mi-task/utils/detect_track.py

import cv2
import numpy as np
import os
import datetime
from sklearn import linear_model
from utils.log_helper import get_logger, debug, info, warning, error, success

def detect_horizontal_track_edge(image, observe=False, delay=1000, save_log=True):
    """
    检测正前方横向黄色赛道的边缘，并返回y值最大的边缘点
    # INFO 原来的版本
    
    参数:
        image: 输入图像，可以是文件路径或者已加载的图像数组
        observe: 是否输出中间状态信息和可视化结果，默认为False
        delay: 展示每个步骤的等待时间(毫秒)
        save_log: 是否保存日志和图像
    返回:
        edge_point: 赛道前方边缘点的坐标 (x, y)
        edge_info: 边缘信息字典
    """
    # observe = False  # TEST
    # 如果输入是字符串（文件路径），则加载图像
    if isinstance(image, str):
        img = cv2.imread(image)
    else:
        img = image.copy()
    
    if img is None:
        error("无法加载图像", "失败")
        return None, None
    
    # 获取图像尺寸
    height, width = img.shape[:2]
    
    # 计算图像中间区域的范围（用于专注于正前方的赛道）
    center_x = width // 2
    search_width = int(width * 2/3)  # 搜索区域宽度为图像宽度的2/3
    search_height = height  # 搜索区域高度为图像高度的1/1
    left_bound = center_x - search_width // 2
    right_bound = center_x + search_width // 2
    bottom_bound = height
    top_bound = height - search_height
    
    if observe:
        debug("步骤1: 原始图像已加载", "加载")
        search_region_img = img.copy()
        # 绘制搜索区域
        cv2.rectangle(search_region_img, (left_bound, top_bound), (right_bound, bottom_bound), (255, 0, 0), 2)
        cv2.line(search_region_img, (center_x, 0), (center_x, height), (0, 0, 255), 2)  # 中线
        cv2.imshow("搜索区域", search_region_img)
        cv2.waitKey(delay)
    
    # 转换到HSV颜色空间以便更容易提取黄色
    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
    
    # 黄色的HSV范围
    lower_yellow = np.array([20, 100, 100])
    upper_yellow = np.array([30, 255, 255])
    
    # 创建黄色的掩码
    mask = cv2.inRange(hsv, lower_yellow, upper_yellow)
    
    # 添加形态学操作以改善掩码
    kernel = np.ones((3, 3), np.uint8)
    mask = cv2.dilate(mask, kernel, iterations=1)
    
    if observe:
        debug("步骤2: 创建黄色掩码", "处理")
        cv2.imshow("黄色掩码", mask)
        cv2.waitKey(delay)
    
    # 应用掩码，只保留黄色部分
    yellow_only = cv2.bitwise_and(img, img, mask=mask)
    
    if observe:
        debug("步骤3: 提取黄色部分", "处理")
        cv2.imshow("只保留黄色", yellow_only)
        cv2.waitKey(delay)
    
    # 裁剪掩码到搜索区域
    search_mask = mask[top_bound:bottom_bound, left_bound:right_bound]
    
    # 找到掩码在搜索区域中最底部的非零点位置
    bottom_points = []
    non_zero_cols = np.where(np.any(search_mask, axis=0))[0]
    
    # 寻找每列的最底部点
    for col in non_zero_cols:
        col_points = np.where(search_mask[:, col] > 0)[0]
        if len(col_points) > 0:
            bottom_row = np.max(col_points)
            bottom_points.append((left_bound + col, top_bound + bottom_row))
    
    if len(bottom_points) < 3:
        # 如果找不到足够的底部点，使用canny+霍夫变换
        edges = cv2.Canny(mask, 50, 150, apertureSize=3)
        
        if observe:
            debug("步骤3.1: 边缘检测", "处理")
            cv2.imshow("边缘检测", edges)
            cv2.waitKey(delay)
        
        # 使用霍夫变换检测直线 - 调低阈值以检测短线段
        lines = cv2.HoughLinesP(edges, 1, np.pi/180, threshold=30, 
                               minLineLength=width*0.1, maxLineGap=30)
        
        if lines is None or len(lines) == 0:
            if observe:
                error("未检测到直线", "失败")
            return None, None
        
        if observe:
            debug(f"步骤4: 检测到 {len(lines)} 条直线", "处理")
            lines_img = img.copy()
            for line in lines:
                x1, y1, x2, y2 = line[0]
                cv2.line(lines_img, (x1, y1), (x2, y2), (0, 255, 0), 2)
            cv2.imshow("检测到的直线", lines_img)
            cv2.waitKey(delay)
        
        # 筛选水平线，但放宽斜率条件
        horizontal_lines = []
        for line in lines:
            x1, y1, x2, y2 = line[0]
            
            # 计算斜率 (避免除零错误)
            if abs(x2 - x1) < 5:  # 几乎垂直的线
                continue
                
            slope = (y2 - y1) / (x2 - x1)
            
            # 筛选接近水平的线 (斜率接近0)，但容许更大的倾斜度
            if abs(slope) < 0.3:
                # 确保线在搜索区域内
                if ((left_bound <= x1 <= right_bound and top_bound <= y1 <= bottom_bound) or
                    (left_bound <= x2 <= right_bound and top_bound <= y2 <= bottom_bound)):
                    # 计算线的中点y坐标
                    mid_y = (y1 + y2) / 2
                    line_length = np.sqrt((x2-x1)**2 + (y2-y1)**2)
                    # 保存线段、其y坐标和长度
                    horizontal_lines.append((line[0], mid_y, slope, line_length))
        
        if not horizontal_lines:
            if observe:
                error("未检测到水平线", "失败")
            return None, None
        
        if observe:
            debug(f"步骤4.1: 找到 {len(horizontal_lines)} 条水平线", "处理")
            h_lines_img = img.copy()
            for line_info in horizontal_lines:
                line, _, slope, _ = line_info
                x1, y1, x2, y2 = line
                cv2.line(h_lines_img, (x1, y1), (x2, y2), (0, 255, 255), 2)
                # 显示斜率
                cv2.putText(h_lines_img, f"{slope:.2f}", ((x1+x2)//2, (y1+y2)//2), 
                            cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 1)
            cv2.imshow("水平线", h_lines_img)
            cv2.waitKey(delay)
        
        # 按y坐标排序 (从大到小，底部的线排在前面)
        horizontal_lines.sort(key=lambda x: x[1], reverse=True)
        
        # 取最靠近底部且足够长的线作为横向赛道线
        selected_line = None
        selected_slope = 0
        for line_info in horizontal_lines:
            line, _, slope, length = line_info
            if length > width * 0.1:  # 确保线足够长
                selected_line = line
                selected_slope = slope
                break
        
        if selected_line is None and horizontal_lines:
            # 如果没有足够长的线，就取最靠近底部的线
            selected_line = horizontal_lines[0][0]
            selected_slope = horizontal_lines[0][2]
        
        if selected_line is None:
            if observe:
                error("无法选择合适的线段", "失败")
            return None, None
            
        x1, y1, x2, y2 = selected_line
    else:
        # 使用底部点拟合直线
        if observe:
            debug("正在处理底部边缘点", "处理")
            bottom_points_img = img.copy()
            for point in bottom_points:
                cv2.circle(bottom_points_img, point, 3, (0, 255, 0), -1)
            cv2.imshow("底部边缘点", bottom_points_img)
            cv2.waitKey(delay)
        
        # 使用RANSAC拟合直线以去除异常值
        x_points = np.array([p[0] for p in bottom_points]).reshape(-1, 1)
        y_points = np.array([p[1] for p in bottom_points])
        
        # 如果点过少或分布不够宽，返回None
        if len(bottom_points) < 3 or np.max(x_points) - np.min(x_points) < width * 0.1:
            if observe:
                warning("底部点太少或分布不够宽", "警告")
            return None, None
        
        ransac = linear_model.RANSACRegressor(residual_threshold=5.0)
        ransac.fit(x_points, y_points)
        
        # 获取拟合参数
        selected_slope = ransac.estimator_.coef_[0]
        intercept = ransac.estimator_.intercept_
        
        # 检查斜率是否在合理范围内
        if abs(selected_slope) > 0.3:
            if observe:
                warning(f"拟合斜率过大: {selected_slope:.4f}", "警告")
            return None, None
        
        # 使用拟合的直线参数计算线段端点
        x1 = left_bound
        y1 = int(selected_slope * x1 + intercept)
        x2 = right_bound
        y2 = int(selected_slope * x2 + intercept)
        
        if observe:
            debug("显示拟合线段", "处理")
            fitted_line_img = img.copy()
            cv2.line(fitted_line_img, (x1, y1), (x2, y2), (0, 255, 255), 2)
            cv2.imshow("拟合线段", fitted_line_img)
            cv2.waitKey(delay)
    
    # 确保x1 < x2
    if x1 > x2:
        x1, x2 = x2, x1
        y1, y2 = y2, y1
    
    # 找到线上y值最大的点作为边缘点(最靠近相机的点)
    if y1 > y2:
        bottom_edge_point = (x1, y1)
    else:
        bottom_edge_point = (x2, y2)
    
    # 获取线上的更多点
    selected_points = []
    step = 5  # 每5个像素取一个点
    for x in range(max(left_bound, int(min(x1, x2))), min(right_bound, int(max(x1, x2)) + 1), step):
        y = int(selected_slope * (x - x1) + y1)
        if top_bound <= y <= bottom_bound:
            selected_points.append((x, y))
    
    if observe:
        debug(f"步骤5: 找到边缘点 {bottom_edge_point}", "检测")
        edge_img = img.copy()
        # 画线
        cv2.line(edge_img, (x1, y1), (x2, y2), (0, 255, 0), 2)
        # 绘制所有点
        for point in selected_points:
            cv2.circle(edge_img, point, 3, (255, 0, 0), -1)
        # 标记边缘点
        cv2.circle(edge_img, bottom_edge_point, 10, (0, 0, 255), -1)
        cv2.imshow("选定的横向线和边缘点", edge_img)
        cv2.waitKey(delay)
    
    # 计算这个点到中线的距离
    distance_to_center = bottom_edge_point[0] - center_x
    
    # 计算中线与检测到的横向线的交点
    # 横向线方程: y = slope * (x - x1) + y1
    # 中线方程: x = center_x
    # 解这个方程组得到交点坐标
    intersection_x = center_x
    intersection_y = selected_slope * (center_x - x1) + y1
    intersection_point = (int(intersection_x), int(intersection_y))
    
    # 计算交点到图像底部的距离（以像素为单位）
    distance_to_bottom = height - intersection_y
    
    result_img = None
    if observe or save_log:
        slope_img = img.copy()
        # 画出检测到的线
        cv2.line(slope_img, (x1, y1), (x2, y2), (0, 255, 0), 2)
        # 标记边缘点
        cv2.circle(slope_img, bottom_edge_point, 10, (0, 0, 255), -1)
        # 画出中线
        cv2.line(slope_img, (center_x, 0), (center_x, height), (0, 0, 255), 2)
        # 标记中线与横向线的交点
        cv2.circle(slope_img, intersection_point, 12, (255, 0, 255), -1)
        cv2.circle(slope_img, intersection_point, 5, (255, 255, 255), -1)
        # 画出交点到底部的距离线
        cv2.line(slope_img, intersection_point, (intersection_x, height), (255, 255, 0), 2)
        
        cv2.putText(slope_img, f"Slope: {selected_slope:.4f}", (10, 30), 
                    cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
        cv2.putText(slope_img, f"Distance to center: {distance_to_center}px", (10, 70), 
                    cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
        cv2.putText(slope_img, f"Distance to bottom: {distance_to_bottom:.1f}px", (10, 110), 
                    cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
        cv2.putText(slope_img, f"中线交点: ({intersection_point[0]}, {intersection_point[1]})", (10, 150), 
                    cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
        
        if observe:
            debug("显示边缘斜率和中线交点", "显示")
            cv2.imshow("边缘斜率和中线交点", slope_img)
            cv2.waitKey(delay)
        
        result_img = slope_img
    
    # 保存日志图像
    if save_log and result_img is not None:
        timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S_%f")
        log_dir = "logs/image"
        os.makedirs(log_dir, exist_ok=True)

        # origin image
        origin_image_path = os.path.join(log_dir, f"origin_horizontal_edge_{timestamp}.jpg")
        cv2.imwrite(origin_image_path, img)
        info(f"保存原始图像到: {origin_image_path}", "日志")

        img_path = os.path.join(log_dir, f"horizontal_edge_{timestamp}.jpg")
        cv2.imwrite(img_path, result_img)
        info(f"保存横向边缘检测结果图像到: {img_path}", "日志")
        
        # 保存文本日志信息
        log_info = {
            "timestamp": timestamp,
            "edge_point": bottom_edge_point,
            "distance_to_center": distance_to_center,
            "slope": selected_slope,
            "distance_to_bottom": distance_to_bottom,
            "intersection_point": intersection_point
        }
        info(f"横向边缘检测结果: {log_info}", "日志")
    
    # 创建边缘信息字典
    edge_info = {
        "x": bottom_edge_point[0],
        "y": bottom_edge_point[1],
        "distance_to_center": distance_to_center,
        "slope": selected_slope,
        "is_horizontal": abs(selected_slope) < 0.05,  # 判断边缘是否接近水平
        "points_count": len(selected_points),  # 该组中点的数量
        "intersection_point": intersection_point,  # 中线与横向线的交点
        "distance_to_bottom": distance_to_bottom,  # 交点到图像底部的距离
        # "points": selected_points  # 添加选定的点组
    }
    
    return bottom_edge_point, edge_info

def detect_horizontal_track_edge_v2(image, observe=False, delay=1000, save_log=True):
    """
    检测正前方横向黄色赛道的边缘，并返回y值最大的边缘点
    优先检测中部和上部的横线，特别是对于远处的横线
    
    参数:
        image: 输入图像，可以是文件路径或者已加载的图像数组
        observe: 是否输出中间状态信息和可视化结果，默认为False
        delay: 展示每个步骤的等待时间(毫秒)
        save_log: 是否保存日志和图像
    返回:
        edge_point: 赛道前方边缘点的坐标 (x, y)
        edge_info: 边缘信息字典
    """
    observe = False  # TEST
    # 如果输入是字符串（文件路径），则加载图像
    if isinstance(image, str):
        img = cv2.imread(image)
    else:
        img = image.copy()
    
    if img is None:
        error("无法加载图像", "失败")
        return None, None
    
    if save_log:
        timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S_%f")
        origin_image_path = os.path.join("logs/image", f"origin_horizontal_edge_{timestamp}.jpg")
        cv2.imwrite(origin_image_path, img)
        info(f"保存原始图像到: {origin_image_path}", "日志")
    
    # 获取图像尺寸
    height, width = img.shape[:2]
    
    # 计算图像中间区域的范围（用于专注于正前方的赛道）
    center_x = width // 2
    search_width = int(width * 2/3)  # 搜索区域宽度为图像宽度的2/3
    search_height = height  # 搜索区域高度为图像高度的1/1
    left_bound = center_x - search_width // 2
    right_bound = center_x + search_width // 2
    bottom_bound = height
    top_bound = height - search_height
    
    # 定义合理的值范围 - 修改为更关注中上部区域
    valid_y_range = (height * 0.1, height * 0.6)  # 有效的y坐标范围（中上部分图像），扩大范围到90%
    max_slope = 0.2  # 最大允许斜率（接近水平）
    min_line_length = width * 0.2  # 最小线长度
    
    if observe:
        debug("步骤1: 原始图像已加载", "加载")
        search_region_img = img.copy()
        # 绘制搜索区域
        cv2.rectangle(search_region_img, (left_bound, top_bound), (right_bound, bottom_bound), (255, 0, 0), 2)
        cv2.line(search_region_img, (center_x, 0), (center_x, height), (0, 0, 255), 2)  # 中线
        cv2.imshow("搜索区域", search_region_img)
        cv2.waitKey(delay)
    
    # 转换到HSV颜色空间以便更容易提取黄色
    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
    
    # 黄色的HSV范围
    lower_yellow = np.array([20, 100, 100])
    upper_yellow = np.array([30, 255, 255])
    
    # 创建黄色的掩码
    mask = cv2.inRange(hsv, lower_yellow, upper_yellow)
    
    # 添加形态学操作以改善掩码
    kernel = np.ones((3, 3), np.uint8)
    mask = cv2.dilate(mask, kernel, iterations=1)
    
    if observe:
        debug("步骤2: 创建黄色掩码", "处理")
        cv2.imshow("黄色掩码", mask)
        cv2.waitKey(delay)
    
    # 应用掩码，只保留黄色部分
    yellow_only = cv2.bitwise_and(img, img, mask=mask)
    
    if observe:
        debug("步骤3: 提取黄色部分", "处理")
        cv2.imshow("只保留黄色", yellow_only)
        cv2.waitKey(delay)
    
    # 裁剪掩码到搜索区域
    search_mask = mask[top_bound:bottom_bound, left_bound:right_bound]
    
    # 找到掩码在搜索区域中最底部的非零点位置
    bottom_points = []
    non_zero_cols = np.where(np.any(search_mask, axis=0))[0]
    
    # 寻找每列的最底部点
    for col in non_zero_cols:
        col_points = np.where(search_mask[:, col] > 0)[0]
        if len(col_points) > 0:
            bottom_row = np.max(col_points)
            bottom_points.append((left_bound + col, top_bound + bottom_row))
    
    # 寻找每列的最顶部点（上边缘点）
    top_points = []
    for col in non_zero_cols:
        col_points = np.where(search_mask[:, col] > 0)[0]
        if len(col_points) > 0:
            top_row = np.min(col_points)
            top_points.append((left_bound + col, top_bound + top_row))
    
    if observe:
        debug("检测底部和顶部边缘点", "处理")
        edge_points_img = img.copy()
        for point in bottom_points:
            cv2.circle(edge_points_img, point, 3, (0, 255, 0), -1)
        for point in top_points:
            cv2.circle(edge_points_img, point, 3, (255, 0, 255), -1)
        cv2.imshow("边缘点", edge_points_img)
        cv2.waitKey(delay)
    
    # 边缘检测
    edges = cv2.Canny(mask, 50, 150, apertureSize=3)
    
    if observe:
        debug("步骤4: 边缘检测", "处理")
        cv2.imshow("边缘检测", edges)
        cv2.waitKey(delay)
    
    # 使用霍夫变换检测直线，降低阈值以检测更多线段
    lines = cv2.HoughLinesP(edges, 1, np.pi/180, threshold=30, 
                           minLineLength=width*0.1, maxLineGap=30)
    
    if lines is None or len(lines) == 0:
        if observe:
            error("未检测到直线", "失败")
        return None, None
    
    if observe:
        debug(f"步骤5: 检测到 {len(lines)} 条直线", "处理")
        print(f"len(lines): {len(lines)}")
        lines_img = img.copy()
        for i, line in enumerate(lines):
            x1, y1, x2, y2 = line[0]
            # 根据线段长度使用不同颜色
            line_length = np.sqrt((x2-x1)**2 + (y2-y1)**2)
            # 使用HSV颜色空间生成不同的颜色
            hue = (i * 30) % 180  # 每30度一个颜色
            color = cv2.cvtColor(np.uint8([[[hue, 255, 255]]]), cv2.COLOR_HSV2BGR)[0][0]
            color = (int(color[0]), int(color[1]), int(color[2]))
            cv2.line(lines_img, (x1, y1), (x2, y2), color, 2)
            cv2.putText(lines_img, f"{i}", ((x1+x2)//2, (y1+y2)//2), 
                        cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 255, 255), 2)
        cv2.imshow("检测到的直线", lines_img)
        cv2.waitKey(delay)
    
    # 过滤和合并相似的线段
    filtered_lines = []
    for line in lines:
        x1, y1, x2, y2 = line[0]
        # 确保x1 < x2
        if x1 > x2:
            x1, x2 = x2, x1
            y1, y2 = y2, y1
        filtered_lines.append([x1, y1, x2, y2])
    
    # 合并相似线段
    merged_lines = []
    used_indices = set()
    
    for i, line1 in enumerate(filtered_lines):
        if i in used_indices:
            continue
            
        x1, y1, x2, y2 = line1
        similar_lines = [line1]
        used_indices.add(i)
        
        # 查找与当前线段相似的其他线段
        for j, line2 in enumerate(filtered_lines):
            if j in used_indices or i == j:
                continue
                
            x3, y3, x4, y4 = line2
            
            # 计算两条线段的斜率
            slope1 = (y2 - y1) / (x2 - x1) if abs(x2 - x1) > 5 else 100
            slope2 = (y4 - y3) / (x4 - x3) if abs(x4 - x3) > 5 else 100
            
            # 计算两条线段的中点
            mid1_x, mid1_y = (x1 + x2) / 2, (y1 + y2) / 2
            mid2_x, mid2_y = (x3 + x4) / 2, (y3 + y4) / 2
            
            # 计算中点之间的距离
            mid_dist = np.sqrt((mid2_x - mid1_x)**2 + (mid2_y - mid1_y)**2)
            
            # 计算线段端点之间的最小距离
            end_dists = [
                np.sqrt((x1-x3)**2 + (y1-y3)**2),
                np.sqrt((x1-x4)**2 + (y1-y4)**2),
                np.sqrt((x2-x3)**2 + (y2-y3)**2),
                np.sqrt((x2-x4)**2 + (y2-y4)**2)
            ]
            min_end_dist = min(end_dists)
            
            # 判断两条线段是否相似：满足以下条件之一
            # 1. 斜率接近且中点距离不太远
            # 2. 斜率接近且端点之间距离很近（可能是连接的线段）
            # 3. 端点非常接近（几乎连接），且斜率差异不太大
            if (abs(slope1 - slope2) < 0.15 and mid_dist < height * 0.15) or \
               (abs(slope1 - slope2) < 0.1 and min_end_dist < height * 0.05) or \
               (min_end_dist < height * 0.03 and abs(slope1 - slope2) < 0.25):
                similar_lines.append(line2)
                used_indices.add(j)
        
        # 如果找到相似线段，合并它们
        if len(similar_lines) > 1:
            # 合并所有相似线段的端点
            all_points = []
            for line in similar_lines:
                all_points.append((line[0], line[1]))  # 起点
                all_points.append((line[2], line[3]))  # 终点
            
            # 找出x坐标的最小值和最大值
            min_x = min(p[0] for p in all_points)
            max_x = max(p[0] for p in all_points)
            
            # 使用所有点拟合一条直线
            x_points = np.array([p[0] for p in all_points]).reshape(-1, 1)
            y_points = np.array([p[1] for p in all_points])
            
            # 使用RANSAC拟合更稳定的直线
            ransac = linear_model.RANSACRegressor(residual_threshold=5.0)
            ransac.fit(x_points, y_points)
            
            # 获取拟合的斜率和截距
            merged_slope = ransac.estimator_.coef_[0]
            merged_intercept = ransac.estimator_.intercept_
            
            # 计算新的端点
            y_min = int(merged_slope * min_x + merged_intercept)
            y_max = int(merged_slope * max_x + merged_intercept)
            
            # 添加合并后的线段
            merged_lines.append([min_x, y_min, max_x, y_max])
        else:
            # 如果没有相似线段，直接添加原线段
            merged_lines.append(line1)
    
    # 将合并后的线段转换为霍夫变换的格式
    merged_hough_lines = []
    for line in merged_lines:
        merged_hough_lines.append(np.array([[line[0], line[1], line[2], line[3]]]))
    
    if observe:
        debug(f"步骤5.1: 合并后剩余 {len(merged_hough_lines)} 条线", "处理")
        merged_img = img.copy()
        for i, line in enumerate(merged_hough_lines):
            x1, y1, x2, y2 = line[0]
            # 使用HSV颜色空间生成不同的颜色
            hue = (i * 50) % 180  # 每50度一个颜色
            color = cv2.cvtColor(np.uint8([[[hue, 255, 255]]]), cv2.COLOR_HSV2BGR)[0][0]
            color = (int(color[0]), int(color[1]), int(color[2]))
            cv2.line(merged_img, (x1, y1), (x2, y2), color, 3)
            # 显示线段编号
            cv2.putText(merged_img, f"{i}", ((x1+x2)//2, (y1+y2)//2), 
                        cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 255, 255), 2)
        cv2.imshow("合并后的线段", merged_img)
        cv2.waitKey(delay)
    
    # 使用合并后的线段继续处理
    lines = merged_hough_lines
    
    # 筛选水平线，但放宽斜率条件
    horizontal_lines = []
    # 分别存储上方和下方的水平线
    lower_horizontal_lines = []
    upper_horizontal_lines = []
    
    # 定义上下分界线位置 - 修改为图像的60%处，使上方区域更大
    lower_upper_boundary = height * 0.6
    
    for line in lines:
        x1, y1, x2, y2 = line[0]
        
        # 计算斜率 (避免除零错误)
        if abs(x2 - x1) < 5:  # 几乎垂直的线
            continue
            
        slope = (y2 - y1) / (x2 - x1)

        # 筛选接近水平的线 (斜率接近0)，但容许更大的倾斜度
        if abs(slope) < max_slope:
            # 确保线在搜索区域内
            if ((left_bound <= x1 <= right_bound and top_bound <= y1 <= bottom_bound) or
                (left_bound <= x2 <= right_bound and top_bound <= y2 <= bottom_bound)):
                # 计算线的中点y坐标
                mid_y = (y1 + y2) / 2
                line_length = np.sqrt((x2-x1)**2 + (y2-y1)**2)
                
                # 过滤掉短线段
                if line_length >= min_line_length:
                    # 计算线段在图像中的位置得分
                    # 修改：更偏好中部和上部的线段，使用高斯函数来优化位置评分
                    optimal_y = height * 0.45  # 最佳高度在图像45%处
                    position_score = np.exp(-0.5 * ((mid_y - optimal_y) / (height * 0.2))**2)
                    
                    # 计算长度得分（越长越好）
                    length_score = min(1.0, line_length / (width * 0.5))
                    
                    # 计算斜率得分（越水平越好）
                    slope_score = max(0.0, 1.0 - abs(slope) / max_slope)
                    
                    # 计算线段位于图像中央的程度
                    mid_x = (x1 + x2) / 2
                    center_score = max(0.0, 1.0 - abs(mid_x - center_x) / (width * 0.3))
                    
                    # 计算综合得分，增加位置得分的权重，强调中上部位置
                    quality_score = position_score * 0.5 + length_score * 0.15 + slope_score * 0.25 + center_score * 0.1
                    
                    # 保存线段、其y坐标、斜率、长度和质量得分
                    line_info = (line[0], mid_y, slope, line_length, quality_score)
                    
                    # 区分上方和下方的线
                    if mid_y >= lower_upper_boundary:
                        lower_horizontal_lines.append(line_info)
                    else:
                        upper_horizontal_lines.append(line_info)
                        
                    # 同时保存到总列表中
                    horizontal_lines.append(line_info)
    
    if observe:
        print(f"horizontal_lines: {horizontal_lines}")

    if not horizontal_lines:
        if observe:
            error("未检测到合格的水平线", "失败")
        return None, None
    
    # 根据质量得分排序上方和下方的水平线
    lower_horizontal_lines.sort(key=lambda x: x[4], reverse=True)
    upper_horizontal_lines.sort(key=lambda x: x[4], reverse=True)
    
    if observe:
        debug(f"步骤6: 找到 {len(horizontal_lines)} 条水平线 (下方: {len(lower_horizontal_lines)}, 上方: {len(upper_horizontal_lines)})", "处理")
        h_lines_img = img.copy()
        
        # 绘制所有水平线
        for line_info in horizontal_lines:
            line, _, slope, _, score = line_info
            x1, y1, x2, y2 = line
            # 根据得分调整线的颜色，得分越高越绿
            color = (int(255 * (1-score)), int(255 * score), 0)
            cv2.line(h_lines_img, (x1, y1), (x2, y2), color, 2)
            # 显示斜率和得分
            cv2.putText(h_lines_img, f"{slope:.2f}|{score:.2f}", ((x1+x2)//2, (y1+y2)//2), 
                        cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 1)
        
        # 绘制上下分界线
        cv2.line(h_lines_img, (0, int(lower_upper_boundary)), (width, int(lower_upper_boundary)), (255, 0, 255), 1)
        cv2.imshow("水平线", h_lines_img)
        cv2.waitKey(delay)
    
    # 修改：优先选择上方的线，如果没有上方的线才考虑下方的线
    if upper_horizontal_lines:
        selected_line = upper_horizontal_lines[0][0]
        selected_slope = upper_horizontal_lines[0][2]
        selected_score = upper_horizontal_lines[0][4]
        if observe:
            debug("选择上方水平线", "处理")
    elif lower_horizontal_lines:
        selected_line = lower_horizontal_lines[0][0]
        selected_slope = lower_horizontal_lines[0][2]
        selected_score = lower_horizontal_lines[0][4]
        if observe:
            debug("没有合适的上方线，选择下方水平线", "处理")
    else:
        # 理论上不会进入这个分支，因为前面已经检查过horizontal_lines非空
        if observe:
            error("未检测到合格的水平线", "失败")
        return None, None
    
    # 提取线段端点
    x1, y1, x2, y2 = selected_line
    
    # 确保x1 < x2
    if x1 > x2:
        x1, x2 = x2, x1
        y1, y2 = y2, y1
    
    # 找到线上y值最大的点作为边缘点(最靠近相机的点)
    if y1 > y2:
        bottom_edge_point = (x1, y1)
    else:
        bottom_edge_point = (x2, y2)
    
    # 如果得分过低，可能是错误识别，尝试使用边缘点拟合
    if selected_score < 0.4 and len(top_points) >= 5:  # 修改：优先使用顶部点进行拟合
        if observe:
            debug(f"线段质量得分过低: {selected_score:.2f}，尝试使用边缘点拟合", "处理")
        
        # 筛选中上部分的点
        valid_points = [p for p in top_points if valid_y_range[0] <= p[1] <= valid_y_range[1]]
        
        if len(valid_points) >= 5:
            # 使用RANSAC拟合直线以去除异常值
            x_points = np.array([p[0] for p in valid_points]).reshape(-1, 1)
            y_points = np.array([p[1] for p in valid_points])
            
            ransac = linear_model.RANSACRegressor(residual_threshold=5.0)
            ransac.fit(x_points, y_points)
            
            # 获取拟合参数
            fitted_slope = ransac.estimator_.coef_[0]
            intercept = ransac.estimator_.intercept_
            
            # 检查斜率是否在合理范围内
            if abs(fitted_slope) < max_slope:
                # 计算拟合线的inliers比例
                inlier_mask = ransac.inlier_mask_
                inlier_ratio = sum(inlier_mask) / len(inlier_mask)
                
                # 如果有足够的内点，使用拟合的直线
                if inlier_ratio > 0.5:
                    # 使用拟合的直线参数计算线段端点
                    x1 = left_bound
                    y1 = int(fitted_slope * x1 + intercept)
                    x2 = right_bound
                    y2 = int(fitted_slope * x2 + intercept)
                    
                    selected_slope = fitted_slope
                    selected_line = [x1, y1, x2, y2]
                    
                    # 重新计算边缘点
                    if y1 > y2:
                        bottom_edge_point = (x1, y1)
                    else:
                        bottom_edge_point = (x2, y2)
                    
                    if observe:
                        debug(f"使用拟合直线，斜率: {fitted_slope:.4f}, 内点比例: {inlier_ratio:.2f}", "处理")
                        fitted_line_img = img.copy()
                        cv2.line(fitted_line_img, (x1, y1), (x2, y2), (0, 255, 255), 2)
                        for i, point in enumerate(valid_points):
                            color = (0, 255, 0) if inlier_mask[i] else (0, 0, 255)
                            cv2.circle(fitted_line_img, point, 3, color, -1)
                        cv2.imshow("拟合线和内点", fitted_line_img)
                        cv2.waitKey(delay)
    
    # 获取线上的更多点
    selected_points = []
    step = 5  # 每5个像素取一个点
    for x in range(max(left_bound, int(min(x1, x2))), min(right_bound, int(max(x1, x2)) + 1), step):
        y = int(selected_slope * (x - x1) + y1)
        if top_bound <= y <= bottom_bound:
            selected_points.append((x, y))
    
    # 对结果进行合理性检查
    valid_result = True
    reason = ""
    
    # 修改：调整有效范围检查，适应中上部分的线
    if not (valid_y_range[0] <= bottom_edge_point[1] <= valid_y_range[1]):
        # 如果线在图像更上方，只要不太高也可以接受
        if bottom_edge_point[1] < valid_y_range[0] and bottom_edge_point[1] > height * 0.2:
            pass  # 接受较高的线
        else:
            valid_result = False
            reason += "边缘点y坐标超出有效范围; "
    
    # 检查线段长度
    line_length = np.sqrt((x2-x1)**2 + (y2-y1)**2)
    if line_length < min_line_length:
        valid_result = False
        reason += "线段长度不足; "
    
    # 检查是否有足够的点
    if len(selected_points) < 5:
        valid_result = False
        reason += "选定点数量不足; "
    
    # 计算这个点到中线的距离
    distance_to_center = bottom_edge_point[0] - center_x
    
    # 检查到中心的距离是否合理
    if abs(distance_to_center) > width * 0.8:
        valid_result = False
        reason += "到中心距离过大; "
    
    # 计算中线与检测到的横向线的交点
    # 横向线方程: y = slope * (x - x1) + y1
    # 中线方程: x = center_x
    # 解这个方程组得到交点坐标
    intersection_x = center_x
    intersection_y = selected_slope * (center_x - x1) + y1
    intersection_point = (int(intersection_x), int(intersection_y))
    
    # 修改：放宽交点y坐标的有效范围检查
    if intersection_y < height * 0.2 or intersection_y > height * 0.95:
        valid_result = False
        reason += "交点y坐标超出有效范围; "
    
    # 计算交点到图像底部的距离（以像素为单位）
    distance_to_bottom = height - intersection_y
    
    # 如果结果无效，可能需要返回失败
    if not valid_result and observe:
        warning(f"检测结果不合理: {reason}", "警告")
    
    result_img = None
    if observe or save_log:
        slope_img = img.copy()
        # 画出检测到的线
        line_color = (0, 255, 0) if valid_result else (0, 0, 255)
        cv2.line(slope_img, (x1, y1), (x2, y2), line_color, 2)
        # 标记边缘点
        cv2.circle(slope_img, bottom_edge_point, 10, (0, 0, 255), -1)
        # 画出中线
        cv2.line(slope_img, (center_x, 0), (center_x, height), (0, 0, 255), 2)
        # 标记中线与横向线的交点
        cv2.circle(slope_img, intersection_point, 12, (255, 0, 255), -1)
        cv2.circle(slope_img, intersection_point, 5, (255, 255, 255), -1)
        # 画出交点到底部的距离线
        cv2.line(slope_img, intersection_point, (intersection_x, height), (255, 255, 0), 2)
        # 画出上下分界线
        cv2.line(slope_img, (0, int(lower_upper_boundary)), (width, int(lower_upper_boundary)), (255, 0, 255), 1)
        # 画出有效高度范围
        cv2.line(slope_img, (0, int(valid_y_range[0])), (width, int(valid_y_range[0])), (255, 255, 0), 1)
        cv2.line(slope_img, (0, int(valid_y_range[1])), (width, int(valid_y_range[1])), (255, 255, 0), 1)
        
        cv2.putText(slope_img, f"Slope: {selected_slope:.4f}", (10, 30), 
                    cv2.FONT_HERSHEY_SIMPLEX, 1, line_color, 2)
        cv2.putText(slope_img, f"Distance to center: {distance_to_center}px", (10, 70), 
                    cv2.FONT_HERSHEY_SIMPLEX, 1, line_color, 2)
        cv2.putText(slope_img, f"Distance to bottom: {distance_to_bottom:.1f}px", (10, 110), 
                    cv2.FONT_HERSHEY_SIMPLEX, 1, line_color, 2)
        cv2.putText(slope_img, f"中线交点: ({intersection_point[0]}, {intersection_point[1]})", (10, 150), 
                    cv2.FONT_HERSHEY_SIMPLEX, 1, line_color, 2)
        cv2.putText(slope_img, f"质量得分: {selected_score:.2f}", (10, 190),
                   cv2.FONT_HERSHEY_SIMPLEX, 1, line_color, 2)
        
        if not valid_result:
            cv2.putText(slope_img, f"警告: {reason}", (10, 230), 
                        cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 255), 2)
        
        if observe:
            debug("显示边缘斜率和中线交点", "显示")
            cv2.imshow("边缘斜率和中线交点", slope_img)
            cv2.waitKey(delay)
        
        result_img = slope_img
    
    # 保存日志图像
    if save_log:
        timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S_%f")
        log_dir = "logs/image"
        os.makedirs(log_dir, exist_ok=True)

        # 保存原图
        origin_image_path = os.path.join(log_dir, f"origin_horizontal_edge_{timestamp}.jpg")
        cv2.imwrite(origin_image_path, img)
        info(f"保存原始图像到: {origin_image_path}", "日志")

        if result_img is not None:
            img_path = os.path.join(log_dir, f"horizontal_edge_{timestamp}.jpg")
            cv2.imwrite(img_path, result_img)
            info(f"保存横向边缘检测结果图像到: {img_path}", "日志")
            
            # 保存文本日志信息
            log_info = {
                "timestamp": timestamp,
                "edge_point": bottom_edge_point,
                "distance_to_center": distance_to_center,
                "slope": selected_slope,
                "distance_to_bottom": distance_to_bottom,
                "intersection_point": intersection_point,
                "score": selected_score,
                "valid": valid_result,
                "reason": reason if not valid_result else "",
                "is_upper_line": len(upper_horizontal_lines) > 0 and selected_line == upper_horizontal_lines[0][0]
            }
            info(f"横向边缘检测结果: {log_info}", "日志")
    
    # 如果结果无效，但检测到了一些线，仍然返回结果，不拒绝太靠近底部的线
    if not valid_result and "边缘点y坐标超出有效范围" in reason and bottom_edge_point[1] > height * 0.8:
        # 仍然接受靠近底部的线
        valid_result = True
        reason = ""
    
    # 如果结果无效，可能需要返回失败
    if not valid_result:
        return None, None
    
    # 创建边缘信息字典
    edge_info = {
        "x": bottom_edge_point[0],
        "y": bottom_edge_point[1],
        "distance_to_center": distance_to_center,
        "slope": selected_slope,
        "is_horizontal": abs(selected_slope) < 0.05,  # 判断边缘是否接近水平
        "points_count": len(selected_points),  # 该组中点的数量
        "intersection_point": intersection_point,  # 中线与横向线的交点
        "distance_to_bottom": distance_to_bottom,  # 交点到图像底部的距离
        "score": selected_score,  # 线段质量得分
        "is_upper_line": len(upper_horizontal_lines) > 0 and selected_line == upper_horizontal_lines[0][0]  # 是否为上方线
        # "points": selected_points  # 添加选定的点组
    }
    
    return bottom_edge_point, edge_info

def detect_dual_track_lines(image, observe=False, delay=1000, save_log=True):
    """
    检测左右两条平行的黄色轨道线
    
    参数:
    image: 输入图像，可以是文件路径或者已加载的图像数组
    observe: 是否输出中间状态信息和可视化结果，默认为False
    delay: 展示每个步骤的等待时间(毫秒)
    save_log: 是否保存日志和图像
    
    返回:
    tuple: (中心线信息, 左轨迹线信息, 右轨迹线信息)
    """
    # 如果输入是字符串（文件路径），则加载图像
    if isinstance(image, str):
        img = cv2.imread(image)
    else:
        img = image.copy()
    
    if img is None:
        error("无法加载图像", "失败")
        return None, None, None
    
    # 获取图像尺寸
    height, width = img.shape[:2]
    
    # 计算图像中间区域的范围
    center_x = width // 2
    
    # 转换到HSV颜色空间以便更容易提取黄色
    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
    
    # 黄色的HSV范围 - 扩大范围以更好地捕捉不同光照条件下的黄色
    lower_yellow = np.array([15, 80, 80])  # 更宽松的黄色下限
    upper_yellow = np.array([35, 255, 255])  # 更宽松的黄色上限
    
    # 创建黄色的掩码
    mask = cv2.inRange(hsv, lower_yellow, upper_yellow)
    
    # 形态学操作以改善掩码
    kernel = np.ones((5, 5), np.uint8)  # 增大kernel尺寸
    mask = cv2.dilate(mask, kernel, iterations=1)
    mask = cv2.erode(mask, np.ones((3, 3), np.uint8), iterations=1)  # 添加腐蚀操作去除噪点
    
    if observe:
        debug("步骤1: 创建黄色掩码", "处理")
        cv2.imshow("黄色掩码", mask)
        cv2.waitKey(delay)
    
    # 裁剪底部区域重点关注近处的黄线
    bottom_roi_height = int(height * 0.4)  # 关注图像底部40%区域
    bottom_roi = mask[height-bottom_roi_height:, :]
    
    if observe:
        debug("步骤1.5: 底部区域掩码", "处理")
        cv2.imshow("底部区域掩码", bottom_roi)
        cv2.waitKey(delay)
    
    # 边缘检测
    edges = cv2.Canny(mask, 50, 150, apertureSize=3)
    
    if observe:
        debug("步骤2: 边缘检测", "处理")
        cv2.imshow("边缘检测", edges)
        cv2.waitKey(delay)
    
    # 霍夫变换检测直线 - 降低minLineLength以检测到较短的线段
    lines = cv2.HoughLinesP(edges, 1, np.pi/180, threshold=25, 
                           minLineLength=width*0.05, maxLineGap=40)  # 更宽松的参数
    
    if lines is None or len(lines) == 0:
        error("未检测到直线", "失败")
        return None, None, None
    
    if observe:
        debug(f"步骤3: 检测到 {len(lines)} 条直线", "处理")
        lines_img = img.copy()
        for line in lines:
            x1, y1, x2, y2 = line[0]
            cv2.line(lines_img, (x1, y1), (x2, y2), (0, 255, 0), 2)
        cv2.imshow("检测到的直线", lines_img)
        cv2.waitKey(delay)
    
    # 筛选近似垂直的线
    vertical_lines = []
    for line in lines:
        x1, y1, x2, y2 = line[0]
        
        # 优先选择图像底部的线
        if y1 < height * 0.5 and y2 < height * 0.5:
            continue  # 忽略上半部分的线
        
        # 计算斜率 (避免除零错误)
        if abs(x2 - x1) < 5:  # 几乎垂直的线
            slope = 100  # 设置一个较大的值表示接近垂直
        else:
            slope = (y2 - y1) / (x2 - x1)
        
        # 筛选接近垂直的线 (斜率较大)，但允许更多倾斜度
        if abs(slope) > 0.75:  # 降低垂直线的斜率阈值，允许更多倾斜的线
            line_length = np.sqrt((x2-x1)**2 + (y2-y1)**2)
            # 计算线的中点x坐标
            mid_x = (x1 + x2) / 2
            # 计算线的中点y坐标
            mid_y = (y1 + y2) / 2
            # 保存线段、其坐标、斜率和长度
            vertical_lines.append((line[0], mid_x, mid_y, slope, line_length))
    
    if len(vertical_lines) < 2:
        error("未检测到足够的垂直线", "失败")
        return None, None, None
    
    if observe:
        debug(f"步骤4: 找到 {len(vertical_lines)} 条垂直线", "处理")
        v_lines_img = img.copy()
        for line_info in vertical_lines:
            line, _, _, slope, _ = line_info
            x1, y1, x2, y2 = line
            cv2.line(v_lines_img, (x1, y1), (x2, y2), (0, 255, 255), 2)
            # 显示斜率
            cv2.putText(v_lines_img, f"{slope:.2f}", ((x1+x2)//2, (y1+y2)//2), 
                        cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 1)
        cv2.imshow("垂直线", v_lines_img)
        cv2.waitKey(delay)
    
    # 优先选择更接近图像底部的线 - 根据y坐标均值排序
    vertical_lines.sort(key=lambda x: x[2], reverse=True)  # 按mid_y从大到小排序
    
    # 按x坐标将线分为左右两组
    left_lines = [line for line in vertical_lines if line[1] < center_x]
    right_lines = [line for line in vertical_lines if line[1] > center_x]
    
    # 如果任一侧没有检测到线，则放宽左右两侧线的分组条件
    if not left_lines or not right_lines:
        # 按x坐标排序所有垂直线
        vertical_lines.sort(key=lambda x: x[1])
        
        # 如果有至少两条线，将最左侧的线作为左轨迹线，最右侧的线作为右轨迹线
        if len(vertical_lines) >= 2:
            left_lines = [vertical_lines[0]]
            right_lines = [vertical_lines[-1]]
        else:
            error("左侧或右侧未检测到轨迹线", "失败")
            return None, None, None
    
    # 从左右两组中各选择一条最佳的线
    # 优先选择同时满足：1. 更靠近底部 2. 足够长 3. 更接近中心的线
    def score_line(line_info, is_left):
        _, mid_x, mid_y, _, length = line_info
        # y越大（越靠近底部）分数越高
        y_score = mid_y / height
        # 线越长分数越高
        length_score = min(1.0, length / (height * 0.3))
        # 与预期位置的接近程度
        expected_x = center_x * 0.3 if is_left else center_x * 1.7
        x_score = 1.0 - min(1.0, abs(mid_x - expected_x) / (center_x * 0.5))
        # 综合评分
        return y_score * 0.5 + length_score * 0.3 + x_score * 0.2
    
    # 对左右线组进行评分并排序
    left_lines = sorted(left_lines, key=lambda line: score_line(line, True), reverse=True)
    right_lines = sorted(right_lines, key=lambda line: score_line(line, False), reverse=True)
    
    left_line = left_lines[0]
    right_line = right_lines[0]
    
    # 获取两条线的坐标
    left_x1, left_y1, left_x2, left_y2 = left_line[0]
    right_x1, right_y1, right_x2, right_y2 = right_line[0]
    
    # 确保线段的顺序是从上到下
    if left_y1 > left_y2:
        left_x1, left_x2 = left_x2, left_x1
        left_y1, left_y2 = left_y2, left_y1
    
    if right_y1 > right_y2:
        right_x1, right_x2 = right_x2, right_x1
        right_y1, right_y2 = right_y2, right_y1
    
    # 计算中心线
    center_line_x1 = (left_x1 + right_x1) // 2
    center_line_y1 = (left_y1 + right_y1) // 2
    center_line_x2 = (left_x2 + right_x2) // 2
    center_line_y2 = (left_y2 + right_y2) // 2
    
    # 计算中心线的斜率
    if abs(center_line_x2 - center_line_x1) < 5:
        center_slope = 100  # 几乎垂直
    else:
        center_slope = (center_line_y2 - center_line_y1) / (center_line_x2 - center_line_x1)
    
    # 计算中心线延伸到图像底部的点
    if abs(center_slope) < 0.01:  # 几乎垂直
        bottom_x = center_line_x1
    else:
        bottom_x = int(center_line_x1 + (height - center_line_y1) / center_slope)
    center_point = (bottom_x, height)
    
    # 计算中心线与图像中心线的偏差
    deviation = bottom_x - center_x
    
    result_img = None
    if observe or save_log:
        result_img = img.copy()
        # 绘制左右轨迹线
        cv2.line(result_img, (left_x1, left_y1), (left_x2, left_y2), (255, 0, 0), 2)
        cv2.line(result_img, (right_x1, right_y1), (right_x2, right_y2), (0, 0, 255), 2)
        # 绘制中心线
        cv2.line(result_img, (center_line_x1, center_line_y1), (center_line_x2, center_line_y2), (0, 255, 0), 2)
        cv2.line(result_img, (center_line_x2, center_line_y2), center_point, (0, 255, 0), 2)
        # 绘制图像中心线
        cv2.line(result_img, (center_x, 0), (center_x, height), (0, 0, 255), 1)
        # 标记中心点
        cv2.circle(result_img, center_point, 10, (255, 0, 255), -1)
        
        # 显示偏差信息
        cv2.putText(result_img, f"Deviation: {deviation}px", (10, 30), 
                    cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
        
        if observe:
            cv2.imshow("轨迹线检测结果", result_img)
            cv2.waitKey(delay)
    
    # 保存日志图像
    if save_log and result_img is not None:
        timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S_%f")
        log_dir = "logs/image"
        os.makedirs(log_dir, exist_ok=True)
        img_path = os.path.join(log_dir, f"dual_track_{timestamp}.jpg")
        cv2.imwrite(img_path, result_img)
        info(f"保存双轨迹线检测结果图像到: {img_path}", "日志")
        
        # 保存文本日志信息
        log_info = {
            "timestamp": timestamp,
            "center_point": center_point,
            "deviation": deviation,
            "left_track_mid_x": left_line[1],
            "right_track_mid_x": right_line[1],
            "track_width": right_line[1] - left_line[1],
            "center_slope": center_slope
        }
        info(f"双轨迹线检测结果: {log_info}", "日志")
    
    # 创建左右轨迹线和中心线信息
    left_track_info = {
        "line": left_line[0],
        "slope": left_line[3],  # 注意：索引更改为3，因为保存结构更改了
        "x_mid": left_line[1]
    }
    
    right_track_info = {
        "line": right_line[0],
        "slope": right_line[3],  # 注意：索引更改为3
        "x_mid": right_line[1]
    }
    
    center_info = {
        "point": center_point,
        "deviation": deviation,
        "slope": center_slope,
        "is_vertical": abs(center_slope) > 5.0,  # 判断是否接近垂直
        "track_width": right_line[1] - left_line[1]  # 两轨迹线之间的距离
    }
    
    return center_info, left_track_info, right_track_info

def detect_left_side_track(image, observe=False, delay=1000, save_log=True):
    """
    检测视野左侧黄色轨道线，用于机器狗左侧靠线移动
    
    参数:
    image: 输入图像，可以是文件路径或者已加载的图像数组
    observe: 是否输出中间状态信息和可视化结果，默认为False
    delay: 展示每个步骤的等待时间(毫秒)
    save_log: 是否保存日志和图像
    
    返回:
    tuple: (线信息字典, 最佳跟踪点)
    """
    # 如果输入是字符串（文件路径），则加载图像
    if isinstance(image, str):
        img = cv2.imread(image)
    else:
        img = image.copy()
    
    if img is None:
        error("无法加载图像", "失败")
        return None, None
    
    # 获取图像尺寸
    height, width = img.shape[:2]
    
    # 计算图像中间和左侧区域的范围
    center_x = width // 2
    # 主要关注视野的左半部分，但稍微扩大一点以确保捕捉到左侧线
    left_region_width = int(center_x * 1.2)  # 扩大左侧搜索区域
    left_bound = 0
    right_bound = min(width, left_region_width)
    bottom_bound = height
    top_bound = 0
    
    if observe:
        debug("步骤1: 原始图像已加载", "加载")
        region_img = img.copy()
        # 绘制左侧搜索区域
        cv2.rectangle(region_img, (left_bound, top_bound), (right_bound, bottom_bound), (255, 0, 0), 2)
        cv2.line(region_img, (center_x, 0), (center_x, height), (0, 0, 255), 2)  # 中线
        cv2.imshow("左侧搜索区域", region_img)
        cv2.waitKey(delay)
    
    # 转换到HSV颜色空间以便更容易提取黄色
    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
    
    # 黄色的HSV范围 - 进一步扩大范围以适应不同光照条件
    lower_yellow = np.array([12, 70, 70])  # 更宽松的黄色下限
    upper_yellow = np.array([38, 255, 255])  # 更宽松的黄色上限
    
    # 创建黄色的掩码
    mask = cv2.inRange(hsv, lower_yellow, upper_yellow)
    
    # 形态学操作以改善掩码
    kernel = np.ones((5, 5), np.uint8)
    mask = cv2.dilate(mask, kernel, iterations=1)
    mask = cv2.erode(mask, np.ones((3, 3), np.uint8), iterations=1)
    
    if observe:
        debug("步骤2: 创建黄色掩码", "处理")
        cv2.imshow("黄色掩码", mask)
        cv2.waitKey(delay)
    
    # 裁剪左侧区域
    left_region_mask = mask[:, left_bound:right_bound]
    
    if observe:
        debug("步骤3: 左侧区域掩码", "处理")
        cv2.imshow("左侧区域掩码", left_region_mask)
        cv2.waitKey(delay)
    
    # 边缘检测
    edges = cv2.Canny(mask, 50, 150, apertureSize=3)
    
    if observe:
        debug("步骤4: 边缘检测", "处理")
        cv2.imshow("边缘检测", edges)
        cv2.waitKey(delay)
    
    # 霍夫变换检测直线 - 降低minLineLength以更好地检测近距离的线段
    lines = cv2.HoughLinesP(edges, 1, np.pi/180, threshold=20, 
                           minLineLength=height*0.1, maxLineGap=60)  # 调整参数以检测更短的线段
    
    if lines is None or len(lines) == 0:
        error("未检测到直线", "失败")
        return None, None
    
    if observe:
        debug(f"步骤5: 检测到 {len(lines)} 条直线", "处理")
        lines_img = img.copy()
        for line in lines:
            x1, y1, x2, y2 = line[0]
            cv2.line(lines_img, (x1, y1), (x2, y2), (0, 255, 0), 2)
        cv2.imshow("检测到的直线", lines_img)
        cv2.waitKey(delay)
    
    # 筛选左侧区域内的近似垂直线，放宽条件以捕获更多可能的线
    left_vertical_lines = []
    for line in lines:
        x1, y1, x2, y2 = line[0]
        
        # 确保线在左侧区域内
        if not (max(x1, x2) <= right_bound):
            continue
        
        # 计算斜率 (避免除零错误)
        if abs(x2 - x1) < 5:  # 几乎垂直的线
            slope = 100  # 设置一个较大的值表示接近垂直
        else:
            slope = (y2 - y1) / (x2 - x1)
        
        # 放宽垂直线的斜率范围，以适应近距离时线的倾斜
        if abs(slope) > 0.5:  # 降低垂直线斜率阈值
            line_length = np.sqrt((x2-x1)**2 + (y2-y1)**2)
            # 计算线的中点坐标
            mid_x = (x1 + x2) / 2
            mid_y = (y1 + y2) / 2
            
            # 保存线段、其坐标、斜率和长度
            left_vertical_lines.append((line[0], mid_x, mid_y, slope, line_length))
    
    if len(left_vertical_lines) == 0:
        error("左侧区域未检测到垂直线", "失败")
        return None, None
    
    if observe:
        debug(f"步骤6: 左侧区域找到 {len(left_vertical_lines)} 条垂直线", "处理")
        left_lines_img = img.copy()
        for line_info in left_vertical_lines:
            line, _, _, slope, _ = line_info
            x1, y1, x2, y2 = line
            cv2.line(left_lines_img, (x1, y1), (x2, y2), (0, 255, 255), 2)
            # 显示斜率
            cv2.putText(left_lines_img, f"{slope:.2f}", ((x1+x2)//2, (y1+y2)//2), 
                        cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 1)
        cv2.imshow("左侧垂直线", left_lines_img)
        cv2.waitKey(delay)
    
    # 修改评分函数，优先选择最左侧的线
    def score_left_line(line_info):
        _, mid_x, mid_y, slope, length = line_info
        
        # 线段越长分数越高，但降低权重
        length_score = min(1.0, length / (height * 0.3))
        
        # 越靠近左边分数越高，增加权重
        position_score = 1.0 - (mid_x / center_x)
        
        # 优先选择在图像下半部分的线段
        height_score = min(1.0, mid_y / (height * 0.6))
        
        # 综合评分，加大位置权重
        return length_score * 0.2 + position_score * 0.5 + height_score * 0.2
    
    # 对线段进行评分并排序
    left_vertical_lines = sorted(left_vertical_lines, key=score_left_line, reverse=True)
    
    # 选择最佳的左侧线段
    best_left_line = left_vertical_lines[0]
    line, mid_x, mid_y, slope, length = best_left_line
    x1, y1, x2, y2 = line
    
    # 确保线段的顺序是从上到下
    if y1 > y2:
        x1, x2 = x2, x1
        y1, y2 = y2, y1
    
    # 计算最佳跟踪点 - 选择线段底部较靠近机器人的点
    tracking_point = (x2, y2) if y2 > y1 else (x1, y1)
    
    # 计算线与地面的交点
    if abs(slope) < 0.01:  # 几乎垂直
        ground_intersection_x = x1
    else:
        ground_intersection_x = x1 + (height - y1) / slope
    ground_intersection = (int(ground_intersection_x), height)
    
    # 计算线与图像左边界的距离（以像素为单位）
    distance_to_left = mid_x
    
    result_img = None
    if observe or save_log:
        result_img = img.copy()
        # 绘制检测到的最佳左侧线
        cv2.line(result_img, (x1, y1), (x2, y2), (255, 0, 0), 2)
        # 绘制图像中线
        cv2.line(result_img, (center_x, 0), (center_x, height), (0, 0, 255), 1)
        # 标记最佳跟踪点和地面交点
        cv2.circle(result_img, tracking_point, 10, (0, 255, 0), -1)
        cv2.circle(result_img, ground_intersection, 10, (0, 0, 255), -1)
        
        # 显示信息
        cv2.putText(result_img, f"斜率: {slope:.2f}", (10, 30), 
                    cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
        cv2.putText(result_img, f"距左边界: {distance_to_left:.1f}px", (10, 70), 
                    cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
        cv2.putText(result_img, f"地面交点: ({ground_intersection[0]}, {ground_intersection[1]})", (10, 110), 
                    cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
        
        if observe:
            debug("步骤7: 左侧最佳跟踪线和点", "显示")
            cv2.imshow("左侧最佳跟踪线和点", result_img)
            cv2.waitKey(delay)
        
    # 保存日志图像
    if save_log and result_img is not None:
        timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S_%f")
        log_dir = "logs/image"
        os.makedirs(log_dir, exist_ok=True)
        
        # 保存原图
        original_img_path = os.path.join(log_dir, f"original_{timestamp}.jpg")
        cv2.imwrite(original_img_path, img)
        info(f"保存原始图像到: {original_img_path}", "日志")
        
        img_path = os.path.join(log_dir, f"left_track_{timestamp}.jpg")
        cv2.imwrite(img_path, result_img)
        info(f"保存左侧轨迹线检测结果图像到: {img_path}", "日志")
        
        # 保存文本日志信息
        log_info = {
            "timestamp": timestamp,
            "tracking_point": tracking_point,
            "ground_intersection": ground_intersection,
            "distance_to_left": distance_to_left,
            "slope": slope,
            "line_mid_x": mid_x
        }
        info(f"左侧轨迹线检测结果: {log_info}", "日志")
    
    # 创建线段信息字典
    track_info = {
        "line": line,
        "slope": slope,
        "tracking_point": tracking_point,
        "ground_intersection": ground_intersection,
        "distance_to_left": distance_to_left,
        "mid_x": mid_x,
        "mid_y": mid_y,
        "is_vertical": abs(slope) > 5.0  # 判断是否接近垂直
    }
    
    return track_info, tracking_point