Base class for
RGBDsegmentation. Input is a pair images which are RGB image (cv::Mat) and HHA map generated with depth map (cv::Mat).
Output is a heatmap where each pixels is predicted with a semantic category, like chair, bed, usual object in indoor.
Sample code:
Mat img_bgr = cv::imread("sample_rgbdsegmentation_bgr.jpg");
Mat img_hha = cv::imread("sample_rgbdsegmentation_hha.jpg");
auto segmentation = vitis::ai::RGBDsegmentation::create("SA-Gate_pt", true);
auto result = segmentation->run(img_bgr, img_hha);
imwrite("result.jpg", result.segmentation);Display of the model results: width=\textwidth
Figure 1. out image
Quick Function Reference
The following table lists all the functions defined in the vitis::ai::RGBDsegmentation class: