jellyfin-plugin-streamlink:一个Jellyfin插件，可使用Streamlink将直播作为LiveTV观看

import numpy as np import cv2 as cv import random as rng from collections import deque import board_demo.ros_robot_controller_sdk as rrc import os import time # 添加时间模块用于帧率计算 # 解决Qt平台插件问题 os.environ["QT_QPA_PLATFORM"] = "xcb" # 使用X11后端替代Wayland # 初始化硬件控制板 board = rrc.Board() board.enable_reception() print("硬件控制板初始化完成") # 设置随机种子 rng.seed(12345) # PID控制器类 class PID: def init(self, Kp=0.31, Ki=0.070, Kd=0.3, setpoint=0.0, integral_max=1000.0, deadzone=30.0): self.Kp = Kp self.Ki = Ki self.Kd = Kd self.setpoint = setpoint self.integral = 0.0 self.derivative = 0.0 self.last_error = 0.0 self.u = 0.0 self.integral_max = integral_max self.deadzone = deadzone # 打印PID参数 print(f"PID参数初始化: Kp={Kp}, Ki={Ki}, Kd={Kd}") print(f"设定点={setpoint}, 积分上限={integral_max}, 死区={deadzone}") def compute(self, measured_value): error = measured_value # 打印测量值和误差 print(f"\n[PID计算] 测量值={measured_value:.2f}, 误差={error:.2f}") # 应用死区 if abs(error) < self.deadzone: print(f"死区生效: |{error:.2f}| < {self.deadzone}, 误差归零") error = 0 # 更新积分项 self.integral += error self.integral = max(min(self.integral, self.integral_max), -self.integral_max) # 计算微分项 alpha = 0.2 derivative = error - self.last_error self.derivative = alpha * derivative + (1 - alpha) * self.derivative self.last_error = error # 计算PID输出 self.u = (self.Kp * error) + (self.Ki * self.integral) + (self.Kd * self.derivative) # 打印详细计算过程 print(f"比例项: Kperror = {self.Kp}{error:.2f} = {self.Kp * error:.2f}") print(f"积分项: Kiintegral = {self.Ki}{self.integral:.2f} = {self.Ki * self.integral:.2f}") print(f"微分项: Kdderivative = {self.Kd}{self.derivative:.2f} = {self.Kd * self.derivative:.2f}") print(f"PID输出: u = {self.u:.2f}") return self.u, error # 舵机控制类 class ServoController: def init(self): self.current_pos = [500, 500] self.horizontal_range = (100, 900) self.vertical_range = (200, 800) self.laser_offset_x = 0 self.laser_offset_y = 0 # 打印舵机参数 print(f"舵机控制初始化: 水平范围={self.horizontal_range}, 垂直范围={self.vertical_range}") print(f"激光偏移量: X={self.laser_offset_x}, Y={self.laser_offset_y}") def set_position(self, horizontal, vertical, move_time=500): target_h = int(horizontal + self.laser_offset_x) target_v = int(vertical + self.laser_offset_y) # 打印原始目标位置 print(f"\n[舵机设置] 原始目标: H={horizontal:.2f}, V={vertical:.2f}") # 限制舵机范围 target_h = max(self.horizontal_range[0], min(self.horizontal_range[1], target_h)) target_v = max(self.vertical_range[0], min(self.vertical_range[1], target_v)) # 打印限制后的位置 if target_h != horizontal + self.laser_offset_x or target_v != vertical + self.laser_offset_y: print(f"位置限制生效: H={target_h}, V={target_v}") self.current_pos = [target_h, target_v] # 发送控制命令 board.bus_servo_set_position(0.5, [[1, target_h], [2, target_v]]) print(f"设置舵机位置: H={target_h}, V={target_v}, 移动时间={move_time}ms") return target_h, target_v def get_position(self): return self.current_pos def main(): # 打开摄像头 cap = cv.VideoCapture(0) if not cap.isOpened(): print("无法打开摄像头") return # 设置分辨率 cap.set(cv.CAP_PROP_FRAME_WIDTH, 640) cap.set(cv.CAP_PROP_FRAME_HEIGHT, 480) print(f"摄像头设置: 宽度={640}, 高度={480}") # 创建边缘检测对象 ed = cv.ximgproc.createEdgeDrawing() EDParams = cv.ximgproc_EdgeDrawing_Params() EDParams.MinPathLength = 160 EDParams.PFmode = True EDParams.MinLineLength = 50 EDParams.NFAValidation = True ed.setParams(EDParams) # 打印边缘检测参数 print(f"边缘检测参数: MinPathLength={EDParams.MinPathLength}, MinLineLength={EDParams.MinLineLength}") center_queue = deque(maxlen=100) cv.namedWindow("Detected Ellipses", cv.WINDOW_NORMAL) # 初始化PID控制器 pid_x = PID(Kp=0.2, Ki=0.01, Kd=0.05, setpoint=0, integral_max=500, deadzone=5) pid_y = PID(Kp=0.2, Ki=0.01, Kd=0.05, setpoint=0, integral_max=500, deadzone=5) servo = ServoController() # 获取图像中心 ret, frame = cap.read() if not ret: print("无法读取摄像头图像") return image_height, image_width = frame.shape[:2] center_x, center_y = image_width // 2, image_height // 2 print(f"图像中心: X={center_x}, Y={center_y}") # 帧率计算变量 frame_count = 0 start_time = time.time() while True: ret, frame = cap.read() if not ret: print("无法读取摄像头图像") break frame_count += 1 # 图像处理 gray = cv.cvtColor(frame, cv.COLOR_BGR2GRAY) blurred = cv.GaussianBlur(gray, (5, 5), 0) ed.detectEdges(blurred) ellipses = ed.detectEllipses() # 绘制图像中心点 cv.circle(frame, (center_x, center_y), 5, (0, 255, 255), -1) cv.line(frame, (center_x - 15, center_y), (center_x + 15, center_y), (0, 255, 255), 1) cv.line(frame, (center_x, center_y - 15), (center_x, center_y + 15), (0, 255, 255), 1) cv.putText(frame, "X", (image_width - 20, center_y - 10), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 255), 1) cv.putText(frame, "Y", (center_x + 10, 20), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 255), 1) ellipse_detected = False if ellipses is not None: print(f"\n检测到 {len(ellipses)} 个椭圆") # 修正椭圆数据结构问题 for i, ellipse in enumerate(ellipses): # 将椭圆数据转换为NumPy数组并展平 ellipse_data = np.array(ellipse).flatten() # 确保有足够的数据点 if len(ellipse_data) < 5: continue # 提取椭圆参数 center = (int(ellipse_data[0]), int(ellipse_data[1])) axis1 = ellipse_data[2] axis2 = ellipse_data[3] angle = ellipse_data[4] # 判断正圆/椭圆 color = (0, 0, 255) # 红色-椭圆 circle_ratio = abs(axis1 - axis2) / max(axis1, axis2) if circle_ratio < 0.1: color = (0, 255, 0) # 绿色-正圆 # 打印椭圆参数 print(f"椭圆 {i+1}: 中心({center[0]},{center[1]}), 轴长({axis1:.2f},{axis2:.2f}), " f"角度={angle:.2f}°, 圆度={circle_ratio:.4f}") # 绘制椭圆 cv.ellipse(frame, center, (int(axis1), int(axis2)), angle, 0, 360, color, 2) cv.circle(frame, center, 3, (255, 0, 0), -1) center_queue.append(center) ellipse_detected = True average_center = None if len(center_queue) > 5: # 计算圆心平均值和标准差 mean_center = np.mean(center_queue, axis=0) std_deviation = np.std(center_queue, axis=0) # 打印队列信息 print(f"\n圆心队列: 数量={len(center_queue)}, 平均值=({mean_center[0]:.2f},{mean_center[1]:.2f}), " f"标准差=({std_deviation[0]:.2f},{std_deviation[1]:.2f})") # 过滤掉偏差过大的点 filtered_centers = [] for center in center_queue: distance = np.linalg.norm(np.array(center) - mean_center) if distance <= 2 * np.linalg.norm(std_deviation): filtered_centers.append(center) if filtered_centers: mean_centers = np.mean(filtered_centers, axis=0) average_center = (int(mean_centers[0]), int(mean_centers[1])) cv.circle(frame, average_center, 6, (255, 0, 0), 2) # 计算偏差 deviation_x = average_center[0] - center_x deviation_y = average_center[1] - center_y print(f"圆心偏差: dx={deviation_x}, dy={deviation_y}") # 绘制偏差线 cv.arrowedLine(frame, (center_x, center_y), average_center, (0, 255, 255), 2) deviation_text = f"dx: {deviation_x:.1f}, dy: {deviation_y:.1f}" cv.putText(frame, deviation_text, (10, 30), cv.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 255), 2) # PID控制 if ellipse_detected: current_h, current_v = servo.get_position() print(f"当前舵机位置: H={current_h}, V={current_v}") u_x, _ = pid_x.compute(deviation_x) u_y, _ = pid_y.compute(deviation_y) # 更新舵机位置 new_h = current_h + u_x * 0.3145 new_v = current_v - u_y * 0.3401 print(f"计算新位置: H={new_h:.2f}, V={new_v:.2f}") # 设置舵机位置 servo.set_position(new_h, new_v, move_time=500) pid_text = f"PID: ux={u_x:.1f}, uy={u_y:.1f}" cv.putText(frame, pid_text, (10, 60), cv.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2) # 检查是否达到目标位置 if abs(deviation_x) < 10 and abs(deviation_y) < 10: target_text = "Target Reached!" cv.putText(frame, target_text, (image_width // 2 - 100, 30), cv.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2) # 计算并显示帧率 elapsed_time = time.time() - start_time if elapsed_time > 1: # 每秒更新一次 fps = frame_count / elapsed_time frame_count = 0 start_time = time.time() print(f"当前帧率: {fps:.1f} FPS") # 显示帧率 if 'fps' in locals(): fps_text = f"FPS: {fps:.1f}" cv.putText(frame, fps_text, (10, image_height - 10), cv.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 255), 1) # 显示图像 cv.imshow("Detected Ellipses", frame) # 退出处理 if cv.waitKey(1) & 0xFF == ord('q'): board.bus_servo_set_position(1, [[1, 500], [2, 500]]) print("退出程序: 舵机归中") break # 释放资源 cap.release() cv.destroyAllWindows() print("程序结束") if name == 'main': main()修改代码，先提取图像中一个黑色边框的长方形，然后再在这个长方形中进行代码中的圆检测，然后追踪

<think>好的，我现在需要帮用户修改他们的Python代码，使其首先提取图像中的一个黑色边框的长......

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