### Python 实现车道线识别（直线与曲线） 以下是完整的 Python 代码，用于识别车道线中的直线和曲线。该代码基于 OpenCV 库并结合了 Hough 变换以及多项式拟合的技术[^1]。 ```python import cv2 import numpy as np def preprocess_image(image): """ 图像预处理函数：灰度化 -> 高斯模糊 -> Canny 边缘检测 """ gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # 将图像转换为灰度图 blur = cv2.GaussianBlur(gray, (5, 5), 0) # 使用高斯滤波平滑图像 canny = cv2.Canny(blur, 50, 150) # 进行Canny边缘检测 return canny def region_of_interest(image): """ 提取感兴趣区域(ROI)，仅保留车道线可能存在的部分 """ height, width = image.shape mask = np.zeros_like(image) polygon = np.array([[ (100, height), (width - 100, height), (width//2 + 100, height//2), (width//2 - 100, height//2)]], dtype=np.int32) cv2.fillPoly(mask, polygon, 255) # 创建掩码 masked_image = cv2.bitwise_and(image, mask) # 对原始图像应用掩码 return masked_image def detect_lines(image): """ 检测直线段，并分类为左侧和右侧车道线 """ lines = cv2.HoughLinesP( image, rho=2, theta=np.pi / 180, threshold=100, minLineLength=40, maxLineGap=5 ) left_lines = [] right_lines = [] if lines is not None: for line in lines: x1, y1, x2, y2 = line.reshape(4) parameters = np.polyfit((x1, x2), (y1, y2), 1) # 计算斜率和截距 slope = parameters[0] intercept = parameters[1] if slope < 0: # 左侧车道线具有负斜率 left_lines.append((slope, intercept)) elif slope > 0: # 右侧车道线具有正斜率 right_lines.append((slope, intercept)) return left_lines, right_lines def average_slope_intercept(lines): """ 平均计算每组车道线的斜率和截距 """ avg_line = np.average(lines, axis=0) return avg_line def display_lines(image, lines): """ 绘制最终的车道线到原图上 """ line_image = np.zeros_like(image) if lines is not None: for line in lines: if line is not None: x1, y1, x2, y2 = line.astype(int) cv2.line(line_image, (x1, y1), (x2, y2), (255, 0, 0), 10) return line_image def main(): cap = cv2.VideoCapture('test_video.mp4') # 加载视频文件 while True: ret, frame = cap.read() if not ret: break lane_image = np.copy(frame) canny = preprocess_image(lane_image) # 预处理图像 cropped_canny = region_of_interest(canny) # 提取感兴趣区域 left_lines, right_lines = detect_lines(cropped_canny) # 检测线条 try: left_avg = average_slope_intercept(left_lines) # 获取平均左侧车道线参数 right_avg = average_slope_intercept(right_lines) # 获取平均右侧车道线参数 # 根据平均参数绘制完整车道线 y_max = lane_image.shape[0] y_min = int(y_max * 3/5) left_x_start = int((y_min - left_avg[1]) / left_avg[0]) left_x_end = int((y_max - left_avg[1]) / left_avg[0]) right_x_start = int((y_min - right_avg[1]) / right_avg[0]) right_x_end = int((y_max - right_avg[1]) / right_avg[0]) final_left_line = [[left_x_start, y_min, left_x_end, y_max]] final_right_line = [[right_x_start, y_min, right_x_end, y_max]] combined_lines = [final_left_line, final_right_line] line_image = display_lines(lane_image, combined_lines) # 显示车道线 combo_image = cv2.addWeighted(lane_image, 0.8, line_image, 1, 1) # 合并显示效果 cv2.imshow("Result", combo_image) except Exception as e: pass if cv2.waitKey(1) & 0xFF == ord('q'): break cap.release() cv2.destroyAllWindows() if __name__ == "__main__": main() ``` 此代码实现了从摄像头或视频流中提取车道线的功能。通过 `preprocess_image` 函数完成图像预处理；使用 `region_of_interest` 来限定 ROI 区域；借助霍夫变换 (`cv2.HoughLinesP`) 和多项式拟合找到车道线位置。 #### 关键点说明： - **Hough Transform**: 利用霍夫变换检测图像中的直线段。 - **Polynomial Fitting**: 使用一阶多项式拟合直线方程 \(y = mx+b\) 的形式表示车道线。 - **Region of Interest (ROI)**: 设置一个梯形区域作为兴趣区，减少无关背景干扰[^3]。 ---