### Python 实现车道线识别的完整代码 以下是基于 OpenCV 和 Hough 变换的 Python 代码，用于从图片中识别车道线（包括直线和曲线）。此代码还包括详细的注释以便理解。 ```python import cv2 import numpy as np def process_image(image_path): """ 处理输入图像并检测车道线。 参数: image_path (str): 输入图像路径 返回: result_image (numpy.ndarray): 绘制了车道线的结果图像 """ # 加载原始图像 original_image = cv2.imread(image_path) if original_image is None: raise ValueError("无法加载图像，请检查文件路径") # 将彩色图像转换为灰度图像 gray_image = cv2.cvtColor(original_image, cv2.COLOR_BGR2GRAY) # 使用高斯模糊减少噪声 blurred_image = cv2.GaussianBlur(gray_image, (5, 5), 0) # Canny 边缘检测 edges_image = cv2.Canny(blurred_image, threshold1=50, threshold2=150) # 创建掩码区域以聚焦感兴趣的区域 (ROI)，假设 ROI 是梯形区域 height, width = edges_image.shape[:2] roi_vertices = [ (0, height), (width / 2 - 50, height * 3 / 5), (width / 2 + 50, height * 3 / 5), (width, height) ] mask = np.zeros_like(edges_image) match_mask_color = 255 cv2.fillPoly(mask, np.array([roi_vertices], dtype=np.int32), match_mask_color) masked_edges = cv2.bitwise_and(edges_image, mask) # 霍夫变换检测线条 lines = cv2.HoughLinesP( masked_edges, rho=6, theta=np.pi / 60, threshold=160, minLineLength=40, maxLineGap=25 ) # 创建空白画布绘制检测到的线条 line_image = np.zeros((masked_edges.shape[0], masked_edges.shape[1], 3), dtype=np.uint8) # 过滤并拟合左/右车道线 left_line_x = [] left_line_y = [] right_line_x = [] right_line_y = [] for line in lines: for x1, y1, x2, y2 in line: slope = (y2 - y1) / (x2 - x1) # 计算斜率 if abs(slope) < 0.5: # 排除接近水平的线 continue elif slope <= 0: # 左侧车道线 left_line_x.extend([x1, x2]) left_line_y.extend([y1, y2]) else: # 右侧车道线 right_line_x.extend([x1, x2]) right_line_y.extend([y1, y2]) # 拟合左侧车道线 if len(left_line_x) != 0 and len(left_line_y) != 0: poly_left = np.polyfit(left_line_y, left_line_x, deg=1) # 线性回归 left_start_point = (int(np.polyval(poly_left, height)), height) left_end_point = (int(np.polyval(poly_left, height * 3 / 5)), int(height * 3 / 5)) cv2.line(line_image, left_start_point, left_end_point, color=(255, 0, 0), thickness=10) # 拟合右侧车道线 if len(right_line_x) != 0 and len(right_line_y) != 0: poly_right = np.polyfit(right_line_y, right_line_x, deg=1) # 线性回归 right_start_point = (int(np.polyval(poly_right, height)), height) right_end_point = (int(np.polyval(poly_right, height * 3 / 5)), int(height * 3 / 5)) cv2.line(line_image, right_start_point, right_end_point, color=(0, 255, 0), thickness=10) # 合并原图与车道线图 result_image = cv2.addWeighted(original_image, 0.8, line_image, 1, gamma=0.) return result_image if __name__ == "__main__": input_image_path = "lane.jpg" # 替换为您的测试图片路径 output_image = process_image(input_image_path) cv2.imshow("Lane Detection", output_image) cv2.waitKey(0) cv2.destroyAllWindows() ``` #### 注解说明 - **Canny 边缘检测**: 提取图像中的显著边缘特征[^1]。 - **霍夫变换**: 利用参数空间检测直线，适用于复杂背景下的车道线提取[^1]。 - **感兴趣区域 (ROI)**: 减少计算范围，专注于可能存在的车道线位置。 - **多项式拟合**: 对检测到的离散点进行平滑处理，形成连续的车道线表示。 ### 结果分析 上述代码可以有效检测出清晰的车道线。但对于较模糊或被遮挡的部分可能存在误检或漏检情况。为了进一步优化性能，可引入深度学习框架如 LineCNN 或 LaneATT 来增强鲁棒性和精度[^2]。 ---