add sample

wangrong

2025-01-23 02819b5c047bb354b0ef2374e7c6a6ac4bbd5bba

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
 
#ifdef VP_WITH_TRT 
#include "vp_trt_yolov8_pose_detector.h" 
 
namespace vp_nodes { 
     
    vp_trt_yolov8_pose_detector::vp_trt_yolov8_pose_detector(std::string node_name,  
                                                    std::string model_path): 
                                                    vp_primary_infer_node(node_name, "") { 
        yolov8_pose_detector = std::make_shared<trt_yolov8::trt_yolov8_pose_detector>(model_path); 
        this->initialized(); 
    } 
     
    vp_trt_yolov8_pose_detector::~vp_trt_yolov8_pose_detector() { 
        deinitialized(); 
    } 
 
    // please refer to vp_infer_node::run_infer_combinations 
    void vp_trt_yolov8_pose_detector::run_infer_combinations(const std::vector<std::shared_ptr<vp_objects::vp_frame_meta>>& frame_meta_with_batch) { 
        assert(frame_meta_with_batch.size() == 1); 
        std::vector<cv::Mat> mats_to_infer; 
 
        // start 
        auto start_time = std::chrono::system_clock::now(); 
 
        // prepare data, as same as base class 
        vp_primary_infer_node::prepare(frame_meta_with_batch, mats_to_infer); 
        auto prepare_time = std::chrono::duration_cast<std::chrono::milliseconds>(std::chrono::system_clock::now() - start_time); 
 
        start_time = std::chrono::system_clock::now(); 
        std::vector<std::vector<Detection>> detections; 
        yolov8_pose_detector->detect(mats_to_infer, detections); 
 
        assert(detections.size() == 1); 
        auto& detection_list = detections[0]; 
        auto& frame_meta = frame_meta_with_batch[0]; 
 
        for (int i = 0; i < detection_list.size(); i++) { 
            auto& objbox = detection_list[i]; 
            auto rect = get_rect_adapt_landmark(frame_meta->frame, objbox.bbox, objbox.keypoints); 
             
            std::vector<vp_objects::vp_pose_keypoint> kps;     
            for (int j = 0; j < 51; j += 3) { 
                kps.push_back(vp_objects::vp_pose_keypoint {j, int(objbox.keypoints[j]), int(objbox.keypoints[j + 1]), objbox.keypoints[j + 2]}); 
            } 
                         
            auto pose_target = std::make_shared<vp_objects::vp_frame_pose_target>(vp_objects::vp_pose_type::yolov8_pose_17, kps); 
            frame_meta->pose_targets.push_back(pose_target);             
        } 
        auto infer_time = std::chrono::duration_cast<std::chrono::milliseconds>(std::chrono::system_clock::now() - start_time); 
 
        // can not calculate preprocess time and postprocess time, set 0 by default. 
        vp_infer_node::infer_combinations_time_cost(mats_to_infer.size(), prepare_time.count(), 0, infer_time.count(), 0); 
    } 
 
    void vp_trt_yolov8_pose_detector::postprocess(const std::vector<cv::Mat>& raw_outputs, const std::vector<std::shared_ptr<vp_objects::vp_frame_meta>>& frame_meta_with_batch) { 
 
    } 
} 
#endif