Python (openCV) Lane Detection

Lane.Detection.2023-11-18.14-47-18.mp4

Code explanation

import cv2
import numpy as np

These lines import the necessary libraries: cv2 for computer vision operations and numpy for numerical operations.

def region_of_interest(img, vertices):
    mask = np.zeros_like(img)
    cv2.fillPoly(mask, vertices, 255)
    masked_img = cv2.bitwise_and(img, mask)
    return masked_img

The region_of_interest function creates a mask for the region of interest in the image defined by the vertices parameter. It's used to focus on the area of the image where the lane lines are expected.

def draw_lines(img, lines, color=(0, 255, 0), thickness=3):
    for line in lines:
        for x1, y1, x2, y2 in line:
            cv2.line(img, (x1, y1), (x2, y2), color, thickness)

The draw_lines function draws lines on the image. It takes a list of lines (lines) and draws each line on the input image (img) using cv2.line.

def process_image(image):
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    blur = cv2.GaussianBlur(gray, (5, 5), 0)
    edges = cv2.Canny(blur, 50, 150)

    height, width = edges.shape
    vertices = np.array([[(0, height), (width / 2, height / 2), (width, height)]], dtype=np.int32)
    roi = region_of_interest(edges, vertices)

    lines = cv2.HoughLinesP(roi, 1, np.pi/180, threshold=50, minLineLength=100, maxLineGap=30)

    if lines is not None:
        line_image = np.zeros_like(image)
        draw_lines(line_image, lines)
        result = cv2.addWeighted(image, 0.8, line_image, 1, 0)
    else:
        result = image

    return result

The process_image function takes an input image and performs the following steps:

• Convert the image to grayscale.
• Apply Gaussian blur to the grayscale image to reduce noise.
• Use the Canny edge detector to find edges in the image.
• Define a region of interest (ROI) to focus on a specific area of the image where the lane lines are expected.
• Use the Hough transform to detect lines in the ROI.
• Draw the detected lines on a black image (line_image).
• Combine the original image with the lines using cv2.addWeighted.

cap = cv2.VideoCapture('path_to_your_video.mp4')

while cap.isOpened():
    ret, frame = cap.read()
    if not ret:
        break

    processed_frame = process_image(frame)

    cv2.imshow('Lane Detection', processed_frame)

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

cap.release()
cv2.destroyAllWindows()

The main part of the code captures frames from a video file, processes each frame using the process_image function, and displays the result in a window. The loop continues until the 'q' key is pressed. Make sure to replace 'video.mp4' with the actual path to your video file, I have left one for testing.