面积大小输出

Reviewed-on: #3

CMakeLists.txt

@@ -23,6 +23,7 @@ add_executable(armor_detector_mdv
     src/Visualizer.cpp
     src/BallisticPredictor.cpp
     src/TTLCommunicator.cpp
+    src/PidController.cpp
 )
 
 # Link OpenCV libraries

README.md

@@ -75,4 +75,29 @@ make -j$(nproc)
 1. 检查相机是否正确连接
 2. 确认MindVision相机驱动是否正确安装
 3. 验证相机ID是否正确
-4. 检查权限（可能需要将用户添加到video组）
+4. 检查权限（可能需要将用户添加到video组）
+
+## MindVision-SDK
+`Linux`: >**wget https://www.mindvision.com.cn/wp-content/uploads/2023/08/linuxSDK_V2.1.0.37.tar.gz**
+
+## PID设置原理及其方式
+
+- **PID初始真实值 = g_pitch(/yaw)_kp（`位于23行`） * 相关系数（`位于410行`）**
+- **PID调节滑块含义** ：`46行`轨迹条回调函数为上下限。例如 pos / 100 ，如果pos范围为0~1000`（位于400行）`则滑块调节范围为 0~10。
+
+###  Kp（比例系数）的作用
+ - 响应速度：Kp值越大，系统对误差的响应越快
+ - 控制力度：直接根据当前误差大小产生控制输出
+
+### Ki（积分系数）的作用
+ - 消除稳态误差：累积历史误差，清除长期存在的小偏差
+ - 系统偏移补偿：补偿机械系统的小偏差和静态误差
+
+### Kd（微分系数）的作用
+ - 预测变化趋势：根据误差的变化率进行调节
+ - 抑制振荡：在系统接近目标时减缓调节速度
+
+**参数调整建议 **
+   - Kp先调：首先调整Kp获得基本响应特性
+   - Kd次调：增加Kd减少振荡
+   - Ki后调：最后调节Ki消除稳态误差

inc/ArmorDetector.h

@@ -23,6 +23,23 @@ struct ArmorPlate {
     double confidence;
     std::pair<LightBar, LightBar> pair;
     std::vector<cv::Point2f> corners_2d;  // Can be computed later for 3D pose
+
+    // Function to get bounding rectangle of the armor plate
+    cv::Rect2d getBoundingRect() const {
+        if (corners_2d.size() >= 4) {
+            cv::Point2f min_pt = corners_2d[0];
+            cv::Point2f max_pt = corners_2d[0];
+            for (const auto& pt : corners_2d) {
+                min_pt.x = std::min(min_pt.x, pt.x);
+                min_pt.y = std::min(min_pt.y, pt.y);
+                max_pt.x = std::max(max_pt.x, pt.x);
+                max_pt.y = std::max(max_pt.y, pt.y);
+            }
+            return cv::Rect2d(min_pt.x, min_pt.y, max_pt.x - min_pt.x, max_pt.y - min_pt.y);
+        }
+        // Fallback: use center and a fixed size
+        return cv::Rect2d(center.x - 30, center.y - 15, 60, 30);
+    }
 };
 
 class ArmorDetector {

inc/MindVisionCamera.h

@@ -12,13 +12,15 @@ extern "C" {
 
 class MindVisionCamera {
 public:
-    int camera_handle;      // MindVision SDK中的相机句柄
+    CameraHandle camera_handle;     // MindVision SDk中的相机句柄
     bool is_opened;
     std::string target_color;
     int width;
     int height;
     int fps;
     unsigned char* g_pRgbBuffer; // 处理后数据缓存区
+    tSdkCameraCapbility capability;  // 相机能力信息
+    tSdkImageResolution image_resolution;  // 分辨率信息
 
     MindVisionCamera(int cam_id = 0, const std::string& target_color = "red");
     ~MindVisionCamera();
@@ -29,6 +31,10 @@ public:
     void release();
     bool switch_color(const std::string& target_color);
 
+    int get_width() const { return width; }
+    int get_height() const { return height; }
+    bool set_resolution(int width, int height);
+
 private:
     void set_camera_parameters();
     bool initialize_camera(int cam_id);

inc/PidController.h

@@ -0,0 +1,46 @@
+#ifndef PID_CONTROLLER_H
+#define PID_CONTROLLER_H
+
+#include <opencv2/opencv.hpp>
+
+class PidController {
+public:
+    // 构造函数
+    PidController(float kp = 0.0f, float ki = 0.0f, float kd = 0.0f);
+    
+    // 更新PID计算
+    float update(float setpoint, float measured_value, float dt);
+    
+    // 重置PID控制器
+    void reset();
+    
+    // 设置PID参数
+    void setKp(float kp);
+    void setKi(float ki);
+    void setKd(float kd);
+    void setParameters(float kp, float ki, float kd);
+    
+    // 获取PID参数
+    float getKp() const;
+    float getKi() const;
+    float getKd() const;
+    
+    // 设置输出限制
+    void setOutputLimits(float min, float max);
+    
+    // 获取输出
+    float getOutput() const;
+    
+    // 获取误差
+    float getError() const;
+
+private:
+    float kp_, ki_, kd_;           // PID参数
+    float last_error_;             // 上一次误差
+    float integral_;               // 积分项
+    float output_;                 // 输出值
+    float output_min_, output_max_; // 输出限制
+    bool first_iteration_;         // 是否为第一次迭代
+};
+
+#endif // PID_CONTROLLER_H

inc/config.h

@@ -36,6 +36,9 @@ const float ARMOR_ANGLE_DIFF_THRESHOLD = 15.0f * CV_PI / 180.0f; // Armor light
 const float KF_PROCESS_NOISE = 0.02f;       // Process noise covariance
 const float KF_MEASUREMENT_NOISE = 0.5f;    // Measurement noise covariance
 
+// Focal length in pixels (from camera calibration)
+const double FOCAL_PIXAL = 600.0;  // This should match your actual camera calibration
+
 // TTL communication settings
 const int TTL_BAUDRATE = 115200;
 

src/MindVisionCamera.cpp

@@ -59,21 +59,19 @@ bool MindVisionCamera::initialize_camera(int cam_id) {
     }
     
     // 获取相机能力
-    tSdkCameraCapbility capability;
     iStatus = CameraGetCapability(camera_handle, &capability);
     if (iStatus != CAMERA_STATUS_SUCCESS) {
         std::cerr << "Failed to get camera capability! Error code: " << iStatus << std::endl;
         return false;
     }
-    
-    // 让SDK进入工作模式 - 根据原始OpenCv项目，应该在设置格式前调用
-    CameraPlay(camera_handle);
 
     // 设置输出格式 - 保持与原始工作项目一致，直接设置为BGR8格式
     CameraSetIspOutFormat(camera_handle, CAMERA_MEDIA_TYPE_BGR8);
 
+     // 让SDK进入工作模式 - 根据原始OpenCv项目，应该在设置格式前调用
+    CameraPlay(camera_handle);
+
     // 获取并设置分辨率为 640x480
-    tSdkImageResolution image_resolution;
     int status = CameraGetImageResolution(camera_handle, &image_resolution);
     std::cout << "Default resolution query returned: " << status << std::endl;
 
@@ -166,6 +164,18 @@ bool MindVisionCamera::read_frame(cv::Mat& frame) {
 
     // 获取一帧数据
     if (CameraGetImageBuffer(camera_handle, &sFrameInfo, &pbyBuffer, 1000) == CAMERA_STATUS_SUCCESS) {
+        // 直接使用CameraGetImageBufferEx获取处理后的RGB数据，提高效率
+        INT width, height;
+        unsigned char* pData = CameraGetImageBufferEx(camera_handle, &width, &height, 2000);
+        if(pData != NULL) {
+            // 创建OpenCV Mat对象，直接使用获取的数据
+            cv::Mat temp_mat(height, width, CV_8UC3, pData);
+            frame = temp_mat.clone(); // clone()确保数据被复制，而不是共享指针
+
+            // CameraGetImageBufferEx方式不需要手动释放缓冲区
+            return true;
+        }
+        
         // 使用全局缓冲区处理图像数据 - 与原始项目一致
         int status = CameraImageProcess(camera_handle, pbyBuffer, g_pRgbBuffer, &sFrameInfo);
         if (status != CAMERA_STATUS_SUCCESS) {
@@ -192,6 +202,38 @@ bool MindVisionCamera::read_frame_with_color_filter(cv::Mat& frame, cv::Mat& raw
         return false;
     }
 
+     // 尝试使用CameraGetImageBufferEx方式获取图像
+    INT width, height;
+    unsigned char* pData = CameraGetImageBufferEx(camera_handle, &width, &height, 2000);
+    if(pData != NULL) {
+        // 创建OpenCV Mat对象，直接使用获取的数据
+        cv::Mat original_img(height, width, CV_8UC3, pData);
+        frame = original_img.clone(); // clone()确保数据被复制，而不是共享指针
+
+        // 创建HSV图像用于颜色过滤
+        cv::Mat hsv_img;
+        cv::cvtColor(frame, hsv_img, cv::COLOR_BGR2HSV);
+
+        // 根据颜色创建原始掩码
+        if (target_color == "red") {
+            // Red color range in HSV
+            cv::Mat mask1, mask2;
+            cv::inRange(hsv_img, cv::Scalar(0, 43, 49), cv::Scalar(25, 255, 255), mask1);
+            cv::inRange(hsv_img, cv::Scalar(156, 46, 49), cv::Scalar(180, 255, 255), mask2);
+            raw_mask = mask1 | mask2;
+        } else if (target_color == "blue") {
+            // Blue color range in HSV
+            cv::inRange(hsv_img, cv::Scalar(83, 200, 44), cv::Scalar(130, 255, 255), raw_mask);
+        } else {
+            raw_mask = cv::Mat::zeros(hsv_img.size(), CV_8UC1);
+        }
+
+        // CameraGetImageBufferEx方式不需要手动释放缓冲区
+        return true;
+    }
+
+    // 如果CameraGetImageBufferEx失败，回退到原来的方式
+
     tSdkFrameHead sFrameInfo;
     BYTE* pbyBuffer;
 
@@ -239,11 +281,16 @@ bool MindVisionCamera::read_frame_with_color_filter(cv::Mat& frame, cv::Mat& raw
 }
 
 void MindVisionCamera::release() {
-    if (camera_handle >= 0) {
+    if (camera_handle != 0) {
         // 停止采集，不管is_opened状态如何都尝试释放
+        CameraPause(camera_handle);
         CameraUnInit(camera_handle);
         camera_handle = -1;
     }
+    if(g_pRgbBuffer) {
+        free(g_pRgbBuffer);
+        g_pRgbBuffer = nullptr;
+    }
     is_opened = false;
 }
 
@@ -259,4 +306,30 @@ bool MindVisionCamera::switch_color(const std::string& new_color) {
         return set_cam_params(); // 返回set_cam_params的结果
     }
     return false;
+}
+
+bool MindVisionCamera::set_resolution(int width, int height){
+    if (!is_opened) {
+        return false;
+    }
+
+    tSdkImageResolution res;
+    int status = CameraGetImageResolution(camera_handle, &res);
+
+    res.iIndex = 0xFF;
+    res.iWidth = width;
+    res.iHeight = height;
+    res.iWidthFOV = capability.sResolutionRange.iWidthMax;
+    res.iHeightFOV =  capability.sResolutionRange.iHeightMax;
+    res.iHOffsetFOV = 0;
+    res.iVOffsetFOV = 0;
+
+    status = CameraSetImageResolution(camera_handle, &res);
+    if (status == CAMERA_STATUS_SUCCESS) {
+        this->width = width;
+        this->height = height;
+        image_resolution = res;
+        return true;
+    }
+    return false;
 }

src/MindVisionMain.cpp

@@ -1,3 +1,4 @@
+#include <cstdlib>
 #include <iostream>
 #include <string>
 #include <vector>
@@ -5,8 +6,9 @@
 #include <thread>
 #include <memory>
 #include <unistd.h>
-
+#include <math.h>
 #include <opencv2/opencv.hpp>
+#include <opencv2/tracking.hpp>
 
 #include "config.h"
 #include "MindVisionCamera.h"  // 使用MindVision相机
@@ -16,53 +18,134 @@
 #include "Visualizer.h"
 #include "BallisticPredictor.h"
 #include "TTLCommunicator.h"
+#include "PidController.h"
+
+// 全局PID参数（使用简单类型避免初始化问题）
+float g_pitch_kp = 0.5217f;
+float g_pitch_ki = 0.006f;
+float g_pitch_kd = 0.0773f;
+float g_yaw_kp = 0.6419f;
+float g_yaw_ki = 0.2409f;
+float g_yaw_kd = 0.4372f;
+
+// PID控制器实例
+PidController pitch_pid(g_pitch_kp, g_pitch_ki, g_pitch_kd);
+PidController yaw_pid(g_yaw_kp, g_yaw_ki, g_yaw_kd);
+
+// 更新PID控制器参数的函数
+void update_pid_controllers() {
+    static bool first_call = true;
+    if (first_call) {
+        first_call = false;
+        return;
+    }
+    pitch_pid.setParameters(g_pitch_kp, g_pitch_ki, g_pitch_kd);
+    yaw_pid.setParameters(g_yaw_kp, g_yaw_ki, g_yaw_kd);
+}
+
+// 轨迹条回调函数
+void on_pitch_kp_trackbar(int pos, void*) {
+    g_pitch_kp = pos / 10000.0f;
+}
+
+void on_pitch_ki_trackbar(int pos, void*) {
+    g_pitch_ki = pos / 100000.0f;
+}
+
+void on_pitch_kd_trackbar(int pos, void*) {
+    g_pitch_kd = pos / 100000.0f;
+}
+
+void on_yaw_kp_trackbar(int pos, void*) {
+    g_yaw_kp = pos / 10000.0f;
+}
+
+void on_yaw_ki_trackbar(int pos, void*) {
+    g_yaw_ki = pos / 100000.1f;
+}
+
+void on_yaw_kd_trackbar(int pos, void*) {
+    g_yaw_kd = pos / 100000.0f;
+}
 
 // Function to output control data to TTL device (with enable control)
 void output_control_data(const cv::Point2f* ballistic_point,
                         const std::string& target_color,
-                        int frame_counter,
                         TTLCommunicator* ttl_communicator,
                         const cv::Point2f& img_center,
                         bool use_ttl) {
-    // Only send data when TTL is enabled, meets frame interval, and has valid target
-    if (use_ttl && frame_counter % 5 == 0 && ballistic_point != nullptr) {
+    // Only send data when TTL is enabled and has valid target
+    if (use_ttl && ballistic_point != nullptr) {
         std::ostringstream send_str;
 
         // Calculate offset (based on actual image center)
-        int ballistic_offset_x = static_cast<int>(ballistic_point->x - img_center.x);
-        int ballistic_offset_y = static_cast<int>(img_center.y - ballistic_point->y);
+        float raw_offset_x = -(ballistic_point->x - img_center.x); // yaw error
+        float raw_offset_y = -(img_center.y - ballistic_point->y ); // pitch error
+
+        // Calculate time delta
+        static auto last_time = std::chrono::high_resolution_clock::now();
+        auto current_time = std::chrono::high_resolution_clock::now();
+        float dt = std::chrono::duration<float>(current_time - last_time).count();
+        if (dt <= 0) dt = 0.01f; // Minimum dt to avoid division by zero
+        last_time = current_time;
+
+        // Update PID controllers with latest parameters
+        update_pid_controllers();
+
+        // Apply PID control to the pitch (vertical) component
+        float pid_pitch_output = pitch_pid.update(0.0f, raw_offset_y, dt); // Setpoint is 0, error is raw_offset_y
+
+        // Apply PID control to the yaw (horizontal) component  
+        float pid_yaw_output = yaw_pid.update(0.0f, raw_offset_x, dt); // Setpoint is 0, error is raw_offset_x
+
+        // Convert PID outputs to the expected format
+        // The PID output might be large, so we might need to scale it
+        int ballistic_offset_yaw = 1.4*(-static_cast<int>(pid_yaw_output));
+        int ballistic_offset_pitch = 2*(-static_cast<int>(pid_pitch_output));
+
 
         // Color simplification mapping
         std::string simplified_color = target_color;
         if (target_color == "red") simplified_color = "r";
         else if (target_color == "blue") simplified_color = "b";
 
-        // Construct send string
-        send_str << "s " << simplified_color << " " << std::to_string(ballistic_offset_x) << " " << std::to_string(ballistic_offset_y) << "\n";
+        // Construct send string (original format)
+        send_str << "#s " << simplified_color << " " << std::to_string(ballistic_offset_yaw) << " " << std::to_string(ballistic_offset_pitch) << "*\n";
 
         // Send data
         if (ttl_communicator != nullptr) {
             ttl_communicator->send_data(send_str.str());
-
         }else{
             std::cerr << "Error: TTLCommunicator is a null pointer!" << std::endl;
         }
     }
 }
 
-void set_camera_resolution(MindVisionCamera& , int width, int height) {
+void set_camera_resolution(MindVisionCamera& camera, int width, int height) {
     // The resolution is set during camera initialization in MindVision
     // We need to implement a method in MindVisionCamera to change resolution
     // For now, we'll just log the intended change
-    std::cout << "Setting camera resolution to: " << width << "x" << height << std::endl;
+   if (camera.set_resolution(width, height)){
+        std::cout << "Successfully set camera resolution to: " << width << "x" << height << std::endl;
+   } else {
+        std::cerr << "Failed to set camera resolution to: " << width << "x" << height << std::endl;
+   }
 }
 
 int main(int /*argc*/, char* /*argv*/[]) {
+    static int Numbe = 0;
     std::string target_color = "red";
     int cam_id = 0;
-    cv::Size default_resolution(640, 480);
+    cv::Size default_resolution(1280, 720); // Changed to 640x480 for consistency with SJTU project
     bool use_ttl = true; // Set to false to disable TTL communication
 
+
+    if (Numbe == 0) {
+        // 执行 shell 命令（注意安全风险！）
+        std::system("sudo chmod 777 /dev/tty*");
+        Numbe++;
+    }
+
     // Define optional resolution list (adjust based on camera support)
     std::vector<cv::Size> resolutions = {
         cv::Size(320, 240),   // Low resolution, high frame rate
@@ -117,6 +200,13 @@ int main(int /*argc*/, char* /*argv*/[]) {
     KalmanFilter kalman_tracker;
     Visualizer visualizer;
 
+    // Initialize KCF tracker
+    cv::Ptr<cv::Tracker> tracker = cv::TrackerKCF::create();
+    bool is_tracking = false;
+    cv::Rect tracking_roi;
+    int tracking_frame_count = 0;
+    const int MAX_TRACKING_FRAMES = 100; // Maximum frames to track before returning to search
+
     int frame_counter = 0; // Counter to control output frequency
     int max_consecutive_predicts = 20; // Maximum consecutive prediction times
     int consecutive_predicts = 0; // Current consecutive prediction count
@@ -125,6 +215,7 @@ int main(int /*argc*/, char* /*argv*/[]) {
     try {
         while (true) {
             // 使用新的颜色过滤方法同时获取图像和原始掩码
+
             cv::Mat raw_mask;
             if (!camera.read_frame_with_color_filter(frame, raw_mask, target_color)) {
                 std::cout << "Cannot read from MindVision camera, exiting!，HERERER" << std::endl;
@@ -142,30 +233,94 @@ int main(int /*argc*/, char* /*argv*/[]) {
             cv::Mat color_only_frame;
             frame.copyTo(color_only_frame, raw_mask);
 
-            // Armor plate detection
+            // Initialize tracking center
+            cv::Point2f* tracking_center = nullptr;
+            std::unique_ptr<cv::Point2f> tracking_point = nullptr;
+
+            if (is_tracking) {
+                // Update tracker
+                bool success = tracker->update(frame, tracking_roi);
+                if (success && tracking_roi.area() > 0) {
+                    // Calculate center of tracked ROI
+                    tracking_point = std::make_unique<cv::Point2f>(
+                        tracking_roi.x + tracking_roi.width / 2.0f,
+                        tracking_roi.y + tracking_roi.height / 2.0f
+                    );
+                    tracking_center = tracking_point.get();
+                    tracking_frame_count++;
+
+                    // If tracking for too long or detection is available, search for armor again
+                    if (tracking_frame_count > MAX_TRACKING_FRAMES) {
+                        is_tracking = false;
+                        tracking_frame_count = 0;
+                    }
+                } else {
+                    // Tracking failed, return to detection mode
+                    is_tracking = false;
+                    tracking_frame_count = 0;
+                }
+            }
+
+            // Armor plate detection - only when not tracking or need to verify tracking
             std::vector<LightBar> valid_light_bars;
             std::vector<ArmorPlate> armor_plates;
-            detector.detect(mask, target_color, valid_light_bars, armor_plates);
+
+            if (!is_tracking || tracking_frame_count % 10 == 0) { // Detect every 10 frames when tracking
+                detector.detect(mask, target_color, valid_light_bars, armor_plates);
+            }
 
             // Kalman filter tracking (modified part: get target speed)
-            cv::Point2f* current_center = nullptr;
+            cv::Point2f* detection_center = nullptr;
             if (!armor_plates.empty()) {
-                current_center = &(armor_plates[0].center);
+                detection_center = &(armor_plates[0].center);
             }
 
             // Use smart pointer for safer memory management
             std::unique_ptr<cv::Point2f> predicted_center = nullptr;
 
-            if (current_center != nullptr) {
+            // Priority: detection -> tracking -> Kalman prediction
+            if (detection_center != nullptr) {
                 // Has detection result: update Kalman filter, reset consecutive prediction count
-                kalman_tracker.update(*current_center);
+                kalman_tracker.update(*detection_center);
                 predicted_center = std::make_unique<cv::Point2f>(kalman_tracker.predict());
 
-                // Get velocity from the Kalman filter
-                target_speed = cv::Point2f(0.0f, 0.0f); // Kalman filter in OpenCV doesn't directly expose velocity
+                // Start tracking if successfully detected
+                if (!is_tracking && !armor_plates.empty() && armor_plates[0].corners_2d.size() >= 4) {
+                    // Only start tracking if we have all 4 corners
+                    cv::Rect2d armor_rect = cv::boundingRect(std::vector<cv::Point2f>{
+                        armor_plates[0].corners_2d[0],
+                        armor_plates[0].corners_2d[1],
+                        armor_plates[0].corners_2d[2],
+                        armor_plates[0].corners_2d[3]
+                    });
+                    // Expand the bounding box slightly for better tracking
+                    cv::Rect2d expanded_rect = cv::Rect2d(
+                        armor_rect.x - armor_rect.width * 0.1,
+                        armor_rect.y - armor_rect.height * 0.1,
+                        armor_rect.width * 1.2,
+                        armor_rect.height * 1.2
+                    );
+                    // Ensure the rectangle is within frame bounds
+                    expanded_rect = expanded_rect & cv::Rect2d(0, 0, frame.cols, frame.rows);
+
+                    // Initialize tracker only if the rectangle has positive area
+                    if (expanded_rect.area() > 0) {
+                        tracker = cv::TrackerKCF::create(); // Create new tracker instance
+                        tracker->init(frame, expanded_rect);
+                        tracking_roi = expanded_rect;
+                        is_tracking = true;
+                        tracking_frame_count = 0;
+                    }
+                }
+
+                consecutive_predicts = 0;
+            } else if (is_tracking && tracking_center != nullptr) {
+                // Use tracking result
+                kalman_tracker.update(*tracking_center);
+                predicted_center = std::make_unique<cv::Point2f>(kalman_tracker.predict());
                 consecutive_predicts = 0;
             } else {
-                // No detection result: only use Kalman prediction, limit consecutive predictions
+                // No detection or tracking result: only use Kalman prediction, limit consecutive predictions
                 consecutive_predicts++;
                 if (consecutive_predicts < max_consecutive_predicts) {
                     cv::Point2f temp_pred = kalman_tracker.predict();
@@ -179,34 +334,80 @@ int main(int /*argc*/, char* /*argv*/[]) {
             }
 
             // Determine display center
-            cv::Point2f* display_center = current_center;
+            cv::Point2f* display_center = detection_center; // Give priority to detection results
+            if (display_center == nullptr && is_tracking && tracking_center != nullptr) {
+                display_center = tracking_center;
+            }
             if (display_center == nullptr && predicted_center != nullptr) {
                 display_center = predicted_center.get();
             }
-            bool is_predicted = (display_center != nullptr) && (current_center == nullptr);
+            bool is_predicted = (display_center != nullptr) && (detection_center == nullptr && (!is_tracking || tracking_center == nullptr));
 
             // Calculate ballistic prediction point
-            cv::Point2f* ballistic_point = ballistic_predictor.predict_ballistic_point(
-                predicted_center.get(), img_center, target_speed
-            );
+            cv::Point2f* ballistic_point = nullptr;
+            if (predicted_center) {
+                ballistic_point = ballistic_predictor.predict_ballistic_point(
+                    predicted_center.get(), img_center, target_speed
+                );
+            }
 
             auto current_time = std::chrono::high_resolution_clock::now();
 
             // Visualization
             visualizer.draw_light_bars(frame, valid_light_bars, target_color);
             if (!armor_plates.empty()) {
+                // Only draw armor plate if it has valid data
                 visualizer.draw_armor_plate(frame, armor_plates[0]);
             }
+            // Draw tracking rectangle if tracking
+            if (is_tracking) {
+                cv::rectangle(frame, tracking_roi, cv::Scalar(0, 255, 0), 2);
+            }
             visualizer.draw_offset_text(frame, display_center, target_color, is_predicted);
-            visualizer.draw_ballistic_point(frame, ballistic_point);
+            if (ballistic_point != nullptr) {
+                visualizer.draw_ballistic_point(frame, ballistic_point);
+            }
 
             // Output control data to TTL (passing use_ttl to control whether to send)
-            output_control_data(display_center, target_color, frame_counter, ttl, img_center, use_ttl);
+            // Now sending on every frame for smoother control
+            // Only send if ballistic_point is not null and we have a valid display center
+            if (display_center != nullptr && ballistic_point != nullptr) {
+                output_control_data(display_center, target_color, ttl, img_center, use_ttl);
+            }
+
+            // Create trackbars for PID parameter tuning
+            static bool initialized_trackbars = false;
+            if (!initialized_trackbars) {
+                cv::namedWindow("PID Tuning", cv::WINDOW_AUTOSIZE);
+                
+                // Create trackbars for pitch PID parameters
+                cv::createTrackbar("Pitch Kp", "PID Tuning", nullptr, 10000, on_pitch_kp_trackbar);
+                cv::createTrackbar("Pitch Ki", "PID Tuning", nullptr, 10000, on_pitch_ki_trackbar);
+                cv::createTrackbar("Pitch Kd", "PID Tuning", nullptr, 10000, on_pitch_kd_trackbar);
+                
+                // Create trackbars for yaw PID parameters
+                cv::createTrackbar("Yaw Kp", "PID Tuning", nullptr, 10000, on_yaw_kp_trackbar);
+                cv::createTrackbar("Yaw Ki", "PID Tuning", nullptr, 10000, on_yaw_ki_trackbar);
+                cv::createTrackbar("Yaw Kd", "PID Tuning", nullptr, 10000, on_yaw_kd_trackbar);
+                
+                // Set initial positions
+                cv::setTrackbarPos("Pitch Kp", "PID Tuning", static_cast<int>(g_pitch_kp * 10000));
+                cv::setTrackbarPos("Pitch Ki", "PID Tuning", static_cast<int>(g_pitch_ki * 10000));
+                cv::setTrackbarPos("Pitch Kd", "PID Tuning", static_cast<int>(g_pitch_kd * 10000));
+                cv::setTrackbarPos("Yaw Kp", "PID Tuning", static_cast<int>(g_yaw_kp * 10000));
+                cv::setTrackbarPos("Yaw Ki", "PID Tuning", static_cast<int>(g_yaw_ki * 10000));
+                cv::setTrackbarPos("Yaw Kd", "PID Tuning", static_cast<int>(g_yaw_kd * 10000));
+               
+                initialized_trackbars = true;
+            }
 
             // Display windows
             cv::imshow("Armor Detection", frame);
-            cv::imshow(target_color + " Mask", mask);
-            cv::imshow(target_color + " Only", color_only_frame);
+            //cv::imshow(target_color + " Mask", mask);
+            //cv::imshow(target_color + " Only", color_only_frame);
+
+            // Only call cv::waitKey to keep GUI responsive
+            cv::waitKey(1);
 
             // Exit on 'q' key press
             if (cv::waitKey(1) == 'q') {
@@ -215,6 +416,17 @@ int main(int /*argc*/, char* /*argv*/[]) {
 
             frame_counter++;
 
+            float area = 0.0; 
+            if (!armor_plates.empty()) {
+            // 获取装甲板配对的第一个灯条
+                const LightBar& light_bar = armor_plates[0].pair.first;
+                // 直接使用灯条的area（即rect_area）赋值
+                area = light_bar.area;
+                // 额外校验：确保灯条面积有效（避免负数/0值）
+                if (area <= 0) {
+                    area = 0.0f;
+                }
+            }
             // Control max frame rate (100 FPS)
             auto end_time = std::chrono::high_resolution_clock::now();
             auto elapsed = std::chrono::duration_cast<std::chrono::milliseconds>(end_time - current_time).count();
@@ -257,4 +469,4 @@ int main(int /*argc*/, char* /*argv*/[]) {
     }
 
     return 0;
-}
+}

src/PidController.cpp

@@ -0,0 +1,87 @@
+#include "PidController.h"
+#include <algorithm>
+#include <cmath>
+
+PidController::PidController(float kp, float ki, float kd) 
+    : kp_(kp), ki_(ki), kd_(kd), last_error_(0.0f), integral_(0.0f), output_(0.0f), 
+      output_min_(-400.0f), output_max_(400.0f), first_iteration_(true) {}
+
+float PidController::update(float setpoint, float measured_value, float dt) {
+    float error = setpoint - measured_value;
+    
+    // 处理积分饱和
+    integral_ += error * dt;
+    
+    // 计算微分（使用前向差分）
+    float derivative = 0.0f;
+    if (!first_iteration_) {
+        derivative = (error - last_error_) / dt;
+    } else {
+        first_iteration_ = false;
+    }
+    
+    // PID计算
+    float proportional = kp_ * error;
+    float integral_contribution = ki_ * integral_;
+    float derivative_contribution = kd_ * derivative;
+    
+    output_ = proportional + integral_contribution + derivative_contribution;
+    
+    // 限制输出
+    output_ = std::max(output_min_, std::min(output_max_, output_));
+    
+    // 保存当前误差用于下次计算微分
+    last_error_ = error;
+    
+    return output_;
+}
+
+void PidController::reset() {
+    last_error_ = 0.0f;
+    integral_ = 0.0f;
+    output_ = 0.0f;
+    first_iteration_ = true;
+}
+
+void PidController::setKp(float kp) {
+    kp_ = kp;
+}
+
+void PidController::setKi(float ki) {
+    ki_ = ki;
+}
+
+void PidController::setKd(float kd) {
+    kd_ = kd;
+}
+
+void PidController::setParameters(float kp, float ki, float kd) {
+    kp_ = kp;
+    ki_ = ki;
+    kd_ = kd;
+}
+
+float PidController::getKp() const {
+    return kp_;
+}
+
+float PidController::getKi() const {
+    return ki_;
+}
+
+float PidController::getKd() const {
+    return kd_;
+}
+
+void PidController::setOutputLimits(float min, float max) {
+    output_min_ = min;
+    output_max_ = max;
+}
+
+float PidController::getOutput() const {
+    return output_;
+}
+
+float PidController::getError() const {
+    return last_error_;
+}