python - OpenCV中的KLT跟踪器无法与Python一起正常使用

Question

我正在使用KLT(Kanade-Lucas-Tomasi跟踪)跟踪算法来跟踪印度的交通流量。我正在正确跟踪流量的一侧的流量，但是根本检测不到帧中移动的流量的另一侧。

算法由cv2.goodFeaturesToTrack和cv2.calcOpticalFlowPyrLK组成，以实现结果。



在图像中，您可以看到红色和银色汽车上没有追踪功能。也不会跟踪左侧的黄色自动。有什么原因吗？角落仍然在那里。
cv2.goodFeaturesToTrack的功能参数:

feature_params = dict( maxCorners = 500,   # How many pts. to locate
                       qualityLevel = 0.1,  # b/w 0 & 1, min. quality below which everyone is rejected
                       minDistance = 7,   # Min eucledian distance b/w corners detected
                       blockSize = 3 ) # Size of an average block for computing a derivative covariation matrix over each pixel neighborhood

cv2.calcOpticalFlowPyrLK的功能参数:

lk_params = dict( winSize  = (15,15),  # size of the search window at each pyramid level
                  maxLevel = 2,   #  0, pyramids are not used (single level), if set to 1, two levels are used, and so on
                  criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03))

我必须使用的视频是60分钟。 long，KLT在5分钟后停止跟踪 ..任何建议或帮助都将非常有用。谢谢。

Answer 1

基本上，您做对了所有事情，您只需要重新初始化进行跟踪的优点，就可以像这样

p0 = cv2.goodFeaturesToTrack(old_gray, mask = None, **feature_params)

每隔5帧说一次或之后
希望能帮助到你 !
以下是我的代码:

import cv2
import numpy as np

cap = cv2.VideoCapture('side.avi')
# params for ShiTomasi corner detection
feature_params = dict( maxCorners = 100,
                       qualityLevel = 0.3,
                       minDistance = 7,
                       blockSize = 7 )
# Parameters for lucas kanade optical flow
lk_params = dict( winSize  = (15,15),
                  maxLevel = 2,
                  criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03))
# Create some random colors
color = np.random.randint(0,255,(100,3))
# Take first frame and find corners in it
ret, old_frame = cap.read()
for i in range(60):
    ret, old_frame = cap.read()
old_gray = cv2.cvtColor(old_frame, cv2.COLOR_BGR2GRAY)
p0 = cv2.goodFeaturesToTrack(old_gray, mask = None, **feature_params)
print(p0)
# Create a mask image for drawing purposes
mask = np.zeros_like(old_frame)
while(1):
    ret,frame = cap.read()
    frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    frame_no = cap.get(cv2.CAP_PROP_POS_FRAMES)
    if int(frame_no)%5 == 0:
        p0 = cv2.goodFeaturesToTrack(old_gray, mask = None, **feature_params)
    # calculate optical flow
    p1, st, err = cv2.calcOpticalFlowPyrLK(old_gray, frame_gray, p0, None, **lk_params)
    # Select good points
    good_new = p1[st==1]
    good_old = p0[st==1]
    # draw the tracks
    for i,(new,old) in enumerate(zip(good_new,good_old)):
        a,b = new.ravel()
        c,d = old.ravel()
        mask = cv2.line(mask, (a,b),(c,d), color[i].tolist(), 2)
        frame = cv2.circle(frame,(a,b),5,color[i].tolist(),-1)
    img = cv2.add(frame,mask)
    cv2.imshow('frame',img)
    k = cv2.waitKey(2000) & 0xff
    if k == 27:
        break
    # Now update the previous frame and previous points
    old_gray = frame_gray.copy()
    p0 = good_new.reshape(-1,1,2)
cv2.destroyAllWindows()
cap.release()