参考：mmpose/projects/rtmpose at main · open-mmlab/mmpose · GitHub

        MMDeploy 提供了一系列工具，帮助我们更轻松的将 OpenMMLab 下的算法部署到各种设备与平台上。目前，MMDeploy 可以把 PyTorch 模型转换为 ONNX、TorchScript 等和设备无关的 IR 模型。也可以将 ONNX 模型转换为推理后端模型。两者相结合，可实现端到端的模型转换，也就是从训练端到生产端的一键式部署。

一、模型转换

        We provide both Python and C++ inference API with MMDeploy SDK.To use SDK, you need to dump the required info during converting the model. Just add --dump-info to the model conversion command.（MMDeploy sdk提供了python和c++的推理api，在使用api进行推理前需要在pc上进行相应文件的转换，如pth->onnx，pth->ncnn...）

        将rtm中的hand_detection模型转为onnx输出，输出后文件夹如下：因为我没装tensorrt，故没有end2end.engine文件，做ncnn部署。

|----{work-dir}
     |----end2end.onnx    # ONNX model
     |----end2end.bin     # ncnn model
     |----end2end.param   # ncnn model
     |----pipeline.json   #
     |----deploy.json     # json files for the SDK
     |----detail.json     #

二、模型SDK推理

1、定义模型位置和输入

DEFINE_string(det_model, "../model/rtm/rtmdet", "Decection model directory");
DEFINE_string(image, "../images/test.bmp", "Input image path");
DEFINE_string(device, "cpu", R"(Device name, e.g. "cpu", "cuda")");
DEFINE_double(det_thr, .5, "Detection score threshold");

2、进行推理，得到预测置信度并截取bbox中的手掌作为后续关键点检测的输入。

    // hand detection
    mmdeploy::Detector detector(mmdeploy::Model{FLAGS_det_model}, mmdeploy::Device{FLAGS_device});

    mmdeploy::Detector::Result dets = detector.Apply(img);

    // get the max socre detection
    mmdeploy_detection_t target_det = dets[0];
    std::cout << "detection max score: " << target_det.score << std::endl;
    if (target_det.score < FLAGS_det_thr) {
        std::cout << "failed detect hand." << std::endl;
        return 0;
    }
    std::cout << "successfully detect hand." << std::endl;

    // crop detection result
    cv::Point2f tl(target_det.bbox.left, target_det.bbox.top);
    cv::Point2f br(target_det.bbox.right, target_det.bbox.bottom);
    cv::Mat det_crop = img(cv::Rect(tl, br)).clone();
    cv::imwrite("../images/det_crop.bmp", det_crop);

 3、可视化bbox

    // visualize detection
    utils::Visualize v;
    auto det_sess = v.get_session(img);
    det_sess.add_det(target_det.bbox, target_det.label_id, target_det.score, target_det.mask, 0);
    cv::imwrite("../images/det_out.bmp", det_sess.get());

 4、做手部关键点预测，同理将crop得到的手部图片作为rtm-pose的输入即可。

    // pose detection
    mmdeploy::PoseDetector pose_detector(mmdeploy::Model{FLAGS_pose_model}, mmdeploy::Device{FLAGS_device});
    mmdeploy::PoseDetector::Result pose_dets = pose_detector.Apply(det_crop);
    mmdeploy_pose_detection_t target_pose = pose_dets[0];

        可视化：

    v.set_skeleton(utils::Skeleton::get("coco-wholebody-hand"));
    auto pose_sess = v.get_session(det_crop);
    pose_sess.add_pose(target_pose.point, target_pose.score, target_pose.length, 0.);
    cv::imwrite("../images/pose_out.bmp", pose_sess.get());