python 车道线检测

Python车道线检测是利用计算机视觉技术来识别和跟踪道路上的车道线的方法。在这个项目中，我们使用OpenCV库中的计算机视觉概念来实现车道线检测。主要步骤包括边缘检测、感兴趣区域提取、霍夫变换、车道线识别和车道线绘制。首先，我们使用Canny算法对输入的图像进行边缘检测，得到图像中的边缘信息。然后，定义一个多边形区域，只保留与车道线相关的部分，过滤掉其他无关的背景信息。接下来，使用霍夫变换对感兴趣区域中的边缘像素进行直线拟合，得到图像中的直线信息。根据直线的斜率和截距，将直线分为左右两组，分别计算每组直线的平均斜率和截距，得到左右两条车道线。最后，在原始图像上绘制出左右两条车道线，并显示出结果。通过这些步骤，我们可以实现对车道线的实时检测。[1][2][3]

相关问题

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()

python车道线检测代码

Python车道线检测代码可以使用OpenCV库实现。以下是一份Python车道线检测代码示例：

import cv2
import numpy as np

# 定义ROI区域
def region_of_interest(img, vertices):
    mask = np.zeros_like(img)
    match_mask_color = 255
    cv2.fillPoly(mask, vertices, match_mask_color)
    masked_image = cv2.bitwise_and(img, mask)
    return masked_image

# 定义Hough变换参数
def hough_lines(img, rho, theta, threshold, min_line_len, max_line_gap):
    lines = cv2.HoughLinesP(img, rho, theta, threshold, np.array([]), minLineLength=min_line_len, maxLineGap=max_line_gap)
    line_img = np.zeros((img.shape[0], img.shape[1], 3), dtype=np.uint8)
    draw_lines(line_img, lines)
    return line_img

# 绘制车道线
def draw_lines(img, lines, color=[255, 0, 0], thickness=2):
    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)
    if len(left_lines) > 0:
        left_avg = np.average(left_lines, axis=0)
        cv2.line(img, (int(left_avg[0][0]), int(left_avg[0][1])), (int(left_avg[0][2]), int(left_avg[0][3])), color, thickness)
    if len(right_lines) > 0:
        right_avg = np.average(right_lines, axis=0)
        cv2.line(img, (int(right_avg[0][0]), int(right_avg[0][1])), (int(right_avg[0][2]), int(right_avg[0][3])), color, thickness)

# 读取图片
image = cv2.imread('test.jpg')
# 转为灰度图
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# 高斯滤波
kernel_size = 5
blur_gray = cv2.GaussianBlur(gray, (kernel_size, kernel_size), 0)
# Canny边缘检测
low_threshold = 50
high_threshold = 150
edges = cv2.Canny(blur_gray, low_threshold, high_threshold)
# ROI区域选择
imshape = image.shape
vertices = np.array([[(0,imshape[0]),(450, 320), (550, 320), (imshape[1],imshape[0])]], dtype=np.int32)
masked_edges = region_of_interest(edges, vertices)
# Hough变换检测车道线
rho = 2
theta = np.pi / 180
threshold = 15
min_line_len = 60
max_line_gap = 30
line_image = hough_lines(masked_edges, rho, theta, threshold, min_line_len, max_line_gap)
# 结果叠加到原图上
result = cv2.addWeighted(image, 0.8, line_image, 1, 0)
# 显示结果
cv2.imshow('result', result)
cv2.waitKey(0)
cv2.destroyAllWindows()

此代码可以检测车道线并绘制出车道线，运行结果会显示在窗口中。你可以自己更改代码中的参数来适配不同的图片。