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Update main.cpp
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hpc203
294
main.cpp
294
main.cpp
@@ -1,56 +1,56 @@
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#define _CRT_SECURE_NO_WARNINGS
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#include <iostream>
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#include <fstream>
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#include <numeric>
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#include <opencv2/imgproc.hpp>
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#include <opencv2/highgui.hpp>
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//#include <cuda_provider_factory.h> ///nvidia-cuda<EFBFBD><EFBFBD><EFBFBD><EFBFBD>
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#include <onnxruntime_cxx_api.h>
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using namespace cv;
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using namespace std;
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using namespace Ort;
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typedef struct BoxInfo
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{
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int xmin;
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int ymin;
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int xmax;
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int ymax;
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float score;
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string name;
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} BoxInfo;
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class Detic
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{
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public:
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Detic(string modelpath);
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vector<BoxInfo> detect(Mat cv_image);
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private:
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void preprocess(Mat srcimg);
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#define _CRT_SECURE_NO_WARNINGS
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#include <iostream>
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#include <fstream>
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#include <numeric>
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#include <opencv2/imgproc.hpp>
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#include <opencv2/highgui.hpp>
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//#include <cuda_provider_factory.h> ///nvidia-cuda加速
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#include <onnxruntime_cxx_api.h>
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using namespace cv;
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using namespace std;
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using namespace Ort;
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typedef struct BoxInfo
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{
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int xmin;
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int ymin;
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int xmax;
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int ymax;
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float score;
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string name;
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} BoxInfo;
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class Detic
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{
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public:
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Detic(string modelpath);
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vector<BoxInfo> detect(Mat cv_image);
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private:
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void preprocess(Mat srcimg);
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vector<float> input_image_;
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int inpWidth;
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int inpHeight;
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vector<string> class_names;
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const int max_size = 800;
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//<EFBFBD>洢<EFBFBD><EFBFBD>ʼ<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>õĿ<EFBFBD>ִ<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
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Env env = Env(ORT_LOGGING_LEVEL_ERROR, "Head Pose Estimation");
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int inpHeight;
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vector<string> class_names;
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const int max_size = 800;
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//存储初始化获得的可执行网络
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Env env = Env(ORT_LOGGING_LEVEL_ERROR, "Detic");
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Ort::Session *ort_session = nullptr;
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SessionOptions sessionOptions = SessionOptions();
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vector<char*> input_names;
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vector<char*> output_names;
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vector<vector<int64_t>> input_node_dims; // >=1 outputs
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vector<vector<int64_t>> output_node_dims; // >=1 outputs
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};
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Detic::Detic(string model_path)
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{
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//OrtStatus* status = OrtSessionOptionsAppendExecutionProvider_CUDA(sessionOptions, 0); ///nvidia-cuda<EFBFBD><EFBFBD><EFBFBD><EFBFBD>
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vector<vector<int64_t>> output_node_dims; // >=1 outputs
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};
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Detic::Detic(string model_path)
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{
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//OrtStatus* status = OrtSessionOptionsAppendExecutionProvider_CUDA(sessionOptions, 0); ///nvidia-cuda加速
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sessionOptions.SetGraphOptimizationLevel(ORT_ENABLE_BASIC);
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std::wstring widestr = std::wstring(model_path.begin(), model_path.end()); ///<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>windowsϵͳ<EFBFBD><EFBFBD><EFBFBD><EFBFBD>ôд
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ort_session = new Session(env, widestr.c_str(), sessionOptions); ///<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>windowsϵͳ<EFBFBD><EFBFBD><EFBFBD><EFBFBD>ôд
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///ort_session = new Session(env, model_path.c_str(), sessionOptions); ///<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>linuxϵͳ<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>ôд
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std::wstring widestr = std::wstring(model_path.begin(), model_path.end()); ///如果在windows系统就这么写
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ort_session = new Session(env, widestr.c_str(), sessionOptions); ///如果在windows系统就这么写
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///ort_session = new Session(env, model_path.c_str(), sessionOptions); ///如果在linux系统，就这么写
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size_t numInputNodes = ort_session->GetInputCount();
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size_t numOutputNodes = ort_session->GetOutputCount();
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@@ -70,46 +70,46 @@ Detic::Detic(string model_path)
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auto output_tensor_info = output_type_info.GetTensorTypeAndShapeInfo();
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auto output_dims = output_tensor_info.GetShape();
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output_node_dims.push_back(output_dims);
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}
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ifstream ifs("imagenet_21k_class_names.txt");
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string line;
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while (getline(ifs, line))
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{
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this->class_names.push_back(line); ///<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>ÿ<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>RGBֵ
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}
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}
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void Detic::preprocess(Mat srcimg)
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{
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Mat dstimg;
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cvtColor(srcimg, dstimg, COLOR_BGR2RGB);
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int im_h = srcimg.rows;
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int im_w = srcimg.cols;
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float oh, ow, scale;
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if (im_h < im_w)
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{
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scale = (float)max_size / (float)im_h;
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oh = max_size;
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ow = scale * (float)im_w;
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}
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else
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{
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scale = (float)max_size / (float)im_h;
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oh = scale * (float)im_h;
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ow = max_size;
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}
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float max_hw = std::max(oh, ow);
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if (max_hw > max_size)
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{
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scale = (float)max_size / max_hw;
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oh *= scale;
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ow *= scale;
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}
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resize(dstimg, dstimg, Size(int(ow + 0.5), int(oh + 0.5)), INTER_LINEAR);
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}
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ifstream ifs("imagenet_21k_class_names.txt");
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string line;
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while (getline(ifs, line))
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{
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this->class_names.push_back(line); ///你可以用随机数给每个类别分配RGB值
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}
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}
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void Detic::preprocess(Mat srcimg)
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{
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Mat dstimg;
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cvtColor(srcimg, dstimg, COLOR_BGR2RGB);
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int im_h = srcimg.rows;
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int im_w = srcimg.cols;
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float oh, ow, scale;
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if (im_h < im_w)
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{
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scale = (float)max_size / (float)im_h;
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oh = max_size;
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ow = scale * (float)im_w;
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}
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else
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{
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scale = (float)max_size / (float)im_h;
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oh = scale * (float)im_h;
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ow = max_size;
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}
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float max_hw = std::max(oh, ow);
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if (max_hw > max_size)
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{
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scale = (float)max_size / max_hw;
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oh *= scale;
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ow *= scale;
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}
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resize(dstimg, dstimg, Size(int(ow + 0.5), int(oh + 0.5)), INTER_LINEAR);
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this->inpHeight = dstimg.rows;
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this->inpWidth = dstimg.cols;
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this->inpWidth = dstimg.cols;
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this->input_image_.resize(this->inpWidth * this->inpHeight * dstimg.channels());
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int k = 0;
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for (int c = 0; c < 3; c++)
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@@ -123,71 +123,71 @@ void Detic::preprocess(Mat srcimg)
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k++;
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}
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}
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}
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}
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vector<BoxInfo> Detic::detect(Mat srcimg)
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{
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int im_h = srcimg.rows;
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int im_w = srcimg.cols;
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this->preprocess(srcimg);
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}
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}
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vector<BoxInfo> Detic::detect(Mat srcimg)
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{
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int im_h = srcimg.rows;
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int im_w = srcimg.cols;
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this->preprocess(srcimg);
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array<int64_t, 4> input_shape_{ 1, 3, this->inpHeight, this->inpWidth };
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auto allocator_info = MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeCPU);
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Value input_tensor_ = Value::CreateTensor<float>(allocator_info, input_image_.data(), input_image_.size(), input_shape_.data(), input_shape_.size());
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// <EFBFBD><EFBFBD>ʼ<EFBFBD><EFBFBD><EFBFBD><EFBFBD>
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vector<Value> ort_outputs = ort_session->Run(RunOptions{ nullptr }, &input_names[0], &input_tensor_, 1, output_names.data(), output_names.size());
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const float *pred_boxes = ort_outputs[0].GetTensorMutableData<float>();
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const float *scores = ort_outputs[1].GetTensorMutableData<float>();
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const int *pred_classes = ort_outputs[2].GetTensorMutableData<int>();
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//const float *pred_masks = ort_outputs[3].GetTensorMutableData<float>();
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int num_box = ort_outputs[0].GetTensorTypeAndShapeInfo().GetShape()[0];
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const float scale_x = float(im_w) / float(inpWidth);
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const float scale_y = float(im_h) / float(inpHeight);
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vector<BoxInfo> preds;
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for (int i = 0; i < num_box; i++)
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{
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float xmin = pred_boxes[i * 4] * scale_x;
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float ymin = pred_boxes[i * 4 + 1] * scale_y;
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float xmax = pred_boxes[i * 4 + 2] * scale_x;
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float ymax = pred_boxes[i * 4 + 3] * scale_y;
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xmin = std::min(std::max(xmin, 0.f), float(im_w));
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ymin = std::min(std::max(ymin, 0.f), float(im_h));
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xmax = std::min(std::max(xmax, 0.f), float(im_w));
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ymax = std::min(std::max(ymax, 0.f), float(im_h));
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const float threshold = 0;
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const float width = xmax - xmin;
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const float height = ymax - ymin;
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if (width > threshold && height > threshold)
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{
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preds.push_back({ int(xmin), int(ymin), int(xmax), int(ymax), scores[i], class_names[pred_classes[i]] });
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}
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}
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return preds;
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}
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int main()
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{
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Detic mynet("weights/Detic_C2_R50_640_4x_in21k.onnx");
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string imgpath = "desk.jpg";
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Mat srcimg = imread(imgpath);
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vector<BoxInfo> preds = mynet.detect(srcimg);
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for (size_t i = 0; i < preds.size(); ++i)
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{
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rectangle(srcimg, Point(preds[i].xmin, preds[i].ymin), Point(preds[i].xmax, preds[i].ymax), Scalar(0, 0, 255), 2);
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string label = format("%.2f", preds[i].score);
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label = preds[i].name + " :" + label;
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putText(srcimg, label, Point(preds[i].xmin, preds[i].ymin - 5), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0), 1);
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}
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//imwrite("result.jpg", srcimg);
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static const string kWinName = "Deep learning object detection in ONNXRuntime";
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namedWindow(kWinName, WINDOW_NORMAL);
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imshow(kWinName, srcimg);
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waitKey(0);
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destroyAllWindows();
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}
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// 开始推理
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vector<Value> ort_outputs = ort_session->Run(RunOptions{ nullptr }, &input_names[0], &input_tensor_, 1, output_names.data(), output_names.size());
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const float *pred_boxes = ort_outputs[0].GetTensorMutableData<float>();
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const float *scores = ort_outputs[1].GetTensorMutableData<float>();
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const int *pred_classes = ort_outputs[2].GetTensorMutableData<int>();
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//const float *pred_masks = ort_outputs[3].GetTensorMutableData<float>();
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int num_box = ort_outputs[0].GetTensorTypeAndShapeInfo().GetShape()[0];
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const float scale_x = float(im_w) / float(inpWidth);
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const float scale_y = float(im_h) / float(inpHeight);
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vector<BoxInfo> preds;
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for (int i = 0; i < num_box; i++)
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{
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float xmin = pred_boxes[i * 4] * scale_x;
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float ymin = pred_boxes[i * 4 + 1] * scale_y;
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float xmax = pred_boxes[i * 4 + 2] * scale_x;
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float ymax = pred_boxes[i * 4 + 3] * scale_y;
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xmin = std::min(std::max(xmin, 0.f), float(im_w));
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ymin = std::min(std::max(ymin, 0.f), float(im_h));
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xmax = std::min(std::max(xmax, 0.f), float(im_w));
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ymax = std::min(std::max(ymax, 0.f), float(im_h));
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const float threshold = 0;
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const float width = xmax - xmin;
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const float height = ymax - ymin;
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if (width > threshold && height > threshold)
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{
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preds.push_back({ int(xmin), int(ymin), int(xmax), int(ymax), scores[i], class_names[pred_classes[i]] });
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}
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}
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return preds;
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}
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int main()
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{
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Detic mynet("weights/Detic_C2_R50_640_4x_in21k.onnx");
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string imgpath = "desk.jpg";
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Mat srcimg = imread(imgpath);
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vector<BoxInfo> preds = mynet.detect(srcimg);
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for (size_t i = 0; i < preds.size(); ++i)
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{
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rectangle(srcimg, Point(preds[i].xmin, preds[i].ymin), Point(preds[i].xmax, preds[i].ymax), Scalar(0, 0, 255), 2);
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string label = format("%.2f", preds[i].score);
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label = preds[i].name + " :" + label;
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putText(srcimg, label, Point(preds[i].xmin, preds[i].ymin - 5), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0), 1);
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}
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//imwrite("result.jpg", srcimg);
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static const string kWinName = "Deep learning object detection in ONNXRuntime";
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namedWindow(kWinName, WINDOW_NORMAL);
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imshow(kWinName, srcimg);
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waitKey(0);
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destroyAllWindows();
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}
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