目录
1、下载权重
我这里之前在做实例分割的时候，项目已经下载到本地，环境也安装好了，只需要下载pose的权重就可以
2、python 推理
yolo task=pose mode=predict model=yolov8n-pose.pt source=0 show=true
3、转ONNX格式
yolo export model=yolov8n-pose.pt format=onnx 输出：
(yolo) jason@honor:~/PycharmProjects/pytorch_learn/yolo/ultralytics-main-yolov8$ yolo export model=yolov8n-pose.pt format=onnxUltralytics YOLOv8.0.94 🚀 Python-3.8.13 torch-2.0.0+cu117 CPUYOLOv8n-pose summary (fused): 187 layers, 3289964 parameters, 0 gradients, 9.2 GFLOPsPyTorch: starting from yolov8n-pose.pt with input shape (1, 3, 640, 640) BCHW and output shape(s) (1, 56, 8400) (6.5 MB)ONNX: starting export with onnx 1.13.1 opset 17...============= Diagnostic Run torch.onnx.export version 2.0.0+cu117 =============verbose: False, log level: Level.ERROR======================= 0 NONE 0 NOTE 0 WARNING 0 ERROR ========================ONNX: export success ✅ 0.8s, saved as yolov8n-pose.onnx (12.9 MB)Export complete (1.4s)Results saved to /home/jason/PycharmProjects/pytorch_learn/yolo/ultralytics-main-yolov8Predict: yolo predict task=pose model=yolov8n-pose.onnx imgsz=640 Validate: yolo val task=pose model=yolov8n-pose.onnx imgsz=640 data=/usr/src/app/ultralytics/datasets/coco-pose.yaml Visualize: https://netron.app用netron查看一下：
如上图所是，YOLOv8n-pose只有一个输出：
output0: float32[1,56,8400]。这里的8400,表示有8400个检测框，56为4边界框坐标信息+人这个类别预测分数，17*3关键点信息。每个关键点由x,y,v组成，v代表该点是否可见，v小于 0.5 时，表示这个关键点可能在图外，可以考虑去除掉。
COCO的annotation一共有17个关节点。
分别是：“nose”,“left_eye”, “right_eye”,“left_ear”, “right_ear”,“left_shoulder”, “right_shoulder”,“left_elbow”, “right_elbow”,“left_wrist”, “right_wrist”,“left_hip”, “right_hip”,“left_knee”, “right_knee”,“left_ankle”, “right_ankle”。示例图如下：
4、ONNX RUNTIME C++ 部署
第二篇参考文章的github项目，以此为参考，实现ONNX RUNTIME C++部署
视频输入,效果如下：
utils.h
#pragma once#include #include struct OutputPose { cv::Rect_ box; int label =0; float confidence =0.0; std::vector kps;};void DrawPred(cv::Mat& img, std::vector& results, const std::vector> &SKELLTON, const std::vector> &KPS_COLORS, const std::vector> &LIMB_COLORS);void LetterBox(const cv::Mat& image, cv::Mat& outImage, cv::Vec4d& params, const cv::Size& newShape = cv::Size(640, 640), bool autoShape = false, bool scaleFill=false, bool scaleUp=true, int stride= 32, const cv::Scalar& color = cv::Scalar(114,114,114)); utils.cpp
#pragma once#include "utils.h"using namespace cv;using namespace std;void LetterBox(const cv::Mat& image, cv::Mat& outImage, cv::Vec4d& params, const cv::Size& newShape, bool autoShape, bool scaleFill, bool scaleUp, int stride, const cv::Scalar& color){ if (false) { int maxLen = MAX(image.rows, image.cols); outImage = Mat::zeros(Size(maxLen, maxLen), CV_8UC3); image.copyTo(outImage(Rect(0, 0, image.cols, image.rows))); params[0] = 1; params[1] = 1; params[3] = 0; params[2] = 0; } // 取较小的缩放比例 cv::Size shape = image.size(); float r = std::min((float)newShape.height / (float)shape.height, (float)newShape.width / (float)shape.width); if (!scaleUp) r = std::min(r, 1.0f); printf("原图尺寸：w:%d * h:%d, 要求尺寸：w:%d * h:%d, 即将采用的缩放比：%f\n", shape.width, shape.height, newShape.width, newShape.height, r); // 依据前面的缩放比例后，原图的尺寸 float ratio[2]{r,r}; int new_un_pad[2] = { (int)std::round((float)shape.width * r), (int)std::round((float)shape.height * r)}; printf("等比例缩放后的尺寸该为：w:%d * h:%d\n", new_un_pad[0], new_un_pad[1]); // 计算距离目标尺寸的padding像素数 auto dw = (float)(newShape.width - new_un_pad[0]); auto dh = (float)(newShape.height - new_un_pad[1]); if (autoShape) { dw = (float)((int)dw % stride); dh = (float)((int)dh % stride); } else if (scaleFill) { dw = 0.0f; dh = 0.0f; new_un_pad[0] = newShape.width; new_un_pad[1] = newShape.height; ratio[0] = (float)newShape.width / (float)shape.width; ratio[1] = (float)newShape.height / (float)shape.height; } dw /= 2.0f; dh /= 2.0f; printf("填充padding: dw=%f , dh=%f\n", dw, dh); // 等比例缩放 if (shape.width != new_un_pad[0] && shape.height != new_un_pad[1]) { cv::resize(image, outImage, cv::Size(new_un_pad[0], new_un_pad[1])); } else{ outImage = image.clone(); } // 图像四周padding填充，至此原图与目标尺寸一致 int top = int(std::round(dh - 0.1f)); int bottom = int(std::round(dh + 0.1f)); int left = int(std::round(dw - 0.1f)); int right = int(std::round(dw + 0.1f)); params[0] = ratio[0]; // width的缩放比例 params[1] = ratio[1]; // height的缩放比例 params[2] = left; // 水平方向两边的padding像素数 params[3] = top; //垂直方向两边的padding像素数 cv::copyMakeBorder(outImage, outImage, top, bottom, left, right, cv::BORDER_CONSTANT, color);}void DrawPred(cv::Mat& img, std::vector& results, const std::vector> &SKELLTON, const std::vector> &KPS_COLORS, const std::vector> &LIMB_COLORS){ const int num_point =17; for (auto &result:results){ int left,top,width, height; left = result.box.x; top = result.box.y; width = result.box.width; height = result.box.height;// printf("x: %d y:%d w:%d h%d\n",(int)left, (int)top, (int)result.box.width, (int)result.box.height); // 框出目标 rectangle(img, result.box,Scalar(0,0,255), 2, 8); // 在目标框左上角标识目标类别以及概率 string label = "person:" + to_string(result.confidence) ; int baseLine; Size labelSize = getTextSize(label, FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine); top = max(top, labelSize.height); putText(img, label, Point(left, top), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0,0,255), 2); // 连线 auto &kps = result.kps;// cout << "该目标关键点：" << kps.size() << endl; for (int k=0; k0.5f ,>0.0f显示效果比较好 // 关键点绘制 if (k 0.0f){ cv::Scalar kps_color = Scalar(KPS_COLORS[k][0],KPS_COLORS[k][1],KPS_COLORS[k][2]); cv::circle(img, {kps_x, kps_y}, 5, kps_color, -1); } } auto &ske = SKELLTON[k]; int pos1_x = std::round(kps[(ske[0] -1) * 3]); int pos1_y = std::round(kps[(ske[0] -1) * 3 + 1]); int pos2_x = std::round(kps[(ske[1] -1) * 3]); int pos2_y = std::round(kps[(ske[1] -1) * 3 + 1]); float pos1_s = kps[(ske[0] -1) * 3 + 2]; float pos2_s = kps[(ske[1] -1) * 3 + 2]; if (pos1_s > 0.0f && pos2_s >0.0f){// 不要设置为>0.5f ,>0.0f显示效果比较好 cv::Scalar limb_color = cv::Scalar(LIMB_COLORS[k][0], LIMB_COLORS[k][1], LIMB_COLORS[k][3]); cv::line(img, {pos1_x, pos1_y}, {pos2_x, pos2_y}, limb_color); } // 跌倒检测 float pt5_x = kps[5*3]; float pt5_y = kps[5*3 + 1]; float pt6_x = kps[6*3]; float pt6_y = kps[6*3+1]; float center_up_x = (pt5_x + pt6_x) /2.0f ; float center_up_y = (pt5_y + pt6_y) / 2.0f; Point center_up = Point((int)center_up_x, (int)center_up_y); float pt11_x = kps[11*3]; float pt11_y = kps[11*3 + 1]; float pt12_x = kps[12*3]; float pt12_y = kps[12*3 + 1]; float center_down_x = (pt11_x + pt12_x) / 2.0f; float center_down_y = (pt11_y + pt12_y) / 2.0f; Point center_down = Point((int)center_down_x, (int)center_down_y); float right_angle_point_x = center_down_x; float righ_angle_point_y = center_up_y; Point right_angl_point = Point((int)right_angle_point_x, (int)righ_angle_point_y); float a = abs(right_angle_point_x - center_up_x); float b = abs(center_down_y - righ_angle_point_y); float tan_value = a / b; float Pi = acos(-1); float angle = atan(tan_value) * 180.0f/ Pi; string angel_label = "angle: " + to_string(angle); putText(img, angel_label, Point(left, top-40), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0,0,255), 2); if (angle > 60.0f || center_down_y <= center_up_y || (double)width/ height > 5.0f/3.0f) // 宽高比小于0.6为站立，大于5/3为跌倒 { string fall_down_label = "person fall down!!!!"; putText(img, fall_down_label , Point(left, top-20), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0,0,255), 2); printf("angel:%f width/height:%f\n",angle, (double)width/ height ); } cv::line(img, center_up, center_down, Scalar(0,0,255), 2, 8); cv::line(img, center_up, right_angl_point, Scalar(0,0,255), 2, 8); cv::line(img, right_angl_point, center_down, Scalar(0,0,255), 2, 8); } }} detect.h
#pragma onece#include #include #include #include "utils.h"#include #include class Yolov8Onnx{private: template T VectorProduct(const std::vector& v) { return std::accumulate(v.begin(), v.end(), 1, std::multiplies()); } int Preprocessing(const std::vector& SrcImgs, std::vector& OutSrcImgs, std::vector& params); const int _netWidth = 640; //ONNX-net-input-width const int _netHeight = 640; //ONNX-net-input-height int _batchSize = 1; //if multi-batch,set this bool _isDynamicShape = false;//onnx support dynamic shape int _anchorLength=56;// pose一个框的信息56个数 float _classThreshold = 0.25; float _nmsThrehold= 0.45; //ONNXRUNTIME Ort::Env _OrtEnv = Ort::Env(OrtLoggingLevel::ORT_LOGGING_LEVEL_ERROR, "Yolov5-Seg"); Ort::SessionOptions _OrtSessionOptions = Ort::SessionOptions(); Ort::Session* _OrtSession = nullptr; Ort::MemoryInfo _OrtMemoryInfo; std::shared_ptr _inputName, _output_name0; std::vector _inputNodeNames; //输入节点名 std::vector _outputNodeNames; // 输出节点名 size_t _inputNodesNum = 0; // 输入节点数 size_t _outputNodesNum = 0; // 输出节点数 ONNXTensorElementDataType _inputNodeDataType; //数据类型 ONNXTensorElementDataType _outputNodeDataType; std::vector _inputTensorShape; // 输入张量shape std::vector _outputTensorShape;public: Yolov8Onnx():_OrtMemoryInfo(Ort::MemoryInfo::CreateCpu(OrtAllocatorType::OrtDeviceAllocator, OrtMemType::OrtMemTypeCPUOutput)) {}; ~Yolov8Onnx() {};// delete _OrtMemoryInfo;public: bool ReadModel(const std::string& modelPath, bool isCuda=false, int cudaId=0, bool warmUp=true); bool OnnxDetect(cv::Mat& srcImg, std::vector& output); bool OnnxBatchDetect(std::vector& srcImgs, std::vector>& output);//public:// std::vector _className = {// "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light",// "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow",// "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee",// "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard",// "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple",// "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch",// "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone",// "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear",// "hair drier", "toothbrush"// };}; detect.cpp
#include "detect.h"using namespace std;using namespace cv;using namespace cv::dnn;using namespace Ort;bool Yolov8Onnx::ReadModel(const std::string &modelPath, bool isCuda, int cudaId, bool warmUp){ if (_batchSize < 1) _batchSize =1; try { std::vector available_providers = GetAvailableProviders(); auto cuda_available = std::find(available_providers.begin(), available_providers.end(), "CUDAExecutionProvider"); if (isCuda && (cuda_available == available_providers.end())) { std::cout << "Your ORT build without GPU. Change to CPU." << std::endl; std::cout << "************* Infer model on CPU! *************" << std::endl; } else if (isCuda && (cuda_available != available_providers.end())) { std::cout << "************* Infer model on GPU! *************" << std::endl;//#if ORT_API_VERSION < ORT_OLD_VISON//OrtCUDAProviderOptions cudaOption;//cudaOption.device_id = cudaID;// _OrtSessionOptions.AppendExecutionProvider_CUDA(cudaOption);//#else//OrtStatus* status = OrtSessionOptionsAppendExecutionProvider_CUDA(_OrtSessionOptions, cudaID);//#endif } else { std::cout << "************* Infer model on CPU! *************" << std::endl; } // _OrtSessionOptions.SetGraphOptimizationLevel(GraphOptimizationLevel::ORT_ENABLE_EXTENDED);#ifdef _WIN32 std::wstring model_path(modelPath.begin(), modelPath.end()); _OrtSession = new Ort::Session(_OrtEnv, model_path.c_str(), _OrtSessionOptions);#else _OrtSession = new Ort::Session(_OrtEnv, modelPath.c_str(), _OrtSessionOptions);#endif Ort::AllocatorWithDefaultOptions allocator; //init input _inputNodesNum = _OrtSession->GetInputCount();#if ORT_API_VERSION < ORT_OLD_VISON _inputName = _OrtSession->GetInputName(0, allocator); _inputNodeNames.push_back(_inputName);#else _inputName = std::move(_OrtSession->GetInputNameAllocated(0, allocator)); _inputNodeNames.push_back(_inputName.get());#endif //cout << _inputNodeNames[0] << endl; Ort::TypeInfo inputTypeInfo = _OrtSession->GetInputTypeInfo(0); auto input_tensor_info = inputTypeInfo.GetTensorTypeAndShapeInfo(); _inputNodeDataType = input_tensor_info.GetElementType(); _inputTensorShape = input_tensor_info.GetShape(); if (_inputTensorShape[0] == -1) { _isDynamicShape = true; _inputTensorShape[0] = _batchSize; } if (_inputTensorShape[2] == -1 || _inputTensorShape[3] == -1) { _isDynamicShape = true; _inputTensorShape[2] = _netHeight; _inputTensorShape[3] = _netWidth; } //init output _outputNodesNum = _OrtSession->GetOutputCount();#if ORT_API_VERSION < ORT_OLD_VISON _output_name0 = _OrtSession->GetOutputName(0, allocator); _outputNodeNames.push_back(_output_name0);#else _output_name0 = std::move(_OrtSession->GetOutputNameAllocated(0, allocator)); _outputNodeNames.push_back(_output_name0.get());#endif Ort::TypeInfo type_info_output0(nullptr); type_info_output0 = _OrtSession->GetOutputTypeInfo(0); //output0 auto tensor_info_output0 = type_info_output0.GetTensorTypeAndShapeInfo(); _outputNodeDataType = tensor_info_output0.GetElementType(); _outputTensorShape = tensor_info_output0.GetShape(); //_outputMaskNodeDataType = tensor_info_output1.GetElementType(); //the same as output0 //_outputMaskTensorShape = tensor_info_output1.GetShape(); //if (_outputTensorShape[0] == -1) //{ //_outputTensorShape[0] = _batchSize; //_outputMaskTensorShape[0] = _batchSize; //} //if (_outputMaskTensorShape[2] == -1) { ////size_t ouput_rows = 0; ////for (int i = 0; i < _strideSize; ++i) { ////ouput_rows += 3 * (_netWidth / _netStride[i]) * _netHeight / _netStride[i]; ////} ////_outputTensorShape[1] = ouput_rows; //_outputMaskTensorShape[2] = _segHeight; //_outputMaskTensorShape[3] = _segWidth; //} //warm up if (isCuda && warmUp) { //draw run cout << "Start warming up" << endl; size_t input_tensor_length = VectorProduct(_inputTensorShape); float* temp = new float[input_tensor_length]; std::vector input_tensors; std::vector output_tensors; input_tensors.push_back(Ort::Value::CreateTensor( _OrtMemoryInfo, temp, input_tensor_length, _inputTensorShape.data(), _inputTensorShape.size())); for (int i = 0; i < 3; ++i) { output_tensors = _OrtSession->Run(Ort::RunOptions{ nullptr }, _inputNodeNames.data(), input_tensors.data(), _inputNodeNames.size(), _outputNodeNames.data(), _outputNodeNames.size()); } delete[]temp; } } catch (const std::exception&) { return false; } return true;}int Yolov8Onnx::Preprocessing(const std::vector &SrcImgs, std::vector &OutSrcImgs, std::vector ¶ms){ OutSrcImgs.clear(); Size input_size = Size(_netWidth, _netHeight); // 信封处理 for (size_t i=0; i 0){ Mat temp_img = Mat::zeros(input_size, CV_8UC3); Vec4d temp_param = {1,1,0,0}; OutSrcImgs.push_back(temp_img); params.push_back(temp_param); } return 0;}bool Yolov8Onnx::OnnxBatchDetect(std::vector &srcImgs, std::vector> &output){ vector params; vector input_images; cv::Size input_size(_netWidth, _netHeight); //preprocessing (信封处理) Preprocessing(srcImgs, input_images, params); // [0~255] --> [0~1]; BGR2RGB Mat blob = cv::dnn::blobFromImages(input_images, 1 / 255.0, input_size, Scalar(0,0,0), true, false); // 前向传播得到推理结果 int64_t input_tensor_length = VectorProduct(_inputTensorShape);// ? std::vector input_tensors; std::vector output_tensors; input_tensors.push_back(Ort::Value::CreateTensor(_OrtMemoryInfo, (float*)blob.data, input_tensor_length, _inputTensorShape.data(), _inputTensorShape.size())); output_tensors = _OrtSession->Run(Ort::RunOptions{ nullptr }, _inputNodeNames.data(), input_tensors.data(), _inputNodeNames.size(), _outputNodeNames.data(), _outputNodeNames.size() ); //post-process float* all_data = output_tensors[0].GetTensorMutableData(); // 第一张图片的输出 _outputTensorShape = output_tensors[0].GetTensorTypeAndShapeInfo().GetShape(); // 一张图片输出的维度信息 [1, 84, 8400] int64_t one_output_length = VectorProduct(_outputTensorShape) / _outputTensorShape[0]; // 一张图片输出所占内存长度 8400*84 for (int img_index = 0; img_index < srcImgs.size(); ++img_index){ Mat output0 = Mat(Size((int)_outputTensorShape[2], (int)_outputTensorShape[1]), CV_32F, all_data).t(); // [1, 56 ,8400] -> [1, 8400, 56] all_data += one_output_length; //指针指向下一个图片的地址 float* pdata = (float*)output0.data; // [classid,x,y,w,h,x,y,...21个点] int rows = output0.rows; // 预测框的数量 8400 // 一张图片的预测框 vector confidences; vector boxes; vector labels; vector> kpss; for (int r=0; r _classThreshold){ // rect [x,y,w,h] float x = (pdata[0] - params[img_index][2]) / params[img_index][0]; //x float y = (pdata[1] - params[img_index][3]) / params[img_index][1]; //y float w = pdata[2] / params[img_index][0]; //w float h = pdata[3] / params[img_index][1]; //h int left = MAX(int(x - 0.5 *w +0.5), 0); int top = MAX(int(y - 0.5*h + 0.5), 0); std::vector kps; for (int k=0; k< 17; k++){ float kps_x = (*(kps_ptr + 3*k) - params[img_index][2]) / params[img_index][0]; float kps_y = (*(kps_ptr + 3*k + 1) - params[img_index][3]) / params[img_index][1]; float kps_s = *(kps_ptr + 3*k +2);// cout << *(kps_ptr + 3*k) << endl; kps.push_back(kps_x); kps.push_back(kps_y); kps.push_back(kps_s); } confidences.push_back(score); labels.push_back(0); kpss.push_back(kps); boxes.push_back(Rect(left, top, int(w + 0.5), int(h + 0.5))); } pdata += _anchorLength; //下一个预测框 } // 对一张图的预测框执行NMS处理 vector nms_result; cv::dnn::NMSBoxes(boxes, confidences, _classThreshold, _nmsThrehold, nms_result); // 还需要classThreshold？ // 对一张图片：依据NMS处理得到的索引，得到类别id、confidence、box，并置于结构体OutputDet的容器中 vector temp_output; for (size_t i=0; i &output){ vector input_data = {srcImg}; vector> temp_output; if(OnnxBatchDetect(input_data, temp_output)){ output = temp_output[0]; return true; } else return false;} main.cpp
#include #include #include "detect.h"#include #include using namespace std;using namespace cv;using namespace cv::dnn;const std::vector> KPS_COLORS = {{0, 255, 0}, {0, 255, 0}, {0, 255, 0}, {0, 255, 0}, {0, 255, 0}, {255, 128, 0}, {255, 128, 0}, {255, 128, 0}, {255, 128, 0}, {255, 128, 0}, {255, 128, 0}, {51, 153, 255}, {51, 153, 255}, {51, 153, 255}, {51, 153, 255}, {51, 153, 255}, {51, 153, 255}};const std::vector> SKELETON = {{16, 14}, {14, 12}, {17, 15}, {15, 13}, {12, 13}, {6, 12}, {7, 13}, {6, 7}, {6, 8}, {7, 9}, {8, 10}, {9, 11}, {2, 3}, {1, 2}, {1, 3}, {2, 4}, {3, 5}, {4, 6}, {5, 7}};const std::vector> LIMB_COLORS = {{51, 153, 255}, {51, 153, 255}, {51, 153, 255}, {51, 153, 255}, {255, 51, 255}, {255, 51, 255}, {255, 51, 255}, {255, 128, 0}, {255, 128, 0}, {255, 128, 0}, {255, 128, 0}, {255, 128, 0}, {0, 255, 0}, {0, 255, 0}, {0, 255, 0}, {0, 255, 0}, {0, 255, 0}, {0, 255, 0}, {0, 255, 0}};int main(){ // 读取模型 string detect_model_path = "/home/jason/PycharmProjects/pytorch_learn/yolo/ultralytics-main-yolov8/yolov8n-pose.onnx"; Yolov8Onnx yolov8; if (yolov8.ReadModel(detect_model_path)) cout << "read Net ok!\n"; else { return -1; } VideoCapture capture; capture.open("/home/jason/work/01-img/fall-down3.mp4"); if (capture.isOpened()) cout << "read video ok!\n"; else cout << "read video err!\n"; int width = capture.get(CAP_PROP_FRAME_WIDTH); int height = capture.get(CAP_PROP_FRAME_HEIGHT); Size size1 = Size(width, height); double delay = 1000/capture.get(CAP_PROP_FPS); int frame_pos = 0; int frame_all = capture.get(CAP_PROP_FRAME_COUNT); VideoWriter writer; writer.open("/home/jason/work/01-img/fall-down-result.mp4", VideoWriter::fourcc('m', 'p', '4', 'v'), delay,size1); Mat frame; struct timeval t1, t2; double timeuse; while (1) { // capture>>frame; if (frame_pos == frame_all-1) break; // YOLOv8检测 vector result; gettimeofday(&t1, NULL); bool find = yolov8.OnnxDetect(frame, result); gettimeofday(&t2, NULL); frame_pos+=1; printf("%d/%d:find %d person!\n",frame_pos, frame_all, (int)result.size()); if(find) { DrawPred(frame, result, SKELETON, KPS_COLORS, LIMB_COLORS); } else { cout << "not find!\n"; } timeuse = (t2.tv_sec - t1.tv_sec) + (double)(t2.tv_usec - t1.tv_usec)/1000000; timeuse *= 1000; string label = "TimeUse: " + to_string(timeuse); putText(frame, label, Point(30,30), FONT_HERSHEY_SIMPLEX, 1, Scalar(0,0,255), 2, 8); writer << frame; imshow("yolov8n-pose", frame); if(waitKey(1)=='q') break; } capture.release();// writer.release(); return 0;} CmakeList.txt
cmake_minimum_required(VERSION 3.5)project(05-YOLOv8-pose-onnruntime LANGUAGES CXX)set(CMAKE_CXX_STANDARD 11)set(CMAKE_CXX_STANDARD_REQUIRED ON)include_directories("/home/jason/下载/onnxruntime-linux-x64-1.14.1/include")#link_directories("/home/jason/下载/onnxruntime-linux-x64-1.14.1/lib")include_directories(./include)aux_source_directory(./src SOURCES)find_package(OpenCV 4 REQUIRED)add_executable(${PROJECT_NAME} ${SOURCES})target_link_libraries(${PROJECT_NAME} ${OpenCV_LIBS})target_link_libraries(${PROJECT_NAME} "/home/jason/下载/onnxruntime-linux-x64-1.14.1/lib/libonnxruntime.so")YOLOv8-Pose 的 TensorRT8 推理尝试 - 知乎
来源地址：https://blog.csdn.net/weixin_45824067/article/details/130618583
