def goGetEm(): minRemovalScore = 0.0001 timeOut = 10 cleanThresh = 5 binNumber = 100 distanceWeight = 0.5 sizeWeight = 0.0 timeWeight = 0.5 weights = (distanceWeight, timeWeight, sizeWeight) writingToFiles = True distDev = 200 timeDev = 0.34 sizeDev = 0.25 devs = (distDev, timeDev, sizeDev) framesback = 2 variables = [minRemovalScore, timeOut, cleanThresh, binNumber, weights, writingToFiles, devs, framesback] vid = Video(0, variables) # vidFile = "outvid.avi" # csvFile = "variable.csv" # if writingToFiles: # vid.openVidWrite(vidFile) # vid.openCSVWrite(csvFile) while (True): vid.readFrame() vid.findFaces() vid.display() # vid.writeToVideo() # exit on escape key key = cv2.waitKey(1) if key == 27: break vid.endWindow()
def main(video=0, dowsample=True): print('QUAD Vision, Python:', version, ' OpenCV:', cv2.__version__) vid = Video(video, 'ORB Detection testing', delay=1, fast=False, skip=20, downsample=downsample) detector = Detector() if ros_mode: import rospy from sensor_msgs.msg import Image from cv_bridge import CvBridge, CvBridgeError from quad.msg import Target_coordinates pub_detection = rospy.Publisher('detection', Target_coordinates, queue_size=5) pub_frame = rospy.Publisher('annotated_frame', msg.image, queue_size=15) rospy.init_node('detection', anonymous=True) rospy.init_node('annotated_frame', anonymous=True) # Detection profiles detector.load_profile(profiles.idea_orange) # detector.load_profile(profiles.quadbox_black) # detector.load_profile(profiles.quadbox_white) # detector.load_profile(profiles.idea_red) # detector.load_profile(profiles.sim_drone) vid.skip(20) t = Timer('Detection') while vid.next(): frame = vid.img t.start() # start timer img, confidence, x, y = detector.detect(frame) print('Detection:', confidence, x, y) t.end() vid.display(img) if ros_mode: detection = Target_coordinates() detection.confidence = confidence detection.x = x detection.y = y try: pub_detection.publish(detection) except CvBridgeError as e: print(e) # ros_img = CvBridge.cv2_to_imgmsg(img, encoding="passthrough") # pub_frame.publish(ros_img) vid.close() t.output()
def main(): vid = Video(video, 'Haar Cascade detection') vid.overlay = True cascade_file = 'data/idea_cascade.xml' detector = CascadeDetector(cascade_file) t = Timer('Cascade Detection') while vid.next(): t.start() img = vid.img message = [] img = preprocess(img) img, confidence = detector.detect(img) t.end() message.append('%0.2f' % confidence) vid.display(img, message) t.output()
def goGetEm(): minRemovalScore = 0.0001 timeOut = 10 cleanThresh = 5 binNumber = 100 distanceWeight = 1 timeWeight = 1 sizeWeight = 1 #weights = (distanceWeight, timeWeight, sizeWeight) #variables = [minRemovalScore, timeOut, cleanThresh, binNumber, weights] variables = (0.25, 10, 5, 100, (1,1,1), False, (200,0.34,0.25), 2) #vid = Video("Slightmovement.MP4", [0,1,0.1,0.1,1000,0]) vid = Video(0, variables) while (True): vid.readFrame() vid.findFaces() vid.display() # exit on escape key key = cv2.waitKey(20) if cv2.waitKey(1) & 0xFF == ord('q'): break vid.endWindow()