python车道线检测

以下是Python实现车道线检测的步骤：

1. 图像加载和预处理：使用OpenCV库中的cv2.imread()函数加载图像，并将其转换为灰度图像。然后使用高斯滤波器平滑图像，以减少噪声。

import cv2
import numpy as np

# 加载图像
img = cv2.imread('test.jpg')

# 转换为灰度图像
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# 高斯滤波
kernel_size = 5
blur_gray = cv2.GaussianBlur(gray, (kernel_size, kernel_size), 0)

2. Canny边缘检测：使用Canny算法检测图像中的边缘。

# Canny边缘检测
low_threshold = 50
high_threshold = 150
edges = cv2.Canny(blur_gray, low_threshold, high_threshold)

3. 感兴趣区域检测：提取感兴趣区域（ROI），以便在后续步骤中检测车道线。

# 定义感兴趣区域的四个顶点坐标
imshape = img.shape
vertices = np.array([[(0, imshape[0]), (450, 320), (490, 320), (imshape[1], imshape[0])]], dtype=np.int32)

# 创建一个掩膜，只保留感兴趣区域内的像素
mask = np.zeros_like(edges)
if len(img.shape) > 2:
    channel_count = img.shape[2]
    ignore_mask_color = (255,) * channel_count
else:
    ignore_mask_color = 255
cv2.fillPoly(mask, vertices, ignore_mask_color)
masked_edges = cv2.bitwise_and(edges, mask)

4. 霍夫直线检测：使用霍夫变换检测图像中的直线。

# 霍夫直线检测
rho = 2
theta = np.pi / 180
threshold = 15
min_line_length = 40
max_line_gap = 20
lines = cv2.HoughLinesP(masked_edges, rho, theta, threshold, np.array([]), min_line_length, max_line_gap)

5. 直线拟合：将检测到的直线拟合成车道线。

# 直线拟合
left_lines = []
right_lines = []
for line in lines:
    for x1, y1, x2, y2 in line:
        k = (y2 - y1) / (x2 - x1)
        if k < 0:
            left_lines.append(line)
        else:
            right_lines.append(line)

left_points = np.array([[(x1, y1) for x1, y1, x2, y2 in left_lines]]).reshape(-1, 2)
right_points = np.array([[(x2, y2) for x1, y1, x2, y2 in right_lines]]).reshape(-1, 2)

left_fit = np.polyfit(left_points[:, 0], left_points[:, 1], 1)
right_fit = np.polyfit(right_points[:, 0], right_points[:, 1], 1)

6. 车道线叠加：将检测到的车道线叠加到原始图像上。

# 车道线叠加
line_img = np.zeros_like(img)
y1 = img.shape[0]
y2 = int(y1 * 0.6)
left_x1 = int((y1 - left_fit[1]) / left_fit[0])
left_x2 = int((y2 - left_fit[1]) / left_fit[0])
right_x1 = int((y1 - right_fit[1]) / right_fit[0])
right_x2 = int((y2 - right_fit[1]) / right_fit[0])
cv2.line(line_img, (left_x1, y1), (left_x2, y2), (255, 0, 0), 10)
cv2.line(line_img, (right_x1, y1), (right_x2, y2), (255, 0, 0), 10)
result = cv2.addWeighted(img, 0.8, line_img, 1, 0)

7. 图片和视频测试：使用上述步骤处理单张图片或视频。

# 图片测试
cv2.imwrite('result.jpg', result)

# 视频测试
cap = cv2.VideoCapture('test.mp4')
while cap.isOpened():
    ret, frame = cap.read()
    if ret:
        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        blur_gray = cv2.GaussianBlur(gray, (kernel_size, kernel_size), 0)
        edges = cv2.Canny(blur_gray, low_threshold, high_threshold)
        mask = np.zeros_like(edges)
        cv2.fillPoly(mask, vertices, ignore_mask_color)
        masked_edges = cv2.bitwise_and(edges, mask)
        lines = cv2.HoughLinesP(masked_edges, rho, theta, threshold, np.array([]), min_line_length, max_line_gap)
        left_lines = []
        right_lines = []
        for line in lines:
            for x1, y1, x2, y2 in line:
                k = (y2 - y1) / (x2 - x1)
                if k < 0:
                    left_lines.append(line)
                else:
                    right_lines.append(line)
        left_points = np.array([[(x1, y1) for x1, y1, x2, y2 in left_lines]]).reshape(-1, 2)
        right_points = np.array([[(x2, y2) for x1, y1, x2, y2 in right_lines]]).reshape(-1, 2)
        left_fit = np.polyfit(left_points[:, 0], left_points[:, 1], 1)
        right_fit = np.polyfit(right_points[:, 0], right_points[:, 1], 1)
        line_img = np.zeros_like(frame)
        y1 = frame.shape[0]
        y2 = int(y1 * 0.6)
        left_x1 = int((y1 - left_fit[1]) / left_fit[0])
        left_x2 = int((y2 - left_fit[1]) / left_fit[0])
        right_x1 = int((y1 - right_fit[1]) / right_fit[0])
        right_x2 = int((y2 - right_fit[1]) / right_fit[0])
        cv2.line(line_img, (left_x1, y1), (left_x2, y2), (255, 0, 0), 10)
        cv2.line(line_img, (right_x1, y1), (right_x2, y2), (255, 0, 0), 10)
        result = cv2.addWeighted(frame, 0.8, line_img, 1, 0)
        cv2.imshow('result', result)
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break
    else:
        break
cap.release()
cv2.destroyAllWindows()