diff --git a/opencv_onnx.cpp b/opencv_onnx.cpp
index c53ab36..2dd258a 100644
--- a/opencv_onnx.cpp
+++ b/opencv_onnx.cpp
@@ -124,7 +124,7 @@ int main() {
 
     timer1.printElapsedTime("Time to preprocess image");
 
-    for (int j = 0; j < 1; j++) {
+    for (int j = 0; j < 10; j++) {
         // Run inference
         cv::Mat output = net.forward();
 
@@ -187,8 +187,8 @@ int main() {
         cv::imshow("Detection Mask", detectionMask);
         
         // Save the result as an image
-        std::string savepath = "C:\\Users\\zjc\\Desktop\\suspect_mask.png";
-        cv::imwrite(savepath, detectionMask);
+        // std::string savepath = "C:\\Users\\zjc\\Desktop\\suspect_mask.png";
+        // cv::imwrite(savepath, detectionMask);
 
         timer1.printElapsedTime("Time to run inference");
     }
diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
index c332fe0..68c580a 100644
--- a/src/CMakeLists.txt
+++ b/src/CMakeLists.txt
@@ -11,7 +11,6 @@ target_include_directories(Matrox PUBLIC ${CMAKE_CURRENT_SOURCE_DIR})
 target_link_libraries(Matrox PUBLIC Qt6::Widgets ${OpenCV_LIBS} ${MIL_LIBS})
 
 
-
 #定义CVDL模块的库
 add_library(CVDL
        CVDL/OnnxRunner.cpp
@@ -19,4 +18,4 @@ add_library(CVDL
 # 头文件路径
 target_include_directories(CVDL PUBLIC ${CMAKE_CURRENT_SOURCE_DIR})
 # 链接依赖库
-target_link_libraries(CVDL PUBLIC Qt6::Widgets ${OpenCV_LIBS} ${MIL_LIBS})
\ No newline at end of file
+target_link_libraries(CVDL PUBLIC Qt6::Widgets Matrox ${OpenCV_LIBS} ${MIL_LIBS})
\ No newline at end of file
diff --git a/src/CVDL/OnnxRunner.cpp b/src/CVDL/OnnxRunner.cpp
index 01475c2..20a04f6 100644
--- a/src/CVDL/OnnxRunner.cpp
+++ b/src/CVDL/OnnxRunner.cpp
@@ -15,7 +15,7 @@ void Timer::printElapsedTime(const std::string& message) {
 }
 
 // Resize and pad input image
-cv::Mat resizeAndPad(const cv::Mat& image, int targetWidth, int targetHeight, int& padTop, int& padLeft, float& scale, const cv::Scalar& padColor) {
+cv::Mat ONNXRunner::resizeAndPad(const cv::Mat& image, int targetWidth, int targetHeight, int& padTop, int& padLeft, float& scale, const cv::Scalar& padColor) {
     int originalWidth = image.cols;
     int originalHeight = image.rows;
 
@@ -38,7 +38,7 @@ cv::Mat resizeAndPad(const cv::Mat& image, int targetWidth, int targetHeight, in
 }
 
 // Create detection mask
-cv::Mat createDetectionMask(const cv::Mat& originalImage, const std::vector<Detection>& detections, float scale, int padTop, int padLeft) {
+cv::Mat ONNXRunner::createDetectionMask(const cv::Mat& originalImage, const std::vector<Detection>& detections, float scale, int padTop, int padLeft) {
     cv::Mat mask = cv::Mat::zeros(originalImage.size(), CV_8UC1);  // Single channel mask
 
     for (const auto& detection : detections) {
@@ -59,7 +59,7 @@ cv::Mat createDetectionMask(const cv::Mat& originalImage, const std::vector<Dete
 }
 
 // Load the ONNX model
-cv::dnn::Net loadModel(const std::string& modelPath) {
+cv::dnn::Net ONNXRunner::loadModel(const std::string& modelPath) {
     cv::dnn::Net net = cv::dnn::readNetFromONNX(modelPath);
     net.setPreferableBackend(cv::dnn::DNN_BACKEND_CUDA);  // Use CUDA backend
     net.setPreferableTarget(cv::dnn::DNN_TARGET_CUDA);    // Run on GPU
@@ -67,7 +67,7 @@ cv::dnn::Net loadModel(const std::string& modelPath) {
 }
 
 // Preprocess image for model input
-cv::Mat preprocessImage(const cv::Mat& image, cv::dnn::Net& net, int& padTop, int& padLeft, float& scale) {
+cv::Mat ONNXRunner::preprocessImage(const cv::Mat& image, cv::dnn::Net& net, int& padTop, int& padLeft, float& scale) const {
     cv::Scalar padColor(128, 128, 128);  // Gray padding
     cv::Mat inputImage = resizeAndPad(image, INPUT_WIDTH, INPUT_HEIGHT, padTop, padLeft, scale, padColor);
     cv::Mat blob = cv::dnn::blobFromImage(inputImage, 1 / 255.0, cv::Size(INPUT_WIDTH, INPUT_HEIGHT), cv::Scalar(0, 0, 0), true, false);
@@ -76,7 +76,7 @@ cv::Mat preprocessImage(const cv::Mat& image, cv::dnn::Net& net, int& padTop, in
 }
 
 // Perform inference on the input image
-std::vector<Detection> performInference(cv::dnn::Net& net, const cv::Mat& inputImage) {
+std::vector<Detection> ONNXRunner::performInference(cv::dnn::Net& net, const cv::Mat& inputImage) const {
     std::vector<Detection> detections;
     cv::Mat output = net.forward();
     float* data = (float*)output.data;
@@ -112,7 +112,7 @@ std::vector<Detection> performInference(cv::dnn::Net& net, const cv::Mat& inputI
 }
 
 // Apply Non-Maximum Suppression
-std::vector<Detection> applyNMS(std::vector<Detection>& detections) {
+std::vector<Detection> ONNXRunner::applyNMS(std::vector<Detection>& detections) const {
     std::vector<int> indices;
     std::vector<cv::Rect> boxes;
     std::vector<float> scores;
@@ -130,3 +130,31 @@ std::vector<Detection> applyNMS(std::vector<Detection>& detections) {
 
     return finalDetections;
 }
+
+
+void ONNXRunner::load(const std::string& modelPath) {
+    this->net = ONNXRunner::loadModel(modelPath);
+    // 创建一个空的输入矩阵作为预热数据（假定模型输入是 RGB 图像）
+    cv::Mat dummyInput = cv::Mat::zeros(INPUT_HEIGHT, INPUT_WIDTH, CV_8UC3); // 640x640 的全零矩阵
+
+    // 调用 predict 方法进行预热
+    this->predict(dummyInput);
+}; // Load the model
+
+std::vector<Detection> ONNXRunner::predict(const cv::Mat &image) {
+    cv::dnn::Net net = this->net;
+
+    cv::Mat inputImage = preprocessImage(image, net, this->pad_top, this->pad_left, this->scale);
+
+    // Perform inference
+    std::vector<Detection> detections = performInference(net, inputImage);
+    // Apply Non-Maximum Suppression
+    std::vector<Detection> finalDetections = applyNMS(detections);
+    return finalDetections;
+}
+
+cv::Mat ONNXRunner::postProcess(const std::vector<Detection>& detections, const cv::Mat& image) const {
+    // Create and show the detection mask
+    cv::Mat detectionMask = ONNXRunner::createDetectionMask(image, detections, this->scale, this->pad_top, this->pad_left);
+    return detectionMask;
+}
\ No newline at end of file
diff --git a/src/CVDL/OnnxRunner.h b/src/CVDL/OnnxRunner.h
index 865429b..9811192 100644
--- a/src/CVDL/OnnxRunner.h
+++ b/src/CVDL/OnnxRunner.h
@@ -27,13 +27,31 @@ public:
 private:
     std::chrono::high_resolution_clock::time_point start_time;
 };
+class ONNXRunner {
+public:
+    ONNXRunner() = default; // Default constructor
+    void load(const std::string& modelPath); // Load the model
+    std::vector<Detection> predict(const cv::Mat& image); // Predict and generate results
+    cv::Mat postProcess(const std::vector<Detection> &detections, const cv::Mat &image) const;
+
+private:
+    cv::dnn::Net net;
+    int pad_top, pad_left;
+    float scale;
+
+    // Function prototypes
+    static cv::Mat resizeAndPad(const cv::Mat& image, int targetWidth, int targetHeight, int& padTop, int& padLeft, float& scale, const cv::Scalar& padColor);
+    static cv::Mat createDetectionMask(const cv::Mat& originalImage, const std::vector<Detection>& detections, float scale, int padTop, int padLeft);
+    static cv::dnn::Net loadModel(const std::string& modelPath);
+    cv::Mat preprocessImage(const cv::Mat& image, cv::dnn::Net& net, int& padTop, int& padLeft, float& scale) const;
+    std::vector<Detection> performInference(cv::dnn::Net& net, const cv::Mat& inputImage) const;
+    std::vector<Detection> applyNMS(std::vector<Detection>& detections) const;
+    // Constants
+    const float CONFIDENCE_THRESHOLD = 0.2f;
+    const float NMS_THRESHOLD = 0.2f;
+    const int INPUT_WIDTH = 640;
+    const int INPUT_HEIGHT = 640;
+};
 
-// Function prototypes
-cv::Mat resizeAndPad(const cv::Mat& image, int targetWidth, int targetHeight, int& padTop, int& padLeft, float& scale, const cv::Scalar& padColor);
-cv::Mat createDetectionMask(const cv::Mat& originalImage, const std::vector<Detection>& detections, float scale, int padTop, int padLeft);
-cv::dnn::Net loadModel(const std::string& modelPath);
-cv::Mat preprocessImage(const cv::Mat& image, cv::dnn::Net& net, int& padTop, int& padLeft, float& scale);
-std::vector<Detection> performInference(cv::dnn::Net& net, const cv::Mat& inputImage);
-std::vector<Detection> applyNMS(std::vector<Detection>& detections);
 
 #endif // ONNXRUNNER_H
diff --git a/src/Matrox/template_matching.cpp b/src/Matrox/template_matching.cpp
index b279084..e70a586 100644
--- a/src/Matrox/template_matching.cpp
+++ b/src/Matrox/template_matching.cpp
@@ -119,8 +119,9 @@ void TemplateMatcher::findModels(const MIL_ID& inputImage,MIL_ID& outputImage)
         return;
     }
     MIL_ID input_image_uint8 = convert_to_uint8(inputImage);
-
-    MdispSelect(MilDisplay, input_image_uint8);
+    if(this->param["isdisplay"] == 1) {
+        MdispSelect(MilDisplay, input_image_uint8);
+    }
 
     // Clear previous annotations
     MgraClear(M_DEFAULT, GraphicList);
@@ -155,13 +156,16 @@ void TemplateMatcher::findModels(const MIL_ID& inputImage,MIL_ID& outputImage)
         MbufClear(outputImage, 0);
 
         // Display results
-        std::cout << "Found " << NumResults << " model(s) in " << Time * 1000.0 << " ms:\n";
-        std::cout << "Result   Model   X Position   Y Position   Angle   Scale   Score\n";
+        if(this->param["debug_mode"] == 1) {
+            std::cout << "Found " << NumResults << " model(s) in " << Time * 1000.0 << " ms:\n";
+            std::cout << "Result   Model   X Position   Y Position   Angle   Scale   Score\n";
+        }
         for (MIL_INT i = 0; i < NumResults; ++i) {
-            std::cout << i << "        " << Models[i] << "        " << XPosition[i] << "        "
-                      << YPosition[i] << "        " << Angle[i] << "        " << Scale[i]
-                      << "        " << Score[i] << "%\n";
-
+            if (this->param["debug_mode"] == 1) {
+                std::cout << i << "        " << Models[i] << "        " << XPosition[i] << "        "
+                          << YPosition[i] << "        " << Angle[i] << "        " << Scale[i]
+                          << "        " << Score[i] << "%\n";
+            }
             // Draw results onto the binary image
             MgraColor(M_DEFAULT, 255); // White color for binary image
             MmodDraw(M_DEFAULT, MilResult, outputImage, M_DRAW_EDGES + M_DRAW_POSITION, i, M_DEFAULT);
@@ -172,15 +176,11 @@ void TemplateMatcher::findModels(const MIL_ID& inputImage,MIL_ID& outputImage)
                      M_DRAW_EDGES + M_DRAW_POSITION, i, M_DEFAULT);
         }
 
-        // Display or save the binary image
-
-        MbufSave(SAVE_PATH2, outputImage);
-
     } else {
         std::cout << "No models found.\n";
     }
-    MosPrintf(MIL_TEXT("Press <Enter> to EXIT.\n\n"));
-    MosGetch();
+    // MosPrintf(MIL_TEXT("Press <Enter> to EXIT.\n\n"));
+    // MosGetch();
     MbufFree(input_image_uint8);
 }
 
@@ -237,15 +237,14 @@ void TemplateMatcher::loadConfig(const std::string& filename,
     file.close();
 }
 
-void TemplateMatcher::LoadTemplate(std::map<std::string, int>& params)
+void TemplateMatcher::LoadConfig(const string& config_path)
 {
     std::vector<std::string> template_paths;
     std::vector<MIL_INT> offsetX, offsetY, sizeX, sizeY;
     std::vector<MIL_DOUBLE> drawColor;
 
     // 调用 loadConfig 并加载配置
-    loadConfig("C:\\Users\\zjc\\Desktop\\config\\template_config.txt",
-        template_paths, offsetX, offsetY, sizeX, sizeY, drawColor);
+    loadConfig(config_path, template_paths, offsetX, offsetY, sizeX, sizeY, drawColor);
 
     // 调用 matcher 的 loadTemplates 方法
     this->loadTemplates(template_paths, offsetX, offsetY, sizeX, sizeY, drawColor);
@@ -256,12 +255,24 @@ void TemplateMatcher::FindTemplates( const MIL_ID& inputImage, MIL_ID& outputIma
 {
     // Perform template matching
     this -> findModels(inputImage,outputImage);
-
-    // Notify user that matching is complete
-    cout << "Template matching completed.\n";
 }
 
-
+void TemplateMatcher::predict(const MIL_ID &inputImage, MIL_ID &outputImage, const std::map<std::string, int> &params) {
+    MIL_ID detection_result, detection_resize, output_image_unresize;
+    pre_process(inputImage, detection_result, params);
+    MbufAlloc2d(MilSystem, MbufInquire(detection_result, M_SIZE_X, M_NULL)/2,
+       MbufInquire(detection_result, M_SIZE_Y, M_NULL)/2, 1 + M_UNSIGNED,
+       M_IMAGE + M_PROC, &detection_resize);
+    MimResize(detection_result,detection_resize,0.5,0.5,M_DEFAULT);
+    this->FindTemplates(detection_resize,output_image_unresize, params);
+    MbufAlloc2d(MilSystem, MbufInquire(detection_result, M_SIZE_X, M_NULL),
+       MbufInquire(detection_result, M_SIZE_Y, M_NULL), 1 + M_UNSIGNED,
+       M_IMAGE + M_PROC, &outputImage);
+    MimResize(output_image_unresize,outputImage,2,2,M_DEFAULT);
+    MbufFree(detection_result);
+    MbufFree(detection_resize);
+    MbufFree(output_image_unresize);
+}
 
 
 // TODO: Opencv ONNX runner,    已
diff --git a/src/Matrox/template_matching.h b/src/Matrox/template_matching.h
index 00f8e88..93be1ce 100644
--- a/src/Matrox/template_matching.h
+++ b/src/Matrox/template_matching.h
@@ -21,7 +21,6 @@ private:
     MIL_ID MilSearchContext;
     MIL_ID MilResult;
     MIL_ID GraphicList;
-#define SAVE_PATH2 MIL_TEXT("C:\\Users\\zjc\\Desktop\\detection.png")
 
     std::vector<std::string> ModelImgPaths;
     std::vector<MIL_INT> ModelsOffsetX;
@@ -38,7 +37,7 @@ public:
     // Constructor
     TemplateMatcher(MIL_ID system, MIL_ID display, std::map<std::string, int>& param);
 
-
+    void LoadConfig(const std::string& config_path);
 
     // Load template models
     void loadTemplates(const std::vector<std::string>& template_paths,
@@ -49,11 +48,13 @@ public:
     // Search for models in the input image
     void findModels(const MIL_ID& inputImage,MIL_ID& outputImage);
 
-    void LoadTemplate(std::map<std::string, int> &params);
+
 
     void FindTemplates(const MIL_ID &inputImage, MIL_ID &outputImage,const std::map<std::string, int> &params);
 
-    void loadConfig(const std::string& filename,
+    void predict(const MIL_ID& inputImage, MIL_ID& outputImage, const std::map<std::string, int> &params);
+
+    static void loadConfig(const std::string& filename,
                 std::vector<std::string>& template_paths,
                 std::vector<MIL_INT>& offsetX,
                 std::vector<MIL_INT>& offsetY,
diff --git a/tests/test_onnx.cpp b/tests/test_onnx.cpp
index 5964892..d01c144 100644
--- a/tests/test_onnx.cpp
+++ b/tests/test_onnx.cpp
@@ -7,7 +7,8 @@ int main() {
     Timer timer1;
 
     // Load the model
-    cv::dnn::Net net = loadModel(modelPath);
+    ONNXRunner runner;
+    runner.load(modelPath);
     timer1.printElapsedTime("Time to load the model");
 
     // Read the input image
@@ -16,30 +17,19 @@ int main() {
         std::cerr << "Could not read the image: " << imagePath << std::endl;
         return -1;
     }
-
-    // Preprocess image
-    int padTop, padLeft;
-    float scale;
-    cv::Mat inputImage = preprocessImage(image, net, padTop, padLeft, scale);
-    timer1.printElapsedTime("Time to preprocess image");
-
-    // Perform inference
-    std::vector<Detection> detections = performInference(net, inputImage);
-
-    // Apply Non-Maximum Suppression
-    std::vector<Detection> finalDetections = applyNMS(detections);
-    std::cout << "Number of detections after NMS: " << finalDetections.size() << std::endl;
-
-    // Create and show the detection mask
-    cv::Mat detectionMask = createDetectionMask(image, finalDetections, scale, padTop, padLeft);
-
-    // cv::imshow("Detection Mask", detectionMask);
-
+    cv::Mat mask;
+    timer1.printElapsedTime("Time to load image.");
+    for(int i = 0; i < 10; i++) {
+        std:: cout << "Run time: " << i << std::endl;
+        std::vector<Detection> result = runner.predict(image);
+        timer1.printElapsedTime("Time to predict result");
+        mask = runner.postProcess(result, image);
+        timer1.printElapsedTime("Time to predict result");
+    }
     // Save the result
-
     std::string savepath = "C:\\Users\\zjc\\Desktop\\suspect_mask.png";
-    cv::imwrite(savepath, detectionMask);
-    timer1.printElapsedTime("Time to run inference");
+    cv::imwrite(savepath, mask);
+    timer1.printElapsedTime("Time to save image");
 
     cv::waitKey(0);
     return 0;
diff --git a/tests/test_template_matching.cpp b/tests/test_template_matching.cpp
index 5ff24dc..c09b1ad 100644
--- a/tests/test_template_matching.cpp
+++ b/tests/test_template_matching.cpp
@@ -37,7 +37,7 @@ int main() {
 
     MIL_ID output_Image= M_NULL;
     TemplateMatcher matcher(MilSystem, MilDisplay, params);
-
+    matcher.LoadConfig("C:\\Users\\zjc\\Desktop\\config\\template_config.txt");
     // Measure execution time
     measure_execution_time([&]()
         {
@@ -46,8 +46,6 @@ int main() {
            MbufInquire(detection_result, M_SIZE_Y, M_NULL)/2, 1 + M_UNSIGNED,
            M_IMAGE + M_PROC, &detection_resize);
         MimResize(detection_result,detection_resize,0.5,0.5,M_DEFAULT);
-
-        matcher.LoadTemplate(params);
         matcher.FindTemplates(detection_resize,output_Image,params);
 
     });
diff --git a/tests/test_total.cpp b/tests/test_total.cpp
index ff83f03..57d3026 100644
--- a/tests/test_total.cpp
+++ b/tests/test_total.cpp
@@ -2,6 +2,8 @@
 #include <iostream>
 #include <map>
 #include <string>
+#include <CVDL/OnnxRunner.h>
+
 #include "Mil.h"
 #include "Matrox/utils.h"
 #include "Matrox/color_range.h"
@@ -10,101 +12,74 @@
 // 宏定义
 #define SAVE_PATH3 MIL_TEXT("C:\\Users\\zjc\\Desktop\\suspect.png")
 #define SAVE_PATH4 MIL_TEXT("C:\\Users\\zjc\\Desktop\\suspect2.png")
-#define IMAGE_PATH MIL_TEXT("C:\\Users\\zjc\\Desktop\\cotton_image_new\\357.bmp")
+#define IMAGE_PATH MIL_TEXT(".\\test_imgs\\357.bmp")
+std::string imagePath = "C:\\Users\\zjc\\Desktop\\dimo.bmp";
 
 MIL_ID MilApplication, MilSystem, MilDisplay;
 std::map<std::string, int> params;
 
-void run_high_sat_detect(MIL_ID MilImage, std::map<std::string, int>& params) {
-    MIL_ID detection_result = M_NULL;
-    read_params_from_file("C:\\Users\\zjc\\Desktop\\config\\color_range_config.txt", params);
-
-    measure_execution_time([&]() {
-        high_sat_detect(MilImage, detection_result, params);
-    });
-
-    MbufSave(SAVE_PATH3, detection_result);
-    MbufFree(detection_result);
-}
-
-void run_pre_process(MIL_ID MilImage, std::map<std::string, int>& params) {
-    MIL_ID detection_result = M_NULL;
-    MIL_ID detection_resize = M_NULL;
-    MIL_ID output_Image = M_NULL;
-
-    read_params_from_file("C:\\Users\\zjc\\Desktop\\config\\template_color_config.txt", params);
-    TemplateMatcher matcher(MilSystem, MilDisplay, params);
-
-    measure_execution_time([&]() {
-        pre_process(MilImage, detection_result, params);
-        MbufAlloc2d(MilSystem, MbufInquire(detection_result, M_SIZE_X, M_NULL) / 2,
-                    MbufInquire(detection_result, M_SIZE_Y, M_NULL) / 2, 1 + M_UNSIGNED,
-                    M_IMAGE + M_PROC, &detection_resize);
-        MimResize(detection_result, detection_resize, 0.5, 0.5, M_DEFAULT);
-
-        matcher.LoadTemplate(params);
-        matcher.FindTemplates(detection_resize, output_Image, params);
-    });
-
-    MbufSave(SAVE_PATH4, detection_result);
-    MbufFree(detection_resize);
-    MbufFree(output_Image);
-    MbufFree(detection_result);
-}
-
+using namespace std;
 int main() {
     // 初始化 MIL 应用
-
     MappAllocDefault(M_DEFAULT, &MilApplication, &MilSystem, &MilDisplay, M_NULL, M_NULL) ;
     // 读取图片
-    MIL_ID MilImage = M_NULL;
+    MIL_ID MilImage=M_NULL, MilHighSatResult = M_NULL, MilTemplateMatchingResult = M_NULL;
     MbufRestore(IMAGE_PATH, MilSystem, &MilImage);
     if (MilImage == M_NULL) {
         std::cerr << "Error: Failed to restore image from " << IMAGE_PATH << std::endl;
         MappFreeDefault(MilApplication, MilSystem, MilDisplay, M_NULL, M_NULL);
         return -1;
     }
+    Timer timer1, timer2;
+    std::map<std::string, int> params;
+    read_params_from_file("..\\config\\color_range_config.txt", params);
+    read_params_from_file("..\\config\\template_color_config.txt", params);
+    TemplateMatcher matcher(MilSystem, MilDisplay, params);
+    matcher.LoadConfig("..\\config\\template_config.txt");
+    ONNXRunner runner;
+    runner.load("C:\\Users\\zjc\\Desktop\\dimo_11.14.onnx");
+    timer1.printElapsedTime("Load config and templates and models");
+    cv::Mat deep_result, high_sat_result, template_result, total_result;
+    cout << "Sequence running start:" << endl;
+    for(int i = 0; i < 10; i++) {
+        cout << i << endl;
+        timer1.restart();
+        timer2.restart();
+        // 艳丽色彩检测
+        high_sat_detect(MilImage, MilHighSatResult, params);
+        timer1.printElapsedTime("High Sat Predict finished");
+        // 模板匹配检测
+        matcher.predict(MilImage, MilTemplateMatchingResult, params);
+        timer1.printElapsedTime("Template Matching Predict finished");
+        // 深度学习检测
+        cv::Mat cv_input = mil2mat(MilImage);
+        std::vector<Detection> result = runner.predict(cv_input);
+        deep_result = runner.postProcess(result, cv_input);
+        std::string savepath = "C:\\Users\\zjc\\Desktop\\suspect_mask2.png";
+        cv::imwrite(savepath, deep_result);
+        timer1.printElapsedTime("Deep Learning Predict finished");
+        high_sat_result = mil2mat(MilHighSatResult);
+        template_result = mil2mat(MilTemplateMatchingResult);
 
-    // 加载参数
-    // 创建两个子进程
-    PROCESS_INFORMATION processInfo1, processInfo2;
-    STARTUPINFO startupInfo1, startupInfo2;
+        if (!deep_result.empty() && !high_sat_result.empty() && !template_result.empty()) {
+            cv::bitwise_and(deep_result, high_sat_result, total_result);
+            cv::bitwise_and(total_result, template_result, total_result);
+        } else {
+            cerr << "Error: One or more detection results are empty!" << endl;
+        }
+        timer2.printElapsedTime("Prediction finished Total");
 
-    ZeroMemory(&startupInfo1, sizeof(startupInfo1));
-    ZeroMemory(&startupInfo2, sizeof(startupInfo2));
-    ZeroMemory(&processInfo1, sizeof(processInfo1));
-    ZeroMemory(&processInfo2, sizeof(processInfo2));
+        cv::imshow("Result", total_result);
+        cv::waitKey(0);
 
-    startupInfo1.cb = sizeof(startupInfo1);
-    startupInfo2.cb = sizeof(startupInfo2);
-
-    // 子进程1
-    if (!CreateProcess(NULL, LPWSTR((LPSTR) "ChildProcess1.exe"), NULL, NULL, FALSE, 0, NULL, NULL, &startupInfo1, &processInfo1)) {
-        std::cerr << "Error: Failed to create process 1. Error code: " << GetLastError() << std::endl;
-        MbufFree(MilImage);
-        MappFreeDefault(MilApplication, MilSystem, MilDisplay, M_NULL, M_NULL);
-        return -1;
     }
 
-    // 子进程2
-    if (!CreateProcess(NULL, LPWSTR((LPSTR) "ChildProcess2.exe"), NULL, NULL, FALSE, 0, NULL, NULL, &startupInfo2, &processInfo2)) {
-        std::cerr << "Error: Failed to create process 2. Error code: " << GetLastError() << std::endl;
-        MbufFree(MilImage);
-        MappFreeDefault(MilApplication, MilSystem, MilDisplay, M_NULL, M_NULL);
-        return -1;
-    }
 
-    // 等待两个子进程完成
-    WaitForSingleObject(processInfo1.hProcess, INFINITE);
-    WaitForSingleObject(processInfo2.hProcess, INFINITE);
-
-    CloseHandle(processInfo1.hProcess);
-    CloseHandle(processInfo1.hThread);
-    CloseHandle(processInfo2.hProcess);
-    CloseHandle(processInfo2.hThread);
 
     // 释放资源
     MbufFree(MilImage);
+    MbufFree(MilHighSatResult);
+    MbufFree(MilTemplateMatchingResult);
     MappFreeDefault(MilApplication, MilSystem, MilDisplay, M_NULL, M_NULL);
 
     std::cout << "所有模块检测已完成！按 <Enter> 退出。" << std::endl;