一、安裝ROS-OpenCV
??安裝OpenCVsudo apt-get install ros-kinetic-vision-opencv libopencv-dev python-opencv
??ROS進行圖像處理是依賴于OpenCV庫的。ROS通過一個叫CvBridge的功能包，將獲取的圖像數(shù)據(jù)轉(zhuǎn)換成OpenCV的格式，OpenCV處理之后，傳回給ROS進行圖像顯示（應(yīng)用），如下圖：
二、簡單案例分析
??我們使用ROS驅(qū)動獲取攝像頭數(shù)據(jù)，將ROS獲得的數(shù)據(jù)通過CvBridge轉(zhuǎn)換成OpenCV需要的格式，調(diào)用OpenCV的算法庫對這個圖片進行處理（如畫一個圓），然后返回給ROS進行rviz顯示。
1.usb_cam.launch
??首先我們建立一個launch文件，可以調(diào)用攝像頭驅(qū)動獲取圖像數(shù)據(jù)。運行l(wèi)aunch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.cv_bridge_test.py
??建立一個py文件，是python2的。實現(xiàn)接收ROS發(fā)的圖像信息，在圖像上畫一個圓后，返回給ROS。返回的話題名稱是cv_bridge_image。運行py文件rosrun xxx(功能包名) cv_bridge_test.py
??如果出現(xiàn)權(quán)限不夠的情況，記得切換到py文件目錄下執(zhí)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):
# 創(chuàng)建cv_bridge，聲明圖像的發(fā)布者和訂閱者
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將ROS的圖像數(shù)據(jù)轉(zhuǎn)換成OpenCV的圖像格式
try:
cv_image = self.bridge.imgmsg_to_cv2(data, "bgr8")
except CvBridgeError as e:
print e
# 在opencv的顯示窗口中繪制一個圓，作為標(biāo)記
(rows,cols,channels) = cv_image.shape
if cols > 60 and rows > 60 :
cv2.circle(cv_image, (60, 60), 30, (0,0,255), -1)
# 顯示Opencv格式的圖像
cv2.imshow("Image window", cv_image)
cv2.waitKey(3)
# 再將opencv格式額數(shù)據(jù)轉(zhuǎn)換成ros image格式的數(shù)據(jù)發(fā)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節(jié)點
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
??在終端下執(zhí)行rqt_image_view，訂閱cv_bridge_image話題，可以發(fā)現(xiàn)OpenCV處理之后的圖像在ROS中顯示出來。
三、CvBridge相關(guān)API
1.imgmsg_to_cv2()
??將ROS圖像消息轉(zhuǎn)換成OpenCV圖像數(shù)據(jù)；
# 使用cv_bridge將ROS的圖像數(shù)據(jù)轉(zhuǎn)換成OpenCV的圖像格式
try:
cv_image = self.bridge.imgmsg_to_cv2(data, "bgr8")
except CvBridgeError as e:
print e
2.cv2_to_imgmsg()
??將OpenCV格式的圖像數(shù)據(jù)轉(zhuǎn)換成ROS圖像消息；
# 再將opencv格式額數(shù)據(jù)轉(zhuǎn)換成ros image格式的數(shù)據(jù)發(fā)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實現(xiàn)人臉檢測案例
1.usb_cam.launch
??這個launch和上一個案例一樣先打開攝像頭驅(qū)動獲取圖像數(shù)據(jù)。運行l(wèi)aunch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
??人臉檢測算法采用基于Harr特征的級聯(lián)分類器對象檢測算法，檢測效果并不佳。但是這里只是為了演示如何使用ROS和OpenCV進行圖像處理，所以不必在乎算法本身效果。整個launch調(diào)用了一個py文件和兩個xml文件，分別如下：
2.1 launch
(注意py文件和xml文件的存放位置)
<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);
# 創(chuàng)建cv_bridge
self.bridge = CvBridge()
self.image_pub = rospy.Publisher("cv_bridge_image", Image, queue_size=1)
# 獲取haar特征的級聯(lián)表的XML文件，文件路徑在launch文件中傳入
cascade_1 = rospy.get_param("~cascade_1", "")
cascade_2 = rospy.get_param("~cascade_2", "")
# 使用級聯(lián)表初始化haar特征檢測器
self.cascade_1 = cv2.CascadeClassifier(cascade_1)
self.cascade_2 = cv2.CascadeClassifier(cascade_2)
# 設(shè)置級聯(lián)表的參數(shù)，優(yōu)化人臉識別，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)
# 初始化訂閱rgb格式圖像數(shù)據(jù)的訂閱者，此處圖像topic的話題名可以在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將ROS的圖像數(shù)據(jù)轉(zhuǎn)換成OpenCV的圖像格式
try:
cv_image = self.bridge.imgmsg_to_cv2(data, "bgr8")
frame = np.array(cv_image, dtype=np.uint8)
except CvBridgeError, e:
print e
# 創(chuàng)建灰度圖像
grey_image = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# 創(chuàng)建平衡直方圖，減少光線影響
grey_image = cv2.equalizeHist(grey_image)
# 嘗試檢測人臉
faces_result = self.detect_face(grey_image)
# 在opencv的窗口中框出所有人臉區(qū)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)
# 將識別后的圖像轉(zhuǎn)換成ROS消息并發(fā)布
self.image_pub.publish(self.bridge.cv2_to_imgmsg(cv_image, "bgr8"))
def detect_face(self, input_image):
# 首先匹配正面人臉的模型
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))
# 如果正面人臉匹配失敗，那么就嘗試匹配側(cè)面人臉的模型
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節(jié)點
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 兩個xml文件
鏈接
3.rqt_image_view
??運行完上述兩個launch文件后，在終端下執(zhí)行rqt_image_view，訂閱cv_bridge_image話題，可以發(fā)現(xiàn)OpenCV處理之后的圖像在ROS中顯示出來。
五、利用ROS+OpenCV實現(xiàn)幀差法物體追蹤
1.usb_cam.launch
??這個launch和前兩個案例一樣先打開攝像頭驅(qū)動獲取圖像數(shù)據(jù)。運行l(wèi)aunch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
??物體追蹤方法采用幀差法，追蹤效果并不佳。但是這里只是為了演示如何使用ROS和OpenCV進行圖像處理，所以不必在乎算法本身效果。整個launch調(diào)用了一個py文件，如下：文章來源：http://www.zghlxwxcb.cn/news/detail-451961.html
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>
2.1 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);
# 創(chuàng)建cv_bridge
self.bridge = CvBridge()
self.image_pub = rospy.Publisher("cv_bridge_image", Image, queue_size=1)
# 設(shè)置參數(shù)：最小區(qū)域、閾值
self.minArea = rospy.get_param("~minArea", 500)
self.threshold = rospy.get_param("~threshold", 25)
self.firstFrame = None
self.text = "Unoccupied"
# 初始化訂閱rgb格式圖像數(shù)據(jù)的訂閱者，此處圖像topic的話題名可以在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將ROS的圖像數(shù)據(jù)轉(zhuǎn)換成OpenCV的圖像格式
try:
cv_image = self.bridge.imgmsg_to_cv2(data, "bgr8")
frame = np.array(cv_image, dtype=np.uint8)
except CvBridgeError, e:
print e
# 創(chuàng)建灰度圖像
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (21, 21), 0)
# 使用兩幀圖像做比較，檢測移動物體的區(qū)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:
# 如果檢測到的區(qū)域小于設(shè)置值，則忽略
if cv2.contourArea(c) < self.minArea:
continue
# 在輸出畫面上框出識別到的物體
(x, y, w, h) = cv2.boundingRect(c)
cv2.rectangle(frame, (x, y), (x + w, y + h), (50, 255, 50), 2)
self.text = "Occupied"
# 在輸出畫面上打當(dāng)前狀態(tài)和時間戳信息
cv2.putText(frame, "Status: {}".format(self.text), (10, 20),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
# 將識別后的圖像轉(zhuǎn)換成ROS消息并發(fā)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節(jié)點
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
??運行完上述兩個launch文件后，在終端下執(zhí)行rqt_image_view，訂閱cv_bridge_image話題，可以發(fā)現(xiàn)OpenCV處理之后的圖像在ROS中顯示出來。(鑒于我的測試環(huán)境比較糟糕，并且這個算法本身精度不高，就不展示最終效果了)文章來源地址http://www.zghlxwxcb.cn/news/detail-451961.html
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