mi-task/utils/detect_dual_track_lines.py

import cv2
import numpy as np
import os
import datetime

from utils.base_line_handler import _merge_collinear_lines_iterative
from utils.log_helper import LogHelper, get_logger, section, info, debug, warning, error, success, timing

def detect_dual_track_lines(image, observe=False, delay=1000, save_log=True,
                            min_slope_threshold=0.4,
                            min_line_length=0.05,
                            max_line_gap=40,
                            detect_height=0.4):
    save_log = False
    """
    检测左右两条平行的黄色轨道线，优化后能够更准确处理各种路况
    
    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

    min_slope_threshold: 最小斜率阈值
    min_line_length: 最小线段长度
    max_line_gap: 最大线段间距
    
    返回:
    tuple: (中心线信息, 左轨迹线信息, 右轨迹线信息)
           如果检测失败返回(None, None, None)
    
    改进:
    - 使用多点采样+多项式拟合计算更准确的中心线
    - 优化对左右轨道线的选择，考虑平行性和合理宽度
    - 增强对倾斜轨道和石板路的处理能力
    """
    # 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)
    
    # 标准黄色的HSV范围
    lower_yellow = np.array([15, 80, 80])
    upper_yellow = np.array([35, 255, 255])
    
    # 创建黄色的掩码
    mask = cv2.inRange(hsv, lower_yellow, upper_yellow)
    
    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1: 创建黄色掩码", "处理")
        mask_display = cv2.cvtColor(mask, cv2.COLOR_GRAY2BGR)
        cv2.putText(mask_display, "Step 1: Yellow Mask", (10, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
        cv2.putText(mask_display, f"Lower: {lower_yellow}", (10, 40), cv2.FONT_HERSHEY_SIMPLEX, 0.4, (0, 255, 0), 1)
        cv2.putText(mask_display, f"Upper: {upper_yellow}", (10, 55), cv2.FONT_HERSHEY_SIMPLEX, 0.4, (0, 255, 0), 1)
        cv2.imshow("黄色掩码", mask_display)
        cv2.waitKey(delay)
    
    # 裁剪底部区域重点关注近处的黄线
    bottom_roi_height = int(height * detect_height)  # 增加关注区域到图像底部60%
    bottom_roi = mask[height-bottom_roi_height:, :]
    
    if observe:
        debug("步骤1.5: 底部区域掩码", "处理")
        bottom_roi_display = cv2.cvtColor(bottom_roi, cv2.COLOR_GRAY2BGR)
        cv2.putText(bottom_roi_display, "Step 1.5: Bottom ROI", (10, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
        cv2.putText(bottom_roi_display, f"ROI Height: {bottom_roi_height}px ({bottom_roi_height/height*100:.0f}%)", (10, 40), cv2.FONT_HERSHEY_SIMPLEX, 0.4, (0, 255, 0), 1)
        cv2.imshow("底部区域掩码", bottom_roi_display)
        cv2.waitKey(delay)
    
    # INFO 边缘检测
    # Apply Canny to bottom_roi instead of the full mask
    edges_roi = cv2.Canny(bottom_roi, 50, 150, apertureSize=3)
    
    if observe:
        debug("步骤2: 边缘检测 (底部ROI)", "处理") # Updated text
        # Displaying edges from bottom_roi directly
        edges_display = cv2.cvtColor(edges_roi, cv2.COLOR_GRAY2BGR) 
        cv2.putText(edges_display, "Step 2: Edge Detection (Canny on Bottom ROI)", (10, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
        cv2.putText(edges_display, "Thresholds: (50, 150)", (10, 40), cv2.FONT_HERSHEY_SIMPLEX, 0.4, (0, 255, 0), 1)
        cv2.imshow("边缘检测 (底部ROI)", edges_display) # Updated window title
        cv2.waitKey(delay)
    
    # INFO 霍夫变换检测直线 (on edges from bottom_roi)
    lines = cv2.HoughLinesP(edges_roi, 1, np.pi/180, threshold=25, 
                               minLineLength=width*min_line_length, maxLineGap=max_line_gap)
    
    # Adjust y-coordinates of lines detected in bottom_roi to be relative to the full image
    if lines is not None:
        offset_y = height - bottom_roi_height
        for i in range(len(lines)):
            # line[0] is [x1, y1, x2, y2]
            lines[i][0][1] += offset_y # y1
            lines[i][0][3] += offset_y # y2
            
    if lines is None or len(lines) == 0:
        error("未检测到直线 (在底部ROI)", "失败") # Updated error message
        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()
        cv2.putText(lines_img, "Step 3: Hough Lines", (10, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)
        cv2.putText(lines_img, f"Th:25, MinLen:{width*0.05:.1f}, MaxGap:{max_line_gap}", (10, 40), cv2.FONT_HERSHEY_SIMPLEX, 0.4, (0, 0, 255), 1)
        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_only_lines = []
    for line in lines:
        x1, y1, x2, y2 = line[0]
        
        # 优先选择图像底部的线
        if y1 < height * 0.4 and y2 < height * 0.4:
            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) > min_slope_threshold:
            vertical_only_lines.append(line[0])

    if observe:
        debug(f"步骤3.2: 筛选出 {len(vertical_only_lines)} 条垂直候选线 (合并前)", "可视化")
        pre_merge_lines_img = img.copy()
        cv2.putText(pre_merge_lines_img, "Step 3.2: Vertical Candidates (Pre-Merge)", (10, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
        colors = [(0, 0, 255), (0, 255, 0), (0, 255, 255), (255, 0, 0), (255, 0, 255), (255, 255, 0)]
        for i, line in enumerate(vertical_only_lines):
            x1, y1, x2, y2 = line
            cv2.line(pre_merge_lines_img, (x1, y1), (x2, y2), colors[i % len(colors)], 2)  # 使用红色显示合并前的线
        cv2.imshow("合并前的垂直候选线", pre_merge_lines_img)
        cv2.waitKey(delay)

    if observe:
        vertical_only_lines_tmp = vertical_only_lines.copy()

    vertical_only_lines = _merge_collinear_lines_iterative(vertical_only_lines,
                                                       min_initial_len=5.0, # 最小初始线段长度
                                                       max_angle_diff_deg=4.0, # 最大允许角度差（度）
                                                       max_ep_gap_abs=max_line_gap, # 端点间最大绝对距离
                                                       max_ep_gap_factor=1, # 端点间最大相对距离因子
                                                       max_p_dist_abs=max_line_gap, # 点到线段最大绝对距离
                                                       max_p_dist_factor=1) # 点到线段最大相对距离因子
    if observe:
        print(f"合并前: {len(vertical_only_lines_tmp)} 条线, 合并后: {len(vertical_only_lines)} 条线")
        debug(f"步骤3.5: 合并筛选出 {len(vertical_only_lines)} 条垂直候选线 (合并后)", "可视化")
        
        # 创建两个图像用于对比显示
        pre_merge_img = img.copy()
        post_merge_img = img.copy()
        
        # 在两张图上添加标题
        cv2.putText(pre_merge_img, "合并前的垂直候选线", (10, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
        cv2.putText(post_merge_img, "合并后的垂直候选线", (10, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
        
        # 为每条线分配不同的颜色
        colors = [
            (255, 0, 0),    # 蓝色
            (0, 255, 0),    # 绿色
            (0, 0, 255),    # 红色
            (255, 255, 0),  # 青色
            (255, 0, 255),  # 品红
            (0, 255, 255),  # 黄色
        ]
        
        # 绘制合并前的线
        for i, line in enumerate(vertical_only_lines_tmp):
            x1, y1, x2, y2 = line
            color = colors[i % len(colors)]
            cv2.line(pre_merge_img, (x1, y1), (x2, y2), color, 2)
            mid_x = (x1 + x2) // 2
            mid_y = (y1 + y2) // 2
            cv2.putText(pre_merge_img, str(i+1), (mid_x, mid_y), 
                       cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 1)
        
        # 绘制合并后的线
        for i, line in enumerate(vertical_only_lines):
            x1, y1, x2, y2 = line[0]
            color = colors[i % len(colors)]
            cv2.line(post_merge_img, (x1, y1), (x2, y2), color, 2)
            mid_x = (x1 + x2) // 2
            mid_y = (y1 + y2) // 2
            cv2.putText(post_merge_img, str(i+1), (mid_x, mid_y), 
                       cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 1)
        
        # 水平拼接两张图片
        combined_img = np.hstack((pre_merge_img, post_merge_img))
        cv2.imshow("合并前后的垂直候选线对比", combined_img)
        cv2.waitKey(delay)

    vertical_lines = []
    for line in vertical_only_lines:
        x1, y1, x2, y2 = line[0]
        
        # 优先选择图像底部的线
        if y1 < height * 0.4 and y2 < height * 0.4:
            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) > min_slope_threshold:
            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:
        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()
        cv2.putText(v_lines_img, "Step 4: Vertical Lines Filtered", (10, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)
        cv2.putText(v_lines_img, f"Min Slope Abs: {min_slope_threshold:.2f}", (10, 40), cv2.FONT_HERSHEY_SIMPLEX, 0.4, (0, 0, 255), 1)
        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)
    
    # 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
    
    if observe:
        debug(f"左侧候选线数量: {len(left_lines)}, 右侧候选线数量: {len(right_lines)}", "线候选")
        
        # 创建左右线可视化图像
        left_right_img = img.copy()
        cv2.putText(left_right_img, "Left and Right Candidate Lines", (10, 20), 
                    cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)
        
        # 绘制左侧候选线（蓝色）
        for line_info in left_lines:
            line = line_info[0]
            x1, y1, x2, y2 = line
            cv2.line(left_right_img, (x1, y1), (x2, y2), (255, 0, 0), 2)
            # 显示斜率
            mid_x = (x1 + x2) // 2
            mid_y = (y1 + y2) // 2
            cv2.putText(left_right_img, f"L:{line_info[3]:.2f}", (mid_x, mid_y), 
                        cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 1)
        
        # 绘制右侧候选线（红色）
        for line_info in right_lines:
            line = line_info[0]
            x1, y1, x2, y2 = line
            cv2.line(left_right_img, (x1, y1), (x2, y2), (0, 0, 255), 2)
            # 显示斜率
            mid_x = (x1 + x2) // 2
            mid_y = (y1 + y2) // 2
            cv2.putText(left_right_img, f"R:{line_info[3]:.2f}", (mid_x, mid_y), 
                        cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1)
        
        cv2.imshow("左右候选线", left_right_img)
        cv2.waitKey(delay)
        
    # 优化说明：在默认模式下，评分函数和线对选择都优先考虑更靠近图像中心的线段
    # 这有助于减少对图像边缘可能出现的干扰线的选择，提高轨道线检测的准确性
    
    # INFO 改进的评分函数 - 同时考虑斜率、位置、长度和在图像中的位置
    def score_line(line_info, is_left):
        _, mid_x, mid_y, slope, length = line_info
        line_points = line_info[0]  # 获取线段的端点坐标
        x1, y1, x2, y2 = line_points
        
        # 确保线段从上到下排序
        if y1 > y2:
            x1, x2 = x2, x1
            y1, y2 = y2, y1
        
        # 线段靠近底部评分 - y越大（越靠近底部）分数越高
        bottom_ratio = mid_y / height
        y_score = min(1.0, bottom_ratio * 1.2)  # 适当提高底部线段的权重
        
        # 线段长度评分 - 线越长分数越高
        length_ratio = length / (height * 0.3)
        length_score = min(1.0, length_ratio * 1.2)
        
        # 计算到图像中心的距离得分 - 更重视靠近垂直中线的线
        center_x = width / 2
        distance_to_center = abs(mid_x - center_x)
        # 使用更陡峭的衰减函数，使得距离中心越近的线得分越高
        center_proximity_score = max(0, 1.0 - (distance_to_center / (width * 0.25)) ** 2)
        
        # 计算底部点与中心的偏差
        bottom_x = x1
        if abs(y2 - y1) > 1:  # 非水平线
            t = (height - y1) / (y2 - y1)
            if t >= 0:  # 确保是向下延伸
                bottom_x = x1 + t * (x2 - x1)
        
        bottom_x_distance = abs(bottom_x - center_x)
        bottom_x_score = max(0, 1.0 - (bottom_x_distance / (width * 0.25)) ** 2)
        
        # 斜率评分 - 轨道线应该有一定的倾斜度
        if abs(slope) > 5:
            slope_score = 0.3
        else:
            ideal_slope_magnitude = 0.8
            ideal_slope = -ideal_slope_magnitude if is_left else ideal_slope_magnitude
            
            if (is_left and slope > 0) or (not is_left and slope < 0):
                slope_sign_score = 0.3
            else:
                slope_sign_score = 1.0
            
            slope_diff = abs(slope - ideal_slope)
            slope_magnitude_score = max(0, 1.0 - slope_diff / 2.0)
            slope_score = slope_sign_score * slope_magnitude_score
        
        # 检查线段是否从底部区域开始
        bottom_region_threshold = height * 0.7
        reaches_bottom = max(y1, y2) > bottom_region_threshold
        bottom_reach_score = 1.0 if reaches_bottom else 0.5
        
        # 综合评分 - 大幅提高中心接近性的权重
        final_score = (
            y_score * 0.1 +                 # 底部接近度
            length_score * 0.05 +           # 线段长度
            center_proximity_score * 0.6 +  # 与中心的接近度 (权重提高)
            bottom_x_score * 0.15 +         # 底部点位置
            slope_score * 0.05 +            # 斜率合适性
            bottom_reach_score * 0.05       # 是否到达底部
        )
        
        return final_score
    # 如果有多条线，评估左右线对是否平行
    if len(left_lines) > 0 and len(right_lines) > 0:
        # 计算最佳的左右线对
        best_pair_score = -1
        best_left_line = None
        best_right_line = None
        
        # 先对左右线按照评分排序，只考虑评分较高的候选线（减少计算量）
        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] if left_lines else None
        right_line = right_lines[0] if right_lines else None
        if observe:
            debug(f"选择最佳线对，评分: {best_pair_score:.2f}", "线对")
            
            # 创建评分可视化图像
            score_img = img.copy()
            cv2.putText(score_img, "Line Scores Visualization", (10, 20), 
                        cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)
            
            # 显示左侧线的评分
            for i, line_info in enumerate(left_lines):
                line = line_info[0]
                x1, y1, x2, y2 = line
                score = score_line(line_info, True)
                cv2.line(score_img, (x1, y1), (x2, y2), (255, 0, 0), 2)
                mid_x = (x1 + x2) // 2
                mid_y = (y1 + y2) // 2
                cv2.putText(score_img, f"L{i+1}:{score:.2f}", (mid_x, mid_y), 
                            cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 1)
            
            # 显示右侧线的评分
            for i, line_info in enumerate(right_lines):
                line = line_info[0]
                x1, y1, x2, y2 = line
                score = score_line(line_info, False)
                cv2.line(score_img, (x1, y1), (x2, y2), (0, 0, 255), 2)
                mid_x = (x1 + x2) // 2
                mid_y = (y1 + y2) // 2
                cv2.putText(score_img, f"R{i+1}:{score:.2f}", (mid_x, mid_y), 
                            cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1)
            
            cv2.imshow("线评分可视化", score_img)
            cv2.waitKey(delay)
    else:
        # 如果只有单侧有线，使用评分最高的线
        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] if left_lines else None
        right_line = right_lines[0] if right_lines else None
    
    # 确保两侧都有线
    if left_line is None or right_line is None:
        error("无法确定合适的左右轨迹线对", "失败")
        return None, None, None
    
    # 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
    
    
    # 尝试延长线段到图像底部，处理被石板路部分遮挡的情况
    left_extended_y2 = height
    if abs(left_x2 - left_x1) < 5:  # 几乎垂直
        left_extended_x2 = left_x2
    else:
        left_slope = (left_y2 - left_y1) / (left_x2 - left_x1)
        left_extended_x2 = left_x1 + (left_extended_y2 - left_y1) / left_slope
    
    right_extended_y2 = height
    if abs(right_x2 - right_x1) < 5:  # 几乎垂直
        right_extended_x2 = right_x2
    else:
        right_slope = (right_y2 - right_y1) / (right_x2 - right_x1)
        right_extended_x2 = right_x1 + (right_extended_y2 - right_y1) / right_slope
    
    # 更新线段端点为延长后的坐标
    left_x2, left_y2 = int(left_extended_x2), left_extended_y2
    right_x2, right_y2 = int(right_extended_x2), right_extended_y2
    
    # 尝试延长线段到图像底部，处理被石板路部分遮挡的情况
    left_extended_y2 = height
    if abs(left_x2 - left_x1) < 5:  # 几乎垂直
        left_extended_x2 = left_x2
    else:
        left_slope = (left_y2 - left_y1) / (left_x2 - left_x1)
        left_extended_x2 = left_x1 + (left_extended_y2 - left_y1) / left_slope
    
    right_extended_y2 = height
    if abs(right_x2 - right_x1) < 5:  # 几乎垂直
        right_extended_x2 = right_x2
    else:
        right_slope = (right_y2 - right_y1) / (right_x2 - right_x1)
        right_extended_x2 = right_x1 + (right_extended_y2 - right_y1) / right_slope
    
    # 更新线段端点为延长后的坐标
    left_x2, left_y2 = int(left_extended_x2), left_extended_y2
    right_x2, right_y2 = int(right_extended_x2), right_extended_y2
    
    # 改进的中心线计算方法
    # 首先确定两条轨迹线的有效部分 - 以两条线段的y坐标重叠部分为准
    min_y = max(left_y1, right_y1)
    max_y = min(left_y2, right_y2)
    
    # 如果两条线没有重叠的y部分，则使用整个图像范围
    if min_y >= max_y:
        min_y = 0
        max_y = height
    
    # 计算中间路径点的方式：在多个高度上计算左右线的中点，然后拟合中心线
    num_points = 20  # 增加采样点数量以提高精度
    center_points = []
    
    # 优化采样策略 - 在底部区域采样更密集
    sample_ys = []
    
    # 前半部分采样点均匀分布
    first_half = np.linspace(min_y, min_y + (max_y - min_y) * 0.5, num_points // 4)
    # 后半部分(更靠近底部)采样点更密集
    second_half = np.linspace(min_y + (max_y - min_y) * 0.5, max_y, num_points - num_points // 4)
    
    sample_ys = np.concatenate([first_half, second_half])
    
    for y in sample_ys:
        # 计算左侧线在当前y值处的x坐标
        if abs(left_y2 - left_y1) < 1:  # 防止除零
            left_x = left_x1
        else:
            t = (y - left_y1) / (left_y2 - left_y1)
            left_x = left_x1 + t * (left_x2 - left_x1)
        
        # 计算右侧线在当前y值处的x坐标
        if abs(right_y2 - right_y1) < 1:  # 防止除零
            right_x = right_x1
        else:
            t = (y - right_y1) / (right_y2 - right_y1)
            right_x = right_x1 + t * (right_x2 - right_x1)
        
        # 计算中心点
        center_x = (left_x + right_x) / 2
        center_points.append((center_x, y))
    
    # 使用采样的中心点拟合中心线
    center_xs = np.array([p[0] for p in center_points])
    center_ys = np.array([p[1] for p in center_points])
    
    # 使用多项式拟合改进中心线 - 选择合适的多项式阶数
    if len(center_points) >= 5:  # 需要更多点来拟合更高阶多项式
        try:
            # 尝试不同阶数的多项式拟合，选择最佳结果
            best_poly_coeffs = None
            best_error = float('inf')
            
            for degree in [1, 2, 3]:  # 尝试1-3阶多项式
                try:
                    # 使用多项式拟合
                    poly_coeffs = np.polyfit(center_ys, center_xs, degree)
                    poly_func = np.poly1d(poly_coeffs)
                    
                    # 计算拟合误差
                    predicted_xs = poly_func(center_ys)
                    err = np.mean(np.abs(predicted_xs - center_xs))
                    
                    # 如果误差更小，更新最佳拟合
                    if err < best_error:
                        best_error = err
                        best_poly_coeffs = poly_coeffs
                except:
                    continue
            
            # 如果找到了有效的拟合，使用它
            if best_poly_coeffs is not None:
                poly_coeffs = best_poly_coeffs
                poly_func = np.poly1d(poly_coeffs)
                
                # 为了提高底部拟合精度，给予底部区域更高的权重重新拟合
                weights = np.ones_like(center_ys)
                
                # 计算距离底部的归一化距离 (0表示底部，1表示顶部)
                normalized_distance = (max_y - center_ys) / max(1, (max_y - min_y))
                
                # 设置权重，底部权重更高
                weights = 1.0 + 2.0 * (1.0 - normalized_distance)
                
                # 使用加权多项式拟合
                try:
                    weighted_poly_coeffs = np.polyfit(center_ys, center_xs, len(best_poly_coeffs) - 1, w=weights)
                    weighted_poly_func = np.poly1d(weighted_poly_coeffs)
                    
                    # 比较加权拟合与原始拟合
                    weighted_predicted_xs = weighted_poly_func(center_ys)
                    weighted_error = np.mean(np.abs(weighted_predicted_xs - center_xs))
                    
                    # 如果加权拟合更好，则使用它
                    if weighted_error < best_error * 1.2:  # 允许一定的误差增加，因为我们更关注底部精度
                        poly_coeffs = weighted_poly_coeffs
                        poly_func = weighted_poly_func
                except:
                    pass  # 如果加权拟合失败，继续使用未加权的拟合
                
                # 使用多项式生成中心线的起点和终点
                center_line_y1 = min_y
                center_line_y2 = height  # 延伸到图像底部
                
                # 计算多项式在这些y值处的x坐标
                center_line_x1 = poly_func(center_line_y1)
                center_line_x2 = poly_func(center_line_y2)
                
                # 计算中心线在图像底部的x坐标 - 用于计算偏离度
                bottom_x = poly_func(height)
                
                # 确保坐标在图像范围内
                bottom_x = max(0, min(width - 1, bottom_x))
                center_point = (int(bottom_x), int(height))
                
                # 计算中心线的加权平均斜率 - 更关注底部区域的斜率
                if abs(center_line_y2 - center_line_y1) < 1:  # 防止除零
                    center_slope = 0
                else:
                    # 计算全局斜率
                    global_slope = (center_line_x2 - center_line_x1) / (center_line_y2 - center_line_y1)
                    
                    # 计算底部区域的斜率，使用多个点来提高精度
                    bottom_slopes = []
                    bottom_region_start = height - height * 0.3  # 底部30%区域
                    
                    if bottom_region_start > min_y:
                        # 在底部区域采样多个点计算局部斜率
                        bottom_sample_count = 5
                        bottom_ys = np.linspace(bottom_region_start, height, bottom_sample_count)
                        
                        for i in range(len(bottom_ys) - 1):
                            y1, y2 = bottom_ys[i], bottom_ys[i+1]
                            x1, x2 = poly_func(y1), poly_func(y2)
                            
                            # 确保坐标有效
                            x1 = max(0, min(width - 1, x1))
                            x2 = max(0, min(width - 1, x2))
                            
                            local_slope = (x2 - x1) / max(0.1, (y2 - y1))
                            bottom_slopes.append(local_slope)
                        
                        # 计算底部斜率的加权平均值，越靠近底部权重越高
                        if bottom_slopes:
                            weights = np.linspace(1, 2, len(bottom_slopes))
                            bottom_slope = np.average(bottom_slopes, weights=weights)
                            
                            # 加权平均，底部斜率权重更高
                            center_slope = bottom_slope * 0.8 + global_slope * 0.2
                        else:
                            center_slope = global_slope
                    else:
                        center_slope = global_slope
            else:
                # 如果所有多项式拟合都失败，退回到简单的线性拟合
                raise Exception("多项式拟合失败")
                
        except Exception as e:
            warning(f"多项式拟合失败，使用简单中点计算: {e}", "拟合")
            # 如果多项式拟合失败，退回到简单的中点计算方法
            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:  # 几乎水平
                bottom_x = center_line_x1
            else:
                bottom_x = center_line_x1 + (height - center_line_y1) / center_slope
            
            bottom_x = max(0, min(width - 1, bottom_x))
            center_point = (int(bottom_x), int(height))
    else:
        # 如果点数不足，退回到简单的中点计算方法
        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:  # 几乎水平
            bottom_x = center_line_x1
        else:
            bottom_x = center_line_x1 + (height - center_line_y1) / center_slope
        
        bottom_x = max(0, min(width - 1, bottom_x))
        center_point = (int(bottom_x), int(height))
    
    # 计算中心线与图像中心线的偏差
    deviation =  width / 2 - bottom_x
    
    result_img = None
    if observe or save_log:
        result_img = img.copy()
        # 绘制左右轨迹线
        try:
            # 确保坐标在图像范围内并且是整数
            left_x1_safe = max(0, min(width - 1, left_x1))
            left_y1_safe = max(0, min(height - 1, left_y1))
            left_x2_safe = max(0, min(width - 1, left_x2))
            left_y2_safe = max(0, min(height - 1, left_y2))
            
            cv2.line(result_img, (int(left_x1_safe), int(left_y1_safe)), 
                    (int(left_x2_safe), int(left_y2_safe)), (255, 0, 0), 2)
        except Exception as e:
            warning(f"绘制左轨迹线错误: {e}", "绘图")
            
        try:
            # 确保坐标在图像范围内并且是整数
            right_x1_safe = max(0, min(width - 1, right_x1))
            right_y1_safe = max(0, min(height - 1, right_y1))
            right_x2_safe = max(0, min(width - 1, right_x2))
            right_y2_safe = max(0, min(height - 1, right_y2))
            
            cv2.line(result_img, (int(right_x1_safe), int(right_y1_safe)), 
                    (int(right_x2_safe), int(right_y2_safe)), (0, 0, 255), 2)
        except Exception as e:
            warning(f"绘制右轨迹线错误: {e}", "绘图")
        
        # 绘制中心线 - 如果有多项式拟合，绘制拟合曲线
        if len(center_points) >= 3:
            # 绘制拟合的中心曲线
            try:
                curve_ys = np.linspace(min_y, height, 100)
                curve_xs = poly_func(curve_ys)
                
                for i in range(len(curve_ys) - 1):
                    try:
                        # 确保坐标在图像范围内并且是整数
                        x1 = max(0, min(width - 1, curve_xs[i]))
                        y1 = max(0, min(height - 1, curve_ys[i]))
                        x2 = max(0, min(width - 1, curve_xs[i+1]))
                        y2 = max(0, min(height - 1, curve_ys[i+1]))
                        
                        pt1 = (int(x1), int(y1))
                        pt2 = (int(x2), int(y2))
                        cv2.line(result_img, pt1, pt2, (0, 255, 0), 2)
                    except Exception as e:
                        warning(f"绘制曲线段错误: {e}", "绘图")
                        continue
                    
                # 绘制采样点
                for pt in center_points:
                    try:
                        # 确保坐标在图像范围内并且是整数
                        x = max(0, min(width - 1, pt[0]))
                        y = max(0, min(height - 1, pt[1]))
                        cv2.circle(result_img, (int(x), int(y)), 3, (0, 255, 255), -1)
                    except Exception as e:
                        warning(f"绘制采样点错误: {e}", "绘图")
                        continue
            except Exception as e:
                warning(f"绘制曲线错误: {e}", "绘图")
        else:
            # 绘制简单中心线
            try:
                # 确保坐标在图像范围内并且是整数
                x1 = max(0, min(width - 1, center_line_x1))
                y1 = max(0, min(height - 1, center_line_y1))
                x2 = max(0, min(width - 1, center_line_x2))
                y2 = max(0, min(height - 1, center_line_y2))
                
                cv2.line(result_img, (int(x1), int(y1)), (int(x2), int(y2)), (0, 255, 0), 2)
            except Exception as e:
                warning(f"绘制中心线错误: {e}", "绘图")
        
        # 绘制图像中心线
        try:
            center_x_safe = max(0, min(width - 1, center_x))
            cv2.line(result_img, (int(center_x_safe), 0), (int(center_x_safe), height), (0, 0, 255), 1)
        except Exception as e:
            warning(f"绘制图像中心线错误: {e}", "绘图")
            
        # 标记中心点
        try:
            # 确保中心点坐标在图像范围内
            center_x_safe = max(0, min(width - 1, center_point[0]))
            center_y_safe = max(0, min(height - 1, center_point[1]))
            cv2.circle(result_img, (int(center_x_safe), int(center_y_safe)), 10, (255, 0, 255), -1)
        except Exception as e:
            warning(f"绘制中心点错误: {e}", "绘图")
        
        # 显示偏差信息
        cv2.putText(result_img, f"Deviation: {deviation:.1f}px", (10, 30), 
                    cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
        cv2.putText(result_img, "Final Result", (10, 60), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
        if 'best_pair_score' in locals() and best_pair_score != -1:
            cv2.putText(result_img, f"Pair Score: {best_pair_score:.2f}", (10, 85), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)
            current_y_offset = 105
        else:
            current_y_offset = 85
        
        cv2.putText(result_img, f"L-Slope: {left_line[3]:.2f}, R-Slope: {right_line[3]:.2f}", (10, current_y_offset), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)
        current_y_offset += 20
        cv2.putText(result_img, f"Track Width: {right_line[1] - left_line[1]:.1f}px", (10, current_y_offset), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)
        
        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)
        
        # 保存结果图像
        result_img_path = os.path.join(log_dir, f"dual_track_{timestamp}.jpg")
        cv2.imwrite(result_img_path, result_img)
        
        # 保存原图
        orig_img_path = os.path.join(log_dir, f"dual_track_orig_{timestamp}.jpg")
        cv2.imwrite(orig_img_path, img)
        
        info(f"保存双轨迹线检测结果图像到: {result_img_path}", "日志")
        info(f"保存原始图像到: {orig_img_path}", "日志")
        
        # 保存文本日志信息
        log_info = {
            "timestamp": timestamp,
            "center_point": (int(center_point[0]), int(center_point[1])),
            "deviation": float(deviation),
            "left_track_mid_x": float(left_line[1]),
            "right_track_mid_x": float(right_line[1]),
            "track_width": float(right_line[1] - left_line[1]),
            "center_slope": float(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_x1, left_y1, left_x2, left_y2),
        "slope": left_line[3],
        "x_mid": left_line[1]
    }
    
    right_track_info = {
        "line": (right_x1, right_y1, right_x2, right_y2),
        "slope": right_line[3],
        "x_mid": right_line[1]
    }
    
    center_info = {
        "point": (int(center_point[0]), int(center_point[1])),
        "deviation": float(deviation),
        "slope": float(center_slope),
        "is_vertical": abs(center_slope) > 5.0,  # 判断是否接近垂直
        "track_width": float(right_line[1] - left_line[1]),  # 两轨迹线之间的距离
    }
    
    return center_info, left_track_info, right_track_info