Car displacement detection using MATLAB

import numpy as np

import cv2 as cv

import argparse

parser = argparse.ArgumentParser(description='This sample demonstrates Lucas-Kanade Optical Flow calculation. \

The example file can be downloaded from: \

https://www.bogotobogo.com/python/OpenCV_Python/images/mean_shift_tracking/slow_traffic_small.mp4')

parser.add_argument('image', type=str, help='path to image file')

args = parser.parse_args()

cap = cv.VideoCapture(args.image)

# 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 = (cv.TERM_CRITERIA_EPS | cv.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()

old_gray = cv.cvtColor(old_frame, cv.COLOR_BGR2GRAY)

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

# Create a mask image for drawing purposes

mask = np.zeros_like(old_frame)

while(1):

ret,frame = cap.read()

frame_gray = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)

# calculate optical flow

p1, st, err = cv.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 = cv.line(mask, (a,b),(c,d), color[i].tolist(), 2)

frame = cv.circle(frame,(a,b),5,color[i].tolist(),-1)

img = cv.add(frame,mask)

cv.imshow('frame',img)

k = cv.waitKey(30) & 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)

I found this opencv program online but i don't have idea about the opencv and i want to use matlab for this same program with matlab function can any one help me to detect car speed and location in video?

Thank You;