Optical flow for a video

import numpy as np
import cv2

def getFeaturesToDetect():
    return

cap = cv2.VideoCapture('/home/rish/Code/Datasets/video-walking/person25_walking_d3_uncomp.avi')
#cap = cv2.VideoCapture(0)
cap.set(cv2.CAP_PROP_FRAME_WIDTH, 500)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 500)

if not cap.isOpened():
    CV_ASSERT("Cam open failed")

feature_params = dict ( maxCorners = 10,
                        qualityLevel = 0.3,
                        minDistance = 7,
                        blockSize = 7)
lk_params = dict (  winSize = (15,15),
                    maxLevel = 2,
                    criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03))

color = np.random.randint(0,255, (100,3))

ret, old_frame = cap.read()
old_gray = cv2.cvtColor(old_frame, cv2.COLOR_BGR2GRAY)
p0 = cv2.goodFeaturesToTrack(old_gray, mask = None, **feature_params)
totalFeatures = len(p0)
mask = np.zeros_like(old_frame)

while(1):
    ret, frame = cap.read()
    frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    p1,st,err = cv2.calcOpticalFlowPyrLK(old_gray, frame_gray, p0, None, **lk_params)

    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("optical flow", img)
    k = cv2.waitKey(100) & 0xff
    if k == 27:
        break

    # update features
    if len(p1) <= totalFeatures / 2:
        old_gray = frame_gray.copy()
        p0 = cv2.goodFeaturesToTrack(old_gray, mask = None, **feature_params)
        totalFeatures = len(p0)
        mask = np.zeros_like(old_frame) # keep this line if you would like to remove all previously drawn flows
    else :
        old_gray = frame_gray.copy()
        p0 = good_new.reshape(-1,1,2)

cv2.destroyAllWindows()
cap.release()