给我一份opencv车道线检测代码

好的，请稍等，以下是车道线检测的代码：

import cv2
import numpy as np

def image_processing(img):
    # Convert image to grayscale
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

    # Apply Gaussian blur to the grayscale image
    blur = cv2.GaussianBlur(gray, (5, 5), 0)

    # Apply Canny edge detection to the blurred image
    edges = cv2.Canny(blur, 50, 150)

    # Create a masked edges image using cv2.fillPoly()
    mask = np.zeros_like(edges)
    ignore_mask_color = 255
    imshape = img.shape
    vertices = np.array([[(0,imshape[0]),(450, 290), (490, 290), (imshape[1],imshape[0])]], dtype=np.int32)
    cv2.fillPoly(mask, vertices, ignore_mask_color)
    masked_edges = cv2.bitwise_and(edges, mask)

    # Define the Hough transform parameters
    rho = 2              # Distance resolution in pixels of the Hough grid
    theta = np.pi/180    # Angular resolution in radians of the Hough grid
    threshold = 40       # Minimum number of votes (intersections in Hough grid cell)
    min_line_length = 50 # Minimum number of pixels making up a line
    max_line_gap = 150   # Maximum gap in pixels between connectable line segments

    # Apply Hough transform to the masked edges image
    lines = cv2.HoughLinesP(masked_edges, rho, theta, threshold, np.array([]),
                                min_line_length, max_line_gap)

    # Create a blank image with the same dimensions as the original image
    line_img = np.zeros((img.shape[0], img.shape[1], 3), dtype=np.uint8)

    # Draw detected lines onto the blank image
    draw_lines(line_img, lines)

    # Combine the original image with the line image
    result = cv2.addWeighted(img, 0.8, line_img, 1, 0)

    return result

def draw_lines(img, lines, color=[255, 0, 0], thickness=2):
    """
    This function draws `lines` with `color` and `thickness`.
    Lines are drawn on the image inplace (mutates the image).
    """
    # Define variables to store the averaged slopes and intercepts of the left and right lane lines
    left_slope = []
    left_intercept = []
    right_slope = []
    right_intercept = []

    # Iterate over the detected lines
    for line in lines:
        for x1,y1,x2,y2 in line:
            # Calculate the slope and intercept of the line
            slope, intercept = np.polyfit((x1, x2), (y1, y2), 1)
            # Skip lines with slopes that are too steep or not steep enough
            if abs(slope) < 0.5 or abs(slope) > 2:
                continue
            # Classify the line as left lane line or right lane line based on the slope and position of the line's midpoint
            midpoint_x = (x1 + x2) / 2
            img_center_x = img.shape[1] / 2
            if slope < 0 and midpoint_x < img_center_x:
                left_slope.append(slope)
                left_intercept.append(intercept)
            elif slope > 0 and midpoint_x > img_center_x:
                right_slope.append(slope)
                right_intercept.append(intercept)

    # Calculate the average slope and intercept for the left lane line and right lane line
    if left_slope and left_intercept:
        left_slope_avg = sum(left_slope) / len(left_slope)
        left_intercept_avg = sum(left_intercept) / len(left_intercept)
    else:
        left_slope_avg = None
        left_intercept_avg = None
    if right_slope and right_intercept:
        right_slope_avg = sum(right_slope) / len(right_slope)
        right_intercept_avg = sum(right_intercept) / len(right_intercept)
    else:
        right_slope_avg = None
        right_intercept_avg = None

    # Define the top and bottom y-coordinates of the lane lines
    y1 = img.shape[0]
    y2 = int(y1 * 0.6)

    # Draw the left lane line
    if left_slope_avg is not None:
        x1 = int((y1 - left_intercept_avg) / left_slope_avg)
        x2 = int((y2 - left_intercept_avg) / left_slope_avg)
        cv2.line(img, (x1, y1), (x2, y2), color, thickness)

    # Draw the right lane line
    if right_slope_avg is not None:
        x1 = int((y1 - right_intercept_avg) / right_slope_avg)
        x2 = int((y2 - right_intercept_avg) / right_slope_avg)
        cv2.line(img, (x1, y1), (x2, y2), color, thickness)

if __name__ == '__main__':
    # Load the test image
    img = cv2.imread('test.jpg')

    # Apply the image processing pipeline to the test image
    result = image_processing(img)

    # Show the result
    cv2.imshow('Result', result)
    cv2.waitKey()
    cv2.destroyAllWindows()

注意：此代码仅做演示用途，并不一定适用于所有情况。具体应用时需要根据实际需求进行修改调整。