Raspberry Pi Camera Tracking 2

# -*- coding: utf-8 -*-
import sys
import time
import cv2
import numpy as np
import os

Kernel_size=15
low_threshold=40
high_threshold=120

rho=10
threshold=15
theta=np.pi/180
minLineLength=10
maxLineGap=1

#Initialize camera
video_capture = cv2.VideoCapture(0)

while True:
    # CAPTURE FRAME-BY-FRAME
    ret, frame = video_capture.read()
    time.sleep(0.1)
    #Convert to Grayscale
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    #Blur image to reduce noise. if Kernel_size is bigger the image will be more blurry
    blurred = cv2.GaussianBlur(gray, (Kernel_size, Kernel_size), 0)

    #Perform canny edge-detection.
    #If a pixel gradient is higher than high_threshold is considered as an edge.
    #if a pixel gradient is lower than low_threshold is is rejected , it is not an edge.
    #Bigger high_threshold values will provoque to find less edges.
    #Canny recommended ratio upper:lower  between 2:1 or 3:1
    edged = cv2.Canny(blurred, low_threshold, high_threshold)
    #Perform hough lines probalistic transform
    lines = cv2.HoughLinesP(edged,rho,theta,threshold,minLineLength,maxLineGap)

    #Draw cicrcles in the center of the picture
    cv2.circle(frame,(320,240),20,(0,0,255),1)
    cv2.circle(frame,(320,240),10,(0,255,0),1)
    cv2.circle(frame,(320,240),2,(255,0,0),2)

    #With this for loops only a dots matrix is painted on the picture
    #for y in range(0,480,20):
            #for x in range(0,640,20):
                #cv2.line(frame,(x,y),(x,y),(0,255,255),2)

    #With this for loops a grid is painted on the picture
    for y in range(0,480,40):
            cv2.line(frame,(0,y),(640,y),(255,0,0),1)
            for x in range(0,640,40):
                cv2.line(frame,(x,0),(x,480),(255,0,0),1)

    #Draw lines on input image
    if(lines != None):
        for x1,y1,x2,y2 in lines[0]:
            cv2.line(frame,(x1,y1),(x2,y2),(0,255,0),2)
            cv2.putText(frame,'lines_detected',(50,50),cv2.FONT_HERSHEY_SIMPLEX,1,(0,255,0),1)
    cv2.imshow("line detect test", frame)

    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# When everything is done, release the capture

video_capture.release()
cv2.destroyAllWindows()