修改文件夹名字

视觉代码python/Center_Predicted.py

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+import numpy as np
+from 新建文件夹.config import ARMOR_WIDTH, ARMOR_HEIGHT  # 装甲板尺寸（毫米）
+
+
+class BallisticPredictor:
+    """弹道预测器：结合卡尔曼预测位置、距离和弹速计算击中预测点"""
+
+    def __init__(self, bullet_speed=16.0):  # 出弹速度默认16m/s，可调节
+        self.bullet_speed = bullet_speed  # 子弹速度（米/秒）
+        self.armor_half_width = ARMOR_WIDTH / 2000.0  # 装甲板半宽（转换为米）
+        self.armor_half_height = ARMOR_HEIGHT / 2000.0  # 装甲板半高（转换为米）
+        self.last_ballistic_point = None  # 记录上一次弹道预测点
+
+    def _calculate_distance(self, armor_center, img_center, focal_length=800):
+        """
+        估算目标距离（简化模型，需根据实际相机内参调整）
+        原理：基于装甲板实际尺寸和图像中尺寸的比例关系
+        """
+        # 假设图像中装甲板宽度约为灯条间距（简化计算，实际可根据检测的灯条尺寸优化）
+        # 此处使用中心偏移量和焦距估算，更精确的方式需结合实际检测的装甲板像素尺寸
+        dx = abs(armor_center[0] - img_center[0])
+        dy = abs(armor_center[1] - img_center[1])
+        pixel_distance = np.sqrt(dx ** 2 + dy ** 2)
+
+        # 若目标在中心附近，避免除以零
+        if pixel_distance < 10:
+            return 5.0  # 默认5米（可根据实际场景调整）
+
+        # 距离估算公式：(实际尺寸 × 焦距) / 像素尺寸
+        distance = (self.armor_half_width * focal_length) / (pixel_distance * 0.5)
+        return max(0.5, distance)  # 限制最小距离为0.5米
+
+    def predict_ballistic_point(self, predicted_center, img_center, target_speed):
+        """
+        计算弹道预测点
+        :param predicted_center: 卡尔曼预测的目标中心（图像坐标，(x,y)）
+        :param img_center: 图像中心坐标（(x,y)）
+        :param target_speed: 目标移动速度（由卡尔曼滤波器获取，(vx, vy)像素/秒）
+        :return: 弹道修正后的预测点（图像坐标，(x,y)）
+        """
+        if predicted_center is None:
+            self.last_ballistic_point = None
+            return None
+
+        # 1. 估算目标距离（米）
+        distance = self._calculate_distance(predicted_center, img_center)
+
+        # 2. 计算子弹飞行时间（秒）：距离 / 子弹速度
+        flight_time = distance / self.bullet_speed if self.bullet_speed > 0 else 0.0
+
+        # 3. 计算飞行时间内目标的移动量（像素）
+        # 目标速度（pixel/s）× 飞行时间（s）= 预测移动像素
+        delta_x = target_speed[0] * flight_time
+        delta_y = target_speed[1] * flight_time
+
+        # 4. 弹道预测点 = 卡尔曼预测中心 + 目标移动补偿
+        ballistic_x = predicted_center[0] + delta_x
+        ballistic_y = predicted_center[1] + delta_y
+        ballistic_point = (ballistic_x, ballistic_y)
+
+        self.last_ballistic_point = ballistic_point
+        return ballistic_point
+
+    def get_last_ballistic_point(self):
+        """获取上一次弹道预测点"""
+        return self.last_ballistic_point

视觉代码python/TTLCommunicator.py

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+import serial
+import serial.tools.list_ports
+
+
+class TTLCommunicator:
+    def __init__(self, baudrate=115200, timeout=0.1):
+        """初始化TTL通信对象"""
+        self.baudrate = baudrate
+        self.timeout = timeout
+        self.ser = None
+        self.connected = False
+
+    @staticmethod  # 声明为静态方法
+    def find_usb_ttl_port():
+        """自动查找USB转TTL设备端口（静态方法，不依赖实例状态）"""
+        ports = list(serial.tools.list_ports.comports())
+        for port in ports:
+            if "USB Serial" in port.description or "CH340" in port.description or "PL2303" in port.description:
+                return port.device
+        return None
+
+    def connect(self, port=None):
+        """连接到USB转TTL设备"""
+        try:
+            if not port:
+                # 调用静态方法时，可直接通过类名或实例调用（此处保持原有逻辑）
+                port = self.find_usb_ttl_port()
+                if not port:
+                    print("未找到USB转TTL设备")
+                    return False
+
+            self.ser = serial.Serial(
+                port=port,
+                baudrate=self.baudrate,
+                timeout=self.timeout,
+                parity=serial.PARITY_NONE,
+                stopbits=1,
+                bytesize=serial.EIGHTBITS
+            )
+
+            if self.ser.is_open:
+                self.connected = True
+                print(f"已连接TTL设备：{port}（波特率：{self.baudrate}）")
+                return True
+            return False
+        except Exception as e:
+            print(f"连接失败：{str(e)}")
+            return False
+
+    def send_data(self, data_str):
+        """发送数据到TTL设备"""
+        if not self.connected or not self.ser:
+            print("未连接TTL设备，请先调用connect()")
+            return False
+
+        try:
+            data = (data_str + "\n").encode("utf-8")  # 加换行符作为结束标志
+            self.ser.write(data)
+            self.ser.flush()
+            return True
+        except Exception as e:
+            print(f"发送失败：{str(e)}")
+            return False
+
+    def close(self):
+        """关闭串口连接"""
+        if self.ser and self.ser.is_open:
+            self.ser.close()
+            self.connected = False
+            print("TTL连接已关闭")

视觉代码python/armor_detector.py

@@ -0,0 +1,223 @@
+import cv2
+import numpy as np
+from 新建文件夹.config import (
+    HORIZONTAL_ANGLE_THRESHOLD, NEARBY_LIGHT_BAR_THRESHOLD,
+    LIGHT_BAR_IOU_THRESHOLD, ARMOR_DISTANCE_RATIO_RANGE,
+    ARMOR_LENGTH_DIFF_RATIO, ARMOR_ANGLE_DIFF_THRESHOLD
+)
+
+
+class ArmorDetector:
+    """装甲板检测类：灯条提取、配对"""
+
+    def __init__(self):
+        self.angle_threshold_rad = np.radians(HORIZONTAL_ANGLE_THRESHOLD)
+
+    def _extract_initial_light_bars(self, mask):
+        """从掩码中提取初始灯条（轮廓+最小外接矩形）"""
+        contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+        initial_light_bars = []
+
+        for contour in contours:
+            rect = cv2.minAreaRect(contour)
+            box = cv2.boxPoints(rect)  # 获取矩形四个顶点
+            if len(box) == 0:  # 确保顶点数有效
+                continue
+            box = np.int32(box)  # 转为int32（OpenCV要求的类型）
+            (x, y), (w, h), angle = rect
+            contour_area = cv2.contourArea(contour)
+            rect_area = w * h
+
+            # 筛选：面积足够 + 填充度高
+            if rect_area > 30 and (contour_area / rect_area if rect_area > 0 else 0) > 0.4:
+                initial_light_bars.append({"rect": rect, "box": box, "area": rect_area})
+
+        return initial_light_bars
+
+    def _merge_nearby_light_bars(self, initial_light_bars):
+        """合并临近灯条（避免同一灯条被拆分）"""
+        processed_boxes = []
+        visited = [False] * len(initial_light_bars)
+
+        for i in range(len(initial_light_bars)):
+            if visited[i]:
+                continue
+
+            nearby_indices = [i]
+            (x1, y1), _, _ = initial_light_bars[i]["rect"]
+
+            for j in range(i + 1, len(initial_light_bars)):
+                if visited[j]:
+                    continue
+                (x2, y2), _, _ = initial_light_bars[j]["rect"]
+                distance = np.sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2)
+
+                # 距离+IOU筛选
+                if distance < NEARBY_LIGHT_BAR_THRESHOLD:
+                    box_i = initial_light_bars[i]["box"]
+                    box_j = initial_light_bars[j]["box"]
+                    if len(box_i) == 0 or len(box_j) == 0:
+                        continue
+                    b1 = cv2.boundingRect(box_i)
+                    b2 = cv2.boundingRect(box_j)
+                    inter_area = self._calculate_iou(b1, b2)
+                    if inter_area > LIGHT_BAR_IOU_THRESHOLD:
+                        nearby_indices.append(j)
+                        visited[j] = True
+
+            # 合并临近灯条的顶点，生成新矩形
+            all_valid_points = []
+            for k in nearby_indices:
+                box_k = initial_light_bars[k]["box"]
+                if len(box_k) > 0:
+                    all_valid_points.append(box_k)
+            if not all_valid_points:
+                continue
+            all_points = np.vstack(all_valid_points)
+            merged_rect = cv2.minAreaRect(all_points)
+            merged_box = np.int32(cv2.boxPoints(merged_rect))  # 转为int32
+            processed_boxes.append(merged_box)
+            visited[i] = True
+
+        return processed_boxes
+
+    def _calculate_iou(self, b1, b2):
+        """计算两个bounding box的IOU"""
+        b1 = [b1[0], b1[1], b1[0] + b1[2], b1[1] + b1[3]]
+        b2 = [b2[0], b2[1], b2[0] + b2[2], b2[1] + b2[3]]
+
+        inter_x1 = max(b1[0], b2[0])
+        inter_y1 = max(b1[1], b2[1])
+        inter_x2 = min(b1[2], b2[2])
+        inter_y2 = min(b1[3], b2[3])
+        if inter_x1 >= inter_x2 or inter_y1 >= inter_y2:
+            return 0.0
+
+        inter_area = (inter_x2 - inter_x1) * (inter_y2 - inter_y1)
+        b1_area = (b1[2] - b1[0]) * (b1[3] - b1[1])
+        b2_area = (b2[2] - b2[0]) * (b2[3] - b2[1])
+        union_area = b1_area + b2_area - inter_area
+        return inter_area / union_area if union_area > 0 else 0.0
+
+    def _filter_valid_light_bars(self, processed_boxes):
+        """筛选有效灯条（排除水平灯条）"""
+        valid_light_bars = []
+
+        for box in processed_boxes:
+            if len(box) == 0:
+                continue
+            rect = cv2.minAreaRect(box)
+            (x, y), (w, h), angle = rect
+
+            length = w if w > h else h
+            width = h if w > h else w
+            main_angle = angle if w > h else angle + 90
+            main_angle = main_angle % 180
+            if main_angle > 90:
+                main_angle -= 180
+            main_angle_rad = np.radians(main_angle)
+
+            center = (x, y)
+            end1 = (
+                center[0] + (length / 2) * np.cos(main_angle_rad),
+                center[1] + (length / 2) * np.sin(main_angle_rad)
+            )
+            end2 = (
+                center[0] - (length / 2) * np.cos(main_angle_rad),
+                center[1] - (length / 2) * np.sin(main_angle_rad)
+            )
+
+            rect_area = length * width
+            contour_area = cv2.contourArea(box)
+            area_ratio = contour_area / rect_area if rect_area > 0 else 0
+            # 筛选：面积足够 + 填充度高 + 非水平
+            if (rect_area > 40 and area_ratio > 0.4 and
+                    abs(main_angle_rad) > self.angle_threshold_rad):
+                valid_light_bars.append({
+                    "center": center,
+                    "size": (length, width),
+                    "angle": main_angle,
+                    "angle_rad": main_angle_rad,
+                    "area": rect_area,
+                    "center_line": [end1, end2],
+                    "center_line_length": length,
+                    "box": box
+                })
+
+        return valid_light_bars
+
+    def _pair_light_bars_to_armor(self, light_bars, target_color):
+        """灯条配对为装甲板（按置信度排序）"""
+        armor_plates = []
+        used_indices = set()
+
+        for i in range(len(light_bars)):
+            if i in used_indices:
+                continue
+            lb1 = light_bars[i]
+
+            for j in range(i + 1, len(light_bars)):
+                if j in used_indices:
+                    continue
+                lb2 = light_bars[j]
+
+                # 角度差异筛选
+                angle_diff = abs(lb1["angle_rad"] - lb2["angle_rad"])
+                angle_diff = min(angle_diff, 2 * np.pi - angle_diff)
+                if angle_diff > ARMOR_ANGLE_DIFF_THRESHOLD:
+                    continue
+
+                # 距离比例筛选
+                dx = lb2["center"][0] - lb1["center"][0]
+                dy = lb2["center"][1] - lb1["center"][1]
+                distance = np.sqrt(dx ** 2 + dy ** 2)
+                avg_length = (lb1["center_line_length"] + lb2["center_line_length"]) / 2
+                distance_ratio = distance / avg_length
+                if not (ARMOR_DISTANCE_RATIO_RANGE[0] < distance_ratio < ARMOR_DISTANCE_RATIO_RANGE[1]):
+                    continue
+
+                # 长度差异筛选
+                length_diff_ratio = abs(lb1["center_line_length"] - lb2["center_line_length"]) / avg_length
+                if length_diff_ratio > ARMOR_LENGTH_DIFF_RATIO:
+                    continue
+
+                armor_center = (
+                    (lb1["center"][0] + lb2["center"][0]) / 2,
+                    (lb1["center"][1] + lb2["center"][1]) / 2
+                )
+                confidence = (lb1["area"] + lb2["area"]) / (distance + 1)
+
+                armor_plates.append({
+                    "color": target_color,
+                    "center": armor_center,
+                    "confidence": confidence,
+                    "pair": (lb1, lb2),
+                    "corners_2d": None
+                })
+
+                used_indices.add(i)
+                used_indices.add(j)
+                break
+
+        return sorted(armor_plates, key=lambda x: x["confidence"], reverse=True)
+
+    def detect(self, mask, target_color):
+        """完整检测流程：灯条提取→合并→筛选→配对→3D估计"""
+        initial_light_bars = self._extract_initial_light_bars(mask)
+        if not initial_light_bars:
+            return [], []
+
+        processed_boxes = self._merge_nearby_light_bars(initial_light_bars)
+        if not processed_boxes:
+            return [], []
+
+        valid_light_bars = self._filter_valid_light_bars(processed_boxes)
+        if len(valid_light_bars) < 2:
+            return valid_light_bars, []
+
+        armor_plates = self._pair_light_bars_to_armor(valid_light_bars, target_color)
+        if not armor_plates:
+            return valid_light_bars, []
+
+
+        return valid_light_bars, armor_plates

视觉代码python/camera_utils.py

@@ -0,0 +1,54 @@
+import cv2
+
+
+class Camera:
+    """摄像头初始化与帧读取（支持颜色参数）"""
+
+    def __init__(self, cam_id=0, target_color="red"):
+
+        self.cap = cv2.VideoCapture(cam_id)
+        self.is_opened = self.cap.isOpened()
+        if not self.is_opened:
+            raise RuntimeError("无法打开摄像头！")
+
+        self.target_color = target_color.lower()
+        if self.target_color not in ["red", "blue"]:
+            raise ValueError("仅支持 'red' 或 'blue'")
+
+        self._set_cam_params()
+
+    def _set_cam_params(self):
+        """根据颜色设置摄像头参数"""
+        self.cap.set(cv2.CAP_PROP_AUTOFOCUS, 0)  # 关闭自动对焦
+        self.cap.set(cv2.CAP_PROP_AUTO_WB, 0)    # 关闭自动白平衡
+        self.cap.set(cv2.CAP_PROP_ISO_SPEED, 0)  # 自动ISO
+
+        if self.target_color == "red":
+            self.cap.set(cv2.CAP_PROP_CONTRAST, 10)
+            self.cap.set(cv2.CAP_PROP_EXPOSURE, -10)
+            self.cap.set(cv2.CAP_PROP_WB_TEMPERATURE, 5)
+        elif self.target_color == "blue":
+            self.cap.set(cv2.CAP_PROP_CONTRAST, 10)
+            self.cap.set(cv2.CAP_PROP_EXPOSURE, -9)
+            self.cap.set(cv2.CAP_PROP_WB_TEMPERATURE, 5)
+
+    def read_frame(self):
+        """读取一帧"""
+        if not self.is_opened:
+            return False, None
+        return self.cap.read()
+
+    def release(self):
+        """释放资源"""
+        if self.is_opened:
+            self.cap.release()
+            self.is_opened = False
+
+    def switch_color(self, target_color):
+        """切换目标颜色并更新参数"""
+        target_color = target_color.lower()
+        if target_color in ["red", "blue"] and target_color != self.target_color:
+            self.target_color = target_color
+            self._set_cam_params()
+            return True
+        return False

视觉代码python/config.py

@@ -0,0 +1,36 @@
+import numpy as np
+
+# 相机内参（需实际标定）
+CAMERA_MATRIX = np.array([
+    [600, 0, 320],
+    [0, 600, 240],
+    [0, 0, 1]
+], dtype=np.float32)
+DIST_COEFFS = np.zeros(5)  # 畸变系数（无畸变设为0）
+
+# 装甲板尺寸（毫米）
+ARMOR_WIDTH = 135
+ARMOR_HEIGHT = 125
+ARMOR_THICKNESS = 20
+ARMOR_3D_POINTS = np.array([
+    [-ARMOR_WIDTH / 2, -ARMOR_HEIGHT / 2, 0],
+    [ARMOR_WIDTH / 2, -ARMOR_HEIGHT / 2, 0],
+    [ARMOR_WIDTH / 2, ARMOR_HEIGHT / 2, 0],
+    [-ARMOR_WIDTH / 2, ARMOR_HEIGHT / 2, 0]
+], dtype=np.float32)
+ARMOR_BOX_3D_POINTS = np.vstack([
+    ARMOR_3D_POINTS,
+    ARMOR_3D_POINTS + np.array([0, 0, -ARMOR_THICKNESS])
+])
+
+# 检测阈值
+HORIZONTAL_ANGLE_THRESHOLD = 25  # 灯条水平角度阈值（度）
+NEARBY_LIGHT_BAR_THRESHOLD = 500  # 灯条合并距离阈值（像素）
+LIGHT_BAR_IOU_THRESHOLD = 0.05    # 灯条合并IOU阈值
+ARMOR_DISTANCE_RATIO_RANGE = (0.6, 3)  # 装甲板灯条距离比例范围
+ARMOR_LENGTH_DIFF_RATIO = 0.5    # 装甲板灯条长度差异比例
+ARMOR_ANGLE_DIFF_THRESHOLD = np.radians(20)  # 装甲板灯条角度差异阈值（弧度）
+
+# 卡尔曼滤波参数
+KF_PROCESS_NOISE = 0.02    # 过程噪声协方差
+KF_MEASUREMENT_NOISE = 0.5  # 测量噪声协方差

视觉代码python/image_preprocessor.py

@@ -0,0 +1,57 @@
+import cv2
+import numpy as np
+
+class ImagePreprocessor:
+    """图像预处理：分离目标颜色并生成掩码"""
+    @staticmethod
+    def process_red_light(frame):
+        """红色灯条预处理"""
+        red_only = frame.copy()
+        red_only[:, :, 0] = 0  # B=0
+        red_only[:, :, 1] = 0  # G=0
+
+        hsv = cv2.cvtColor(red_only, cv2.COLOR_BGR2HSV)
+        v_mask = cv2.inRange(hsv[:, :, 2], 70, 190)
+
+        kernel_close = np.ones((7, 7), np.uint8)
+        kernel_dilate = np.ones((2, 2), np.uint8)
+        mask = cv2.morphologyEx(v_mask, cv2.MORPH_CLOSE, kernel_close)
+        mask = cv2.dilate(mask, kernel_dilate, iterations=1)
+
+        kernel = np.ones((4, 4), np.uint8)
+        mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel, iterations=1)
+        mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel, iterations=1)
+        mask = cv2.dilate(mask, kernel, iterations=1)
+
+        return mask, red_only
+
+    @staticmethod
+    def process_blue_light(frame):
+        """蓝色灯条预处理"""
+        blue_only = frame.copy()
+        blue_only[:, :, 1] = 0  # G=0
+        blue_only[:, :, 2] = 0  # R=0
+
+        hsv = cv2.cvtColor(blue_only, cv2.COLOR_BGR2HSV)
+        v_mask = cv2.inRange(hsv[:, :, 2], 120, 230)
+
+        kernel_close = np.ones((7, 7), np.uint8)
+        kernel_dilate = np.ones((2, 2), np.uint8)
+        mask = cv2.morphologyEx(v_mask, cv2.MORPH_CLOSE, kernel_close)
+        mask = cv2.dilate(mask, kernel_dilate, iterations=1)
+
+        kernel = np.ones((4, 4), np.uint8)
+        mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel, iterations=1)
+        mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel, iterations=1)
+        mask = cv2.dilate(mask, kernel, iterations=1)
+
+        return mask, blue_only
+
+    def get_mask(self, frame, target_color):
+        """根据颜色选择预处理逻辑"""
+        if target_color == "red":
+            return self.process_red_light(frame)
+        elif target_color == "blue":
+            return self.process_blue_light(frame)
+        else:
+            raise ValueError("仅支持 'red' 或 'blue'")

视觉代码python/main.py

@@ -0,0 +1,175 @@
+import cv2
+import time
+from TTLCommunicator import TTLCommunicator  # 导入入模块化的TTL通信类
+from 新建文件夹.camera_utils import Camera
+from 新建文件夹.image_preprocessor import ImagePreprocessor
+from 新建文件夹.armor_detector import ArmorDetector
+from tracker import KalmanFilter
+from visualizer import Visualizer
+from 新建文件夹.Center_Predicted import BallisticPredictor
+
+
+def output_control_data(ballistic_point, target_color, frame_counter, ttl_communicator, img_center, use_ttl):
+    """输出控制数据到TTL设备（带启用控制）"""
+    # 仅当启用TTL且且满足帧间隔时发送数据
+    if use_ttl and frame_counter % 5 == 0:
+        if ballistic_point:
+            # 计算偏移量（基于实际图像中心）
+            ballistic_offset_x = int(ballistic_point[0] - img_center[0])
+            ballistic_offset_y = int(ballistic_point[1] - img_center[1])
+
+            # 颜色简化映射
+            color_map = {"red": "r", "blue": "b"}
+            simplified_color = color_map.get(target_color, target_color)
+
+            # 构造发送字符串
+            send_str = f"s {simplified_color} {ballistic_offset_x} {ballistic_offset_y}"
+        else:
+            # 无检测结果时的格式
+            send_str = "s u 0 0"
+
+        # 发送数据
+        ttl_communicator.send_data(send_str)
+
+
+def set_camera_resolution(cap, width, height):
+    """设置摄像头分辨率"""
+    cap.set(cv2.CAP_PROP_FRAME_WIDTH, width)
+    cap.set(cv2.CAP_PROP_FRAME_HEIGHT, height)
+    # 验证实际设置的分辨率
+    actual_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+    actual_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    return actual_width, actual_height
+
+
+def main(target_color="red", cam_id=0, default_resolution=(640, 480), use_ttl=True):
+    """主函数：整合模块运行检测流程与TTL通信（带TTL启用控制）"""
+    # 定义可选分辨率列表（根据摄像头支持情况调整）
+    resolutions = [
+        (320, 240),  # 低分辨率，高帧率
+        (640, 480),  # 标准分辨率
+        (1280, 720),  # HD分辨率
+        (1920, 1080)  # 全高清分辨率
+    ]
+    res_index = resolutions.index(default_resolution) if default_resolution in resolutions else 1
+
+    # 初始化TTL通信（仅当启用时）
+    ttl = None
+    if use_ttl:
+        ttl = TTLCommunicator(baudrate=115200)
+        if not ttl.connect():
+            print("警告：无法建立TTL连接，将继续运行但不发送数据")
+    else:
+        print("TTL通讯已禁用")
+
+    # 初始化视觉处理模块
+    camera = Camera(cam_id, target_color)
+
+    # 初始化弹道预测器（可调节弹速，如16m/s）
+    ballistic_predictor = BallisticPredictor(bullet_speed=16.0)
+    # 新增：用于记录目标速度（从卡尔曼滤波器获取）
+    target_speed = (0.0, 0.0)
+
+    # 设置初始分辨率
+    current_res = set_camera_resolution(camera.cap, resolutions[res_index][0], resolutions[res_index][1])
+    print(f"初始摄像头分辨率: {current_res[0]}x{current_res[1]}")
+
+    preprocessor = ImagePreprocessor()
+    detector = ArmorDetector()
+    kalman_tracker = KalmanFilter()
+    visualizer = Visualizer()
+
+    frame_counter = 0  # 控制输出频率的计数器
+    max_consecutive_predicts = 20  # 最大连续预测次数
+    consecutive_predicts = 0  # 当前连续预测计数
+
+    try:
+        while True:
+            ret, frame = camera.read_frame()
+            if not ret:
+                print("无法读取摄像头，退出！")
+                break
+
+            # 获取图像实际尺寸并计算中心
+            img_height, img_width = frame.shape[:2]
+            img_center = (img_width // 2, img_height // 2)
+
+            # 预处理
+            mask, color_only_frame = preprocessor.get_mask(frame, target_color)
+
+            # 装甲板检测
+            valid_light_bars, armor_plates = detector.detect(mask, target_color)
+
+            # 卡尔曼滤波跟踪（修改部分：获取目标速度）
+            current_center = armor_plates[0]["center"] if (armor_plates and len(armor_plates) > 0) else None
+
+            if current_center:
+                # 有检测结果：更新卡尔曼滤波器，重置连续预测计数
+                kalman_tracker.update(current_center)
+                predicted_center = kalman_tracker.predict()
+                # 获取卡尔曼滤波器中的速度状态（dx, dy）
+                # 假设KalmanFilter类有state属性，包含[x, y, dx, dy]
+                if hasattr(kalman_tracker, 'kf'):
+                    state = kalman_tracker.kf.statePost
+                    target_speed = (state[2, 0], state[3, 0])  # dx, dy（像素/秒）
+                consecutive_predicts = 0
+            else:
+                # 无检测结果：仅用卡尔曼预测，限制连续预测次数
+                consecutive_predicts += 1
+                if consecutive_predicts < max_consecutive_predicts:
+                    predicted_center = kalman_tracker.predict()
+                else:
+                    predicted_center = None
+                    target_speed = (0.0, 0.0)
+
+            # 确定显示的中心
+            display_center = current_center if current_center else predicted_center
+            is_predicted = (display_center is not None) and (current_center is None)
+
+            # 计算弹道预测点
+            ballistic_point = ballistic_predictor.predict_ballistic_point(
+                predicted_center, img_center, target_speed
+            )
+
+            current_time = time.time()
+
+            # 可视化
+            frame = visualizer.draw_light_bars(frame, valid_light_bars, target_color)
+            if armor_plates and len(armor_plates) > 0:
+                frame = visualizer.draw_armor_plate(frame, armor_plates[0])
+            frame = visualizer.draw_offset_text(frame, display_center, target_color, is_predicted)
+
+            # 输出控制数据到TTL（传入use_ttl控制是否发送）
+            output_control_data(display_center, target_color, frame_counter, ttl, img_center, use_ttl)
+
+            # 显示窗口
+            cv2.imshow("Armor Detection", frame)
+            cv2.imshow(f"{target_color.capitalize()} Mask", mask)
+            cv2.imshow(f"{target_color.capitalize()} Only", color_only_frame)
+
+            # 仅保留基础退出功能（按q退出）
+            if cv2.waitKey(1) == ord("q"):
+                break
+
+            frame_counter += 1
+            # 控制最大帧率（100 FPS）
+            time.sleep(max(0.0, 0.01 - (time.time() - current_time)))
+
+    except KeyboardInterrupt:
+        print("\n用户终止程序")
+    finally:
+        camera.release()
+        cv2.destroyAllWindows()
+        # 仅当启用且初始化成功时关闭TTL连接
+        if use_ttl and ttl is not None:
+            ttl.close()
+
+
+if __name__ == "__main__":
+    # 可在此处设置是否启用TTL通讯（use_ttl=True/False）
+    main(
+        target_color="red",
+        cam_id=1,
+        default_resolution=(640, 480),
+        use_ttl=False  # 设为False禁用TTL通讯
+    )

视觉代码python/tracker.py

@@ -0,0 +1,56 @@
+import cv2
+import numpy as np
+from 新建文件夹.config import KF_PROCESS_NOISE, KF_MEASUREMENT_NOISE
+
+class KalmanFilter:
+    """卡尔曼滤波器：平滑并预测装甲板位置"""
+    def __init__(self):
+        self.kf = cv2.KalmanFilter(4, 2)  # 4状态(x,y,dx,dy)，2测量(x,y)
+        self._init_params()
+        self.initialized = False
+        self.last_measurement = None
+        self.last_prediction = None
+
+    def _init_params(self):
+        """初始化卡尔曼参数"""
+        self.kf.transitionMatrix = np.array([
+            [1, 0, 1, 0],
+            [0, 1, 0, 1],
+            [0, 0, 1, 0],
+            [0, 0, 0, 1]
+        ], np.float32)
+
+        self.kf.measurementMatrix = np.array([
+            [1, 0, 0, 0],
+            [0, 1, 0, 0]
+        ], np.float32)
+
+        self.kf.processNoiseCov = np.eye(4, dtype=np.float32) * KF_PROCESS_NOISE
+        self.kf.measurementNoiseCov = np.eye(2, dtype=np.float32) * KF_MEASUREMENT_NOISE
+        self.kf.errorCovPost = np.eye(4, dtype=np.float32)
+
+    def update(self, measurement):
+        """用测量值更新滤波器"""
+        measurement = np.array([[np.float32(measurement[0])], [np.float32(measurement[1])]])
+
+        if not self.initialized:
+            self.kf.statePost = np.array([
+                [measurement[0, 0]],
+                [measurement[1, 0]],
+                [0.0],
+                [0.0]
+            ], np.float32)
+            self.initialized = True
+        else:
+            self.kf.correct(measurement)
+
+        self.last_measurement = (measurement[0, 0], measurement[1, 0])
+
+    def predict(self):
+        """预测下一帧位置"""
+        if not self.initialized:
+            return None
+
+        prediction = self.kf.predict()
+        self.last_prediction = (prediction[0, 0], prediction[1, 0])
+        return self.last_prediction

视觉代码python/visualizer.py

@@ -0,0 +1,80 @@
+import cv2
+import numpy as np
+
+class Visualizer:
+    """可视化：绘制灯条、装甲板、预测位置等"""
+    def __init__(self):
+        self.colors = {
+            "red": (0, 0, 255),
+            "blue": (255, 0, 0),
+            "light_bar": (0, 255, 255),
+            "armor": (0, 255, 0),
+            "prediction": (0, 165, 255),
+            "text": (255, 255, 255)
+        }
+
+    def draw_light_bars(self, frame, light_bars, target_color):
+        """绘制灯条（矩形+中心线+标签）"""
+        bar_color = self.colors[target_color]
+        line_color = self.colors["light_bar"]
+
+        for lb in light_bars:
+            cv2.drawContours(frame, [lb["box"]], 0, bar_color, 2)
+            end1 = tuple(map(int, lb["center_line"][0]))
+            end2 = tuple(map(int, lb["center_line"][1]))
+            cv2.line(frame, end1, end2, line_color, 2)
+            center = tuple(map(int, lb["center"]))
+            cv2.putText(
+                frame, target_color, (center[0], center[1] - 8),
+                cv2.FONT_HERSHEY_SIMPLEX, 0.4, bar_color, 1
+            )
+
+        return frame
+
+    def draw_armor_plate(self, frame, armor):
+        """绘制装甲板（2D框+3D信息）"""
+        corners = armor.get("corners_2d")
+        if corners is not None:
+            # 关键修改：np.int0 替换为 np.int32
+            cv2.polylines(frame, [np.int32(corners)], True, self.colors["armor"], 2)
+
+        center = tuple(map(int, armor["center"]))
+        cv2.circle(frame, center, 3, self.colors["armor"], -1)
+        cv2.putText(
+            frame, "Armor", (center[0] + 5, center[1] - 5),
+            cv2.FONT_HERSHEY_SIMPLEX, 0.5, self.colors["armor"], 2
+        )
+
+        return frame
+
+    def draw_prediction(self, frame, predicted_pos, label="Predicted"):
+        """绘制预测位置（橙色圆点+标签）"""
+        if predicted_pos is None:
+            return frame
+
+        pos = tuple(map(int, predicted_pos))
+        cv2.circle(frame, pos, 5, self.colors["prediction"], -1)
+        cv2.putText(
+            frame, label, (pos[0] + 10, pos[1] - 10),
+            cv2.FONT_HERSHEY_SIMPLEX, 0.5, self.colors["prediction"], 2
+        )
+
+        return frame
+
+    def draw_offset_text(self, frame, display_center, target_color, is_predicted):
+        """绘制装甲板相对图像中心的偏移量"""
+        if display_center is None:
+            text = "Armor: unknown | Offset: (0, 0)"
+        else:
+            img_center = (frame.shape[1] // 2, frame.shape[0] // 2)
+            offset_x = int(display_center[0] - img_center[0])
+            offset_y = int(display_center[1] - img_center[1])
+            color_text = f"{target_color} (predicted)" if is_predicted else target_color
+            text = f"Armor: {color_text} | Offset: ({offset_x}, {offset_y})"
+
+        cv2.putText(
+            frame, text, (10, 30),
+            cv2.FONT_HERSHEY_SIMPLEX, 0.8, self.colors["text"], 2
+        )
+
+        return frame