opencv code doesn't run on raspberry pi

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hello, I have been working on gesture recognition using raspberry pi and opencv, I am able to run the following code on my linux and windows machines but not on the raspberry pi, note that I am able to run other scripts on the raspberry pi

import cv2
import numpy as np
import math
import imutils
import time
from collections import deque
from imutils.video import VideoStream
import argparse
           # construct the argument parse
ap = argparse.ArgumentParser()
ap.add_argument("-v", "--video",
    help="path to the (optional) video file")
ap.add_argument("-b", "--buffer", type=int, default=32,
    help="max buffer size")
args = vars(ap.parse_args())

cap = cv2.VideoCapture(0)
pts = deque(maxlen=args["buffer"])
counter = 0
(dX, dY) = (0, 0)   
while(1):

    try:  #an error comes if it does not find anything in window as it cannot find contour of max area
          #therefore this try error statement

        ret, frame = cap.read()
        ret2, frame2 = cap.read()
        frame=cv2.flip(frame,1)
        frame2=cv2.flip(frame2,1)
        kernel = np.ones((3,3),np.uint8)
        frame2 = imutils.resize(frame2, width=600)
        blurred = cv2.GaussianBlur(frame2, (11, 11), 0)
        hsv2 = cv2.cvtColor(blurred, cv2.COLOR_BGR2HSV)



        #define region of interest
        roi=frame[100:300, 100:300]


        cv2.rectangle(frame,(50,50),(500,500),(0,255,0),0)    
        hsv = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV)



    # define range of skin color in HSV
        lower_skin = np.array([0,20,70], dtype=np.uint8)
        upper_skin = np.array([20,255,255], dtype=np.uint8)




     #extract skin colur imagw  
        mask = cv2.inRange(hsv, lower_skin, upper_skin)
        mask2 = cv2.inRange(hsv2, lower_skin, upper_skin)
        mask2 = cv2.erode(mask2, None, iterations=2)


         #extrapolate the hand to fill dark spots within
        mask = cv2.dilate(mask,kernel,iterations = 4)
        mask2 = cv2.dilate(mask2,None, iterations=2)
          #blur the image
        mask = cv2.GaussianBlur(mask,(5,5),100) 



          #find contours
        cnts,contours,hierarchy= cv2.findContours(mask,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)

          #find contour of max area(hand)
        cnt = max(contours, key = lambda x: cv2.contourArea(x))

          #approx the contour a little
        epsilon = 0.0005*cv2.arcLength(cnt,True)
        approx= cv2.approxPolyDP(cnt,epsilon,True)


           #make convex hull around hand
        hull = cv2.convexHull(cnt)

               #define area of hull and area of hand
        areahull = cv2.contourArea(hull)
        areacnt = cv2.contourArea(cnt)

              #find the percentage of area not covered by hand in convex hull
        arearatio=((areahull-areacnt)/areacnt)*100

          #find the defects in convex hull with respect to hand
        hull = cv2.convexHull(approx, returnPoints=False)
        defects = cv2.convexityDefects(approx, hull)

            # l = no. of defects
        l=0

        cnts2 = cv2.findContours(mask2.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
        cnts2 = imutils.grab_contours(cnts2)
        center = None
        c = max(cnts2, key=cv2.contourArea)
        ((x, y), radius) = cv2.minEnclosingCircle(c)
        M = cv2.moments(c)
        center = (int(M["m10"] / M["m00"]), int(M["m01"] / M["m00"]))
        cv2.circle(frame, (int(x), int(y)),  10, [255,125,0], -1)
        pts.appendleft(center)

        for i in np.arange(1, len(pts)):

            if pts[i - 1] is None or pts[i] is None:

                continue


            if counter >= 5 and i == 1 and pts[-5] is not None:
                # compute ...