# import numpy as np
all_gt = np.loadtxt(imdb_path+'/groundtruth.txt',delimiter=',')
<<<<<<< HEAD
t = dlib.correlation_tracker()
ol = np.zeros(img_count-10)
for i in xrange(img_count-10):
img = cv2.imread(imdb_path+'/%08d.jpg'%(i+1))
t.start_track(img, dlib.rectangle(*all_gt[i]))
img2 = cv2.imread(imdb_path+'/%08d.jpg'%(i+10))
t.update()
rect = tracker.get_position()
box = [int(rect.left()),int(rect.top()),int(rect.right()),int(rect.bottom())]
ol[i] = overlap(box, all_gt[i+9])
import matplotlib.pyplot as plt
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# print all_gt.shape
all_gt = all_gt[:,[2,3,6,7]].astype(np.int)
print all_gt.shape
t = dlib.correlation_tracker()
ol = np.zeros(img_count-60)
for i in xrange(img_count-60):
img = cv2.imread(imdb_path+'/%08d.jpg'%(i+1))
t.start_track(img, dlib.rectangle(*all_gt[i]))
img2 = cv2.imread(imdb_path+'/%08d.jpg'%(i+60))
logfile_path = os.path.join(this_file_path,'logfile', logfile)
print 'IMG path:', imdb_path
assert os.path.exists(imdb_path)
_, _, files = os.walk(imdb_path).next()
img_count = len(files) - 7
print 'IMG count:', img_count
all_gt = np.loadtxt(imdb_path+'/groundtruth.txt',delimiter=',')
result = np.loadtxt(logfile_path)
# print result[:,0]
gt = all_gt[result[:-1,0].astype(np.int)-1,:]
#print gt
gt = gt[:,[2,3,6,7]]
print gt
boxes = result[:-1,1:]
# su = success(boxes,gt,100)
fc = boxes.shape[0]
su = np.zeros(fc)
for i in xrange(fc):
su[i] = overlap(gt[i],boxes[i])
plt.plot(su)
plt.show()