如何檢測dlib的correlation_tracker丟失了目標圖像？

Question

我在多線程人臉tracking script中添加了一個correlation_tracker，奇怪的是，它通常很好地跟蹤屏幕上的臉部，但是當您將手放在相機上時，它會一直說相同的座標並突出顯示相同的區域。有沒有一種很好的方法來檢測被跟蹤物體何時離開？或者這是否需要在一段時間內處理較慢的全方位探測器對象？

from __future__ import division 
import sys 
from time import time, sleep 
import threading 

import dlib 
#from skimage import io 


detector = dlib.get_frontal_face_detector() 
win = dlib.image_window() 

def adjustForOpenCV(image): 
    """ OpenCV use bgr not rgb. odd. """ 
    for row in image: 
     for px in row: 
      #rgb expected... but the array is bgr? 
      r = px[2] 
      px[2] = px[0] 
      px[0] = r 
    return image 

class webCamGrabber(threading.Thread): 
    def __init__(self): 
     threading.Thread.__init__(self) 
     #Lock for when you can read/write self.image: 
     #self.imageLock = threading.Lock() 
     self.image = False 

     from cv2 import VideoCapture, cv 
     from time import time 

     self.cam = VideoCapture(0) #set the port of the camera as before 
     #Doesn't seem to work: 
     self.cam.set(cv.CV_CAP_PROP_FRAME_WIDTH, 160) 
     self.cam.set(cv.CV_CAP_PROP_FRAME_WIDTH, 120) 
     #self.cam.set(cv.CV_CAP_PROP_FPS, 1) 


    def run(self): 
     while True: 
      start = time() 
      #self.imageLock.acquire() 
      retval, self.image = self.cam.read() #return a True bolean and and the image if all go right 
      #print("readimage: " + str(time() - start)) 
      #sleep(0.1) 

if len(sys.argv[1:]) == 0: 

    #Start webcam reader thread: 
    camThread = webCamGrabber() 
    camThread.start() 

    #Setup window for results 
    detector = dlib.get_frontal_face_detector() 
    win = dlib.image_window() 

    while True: 
     #camThread.imageLock.acquire() 
     if camThread.image is not False: 
      print("enter") 
      start = time() 

      myimage = camThread.image 
      for row in myimage: 
       for px in row: 
        #rgb expected... but the array is bgr? 
        r = px[2] 
        px[2] = px[0] 
        px[0] = r 


      dets = detector(myimage, 0) 
      #camThread.imageLock.release() 
      print "your faces:" +str(len(dets)) 
      nearFace = None 
      nearFaceArea = 0 

      for i, d in enumerate(dets): 
       #print("Detection {}: Left: {} Top: {} Right: {} Bottom: {}".format(
       # i, d.left(), d.top(), d.right(), d.bottom())) 
       screenArea = (d.right() - d.left()) * (d.bottom() - d.top()) 
       #print 'area', screenArea 
       if screenArea > nearFaceArea: 
        nearFace = d 
      print("face-find-time: " + str(time() - start)) 

      print("from left: {}".format(((nearFace.left() + nearFace.right())/2)/len(camThread.image[0]))) 
      print("from top: {}".format(((nearFace.top() + nearFace.bottom())/2)/len(camThread.image))) 

      start = time() 
      win.clear_overlay() 
      win.set_image(myimage) 
      win.add_overlay(nearFace) 
      print("show: " + str(time() - start)) 

      if nearFace != None: 
       points = (nearFace.left(), nearFace.top(), nearFace.right(), nearFace.bottom()) 
       tracker = dlib.correlation_tracker() 
       tracker.start_track(myimage, dlib.rectangle(*points)) 

       while True: 
        myImage = adjustForOpenCV(camThread.image) 

        tracker.update(myImage) 
        rect = tracker.get_position() 
        cx = (rect.right() + rect.left())/2 
        cy = (rect.top() + rect.bottom())/2 
        print('correlationTracker %s,%s' % (cx, cy)) 
        print rect 
        win.clear_overlay() 
        win.set_image(myImage) 
        win.add_overlay(rect) 
        sleep(0.1) 

      #dlib.hit_enter_to_continue() 




for f in sys.argv[1:]: 
    print("Processing file: {}".format(f)) 
    img = io.imread(f) 
    # The 1 in the second argument indicates that we should upsample the image 
    # 1 time. This will make everything bigger and allow us to detect more 
    # faces. 
    dets = detector(img, 1) 
    print("Number of faces detected: {}".format(len(dets))) 
    for i, d in enumerate(dets): 
     print("Detection {}: Left: {} Top: {} Right: {} Bottom: {}".format(
      i, d.left(), d.top(), d.right(), d.bottom())) 

    win.clear_overlay() 
    win.set_image(img) 
    win.add_overlay(dets) 
    dlib.hit_enter_to_continue() 


# Finally, if you really want to you can ask the detector to tell you the score 
# for each detection. The score is bigger for more confident detections. 
# Also, the idx tells you which of the face sub-detectors matched. This can be 
# used to broadly identify faces in different orientations. 
if (len(sys.argv[1:]) > 0): 
    img = io.imread(sys.argv[1]) 
    dets, scores, idx = detector.run(img, 1) 
    for i, d in enumerate(dets): 
     print("Detection {}, score: {}, face_type:{}".format(
      d, scores[i], idx[i])) 

Answer 1

您必須時不時運行人臉檢測器以查看人臉是否仍然存在。