项目结构大变

inc/BallisticPredictor.h

@@ -6,21 +6,22 @@
 class BallisticPredictor {
 public:
     BallisticPredictor(float bullet_speed = 16.0f);
-    
+    ~BallisticPredictor(); // Added destructor to properly manage memory
+
     // Predict ballistic point considering target movement and flight time
-    cv::Point2f* predict_ballistic_point(const cv::Point2f* predicted_center, 
-                                        const cv::Point2f& img_center, 
+    cv::Point2f* predict_ballistic_point(const cv::Point2f* predicted_center,
+                                        const cv::Point2f& img_center,
                                         const cv::Point2f& target_speed);
-    
+
     // Get last ballistic point
     const cv::Point2f* get_last_ballistic_point() const { return last_ballistic_point; }
-    
+
 private:
     float bullet_speed;            // Bullet speed (m/s)
     float armor_half_width;       // Armor half width (converted to meters)
     float armor_half_height;      // Armor half height (converted to meters)
     cv::Point2f* last_ballistic_point;  // Last ballistic prediction
-    
+
     // Calculate distance to target
     float calculate_distance(const cv::Point2f& armor_center, const cv::Point2f& img_center, float focal_length = 800.0f);
 };

inc/ImagePreprocessor.h

@@ -8,16 +8,12 @@ class ImagePreprocessor {
 public:
     ImagePreprocessor();
     ~ImagePreprocessor();
-    
-    // Get mask based on target color
-    cv::Mat get_mask(const cv::Mat& frame, const std::string& target_color);
-    
-    // Get frame with only the target color
-    cv::Mat get_color_only_frame(const cv::Mat& frame, const std::string& target_color);
-    
-    // Combined function to return both mask and color-only frame
-    void process_frame(const cv::Mat& frame, const std::string& target_color, 
-                      cv::Mat& mask, cv::Mat& color_only_frame);
+
+    // Apply morphological operations to a mask
+    void apply_morphology(const cv::Mat& input_mask, cv::Mat& output_mask);
+
+    // Apply Gaussian blur to reduce noise
+    void apply_gaussian_blur(const cv::Mat& input, cv::Mat& output, int kernel_size = 6);
 };
 
 #endif // IMAGEPREPROCESSOR_H

inc/KalmanFilter.h

@@ -6,12 +6,16 @@
 class KalmanFilter {
 public:
     KalmanFilter();
-    
+
     void update(const cv::Point2f& measurement);
     cv::Point2f predict();
-    
+
+    // Method to update the process noise based on target movement characteristics
+    void updateProcessNoise(const cv::Point2f& current_measurement);
+
     cv::Point2f get_last_measurement() const { return last_measurement; }
     cv::Point2f get_last_prediction() const { return last_prediction; }
+    cv::Point2f get_current_velocity() const;  // New method to get current velocity
     bool is_initialized() const { return initialized; }
 
 private:
@@ -19,8 +23,16 @@ private:
     bool initialized;
     cv::Point2f last_measurement;
     cv::Point2f last_prediction;
-    
+    cv::Point2f prev_measurement;
+    bool has_prev_measurement;
+
+    // Store previous states for velocity and acceleration estimation
+    cv::Point2f prev_velocity;
+    double time_elapsed;  // Time in seconds between measurements
+    bool has_prev_velocity;
+
     void init_params();
+    cv::Point2f estimate_velocity(const cv::Point2f& current, const cv::Point2f& previous);
 };
 
 #endif // KALMANFILTER_H

inc/MindVisionCamera.h

@@ -3,6 +3,7 @@
 
 #include <opencv2/opencv.hpp>
 #include <string>
+#include <memory>
 
 // MindVision SDK 头文件 - 可能需要根据实际SDK文件调整
 extern "C" {
@@ -17,12 +18,14 @@ public:
     int width;
     int height;
     int fps;
+    unsigned char* g_pRgbBuffer; // 处理后数据缓存区
 
     MindVisionCamera(int cam_id = 0, const std::string& target_color = "red");
     ~MindVisionCamera();
 
-    void set_cam_params();
+    bool set_cam_params();
     bool read_frame(cv::Mat& frame);
+    bool read_frame_with_color_filter(cv::Mat& frame, cv::Mat& mask, const std::string& target_color);
     void release();
     bool switch_color(const std::string& target_color);
 

inc/TTLCommunicator.h

@@ -3,17 +3,11 @@
 
 #include <string>
 
-// Placeholder for TTL communication class
-// In a real implementation, you would use a C++ serial library like:
-// - serial (https://github.com/wjwwood/serial)
-// - Boost.Asio
-// - or a system-specific approach
-
 class TTLCommunicator {
 public:
-    TTLCommunicator(int baudrate = 115200);
+    TTLCommunicator(const std::string& port_name = "/dev/ttyUSB0", int baudrate = 115200);
     ~TTLCommunicator();
-    
+
     bool connect();
     void close();
     bool send_data(const std::string& data);
@@ -23,8 +17,9 @@ private:
     std::string port_name;
     int baudrate;
     bool connected;
-    
-    // These would be implemented with actual serial communication code
+
+    int serial_fd;
+
     bool open_serial_port();
     void close_serial_port();
 };

inc/config.h

@@ -28,9 +28,9 @@ const float HORIZONTAL_ANGLE_THRESHOLD_RAD = HORIZONTAL_ANGLE_THRESHOLD * CV_PI
 const float NEARBY_LIGHT_BAR_THRESHOLD = 500.0f; // Light bar merging distance threshold (pixels)
 const float LIGHT_BAR_IOU_THRESHOLD = 0.05f;     // Light bar merging IOU threshold
 const float ARMOR_DISTANCE_RATIO_MIN = 0.6f;     // Armor light bar distance ratio range
-const float ARMOR_DISTANCE_RATIO_MAX = 3.0f;
+const float ARMOR_DISTANCE_RATIO_MAX = 4.0f;
 const float ARMOR_LENGTH_DIFF_RATIO = 0.5f;      // Armor light bar length difference ratio
-const float ARMOR_ANGLE_DIFF_THRESHOLD = 20.0f * CV_PI / 180.0f; // Armor light bar angle difference threshold (radians)
+const float ARMOR_ANGLE_DIFF_THRESHOLD = 15.0f * CV_PI / 180.0f; // Armor light bar angle difference threshold (radians)
 
 // Kalman filter parameters
 const float KF_PROCESS_NOISE = 0.02f;       // Process noise covariance