def callback(self, ros_data): '''Callback function of subscribed topic. Here images get converted and features detected''' if VERBOSE : print 'received image of type: "%s"' % ros_data.format #### direct conversion to CV2 #### np_arr = np.fromstring(ros_data.data, np.uint8) image_np = cv2.imdecode(np_arr, cv2.CV_LOAD_IMAGE_COLOR) #### Feature detectors using CV2 #### # "","Grid","Pyramid" + # "FAST","GFTT","HARRIS","MSER","ORB","SIFT","STAR","SURF" #method = "FAST" #feat_det = cv2.FeatureDetector_create(method) #convert np image to grayscale # grey_imagenp = cv2.cvtColor(image_np, cv2.COLOR_BGR2GRAY) # print 'The image is being processed' predictionArray, centroidArray = tS.processAndClassify(image_np) #featPoints,des,keyImage = dd.featureDetectDesORB(grey_imagenp) # # for featpoint in featPoints: # x,y = featpoint.pt # cv2.circle(image_np,(int(x),int(y)), 3, (0,0,255), -1) # print 'The image is being processed' cv2.imshow('cv_img', frameBoundingBox) cv2.waitKey(2) #### Create CompressedIamge #### msg = CompressedImage() msg.header.stamp = rospy.Time.now() msg.format = "jpeg" msg.data = np.array(cv2.imencode('.jpg', image_np)[1]).tostring() # Publish new image self.image_pub.publish(msg)
def callback(self, ros_data): '''Callback function of subscribed topic. Here images get converted and features detected''' if VERBOSE: print 'received image of type: "%s"' % ros_data.format #### direct conversion to CV2 #### np_arr = np.fromstring(ros_data.data, np.uint8) image_np = cv2.imdecode(np_arr, cv2.CV_LOAD_IMAGE_COLOR) #### Feature detectors using CV2 #### # "","Grid","Pyramid" + # "FAST","GFTT","HARRIS","MSER","ORB","SIFT","STAR","SURF" #method = "FAST" #feat_det = cv2.FeatureDetector_create(method) #convert np image to grayscale # grey_imagenp = cv2.cvtColor(image_np, cv2.COLOR_BGR2GRAY) # print 'The image is being processed' predictionArray, centroidArray = tS.processAndClassify(image_np) #featPoints,des,keyImage = dd.featureDetectDesORB(grey_imagenp) # # for featpoint in featPoints: # x,y = featpoint.pt # cv2.circle(image_np,(int(x),int(y)), 3, (0,0,255), -1) # print 'The image is being processed' cv2.imshow('cv_img', frameBoundingBox) cv2.waitKey(2) #### Create CompressedIamge #### msg = CompressedImage() msg.header.stamp = rospy.Time.now() msg.format = "jpeg" msg.data = np.array(cv2.imencode('.jpg', image_np)[1]).tostring() # Publish new image self.image_pub.publish(msg)
def processImage(rosImage): global predictionArray, centroidArray,labels frame = np.array(rosImage, dtype=np.uint8) # Process the frame using the process_image() function predictionArray, centroidArray,labels = tS.processAndClassify(frame) return predictionArray, centroidArray,labels
def processImage(self, frame, codeBookCenters, svm): predictionArray, centroidArray,labels = tS.processAndClassify(frame, codeBookCenters, svm) return predictionArray, centroidArray,labels
# -*- coding: utf-8 -*- """ Created on Fri Jan 15 19:28:03 2016 @author: 4chennur """ import cv2 import testSegmented as tS frame = cv2.imread('frame0032.jpg') tS.processAndClassify(frame)
def processImage(self, frame, codeBookCenters, svm): predictionArray, centroidArray, labels = tS.processAndClassify( frame, codeBookCenters, svm) return predictionArray, centroidArray, labels