Lane Detection OpenCV Python With Source Code

Lane Detection OpenCV Python With Source Code

The Lane Detection OpenCV Python Code was developed using Python OpenCV, Self Driving Car is one of AI’s most innovative technologies.

Self Driving Cars use lane detection OpenCV features to detect lanes on the roads and they are trained not to drive outside of the lane.

What is OpenCV?

OpenCV (Open Source Computer Vision Library) is an open-source computer vision and machine learning software library. OpenCV was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in commercial products.

A Lane Detection OpenCV consists of 6 Algorithms for the Python openCV project.

1. Capturing and decoding video file frame by frame
2. Conversion of the Image to GrayScale
3. Applying filters to reduce noise in video frames
4. Edge Detection Using Canny Edge detection method
5. Finding the region of interest and working on that part
6. Detecting lanes using Hough line transform

In this Python OpenCV Project also includes a downloadable Python Project With Source Code for free, just find the downloadable source code below and click to start downloading.

By the way if you are new to python programming and you don’t know what would be the the Python IDE to use, I have here a list of Best Python IDE for Windows, Linux, Mac OS that will suit for you. I also have here How to Download and Install Latest Version of Python on Windows.

To start executing Lane Detection OpenCV Python With Source Code, make sure that you have installed Python 3.9 and PyCharm in your computer.

Lane Detection OpenCV Python With Source Code : Steps on how to run the project

Installed Libraries

import cv2 as cv
import numpy as np
from matplotlib import pyplot as plt

Complete Source Code

import cv2 as cv
import numpy as np
from matplotlib import pyplot as plt


def lanesDetection(img):
    # img = cv.imread("./img/road.jpg")
    # img = cv.cvtColor(img, cv.COLOR_BGR2RGB)

    # print(img.shape)
    height = img.shape[0]
    width = img.shape[1]

    region_of_interest_vertices = [
        (200, height), (width/2, height/1.37), (width-300, height)
    ]
    gray_img = cv.cvtColor(img, cv.COLOR_RGB2GRAY)
    edge = cv.Canny(gray_img, 50, 100, apertureSize=3)
    cropped_image = region_of_interest(
        edge, np.array([region_of_interest_vertices], np.int32))

    lines = cv.HoughLinesP(cropped_image, rho=2, theta=np.pi/180,
                           threshold=50, lines=np.array([]), minLineLength=10, maxLineGap=30)
    image_with_lines = draw_lines(img, lines)
    # plt.imshow(image_with_lines)
    # plt.show()
    return image_with_lines


def region_of_interest(img, vertices):
    mask = np.zeros_like(img)
    # channel_count = img.shape[2]
    match_mask_color = (255)
    cv.fillPoly(mask, vertices, match_mask_color)
    masked_image = cv.bitwise_and(img, mask)
    return masked_image


def draw_lines(img, lines):
    img = np.copy(img)
    blank_image = np.zeros((img.shape[0], img.shape[1], 3), np.uint8)

    for line in lines:
        for x1, y1, x2, y2 in line:
            cv.line(blank_image, (x1, y1), (x2, y2), (0, 255, 0), 2)

    img = cv.addWeighted(img, 0.8, blank_image, 1, 0.0)
    return img


def videoLanes():
    cap = cv.VideoCapture('./img/Lane.mp4')
    while(cap.isOpened()):
        ret, frame = cap.read()
        frame = lanesDetection(frame)
        cv.imshow('Lanes Detection', frame)
        if cv.waitKey(1) & 0xFF == ord('q'):
            break

    cap.release()
    cv.destroyAllWindows()


if __name__ == "__main__":
    videoLanes()

Output

Download the Source Code below

Anyway, if you want to level up your programming knowledge, especially Python OpenCV, try this new article I’ve made for you Best OpenCV Projects With Source Code For Beginners.

Summary

Self-driving Car is one of AI’s most innovative technologies. Self Driving Cars use lane detection OpenCV features to detect lanes of the roads and they are trained not to drive outside of the lane.

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Inquiries

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