From f7c7606aee010e0912e99cfce152d501d1a7c889 Mon Sep 17 00:00:00 2001 From: hpc203 <1749069040@qq.com> Date: Sun, 30 Jul 2023 16:37:12 +0800 Subject: [PATCH] Update main.cpp --- main.cpp | 294 +++++++++++++++++++++++++++---------------------------- 1 file changed, 147 insertions(+), 147 deletions(-) diff --git a/main.cpp b/main.cpp index 8195fad..0f43d60 100644 --- a/main.cpp +++ b/main.cpp @@ -1,56 +1,56 @@ -#define _CRT_SECURE_NO_WARNINGS -#include -#include -#include -#include -#include -//#include ///nvidia-cuda -#include - -using namespace cv; -using namespace std; -using namespace Ort; - -typedef struct BoxInfo -{ - int xmin; - int ymin; - int xmax; - int ymax; - float score; - string name; -} BoxInfo; - -class Detic -{ -public: - Detic(string modelpath); - vector detect(Mat cv_image); -private: - void preprocess(Mat srcimg); +#define _CRT_SECURE_NO_WARNINGS +#include +#include +#include +#include +#include +//#include ///nvidia-cuda加速 +#include + +using namespace cv; +using namespace std; +using namespace Ort; + +typedef struct BoxInfo +{ + int xmin; + int ymin; + int xmax; + int ymax; + float score; + string name; +} BoxInfo; + +class Detic +{ +public: + Detic(string modelpath); + vector detect(Mat cv_image); +private: + void preprocess(Mat srcimg); vector input_image_; int inpWidth; - int inpHeight; - vector class_names; - const int max_size = 800; - - //洢ʼõĿִ - Env env = Env(ORT_LOGGING_LEVEL_ERROR, "Head Pose Estimation"); + int inpHeight; + vector class_names; + const int max_size = 800; + + //存储初始化获得的可执行网络 + Env env = Env(ORT_LOGGING_LEVEL_ERROR, "Detic"); Ort::Session *ort_session = nullptr; SessionOptions sessionOptions = SessionOptions(); vector input_names; vector output_names; vector> input_node_dims; // >=1 outputs - vector> output_node_dims; // >=1 outputs -}; - -Detic::Detic(string model_path) -{ - //OrtStatus* status = OrtSessionOptionsAppendExecutionProvider_CUDA(sessionOptions, 0); ///nvidia-cuda + vector> output_node_dims; // >=1 outputs +}; + +Detic::Detic(string model_path) +{ + //OrtStatus* status = OrtSessionOptionsAppendExecutionProvider_CUDA(sessionOptions, 0); ///nvidia-cuda加速 sessionOptions.SetGraphOptimizationLevel(ORT_ENABLE_BASIC); - std::wstring widestr = std::wstring(model_path.begin(), model_path.end()); ///windowsϵͳôд - ort_session = new Session(env, widestr.c_str(), sessionOptions); ///windowsϵͳôд - ///ort_session = new Session(env, model_path.c_str(), sessionOptions); ///linuxϵͳôд + std::wstring widestr = std::wstring(model_path.begin(), model_path.end()); ///如果在windows系统就这么写 + ort_session = new Session(env, widestr.c_str(), sessionOptions); ///如果在windows系统就这么写 + ///ort_session = new Session(env, model_path.c_str(), sessionOptions); ///如果在linux系统，就这么写 size_t numInputNodes = ort_session->GetInputCount(); size_t numOutputNodes = ort_session->GetOutputCount(); @@ -70,46 +70,46 @@ Detic::Detic(string model_path) auto output_tensor_info = output_type_info.GetTensorTypeAndShapeInfo(); auto output_dims = output_tensor_info.GetShape(); output_node_dims.push_back(output_dims); - } - - ifstream ifs("imagenet_21k_class_names.txt"); - string line; - while (getline(ifs, line)) - { - this->class_names.push_back(line); ///ÿRGBֵ - } -} - -void Detic::preprocess(Mat srcimg) -{ - Mat dstimg; - cvtColor(srcimg, dstimg, COLOR_BGR2RGB); - int im_h = srcimg.rows; - int im_w = srcimg.cols; - float oh, ow, scale; - if (im_h < im_w) - { - scale = (float)max_size / (float)im_h; - oh = max_size; - ow = scale * (float)im_w; - } - else - { - scale = (float)max_size / (float)im_h; - oh = scale * (float)im_h; - ow = max_size; - } - float max_hw = std::max(oh, ow); - if (max_hw > max_size) - { - scale = (float)max_size / max_hw; - oh *= scale; - ow *= scale; - } - - resize(dstimg, dstimg, Size(int(ow + 0.5), int(oh + 0.5)), INTER_LINEAR); + } + + ifstream ifs("imagenet_21k_class_names.txt"); + string line; + while (getline(ifs, line)) + { + this->class_names.push_back(line); ///你可以用随机数给每个类别分配RGB值 + } +} + +void Detic::preprocess(Mat srcimg) +{ + Mat dstimg; + cvtColor(srcimg, dstimg, COLOR_BGR2RGB); + int im_h = srcimg.rows; + int im_w = srcimg.cols; + float oh, ow, scale; + if (im_h < im_w) + { + scale = (float)max_size / (float)im_h; + oh = max_size; + ow = scale * (float)im_w; + } + else + { + scale = (float)max_size / (float)im_h; + oh = scale * (float)im_h; + ow = max_size; + } + float max_hw = std::max(oh, ow); + if (max_hw > max_size) + { + scale = (float)max_size / max_hw; + oh *= scale; + ow *= scale; + } + + resize(dstimg, dstimg, Size(int(ow + 0.5), int(oh + 0.5)), INTER_LINEAR); this->inpHeight = dstimg.rows; - this->inpWidth = dstimg.cols; + this->inpWidth = dstimg.cols; this->input_image_.resize(this->inpWidth * this->inpHeight * dstimg.channels()); int k = 0; for (int c = 0; c < 3; c++) @@ -123,71 +123,71 @@ void Detic::preprocess(Mat srcimg) k++; } } - } -} - -vector Detic::detect(Mat srcimg) -{ - int im_h = srcimg.rows; - int im_w = srcimg.cols; - this->preprocess(srcimg); + } +} + +vector Detic::detect(Mat srcimg) +{ + int im_h = srcimg.rows; + int im_w = srcimg.cols; + this->preprocess(srcimg); array input_shape_{ 1, 3, this->inpHeight, this->inpWidth }; auto allocator_info = MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeCPU); Value input_tensor_ = Value::CreateTensor(allocator_info, input_image_.data(), input_image_.size(), input_shape_.data(), input_shape_.size()); - // ʼ - vector ort_outputs = ort_session->Run(RunOptions{ nullptr }, &input_names[0], &input_tensor_, 1, output_names.data(), output_names.size()); - - const float *pred_boxes = ort_outputs[0].GetTensorMutableData(); - const float *scores = ort_outputs[1].GetTensorMutableData(); - const int *pred_classes = ort_outputs[2].GetTensorMutableData(); - //const float *pred_masks = ort_outputs[3].GetTensorMutableData(); - - int num_box = ort_outputs[0].GetTensorTypeAndShapeInfo().GetShape()[0]; - const float scale_x = float(im_w) / float(inpWidth); - const float scale_y = float(im_h) / float(inpHeight); - vector preds; - for (int i = 0; i < num_box; i++) - { - float xmin = pred_boxes[i * 4] * scale_x; - float ymin = pred_boxes[i * 4 + 1] * scale_y; - float xmax = pred_boxes[i * 4 + 2] * scale_x; - float ymax = pred_boxes[i * 4 + 3] * scale_y; - xmin = std::min(std::max(xmin, 0.f), float(im_w)); - ymin = std::min(std::max(ymin, 0.f), float(im_h)); - xmax = std::min(std::max(xmax, 0.f), float(im_w)); - ymax = std::min(std::max(ymax, 0.f), float(im_h)); - - const float threshold = 0; - const float width = xmax - xmin; - const float height = ymax - ymin; - if (width > threshold && height > threshold) - { - preds.push_back({ int(xmin), int(ymin), int(xmax), int(ymax), scores[i], class_names[pred_classes[i]] }); - } - } - return preds; -} - -int main() -{ - Detic mynet("weights/Detic_C2_R50_640_4x_in21k.onnx"); - string imgpath = "desk.jpg"; - Mat srcimg = imread(imgpath); - vector preds = mynet.detect(srcimg); - for (size_t i = 0; i < preds.size(); ++i) - { - rectangle(srcimg, Point(preds[i].xmin, preds[i].ymin), Point(preds[i].xmax, preds[i].ymax), Scalar(0, 0, 255), 2); - string label = format("%.2f", preds[i].score); - label = preds[i].name + " :" + label; - putText(srcimg, label, Point(preds[i].xmin, preds[i].ymin - 5), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0), 1); - } - - //imwrite("result.jpg", srcimg); - static const string kWinName = "Deep learning object detection in ONNXRuntime"; - namedWindow(kWinName, WINDOW_NORMAL); - imshow(kWinName, srcimg); - waitKey(0); - destroyAllWindows(); -} \ No newline at end of file + // 开始推理 + vector ort_outputs = ort_session->Run(RunOptions{ nullptr }, &input_names[0], &input_tensor_, 1, output_names.data(), output_names.size()); + + const float *pred_boxes = ort_outputs[0].GetTensorMutableData(); + const float *scores = ort_outputs[1].GetTensorMutableData(); + const int *pred_classes = ort_outputs[2].GetTensorMutableData(); + //const float *pred_masks = ort_outputs[3].GetTensorMutableData(); + + int num_box = ort_outputs[0].GetTensorTypeAndShapeInfo().GetShape()[0]; + const float scale_x = float(im_w) / float(inpWidth); + const float scale_y = float(im_h) / float(inpHeight); + vector preds; + for (int i = 0; i < num_box; i++) + { + float xmin = pred_boxes[i * 4] * scale_x; + float ymin = pred_boxes[i * 4 + 1] * scale_y; + float xmax = pred_boxes[i * 4 + 2] * scale_x; + float ymax = pred_boxes[i * 4 + 3] * scale_y; + xmin = std::min(std::max(xmin, 0.f), float(im_w)); + ymin = std::min(std::max(ymin, 0.f), float(im_h)); + xmax = std::min(std::max(xmax, 0.f), float(im_w)); + ymax = std::min(std::max(ymax, 0.f), float(im_h)); + + const float threshold = 0; + const float width = xmax - xmin; + const float height = ymax - ymin; + if (width > threshold && height > threshold) + { + preds.push_back({ int(xmin), int(ymin), int(xmax), int(ymax), scores[i], class_names[pred_classes[i]] }); + } + } + return preds; +} + +int main() +{ + Detic mynet("weights/Detic_C2_R50_640_4x_in21k.onnx"); + string imgpath = "desk.jpg"; + Mat srcimg = imread(imgpath); + vector preds = mynet.detect(srcimg); + for (size_t i = 0; i < preds.size(); ++i) + { + rectangle(srcimg, Point(preds[i].xmin, preds[i].ymin), Point(preds[i].xmax, preds[i].ymax), Scalar(0, 0, 255), 2); + string label = format("%.2f", preds[i].score); + label = preds[i].name + " :" + label; + putText(srcimg, label, Point(preds[i].xmin, preds[i].ymin - 5), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0), 1); + } + + //imwrite("result.jpg", srcimg); + static const string kWinName = "Deep learning object detection in ONNXRuntime"; + namedWindow(kWinName, WINDOW_NORMAL); + imshow(kWinName, srcimg); + waitKey(0); + destroyAllWindows(); +}