ROS2下编写package利用orbbec相机进行yolov8实时目标检测

在《ROS2下编写orbbec相机C++ package并Rviz显示》的基础上，继续添加对获取的图像使用YOLO进行目标检测

首先安装YOLO以及相关库

pip3 install ultralytics

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指定依赖项 rclpy（ROS 2 Python 客户端库）、sensor_msgs（用于处理图像消息）和 cv_bridge（用于在 ROS 图像消息和 OpenCV 图像之间进行转换）

在

src/yolo_target_detection/yolo_target_detection目录下创建一个 Python 脚本，例如yolo_target_detection.py，并添加以下代码：

import rclpy from rclpy.node import Node from sensor_msgs.msg import Image from cv_bridge import CvBridge import cv2 from ultralytics import YOLO import numpy as np from std_msgs.msg import Header from sensor_msgs.msg import Image as ROSImage class YoloTargetDetectionNode(Node): def __init__(self): super().__init__('yolo_target_detection_node') # Initialize the YOLOv8 model self.model = YOLO("yolov8n.pt") # 选择你训练的模型 self.bridge = CvBridge() # Create a subscriber for RGB image self.image_sub = self.create_subscription( Image, '/rgb_image', # 修改为你订阅的topic self.image_callback, 10 ) # Create a publisher for output image with bounding boxes self.obb_pub = self.create_publisher( ROSImage, '/obb_image', 10 ) def image_callback(self, msg): try: # Convert ROS Image message to OpenCV image cv_image = self.bridge.imgmsg_to_cv2(msg, "bgr8") except Exception as e: self.get_logger().error(f"Error converting image: {e}") return # Perform object detection using YOLOv8 results = self.model(cv_image) # YOLOv8 returns a list of results, each result is a Results object result = results[0] # Get the first result (assuming single image inference) # Get bounding boxes, class IDs, and confidences boxes = result.boxes.xywh.cpu().numpy() # Bounding boxes (x_center, y_center, width, height) confidences = result.boxes.conf.cpu().numpy() # Confidence scores class_ids = result.boxes.cls.cpu().numpy() # Class IDs labels = result.names # Class names # Draw bounding boxes on the image for i, box in enumerate(boxes): x_center, y_center, width, height = box[:4] confidence = confidences[i] class_id = int(class_ids[i]) # Get the class ID label = labels[class_id] # Get the class label # Convert center to top-left coordinates for cv2 x1 = int((x_center - width / 2)) y1 = int((y_center - height / 2)) x2 = int((x_center + width / 2)) y2 = int((y_center + height / 2)) # Draw bounding box and label on the image cv2.rectangle(cv_image, (x1, y1), (x2, y2), (0, 255, 0), 2) cv2.putText(cv_image, f"{label} {confidence:.2f}", (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 0, 0), 2) # Convert the image with bounding boxes back to ROS message try: obb_image_msg = self.bridge.cv2_to_imgmsg(cv_image, encoding="bgr8") obb_image_msg.header = Header() obb_image_msg.header.stamp = self.get_clock().now().to_msg() obb_image_msg.header.frame_id = "camera_frame" # 根据你的相机frame进行调整 # Publish the image with bounding boxes self.obb_pub.publish(obb_image_msg) self.get_logger().info("Published object-bound box image.") except Exception as e: self.get_logger().error(f"Error publishing image: {e}") def main(args=None): rclpy.init(args=args) node = YoloTargetDetectionNode() try: rclpy.spin(node) except KeyboardInterrupt: pass finally: node.destroy_node() rclpy.shutdown() if __name__ == '__main__': main()