Merge branch 'main-v2' of ssh://120.27.199.238:222/Havoc420mac/mi-task into main-v2

This commit is contained in:
hav 2025-08-20 13:20:32 +08:00
parent 3eaf411bde
commit 10169de57a
2 changed files with 145 additions and 26 deletions

119
utils/fisheye.py Normal file
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@ -0,0 +1,119 @@
import cv2
import numpy as np
import os
def detect_yellow_distance_from_bottom(image_path, visualize=False):
"""
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
参数:
image_path: 图像路径
visualize: 是否显示检测过程可视化结果
返回:
distance: 黄点到图像底部的距离(像素)
center_x: 黄点的x坐标(用于垂线参考)
mask: 黄色区域掩模(可视化时使用)
"""
# 1. 读取图像并转换色彩空间
img = cv2.imread(image_path)
if img is None:
raise ValueError(f"无法读取图像,请检查路径: {image_path}")
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
height, width = img.shape[:2]
# 2. 定义黄色颜色范围 (考虑不同光照条件)
lower_yellow = np.array([20, 100, 100])
upper_yellow = np.array([30, 255, 255])
# 3. 创建黄色区域掩模
mask = cv2.inRange(hsv, lower_yellow, upper_yellow)
# 4. 形态学处理去除噪声
kernel = np.ones((5,5), np.uint8)
mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel)
mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)
# 5. 寻找轮廓
contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
if not contours:
print("未检测到黄色区域")
return None, None, mask
# 6. 找到所有黄色区域的中心点
yellow_points = []
for cnt in contours:
M = cv2.moments(cnt)
if M["m00"] > 100: # 忽略太小的区域
cx = int(M["m10"] / M["m00"])
cy = int(M["m01"] / M["m00"])
yellow_points.append((cx, cy))
if not yellow_points:
print("未找到有效的黄色中心点")
return None, None, mask
# 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)
center_x = width // 2
vertical_line_threshold = width * 0.1 # 垂线左右10%的容差范围
# 8. 筛选在垂线附近的黄点
vertical_points = [p for p in yellow_points if abs(p[0] - center_x) < vertical_line_threshold]
if not vertical_points:
# 如果没有完全垂直的点,选择最接近垂线的点
vertical_points = sorted(yellow_points, key=lambda p: abs(p[0] - center_x))[:1]
print(f"警告: 没有严格垂直的点,使用最接近垂线的点: {vertical_points[0]}")
# 9. 找出最下方的黄点
lowest_point = max(vertical_points, key=lambda p: p[1])
# 10. 计算到图像底部的距离
distance = height - lowest_point[1]
# 可视化结果
if visualize:
vis = img.copy()
# 标记所有黄点
for (cx, cy) in yellow_points:
cv2.circle(vis, (cx, cy), 5, (0, 255, 255), -1)
# 标记垂线区域
cv2.line(vis, (center_x, 0), (center_x, height), (0, 255, 0), 1)
cv2.line(vis, (int(center_x - vertical_line_threshold), 0),
(int(center_x - vertical_line_threshold), height), (0, 255, 0), 1)
cv2.line(vis, (int(center_x + vertical_line_threshold), 0),
(int(center_x + vertical_line_threshold), height), (0, 255, 0), 1)
# 标记最低黄点
cv2.circle(vis, lowest_point, 10, (0, 0, 255), -1)
cv2.line(vis, (lowest_point[0], lowest_point[1]),
(lowest_point[0], height), (0, 0, 255), 2)
cv2.putText(vis, f"Distance: {distance}px", (10, 30),
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
# 确保保存目录存在
os.makedirs("saved_images", exist_ok=True)
# 保存原始图像和结果图像
cv2.imwrite("saved_images/Original_Image.jpg", img)
cv2.imwrite("saved_images/Detection_Result.jpg", vis)
return distance, center_x, mask
# 使用示例
try:
distance, center_x, _ = detect_yellow_distance_from_bottom(
"/home/mi-task/saved_images/right_20250820_120541_263023.jpg",
visualize=True
)
if distance is not None:
print(f"黄点到图像底部的距离: {distance} 像素")
print(f"参考垂线x坐标: {center_x}")
else:
print("未能检测到有效的黄色点")
except Exception as e:
print(f"发生错误: {str(e)}")

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@ -63,6 +63,7 @@ from threading import Thread, Lock
import time
import queue
from datetime import datetime
from utils.log_helper import get_logger
# 导入AI相机服务
from protocol.srv import CameraService
# qrcode
@ -80,8 +81,7 @@ class ImageSubscriber(Node):
# 创建服务客户端
self.camera_client = self.create_client(CameraService, '/camera_service')
while not self.camera_client.wait_for_service(timeout_sec=1.0):
print('waiting for camera service...')
# self.get_logger().info('等待AI相机服务...')
self.get_logger().info('等待AI相机服务...')
# 图像订阅
self.image_sub = self.create_subscription(
@ -137,14 +137,14 @@ class ImageSubscriber(Node):
rclpy.spin_until_future_complete(self, future)
result = future.result()
print(f'服务返回: [code={result.result}, msg="{result.msg}"]')
self.get_logger().info(f'服务返回: [code={result.result}, msg="{result.msg}"]')
if result.result == 0:
print('相机启动成功')
self.get_logger().info('相机启动成功')
self.camera_started = True
return True
else:
print(f'启动失败 (错误码 {result.result})')
self.get_logger().error(f'启动失败 (错误码 {result.result})')
return False
def stop_camera(self):
@ -160,14 +160,14 @@ class ImageSubscriber(Node):
rclpy.spin_until_future_complete(self, future, timeout_sec=2.0)
if future.result().result == 0:
print('相机已停止')
self.get_logger().info('相机已停止')
self.camera_started = False
return True
else:
print(f'停止失败: {future.result().msg}')
self.get_logger().error(f'停止失败: {future.result().msg}')
return False
except Exception as e:
print(f'停止异常: {str(e)}')
self.get_logger().error(f'停止异常: {str(e)}')
return False
def save_image(self, image, prefix):
@ -179,10 +179,10 @@ class ImageSubscriber(Node):
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S_%f")
filename = f"{self.save_dir}/{prefix}_{timestamp}.jpg"
cv2.imwrite(filename, image)
print(f"已保存 {prefix} 图像: {filename}")
# self.get_logger().info(f"已保存 {prefix} 图像: {filename}")
return True
except Exception as e:
print(f"保存{prefix}图像失败: {str(e)}")
self.get_logger().error(f"保存{prefix}图像失败: {str(e)}")
return False
def image_callback_rgb(self, msg):
@ -200,7 +200,7 @@ class ImageSubscriber(Node):
self.save_image(self.cv_image_rgb, 'rgb')
self.last_save_time['rgb'] = time.time()
except Exception as e:
print(f"RGB图像处理错误: {str(e)}")
self.get_logger().error(f"RGB图像处理错误: {str(e)}")
def image_callback_left(self, msg):
try:
@ -209,7 +209,7 @@ class ImageSubscriber(Node):
self.save_image(self.cv_image_left, 'left')
self.last_save_time['left'] = time.time()
except Exception as e:
print(f"左图像处理错误: {str(e)}")
self.get_logger().error(f"左图像处理错误: {str(e)}")
def image_callback_right(self, msg):
try:
@ -218,7 +218,7 @@ class ImageSubscriber(Node):
self.save_image(self.cv_image_right, 'right')
self.last_save_time['right'] = time.time()
except Exception as e:
print(f"右图像处理错误: {str(e)}")
self.get_logger().error(f"右图像处理错误: {str(e)}")
def image_callback_ai(self, msg):
try:
@ -227,7 +227,7 @@ class ImageSubscriber(Node):
self.save_image(self.cv_image_ai, 'ai')
self.last_save_time['ai'] = time.time()
except Exception as e:
print(f"ai图像处理错误: {str(e)}")
self.get_logger().error(f"ai图像处理错误: {str(e)}")
def safe_spin(self):
"""安全spin循环"""
@ -366,8 +366,8 @@ class ImageProcessor:
interval: 扫描间隔单位秒
"""
if self.scan_thread is not None and self.scan_thread.is_alive():
# self.log.warning("异步扫描已经在运行中", "警告")
print('[ImageProcessor] scan,warn')
self.log.warning("异步扫描已经在运行中", "警告")
print('scan,warn')
return
self.enable_async_scan = True
@ -375,16 +375,16 @@ class ImageProcessor:
self.scan_thread = Thread(target=self._async_scan_worker, args=(interval,))
self.scan_thread.daemon = True # 设为守护线程,主线程结束时自动结束
self.scan_thread.start()
# self.log.info("启动异步 QR 码扫描线程", "启动")
print('[ImageProcessor] start async scan')
self.log.info("启动异步 QR 码扫描线程", "启动")
print('start')
def stop_async_scan(self):
"""停止异步 QR 码扫描"""
self.enable_async_scan = False
if self.scan_thread and self.scan_thread.is_alive():
self.scan_thread.join(timeout=1.0)
# self.log.info("异步 QR 码扫描线程已停止", "停止")
print('[ImageProcessor] stop async scan')
self.log.info("异步 QR 码扫描线程已停止", "停止")
print('stop')
def _async_scan_worker(self, interval):
"""异步扫描工作线程"""
@ -400,21 +400,21 @@ class ImageProcessor:
try:
self.is_scanning = True
qr_data = self.decode_all_qrcodes(img)
print(f"[ImageProcessor] 异步扫描到 QR 码: {qr_data}")
print(qr_data)
self.is_scanning = False
with self.scan_lock:
if qr_data:
self.last_qr_result = qr_data
self.last_qr_time = current_time
# self.log.success(f"异步扫描到 QR 码: {qr_data}", "扫描")
print(f"[ImageProcessor] 异步扫描到 QR 码: {qr_data}")
self.log.success(f"异步扫描到 QR 码: {qr_data}", "扫描")
print(f"异步扫描到 QR 码: {qr_data}")
except Exception as e:
self.is_scanning = False
# self.log.error(f"异步 QR 码扫描出错: {e}", "错误")
print(f"[ImageProcessor] 异步 QR 码扫描出错: {e}")
self.log.error(f"异步 QR 码扫描出错: {e}", "错误")
print(f"异步 QR 码扫描出错: {e}")
else:
print('[ImageProcessor] no img')
print('no img')
last_scan_time = current_time