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 lane of 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 consist of 6 Algorithm for the python opencv project.
- Capturing and decoding video file frame by frame
- Conversion of the Image to GrayScale
- Applying filters to reduce noise in video frames
- Edge Detection Using Canny Edge detection method
- Finding the region of interest and working on that part
- 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
Time needed: 5 minutes
These are the steps on how to run Lane Detection OpenCV Python With Source Code
- Step 1: Download the given source code below.
First, download the given source code below and unzip the source code.
- Step 2: Import the project to your PyCharm IDE.
Next, import the source code you’ve download to your PyCharm IDE.
- Step 3: Run the project.
last, run the project with the command “py main.py”
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 width = img.shape 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 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, img.shape, 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()
Download 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 2021.
Self Driving Car is one of AI’s most innovative technologies. Self Driving Cars use lane detection OpenCV features to detect lane of the roads and they are trained not to drive outside of the lane.
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