添加opencv调用onnx的模块

CMakeLists.txt

@@ -64,4 +64,8 @@ target_link_libraries(ui Qt6::Widgets)
 
 
 add_executable(onnx Matrox/onnx_running.cpp)
-target_link_libraries(onnx Qt6::Widgets ${MIL_LIBS})
+target_link_libraries(onnx Qt6::Widgets ${MIL_LIBS})
+
+add_executable(opencv_onnx opencv_onnx.cpp)
+# 链接 OpenCV 和 Qt 库
+target_link_libraries(opencv_onnx Qt6::Widgets ${OpenCV_LIBS} comdlg32)

opencv_onnx.cpp

@@ -0,0 +1,134 @@
+#include <opencv2/opencv.hpp>
+#include <opencv2/dnn/dnn.hpp>
+#include <iostream>
+
+// 参数
+const float CONFIDENCE_THRESHOLD = 0.2; // 置信度阈值
+const float NMS_THRESHOLD = 0.2;       // 非极大值抑制阈值
+const int INPUT_WIDTH = 640;            // 模型输入宽度
+const int INPUT_HEIGHT = 640;           // 模型输入高度
+
+// 检测结构体
+struct Detection {
+    cv::Rect box;
+    float confidence;
+};
+
+// 在图像上绘制检测框
+void drawDetections(cv::Mat& image, const std::vector<Detection>& detections) {
+    for (const auto& detection : detections) {
+        cv::rectangle(image, detection.box, cv::Scalar(0, 255, 0), 2);
+        std::string label = "Object: " + cv::format("%.2f", detection.confidence);
+        int baseLine;
+        cv::Size labelSize = cv::getTextSize(label, cv::FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);
+        cv::rectangle(image, cv::Point(detection.box.x, detection.box.y - labelSize.height - baseLine),
+                      cv::Point(detection.box.x + labelSize.width, detection.box.y), cv::Scalar(0, 255, 0), cv::FILLED);
+        cv::putText(image, label, cv::Point(detection.box.x, detection.box.y - baseLine), cv::FONT_HERSHEY_SIMPLEX, 0.5, cv::Scalar(0, 0, 0), 1);
+    }
+}
+
+int main() {
+    // 模型路径和图片路径
+    std::string modelPath = "C:\\Users\\zjc\\Desktop\\dimo_11.14.onnx";
+    std::string imagePath = "C:\\Users\\zjc\\Desktop\\yolo_resized_image_640x640.png";
+
+    // 加载模型
+    cv::dnn::Net net = cv::dnn::readNetFromONNX(modelPath);
+
+    // 读取输入图像
+    cv::Mat image = cv::imread(imagePath);
+    if (image.empty()) {
+        std::cerr << "Could not read the image: " << imagePath << std::endl;
+        return -1;
+    }
+
+    // 预处理图像
+    cv::Mat blob = cv::dnn::blobFromImage(image, 1 / 255.0, cv::Size(INPUT_WIDTH, INPUT_HEIGHT), cv::Scalar(0, 0, 0), true, false);
+    net.setInput(blob);
+
+
+    // 推理模型
+    cv::Mat output = net.forward();
+
+    // 处理输出数据
+    std::vector<Detection> detections;
+    float* data = (float*)output.data;
+    for (int i = 0; i < 25200; ++i) {
+        float confidence = data[i * 6 + 4]; // 置信度
+        if (confidence >= CONFIDENCE_THRESHOLD) {
+            // 获取检测框并映射到图像坐标
+            // Remove the unnecessary multiplication
+            float cx = data[i * 6];
+            float cy = data[i * 6 + 1];
+            float w = data[i * 6 + 2];
+            float h = data[i * 6 + 3];
+
+            // If needed, adjust for differences between input image size and model input size
+            // Since they are the same in your case, this step can be omitted or kept as is
+            cx = cx * image.cols / INPUT_WIDTH;
+            cy = cy * image.rows / INPUT_HEIGHT;
+            w = w * image.cols / INPUT_WIDTH;
+            h = h * image.rows / INPUT_HEIGHT;
+
+            // Proceed with the rest of your code
+            int left = static_cast<int>(cx - w / 2);
+            int top = static_cast<int>(cy - h / 2);
+            int width = static_cast<int>(w);
+            int height = static_cast<int>(h);
+
+            // Ensure coordinates are within image bounds
+            left = std::max(0, std::min(left, image.cols - 1));
+            top = std::max(0, std::min(top, image.rows - 1));
+            width = std::min(width, image.cols - left);
+            height = std::min(height, image.rows - top);
+
+            // Add detection
+            detections.push_back({cv::Rect(left, top, width, height), confidence});
+
+        }
+    }
+
+
+
+    // 非极大值抑制
+    std::vector<int> indices;
+    std::vector<cv::Rect> boxes;
+    std::vector<float> scores;
+    for (const auto& detection : detections) {
+        boxes.push_back(detection.box);
+
+        scores.push_back(detection.confidence);
+    }
+    cv::dnn::NMSBoxes(boxes, scores, CONFIDENCE_THRESHOLD, NMS_THRESHOLD, indices);
+    std::cout << "Number of detections after NMS: " << indices.size() << std::endl;
+    if (indices.empty()) {
+        std::cout << "No boxes passed NMS." << std::endl;
+    }
+    for (int idx : indices) {
+        Detection detection = detections[idx];
+        std::cout << "Drawing box at: (" << detection.box.x << ", " << detection.box.y
+                  << "), width: " << detection.box.width << ", height: " << detection.box.height << std::endl;
+        drawDetections(image, {detection});
+    }
+
+    std::vector<Detection> finalDetections;
+    for (int idx : indices) {
+        finalDetections.push_back(detections[idx]);
+    }
+    for (int i = 0; i < 25200; ++i) {
+        float confidence = data[i * 6 + 4];
+        if (confidence >= CONFIDENCE_THRESHOLD) {
+            std::cout << "Detection " << i << ": confidence=" << confidence << std::endl;
+        }
+    }
+
+    // 绘制检测框并显示图像
+    drawDetections(image, finalDetections);
+    cv::imshow("Detections", image);
+    cv::waitKey(0);
+
+    return 0;
+}
+//
+// Created by zjc on 24-11-19.
+//