Functions
VX_API_ENTRY vx_node VX_API_CALL	vxScanClassifierNode (vx_graph graph, vx_tensor input_feature_map, vx_classifier_model model, vx_int32 scan_window_width, vx_int32 scan_window_height, vx_int32 step_x, vx_int32 step_y, vx_array object_confidences, vx_array object_rectangles, vx_scalar num_objects)
	[Graph] Scans a feature-map (input_feature_map) and detect the classification for each scan-window.

Detailed Description

Function Documentation

VX_API_ENTRY vx_node VX_API_CALL vxScanClassifierNode	(	vx_graph	graph,
		vx_tensor	input_feature_map,
		vx_classifier_model	model,
		vx_int32	scan_window_width,
		vx_int32	scan_window_height,
		vx_int32	step_x,
		vx_int32	step_y,
		vx_array	object_confidences,
		vx_array	object_rectangles,
		vx_scalar	num_objects )

[Graph] Scans a feature-map (input_feature_map) and detect the classification for each scan-window.

Parameters

[in]	graph	The reference to the graph
[in]	input_feature_map	The Feature-map, example is the output of `vxHOGFeaturesNode`.
[in]	model	The pre-trained model loaded. Loaded using `vxImportClassifierModel`
[in]	scan_window_width	Width of the scan window
[in]	scan_window_height	Height of the scan window
[in]	step_x	Horizontal step-size (along x-axis)
[in]	step_y	Vertical step-size (along y-axis)
[out]	object_confidences	[Optional] An array of confidences measure, the measure is of type `VX_TYPE_UINT16`. The confidence measure is defined by the extensions which define classification model with defined binary format. This output can be used as class index as well. In case we detect several different classes in single execution. The output will be an array of indexes of the classes.
[out]	object_rectangles	An array of object positions, in `VX_TYPE_RECTANGLE`
[out]	num_objects	[optional] The number of object detected in a `vx_size` scalar

Note: The border mode VX_NODE_BORDER value VX_BORDER_UNDEFINED is supported.

Return values

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