Python中ROS和OpenCV结合处理图像问题_python_

#!/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() 

# 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

# 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

 haar_scaleFactor: 1.2 haar_minNeighbors: 2 haar_minSize: 40 haar_maxSize: 60 

#!/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() 

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