ros yolov5实例分割 代码

以下是使用ROS和YOLOv5进行实例分割的示例代码：

首先，需要安装ROS和YOLOv5的依赖项。在Ubuntu中，可以使用以下命令安装：

sudo apt-get install ros-<your_ros_version>-cv-bridge ros-<your_ros_version>-image-transport

然后，下载YOLOv5的代码并将其放置在ROS包中。在终端中，进入ROS包的目录并运行以下命令：

git clone https://github.com/ultralytics/yolov5.git

接下来，创建一个ROS节点并在其中运行YOLOv5。在ROS包的`src`目录下创建一个Python脚本，例如`yolov5_node.py`，并添加以下代码：

#!/usr/bin/env python

import rospy
from sensor_msgs.msg import Image
from cv_bridge import CvBridge
import cv2
from yolov5.models.experimental import attempt_load
from yolov5.utils.general import non_max_suppression, scale_coords
from yolov5.utils.torch_utils import select_device


class YOLOv5Node:

    def __init__(self):
        # Load YOLOv5 model
        self.device = select_device('')
        self.model = attempt_load('yolov5s.pt', map_location=self.device)
        self.names = self.model.module.names if hasattr(self.model, 'module') else self.model.names
        self.colors = [[0, 255, 0]]

        # Set up ROS node
        rospy.init_node('yolov5_node')
        self.bridge = CvBridge()
        self.image_sub = rospy.Subscriber('/camera/image_raw', Image, self.image_callback)
        self.image_pub = rospy.Publisher('/camera/image_processed', Image, queue_size=1)

    def image_callback(self, data):
        # Convert ROS image message to OpenCV image
        cv_image = self.bridge.imgmsg_to_cv2(data, 'bgr8')

        # Run YOLOv5 inference on image
        img = self.model.preprocess(cv_image)
        img = img.to(self.device)
        pred = self.model(img, augment=False)[0]
        pred = non_max_suppression(pred, conf_thres=0.25, iou_thres=0.45, agnostic=False)

        # Draw bounding boxes on image
        for i, det in enumerate(pred):
            if len(det):
                det[:, :4] = scale_coords(img.shape[2:], det[:, :4], cv_image.shape).round()
                for *xyxy, conf, cls in reversed(det):
                    label = f'{self.names[int(cls)]} {conf:.2f}'
                    color = self.colors[int(cls) % len(self.colors)]
                    cv2.rectangle(cv_image, (int(xyxy[0]), int(xyxy[1])), (int(xyxy[2]), int(xyxy[3])), color, thickness=2)
                    cv2.putText(cv_image, label, (int(xyxy[0]), int(xyxy[1]) - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, thickness=2)

        # Convert OpenCV image to ROS image message and publish
        image_msg = self.bridge.cv2_to_imgmsg(cv_image, 'bgr8')
        self.image_pub.publish(image_msg)


if __name__ == '__main__':
    try:
        node = YOLOv5Node()
        rospy.spin()
    except rospy.ROSInterruptException:
        pass

在`__init__`方法中，YOLOv5模型被加载并初始化ROS节点。`image_callback`方法会在每次接收到ROS图像消息时被调用，并使用YOLOv5进行实例分割和边框绘制。最后，绘制的图像被转换为ROS图像消息并发布到`/camera/image_processed`话题。

在终端中，运行以下命令启动ROS节点：

rosrun <your_package_name> yolov5_node.py

接下来，使用ROS的`image_view`节点查看分割后的图像。在终端中运行以下命令：

rosrun image_view image_view image:=/camera/image_processed

应该可以看到实例分割和边框绘制后的图像了。