60、在Visual Studio 2019 环境下，使用C#调用C++生成的dll实现yolov5的图片检测_sxj731533730_c# yolov5

未知 02-07 3826

基本思想：写了一个简单的c#调用c++的dll库图形化界面程序，完成yolov5检测图片的分类

一、创建一个c++工程，详细的构建和配置环境就不详细累述了，贴代码吧，导入opencv和ncnn库即可，因为需要考虑使用C#调用，所以改的代码尽量迎合C#调用的风格

main.cpp

#include "connect.h" int main(int argc, char** argv) { cv::Mat image = cv::imread("E:\\images\\test\\test1.jpg"); unsigned char* src = image.data; cv::Mat result = cv::Mat(image.rows, image.cols, CV_8UC3, src); unsigned char* dest = result.data; const char* model_param = "G:\\ncnn-20210525-windows-vs2019\\ncnn-20210525-windows-vs2019\\x64\\bin\\best_sim211223.param"; const char* model_bin = "G:\\ncnn-20210525-windows-vs2019\\ncnn-20210525-windows-vs2019\\x64\\bin\\best_sim211223.bin"; ConnectCppWrapper::init_model(model_param, model_bin); ConnectCppWrapper::detect_image(src, dest, image.rows, image.cols); cv::imshow("demo", result); cv::waitKey(0); return 0; }

connect.h

#pragma once #include "yolov5.h" namespace ConnectCppWrapper { extern "C" __declspec(dllexport) int __stdcall init_model(const char* model_param, const char* bin_param); extern "C" __declspec(dllexport) int __stdcall detect_image(unsigned char* ImageBuffer, unsigned char* ImageResult, int height, int width); }

connect.cpp

yolov5.h? 这部分代码和模型来自ncnn的example

#pragma once #include "layer.h" #include "net.h" #if defined(USE_NCNN_SIMPLEOCV) #include "simpleocv.h" #else #include <opencv2/core/core.hpp> #include <opencv2/highgui/highgui.hpp> #include <opencv2/imgproc/imgproc.hpp> #endif #include <float.h> #include <stdio.h> #include <vector> #define YOLOV5_V60 1 //YOLOv5 v6.0 using namespace std; using namespace ncnn; struct Object { cv::Rect_<float> rect; int label; float prob; }; class Yolov5 { public: Yolov5(); ~Yolov5(); private: float intersection_area(const Object& a, const Object& b); void qsort_descent_inplace(std::vector<Object>& faceobjects, int left, int right); void qsort_descent_inplace(std::vector<Object>& faceobjects); void nms_sorted_bboxes(const std::vector<Object>& faceobjects, std::vector<int>& picked, float nms_threshold); void generate_proposals(const ncnn::Mat& anchors, int stride, const ncnn::Mat& in_pad, const ncnn::Mat& feat_blob, float prob_threshold, std::vector<Object>& objects); float sigmoid(float x); public: int detect_yolov5(cv::Mat image); void draw_objects(const cv::Mat& image, const std::vector<Object>& objects); int init_model(const char* param_file, const char* bin_file); private: ncnn::Net m_yolov5; const int m_target_size = 640; const float m_prob_threshold = 0.25f; const float m_nms_threshold = 0.45f; };

yolov5.cpp

#include "yolov5.h" #if YOLOV5_V60 #define MAX_STRIDE 64 #else #define MAX_STRIDE 32 class YoloV5Focus : public ncnn::Layer { public: YoloV5Focus() { one_blob_only = true; } virtual int forward(const ncnn::Mat& bottom_blob, ncnn::Mat& top_blob, const ncnn::Option& opt) const { int w = bottom_blob.w; int h = bottom_blob.h; int channels = bottom_blob.c; int outw = w / 2; int outh = h / 2; int outc = channels * 4; top_blob.create(outw, outh, outc, 4u, 1, opt.blob_allocator); if (top_blob.empty()) return -100; #pragma omp parallel for num_threads(opt.num_threads) for (int p = 0; p < outc; p++) { const float* ptr = bottom_blob.channel(p % channels).row((p / channels) % 2) + ((p / channels) / 2); float* outptr = top_blob.channel(p); for (int i = 0; i < outh; i++) { for (int j = 0; j < outw; j++) { *outptr = *ptr; outptr += 1; ptr += 2; } ptr += w; } } return 0; } }; DEFINE_LAYER_CREATOR(YoloV5Focus) #endif //YOLOV5_V60 Yolov5::Yolov5() { } Yolov5::~Yolov5() { } float Yolov5::intersection_area(const Object& a, const Object& b) { cv::Rect_<float> inter = a.rect & b.rect; return inter.area(); } void Yolov5::qsort_descent_inplace(std::vector<Object>& faceobjects, int left, int right) { int i = left; int j = right; float p = faceobjects[(left + right) / 2].prob; while (i <= j) { while (faceobjects[i].prob > p) i++; while (faceobjects[j].prob < p) j--; if (i <= j) { // swap std::swap(faceobjects[i], faceobjects[j]); i++; j--; } } #pragma omp parallel sections { #pragma omp section { if (left < j) qsort_descent_inplace(faceobjects, left, j); } #pragma omp section { if (i < right) qsort_descent_inplace(faceobjects, i, right); } } } void Yolov5::qsort_descent_inplace(std::vector<Object>& faceobjects) { if (faceobjects.empty()) return; qsort_descent_inplace(faceobjects, 0, faceobjects.size() - 1); } void Yolov5::nms_sorted_bboxes(const std::vector<Object>& faceobjects, std::vector<int>& picked, float nms_threshold) { picked.clear(); const int n = faceobjects.size(); std::vector<float> areas(n); for (int i = 0; i < n; i++) { areas[i] = faceobjects[i].rect.area(); } for (int i = 0; i < n; i++) { const Object& a = faceobjects[i]; int keep = 1; for (int j = 0; j < (int)picked.size(); j++) { const Object& b = faceobjects[picked[j]]; // intersection over union float inter_area = intersection_area(a, b); float union_area = areas[i] + areas[picked[j]] - inter_area; // float IoU = inter_area / union_area if (inter_area / union_area > nms_threshold) keep = 0; } if (keep) picked.push_back(i); } } float Yolov5::sigmoid(float x) { return static_cast<float>(1.f / (1.f + exp(-x))); } void Yolov5::generate_proposals(const ncnn::Mat& anchors, int stride, const ncnn::Mat& in_pad, const ncnn::Mat& feat_blob, float prob_threshold, std::vector<Object>& objects) { const int num_grid = feat_blob.h; int num_grid_x; int num_grid_y; if (in_pad.w > in_pad.h) { num_grid_x = in_pad.w / stride; num_grid_y = num_grid / num_grid_x; } else { num_grid_y = in_pad.h / stride; num_grid_x = num_grid / num_grid_y; } const int num_class = feat_blob.w - 5; const int num_anchors = anchors.w / 2; for (int q = 0; q < num_anchors; q++) { const float anchor_w = anchors[q * 2]; const float anchor_h = anchors[q * 2 + 1]; const ncnn::Mat feat = feat_blob.channel(q); for (int i = 0; i < num_grid_y; i++) { for (int j = 0; j < num_grid_x; j++) { const float* featptr = feat.row(i * num_grid_x + j); // find class index with max class score int class_index = 0; float class_score = -FLT_MAX; for (int k = 0; k < num_class; k++) { float score = featptr[5 + k]; if (score > class_score) { class_index = k; class_score = score; } } float box_score = featptr[4]; float confidence = sigmoid(box_score) * sigmoid(class_score); if (confidence >= prob_threshold) { // yolov5/models/yolo.py Detect forward // y = x[i].sigmoid() // y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + self.grid[i].to(x[i].device)) * self.stride[i] # xy // y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh float dx = sigmoid(featptr[0]); float dy = sigmoid(featptr[1]); float dw = sigmoid(featptr[2]); float dh = sigmoid(featptr[3]); float pb_cx = (dx * 2.f - 0.5f + j) * stride; float pb_cy = (dy * 2.f - 0.5f + i) * stride; float pb_w = pow(dw * 2.f, 2) * anchor_w; float pb_h = pow(dh * 2.f, 2) * anchor_h; float x0 = pb_cx - pb_w * 0.5f; float y0 = pb_cy - pb_h * 0.5f; float x1 = pb_cx + pb_w * 0.5f; float y1 = pb_cy + pb_h * 0.5f; Object obj; obj.rect.x = x0; obj.rect.y = y0; obj.rect.width = x1 - x0; obj.rect.height = y1 - y0; obj.label = class_index; obj.prob = confidence; objects.push_back(obj); } } } } } int Yolov5::init_model(const char* param_file, const char* bin_file) { m_yolov5.opt.use_vulkan_compute = true; // yolov5.opt.use_bf16_storage = true; // original pretrained model from https://github.com/ultralytics/yolov5 // the ncnn model https://github.com/nihui/ncnn-assets/tree/master/models #if YOLOV5_V60 int ok0= m_yolov5.load_param(param_file); int ok1 = m_yolov5.load_model(bin_file); #else yolov5.register_custom_layer("YoloV5Focus", YoloV5Focus_layer_creator); yolov5.load_param("yolov5s.param"); yolov5.load_model("yolov5s.bin"); #endif return ok0 + ok1; } int Yolov5::detect_yolov5(cv::Mat image) { std::vector<Object> objects; int img_w = image.cols; int img_h = image.rows; // letterbox pad to multiple of MAX_STRIDE int w = img_w; int h = img_h; float scale = 1.f; if (w > h) { scale = (float)m_target_size / w; w = m_target_size; h = h * scale; } else { scale = (float)m_target_size / h; h = m_target_size; w = w * scale; } ncnn::Mat in = ncnn::Mat::from_pixels_resize(image.data, ncnn::Mat::PIXEL_BGR2RGB, img_w, img_h, w, h); // pad to target_size rectangle // yolov5/utils/datasets.py letterbox int wpad = (w + MAX_STRIDE - 1) / MAX_STRIDE * MAX_STRIDE - w; int hpad = (h + MAX_STRIDE - 1) / MAX_STRIDE * MAX_STRIDE - h; ncnn::Mat in_pad; ncnn::copy_make_border(in, in_pad, hpad / 2, hpad - hpad / 2, wpad / 2, wpad - wpad / 2, ncnn::BORDER_CONSTANT, 114.f); const float norm_vals[3] = { 1 / 255.f, 1 / 255.f, 1 / 255.f }; in_pad.substract_mean_normalize(0, norm_vals); ncnn::Extractor ex = m_yolov5.create_extractor(); ex.input("images", in_pad); std::vector<Object> proposals; // anchor setting from yolov5/models/yolov5s.yaml // stride 8 { ncnn::Mat out; ex.extract("output", out); ncnn::Mat anchors(6); anchors[0] = 10.f; anchors[1] = 13.f; anchors[2] = 16.f; anchors[3] = 30.f; anchors[4] = 33.f; anchors[5] = 23.f; std::vector<Object> objects8; generate_proposals(anchors, 8, in_pad, out, m_prob_threshold, objects8); proposals.insert(proposals.end(), objects8.begin(), objects8.end()); } // stride 16 { ncnn::Mat out; #if YOLOV5_V60 ex.extract("376", out); #else ex.extract("781", out); #endif ncnn::Mat anchors(6); anchors[0] = 30.f; anchors[1] = 61.f; anchors[2] = 62.f; anchors[3] = 45.f; anchors[4] = 59.f; anchors[5] = 119.f; std::vector<Object> objects16; generate_proposals(anchors, 16, in_pad, out, m_prob_threshold, objects16); proposals.insert(proposals.end(), objects16.begin(), objects16.end()); } // stride 32 { ncnn::Mat out; #if YOLOV5_V60 ex.extract("401", out); #else ex.extract("801", out); #endif ncnn::Mat anchors(6); anchors[0] = 116.f; anchors[1] = 90.f; anchors[2] = 156.f; anchors[3] = 198.f; anchors[4] = 373.f; anchors[5] = 326.f; std::vector<Object> objects32; generate_proposals(anchors, 32, in_pad, out, m_prob_threshold, objects32); proposals.insert(proposals.end(), objects32.begin(), objects32.end()); } // sort all proposals by score from highest to lowest qsort_descent_inplace(proposals); // apply nms with nms_threshold std::vector<int> picked; nms_sorted_bboxes(proposals, picked, m_nms_threshold); int count = picked.size(); objects.resize(count); for (int i = 0; i < count; i++) { objects[i] = proposals[picked[i]]; // adjust offset to original unpadded float x0 = (objects[i].rect.x - (wpad / 2)) / scale; float y0 = (objects[i].rect.y - (hpad / 2)) / scale; float x1 = (objects[i].rect.x + objects[i].rect.width - (wpad / 2)) / scale; float y1 = (objects[i].rect.y + objects[i].rect.height - (hpad / 2)) / scale; // clip x0 = std::max(std::min(x0, (float)(img_w - 1)), 0.f); y0 = std::max(std::min(y0, (float)(img_h - 1)), 0.f); x1 = std::max(std::min(x1, (float)(img_w - 1)), 0.f); y1 = std::max(std::min(y1, (float)(img_h - 1)), 0.f); objects[i].rect.x = x0; objects[i].rect.y = y0; objects[i].rect.width = x1 - x0; objects[i].rect.height = y1 - y0; } draw_objects(image, objects); return 0; } void Yolov5::draw_objects(const cv::Mat& image, const std::vector<Object>& objects) { static const char* class_names[] = { "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" }; for (size_t i = 0; i < objects.size(); i++) { const Object& obj = objects[i]; fprintf(stderr, "%d = %.5f at %.2f %.2f %.2f x %.2f\n", obj.label, obj.prob, obj.rect.x, obj.rect.y, obj.rect.width, obj.rect.height); cv::rectangle(image, obj.rect, cv::Scalar(255, 0, 0)); char text[256]; sprintf(text, "%s %.1f%%", class_names[obj.label], obj.prob * 100); int baseLine = 0; cv::Size label_size = cv::getTextSize(text, cv::FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine); int x = obj.rect.x; int y = obj.rect.y - label_size.height - baseLine; if (y < 0) y = 0; if (x + label_size.width > image.cols) x = image.cols - label_size.width; cv::rectangle(image, cv::Rect(cv::Point(x, y), cv::Size(label_size.width, label_size.height + baseLine)), cv::Scalar(255, 255, 255), -1); cv::putText(image, text, cv::Point(x, y + label_size.height), cv::FONT_HERSHEY_SIMPLEX, 0.5, cv::Scalar(0, 0, 0)); } // cv::imshow("demo", image); //cv::waitKey(0); //return image; }

先使用vs测试一下

然后再生dll库

??58、Visual studio 2019+C#传递Mat数据给C++动态包处理,并将处理结果Mat返回给C#显示、保存_sxj731533730-CSDN博客

二、然后在创建.NET工程，拖拽三个按钮和两个pictureBox画布

Program.cs

using System; using System.Collections.Generic; using System.Linq; using System.Threading.Tasks; using System.Windows.Forms; using System.IO; using System.Runtime.InteropServices; using OpenCvSharp; using System.Drawing; using OpenCvSharp.Extensions; using System.Text; namespace WindowsFormsApp1 { static class Program { [DllImport(@"F:\sxj\20211108\detectYolov5Ncnn\x64\Release\detectYolov5Ncnn.dll", CharSet = CharSet.Ansi, CallingConvention = CallingConvention.StdCall)] public static extern int init_model(StringBuilder model_param, StringBuilder model_bin); /// <summary> /// 应用程序的主入口点。 /// </summary> [STAThread] static void Main() { StringBuilder model_param = new StringBuilder("F:\\sxj\\20211201\\yolov5s_6.0.param"); StringBuilder model_bin = new StringBuilder("F:\\sxj\\20211201\\yolov5s_6.0.bin"); init_model(model_param, model_bin); Application.EnableVisualStyles(); Application.SetCompatibleTextRenderingDefault(false); Application.Run(new Form1()); } } }

Form1.cs

测试效果图

（1）初始化界面

（2）选择一张图

（3）检测出结果

三、如果想把检测的目标坐标和类别置信度返回给c# 可以这样写（目前只写了一个支持单类检测的，多类别信息需要变成数组即可）

main.cpp函数

#include "connect.h" int main(int argc, char** argv) { ConnectCppWrapper::DefectResult* data = new ConnectCppWrapper::DefectResult; printf("data.prob= %f\n", data->prob); printf("data.label= %d\n", data->label); printf("data.x= %d\n", data->x); printf("data.y= %d\n", data->y); printf("data.width= %d\n", data->width); printf("data.height= %d\n", data->height); cv::Mat image = cv::imread("F:\\sxj\\predictions.jpg"); unsigned char* src = image.data; cv::Mat result = cv::Mat( image.rows,image.cols, CV_8UC3, src); unsigned char* dest = result.data; const char* model_param = "F:\\sxj\\20211201\\yolov5s_6.0.param"; const char* model_bin = "F:\\sxj\\20211201\\yolov5s_6.0.bin"; ConnectCppWrapper::init_model(model_param, model_bin); ConnectCppWrapper::detect_image(src, dest, image.rows,image.cols, data); printf("data.prob= %f\n", data->prob); printf("data.label= %d\n", data->label); printf("data.x= %d\n", data->x); printf("data.y= %d\n", data->y); printf("data.width= %d\n", data->width); printf("data.height= %d\n", data->height); cv::imshow("demo", result); cv::waitKey(0); return 0; }

connect.h头文件

#pragma once #include "yolov5.h" namespace ConnectCppWrapper { struct DefectResult { int label = 0; float prob = 0; int x = 0; int y = 0; int width = 0; int height = 0; }; extern "C" __declspec(dllexport) int __stdcall init_model(const char* model_param, const char* bin_param); extern "C" __declspec(dllexport) int __stdcall detect_image(unsigned char* ImageBuffer, unsigned char* ImageResult,int height, int width, DefectResult * data); }

connect.cpp文件

#include "connect.h" namespace ConnectCppWrapper { Yolov5* yolov5Item = new Yolov5(); int __stdcall init_model(const char* model_param, const char* bin_param) { return yolov5Item->init_model(model_param, bin_param); } int __stdcall detect_image(unsigned char* ImageBuffer, unsigned char* ImageResult, int height,int width, DefectResult* data) { cv::Mat result; cv::Mat image = cv::Mat(height,width , CV_8UC3, ImageBuffer); std::vector<Object> objects; int ok = yolov5Item->detect_yolov5(image, objects); if (ok == 0) { printf("detect image is successful\n"); } for (int i = 0; i < objects.size(); i++) { data->label = objects[i].label; data->prob = objects[i].prob; data->x = objects[i].rect.x; data->y = objects[i].rect.x; data->width = objects[i].rect.width; data->height = objects[i].rect.height; printf("%f %f %f %f %d %f\n", objects[i].rect.x, objects[i].rect.y, objects[i].rect.width, objects[i].rect.height, objects[i].label, objects[i].prob); } int length = (int)(result.total() * result.elemSize()); unsigned char* buffer = new unsigned char[length]; memcpy(ImageResult, result.data, length * sizeof(unsigned char)); return 0; } }

yolov5.h头文件

#pragma once #include "layer.h" #include "net.h" #if defined(USE_NCNN_SIMPLEOCV) #include "simpleocv.h" #else #include <opencv2/core/core.hpp> #include <opencv2/highgui/highgui.hpp> #include <opencv2/imgproc/imgproc.hpp> #endif #include <float.h> #include <stdio.h> #include <vector> #define YOLOV5_V60 1 //YOLOv5 v6.0 using namespace std; using namespace ncnn; struct Object { cv::Rect_<float> rect; int label; float prob; }; class Yolov5 { public: Yolov5(); ~Yolov5(); private: float intersection_area(const Object& a, const Object& b); void qsort_descent_inplace(std::vector<Object>& faceobjects, int left, int right); void qsort_descent_inplace(std::vector<Object>& faceobjects); void nms_sorted_bboxes(const std::vector<Object>& faceobjects, std::vector<int>& picked, float nms_threshold); void generate_proposals(const ncnn::Mat& anchors, int stride, const ncnn::Mat& in_pad, const ncnn::Mat& feat_blob, float prob_threshold, std::vector<Object>& objects); float sigmoid(float x); public: int detect_yolov5(cv::Mat image, std::vector<Object>& objects); void draw_objects(const cv::Mat& image, const std::vector<Object>& objects); int init_model(const char* param_file, const char* bin_file); private: ncnn::Net m_yolov5; const int m_target_size = 640; const float m_prob_threshold = 0.25f; const float m_nms_threshold = 0.45f; };

yolov5.cpp文件

#include "yolov5.h" #if YOLOV5_V60 #define MAX_STRIDE 64 #else #define MAX_STRIDE 32 class YoloV5Focus : public ncnn::Layer { public: YoloV5Focus() { one_blob_only = true; } virtual int forward(const ncnn::Mat& bottom_blob, ncnn::Mat& top_blob, const ncnn::Option& opt) const { int w = bottom_blob.w; int h = bottom_blob.h; int channels = bottom_blob.c; int outw = w / 2; int outh = h / 2; int outc = channels * 4; top_blob.create(outw, outh, outc, 4u, 1, opt.blob_allocator); if (top_blob.empty()) return -100; #pragma omp parallel for num_threads(opt.num_threads) for (int p = 0; p < outc; p++) { const float* ptr = bottom_blob.channel(p % channels).row((p / channels) % 2) + ((p / channels) / 2); float* outptr = top_blob.channel(p); for (int i = 0; i < outh; i++) { for (int j = 0; j < outw; j++) { *outptr = *ptr; outptr += 1; ptr += 2; } ptr += w; } } return 0; } }; DEFINE_LAYER_CREATOR(YoloV5Focus) #endif //YOLOV5_V60 Yolov5::Yolov5() { } Yolov5::~Yolov5() { } float Yolov5::intersection_area(const Object& a, const Object& b) { cv::Rect_<float> inter = a.rect & b.rect; return inter.area(); } void Yolov5::qsort_descent_inplace(std::vector<Object>& faceobjects, int left, int right) { int i = left; int j = right; float p = faceobjects[(left + right) / 2].prob; while (i <= j) { while (faceobjects[i].prob > p) i++; while (faceobjects[j].prob < p) j--; if (i <= j) { // swap std::swap(faceobjects[i], faceobjects[j]); i++; j--; } } #pragma omp parallel sections { #pragma omp section { if (left < j) qsort_descent_inplace(faceobjects, left, j); } #pragma omp section { if (i < right) qsort_descent_inplace(faceobjects, i, right); } } } void Yolov5::qsort_descent_inplace(std::vector<Object>& faceobjects) { if (faceobjects.empty()) return; qsort_descent_inplace(faceobjects, 0, faceobjects.size() - 1); } void Yolov5::nms_sorted_bboxes(const std::vector<Object>& faceobjects, std::vector<int>& picked, float nms_threshold) { picked.clear(); const int n = faceobjects.size(); std::vector<float> areas(n); for (int i = 0; i < n; i++) { areas[i] = faceobjects[i].rect.area(); } for (int i = 0; i < n; i++) { const Object& a = faceobjects[i]; int keep = 1; for (int j = 0; j < (int)picked.size(); j++) { const Object& b = faceobjects[picked[j]]; // intersection over union float inter_area = intersection_area(a, b); float union_area = areas[i] + areas[picked[j]] - inter_area; // float IoU = inter_area / union_area if (inter_area / union_area > nms_threshold) keep = 0; } if (keep) picked.push_back(i); } } float Yolov5::sigmoid(float x) { return static_cast<float>(1.f / (1.f + exp(-x))); } void Yolov5::generate_proposals(const ncnn::Mat& anchors, int stride, const ncnn::Mat& in_pad, const ncnn::Mat& feat_blob, float prob_threshold, std::vector<Object>& objects) { const int num_grid = feat_blob.h; int num_grid_x; int num_grid_y; if (in_pad.w > in_pad.h) { num_grid_x = in_pad.w / stride; num_grid_y = num_grid / num_grid_x; } else { num_grid_y = in_pad.h / stride; num_grid_x = num_grid / num_grid_y; } const int num_class = feat_blob.w - 5; const int num_anchors = anchors.w / 2; for (int q = 0; q < num_anchors; q++) { const float anchor_w = anchors[q * 2]; const float anchor_h = anchors[q * 2 + 1]; const ncnn::Mat feat = feat_blob.channel(q); for (int i = 0; i < num_grid_y; i++) { for (int j = 0; j < num_grid_x; j++) { const float* featptr = feat.row(i * num_grid_x + j); // find class index with max class score int class_index = 0; float class_score = -FLT_MAX; for (int k = 0; k < num_class; k++) { float score = featptr[5 + k]; if (score > class_score) { class_index = k; class_score = score; } } float box_score = featptr[4]; float confidence = sigmoid(box_score) * sigmoid(class_score); if (confidence >= prob_threshold) { // yolov5/models/yolo.py Detect forward // y = x[i].sigmoid() // y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + self.grid[i].to(x[i].device)) * self.stride[i] # xy // y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh float dx = sigmoid(featptr[0]); float dy = sigmoid(featptr[1]); float dw = sigmoid(featptr[2]); float dh = sigmoid(featptr[3]); float pb_cx = (dx * 2.f - 0.5f + j) * stride; float pb_cy = (dy * 2.f - 0.5f + i) * stride; float pb_w = pow(dw * 2.f, 2) * anchor_w; float pb_h = pow(dh * 2.f, 2) * anchor_h; float x0 = pb_cx - pb_w * 0.5f; float y0 = pb_cy - pb_h * 0.5f; float x1 = pb_cx + pb_w * 0.5f; float y1 = pb_cy + pb_h * 0.5f; Object obj; obj.rect.x = x0; obj.rect.y = y0; obj.rect.width = x1 - x0; obj.rect.height = y1 - y0; obj.label = class_index; obj.prob = confidence; objects.push_back(obj); } } } } } int Yolov5::init_model(const char* param_file, const char* bin_file) { m_yolov5.opt.use_vulkan_compute = true; // yolov5.opt.use_bf16_storage = true; // original pretrained model from https://github.com/ultralytics/yolov5 // the ncnn model https://github.com/nihui/ncnn-assets/tree/master/models #if YOLOV5_V60 int ok0 = m_yolov5.load_param(param_file); int ok1 = m_yolov5.load_model(bin_file); #else yolov5.register_custom_layer("YoloV5Focus", YoloV5Focus_layer_creator); yolov5.load_param("yolov5s.param"); yolov5.load_model("yolov5s.bin"); #endif return ok0 + ok1; } int Yolov5::detect_yolov5(cv::Mat image, std::vector<Object>& objects) { int img_w = image.cols; int img_h = image.rows; // letterbox pad to multiple of MAX_STRIDE int w = img_w; int h = img_h; float scale = 1.f; if (w > h) { scale = (float)m_target_size / w; w = m_target_size; h = h * scale; } else { scale = (float)m_target_size / h; h = m_target_size; w = w * scale; } ncnn::Mat in = ncnn::Mat::from_pixels_resize(image.data, ncnn::Mat::PIXEL_BGR2RGB, img_w, img_h, w, h); // pad to target_size rectangle // yolov5/utils/datasets.py letterbox int wpad = (w + MAX_STRIDE - 1) / MAX_STRIDE * MAX_STRIDE - w; int hpad = (h + MAX_STRIDE - 1) / MAX_STRIDE * MAX_STRIDE - h; ncnn::Mat in_pad; ncnn::copy_make_border(in, in_pad, hpad / 2, hpad - hpad / 2, wpad / 2, wpad - wpad / 2, ncnn::BORDER_CONSTANT, 114.f); const float norm_vals[3] = { 1 / 255.f, 1 / 255.f, 1 / 255.f }; in_pad.substract_mean_normalize(0, norm_vals); ncnn::Extractor ex = m_yolov5.create_extractor(); ex.input("images", in_pad); std::vector<Object> proposals; // anchor setting from yolov5/models/yolov5s.yaml // stride 8 { ncnn::Mat out; ex.extract("output", out); ncnn::Mat anchors(6); anchors[0] = 10.f; anchors[1] = 13.f; anchors[2] = 16.f; anchors[3] = 30.f; anchors[4] = 33.f; anchors[5] = 23.f; std::vector<Object> objects8; generate_proposals(anchors, 8, in_pad, out, m_prob_threshold, objects8); proposals.insert(proposals.end(), objects8.begin(), objects8.end()); } // stride 16 { ncnn::Mat out; #if YOLOV5_V60 ex.extract("376", out); #else ex.extract("781", out); #endif ncnn::Mat anchors(6); anchors[0] = 30.f; anchors[1] = 61.f; anchors[2] = 62.f; anchors[3] = 45.f; anchors[4] = 59.f; anchors[5] = 119.f; std::vector<Object> objects16; generate_proposals(anchors, 16, in_pad, out, m_prob_threshold, objects16); proposals.insert(proposals.end(), objects16.begin(), objects16.end()); } // stride 32 { ncnn::Mat out; #if YOLOV5_V60 ex.extract("401", out); #else ex.extract("801", out); #endif ncnn::Mat anchors(6); anchors[0] = 116.f; anchors[1] = 90.f; anchors[2] = 156.f; anchors[3] = 198.f; anchors[4] = 373.f; anchors[5] = 326.f; std::vector<Object> objects32; generate_proposals(anchors, 32, in_pad, out, m_prob_threshold, objects32); proposals.insert(proposals.end(), objects32.begin(), objects32.end()); } // sort all proposals by score from highest to lowest qsort_descent_inplace(proposals); // apply nms with nms_threshold std::vector<int> picked; nms_sorted_bboxes(proposals, picked, m_nms_threshold); int count = picked.size(); objects.resize(count); for (int i = 0; i < count; i++) { objects[i] = proposals[picked[i]]; // adjust offset to original unpadded float x0 = (objects[i].rect.x - (wpad / 2)) / scale; float y0 = (objects[i].rect.y - (hpad / 2)) / scale; float x1 = (objects[i].rect.x + objects[i].rect.width - (wpad / 2)) / scale; float y1 = (objects[i].rect.y + objects[i].rect.height - (hpad / 2)) / scale; // clip x0 = std::max(std::min(x0, (float)(img_w - 1)), 0.f); y0 = std::max(std::min(y0, (float)(img_h - 1)), 0.f); x1 = std::max(std::min(x1, (float)(img_w - 1)), 0.f); y1 = std::max(std::min(y1, (float)(img_h - 1)), 0.f); objects[i].rect.x = x0; objects[i].rect.y = y0; objects[i].rect.width = x1 - x0; objects[i].rect.height = y1 - y0; } draw_objects(image, objects); return 0; } void Yolov5::draw_objects(const cv::Mat& image, const std::vector<Object>& objects) { static const char* class_names[] = { "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" }; for (size_t i = 0; i < objects.size(); i++) { const Object& obj = objects[i]; fprintf(stderr, "%d = %.5f at %.2f %.2f %.2f x %.2f\n", obj.label, obj.prob, obj.rect.x, obj.rect.y, obj.rect.width, obj.rect.height); cv::rectangle(image, obj.rect, cv::Scalar(255, 0, 0)); char text[256]; sprintf(text, "%s %.1f%%", class_names[obj.label], obj.prob * 100); int baseLine = 0; cv::Size label_size = cv::getTextSize(text, cv::FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine); int x = obj.rect.x; int y = obj.rect.y - label_size.height - baseLine; if (y < 0) y = 0; if (x + label_size.width > image.cols) x = image.cols - label_size.width; cv::rectangle(image, cv::Rect(cv::Point(x, y), cv::Size(label_size.width, label_size.height + baseLine)), cv::Scalar(255, 255, 255), -1); cv::putText(image, text, cv::Point(x, y + label_size.height), cv::FONT_HERSHEY_SIMPLEX, 0.5, cv::Scalar(0, 0, 0)); } // cv::imshow("demo", image); //cv::waitKey(0); //return image; }

同时将构建的工程设置一下

?先测试一下生成exe是没有问题的，然后在生成dll动态库，去修改一下c#的代码，进行联调即可

c#的工程也需要设置一下

c# Program.cs文件内容不变

Form1.cs文件修改一下，写了个只支持单检测类，好像很复杂 c#和c++ 进行托管内存和非托管内存传递，看的msdn有点复杂

using System; using System.Collections.Generic; using System.ComponentModel; using System.Data; using System.Drawing; using System.IO; using System.Linq; using System.Text; using System.Threading.Tasks; using System.Windows.Forms; using OpenCvSharp; using System.Runtime.InteropServices; using OpenCvSharp.Extensions; using System.IO.Compression; using System.Drawing.Imaging; namespace WindowsFormsApp1 { public partial class Form1 : Form { [DllImport(@"F:\sxj\20211108\detectYolov5Ncnn\x64\Release\detectYolov5Ncnn.dll", CallingConvention = CallingConvention.StdCall)] public static extern int detect_image(byte[] ImageBuffer, byte[] ImageResult, int imageHeight, int imageWidth , IntPtr ptrItem); [StructLayoutAttribute(LayoutKind.Sequential, CharSet = CharSet.Ansi, Pack = 1)] public struct DefectResult { public int label; public float prob; public int x; public int y; public int width; public int height; } public Form1() { InitializeComponent(); } private void button1_Click(object sender, EventArgs e) { OpenFileDialog openFileDialog = new OpenFileDialog(); openFileDialog.Filter = @"jpeg|*.jpg|bmp|*.bmp|gif|*.gif"; if (openFileDialog.ShowDialog() == DialogResult.OK) { string fullpath = openFileDialog.FileName; FileStream fs = new FileStream(fullpath, FileMode.Open); byte[] picturebytes = new byte[fs.Length]; BinaryReader br = new BinaryReader(fs); picturebytes = br.ReadBytes(Convert.ToInt32(fs.Length)); MemoryStream ms = new MemoryStream(picturebytes); Bitmap bmpt = new Bitmap(ms); pictureBox1.Image = bmpt; pictureBox1.SizeMode = PictureBoxSizeMode.StretchImage; } else { MessageBox.Show("图片打开失败"); } } private void button2_Click(object sender, EventArgs e) { SaveFileDialog saveImageDialog = new SaveFileDialog(); saveImageDialog.Title = "图片保存"; saveImageDialog.Filter = @"jpeg|*.jpg|bmp|*.bmp"; saveImageDialog.FileName = System.DateTime.Now.ToString("yyyyMMddHHmmss");//设置默认文件名 if (saveImageDialog.ShowDialog() == DialogResult.OK) { string fileName = saveImageDialog.FileName.ToString(); //Console.WriteLine("fileName" + fileName); if (fileName != "" && fileName != null) { string fileExtName = fileName.Substring(fileName.LastIndexOf(".") + 1).ToString(); //Console.WriteLine("fileExtName" + fileExtName); System.Drawing.Imaging.ImageFormat imgformat = null; if (fileExtName != "") { switch (fileExtName) { case "jpg": imgformat = System.Drawing.Imaging.ImageFormat.Jpeg; break; case "bmp": imgformat = System.Drawing.Imaging.ImageFormat.Bmp; break; default: imgformat = System.Drawing.Imaging.ImageFormat.Jpeg; break; } try { Bitmap bit = new Bitmap(pictureBox2.Image); MessageBox.Show(fileName); pictureBox2.Image.Save(fileName, imgformat); } catch { } } } } } private void button3_Click(object sender, EventArgs e) { int workStationCount = 1; int size = Marshal.SizeOf(typeof(DefectResult)) * workStationCount; byte[] bytes = new byte[size]; IntPtr infosIntptr = Marshal.AllocHGlobal(size); DefectResult[] pClass = new DefectResult[workStationCount]; Bitmap bmp = (Bitmap)pictureBox1.Image.Clone(); byte[] source = GetBGRValues(bmp); byte[] dest = source; detect_image(source, dest, bmp.Height, bmp.Width, infosIntptr); string[] class_names = new string[]{ "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" }; for (int inkIndex = 0; inkIndex < workStationCount; inkIndex++) { IntPtr pPonitor = new IntPtr(infosIntptr.ToInt64() + Marshal.SizeOf(typeof(DefectResult)) * inkIndex); pClass[inkIndex] = (DefectResult)Marshal.PtrToStructure(pPonitor, typeof(DefectResult)); Console.WriteLine("{0} ", pClass[inkIndex].prob); Console.WriteLine("{0} ", class_names[pClass[inkIndex].label]); Console.WriteLine("{0} ", pClass[inkIndex].x); Console.WriteLine("{0} ", pClass[inkIndex].y); Console.WriteLine("{0} ", pClass[inkIndex].width); Console.WriteLine("{0} ", pClass[inkIndex].height); } Marshal.FreeHGlobal(infosIntptr); Bitmap bmpConvert = Byte2Bitmap(dest, bmp.Width, bmp.Height); Image images = bmpConvert; pictureBox2.Image = images; pictureBox2.SizeMode = PictureBoxSizeMode.StretchImage; } public static byte[] GetBGRValues(Bitmap bmp) { var rect = new Rectangle(0, 0, bmp.Width, bmp.Height); var bmpData = bmp.LockBits(rect, System.Drawing.Imaging.ImageLockMode.ReadOnly, bmp.PixelFormat); var rowBytes = bmpData.Width * Image.GetPixelFormatSize(bmp.PixelFormat) / 8; var imgBytes = bmp.Height * rowBytes; byte[] rgbValues = new byte[imgBytes]; IntPtr ptr = bmpData.Scan0; for (var i = 0; i < bmp.Height; i++) { Marshal.Copy(ptr, rgbValues, i * rowBytes, rowBytes); ptr += bmpData.Stride; } bmp.UnlockBits(bmpData); return rgbValues; } public static Bitmap Byte2Bitmap(Byte[] data, int width, int height) { Bitmap image = new Bitmap(width, height, System.Drawing.Imaging.PixelFormat.Format24bppRgb); Rectangle rect = new Rectangle(0, 0, image.Width, image.Height); BitmapData bmData = image.LockBits(rect, ImageLockMode.ReadWrite, image.PixelFormat); IntPtr ptr = bmData.Scan0; for (int i = 0; i < image.Height; i++) { Marshal.Copy(data, i * image.Width * 3, ptr, image.Width * 3); ptr = (IntPtr)(ptr.ToInt64() + bmData.Stride); } image.UnlockBits(bmData); return image; } private void pictureBox1_Click(object sender, EventArgs e) { } } }

然后使用debug模式或者直接release模式输出一下就可看到在c++ 中完成了绘图功能，也可以把类别信息返回了

附录一下debug调试结果

还是那位可爱的小姐姐

注意 c#中的图片高宽的参数与 c++的高宽对应一致~~~