在作kcf跟踪时，set_activity/type的作用是什么

在使用 KCF 跟踪算法时，`set_activity` 和 `set_type` 是 Tracker 类中的两个重要方法。它们的作用如下：

- `set_activity`: 设置跟踪器的活跃状态。当跟踪目标时，跟踪器的活跃状态应该为 true，否则为 false。设置跟踪器的活跃状态可以控制跟踪器在跟踪目标时的计算量，从而提高跟踪的效率。
- `set_type`: 设置跟踪器的类型。KCF 跟踪算法有多种不同的变体，每个变体都有不同的参数和性能特点。通过设置跟踪器的类型，可以选择最适合当前跟踪任务的算法变体，从而提高跟踪的精度和效率。

总的来说，`set_activity` 和 `set_type` 方法可以让我们更好地控制和优化 KCF 跟踪算法的性能，从而实现更好的跟踪效果。

相关问题

kcf无人机跟踪小车ros代码,要求订阅/iris/usb_cam/image_raw话题，发布mavros/setpoint_position/local和/mavros/setpoint_velocity/cmd_vel_unstamped

以下是一个简单的ROS节点示例，用于使用KCF算法跟踪小车并发布位置和速度指令到MAVROS。

#!/usr/bin/env python

import rospy
from sensor_msgs.msg import Image
from cv_bridge import CvBridge
import cv2
import numpy as np
from pyimagesearch.centroidtracker import CentroidTracker
from pyimagesearch.trackableobject import TrackableObject
from geometry_msgs.msg import PoseStamped, Twist

class KCFTrackerNode:
    def __init__(self):
        rospy.init_node('kcf_tracker_node', anonymous=True)

        self.bridge = CvBridge()
        self.ct = CentroidTracker()
        self.trackers = []
        self.trackable_objects = {}

        self.image_sub = rospy.Subscriber('/iris/usb_cam/image_raw', Image, self.image_callback)
        self.position_pub = rospy.Publisher('/mavros/setpoint_position/local', PoseStamped, queue_size=1)
        self.velocity_pub = rospy.Publisher('/mavros/setpoint_velocity/cmd_vel_unstamped', Twist, queue_size=1)

        self.image_width = 640
        self.image_height = 480
        self.focal_length = 600
        self.real_width = 0.5
        self.target_width = 0.1

    def image_callback(self, data):
        cv_image = self.bridge.imgmsg_to_cv2(data, 'bgr8')

        if len(self.trackers) == 0:
            # initialize trackers
            objects = self.ct.update([(0, 0, self.image_width, self.image_height)])
            for (object_id, centroid) in objects.items():
                tracker = cv2.TrackerKCF_create()
                tracker.init(cv_image, (centroid[0], centroid[1], self.target_width * self.focal_length, self.target_width * self.focal_length))
                self.trackers.append(tracker)
                self.trackable_objects[object_id] = TrackableObject(object_id, centroid)

        else:
            # update trackers
            for tracker in self.trackers:
                success, box = tracker.update(cv_image)
                if success:
                    (x, y, w, h) = [int(v) for v in box]
                    centroid = (x + w / 2, y + h / 2)
                    object_id = self.ct.register(centroid)
                    to = self.trackable_objects.get(object_id, None)
                    if to is None:
                        to = TrackableObject(object_id, centroid)
                        self.trackable_objects[object_id] = to
                    else:
                        to.centroids.append(centroid)

        # filter out small objects
        self.trackable_objects = {k: v for k, v in self.trackable_objects.items() if len(v.centroids) > 5 and v.width() > self.image_width * 0.1}

        # update position and velocity commands
        for object_id, to in self.trackable_objects.items():
            x = (to.centroids[-1][0] - self.image_width / 2) * self.real_width / self.focal_length
            y = (to.centroids[-1][1] - self.image_height / 2) * self.real_width / self.focal_length
            z = 2.0

            pose_msg = PoseStamped()
            pose_msg.header.stamp = rospy.Time.now()
            pose_msg.pose.position.x = x
            pose_msg.pose.position.y = y
            pose_msg.pose.position.z = z
            self.position_pub.publish(pose_msg)

            vx = (to.centroids[-1][0] - to.centroids[-2][0]) * self.real_width / self.focal_length
            vy = (to.centroids[-1][1] - to.centroids[-2][1]) * self.real_width / self.focal_length
            vz = 0.0

            vel_msg = Twist()
            vel_msg.linear.x = vx
            vel_msg.linear.y = vy
            vel_msg.linear.z = vz
            self.velocity_pub.publish(vel_msg)

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

请注意，此节点使用了pyimagesearch库中的CentroidTracker和TrackableObject类，您需要先安装此库：

pip install imutils

此外，这里的代码将图像中间作为目标点，将图像宽度的10％用作最小目标宽度，将真实世界中的实际宽度设置为0.5米。您可能需要根据您的具体应用程序进行一些修改。

Running command: "make cmake_check_build_system" in "/home/mps/kcf_ws/build

回答: 根据引用\[1\]的内容，出现了一个错误信息，指出在执行命令"make -j2 -l2"时，没有找到目标文件'robot_voice/CMakeFiles/iat_publish.dir/build'，导致编译失败。这可能是由于缺少相关的依赖或配置错误所致。而引用\[2\]提供了一个解决方法，针对Ubuntu 20.04和ROS Noetic版本，可以尝试使用以下命令来安装serial包: sudo apt-get install ros-noetic-serial。这样可以解决在ROS Noetic版本中缺少serial包的问题。请注意，这个解决方法是基于引用\[2\]中提供的信息，如果你的情况不同，请提供更多的上下文信息以便我能够更准确地回答你的问题。
#### 引用[.reference_title]
- *1* [######## Running command: “make cmake_check_build_system“ in “/home/jjy/catkin_ws/build“########](https://blog.csdn.net/weixin_45684863/article/details/122074533)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v91^insertT0,239^v3^insert_chatgpt"}} ] [.reference_item]
- *2* [“make cmake_check_build_system”](https://blog.csdn.net/weixin_44439003/article/details/121396180)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v91^insertT0,239^v3^insert_chatgpt"}} ] [.reference_item]
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