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MIVisionX: /home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-mivisionx/checkouts/docs-5.7.1/amd_openvx/openvx/include/VX/vx_nodes.h File Reference

The "Simple" API interface for OpenVX. These APIs are just wrappers around the more verbose functions defined in vx_api.h. More...

Go to the source code of this file.

Functions
VX_API_ENTRY vx_node VX_API_CALL	vxColorConvertNode (vx_graph graph, vx_image input, vx_image output)
	[Graph] Creates a color conversion node. More...

VX_API_ENTRY vx_node VX_API_CALL	vxChannelExtractNode (vx_graph graph, vx_image input, vx_enum channel, vx_image output)
	[Graph] Creates a channel extract node. More...

VX_API_ENTRY vx_node VX_API_CALL	vxChannelCombineNode (vx_graph graph, vx_image plane0, vx_image plane1, vx_image plane2, vx_image plane3, vx_image output)
	[Graph] Creates a channel combine node. More...

VX_API_ENTRY vx_node VX_API_CALL	vxPhaseNode (vx_graph graph, vx_image grad_x, vx_image grad_y, vx_image orientation)
	[Graph] Creates a Phase node. More...

VX_API_ENTRY vx_node VX_API_CALL	vxSobel3x3Node (vx_graph graph, vx_image input, vx_image output_x, vx_image output_y)
	[Graph] Creates a Sobel3x3 node. More...

VX_API_ENTRY vx_node VX_API_CALL	vxMagnitudeNode (vx_graph graph, vx_image grad_x, vx_image grad_y, vx_image mag)
	[Graph] Create a Magnitude node. More...

VX_API_ENTRY vx_node VX_API_CALL	vxScaleImageNode (vx_graph graph, vx_image src, vx_image dst, vx_enum type)
	[Graph] Creates a Scale Image Node. More...

VX_API_ENTRY vx_node VX_API_CALL	vxTableLookupNode (vx_graph graph, vx_image input, vx_lut lut, vx_image output)
	[Graph] Creates a Table Lookup node. If a value from the input image is not present in the lookup table, the result is undefined. More...

VX_API_ENTRY vx_node VX_API_CALL	vxHistogramNode (vx_graph graph, vx_image input, vx_distribution distribution)
	[Graph] Creates a Histogram node. More...

VX_API_ENTRY vx_node VX_API_CALL	vxEqualizeHistNode (vx_graph graph, vx_image input, vx_image output)
	[Graph] Creates a Histogram Equalization node. More...

VX_API_ENTRY vx_node VX_API_CALL	vxAbsDiffNode (vx_graph graph, vx_image in1, vx_image in2, vx_image out)
	[Graph] Creates an AbsDiff node. More...

VX_API_ENTRY vx_node VX_API_CALL	vxMeanStdDevNode (vx_graph graph, vx_image input, vx_scalar mean, vx_scalar stddev)
	[Graph] Creates a mean value and optionally, a standard deviation node. More...

VX_API_ENTRY vx_node VX_API_CALL	vxThresholdNode (vx_graph graph, vx_image input, vx_threshold thresh, vx_image output)
	[Graph] Creates a Threshold node and returns a reference to it. More...

VX_API_ENTRY vx_node VX_API_CALL	vxNonMaxSuppressionNode (vx_graph graph, vx_image input, vx_image mask, vx_int32 win_size, vx_image output)
	[Graph] Creates a Non-Maxima Suppression node. More...

VX_API_ENTRY vx_node VX_API_CALL	vxIntegralImageNode (vx_graph graph, vx_image input, vx_image output)
	[Graph] Creates an Integral Image Node. More...

VX_API_ENTRY vx_node VX_API_CALL	vxErode3x3Node (vx_graph graph, vx_image input, vx_image output)
	[Graph] Creates an Erosion Image Node. More...

VX_API_ENTRY vx_node VX_API_CALL	vxDilate3x3Node (vx_graph graph, vx_image input, vx_image output)
	[Graph] Creates a Dilation Image Node. More...

VX_API_ENTRY vx_node VX_API_CALL	vxMedian3x3Node (vx_graph graph, vx_image input, vx_image output)
	[Graph] Creates a Median Image Node. More...

VX_API_ENTRY vx_node VX_API_CALL	vxBox3x3Node (vx_graph graph, vx_image input, vx_image output)
	[Graph] Creates a Box Filter Node. More...

VX_API_ENTRY vx_node VX_API_CALL	vxGaussian3x3Node (vx_graph graph, vx_image input, vx_image output)
	[Graph] Creates a Gaussian Filter Node. More...

VX_API_ENTRY vx_node VX_API_CALL	vxNonLinearFilterNode (vx_graph graph, vx_enum function, vx_image input, vx_matrix mask, vx_image output)
	[Graph] Creates a Non-linear Filter Node. More...

VX_API_ENTRY vx_node VX_API_CALL	vxConvolveNode (vx_graph graph, vx_image input, vx_convolution conv, vx_image output)
	[Graph] Creates a custom convolution node. More...

VX_API_ENTRY vx_node VX_API_CALL	vxGaussianPyramidNode (vx_graph graph, vx_image input, vx_pyramid gaussian)
	[Graph] Creates a node for a Gaussian Image Pyramid. More...

VX_API_ENTRY vx_node VX_API_CALL	vxLaplacianPyramidNode (vx_graph graph, vx_image input, vx_pyramid laplacian, vx_image output)
	[Graph] Creates a node for a Laplacian Image Pyramid. More...

VX_API_ENTRY vx_node VX_API_CALL	vxLaplacianReconstructNode (vx_graph graph, vx_pyramid laplacian, vx_image input, vx_image output)
	[Graph] Reconstructs an image from a Laplacian Image pyramid. More...

VX_API_ENTRY vx_node VX_API_CALL	vxWeightedAverageNode (vx_graph graph, vx_image img1, vx_scalar alpha, vx_image img2, vx_image output)
	[Graph] Creates a image weighted average node. More...

VX_API_ENTRY vx_node VX_API_CALL	vxMinMaxLocNode (vx_graph graph, vx_image input, vx_scalar minVal, vx_scalar maxVal, vx_array minLoc, vx_array maxLoc, vx_scalar minCount, vx_scalar maxCount)
	[Graph] Creates a min,max,loc node. More...

VX_API_ENTRY vx_node VX_API_CALL	vxMinNode (vx_graph graph, vx_image in1, vx_image in2, vx_image out)
	[Graph] Creates a pixel-wise minimum kernel. More...

VX_API_ENTRY vx_node VX_API_CALL	vxMaxNode (vx_graph graph, vx_image in1, vx_image in2, vx_image out)
	[Graph] Creates a pixel-wise maximum kernel. More...

VX_API_ENTRY vx_node VX_API_CALL	vxAndNode (vx_graph graph, vx_image in1, vx_image in2, vx_image out)
	[Graph] Creates a bitwise AND node. More...

VX_API_ENTRY vx_node VX_API_CALL	vxOrNode (vx_graph graph, vx_image in1, vx_image in2, vx_image out)
	[Graph] Creates a bitwise INCLUSIVE OR node. More...

VX_API_ENTRY vx_node VX_API_CALL	vxXorNode (vx_graph graph, vx_image in1, vx_image in2, vx_image out)
	[Graph] Creates a bitwise EXCLUSIVE OR node. More...

VX_API_ENTRY vx_node VX_API_CALL	vxNotNode (vx_graph graph, vx_image input, vx_image output)
	[Graph] Creates a bitwise NOT node. More...

VX_API_ENTRY vx_node VX_API_CALL	vxScalarOperationNode (vx_graph graph, vx_enum scalar_operation, vx_scalar a, vx_scalar b, vx_scalar output)
	[Graph] Creates a scalar operation node. More...

VX_API_ENTRY vx_node VX_API_CALL	vxSelectNode (vx_graph graph, vx_scalar condition, vx_reference true_value, vx_reference false_value, vx_reference output)
	[Graph] Selects one of two data objects depending on the the value of a condition (boolean scalar), and copies its data into another data object. More...

VX_API_ENTRY vx_node VX_API_CALL	vxMultiplyNode (vx_graph graph, vx_image in1, vx_image in2, vx_scalar scale, vx_enum overflow_policy, vx_enum rounding_policy, vx_image out)
	[Graph] Creates an pixelwise-multiplication node. More...

VX_API_ENTRY vx_node VX_API_CALL	vxAddNode (vx_graph graph, vx_image in1, vx_image in2, vx_enum policy, vx_image out)
	[Graph] Creates an arithmetic addition node. More...

VX_API_ENTRY vx_node VX_API_CALL	vxSubtractNode (vx_graph graph, vx_image in1, vx_image in2, vx_enum policy, vx_image out)
	[Graph] Creates an arithmetic subtraction node. More...

VX_API_ENTRY vx_node VX_API_CALL	vxConvertDepthNode (vx_graph graph, vx_image input, vx_image output, vx_enum policy, vx_scalar shift)
	[Graph] Creates a bit-depth conversion node. More...

VX_API_ENTRY vx_node VX_API_CALL	vxCannyEdgeDetectorNode (vx_graph graph, vx_image input, vx_threshold hyst, vx_int32 gradient_size, vx_enum norm_type, vx_image output)
	[Graph] Creates a Canny Edge Detection Node. More...

VX_API_ENTRY vx_node VX_API_CALL	vxWarpAffineNode (vx_graph graph, vx_image input, vx_matrix matrix, vx_enum type, vx_image output)
	[Graph] Creates an Affine Warp Node. More...

VX_API_ENTRY vx_node VX_API_CALL	vxWarpPerspectiveNode (vx_graph graph, vx_image input, vx_matrix matrix, vx_enum type, vx_image output)
	[Graph] Creates a Perspective Warp Node. More...

VX_API_ENTRY vx_node VX_API_CALL	vxHarrisCornersNode (vx_graph graph, vx_image input, vx_scalar strength_thresh, vx_scalar min_distance, vx_scalar sensitivity, vx_int32 gradient_size, vx_int32 block_size, vx_array corners, vx_scalar num_corners)
	[Graph] Creates a Harris Corners Node. More...

VX_API_ENTRY vx_node VX_API_CALL	vxFastCornersNode (vx_graph graph, vx_image input, vx_scalar strength_thresh, vx_bool nonmax_suppression, vx_array corners, vx_scalar num_corners)
	[Graph] Creates a FAST Corners Node. More...

VX_API_ENTRY vx_node VX_API_CALL	vxOpticalFlowPyrLKNode (vx_graph graph, vx_pyramid old_images, vx_pyramid new_images, vx_array old_points, vx_array new_points_estimates, vx_array new_points, vx_enum termination, vx_scalar epsilon, vx_scalar num_iterations, vx_scalar use_initial_estimate, vx_size window_dimension)
	[Graph] Creates a Lucas Kanade Tracking Node. More...

VX_API_ENTRY vx_node VX_API_CALL	vxRemapNode (vx_graph graph, vx_image input, vx_remap table, vx_enum policy, vx_image output)
	[Graph] Creates a Remap Node. More...

VX_API_ENTRY vx_node VX_API_CALL	vxHalfScaleGaussianNode (vx_graph graph, vx_image input, vx_image output, vx_int32 kernel_size)
	[Graph] Performs a Gaussian Blur on an image then half-scales it. The interpolation mode used is nearest-neighbor. More...

VX_API_ENTRY vx_node VX_API_CALL	vxMatchTemplateNode (vx_graph graph, vx_image src, vx_image templateImage, vx_enum matchingMethod, vx_image output)
	[Graph] The Node Compares an image template against overlapped image regions. More...

VX_API_ENTRY vx_node VX_API_CALL	vxLBPNode (vx_graph graph, vx_image in, vx_enum format, vx_int8 kernel_size, vx_image out)
	[Graph] Creates a node that extracts LBP image from an input image More...

VX_API_ENTRY vx_node VX_API_CALL	vxHOGCellsNode (vx_graph graph, vx_image input, vx_int32 cell_width, vx_int32 cell_height, vx_int32 num_bins, vx_tensor magnitudes, vx_tensor bins)
	[Graph] Performs cell calculations for the average gradient magnitude and gradient orientation histograms. More...

VX_API_ENTRY vx_node VX_API_CALL	vxHOGFeaturesNode (vx_graph graph, vx_image input, vx_tensor magnitudes, vx_tensor bins, const vx_hog_t *params, vx_size hog_param_size, vx_tensor features)
	[Graph] The node produces HOG features for the W1xW2 window in a sliding window fashion over the whole input image. Each position produces a HOG feature vector. More...

VX_API_ENTRY vx_node VX_API_CALL	vxHoughLinesPNode (vx_graph graph, vx_image input, const vx_hough_lines_p_t *params, vx_array lines_array, vx_scalar num_lines)
	[Graph] Finds the Probabilistic Hough Lines detected in the input binary image, each line is stored in the output array as a set of points (x1, y1, x2, y2) . More...

VX_API_ENTRY vx_node VX_API_CALL	vxBilateralFilterNode (vx_graph graph, vx_tensor src, vx_int32 diameter, vx_float32 sigmaSpace, vx_float32 sigmaValues, vx_tensor dst)
	[Graph] The function applies bilateral filtering to the input tensor. More...

VX_API_ENTRY vx_node VX_API_CALL	vxTensorMultiplyNode (vx_graph graph, vx_tensor input1, vx_tensor input2, vx_scalar scale, vx_enum overflow_policy, vx_enum rounding_policy, vx_tensor output)
	[Graph] Performs element wise multiplications on element values in the input tensor data with a scale. More...

VX_API_ENTRY vx_node VX_API_CALL	vxTensorAddNode (vx_graph graph, vx_tensor input1, vx_tensor input2, vx_enum policy, vx_tensor output)
	[Graph] Performs arithmetic addition on element values in the input tensor data. More...

VX_API_ENTRY vx_node VX_API_CALL	vxTensorSubtractNode (vx_graph graph, vx_tensor input1, vx_tensor input2, vx_enum policy, vx_tensor output)
	[Graph] Performs arithmetic subtraction on element values in the input tensor data. More...

VX_API_ENTRY vx_node VX_API_CALL	vxTensorTableLookupNode (vx_graph graph, vx_tensor input1, vx_lut lut, vx_tensor output)
	[Graph] Performs LUT on element values in the input tensor data. More...

VX_API_ENTRY vx_node VX_API_CALL	vxTensorTransposeNode (vx_graph graph, vx_tensor input, vx_tensor output, vx_size dimension1, vx_size dimension2)
	[Graph] Performs transpose on the input tensor. The node transpose the tensor according to a specified 2 indexes in the tensor (0-based indexing) More...

VX_API_ENTRY vx_node VX_API_CALL	vxTensorConvertDepthNode (vx_graph graph, vx_tensor input, vx_enum policy, vx_scalar norm, vx_scalar offset, vx_tensor output)
	[Graph] Creates a bit-depth conversion node. More...

VX_API_ENTRY vx_node VX_API_CALL	vxTensorMatrixMultiplyNode (vx_graph graph, vx_tensor input1, vx_tensor input2, vx_tensor input3, const vx_tensor_matrix_multiply_params_t *matrix_multiply_params, vx_tensor output)
	[Graph] Creates a generalized matrix multiplication node. More...

VX_API_ENTRY vx_node VX_API_CALL	vxCopyNode (vx_graph graph, vx_reference input, vx_reference output)
	Copy data from one object to another. More...

Detailed Description

The "Simple" API interface for OpenVX. These APIs are just wrappers around the more verbose functions defined in vx_api.h.

Function Documentation

◆ vxAbsDiffNode()

VX_API_ENTRY vx_node VX_API_CALL vxAbsDiffNode	(	vx_graph	graph,
		vx_image	in1,
		vx_image	in2,
		vx_image	out
	)

[Graph] Creates an AbsDiff node.

Parameters

[in]	graph	The reference to the graph.
[in]	in1	An input image in `VX_DF_IMAGE_U8` or `VX_DF_IMAGE_S16` format.
[in]	in2	An input image in `VX_DF_IMAGE_U8` or `VX_DF_IMAGE_S16` format.
[out]	out	The output image in `VX_DF_IMAGE_U8` or `VX_DF_IMAGE_S16` format, which must have the same dimensions as the input image.

Return values

vx_node A node reference. Any possible errors preventing a successful creation should be checked using vxGetStatus

◆ vxAddNode()

VX_API_ENTRY vx_node VX_API_CALL vxAddNode	(	vx_graph	graph,
		vx_image	in1,
		vx_image	in2,
		vx_enum	policy,
		vx_image	out
	)

[Graph] Creates an arithmetic addition node.

Parameters

[in]	graph	The reference to the graph.
[in]	in1	An input image, `VX_DF_IMAGE_U8` or `VX_DF_IMAGE_S16`.
[in]	in2	An input image, `VX_DF_IMAGE_U8` or `VX_DF_IMAGE_S16`.
[in]	policy	A `VX_TYPE_ENUM` of the vx_convert_policy_e enumeration.
[out]	out	The output image, a `VX_DF_IMAGE_U8` or `VX_DF_IMAGE_S16` image, which must have the same dimensions as the input images.

Returns: vx_node.

Return values

vx_node A node reference. Any possible errors preventing a successful creation should be checked using vxGetStatus

◆ vxAndNode()

VX_API_ENTRY vx_node VX_API_CALL vxAndNode	(	vx_graph	graph,
		vx_image	in1,
		vx_image	in2,
		vx_image	out
	)

[Graph] Creates a bitwise AND node.

Parameters

[in]	graph	The reference to the graph.
[in]	in1	A `VX_DF_IMAGE_U8` or `VX_DF_IMAGE_U1` input image.
[in]	in2	A `VX_DF_IMAGE_U8` or `VX_DF_IMAGE_U1` input image.
[out]	out	The `VX_DF_IMAGE_U8` or `VX_DF_IMAGE_U1` output image, which must have the same dimensions and type as the input images.

Returns: vx_node.

Return values

vx_node A node reference. Any possible errors preventing a successful creation should be checked using vxGetStatus

◆ vxBilateralFilterNode()

VX_API_ENTRY vx_node VX_API_CALL vxBilateralFilterNode	(	vx_graph	graph,
		vx_tensor	src,
		vx_int32	diameter,
		vx_float32	sigmaSpace,
		vx_float32	sigmaValues,
		vx_tensor	dst
	)

[Graph] The function applies bilateral filtering to the input tensor.

Parameters

[in]	graph	The reference to the graph.
[in]	src	The input data a `vx_tensor`. maximum 3 dimension and minimum 2. The tensor is of type `VX_TYPE_UINT8` or `VX_TYPE_INT16`. dimensions are [radiometric ,width,height] or [width,height].See `vxCreateTensor` and `vxCreateVirtualTensor`.
[in]	diameter	of each pixel neighbourhood that is used during filtering. Values of diameter must be odd. Bigger then 3 and smaller then 10.
[in]	sigmaValues	Filter sigma in the radiometric space. Supported values are bigger then 0 and smaller or equal 20.
[in]	sigmaSpace	Filter sigma in the spatial space. Supported values are bigger then 0 and smaller or equal 20.
[out]	dst	The output data a `vx_tensor`,Of type `VX_TYPE_UINT8` or `VX_TYPE_INT16`. And must be the same type and size of the input.

Note: The border modes VX_NODE_BORDER value VX_BORDER_REPLICATE and VX_BORDER_CONSTANT are supported.

Returns: vx_node.

Return values

vx_node A node reference. Any possible errors preventing a successful creation should be checked using vxGetStatus

◆ vxBox3x3Node()

VX_API_ENTRY vx_node VX_API_CALL vxBox3x3Node	(	vx_graph	graph,
		vx_image	input,
		vx_image	output
	)

[Graph] Creates a Box Filter Node.

Parameters

[in]	graph	The reference to the graph.
[in]	input	The input image in `VX_DF_IMAGE_U8` format.
[out]	output	The output image in `VX_DF_IMAGE_U8` format, which must have the same dimensions as the input image.

Returns: vx_node.

Return values

vx_node A node reference. Any possible errors preventing a successful creation should be checked using vxGetStatus

◆ vxCannyEdgeDetectorNode()

VX_API_ENTRY vx_node VX_API_CALL vxCannyEdgeDetectorNode	(	vx_graph	graph,
		vx_image	input,
		vx_threshold	hyst,
		vx_int32	gradient_size,
		vx_enum	norm_type,
		vx_image	output
	)

[Graph] Creates a Canny Edge Detection Node.

Parameters

[in]	graph	The reference to the graph.
[in]	input	The input `VX_DF_IMAGE_U8` image.
[in]	hyst	The double threshold for hysteresis. The `VX_THRESHOLD_INPUT_FORMAT` shall be either `VX_DF_IMAGE_U8` or `VX_DF_IMAGE_S16`. The `VX_THRESHOLD_OUTPUT_FORMAT` is ignored.
[in]	gradient_size	The size of the Sobel filter window, must support at least 3, 5, and 7.
[in]	norm_type	A flag indicating the norm used to compute the gradient, `VX_NORM_L1` or `VX_NORM_L2`.
[out]	output	The binary output image in `VX_DF_IMAGE_U1` or `VX_DF_IMAGE_U8` format with values either 0 and 1 (`VX_DF_IMAGE_U1`), or 0 and 255 (`VX_DF_IMAGE_U8`).

Returns: vx_node.

Return values

vx_node A node reference. Any possible errors preventing a successful creation should be checked using vxGetStatus

◆ vxChannelCombineNode()

VX_API_ENTRY vx_node VX_API_CALL vxChannelCombineNode	(	vx_graph	graph,
		vx_image	plane0,
		vx_image	plane1,
		vx_image	plane2,
		vx_image	plane3,
		vx_image	output
	)

[Graph] Creates a channel combine node.

Parameters

[in]	graph	The graph reference.
[in]	plane0	The plane that forms channel 0. Must be `VX_DF_IMAGE_U8`.
[in]	plane1	The plane that forms channel 1. Must be `VX_DF_IMAGE_U8`.
[in]	plane2	[optional] The plane that forms channel 2. Must be `VX_DF_IMAGE_U8`.
[in]	plane3	[optional] The plane that forms channel 3. Must be `VX_DF_IMAGE_U8`.
[out]	output	The output image. The format of the image must be defined, even if the image is virtual. Must have the same dimensions as the input images

See also: VX_KERNEL_CHANNEL_COMBINE

Returns: vx_node.

Return values

vx_node A node reference. Any possible errors preventing a successful creation should be checked using vxGetStatus

◆ vxChannelExtractNode()

VX_API_ENTRY vx_node VX_API_CALL vxChannelExtractNode	(	vx_graph	graph,
		vx_image	input,
		vx_enum	channel,
		vx_image	output
	)

[Graph] Creates a channel extract node.

Parameters

[in]	graph	The reference to the graph.
[in]	input	The input image. Must be one of the defined vx_df_image_e multi-channel formats.
[in]	channel	The `vx_channel_e` channel to extract.
[out]	output	The output image. Must be `VX_DF_IMAGE_U8`, and must have the same dimensions as the input image.

See also: VX_KERNEL_CHANNEL_EXTRACT

Returns: vx_node.

Return values

vx_node A node reference. Any possible errors preventing a successful creation should be checked using vxGetStatus

◆ vxColorConvertNode()

VX_API_ENTRY vx_node VX_API_CALL vxColorConvertNode	(	vx_graph	graph,
		vx_image	input,
		vx_image	output
	)

[Graph] Creates a color conversion node.

Parameters

[in]	graph	The reference to the graph.
[in]	input	The input image from which to convert.
[out]	output	The output image to which to convert, which must have the same dimensions as the input image.

See also: VX_KERNEL_COLOR_CONVERT

Returns: vx_node.

Return values

vx_node A node reference. Any possible errors preventing a successful creation should be checked using vxGetStatus

◆ vxConvertDepthNode()

VX_API_ENTRY vx_node VX_API_CALL vxConvertDepthNode	(	vx_graph	graph,
		vx_image	input,
		vx_image	output,
		vx_enum	policy,
		vx_scalar	shift
	)

[Graph] Creates a bit-depth conversion node.

Parameters

[in]	graph	The reference to the graph.
[in]	input	The input image.
[out]	output	The output image with the same dimensions of the input image.
[in]	policy	A `VX_TYPE_ENUM` of the `vx_convert_policy_e` enumeration.
[in]	shift	A scalar containing a `VX_TYPE_INT32` of the shift value.

Returns: vx_node.

Return values

vx_node A node reference. Any possible errors preventing a successful creation should be checked using vxGetStatus

◆ vxConvolveNode()

VX_API_ENTRY vx_node VX_API_CALL vxConvolveNode	(	vx_graph	graph,
		vx_image	input,
		vx_convolution	conv,
		vx_image	output
	)

[Graph] Creates a custom convolution node.

Parameters

[in]	graph	The reference to the graph.
[in]	input	The input image in `VX_DF_IMAGE_U8` format.
[in]	conv	The `vx_int16` convolution matrix.
[out]	output	The output image in `VX_DF_IMAGE_U8` or `VX_DF_IMAGE_S16` format, which must have the same dimensions as the input image.

Returns: vx_node.

Return values

vx_node A node reference. Any possible errors preventing a successful creation should be checked using vxGetStatus

◆ vxCopyNode()

VX_API_ENTRY vx_node VX_API_CALL vxCopyNode	(	vx_graph	graph,
		vx_reference	input,
		vx_reference	output
	)

Copy data from one object to another.

Note: An implementation may optimize away the copy when virtual data objects are used.

Parameters

[in]	graph	The reference to the graph.
[in]	input	The input data object.
[out]	output	The output data object with meta-data identical to the input data object.

Returns: vx_node.

Return values

vx_node A node reference. Any possible errors preventing a successful creation should be checked using vxGetStatus

◆ vxDilate3x3Node()

VX_API_ENTRY vx_node VX_API_CALL vxDilate3x3Node	(	vx_graph	graph,
		vx_image	input,
		vx_image	output
	)

[Graph] Creates a Dilation Image Node.

Parameters

[in]	graph	The reference to the graph.
[in]	input	The input image in `VX_DF_IMAGE_U8` or `VX_DF_IMAGE_U1` format.
[out]	output	The output image in `VX_DF_IMAGE_U8` or `VX_DF_IMAGE_U1` format, which must have the same dimensions and type as the input image.

Returns: vx_node.

Return values

vx_node A node reference. Any possible errors preventing a successful creation should be checked using vxGetStatus

◆ vxEqualizeHistNode()

VX_API_ENTRY vx_node VX_API_CALL vxEqualizeHistNode	(	vx_graph	graph,
		vx_image	input,
		vx_image	output
	)

[Graph] Creates a Histogram Equalization node.

Parameters

[in]	graph	The reference to the graph.
[in]	input	The grayscale input image in `VX_DF_IMAGE_U8`.
[out]	output	The grayscale output image of type `VX_DF_IMAGE_U8` with equalized brightness and contrast and same size as the input image.

Returns: vx_node.

Return values

vx_node A node reference. Any possible errors preventing a successful creation should be checked using vxGetStatus

◆ vxErode3x3Node()

VX_API_ENTRY vx_node VX_API_CALL vxErode3x3Node	(	vx_graph	graph,
		vx_image	input,
		vx_image	output
	)

[Graph] Creates an Erosion Image Node.

Parameters

[in]	graph	The reference to the graph.
[in]	input	The input image in `VX_DF_IMAGE_U8` or `VX_DF_IMAGE_U1` format.
[out]	output	The output image in `VX_DF_IMAGE_U8` or `VX_DF_IMAGE_U1` format, which must have the same dimensions and type as the input image.

Returns: vx_node.

Return values

vx_node A node reference. Any possible errors preventing a successful creation should be checked using vxGetStatus

◆ vxFastCornersNode()

VX_API_ENTRY vx_node VX_API_CALL vxFastCornersNode	(	vx_graph	graph,
		vx_image	input,
		vx_scalar	strength_thresh,
		vx_bool	nonmax_suppression,
		vx_array	corners,
		vx_scalar	num_corners
	)

[Graph] Creates a FAST Corners Node.

Parameters

[in]	graph	The reference to the graph.
[in]	input	The input `VX_DF_IMAGE_U8` image.
[in]	strength_thresh	Threshold on difference between intensity of the central pixel and pixels on Bresenham's circle of radius 3 (`VX_TYPE_FLOAT32` scalar), with a value in the range of 0.0 $\le$ strength_thresh < 256.0. Any fractional value will be truncated to an integer.
[in]	nonmax_suppression	If true, non-maximum suppression is applied to detected corners before being placed in the `vx_array` of `VX_TYPE_KEYPOINT` objects.
[out]	corners	Output corner `vx_array` of `VX_TYPE_KEYPOINT`. The order of the keypoints in this array is implementation dependent.
[out]	num_corners	[optional] The total number of detected corners in image. Use a VX_TYPE_SIZE scalar.

Returns: vx_node.

Return values

vx_node A node reference. Any possible errors preventing a successful creation should be checked using vxGetStatus

◆ vxGaussian3x3Node()

VX_API_ENTRY vx_node VX_API_CALL vxGaussian3x3Node	(	vx_graph	graph,
		vx_image	input,
		vx_image	output
	)

[Graph] Creates a Gaussian Filter Node.

Parameters

[in]	graph	The reference to the graph.
[in]	input	The input image in `VX_DF_IMAGE_U8` format.
[out]	output	The output image in `VX_DF_IMAGE_U8` format, which must have the same dimensions as the input image.

Returns: vx_node.

Return values

vx_node A node reference. Any possible errors preventing a successful creation should be checked using vxGetStatus

◆ vxGaussianPyramidNode()

VX_API_ENTRY vx_node VX_API_CALL vxGaussianPyramidNode	(	vx_graph	graph,
		vx_image	input,
		vx_pyramid	gaussian
	)

[Graph] Creates a node for a Gaussian Image Pyramid.

Parameters

[in]	graph	The reference to the graph.
[in]	input	The input image in `VX_DF_IMAGE_U8` format.
[out]	gaussian	The Gaussian pyramid with `VX_DF_IMAGE_U8` to construct.

See also: group_pyramid

Returns: vx_node.

Return values

vx_node A node reference. Any possible errors preventing a successful creation should be checked using vxGetStatus

◆ vxHalfScaleGaussianNode()

VX_API_ENTRY vx_node VX_API_CALL vxHalfScaleGaussianNode	(	vx_graph	graph,
		vx_image	input,
		vx_image	output,
		vx_int32	kernel_size
	)

[Graph] Performs a Gaussian Blur on an image then half-scales it. The interpolation mode used is nearest-neighbor.

The output image size is determined by:

$W_{output} = \frac{W_{input} + 1}{2} \\ , H_{output} = \frac{H_{input} + 1}{2}$

Parameters

[in]	graph	The reference to the graph.
[in]	input	The input `VX_DF_IMAGE_U8` image.
[out]	output	The output `VX_DF_IMAGE_U8` image.
[in]	kernel_size	The input size of the Gaussian filter. Supported values are 1, 3 and 5.

Returns: vx_node.

Return values

vx_node A node reference. Any possible errors preventing a successful creation should be checked using vxGetStatus

◆ vxHarrisCornersNode()

VX_API_ENTRY vx_node VX_API_CALL vxHarrisCornersNode	(	vx_graph	graph,
		vx_image	input,
		vx_scalar	strength_thresh,
		vx_scalar	min_distance,
		vx_scalar	sensitivity,
		vx_int32	gradient_size,
		vx_int32	block_size,
		vx_array	corners,
		vx_scalar	num_corners
	)

[Graph] Creates a Harris Corners Node.

Parameters

[in]	graph	The reference to the graph.
[in]	input	The input `VX_DF_IMAGE_U8` image.
[in]	strength_thresh	The `VX_TYPE_FLOAT32` minimum threshold with which to eliminate Harris Corner scores (computed using the normalized Sobel kernel).
[in]	min_distance	The `VX_TYPE_FLOAT32` radial Euclidean distance for non-maximum suppression.
[in]	sensitivity	The `VX_TYPE_FLOAT32` scalar sensitivity threshold from the Harris-Stephens equation.
[in]	gradient_size	The gradient window size to use on the input. The implementation must support at least 3, 5, and 7.
[in]	block_size	The block window size used to compute the Harris Corner score. The implementation must support at least 3, 5, and 7.
[out]	corners	The array of `VX_TYPE_KEYPOINT` objects. The order of the keypoints in this array is implementation dependent.
[out]	num_corners	[optional] The total number of detected corners in image. Use a VX_TYPE_SIZE scalar.

Returns: vx_node.

Return values

vx_node A node reference. Any possible errors preventing a successful creation should be checked using vxGetStatus

◆ vxHistogramNode()

VX_API_ENTRY vx_node VX_API_CALL vxHistogramNode	(	vx_graph	graph,
		vx_image	input,
		vx_distribution	distribution
	)

[Graph] Creates a Histogram node.

Parameters

[in]	graph	The reference to the graph.
[in]	input	The input image in `VX_DF_IMAGE_U8`.
[out]	distribution	The output distribution.

Returns: vx_node.

Return values

vx_node A node reference. Any possible errors preventing a successful creation should be checked using vxGetStatus

◆ vxHOGCellsNode()

VX_API_ENTRY vx_node VX_API_CALL vxHOGCellsNode	(	vx_graph	graph,
		vx_image	input,
		vx_int32	cell_width,
		vx_int32	cell_height,
		vx_int32	num_bins,
		vx_tensor	magnitudes,
		vx_tensor	bins
	)

[Graph] Performs cell calculations for the average gradient magnitude and gradient orientation histograms.

Firstly, the gradient magnitude and gradient orientation are computed for each pixel in the input image. Two 1-D centred, point discrete derivative masks are applied to the input image in the horizontal and vertical directions.

$M_h = [-1, 0, 1]$

and

$M_v = [-1, 0, 1]^T$

$G_v$ is the result of applying mask $M_v$ to the input image, and $G_h$ is the result of applying mask $M_h$ to the input image. The border mode used for the gradient calculation is implementation dependent. Its behavior should be similar to VX_BORDER_UNDEFINED. The gradient magnitudes and gradient orientations for each pixel are then calculated in the following manner.

$G(x,y) = \sqrt{G_v(x,y)^2 + G_h(x,y)^2}$

$\theta(x,y) = arctan(G_v(x,y), G_h(x,y))$

where $arctan(v, h)$ is $tan^{-1}(v/h)$ when $h!=0$ ,

$ -pi/2 $ if $v<0$ and $h==0$ ,

$ pi/2 $ if $v>0$ and $h==0$

and $ 0 $ if $v==0$ and $h==0$

Secondly, the gradient magnitudes and orientations are used to compute the bins output tensor and optional magnitudes output tensor. These tensors are computed on a cell level where the cells are rectangular in shape. The magnitudes tensor contains the average gradient magnitude for each cell.

$magnitudes(c) = \frac{1}{(cell\_width * cell\_height)}\sum\limits_{w=0}^{cell\_width} \sum\limits_{h=0}^{cell\_height} G_c(w,h)$

where $G_c$ is the gradient magnitudes related to cell $c$ . The bins tensor contains histograms of gradient orientations for each cell. The gradient orientations at each pixel range from 0 to 360 degrees. These are quantised into a set of histogram bins based on the num_bins parameter. Each pixel votes for a specific cell histogram bin based on its gradient orientation. The vote itself is the pixel's gradient magnitude.

$bins(c, n) = \sum\limits_{w=0}^{cell\_width} \sum\limits_{h=0}^{cell\_height} G_c(w,h) * 1[B_c(w, h, num\_bins) == n]$

where $B_c$ produces the histogram bin number based on the gradient orientation of the pixel at location ( $w$ , $h$ ) in cell $c$ based on the $num\_bins$ and

$1[B_c(w, h, num\_bins) == n]$

is a delta-function with value 1 when $B_c(w, h, num\_bins) == n$ or 0 otherwise.

Parameters

[in]	graph	The reference to the graph.
[in]	input	The input image of type `VX_DF_IMAGE_U8`.
[in]	cell_width	The histogram cell width of type `VX_TYPE_INT32`.
[in]	cell_height	The histogram cell height of type `VX_TYPE_INT32`.
[in]	num_bins	The histogram size of type `VX_TYPE_INT32`.
[out]	magnitudes	(Optional) The output average gradient magnitudes per cell of `vx_tensor` of type `VX_TYPE_INT16` of size $[floor(image_{width}/cell_{width}) ,floor(image_{height}/cell_{height}) ]$ .
[out]	bins	The output gradient orientation histograms per cell of `vx_tensor` of type `VX_TYPE_INT16` of size $[floor(image_{width}/cell_{width}) ,floor(image_{height}/cell_{height}), num_{bins}]$ .

Returns: vx_node.

Return values

0	Node could not be created.
*	Node handle.

◆ vxHOGFeaturesNode()

VX_API_ENTRY vx_node VX_API_CALL vxHOGFeaturesNode	(	vx_graph	graph,
		vx_image	input,
		vx_tensor	magnitudes,
		vx_tensor	bins,
		const vx_hog_t *	params,
		vx_size	hog_param_size,
		vx_tensor	features
	)

[Graph] The node produces HOG features for the W1xW2 window in a sliding window fashion over the whole input image. Each position produces a HOG feature vector.

Firstly if a magnitudes tensor is provided the cell histograms in the bins tensor are normalised by the average cell gradient magnitudes.

$bins(c,n) = \frac{bins(c,n)}{magnitudes(c)}$

To account for changes in illumination and contrast the cell histograms must be locally normalized which requires grouping the cell histograms together into larger spatially connected blocks. Blocks are rectangular grids represented by three parameters: the number of cells per block, the number of pixels per cell, and the number of bins per cell histogram. These blocks typically overlap, meaning that each cell histogram contributes more than once to the final descriptor. To normalize a block its cell histograms $h$ are grouped together to form a vector $v = [h_1, h_2, h_3, ... , h_n]$ . This vector is normalised using L2-Hys which means performing L2-norm on this vector; clipping the result (by limiting the maximum values of v to be threshold) and renormalizing again. If the threshold is equal to zero then L2-Hys normalization is not performed.

$L2norm(v) = \frac{v}{\sqrt{\|v\|_2^2 + \epsilon^2}}$

where $\|v\|_k$ be its k-norm for k=1, 2, and $\epsilon$ be a small constant. For a specific window its HOG descriptor is then the concatenated vector of the components of the normalized cell histograms from all of the block regions contained in the window. The W1xW2 window starting position is at coordinates 0x0. If the input image has dimensions that are not an integer multiple of W1xW2 blocks with the specified stride, then the last positions that contain only a partial W1xW2 window will be calculated with the remaining part of the W1xW2 window padded with zeroes. The Window W1xW2 must also have a size so that it contains an integer number of cells, otherwise the node is not well-defined. The final output tensor will contain HOG descriptors equal to the number of windows in the input image. The output features tensor has 3 dimensions, given by:

$[ (floor((image_{width}-window_{width})/window_{stride}) + 1),$

$(floor((image_{height}-window_{height})/window_{stride}) + 1),$

$floor((window_{width} - block_{width})/block_{stride} + 1) * floor((window_{height} - block_{height})/block_{stride} + 1) *$

$(((block_{width} * block_{height}) / (cell_{width} * cell_{height})) * num_{bins})]$

See vxCreateTensor and vxCreateVirtualTensor. We recommend the output tensors always be virtual objects, with this node connected directly to the classifier. The output tensor will be very large, and using non-virtual tensors will result in a poorly optimized implementation. Merging of this node with a classifier node such as that described in the classifier extension will result in better performance. Notice that this node creation function has more parameters than the corresponding kernel. Numbering of kernel parameters (required if you create this node using the generic interface) is explicitly specified here.

Parameters

[in]	graph	The reference to the graph.
[in]	input	The input image of type `VX_DF_IMAGE_U8`. (Kernel parameter #0)
[in]	magnitudes	(Optional) The gradient magnitudes per cell of `vx_tensor` of type `VX_TYPE_INT16`. It is the output of `vxHOGCellsNode`. (Kernel parameter #1)
[in]	bins	The gradient orientation histograms per cell of `vx_tensor` of type `VX_TYPE_INT16`. It is the output of `vxHOGCellsNode`. (Kernel parameter #2)
[in]	params	The parameters of type `vx_hog_t`. (Kernel parameter #3)
[in]	hog_param_size	Size of `vx_hog_t` in bytes. Note that this parameter is not counted as one of the kernel parameters.
[out]	features	The output HOG features of `vx_tensor` of type `VX_TYPE_INT16`. (Kernel parameter #4)

Returns: vx_node.

Return values

0	Node could not be created.
*	Node handle.

◆ vxHoughLinesPNode()

VX_API_ENTRY vx_node VX_API_CALL vxHoughLinesPNode	(	vx_graph	graph,
		vx_image	input,
		const vx_hough_lines_p_t *	params,
		vx_array	lines_array,
		vx_scalar	num_lines
	)

[Graph] Finds the Probabilistic Hough Lines detected in the input binary image, each line is stored in the output array as a set of points (x1, y1, x2, y2) .

Some implementations of the algorithm may have a random or non-deterministic element. If the target application is in a safety-critical environment this should be borne in mind and steps taken in the implementation, the application or both to achieve the level of determinism required by the system design.

Parameters

[in]	graph	graph handle
[in]	input	A single channel binary source image of type `VX_DF_IMAGE_U8` or `VX_DF_IMAGE_U1`.
[in]	params	parameters of the struct `vx_hough_lines_p_t`
[out]	lines_array	lines_array contains array of lines, see `vx_line2d_t` The order of lines in implementation dependent
[out]	num_lines	[optional] The total number of detected lines in image. Use a VX_TYPE_SIZE scalar

Returns: vx_node.

Return values

vx_node A node reference. Any possible errors preventing a successful creation should be checked using vxGetStatus

◆ vxIntegralImageNode()

VX_API_ENTRY vx_node VX_API_CALL vxIntegralImageNode	(	vx_graph	graph,
		vx_image	input,
		vx_image	output
	)

[Graph] Creates an Integral Image Node.

Parameters

[in]	graph	The reference to the graph.
[in]	input	The input image in `VX_DF_IMAGE_U8` format.
[out]	output	The output image in `VX_DF_IMAGE_U32` format, which must have the same dimensions as the input image.

Returns: vx_node.

Return values

vx_node A node reference. Any possible errors preventing a successful creation should be checked using vxGetStatus

◆ vxLaplacianPyramidNode()

VX_API_ENTRY vx_node VX_API_CALL vxLaplacianPyramidNode	(	vx_graph	graph,
		vx_image	input,
		vx_pyramid	laplacian,
		vx_image	output
	)

[Graph] Creates a node for a Laplacian Image Pyramid.

Parameters

[in]	graph	The reference to the graph.
[in]	input	The input image in `VX_DF_IMAGE_U8` or `VX_DF_IMAGE_S16` format.
[out]	laplacian	The Laplacian pyramid with `VX_DF_IMAGE_S16` to construct.
[out]	output	The lowest resolution image in `VX_DF_IMAGE_U8` or `VX_DF_IMAGE_S16` format necessary to reconstruct the input image from the pyramid. The output image format should be same as input image format.

See also: group_pyramid

Returns: vx_node.

Return values

vx_node A node reference. Any possible errors preventing a successful creation should be checked using vxGetStatus

◆ vxLaplacianReconstructNode()

VX_API_ENTRY vx_node VX_API_CALL vxLaplacianReconstructNode	(	vx_graph	graph,
		vx_pyramid	laplacian,
		vx_image	input,
		vx_image	output
	)

[Graph] Reconstructs an image from a Laplacian Image pyramid.

Parameters

[in]	graph	The reference to the graph.
[in]	laplacian	The Laplacian pyramid with `VX_DF_IMAGE_S16` format.
[in]	input	The lowest resolution image in `VX_DF_IMAGE_U8` or `VX_DF_IMAGE_S16` format for the Laplacian pyramid.
[out]	output	The output image in `VX_DF_IMAGE_U8` or `VX_DF_IMAGE_S16` format with the highest possible resolution reconstructed from the Laplacian pyramid. The output image format should be same as input image format.

See also: group_pyramid

Returns: vx_node.

Return values

0	Node could not be created.
*	Node handle.

◆ vxLBPNode()

VX_API_ENTRY vx_node VX_API_CALL vxLBPNode	(	vx_graph	graph,
		vx_image	in,
		vx_enum	format,
		vx_int8	kernel_size,
		vx_image	out
	)

[Graph] Creates a node that extracts LBP image from an input image

Parameters

[in]	graph	The reference to the graph.
[in]	in	An input image in `vx_image`. Or in the equations. the image is of type `VX_DF_IMAGE_U8`
[in]	format	A variation of LBP like original LBP and mLBP. see `vx_lbp_format_e`
[in]	kernel_size	Kernel size. Only size of 3 and 5 are supported
[out]	out	An output image in `vx_image`.Or in the equations. the image is of type `VX_DF_IMAGE_U8` with the same dimensions as the input image.

Returns: vx_node.

Return values

vx_node A node reference. Any possible errors preventing a successful creation should be checked using vxGetStatus

◆ vxMagnitudeNode()

VX_API_ENTRY vx_node VX_API_CALL vxMagnitudeNode	(	vx_graph	graph,
		vx_image	grad_x,
		vx_image	grad_y,
		vx_image	mag
	)

[Graph] Create a Magnitude node.

Parameters

[in]	graph	The reference to the graph.
[in]	grad_x	The input x image. This must be in `VX_DF_IMAGE_S16` format.
[in]	grad_y	The input y image. This must be in `VX_DF_IMAGE_S16` format.
[out]	mag	The magnitude image. This is in `VX_DF_IMAGE_S16` format. Must have the same dimensions as the input image.

See also: VX_KERNEL_MAGNITUDE

Returns: vx_node.

Return values

vx_node A node reference. Any possible errors preventing a successful creation should be checked using vxGetStatus

◆ vxMatchTemplateNode()

VX_API_ENTRY vx_node VX_API_CALL vxMatchTemplateNode	(	vx_graph	graph,
		vx_image	src,
		vx_image	templateImage,
		vx_enum	matchingMethod,
		vx_image	output
	)

[Graph] The Node Compares an image template against overlapped image regions.

The detailed equation to the matching can be found in vx_comp_metric_e. The output of the template matching node is a comparison map as described in vx_comp_metric_e. The Node have a limitation on the template image size (width*height). It should not be larger then 65535. If the valid region of the template image is smaller than the entire template image, the result in the destination image is implementation-dependent.

Parameters

[in]	graph	The reference to the graph.
[in]	src	The input image of type `VX_DF_IMAGE_U8`.
[in]	templateImage	Searched template of type `VX_DF_IMAGE_U8`.
[in]	matchingMethod	attribute specifying the comparison method `vx_comp_metric_e`. This function support only `VX_COMPARE_CCORR_NORM` and `VX_COMPARE_L2`.
[out]	output	Map of comparison results. The output is an image of type VX_DF_IMAGE_S16

Returns: vx_node.

Return values

vx_node A node reference. Any possible errors preventing a successful creation should be checked using vxGetStatus

◆ vxMaxNode()

VX_API_ENTRY vx_node VX_API_CALL vxMaxNode	(	vx_graph	graph,
		vx_image	in1,
		vx_image	in2,
		vx_image	out
	)

[Graph] Creates a pixel-wise maximum kernel.

Parameters

[in]	graph	The reference to the graph where to create the node.
[in]	in1	The first input image. Must be of type `VX_DF_IMAGE_U8` or `VX_DF_IMAGE_S16`.
[in]	in2	The second input image. Must be of type `VX_DF_IMAGE_U8` or `VX_DF_IMAGE_S16`.
[out]	out	The output image which will hold the result of max and will have the same type and dimensions of the imput images.

Returns: vx_node.

Return values

vx_node A node reference. Any possible errors preventing a successful creation should be checked using vxGetStatus

◆ vxMeanStdDevNode()

VX_API_ENTRY vx_node VX_API_CALL vxMeanStdDevNode	(	vx_graph	graph,
		vx_image	input,
		vx_scalar	mean,
		vx_scalar	stddev
	)

[Graph] Creates a mean value and optionally, a standard deviation node.

Parameters

[in]	graph	The reference to the graph.
[in]	input	The input image. `VX_DF_IMAGE_U8` and `VX_DF_IMAGE_U1` are supported.
[out]	mean	The `VX_TYPE_FLOAT32` average pixel value.
[out]	stddev	[optional] The `VX_TYPE_FLOAT32` standard deviation of the pixel values.

Returns: vx_node.

Return values

vx_node A node reference. Any possible errors preventing a successful creation should be checked using vxGetStatus

◆ vxMedian3x3Node()

VX_API_ENTRY vx_node VX_API_CALL vxMedian3x3Node	(	vx_graph	graph,
		vx_image	input,
		vx_image	output
	)

[Graph] Creates a Median Image Node.

Parameters

[in]	graph	The reference to the graph.
[in]	input	The input image in `VX_DF_IMAGE_U8` or `VX_DF_IMAGE_U1` format.
[out]	output	The output image in `VX_DF_IMAGE_U8` or `VX_DF_IMAGE_U1` format, which must have the same dimensions and type as the input image.

Returns: vx_node.

Return values

vx_node A node reference. Any possible errors preventing a successful creation should be checked using vxGetStatus

◆ vxMinMaxLocNode()

VX_API_ENTRY vx_node VX_API_CALL vxMinMaxLocNode	(	vx_graph	graph,
		vx_image	input,
		vx_scalar	minVal,
		vx_scalar	maxVal,
		vx_array	minLoc,
		vx_array	maxLoc,
		vx_scalar	minCount,
		vx_scalar	maxCount
	)

[Graph] Creates a min,max,loc node.

Parameters

[in]	graph	The reference to create the graph.
[in]	input	The input image in `VX_DF_IMAGE_U8` or `VX_DF_IMAGE_S16` format.
[out]	minVal	The minimum value in the image, which corresponds to the type of the input.
[out]	maxVal	The maximum value in the image, which corresponds to the type of the input.
[out]	minLoc	[optional] The minimum `VX_TYPE_COORDINATES2D` locations. If the input image has several minimums, the kernel will return up to the capacity of the array.
[out]	maxLoc	[optional] The maximum `VX_TYPE_COORDINATES2D` locations. If the input image has several maximums, the kernel will return up to the capacity of the array.
[out]	minCount	[optional] The total number of detected minimums in image. Use a `VX_TYPE_SIZE` scalar.
[out]	maxCount	[optional] The total number of detected maximums in image. Use a `VX_TYPE_SIZE` scalar.

Returns: vx_node.

Return values

vx_node A node reference. Any possible errors preventing a successful creation should be checked using vxGetStatus

◆ vxMinNode()

VX_API_ENTRY vx_node VX_API_CALL vxMinNode	(	vx_graph	graph,
		vx_image	in1,
		vx_image	in2,
		vx_image	out
	)

[Graph] Creates a pixel-wise minimum kernel.

Parameters

[in]	graph	The reference to the graph where to create the node.
[in]	in1	The first input image. Must be of type `VX_DF_IMAGE_U8` or `VX_DF_IMAGE_S16`.
[in]	in2	The second input image. Must be of type `VX_DF_IMAGE_U8` or `VX_DF_IMAGE_S16`.
[out]	out	The output image which will hold the result of min and will have the same type and dimensions of the imput images.

Returns: vx_node.

Return values

vx_node A node reference. Any possible errors preventing a successful creation should be checked using vxGetStatus

◆ vxMultiplyNode()

VX_API_ENTRY vx_node VX_API_CALL vxMultiplyNode	(	vx_graph	graph,
		vx_image	in1,
		vx_image	in2,
		vx_scalar	scale,
		vx_enum	overflow_policy,
		vx_enum	rounding_policy,
		vx_image	out
	)

[Graph] Creates an pixelwise-multiplication node.

Parameters

[in]	graph	The reference to the graph.
[in]	in1	An input image, `VX_DF_IMAGE_U8` or `VX_DF_IMAGE_S16`.
[in]	in2	An input image, `VX_DF_IMAGE_U8` or `VX_DF_IMAGE_S16`.
[in]	scale	A non-negative `VX_TYPE_FLOAT32` multiplied to each product before overflow handling.
[in]	overflow_policy	A `VX_TYPE_ENUM` of the `vx_convert_policy_e` enumeration.
[in]	rounding_policy	A `VX_TYPE_ENUM` of the `vx_round_policy_e` enumeration.
[out]	out	The output image, a `VX_DF_IMAGE_U8` or `VX_DF_IMAGE_S16` image. Must have the same type and dimensions of the imput images.

Returns: vx_node.

Return values

vx_node A node reference. Any possible errors preventing a successful creation should be checked using vxGetStatus

◆ vxNonLinearFilterNode()

VX_API_ENTRY vx_node VX_API_CALL vxNonLinearFilterNode	(	vx_graph	graph,
		vx_enum	function,
		vx_image	input,
		vx_matrix	mask,
		vx_image	output
	)

[Graph] Creates a Non-linear Filter Node.

Parameters

[in]	graph	The reference to the graph.
[in]	function	The non-linear filter function. See `vx_non_linear_filter_e`.
[in]	input	The input image in `VX_DF_IMAGE_U8` or `VX_DF_IMAGE_U1` format.
[in]	mask	The mask to be applied to the Non-linear function. `VX_MATRIX_ORIGIN` attribute is used to place the mask appropriately when computing the resulting image. See `vxCreateMatrixFromPattern`.
[out]	output	The output image in `VX_DF_IMAGE_U8` or `VX_DF_IMAGE_U1` format, which must have the same dimensions and type as the input image.

Returns: vx_node.

Return values

vx_node A node reference. Any possible errors preventing a successful creation should be checked using vxGetStatus

◆ vxNonMaxSuppressionNode()

VX_API_ENTRY vx_node VX_API_CALL vxNonMaxSuppressionNode	(	vx_graph	graph,
		vx_image	input,
		vx_image	mask,
		vx_int32	win_size,
		vx_image	output
	)

[Graph] Creates a Non-Maxima Suppression node.

Parameters

[in]	graph	The reference to the graph.
[in]	input	The input image in `VX_DF_IMAGE_U8` or `VX_DF_IMAGE_S16` format.
[in]	mask	[optional] Constrict suppression to a ROI. The mask image is of type `VX_DF_IMAGE_U8` or `VX_DF_IMAGE_U1` and must have the same dimensions as the input image.
[in]	win_size	The size of window over which to perform the localized non-maxima suppression. Must be odd, and less than or equal to the smallest dimension of the input image.
[out]	output	The output image, of the same type and size as the input, that has been non-maxima suppressed.

Returns: vx_node.

Return values

vx_node A node reference. Any possible errors preventing a successful creation should be checked using vxGetStatus

◆ vxNotNode()

VX_API_ENTRY vx_node VX_API_CALL vxNotNode	(	vx_graph	graph,
		vx_image	input,
		vx_image	output
	)

[Graph] Creates a bitwise NOT node.

Parameters

[in]	graph	The reference to the graph.
[in]	input	A `VX_DF_IMAGE_U8` or `VX_DF_IMAGE_U1` input image.
[out]	output	The `VX_DF_IMAGE_U8` or `VX_DF_IMAGE_U1` output image, which must have the same dimensions and type as the input image.

Returns: vx_node.

Return values

vx_node A node reference. Any possible errors preventing a successful creation should be checked using vxGetStatus

◆ vxOpticalFlowPyrLKNode()

VX_API_ENTRY vx_node VX_API_CALL vxOpticalFlowPyrLKNode	(	vx_graph	graph,
		vx_pyramid	old_images,
		vx_pyramid	new_images,
		vx_array	old_points,
		vx_array	new_points_estimates,
		vx_array	new_points,
		vx_enum	termination,
		vx_scalar	epsilon,
		vx_scalar	num_iterations,
		vx_scalar	use_initial_estimate,
		vx_size	window_dimension
	)

[Graph] Creates a Lucas Kanade Tracking Node.

Parameters

[in]	graph	The reference to the graph.
[in]	old_images	Input of first (old) image pyramid in `VX_DF_IMAGE_U8`.
[in]	new_images	Input of destination (new) image pyramid `VX_DF_IMAGE_U8`.
[in]	old_points	An array of key points in a `vx_array` of `VX_TYPE_KEYPOINT`; those key points are defined at the old_images high resolution pyramid.
[in]	new_points_estimates	An array of estimation on what is the output key points in a `vx_array` of `VX_TYPE_KEYPOINT`; those keypoints are defined at the new_images high resolution pyramid.
[out]	new_points	An output array of key points in a `vx_array` of `VX_TYPE_KEYPOINT`; those key points are defined at the new_images high resolution pyramid.
[in]	termination	The termination can be `VX_TERM_CRITERIA_ITERATIONS` or `VX_TERM_CRITERIA_EPSILON` or `VX_TERM_CRITERIA_BOTH`.
[in]	epsilon	The `vx_float32` error for terminating the algorithm.
[in]	num_iterations	The number of iterations. Use a `VX_TYPE_UINT32` scalar.
[in]	use_initial_estimate	Use a `VX_TYPE_BOOL` scalar.
[in]	window_dimension	The size of the window on which to perform the algorithm. See `VX_CONTEXT_OPTICAL_FLOW_MAX_WINDOW_DIMENSION`

Returns: vx_node.

Return values

vx_node A node reference. Any possible errors preventing a successful creation should be checked using vxGetStatus

◆ vxOrNode()

VX_API_ENTRY vx_node VX_API_CALL vxOrNode	(	vx_graph	graph,
		vx_image	in1,
		vx_image	in2,
		vx_image	out
	)

[Graph] Creates a bitwise INCLUSIVE OR node.

Parameters

[in]	graph	The reference to the graph.
[in]	in1	A `VX_DF_IMAGE_U8` or `VX_DF_IMAGE_U1` input image.
[in]	in2	A `VX_DF_IMAGE_U8` or `VX_DF_IMAGE_U1` input image.
[out]	out	The `VX_DF_IMAGE_U8` or `VX_DF_IMAGE_U1` output image, which must have the same dimensions and type as the input images.

Returns: vx_node.

Return values

vx_node A node reference. Any possible errors preventing a successful creation should be checked using vxGetStatus

◆ vxPhaseNode()

VX_API_ENTRY vx_node VX_API_CALL vxPhaseNode	(	vx_graph	graph,
		vx_image	grad_x,
		vx_image	grad_y,
		vx_image	orientation
	)

[Graph] Creates a Phase node.

Parameters

[in]	graph	The reference to the graph.
[in]	grad_x	The input x image. This must be in `VX_DF_IMAGE_S16` format.
[in]	grad_y	The input y image. This must be in `VX_DF_IMAGE_S16` format.
[out]	orientation	The phase image. This is in `VX_DF_IMAGE_U8` format, and must have the same dimensions as the input images.

See also: VX_KERNEL_PHASE

Returns: vx_node.

Return values

vx_node A node reference. Any possible errors preventing a successful creation should be checked using vxGetStatus

◆ vxRemapNode()

VX_API_ENTRY vx_node VX_API_CALL vxRemapNode	(	vx_graph	graph,
		vx_image	input,
		vx_remap	table,
		vx_enum	policy,
		vx_image	output
	)

[Graph] Creates a Remap Node.

Parameters

[in]	graph	The reference to the graph that will contain the node.
[in]	input	The input `VX_DF_IMAGE_U8` image.
[in]	table	The remap table object.
[in]	policy	An interpolation type from `vx_interpolation_type_e`. `VX_INTERPOLATION_AREA` is not supported.
[out]	output	The output `VX_DF_IMAGE_U8` image with the same dimensions as the input image.

Note: The border modes VX_NODE_BORDER value VX_BORDER_UNDEFINED and VX_BORDER_CONSTANT are supported.

Returns: A vx_node.

Return values

vx_node A node reference. Any possible errors preventing a successful creation should be checked using vxGetStatus

◆ vxScalarOperationNode()

VX_API_ENTRY vx_node VX_API_CALL vxScalarOperationNode	(	vx_graph	graph,
		vx_enum	scalar_operation,
		vx_scalar	a,
		vx_scalar	b,
		vx_scalar	output
	)

[Graph] Creates a scalar operation node.

Parameters

[in]	graph	The reference to the graph.
[in]	scalar_operation	A `VX_TYPE_ENUM` of the `vx_scalar_operation_e` enumeration.
[in]	a	First scalar operand.
[in]	b	Second scalar operand.
[out]	output	Result of the scalar operation.

Returns: vx_node.

Return values

vx_node A node reference. Any possible errors preventing a successful creation should be checked using vxGetStatus

◆ vxScaleImageNode()

VX_API_ENTRY vx_node VX_API_CALL vxScaleImageNode	(	vx_graph	graph,
		vx_image	src,
		vx_image	dst,
		vx_enum	type
	)

[Graph] Creates a Scale Image Node.

Parameters

[in]	graph	The reference to the graph.
[in]	src	The source image of type `VX_DF_IMAGE_U8` or `VX_DF_IMAGE_U1`.
[out]	dst	The destination image of type `VX_DF_IMAGE_U8` or `VX_DF_IMAGE_U1`. The output type must be the same as that of the input image.
[in]	type	The interpolation type to use.

See also: vx_interpolation_type_e.

Note: The destination image must have a defined size and format. The border modes VX_NODE_BORDER value VX_BORDER_UNDEFINED, VX_BORDER_REPLICATE and VX_BORDER_CONSTANT are supported.

Returns: vx_node.

Return values

vx_node A node reference. Any possible errors preventing a successful creation should be checked using vxGetStatus

◆ vxSelectNode()

VX_API_ENTRY vx_node VX_API_CALL vxSelectNode	(	vx_graph	graph,
		vx_scalar	condition,
		vx_reference	true_value,
		vx_reference	false_value,
		vx_reference	output
	)

[Graph] Selects one of two data objects depending on the the value of a condition (boolean scalar), and copies its data into another data object.

This node supports predicated execution flow within a graph. All the data objects passed to this kernel shall have the same object type and meta data. It is important to note that an implementation may optimize away the select and copy when virtual data objects are used.
If there is a kernel node that contribute only into virtual data objects during the graph execution due to certain data path being eliminated by not taken argument of select node, then the OpenVX implementation guarantees that there will not be any side effects to graph execution and node state.
If the path to a select node contains non-virtual objects, user nodes, or nodes with completion callbacks, then that path may not be "optimized out" because the callback must be executed and the non-virtual objects must be modified.

Parameters

[in]	graph	The reference to the graph.
[in]	condition	`VX_TYPE_BOOL` predicate variable.
[in]	true_value	Data object for true.
[in]	false_value	Data object for false.
[out]	output	Output data object.

Returns: vx_node.

Return values

vx_node A node reference. Any possible errors preventing a successful creation should be checked using vxGetStatus

◆ vxSobel3x3Node()

VX_API_ENTRY vx_node VX_API_CALL vxSobel3x3Node	(	vx_graph	graph,
		vx_image	input,
		vx_image	output_x,
		vx_image	output_y
	)

[Graph] Creates a Sobel3x3 node.

Parameters

[in]	graph	The reference to the graph.
[in]	input	The input image in `VX_DF_IMAGE_U8` format.
[out]	output_x	[optional] The output gradient in the x direction in `VX_DF_IMAGE_S16`. Must have the same dimensions as the input image.
[out]	output_y	[optional] The output gradient in the y direction in `VX_DF_IMAGE_S16`. Must have the same dimensions as the input image.

See also: VX_KERNEL_SOBEL_3x3

Returns: vx_node.

Return values

vx_node A node reference. Any possible errors preventing a successful creation should be checked using vxGetStatus

◆ vxSubtractNode()

VX_API_ENTRY vx_node VX_API_CALL vxSubtractNode	(	vx_graph	graph,
		vx_image	in1,
		vx_image	in2,
		vx_enum	policy,
		vx_image	out
	)

[Graph] Creates an arithmetic subtraction node.

Parameters

[in]	graph	The reference to the graph.
[in]	in1	An input image, `VX_DF_IMAGE_U8` or `VX_DF_IMAGE_S16`, the minuend.
[in]	in2	An input image, `VX_DF_IMAGE_U8` or `VX_DF_IMAGE_S16`, the subtrahend.
[in]	policy	A `VX_TYPE_ENUM` of the vx_convert_policy_e enumeration.
[out]	out	The output image, a `VX_DF_IMAGE_U8` or `VX_DF_IMAGE_S16` image, which must have the same dimensions as the input images.

Returns: vx_node.

Return values

vx_node A node reference. Any possible errors preventing a successful creation should be checked using vxGetStatus

◆ vxTableLookupNode()

VX_API_ENTRY vx_node VX_API_CALL vxTableLookupNode	(	vx_graph	graph,
		vx_image	input,
		vx_lut	lut,
		vx_image	output
	)

[Graph] Creates a Table Lookup node. If a value from the input image is not present in the lookup table, the result is undefined.

Parameters

[in]	graph	The reference to the graph.
[in]	input	The input image in `VX_DF_IMAGE_U8` or `VX_DF_IMAGE_S16`.
[in]	lut	The LUT which is of type `VX_TYPE_UINT8` if input image is `VX_DF_IMAGE_U8` or `VX_TYPE_INT16` if input image is `VX_DF_IMAGE_S16`.
[out]	output	The output image of the same size as the input image.

Returns: vx_node.

Return values

vx_node A node reference. Any possible errors preventing a successful creation should be checked using vxGetStatus.

◆ vxTensorAddNode()

VX_API_ENTRY vx_node VX_API_CALL vxTensorAddNode	(	vx_graph	graph,
		vx_tensor	input1,
		vx_tensor	input2,
		vx_enum	policy,
		vx_tensor	output
	)

[Graph] Performs arithmetic addition on element values in the input tensor data.

Parameters

[in]	graph	The handle to the graph.
[in]	input1	Input tensor data. Implementations must support input tensor data type `VX_TYPE_INT16` with fixed_point_position 8, and tensor data types `VX_TYPE_UINT8` and `VX_TYPE_INT8`, with fixed_point_position 0.
[in]	input2	Input tensor data. The dimensions and sizes of input2 match those of input1, unless the vx_tensor of one or more dimensions in input2 is 1. In this case, those dimensions are treated as if this tensor was expanded to match the size of the corresponding dimension of input1, and data was duplicated on all terms in that dimension. After this expansion, the dimensions will be equal. The data type must match the data type of Input1.
[in]	policy	A `vx_convert_policy_e` enumeration.
[out]	output	The output tensor data with the same dimensions as the input tensor data.

Returns: vx_node.; A node reference vx_node. Any possible errors preventing a successful creation should be checked using vxGetStatus.

◆ vxTensorConvertDepthNode()

VX_API_ENTRY vx_node VX_API_CALL vxTensorConvertDepthNode	(	vx_graph	graph,
		vx_tensor	input,
		vx_enum	policy,
		vx_scalar	norm,
		vx_scalar	offset,
		vx_tensor	output
	)

[Graph] Creates a bit-depth conversion node.

Parameters

[in]	graph	The reference to the graph.
[in]	input	The input tensor. Implementations must support input tensor data type `VX_TYPE_INT16` with fixed_point_position 8, and tensor data types `VX_TYPE_UINT8` and `VX_TYPE_INT8`, with fixed_point_position 0.
[in]	policy	A `VX_TYPE_ENUM` of the `vx_convert_policy_e` enumeration.
[in]	norm	A scalar containing a `VX_TYPE_FLOAT32` of the normalization value.
[in]	offset	A scalar containing a `VX_TYPE_FLOAT32` of the offset value subtracted before normalization.
[out]	output	The output tensor. Implementations must support input tensor data type `VX_TYPE_INT16`. with fixed_point_position 8. And `VX_TYPE_UINT8` with fixed_point_position 0.

Returns: vx_node.

Return values

vx_node A node reference. Any possible errors preventing a successful creation should be checked using vxGetStatus

◆ vxTensorMatrixMultiplyNode()

VX_API_ENTRY vx_node VX_API_CALL vxTensorMatrixMultiplyNode	(	vx_graph	graph,
		vx_tensor	input1,
		vx_tensor	input2,
		vx_tensor	input3,
		const vx_tensor_matrix_multiply_params_t *	matrix_multiply_params,
		vx_tensor	output
	)

[Graph] Creates a generalized matrix multiplication node.

Parameters

[in]	graph	The reference to the graph.
[in]	input1	The first input 2D tensor of type `VX_TYPE_INT16` with fixed_point_pos 8, or tensor data types `VX_TYPE_UINT8` or `VX_TYPE_INT8`, with fixed_point_pos 0.
[in]	input2	The second 2D tensor. Must be in the same data type as input1.
[in]	input3	The third 2D tensor. Must be in the same data type as input1. [optional].
[in]	matrix_multiply_params	Matrix multiply parameters, see `vx_tensor_matrix_multiply_params_t` .
[out]	output	The output 2D tensor. Must be in the same data type as input1. Output dimension must agree the formula in the description.

Returns: vx_node.; A node reference vx_node. Any possible errors preventing a successful creation should be checked using vxGetStatus.

◆ vxTensorMultiplyNode()

VX_API_ENTRY vx_node VX_API_CALL vxTensorMultiplyNode	(	vx_graph	graph,
		vx_tensor	input1,
		vx_tensor	input2,
		vx_scalar	scale,
		vx_enum	overflow_policy,
		vx_enum	rounding_policy,
		vx_tensor	output
	)

[Graph] Performs element wise multiplications on element values in the input tensor data with a scale.

Parameters

[in]	graph	The handle to the graph.
[in]	input1	Input tensor data. Implementations must support input tensor data type `VX_TYPE_INT16` with fixed_point_position 8, and tensor data types `VX_TYPE_UINT8` and `VX_TYPE_INT8`, with fixed_point_position 0.
[in]	input2	Input tensor data. The dimensions and sizes of input2 match those of input1, unless the vx_tensor of one or more dimensions in input2 is 1. In this case, those dimensions are treated as if this tensor was expanded to match the size of the corresponding dimension of input1, and data was duplicated on all terms in that dimension. After this expansion, the dimensions will be equal. The data type must match the data type of Input1.
[in]	scale	A non-negative `VX_TYPE_FLOAT32` multiplied to each product before overflow handling.
[in]	overflow_policy	A `vx_convert_policy_e` enumeration.
[in]	rounding_policy	A `vx_round_policy_e` enumeration.
[out]	output	The output tensor data with the same dimensions as the input tensor data.

Returns: vx_node.; A node reference vx_node. Any possible errors preventing a successful creation should be checked using vxGetStatus.

◆ vxTensorSubtractNode()

VX_API_ENTRY vx_node VX_API_CALL vxTensorSubtractNode	(	vx_graph	graph,
		vx_tensor	input1,
		vx_tensor	input2,
		vx_enum	policy,
		vx_tensor	output
	)

[Graph] Performs arithmetic subtraction on element values in the input tensor data.

Parameters

[in]	graph	The handle to the graph.
[in]	input1	Input tensor data. Implementations must support input tensor data type `VX_TYPE_INT16` with fixed_point_position 8, and tensor data types `VX_TYPE_UINT8` and `VX_TYPE_INT8`, with fixed_point_position 0.
[in]	input2	Input tensor data. The dimensions and sizes of input2 match those of input1, unless the vx_tensor of one or more dimensions in input2 is 1. In this case, those dimensions are treated as if this tensor was expanded to match the size of the corresponding dimension of input1, and data was duplicated on all terms in that dimension. After this expansion, the dimensions will be equal. The data type must match the data type of Input1.
[in]	policy	A `vx_convert_policy_e` enumeration.
[out]	output	The output tensor data with the same dimensions as the input tensor data.

Returns: vx_node.; A node reference vx_node. Any possible errors preventing a successful creation should be checked using vxGetStatus.

◆ vxTensorTableLookupNode()

VX_API_ENTRY vx_node VX_API_CALL vxTensorTableLookupNode	(	vx_graph	graph,
		vx_tensor	input1,
		vx_lut	lut,
		vx_tensor	output
	)

[Graph] Performs LUT on element values in the input tensor data.

Parameters

[in]	graph	The handle to the graph.
[in]	input1	Input tensor data. Implementations must support input tensor data type `VX_TYPE_INT16` with fixed_point_position 8, and tensor data types `VX_TYPE_UINT8`, with fixed_point_position 0.
[in]	lut	The look-up table to use, of type `vx_lut`. The elements of input1 are treated as unsigned integers to determine an index into the look-up table. The data type of the items in the look-up table must match that of the output tensor.
[out]	output	The output tensor data with the same dimensions as the input tensor data.

Returns: vx_node.; A node reference vx_node. Any possible errors preventing a successful creation should be checked using vxGetStatus.

◆ vxTensorTransposeNode()

VX_API_ENTRY vx_node VX_API_CALL vxTensorTransposeNode	(	vx_graph	graph,
		vx_tensor	input,
		vx_tensor	output,
		vx_size	dimension1,
		vx_size	dimension2
	)

[Graph] Performs transpose on the input tensor. The node transpose the tensor according to a specified 2 indexes in the tensor (0-based indexing)

Parameters

[in]	graph	The handle to the graph.
[in]	input	Input tensor data, Implementations must support input tensor data type `VX_TYPE_INT16` with fixed_point_position 8, and tensor data types `VX_TYPE_UINT8` and `VX_TYPE_INT8`, with fixed_point_position 0.
[out]	output	output tensor data,
[in]	dimension1	Dimension index that is transposed with dim 2.
[in]	dimension2	Dimension index that is transposed with dim 1.

Returns: vx_node.; A node reference vx_node. Any possible errors preventing a successful creation should be checked using vxGetStatus.

◆ vxThresholdNode()

VX_API_ENTRY vx_node VX_API_CALL vxThresholdNode	(	vx_graph	graph,
		vx_image	input,
		vx_threshold	thresh,
		vx_image	output
	)

[Graph] Creates a Threshold node and returns a reference to it.

Parameters

[in]	graph	The reference to the graph in which the node is created.
[in]	input	The input image. Only images with format `VX_DF_IMAGE_U8` and `VX_DF_IMAGE_S16` are supported.
[in]	thresh	The thresholding object that defines the parameters of the operation. The `VX_THRESHOLD_INPUT_FORMAT` must be the same as the input image format and the `VX_THRESHOLD_OUTPUT_FORMAT` must be the same as the output image format.
[out]	output	The output image, that will contain as pixel value true and false values defined by `thresh`. Images with format `VX_DF_IMAGE_U8` or `VX_DF_IMAGE_U1` are supported. The dimensions are the same as the input image.

Returns: vx_node.

Return values

vx_node A node reference. Any possible errors preventing a successful creation should be checked using vxGetStatus

◆ vxWarpAffineNode()

VX_API_ENTRY vx_node VX_API_CALL vxWarpAffineNode	(	vx_graph	graph,
		vx_image	input,
		vx_matrix	matrix,
		vx_enum	type,
		vx_image	output
	)

[Graph] Creates an Affine Warp Node.

Parameters

[in]	graph	The reference to the graph.
[in]	input	The input `VX_DF_IMAGE_U8` or `VX_DF_IMAGE_U1` image.
[in]	matrix	The affine matrix. Must be 2x3 of type VX_TYPE_FLOAT32.
[in]	type	The interpolation type from `vx_interpolation_type_e`. `VX_INTERPOLATION_AREA` is not supported.
[out]	output	The output `VX_DF_IMAGE_U8` or `VX_DF_IMAGE_U1` image with the same format as the input image.

Note: The border modes VX_NODE_BORDER value VX_BORDER_UNDEFINED and VX_BORDER_CONSTANT are supported.

Returns: vx_node.

Return values

vx_node A node reference. Any possible errors preventing a successful creation should be checked using vxGetStatus

◆ vxWarpPerspectiveNode()

VX_API_ENTRY vx_node VX_API_CALL vxWarpPerspectiveNode	(	vx_graph	graph,
		vx_image	input,
		vx_matrix	matrix,
		vx_enum	type,
		vx_image	output
	)

[Graph] Creates a Perspective Warp Node.

Parameters

[in]	graph	The reference to the graph.
[in]	input	The input `VX_DF_IMAGE_U8` image.
[in]	matrix	The perspective matrix. Must be 3x3 of type `VX_TYPE_FLOAT32`.
[in]	type	The interpolation type from `vx_interpolation_type_e`. `VX_INTERPOLATION_AREA` is not supported.
[out]	output	The output `VX_DF_IMAGE_U8` image.

Note: The border modes VX_NODE_BORDER value VX_BORDER_UNDEFINED and VX_BORDER_CONSTANT are supported.

Returns: vx_node.

Return values

vx_node A node reference. Any possible errors preventing a successful creation should be checked using vxGetStatus

◆ vxWeightedAverageNode()

VX_API_ENTRY vx_node VX_API_CALL vxWeightedAverageNode	(	vx_graph	graph,
		vx_image	img1,
		vx_scalar	alpha,
		vx_image	img2,
		vx_image	output
	)

[Graph] Creates a image weighted average node.

Parameters

[in]	graph	The reference to the graph.
[in]	img1	The first input `VX_DF_IMAGE_U8` image.
[in]	alpha	The input `VX_TYPE_FLOAT32` scalar value with a value in the range of $0.0 \le \alpha \le 1.0$ .
[in]	img2	The second `VX_DF_IMAGE_U8` image, which must have the same dimensions as the img1.
[out]	output	The output `VX_DF_IMAGE_U8` image, which must have the same dimensions as the img1.

Returns: vx_node.

Return values

vx_node A node reference. Any possible errors preventing a successful creation should be checked using vxGetStatus

◆ vxXorNode()

VX_API_ENTRY vx_node VX_API_CALL vxXorNode	(	vx_graph	graph,
		vx_image	in1,
		vx_image	in2,
		vx_image	out
	)

[Graph] Creates a bitwise EXCLUSIVE OR node.

Parameters

[in]	graph	The reference to the graph.
[in]	in1	A `VX_DF_IMAGE_U8` or `VX_DF_IMAGE_U1` input image.
[in]	in2	A `VX_DF_IMAGE_U8` or `VX_DF_IMAGE_U1` input image.
[out]	out	The `VX_DF_IMAGE_U8` or `VX_DF_IMAGE_U1` output image, which must have the same dimensions and type as the input images.

Returns: vx_node.

Return values

vx_node A node reference. Any possible errors preventing a successful creation should be checked using vxGetStatus

/home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-mivisionx/checkouts/docs-5.7.1/amd_openvx/openvx/include/VX/vx_nodes.h File Reference

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Functions

Detailed Description

Function Documentation

◆ vxAbsDiffNode()

◆ vxAddNode()

◆ vxAndNode()

◆ vxBilateralFilterNode()

◆ vxBox3x3Node()

◆ vxCannyEdgeDetectorNode()

◆ vxChannelCombineNode()

◆ vxChannelExtractNode()

◆ vxColorConvertNode()

◆ vxConvertDepthNode()

◆ vxConvolveNode()

◆ vxCopyNode()

◆ vxDilate3x3Node()

◆ vxEqualizeHistNode()

◆ vxErode3x3Node()

◆ vxFastCornersNode()

◆ vxGaussian3x3Node()

◆ vxGaussianPyramidNode()

◆ vxHalfScaleGaussianNode()

◆ vxHarrisCornersNode()

◆ vxHistogramNode()

◆ vxHOGCellsNode()

◆ vxHOGFeaturesNode()

◆ vxHoughLinesPNode()

◆ vxIntegralImageNode()

◆ vxLaplacianPyramidNode()

◆ vxLaplacianReconstructNode()

◆ vxLBPNode()

◆ vxMagnitudeNode()

◆ vxMatchTemplateNode()

◆ vxMaxNode()

◆ vxMeanStdDevNode()

◆ vxMedian3x3Node()

◆ vxMinMaxLocNode()

◆ vxMinNode()

◆ vxMultiplyNode()

◆ vxNonLinearFilterNode()

◆ vxNonMaxSuppressionNode()

◆ vxNotNode()

◆ vxOpticalFlowPyrLKNode()

◆ vxOrNode()

◆ vxPhaseNode()

◆ vxRemapNode()

◆ vxScalarOperationNode()

◆ vxScaleImageNode()

◆ vxSelectNode()

◆ vxSobel3x3Node()

◆ vxSubtractNode()

◆ vxTableLookupNode()

◆ vxTensorAddNode()

◆ vxTensorConvertDepthNode()

◆ vxTensorMatrixMultiplyNode()

◆ vxTensorMultiplyNode()

◆ vxTensorSubtractNode()

◆ vxTensorTableLookupNode()

◆ vxTensorTransposeNode()

◆ vxThresholdNode()

◆ vxWarpAffineNode()

◆ vxWarpPerspectiveNode()

◆ vxWeightedAverageNode()

◆ vxXorNode()