def success(boxes, grdTruth, n=100):
assert boxes.shape == grdTruth.shape
nFrame = boxes.shape[0]
count = np.zeros(n)
for i in xrange(nFrame):
score = overlap(boxes[i], grdTruth[i])
count[:int(np.floor(n * score))] += 1
return count / nFrame
def success(boxes, grdTruth, n=10): assert boxes.shape == grdTruth.shape nFrame = boxes.shape[0] count = np.zeros(n) for i in xrange(nFrame): score = overlap(boxes[i], grdTruth[i]) count[:int(np.floor(n*score))] += 1 return count/nFrame
GT = readGT(imdb_name)
for idx in xrange(0,img_count):
# print 'Frame', idx + 1
img = cv2.imread(imdb_path+'/%08d.jpg'%(idx+1))
# print img.shape
assert img != None
try:
tracker
except NameError:
tracker = dlib.correlation_tracker()
tracker.start_track(img, dlib.rectangle(*initbox))
tracker.update(img)
rect = tracker.get_position()
box = [int(rect.left()), int(rect.top()),\
int(rect.right()), int(rect.bottom())]
print overlap(box,GT[idx])
# pt1 = (int(rect.left()),int(rect.top()))
# pt2 = (int(rect.right()),int(rect.bottom()))
# print pt1, pt2
# cv2.rectangle(img,pt1,pt2,(255,255,255),2)
# cv2.imshow('Vedio', img)
# cv2.waitKey(100)
print tracker