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): """ 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 * 0.4) # 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