`

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 `rppCreate()`

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 `rppCreate()`

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 `rppCreate()`

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 `rppCreate()`

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 `rppCreate()`

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 `rppCreate()`

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 `rppCreate()`

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 `rppCreate()`

Returns: A RppStatus enumeration.

Return values

RPP_SUCCESS	Successful completion.
RPP_ERROR*	Unsuccessful completion.

◆ rppt_jpeg_compression_distortion_gpu()

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

JPEG Compression Distortion on HIP backend.

This function simulates JPEG compression distortion on an image tensor in hip memory. It introduces artifacts seen in lossy JPEG compression by converting the image to the frequency domain using the Discrete Cosine Transform (DCT), applying quantization, and then reconstructing the image using the inverse DCT (IDCT). This process introduces compression-related distortions similar to those in JPEG images.

Sample Input

Sample Output

Parameters

[in]	srcPtr	source tensor in HIP memory
[in]	srcGenericDescPtr	source tensor descriptor
[out]	dstPtr	destination tensor in HIP memory
[in]	dstGenericDescPtr	destination tensor descriptor
[in]	qualityTensor	JPEG quality factor that controls the amount of distortion (0 < quality < 100).
[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 `rppCreate()`

Returns: A RppStatus enumeration.

Return values

RPP_SUCCESS	Successful completion.
RPP_ERROR*	Unsuccessful completion.

◆ rppt_jpeg_compression_distortion_host()

RppStatus rppt_jpeg_compression_distortion_host	(	RppPtr_t	srcPtr,
		RpptDescPtr	srcDescPtr,
		RppPtr_t	dstPtr,
		RpptDescPtr	dstDescPtr,
		Rpp32s *	qualityTensor,
		RpptROIPtr	roiTensorPtrSrc,
		RpptRoiType	roiType,
		rppHandle_t	rppHandle
	)

JPEG Compression Distortion on HOST backend.

This function simulates JPEG compression distortion on an image tensor in host memory. It introduces artifacts seen in lossy JPEG compression by converting the image to the frequency domain using the Discrete Cosine Transform (DCT), applying quantization, and then reconstructing the image using the inverse DCT (IDCT). This process introduces compression-related distortions similar to those in JPEG images.

Sample Input

Sample Output

Parameters

[in]	srcPtr	source tensor in HOST memory
[in]	srcGenericDescPtr	source tensor descriptor
[out]	dstPtr	destination tensor in HOST memory
[in]	dstGenericDescPtr	destination tensor descriptor
[in]	qualityTensor	JPEG quality factor controlling the level of compression. Valid range is 1 to 99 (inclusive).
[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 `rppCreate()`

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 `rppCreate()`

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 `rppCreate()`

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 `rppCreate()`

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 `rppCreate()`

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 roiTensorPtrSrc)
[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 roiTensorPtrSrc)
[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 `rppCreate()`

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 roiTensorPtrSrc)
[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 roiTensorPtrSrc)
[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 `rppCreate()`

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 `rppCreate()`

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 `rppCreate()`

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 `rppCreate()`

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 `rppCreate()`

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 `rppCreate()`

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 `rppCreate()`

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 `rppCreate()`

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 `rppCreate()`

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 `rppCreate()`

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 `rppCreate()`

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 `rppCreate()`

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 `rppCreate()`

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 `rppCreate()`

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 `rppCreate()`

Returns: A RppStatus enumeration.

Return values

RPP_SUCCESS	Successful completion.
RPP_ERROR*	Unsuccessful completion.

◆ rppt_warp_perspective_gpu()

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

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

The warp perspective augmentation performs perspective 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]	perspectiveTensor	perspective matrix values for transformation calculation (2D tensor in pinned/HIP memory, of size batchSize * 9 for each image in batch)
[in]	interpolationType	Interpolation type used (RpptInterpolationType::BILINEAR or RpptRoiType::NEAREST_NEIGHBOR)
[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 `rppCreate()`

Returns: A RppStatus enumeration.

Return values

RPP_SUCCESS	Successful completion.
RPP_ERROR*	Unsuccessful completion.

◆ rppt_warp_perspective_host()

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

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

The warp perspective performs perspective 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]	perspectiveTensor	perspective matrix values for transformation calculation (2D tensor in HOST memory, of size batchSize * 9 for each image in batch)
[in]	interpolationType	Interpolation type used (RpptInterpolationType::BILINEAR or RpptRoiType::NEAREST_NEIGHBOR)
[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 `rppCreate()`

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_concat_gpu()

◆ rppt_concat_host()

◆ 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_jpeg_compression_distortion_gpu()

◆ rppt_jpeg_compression_distortion_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()

◆ rppt_warp_perspective_gpu()

◆ rppt_warp_perspective_host()