Merge branch 'main-v2' of ssh://120.27.199.238:222/Havoc420mac/mi-task into main-v2
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119
utils/fisheye.py
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119
utils/fisheye.py
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import cv2
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import numpy as np
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import os
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def detect_yellow_distance_from_bottom(image_path, visualize=False):
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"""
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检测鱼眼图像中垂线上最靠近下方的黄点到图像底部的距离
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参数:
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image_path: 图像路径
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visualize: 是否显示检测过程可视化结果
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返回:
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distance: 黄点到图像底部的距离(像素)
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center_x: 黄点的x坐标(用于垂线参考)
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mask: 黄色区域掩模(可视化时使用)
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"""
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# 1. 读取图像并转换色彩空间
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img = cv2.imread(image_path)
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if img is None:
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raise ValueError(f"无法读取图像,请检查路径: {image_path}")
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hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
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height, width = img.shape[:2]
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# 2. 定义黄色颜色范围 (考虑不同光照条件)
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lower_yellow = np.array([20, 100, 100])
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upper_yellow = np.array([30, 255, 255])
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# 3. 创建黄色区域掩模
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mask = cv2.inRange(hsv, lower_yellow, upper_yellow)
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# 4. 形态学处理去除噪声
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kernel = np.ones((5,5), np.uint8)
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mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel)
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mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)
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# 5. 寻找轮廓
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contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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if not contours:
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print("未检测到黄色区域")
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return None, None, mask
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# 6. 找到所有黄色区域的中心点
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yellow_points = []
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for cnt in contours:
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M = cv2.moments(cnt)
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if M["m00"] > 100: # 忽略太小的区域
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cx = int(M["m10"] / M["m00"])
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cy = int(M["m01"] / M["m00"])
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yellow_points.append((cx, cy))
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if not yellow_points:
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print("未找到有效的黄色中心点")
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return None, None, mask
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# 7. 计算图像中心垂线 (考虑鱼眼畸变,使用图像中心作为参考)
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center_x = width // 2
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vertical_line_threshold = width * 0.1 # 垂线左右10%的容差范围
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# 8. 筛选在垂线附近的黄点
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vertical_points = [p for p in yellow_points if abs(p[0] - center_x) < vertical_line_threshold]
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if not vertical_points:
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# 如果没有完全垂直的点,选择最接近垂线的点
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vertical_points = sorted(yellow_points, key=lambda p: abs(p[0] - center_x))[:1]
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print(f"警告: 没有严格垂直的点,使用最接近垂线的点: {vertical_points[0]}")
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# 9. 找出最下方的黄点
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lowest_point = max(vertical_points, key=lambda p: p[1])
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# 10. 计算到图像底部的距离
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distance = height - lowest_point[1]
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# 可视化结果
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if visualize:
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vis = img.copy()
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# 标记所有黄点
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for (cx, cy) in yellow_points:
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cv2.circle(vis, (cx, cy), 5, (0, 255, 255), -1)
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# 标记垂线区域
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cv2.line(vis, (center_x, 0), (center_x, height), (0, 255, 0), 1)
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cv2.line(vis, (int(center_x - vertical_line_threshold), 0),
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(int(center_x - vertical_line_threshold), height), (0, 255, 0), 1)
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cv2.line(vis, (int(center_x + vertical_line_threshold), 0),
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(int(center_x + vertical_line_threshold), height), (0, 255, 0), 1)
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# 标记最低黄点
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cv2.circle(vis, lowest_point, 10, (0, 0, 255), -1)
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cv2.line(vis, (lowest_point[0], lowest_point[1]),
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(lowest_point[0], height), (0, 0, 255), 2)
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cv2.putText(vis, f"Distance: {distance}px", (10, 30),
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cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
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# 确保保存目录存在
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os.makedirs("saved_images", exist_ok=True)
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# 保存原始图像和结果图像
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cv2.imwrite("saved_images/Original_Image.jpg", img)
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cv2.imwrite("saved_images/Detection_Result.jpg", vis)
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return distance, center_x, mask
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# 使用示例
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try:
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distance, center_x, _ = detect_yellow_distance_from_bottom(
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"/home/mi-task/saved_images/right_20250820_120541_263023.jpg",
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visualize=True
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)
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if distance is not None:
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print(f"黄点到图像底部的距离: {distance} 像素")
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print(f"参考垂线x坐标: {center_x}")
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else:
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print("未能检测到有效的黄色点")
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except Exception as e:
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print(f"发生错误: {str(e)}")
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@ -63,6 +63,7 @@ from threading import Thread, Lock
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import time
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import queue
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from datetime import datetime
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from utils.log_helper import get_logger
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# 导入AI相机服务
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from protocol.srv import CameraService
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# qrcode
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@ -80,8 +81,7 @@ class ImageSubscriber(Node):
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# 创建服务客户端
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self.camera_client = self.create_client(CameraService, '/camera_service')
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while not self.camera_client.wait_for_service(timeout_sec=1.0):
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print('waiting for camera service...')
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# self.get_logger().info('等待AI相机服务...')
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self.get_logger().info('等待AI相机服务...')
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# 图像订阅
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self.image_sub = self.create_subscription(
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@ -137,14 +137,14 @@ class ImageSubscriber(Node):
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rclpy.spin_until_future_complete(self, future)
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result = future.result()
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print(f'服务返回: [code={result.result}, msg="{result.msg}"]')
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self.get_logger().info(f'服务返回: [code={result.result}, msg="{result.msg}"]')
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if result.result == 0:
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print('相机启动成功')
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self.get_logger().info('相机启动成功')
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self.camera_started = True
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return True
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else:
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print(f'启动失败 (错误码 {result.result})')
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self.get_logger().error(f'启动失败 (错误码 {result.result})')
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return False
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def stop_camera(self):
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@ -160,14 +160,14 @@ class ImageSubscriber(Node):
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rclpy.spin_until_future_complete(self, future, timeout_sec=2.0)
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if future.result().result == 0:
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print('相机已停止')
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self.get_logger().info('相机已停止')
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self.camera_started = False
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return True
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else:
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print(f'停止失败: {future.result().msg}')
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self.get_logger().error(f'停止失败: {future.result().msg}')
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return False
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except Exception as e:
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print(f'停止异常: {str(e)}')
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self.get_logger().error(f'停止异常: {str(e)}')
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return False
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def save_image(self, image, prefix):
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timestamp = datetime.now().strftime("%Y%m%d_%H%M%S_%f")
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filename = f"{self.save_dir}/{prefix}_{timestamp}.jpg"
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cv2.imwrite(filename, image)
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print(f"已保存 {prefix} 图像: {filename}")
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# self.get_logger().info(f"已保存 {prefix} 图像: {filename}")
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return True
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except Exception as e:
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print(f"保存{prefix}图像失败: {str(e)}")
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self.get_logger().error(f"保存{prefix}图像失败: {str(e)}")
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return False
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def image_callback_rgb(self, msg):
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@ -200,7 +200,7 @@ class ImageSubscriber(Node):
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self.save_image(self.cv_image_rgb, 'rgb')
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self.last_save_time['rgb'] = time.time()
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except Exception as e:
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print(f"RGB图像处理错误: {str(e)}")
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self.get_logger().error(f"RGB图像处理错误: {str(e)}")
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def image_callback_left(self, msg):
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try:
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@ -209,7 +209,7 @@ class ImageSubscriber(Node):
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self.save_image(self.cv_image_left, 'left')
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self.last_save_time['left'] = time.time()
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except Exception as e:
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print(f"左图像处理错误: {str(e)}")
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self.get_logger().error(f"左图像处理错误: {str(e)}")
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def image_callback_right(self, msg):
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try:
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@ -218,7 +218,7 @@ class ImageSubscriber(Node):
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self.save_image(self.cv_image_right, 'right')
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self.last_save_time['right'] = time.time()
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except Exception as e:
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print(f"右图像处理错误: {str(e)}")
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self.get_logger().error(f"右图像处理错误: {str(e)}")
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def image_callback_ai(self, msg):
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try:
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@ -227,7 +227,7 @@ class ImageSubscriber(Node):
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self.save_image(self.cv_image_ai, 'ai')
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self.last_save_time['ai'] = time.time()
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except Exception as e:
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print(f"ai图像处理错误: {str(e)}")
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self.get_logger().error(f"ai图像处理错误: {str(e)}")
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def safe_spin(self):
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"""安全spin循环"""
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@ -366,8 +366,8 @@ class ImageProcessor:
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interval: 扫描间隔,单位秒
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"""
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if self.scan_thread is not None and self.scan_thread.is_alive():
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# self.log.warning("异步扫描已经在运行中", "警告")
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print('[ImageProcessor] scan,warn')
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self.log.warning("异步扫描已经在运行中", "警告")
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print('scan,warn')
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return
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self.enable_async_scan = True
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self.scan_thread = Thread(target=self._async_scan_worker, args=(interval,))
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self.scan_thread.daemon = True # 设为守护线程,主线程结束时自动结束
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self.scan_thread.start()
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# self.log.info("启动异步 QR 码扫描线程", "启动")
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print('[ImageProcessor] start async scan')
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self.log.info("启动异步 QR 码扫描线程", "启动")
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print('start')
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def stop_async_scan(self):
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"""停止异步 QR 码扫描"""
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self.enable_async_scan = False
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if self.scan_thread and self.scan_thread.is_alive():
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self.scan_thread.join(timeout=1.0)
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# self.log.info("异步 QR 码扫描线程已停止", "停止")
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print('[ImageProcessor] stop async scan')
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self.log.info("异步 QR 码扫描线程已停止", "停止")
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print('stop')
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def _async_scan_worker(self, interval):
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"""异步扫描工作线程"""
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try:
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self.is_scanning = True
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qr_data = self.decode_all_qrcodes(img)
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print(f"[ImageProcessor] 异步扫描到 QR 码: {qr_data}")
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print(qr_data)
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self.is_scanning = False
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with self.scan_lock:
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if qr_data:
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self.last_qr_result = qr_data
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self.last_qr_time = current_time
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# self.log.success(f"异步扫描到 QR 码: {qr_data}", "扫描")
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print(f"[ImageProcessor] 异步扫描到 QR 码: {qr_data}")
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self.log.success(f"异步扫描到 QR 码: {qr_data}", "扫描")
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print(f"异步扫描到 QR 码: {qr_data}")
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except Exception as e:
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self.is_scanning = False
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# self.log.error(f"异步 QR 码扫描出错: {e}", "错误")
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print(f"[ImageProcessor] 异步 QR 码扫描出错: {e}")
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self.log.error(f"异步 QR 码扫描出错: {e}", "错误")
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print(f"异步 QR 码扫描出错: {e}")
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else:
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print('[ImageProcessor] no img')
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print('no img')
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last_scan_time = current_time
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