Published November 19, 2017 © GPL3+

Line tracking with Raspberry pi 3 python2 and Open CV

The idea is to create a program in Python 2 using OpenCV 2 to find the center of a line for a Linefollower vehicle.

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Line tracking with Raspberry pi 3 python2 and Open CV

Things used in this project

Hardware components

Raspberry Pi 3 Model B

Logitech C270 HD - Webcam

Software apps and online services

OpenCV

Story

I want use the line detection to guide a vehicle to maintain the vehicle always at the center of the line. I will use the ROI function to find the line cut by a ROI of a rectangle of 640 pixels wide and 4 pixels high. The vision system will look at the ROI as a cat eye an the x value of the lines detected will be used to move the motor to keep the line in the middle, it means around x=320 aprox.

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Next step:

Create a ROI region of interest. Rectangular shape. 640x4 pixels

Calculate the desviation Of the line location from the center, the center nominal should be at 320 in X

Calculate the difference between real X and 320 and give a value as an tracking error.

I will add a PID algorithm

I will add the H bridge control subroutine for two motors to speed and change the direction to correct and move the vehicle to locate the center of the line at the center of the Camera image.

....

For complete example for line tracking using a video to check the code, please visit

https://github.com/naokishibuya/car-finding-lane-lines

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thanks

Instalation of OpenCV2 for Windows to use it in Python.

https://sourceforge.net/projects/opencvlibrary/files/opencv-win/2.0/OpenCV-2.0.0a-win32.exe/download

Code

# -*- 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()