基于OpenCV实现车道线检测(自动驾驶 机器视觉)

# 示例代码，作者丹成学长：Q746876041
mask = np.zeros_like(edges)

    ignore_mask_color = 255 
    # 获取图片尺寸
    imshape = img.shape
    # 定义 mask 顶点
    vertices = np.array([[(0,imshape[0]),(450, 290), (490, 290), (imshape[1],imshape[0])]], dtype=np.int32)
    # 使用 fillpoly 来绘制 mask
    cv2.fillPoly(mask, vertices, ignore_mask_color)
    masked_edges = cv2.bitwise_and(edges, mask)
    # 定义Hough 变换的参数
    rho = 1 
    theta = np.pi/180
    threshold = 2

    min_line_length = 4 # 组成一条线的最小像素数
    max_line_gap = 5    # 可连接线段之间的最大像素间距
    # 创建一个用于绘制车道线的图片
    line_image = np.copy(img)*0 

    # 对于 canny 边缘检测结果应用 Hough 变换
    # 输出“线”是一个数组，其中包含检测到的线段的端点
    lines = cv2.HoughLinesP(masked_edges, rho, theta, threshold, np.array([]),
                                min_line_length, max_line_gap)

    # 遍历“线”的数组来在 line_image 上绘制
    for line in lines:
        for x1,y1,x2,y2 in line:
            cv2.line(line_image,(x1,y1),(x2,y2),(255,0,0),10)

    color_edges = np.dstack((edges, edges, edges)) 

import math
import cv2
import numpy as np

"""
Gray Scale
Gaussian Smoothing
Canny Edge Detection
Region Masking
Hough Transform
Draw Lines [Mark Lane Lines with different Color]
"""

class SimpleLaneLineDetector(object):
    def __init__(self):
        pass

    def detect(self,img):
        # 图像灰度处理
        gray_img = self.grayscale(img)
        print(gray_img)
        #图像高斯平滑处理
        smoothed_img = self.gaussian_blur(img = gray_img, kernel_size = 5)
        #canny 边缘检测
        canny_img = self.canny(img = smoothed_img, low_threshold = 180, high_threshold = 240)
        #区域 Mask
        masked_img = self.region_of_interest(img = canny_img, vertices = self.get_vertices(img))
        #霍夫变换
        houghed_lines = self.hough_lines(img = masked_img, rho = 1, theta = np.pi/180, threshold = 20, min_line_len = 20, max_line_gap = 180)
        # 绘制车道线
        output = self.weighted_img(img = houghed_lines, initial_img = img, alpha=0.8, beta=1., gamma=0.)
        
        return output
    def grayscale(self,img):
        return cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)

    def canny(self,img, low_threshold, high_threshold):
        return cv2.Canny(img, low_threshold, high_threshold)

    def gaussian_blur(self,img, kernel_size):
        return cv2.GaussianBlur(img, (kernel_size, kernel_size), 0)

    def region_of_interest(self,img, vertices):
        mask = np.zeros_like(img)   
    
        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_image = cv2.bitwise_and(img, mask)
        return masked_image
    def draw_lines(self,img, lines, color=[255, 0, 0], thickness=10):
        for line in lines:
            for x1,y1,x2,y2 in line:
                cv2.line(img, (x1, y1), (x2, y2), color, thickness)

    def slope_lines(self,image,lines):
        img = image.copy()
        poly_vertices = []
        order = [0,1,3,2]

        left_lines = [] 
        right_lines = [] 
        for line in lines:
            for x1,y1,x2,y2 in line:

                if x1 == x2:
                    pass 
                else:
                    m = (y2 - y1) / (x2 - x1)
                    c = y1 - m * x1

                    if m < 0:
                        left_lines.append((m,c))
                    elif m >= 0:
                        right_lines.append((m,c))

        left_line = np.mean(left_lines, axis=0)
        right_line = np.mean(right_lines, axis=0)


        for slope, intercept in [left_line, right_line]:

            rows, cols = image.shape[:2]
            y1= int(rows) 

            y2= int(rows*0.6)

            x1=int((y1-intercept)/slope)
            x2=int((y2-intercept)/slope)
            poly_vertices.append((x1, y1))
            poly_vertices.append((x2, y2))
            self.draw_lines(img, np.array([[[x1,y1,x2,y2]]]))
        
        poly_vertices = [poly_vertices[i] for i in order]
        cv2.fillPoly(img, pts = np.array([poly_vertices],'int32'), color = (0,255,0))
        return cv2.addWeighted(image,0.7,img,0.4,0.)

    def hough_lines(self,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)
        line_img = self.slope_lines(line_img,lines)
        return line_img

    def weighted_img(self,img, initial_img, alpha=0.1, beta=1., gamma=0.):

        lines_edges = cv2.addWeighted(initial_img, alpha, img, beta, gamma)
        return lines_edges
        
    def get_vertices(self,image):
        rows, cols = image.shape[:2]
        bottom_left  = [cols*0.15, rows]
        top_left     = [cols*0.45, rows*0.6]
        bottom_right = [cols*0.95, rows]
        top_right    = [cols*0.55, rows*0.6] 
        
        ver = np.array([[bottom_left, top_left, top_right, bottom_right]], dtype=np.int32)
        return ver

lines = cv2.HoughLinesP(cropped_image,2,np.pi/180,100,np.array([]),minLineLength=40,maxLineGap=5)