YOLOv8 人体姿态估计(关键点检测) python推理 && ONNX RUNTIME C++部署

目录

1、下载权重

​编辑2、python 推理

3、转ONNX格式

4、ONNX RUNTIME C++ 部署

utils.h

utils.cpp

detect.h

detect.cpp

main.cpp

CmakeList.txt

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.ptsource=0show=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 YOLOv8.0.94Python-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 structOutputPose {cv::Rect_ box;int label =0;float confidence =0.0;std::vector kps;};void DrawPred(cv::Mat& img, std::vector& results,const std::vector<std::vector> &SKELLTON,const std::vector<std::vector> &KPS_COLORS,const std::vector<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<std::vector> &SKELLTON,const std::vector<std::vector> &KPS_COLORS,const std::vector<std::vector> &LIMB_COLORS){const int num_point =17;for (auto &result:results){intleft,top,width, height;left = result.box.x;top = result.box.y;width = result.box.width;height = result.box.height;//printf("x: %dy:%dw:%dh%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  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:templateT 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-widthconst int _netHeight = 640;//ONNX-net-input-heightint _batchSize = 1; //if multi-batch,set thisbool _isDynamicShape = false;//onnx support dynamic shapeint _anchorLength=56;// pose一个框的信息56个数float _classThreshold = 0.25;float _nmsThrehold= 0.45;//ONNXRUNTIMEOrt::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shapestd::vector _outputTensorShape;public:Yolov8Onnx():_OrtMemoryInfo(Ort::MemoryInfo::CreateCpu(OrtAllocatorType::OrtDeviceAllocator, OrtMemType::OrtMemTypeCPUOutput)) {};~Yolov8Onnx() {};// delete _OrtMemoryInfo;public:/** \brief Read onnx-model* \param[in] modelPath:onnx-model path* \param[in] isCuda:if true,use Ort-GPU,else run it on cpu.* \param[in] cudaID:if isCuda==true,run Ort-GPU on cudaID.* \param[in] warmUp:if isCuda==true,warm up GPU-model.*/bool ReadModel(const std::string& modelPath, bool isCuda=false, int cudaId=0, bool warmUp=true);/** \briefdetect.* \param[in] srcImg:a 3-channels image.* \param[out] output:detection results of input image.*/bool OnnxDetect(cv::Mat& srcImg, std::vector& output);/** \briefdetect,batch size= _batchSize* \param[in] srcImg:A batch of images.* \param[out] output:detection results of input images.*/bool OnnxBatchDetect(std::vector& srcImgs, std::vector<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! *************" <GetInputCount();#if ORT_API_VERSION GetInputName(0, allocator);_inputNodeNames.push_back(_inputName);#else_inputName = std::move(_OrtSession->GetInputNameAllocated(0, allocator));_inputNodeNames.push_back(_inputName.get());#endif//cout << _inputNodeNames[0] <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 GetOutputName(0, allocator);_outputNodeNames.push_back(_output_name0);#else_output_name0 = std::move(_OrtSession->GetOutputNameAllocated(0, allocator));_outputNodeNames.push_back(_output_name0.get());#endifOrt::TypeInfo type_info_output0(nullptr);type_info_output0 = _OrtSession->GetOutputTypeInfo(0);//output0auto 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 upif (isCuda && warmUp) {//draw runcout << "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 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 &params){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<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]; BGR2RGBMat 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);// " />main.cpp 
#include #include #include "detect.h"#include #include using namespace std;using namespace cv;using namespace cv::dnn;const std::vector<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<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<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";elsecout <>frame;if (frame_pos == frame_all-1) break;// YOLOv8检测vector result;gettimeofday(&t1, NULL);boolfind = 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")
 参考：
Yolov8 姿态估计 - 知乎
YOLOv8-Pose 的 TensorRT8 推理尝试 - 知乎