self.get_box(image_name)这段代码什么意思?
时间: 2024-04-05 19:34:59 浏览: 50
根据命名可以推测,这个代码应该是一个函数,接受一个参数 `image_name`,返回一个“box”的对象或者列表。但是具体实现和返回值的含义要看函数的具体实现。可能这个函数是用于处理图像的,根据图片名称获取该图片的“box”信息。但是需要注意的是,这只是一个猜测,具体的实现和含义需根据上下文进一步确定。
相关问题
def detect(self, srcimg): img, newh, neww, top, left = self.resize_image(srcimg) img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) img = img.astype(np.float32) / 255.0 blob = np.expand_dims(np.transpose(img, (2, 0, 1)), axis=0) outs = self.net.run(None, {self.net.get_inputs()[0].name: blob})[0].squeeze(axis=0) box_index,boxes = self.postprocess(srcimg, outs, (newh, neww, top, left)) return box_index,boxes 解释代码
这段代码是一个对象的方法,其中包含了目标检测的一些过程。
首先,输入的原始图像被调用对象的 `resize_image` 方法进行缩放,并返回了缩放后的图像、新的高度和宽度,以及在新图像中的顶部和左侧位置。
然后,图像被转换成 RGB 格式,并将像素值归一化到 0 到 1 之间。
接下来,使用 `np.transpose` 方法将图像的维度从 (H, W, C) 改变为 (C, H, W),并使用 `np.expand_dims` 方法将其扩展为 4 维,以适应神经网络输入的要求。
然后,使用 `self.net.run` 方法将图像通过神经网络进行前向传播,获得输出,即目标检测的结果。
最后,调用对象的 `postprocess` 方法对目标检测结果进行后处理,包括筛选出置信度高的目标框和对其进行位置还原,并返回目标框的索引和位置信息。最终,这些信息被返回给调用者。
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。
```python
#!/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:
```bash
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。