import cv2 import numpy as np from sklearn.cluster import KMeans from sklearn.metrics import silhouette_score from sklearn import linear_model def detect_horizontal_track_edge(image, observe=False, delay=1000): """ 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 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) 返回: edge_point: 赛道前方边缘点的坐标 (x, y) edge_info: 边缘信息字典 """ # 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: print("无法加载图像") 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: print("步骤1: 原始图像已加载") 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) if observe: print("步骤2: 创建黄色掩码") cv2.imshow("黄色掩码", mask) cv2.waitKey(delay) # 使用形态学操作改善掩码质量 kernel = np.ones((5, 5), np.uint8) mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel) # 闭操作填充小空洞 mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel) # 开操作移除小噪点 if observe: print("步骤2.1: 形态学处理后的掩码") cv2.imshow("处理后的掩码", 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: print("步骤3: 提取黄色部分") cv2.imshow("只保留黄色", yellow_only) cv2.waitKey(delay) # 查找轮廓 contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) # 如果没有找到轮廓，返回None if not contours: if observe: print("未找到轮廓") return None, None if observe: print(f"步骤4: 找到 {len(contours)} 个轮廓") contour_img = img.copy() cv2.drawContours(contour_img, contours, -1, (0, 255, 0), 2) cv2.imshow("所有轮廓", contour_img) cv2.waitKey(delay) # 筛选可能属于横向赛道的轮廓 horizontal_contours = [] for contour in contours: # 计算轮廓的边界框 x, y, w, h = cv2.boundingRect(contour) # 计算轮廓的宽高比 aspect_ratio = float(w) / max(h, 1) # 在搜索区域内且宽高比大于1（更宽而非更高）的轮廓更可能是横向线段 if (left_bound <= x + w // 2 <= right_bound and top_bound <= y + h // 2 <= bottom_bound and aspect_ratio > 1.0): horizontal_contours.append(contour) if not horizontal_contours: if observe: print("未找到符合条件的横向轮廓") # 如果没有找到符合条件的横向轮廓，尝试使用所有在搜索区域内的轮廓 for contour in contours: x, y, w, h = cv2.boundingRect(contour) if (left_bound <= x + w // 2 <= right_bound and top_bound <= y + h // 2 <= bottom_bound): horizontal_contours.append(contour) if not horizontal_contours: if observe: print("在搜索区域内未找到任何轮廓") return None, None if observe: print(f"步骤4.1: 找到 {len(horizontal_contours)} 个可能的横向轮廓") horizontal_img = img.copy() cv2.drawContours(horizontal_img, horizontal_contours, -1, (0, 255, 0), 2) cv2.imshow("横向轮廓", horizontal_img) cv2.waitKey(delay) # 收集所有可能的横向轮廓点 all_horizontal_points = [] for contour in horizontal_contours: for point in contour: x, y = point[0] if (left_bound <= x <= right_bound and top_bound <= y <= bottom_bound): all_horizontal_points.append((x, y)) if not all_horizontal_points: if observe: print("在搜索区域内未找到有效点") return None, None # 按y值对点进行分组（针对不同的水平线段） # 使用聚类方法将点按y值分组 y_values = np.array([p[1] for p in all_horizontal_points]) y_values = y_values.reshape(-1, 1) # 转换为列向量 # 如果点较少，直接按y值简单分组 if len(y_values) < 10: # 简单分组：通过y值差异判断是否属于同一水平线 y_groups = [] current_group = [all_horizontal_points[0]] current_y = all_horizontal_points[0][1] for i in range(1, len(all_horizontal_points)): point = all_horizontal_points[i] if abs(point[1] - current_y) < 10: # 如果y值接近当前组的y值 current_group.append(point) else: y_groups.append(current_group) current_group = [point] current_y = point[1] if current_group: y_groups.append(current_group) else: # 使用K-means聚类按y值将点分为不同组 max_clusters = min(5, len(y_values) // 2) # 最多5个聚类或点数的一半 # 尝试不同数量的聚类，找到最佳分组 best_score = -1 best_labels = None for n_clusters in range(1, max_clusters + 1): kmeans = KMeans(n_clusters=n_clusters, n_init=10, random_state=0).fit(y_values) score = silhouette_score(y_values, kmeans.labels_) if n_clusters > 1 else 0 if score > best_score: best_score = score best_labels = kmeans.labels_ # 根据聚类结果分组 y_groups = [[] for _ in range(max(best_labels) + 1)] for i, point in enumerate(all_horizontal_points): group_idx = best_labels[i] y_groups[group_idx].append(point) if observe: clusters_img = img.copy() colors = [(0, 0, 255), (0, 255, 0), (255, 0, 0), (255, 255, 0), (0, 255, 255)] for i, group in enumerate(y_groups): color = colors[i % len(colors)] for point in group: cv2.circle(clusters_img, point, 3, color, -1) cv2.imshow("按Y值分组的点", clusters_img) cv2.waitKey(delay) # 为每个组计算平均y值 avg_y_values = [] for group in y_groups: avg_y = sum(p[1] for p in group) / len(group) avg_y_values.append((avg_y, group)) # 按平均y值降序排序（越大的y值越靠近底部，也就是越靠近相机） avg_y_values.sort(reverse=True) # 从y值最大的组开始分析，找到符合横向赛道特征的组 selected_group = None selected_slope = 0 for avg_y, group in avg_y_values: # 计算该组点的斜率 if len(group) < 2: continue x_coords = np.array([p[0] for p in group]) y_coords = np.array([p[1] for p in group]) if np.std(x_coords) <= 0: continue slope, _ = np.polyfit(x_coords, y_coords, 1) # 判断该组是否可能是横向赛道 # 横向赛道的斜率应该比较小（接近水平） if abs(slope) < 0.5: # 允许一定的倾斜 selected_group = group selected_slope = slope break # 如果没有找到符合条件的组，使用y值最大的组 if selected_group is None and avg_y_values: selected_group = avg_y_values[0][1] # 重新计算斜率 if len(selected_group) >= 2: x_coords = np.array([p[0] for p in selected_group]) y_coords = np.array([p[1] for p in selected_group]) if np.std(x_coords) > 0: selected_slope, _ = np.polyfit(x_coords, y_coords, 1) if selected_group is None: if observe: print("未能找到有效的横向赛道线") return None, None # 找出选定组中y值最大的点（最靠近相机的点） bottom_edge_point = max(selected_group, key=lambda p: p[1]) if observe: print(f"步骤5: 找到边缘点 {bottom_edge_point}") edge_img = img.copy() # 绘制选定的组 for point in selected_group: 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 # 改进斜率计算，使用BFS找到同一条边缘线上的更多点 def get_better_slope(start_point, points, max_distance=20): """使用BFS算法寻找同一条边缘线上的点，并计算更准确的斜率""" queue = [start_point] visited = {start_point} line_points = [start_point] # BFS搜索相连的点 while queue and len(line_points) < 200: # 增加最大点数 current = queue.pop(0) cx, cy = current # 对所有未访问点计算距离 for point in points: if point in visited: continue px, py = point # 计算欧氏距离 dist = np.sqrt((px - cx) ** 2 + (py - cy) ** 2) # 如果距离在阈值内，认为是同一条线上的点 # 降低距离阈值，使连接更精确 if dist < max_distance: queue.append(point) visited.add(point) line_points.append(point) # 如果找到足够多的点，计算斜率 if len(line_points) >= 5: # 至少需要更多点来拟合 x_coords = np.array([p[0] for p in line_points]) y_coords = np.array([p[1] for p in line_points]) # 使用RANSAC算法拟合直线，更加鲁棒 # 尝试使用RANSAC进行更鲁棒的拟合 try: # 创建RANSAC对象 ransac = linear_model.RANSACRegressor() X = x_coords.reshape(-1, 1) # 拟合模型 ransac.fit(X, y_coords) new_slope = ransac.estimator_.coef_[0] # 获取内点（符合模型的点） inlier_mask = ransac.inlier_mask_ inlier_points = [line_points[i] for i in range(len(line_points)) if inlier_mask[i]] # 至少需要3个内点 if len(inlier_points) >= 3: return new_slope, inlier_points except: # 如果RANSAC失败，回退到普通拟合 pass # 标准拟合方法作为后备 if np.std(x_coords) > 0: new_slope, _ = np.polyfit(x_coords, y_coords, 1) return new_slope, line_points return selected_slope, line_points # 尝试获取更准确的斜率 improved_slope, better_line_points = get_better_slope(bottom_edge_point, selected_group) # 使用改进后的斜率 slope = improved_slope if observe: improved_slope_img = img.copy() # 画出底部边缘点 cv2.circle(improved_slope_img, bottom_edge_point, 10, (0, 0, 255), -1) # 画出改进后找到的所有点 for point in better_line_points: cv2.circle(improved_slope_img, point, 3, (255, 255, 0), -1) # 使用改进后的斜率画线 line_length = 300 # 确保线条经过边缘点 mid_x = bottom_edge_point[0] mid_y = bottom_edge_point[1] # 计算线条起点和终点 end_x = mid_x + line_length end_y = int(mid_y + improved_slope * line_length) start_x = mid_x - line_length start_y = int(mid_y - improved_slope * line_length) # 绘制线条 cv2.line(improved_slope_img, (start_x, start_y), (end_x, end_y), (0, 255, 0), 2) # 添加文本显示信息 cv2.putText(improved_slope_img, f"原始斜率: {selected_slope:.4f}", (10, 150), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2) cv2.putText(improved_slope_img, f"改进斜率: {improved_slope:.4f}", (10, 190), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 255), 2) cv2.putText(improved_slope_img, f"找到点数: {len(better_line_points)}", (10, 230), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 255), 2) # 显示所有原始点和改进算法选择的点之间的比较 cv2.putText(improved_slope_img, f"原始点数: {len(selected_group)}", (10, 270), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2) cv2.imshow("改进的斜率计算", improved_slope_img) cv2.waitKey(delay) # 计算中线与检测到的横向线的交点 # 横向线方程: y = slope * (x - edge_x) + edge_y # 中线方程: x = center_x # 解这个方程组得到交点坐标 edge_x, edge_y = bottom_edge_point intersection_x = center_x intersection_y = slope * (center_x - edge_x) + edge_y 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 if observe: slope_img = img.copy() # 画出底部边缘点 cv2.circle(slope_img, bottom_edge_point, 10, (0, 0, 255), -1) # 画出选定组中的所有点 for point in selected_group: cv2.circle(slope_img, point, 3, (255, 0, 0), -1) # 使用斜率画一条线来表示边缘方向 line_length = 200 end_x = bottom_edge_point[0] + line_length end_y = int(bottom_edge_point[1] + slope * line_length) start_x = bottom_edge_point[0] - line_length start_y = int(bottom_edge_point[1] - slope * line_length) cv2.line(slope_img, (start_x, start_y), (end_x, end_y), (0, 255, 0), 2) # 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 (高亮显示) 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: {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) cv2.imshow("边缘斜率和中线交点", slope_img) cv2.imwrite("res/path/test/edge_img.png", slope_img) cv2.waitKey(delay) # 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": slope, "is_horizontal": abs(slope) < 0.05, # 判断边缘是否接近水平 "points_count": len(selected_group), # 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_group # 添加选定的点组 } return bottom_edge_point, edge_info # 用法示例 if __name__ == "__main__": pass