python+opencv车道线检测（简易实现） 车道线检测（简易实现） python+opencv车道线检测（简易实现） 车道线检测（简易实现） 技术栈：python+opencv 技术栈： 实现思路： 实现思路： canny边缘检测获取图中的边缘信息； 霍夫变换寻找图中直线； 绘制梯形感兴趣区域获得车前范围； 得到并绘制车道线； 效果展示： 效果展示： 代码实现： 代码实现： import cv2 import numpy as np def canny(): gray = cv2.cvtColor(lane_image, cv2.COLOR_RGB2GRAY) #高斯滤波 blur = cv2.GaussianBlur(gray, (5, 5), 0) #边缘检测 canny_img = cv2.Canny(blur, 50, 150) return canny_img def region_of_interest(r_image): h = r_image.shape[0] w = r_image.shape[1] # 这个区域不稳定，需要根据图片更换 poly = np.array([ [(100, h), (500, h), (290, 180), (250, 180)] ]) mask = np.zeros_like(r_image) # 绘制掩膜图像 cv2.fillPoly(mask, poly, 255) # 获得ROI区域 masked_image = cv2.bitwise_and(r_image, mask) return masked_image if __name__ == '__main__': image = cv2.imread('test.jpg') lane_image = np.copy(image) canny = canny() cropped_image = region_of_interest(canny) cv2.imshow("result", cropped_image) cv2.waitKey(0) 霍夫变换加线性拟合改良： 霍夫变换加线性拟合改良： 效果图： 效果图： 代码实现： 代码实现： 主要增加了根据斜率作线性拟合过滤无用点后连线的操作； import cv2 import numpy as np def canny(): gray = cv2.cvtColor(lane_image, cv2.COLOR_RGB2GRAY) 

blur = cv2.GaussianBlur(gray, (5, 5), 0) canny_img = cv2.Canny(blur, 50, 150) return canny_img def region_of_interest(r_image): h = r_image.shape[0] w = r_image.shape[1] poly = np.array([ [(100, h), (500, h), (280, 180), (250, 180)] ]) mask = np.zeros_like(r_image) cv2.fillPoly(mask, poly, 255) masked_image = cv2.bitwise_and(r_image, mask) return masked_image def get_lines(img_lines): if img_lines is not None: for line in lines: for x1, y1, x2, y2 in line: # 分左右车道 k = (y2 - y1) / (x2 - x1) if k 0: mean = np.mean(slope) # 计算平均斜率 diff = [abs(s - mean) for s in slope] # 每条线斜率与平均斜率的差距 idx = np.argmax(diff) # 找到最大斜率的索引 if diff[idx] > slo_th: # 大于预设的阈值选取 slope.pop(idx) after_lines.pop(idx) else: break return after_lines def clac_edgepoints(points, y_min, y_max): x = [p[0] for p in points] y = [p[1] for p in points] k = np.polyfit(y, x, 1) # 曲线拟合的函数，找到xy的拟合关系斜率 func = np.poly1d(k) # 斜率代入可以得到一个y=kx的函数 x_min = int(func(y_min)) # y_min = 325其实是近似找了一个 x_max = int(func(y_max)) return [(x_min, y_min), (x_max, y_max)] if __name__ == '__main__': image = cv2.imread('F:\\A_javaPro\\test.jpg') lane_image = np.copy(image) canny_img = canny() cropped_image = region_of_interest(canny_img) lefts = [] rights = [] lines = cv2.HoughLinesP(cropped_image, 1, np.pi / 180, 15, np.array([]), minLineLength=40, maxLineGap=20) get_lines(lines) # 分别得到左右车道线的图片 good_leftlines = choose_lines(lefts, 0.1) # 处理后的点 good_rightlines = choose_lines(rights, 0.1) leftpoints = [(x1, y1) for left in good_leftlines for x1, y1, x2, y2 in left] leftpoints = leftpoints + [(x2, y2) for left in good_leftlines for x1, y1, x2, y2 in left] rightpoints = [(x1, y1) for right in good_rightlines for x1, y1, x2, y2 in right] rightpoints = rightpoints + [(x2, y2) for right in good_rightlines for x1, y1, x2, y2 in right] lefttop = clac_edgepoints(leftpoints, 180, image.shape[0]) # 要画左右车道线的端点 righttop = clac_edgepoints(rightpoints, 180, image.shape[0]) src = np.zeros_like(image) cv2.line(src, lefttop[0], lefttop[1], (255, 255, 0), 7) cv2.line(src, righttop[0], righttop[1], (255, 255, 0), 7) cv2.imshow('line Image', src) src_2 = cv2.addWeighted(image, 0.8, src, 1, 0) cv2.imshow('Finally Image', src_2) cv2.waitKey(0) 待改进： 待改进： 代码实用性差，几乎不能用于实际，但是可以作为初学者的练手项目； 斑马线检测思路 斑马线检测思路：获取车前感兴趣区域，判断白色像素点比例即可实现； 行人检测思路：opencv有内置行人检测函数，基于内置的训练好的数据集； 行人检测思路 作者：毛钱儿