基于ONNXRuntime部署3D人脸重建、人头姿态估计、人脸mesh项目源码+模型(python和c++两个版本).zip

#define _CRT_SECURE_NO_WARNINGS #include <fstream> #include <sstream> #include <iostream> #include <opencv2/imgproc.hpp> #include <opencv2/highgui.hpp> //#include <cuda_provider_factory.h> #include <onnxruntime_cxx_api.h> #include"utils.h" using namespace std; using namespace cv; using namespace Ort; typedef struct BoxInfo { int x1; int y1; int x2; int y2; float score; int label; } BoxInfo; class Detect_Face { public: Detect_Face(float confThreshold); vector<BoxInfo> detect(Mat frame); void drawPred(Mat &frame, vector<BoxInfo> bboxes); private: int inpWidth; int inpHeight; int num_proposal; int nout; float confThreshold; vector<float> input_image_; void normalize_(Mat img); Env env = Env(ORT_LOGGING_LEVEL_ERROR, "Detect Face"); Ort::Session *ort_session = nullptr; SessionOptions sessionOptions = SessionOptions(); vector<char*> input_names; vector<char*> output_names; vector<vector<int64_t>> input_node_dims; // >=1 outputs vector<vector<int64_t>> output_node_dims; // >=1 outputs }; Detect_Face::Detect_Face(float confThreshold) { this->confThreshold = confThreshold; string model_path = "weights/RFB-320_240x320_post.onnx"; std::wstring widestr = std::wstring(model_path.begin(), model_path.end()); //OrtStatus* status = OrtSessionOptionsAppendExecutionProvider_CUDA(sessionOptions, 0); sessionOptions.SetGraphOptimizationLevel(ORT_ENABLE_BASIC); ort_session = new Session(env, widestr.c_str(), sessionOptions); size_t numInputNodes = ort_session->GetInputCount(); size_t numOutputNodes = ort_session->GetOutputCount(); AllocatorWithDefaultOptions allocator; for (int i = 0; i < numInputNodes; i++) { input_names.push_back(ort_session->GetInputName(i, allocator)); Ort::TypeInfo input_type_info = ort_session->GetInputTypeInfo(i); auto input_tensor_info = input_type_info.GetTensorTypeAndShapeInfo(); auto input_dims = input_tensor_info.GetShape(); input_node_dims.push_back(input_dims); } for (int i = 0; i < numOutputNodes; i++) { output_names.push_back(ort_session->GetOutputName(i, allocator)); Ort::TypeInfo output_type_info = ort_session->GetOutputTypeInfo(i); auto output_tensor_info = output_type_info.GetTensorTypeAndShapeInfo(); auto output_dims = output_tensor_info.GetShape(); output_node_dims.push_back(output_dims); } this->inpHeight = input_node_dims[0][2]; this->inpWidth = input_node_dims[0][3]; } void Detect_Face::normalize_(Mat img) { // img.convertTo(img, CV_32F); int row = img.rows; int col = img.cols; this->input_image_.resize(row * col * img.channels()); for (int c = 0; c < 3; c++) { for (int i = 0; i < row; i++) { for (int j = 0; j < col; j++) { float pix = img.ptr<uchar>(i)[j * 3 + c]; this->input_image_[c * row * col + i * col + j] = (pix - 127.5) / 127.5; } } } } void Detect_Face::drawPred(Mat &frame, vector<BoxInfo> bboxes) { for (size_t i = 0; i < bboxes.size(); ++i) { int xmin = bboxes[i].x1; int ymin = bboxes[i].y1; rectangle(frame, Point(xmin, ymin), Point(bboxes[i].x2, bboxes[i].y2), Scalar(0, 0, 255), 2); string label = format("Face:%.2f", bboxes[i].score); //label = "Face:" + label; putText(frame, label, Point(xmin, ymin - 5), FONT_HERSHEY_SIMPLEX, 0.75, Scalar(0, 255, 0), 1); } } vector<BoxInfo> Detect_Face::detect(Mat frame) { Mat dstimg; cvtColor(frame, dstimg, COLOR_BGR2RGB); resize(dstimg, dstimg, Size(this->inpWidth, this->inpHeight)); this->normalize_(dstimg); array<int64_t, 4> input_shape_{ 1, 3, this->inpHeight, this->inpWidth }; auto allocator_info = MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeCPU); Value input_tensor_ = Value::CreateTensor<float>(allocator_info, input_image_.data(), input_image_.size(), input_shape_.data(), input_shape_.size()); // 开始推理 vector<Value> ort_outputs = ort_session->Run(RunOptions{ nullptr }, &input_names[0], &input_tensor_, 1, output_names.data(), output_names.size()); // 开始推理 Ort::Value &predictions = ort_outputs.at(0); auto pred_dims = predictions.GetTensorTypeAndShapeInfo().GetShape(); num_proposal = pred_dims.at(0); nout = pred_dims.at(1); vector<BoxInfo> bboxes; int n = 0; ///batchno , classid , score , x1y1x2y2 const float* pdata = predictions.GetTensorMutableData<float>(); for (n = 0; n < this->num_proposal; n++) ///特征图尺度 { const float class_socre = pdata[2]; if (class_socre >= this->confThreshold) { int xmin = int(pdata[3] * (float)frame.cols); int ymin = int(pdata[4] * (float)frame.rows); int xmax = int(pdata[5] * (float)frame.cols); int ymax = int(pdata[6] * (float)frame.rows); bboxes.push_back(BoxInfo{ xmin, ymin, xmax, ymax, class_socre, int(pdata[1]) }); } pdata += nout; } return bboxes; } class Face_Mesh { public: Face_Mesh(string mode); void detect(Mat &frame, vector<BoxInfo> faces); ~Face_Mesh(); // 这是析构函数, 释放内存 private: int inpWidth; int inpHeight; string mode; int edge_size; const int ntri = 76073; int* triangles; vector<float> input_image_; void normalize_(vector<Mat> imgs); Env env = Env(ORT_LOGGING_LEVEL_ERROR, "Face Mesh"); Ort::Session *ort_session = nullptr; SessionOptions sessionOptions = SessionOptions(); vector<char*> input_names; vector<char*> output_names; vector<vector<int64_t>> input_node_dims; // >=1 outputs vector<vector<int64_t>> output_node_dims; // >=1 outputs }; Face_Mesh::Face_Mesh(string mode) { this->mode = mode; string model_path; if (mode == "pose" || mode == "sparse") { model_path = "weights/sparse_face_Nx3x120x120.onnx"; } else if (mode == "dense" || mode == "mesh") { model_path = "weights/dense_face_Nx3x120x120.onnx"; } else { cout << "input mode is error" << endl; exit(100); } std::wstring widestr = std::wstring(model_path.begin(), model_path.end()); //OrtStatus* status = OrtSessionOptionsAppendExecutionProvider_CUDA(sessionOptions, 0); sessionOptions.SetGraphOptimizationLevel(ORT_ENABLE_BASIC); ort_session = new Session(env, widestr.c_str(), sessionOptions); size_t numInputNodes = ort_session->GetInputCount(); size_t numOutputNodes = ort_session->GetOutputCount(); AllocatorWithDefaultOptions allocator; for (int i = 0; i < numInputNodes; i++) { input_names.push_back(ort_session->GetInputName(i, allocator)); Ort::TypeInfo input_type_info = ort_session->GetInputTypeInfo(i); auto input_tensor_info = input_type_info.GetTensorTypeAndShapeInfo(); auto input_dims = input_tensor_info.GetShape(); input_node_dims.push_back(input_dims); } for (int i = 0; i < numOutputNodes; i++) { output_names.push_back(ort_session->GetOutputName(i, allocator)); Ort::TypeInfo output_type_info = ort_session->GetOutputTypeInfo(i); auto output_tensor_info = output_type_info.GetTensorTypeAndShapeInfo(); auto output_dims = output_tensor_info.GetShape(); output_node_dims.push_back(output_dims); } this->inpHeight = input_node_dims[0][2]; this->inpWidth = input_node_dims[0][3]; this->edge_size = input_node_dims[0][3]; const int len = this->ntri * 3; this->triangles = new int[len]; FILE* fp = fopen("triangles.bin", "rb"); fread(triangles, sizeof(int), len, fp);//导入数据 fclose(fp);//关闭文件。 } Face_Mesh::~Face_Mesh() { delete[] triangles; triangles = NULL; } void Face_Mesh::normalize_(vector<Mat> imgs) { const int imgnum = imgs.size(); const int img_area = this->inpHeight * this->inpWidth; this->input_image_.resize(imgnum * img_area * 3); ////也可以用opencv里的merge函数 for (int n = 0; n < imgnum; n++) { for (int c = 0; c < 3; c++) { for (int i = 0; i < this->inpHeight; i++) { for (int j = 0; j < this->inpWidth; j++) { float pix = imgs[n].ptr<float>(i)[j * 3 + c]; this->input_im