Lane detection — computer vision in the browser

A classic computer-vision demo: detect road lane lines from a live camera. OpenCV Canny edges + a region-of-interest mask + cv2.HoughLinesP find the lane segments. Point the (rear) camera at a road, press Run, and allow the camera.

Example code (Python / OpenCV)

import cv2
import numpy as np

# Point the camera at a road and press Run.
# Tip: use the flip (⟳) button on the preview to switch to the REAR camera.
cap = cv2.VideoCapture(0)

def region_of_interest(edges):
    # Keep only a trapezoid over the road ahead (lower-center of the frame).
    h, w = edges.shape
    mask = np.zeros_like(edges)
    poly = np.array([[
        (int(0.05 * w), h),
        (int(0.45 * w), int(0.60 * h)),
        (int(0.55 * w), int(0.60 * h)),
        (int(0.95 * w), h),
    ]], dtype=np.int32)
    cv2.fillPoly(mask, poly, 255)
    return cv2.bitwise_and(edges, mask)

while True:
    ret, frame = cap.read()
    if not ret:
        print("No camera frame — allow camera access when prompted.")
        break

    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    blur = cv2.GaussianBlur(gray, (5, 5), 0)
    edges = cv2.Canny(blur, 60, 150)
    roi = region_of_interest(edges)

    lines = cv2.HoughLinesP(roi, 1, np.pi / 180, threshold=40,
                            minLineLength=30, maxLineGap=60)
    overlay = frame.copy()
    if lines is not None:
        for x1, y1, x2, y2 in lines[:, 0]:
            if abs(y2 - y1) > 0.3 * abs(x2 - x1):   # drop near-horizontal clutter
                cv2.line(overlay, (x1, y1), (x2, y2), (0, 255, 0), 4)

    cv2.imshow("lanes", overlay)
    cv2.imshow("edges", roi)

    if cv2.waitKey(1) == 27:  # Esc / Stop
        break

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