使用opencv和python实现图像的智能处理,python opencv图像识别教程整理
ROS通过一个名为CvBridge的函数包将采集到的图像数据转换成OpenCV格式，经过OpenCV处理后送回ROS进行图像显示(应用)。本文主要介绍如何在Python中结合ROS和OpenCV处理图像，有需要的可以参考一下。
00-1010一、安装ROS-OpenCV II。简单案例分析1。USB _ cam . launch 2 . cv _ bridge _ test . py3 . rqt _ image _ view III。与CvBridge 1相关的API。imgmsg _ to _ cv2 () 2。cv2 _ to _ imgmsg()四、用ROS OpenCV实现人脸检测案例1。USB _ cam . launch 2 . face _ detector . launch 2.1 launch 2.2 face _ detector . py 2.3两个xml文件3.rqt_image_view V、用ROS OpenCV实现帧差法物体跟踪1。USB _ cam . launch 2 . motion _ detector . launch 2.1 launch 2.2 motion _ detector . py3 . rqt _ image _ view
目录
安装opencv sudo apt-get安装ROS-kinetic-vision-opencv libopencv-dev python-opencv
ROS图像处理依赖于OpenCV库。ROS通过一个名为CvBridge的函数包将采集到的图像数据转换成OpenCV格式，经过OpenCV处理后送回ROS进行图像显示(应用)，如下图所示：
一、安装ROS-OpenCV
我们使用ROS驱动获取摄像头数据，通过CvBridge将ROS获取的数据转换成OpenCV要求的格式，调用OpenCV的算法库对这张图片进行处理(比如画一个圆)，然后返回给ROS进行rviz显示。
二、简单案例分析
首先，我们建立一个启动文件，它可以调用相机驱动程序来获取图像数据。运行启动文件roslaunch xxx(功能包名称)usb_cam.launch
发动
node name= USB _ cam pkg= USB _ cam type= USB _ cam _ node output= screen
param name= video _ device value=/dev/video 0 /
param name= image _ width value= 1280 /
param name= image _ height value= 720 /
param name= pixel _ format value= yuyv /
param name= camera _ frame _ id value= USB _ cam /
param name= io _ method value= mmap /
/节点
/启动
1.usb_cam.launch
.py
建立一个py文件，是python2的。实现接收ROS发的图像信息，在图像上画一个圆后，返回给ROS。返回的话题名称是cv_bridge_image。运行py文件rosrun xxx(功能包名) cv_bridge_test.py
如果出现权限不够的情况，记得切换到py文件目录下执行：sudo chmod +x *.py
#!/usr/bin/env python# -*- coding: utf-8 -*-
import rospy
import cv2
from cv_bridge import CvBridge, CvBridgeError
from sensor_msgs.msg import Image
class image_converter:
def __init__(self):
# 创建cv_bridge，声明图像的发布者和订阅者
self.image_pub = rospy.Publisher("cv_bridge_image", Image, queue_size=1)
self.bridge = CvBridge()
self.image_sub = rospy.Subscriber("/usb_cam/image_raw", Image, self.callback)
def callback(self,data):
# 使用cv_bridge将ROS的图像数据转换成OpenCV的图像格式
try:
cv_image = self.bridge.imgmsg_to_cv2(data, "bgr8")
except CvBridgeError as e:
print e
# 在opencv的显示窗口中绘制一个圆，作为标记
(rows,cols,channels) = cv_image.shape
if cols > 60 and rows > 60 :
cv2.circle(cv_image, (60, 60), 30, (0,0,255), -1)
# 显示Opencv格式的图像
cv2.imshow("Image window", cv_image)
cv2.waitKey(3)
# 再将opencv格式额数据转换成ros image格式的数据发布
try:
self.image_pub.publish(self.bridge.cv2_to_imgmsg(cv_image, "bgr8"))
except CvBridgeError as e:
print e
if __name__ == __main__:
try:
# 初始化ros节点
rospy.init_node("cv_bridge_test")
rospy.loginfo("Starting cv_bridge_test node")
image_converter()
rospy.spin()
except KeyboardInterrupt:
print "Shutting down cv_bridge_test node."
cv2.destroyAllWindows()
3.rqt_image_view
在终端下执行rqt_image_view，订阅cv_bridge_image话题，可以发现OpenCV处理之后的图像在ROS中显示出来。
三、CvBridge相关API
1.imgmsg_to_cv2()
将ROS图像消息转换成OpenCV图像数据；
# 使用cv_bridge将ROS的图像数据转换成OpenCV的图像格式try:
cv_image = self.bridge.imgmsg_to_cv2(data, "bgr8")
except CvBridgeError as e:
print e
2.cv2_to_imgmsg()
将OpenCV格式的图像数据转换成ROS图像消息；
# 再将opencv格式额数据转换成ros image格式的数据发布try:
self.image_pub.publish(self.bridge.cv2_to_imgmsg(cv_image, "bgr8"))
except CvBridgeError as e:
print e
四、利用ROS+OpenCV实现人脸检测案例
1.usb_cam.launch
这个launch和上一个案例一样先打开摄像头驱动获取图像数据。运行launch文件roslaunch xxx(功能包名) usb_cam.launch
<launch><node name="usb_cam" pkg="usb_cam" type="usb_cam_node" output="screen" >
<param name="video_device" value="/dev/video0" />
<param name="image_width" value="1280" />
<param name="image_height" value="720" />
<param name="pixel_format" value="yuyv" />
<param name="camera_frame_id" value="usb_cam" />
<param name="io_method" value="mmap"/>
</node>
</launch>
2.face_detector.launch
人脸检测算法采用基于Harr特征的级联分类器对象检测算法，检测效果并不佳。但是这里只是为了演示如何使用ROS和OpenCV进行图像处理，所以不必在乎算法本身效果。整个launch调用了一个py文件和两个xml文件，分别如下：
2.1 launch
<launch><node pkg="robot_vision" name="face_detector" type="face_detector.py" output="screen">
<remap from="input_rgb_image" to="/usb_cam/image_raw" />
<rosparam>
haar_scaleFactor: 1.2
haar_minNeighbors: 2
haar_minSize: 40
haar_maxSize: 60
</rosparam>
<param name="cascade_1" value="$(find robot_vision)/data/haar_detectors/haarcascade_frontalface_alt.xml" />
<param name="cascade_2" value="$(find robot_vision)/data/haar_detectors/haarcascade_profileface.xml" />
</node>
</launch>
2.2 face_detector.py
#!/usr/bin/env python# -*- coding: utf-8 -*-
import rospy
import cv2
import numpy as np
from sensor_msgs.msg import Image, RegionOfInterest
from cv_bridge import CvBridge, CvBridgeError
class faceDetector:
def __init__(self):
rospy.on_shutdown(self.cleanup);
# 创建cv_bridge
self.bridge = CvBridge()
self.image_pub = rospy.Publisher("cv_bridge_image", Image, queue_size=1)
# 获取haar特征的级联表的XML文件，文件路径在launch文件中传入
cascade_1 = rospy.get_param("~cascade_1", "")
cascade_2 = rospy.get_param("~cascade_2", "")
# 使用级联表初始化haar特征检测器
self.cascade_1 = cv2.CascadeClassifier(cascade_1)
self.cascade_2 = cv2.CascadeClassifier(cascade_2)
# 设置级联表的参数，优化人脸识别，可以在launch文件中重新配置
self.haar_scaleFactor = rospy.get_param("~haar_scaleFactor", 1.2)
self.haar_minNeighbors = rospy.get_param("~haar_minNeighbors", 2)
self.haar_minSize = rospy.get_param("~haar_minSize", 40)
self.haar_maxSize = rospy.get_param("~haar_maxSize", 60)
self.color = (50, 255, 50)
# 初始化订阅rgb格式图像数据的订阅者，此处图像topic的话题名可以在launch文件中重映射
self.image_sub = rospy.Subscriber("input_rgb_image", Image, self.image_callback, queue_size=1)
def image_callback(self, data):
# 使用cv_bridge将ROS的图像数据转换成OpenCV的图像格式
try:
cv_image = self.bridge.imgmsg_to_cv2(data, "bgr8")
frame = np.array(cv_image, dtype=np.uint8)
except CvBridgeError, e:
print e
# 创建灰度图像
grey_image = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# 创建平衡直方图，减少光线影响
grey_image = cv2.equalizeHist(grey_image)
# 尝试检测人脸
faces_result = self.detect_face(grey_image)
# 在opencv的窗口中框出所有人脸区域
if len(faces_result)>0:
for face in faces_result:
x, y, w, h = face
cv2.rectangle(cv_image, (x, y), (x+w, y+h), self.color, 2)
# 将识别后的图像转换成ROS消息并发布
self.image_pub.publish(self.bridge.cv2_to_imgmsg(cv_image, "bgr8"))
def detect_face(self, input_image):
# 首先匹配正面人脸的模型
if self.cascade_1:
faces = self.cascade_1.detectMultiScale(input_image,
self.haar_scaleFactor,
self.haar_minNeighbors,
cv2.CASCADE_SCALE_IMAGE,
(self.haar_minSize, self.haar_maxSize))
# 如果正面人脸匹配失败，那么就尝试匹配侧面人脸的模型
if len(faces) == 0 and self.cascade_2:
faces = self.cascade_2.detectMultiScale(input_image,
self.haar_scaleFactor,
self.haar_minNeighbors,
cv2.CASCADE_SCALE_IMAGE,
(self.haar_minSize, self.haar_maxSize))
return faces
def cleanup(self):
print "Shutting down vision node."
cv2.destroyAllWindows()
if __name__ == __main__:
try:
# 初始化ros节点
rospy.init_node("face_detector")
faceDetector()
rospy.loginfo("Face detector is started..")
rospy.loginfo("Please subscribe the ROS image.")
rospy.spin()
except KeyboardInterrupt:
print "Shutting down face detector node."
cv2.destroyAllWindows()
2.3 两个xml文件
链接
3.rqt_image_view
运行完上述两个launch文件后，在终端下执行rqt_image_view，订阅cv_bridge_image话题，可以发现OpenCV处理之后的图像在ROS中显示出来。
五、利用ROS+OpenCV实现帧差法物体追踪
1.usb_cam.launch
这个launch和前两个案例一样先打开摄像头驱动获取图像数据。运行launch文件roslaunch xxx(功能包名) usb_cam.launch
<launch><node name="usb_cam" pkg="usb_cam" type="usb_cam_node" output="screen" >
<param name="video_device" value="/dev/video0" />
<param name="image_width" value="1280" />
<param name="image_height" value="720" />
<param name="pixel_format" value="yuyv" />
<param name="camera_frame_id" value="usb_cam" />
<param name="io_method" value="mmap"/>
</node>
</launch>
2.motion_detector.launch
物体追踪方法采用帧差法，追踪效果并不佳。但是这里只是为了演示如何使用ROS和OpenCV进行图像处理，所以不必在乎算法本身效果。整个launch调用了一个py文件，如下：
2.1 launch
<launch><node pkg="robot_vision" name="motion_detector" type="motion_detector.py" output="screen">
<remap from="input_rgb_image" to="/usb_cam/image_raw" />
<rosparam>
minArea: 500
threshold: 25
</rosparam>
</node>
</launch>
2.2 motion_detector.py
#!/usr/bin/env python# -*- coding: utf-8 -*-
import rospy
import cv2
import numpy as np
from sensor_msgs.msg import Image, RegionOfInterest
from cv_bridge import CvBridge, CvBridgeError
class motionDetector:
def __init__(self):
rospy.on_shutdown(self.cleanup);
# 创建cv_bridge
self.bridge = CvBridge()
self.image_pub = rospy.Publisher("cv_bridge_image", Image, queue_size=1)
# 设置参数：最小区域、阈值
self.minArea = rospy.get_param("~minArea", 500)
self.threshold = rospy.get_param("~threshold", 25)
self.firstFrame = None
self.text = "Unoccupied"
# 初始化订阅rgb格式图像数据的订阅者，此处图像topic的话题名可以在launch文件中重映射
self.image_sub = rospy.Subscriber("input_rgb_image", Image, self.image_callback, queue_size=1)
def image_callback(self, data):
# 使用cv_bridge将ROS的图像数据转换成OpenCV的图像格式
try:
cv_image = self.bridge.imgmsg_to_cv2(data, "bgr8")
frame = np.array(cv_image, dtype=np.uint8)
except CvBridgeError, e:
print e
# 创建灰度图像
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (21, 21), 0)
# 使用两帧图像做比较，检测移动物体的区域
if self.firstFrame is None:
self.firstFrame = gray
return
frameDelta = cv2.absdiff(self.firstFrame, gray)
thresh = cv2.threshold(frameDelta, self.threshold, 255, cv2.THRESH_BINARY)[1]
thresh = cv2.dilate(thresh, None, iterations=2)
binary, cnts, hierarchy= cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
for c in cnts:
# 如果检测到的区域小于设置值，则忽略
if cv2.contourArea(c) < self.minArea:
continue
# 在输出画面上框出识别到的物体
(x, y, w, h) = cv2.boundingRect(c)
cv2.rectangle(frame, (x, y), (x + w, y + h), (50, 255, 50), 2)
self.text = "Occupied"
# 在输出画面上打当前状态和时间戳信息
cv2.putText(frame, "Status: {}".format(self.text), (10, 20),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
# 将识别后的图像转换成ROS消息并发布
self.image_pub.publish(self.bridge.cv2_to_imgmsg(frame, "bgr8"))
def cleanup(self):
print "Shutting down vision node."
cv2.destroyAllWindows()
if __name__ == __main__:
try:
# 初始化ros节点
rospy.init_node("motion_detector")
rospy.loginfo("motion_detector node is started...")
rospy.loginfo("Please subscribe the ROS image.")
motionDetector()
rospy.spin()
except KeyboardInterrupt:
print "Shutting down motion detector node."
cv2.destroyAllWindows()
3.rqt_image_view
运行完上述两个launch文件后，在终端下执行rqt_image_view，订阅cv_bridge_image话题，可以发现OpenCV处理之后的图像在ROS中显示出来。(鉴于我的测试环境比较糟糕，并且这个算法本身精度不高，就不展示最终效果了)
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