import cv2 import numpy as np from sklearn.cluster import KMeans from sklearn.metrics import silhouette_score from sklearn import linear_model def preprocess_image(image): """ 预处理图像以便进行赛道检测 参数: image: 输入图像，可以是文件路径或者已加载的图像数组 返回: edges: 处理后的边缘图像 """ # 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 # 使用提供的预处理步骤 gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) blurred = cv2.GaussianBlur(gray, (5, 5), 0) edges = cv2.Canny(blurred, 50, 150) # 调整阈值以适应不同光照条件 cv2.imshow("edges", edges) key = cv2.waitKey(0) if key == ord('q'): # 按q键退出 cv2.destroyAllWindows() else: cv2.destroyAllWindows() return edges def detect_yellow_track(image, observe=False, delay=1500): """ 从图像中提取黄色赛道并估算距离 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)，默认为1500ms 返回: distance: 估算的赛道到摄像机的距离 path_info: 赛道路径信息字典 """ # 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 if observe: print("步骤1: 原始图像已加载") cv2.imshow("原始图像", 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) if observe: print("步骤2: 转换到HSV颜色空间") cv2.imshow("HSV图像", hsv) cv2.waitKey(delay) # 黄色的HSV范围 # 调整这些值以匹配图像中黄色的具体色调 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3: 创建黄色掩码") 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: print("步骤4: 提取黄色部分") cv2.imshow("只保留黄色", yellow_only) cv2.waitKey(delay) # 将掩码转为灰度图 gray = mask.copy() # 查找轮廓 contours, _ = cv2.findContours(gray, 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5: 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) # 获取图像尺寸 height, width = img.shape[:2] # 在图像上绘制三条竖线并计算与轮廓的交点 vertical_lines = [ width // 2, # 中线 int(width * 2 / 5), # 2/5位置的线 int(width * 3 / 5) # 3/5位置的线 ] intersection_points = [] vertical_line_images = [] if observe: print("步骤5.1: 绘制三条竖线并计算与轮廓的交点") for i, x in enumerate(vertical_lines): # 为每条竖线创建单独的图像用于可视化 line_img = img.copy() cv2.line(line_img, (x, 0), (x, height), (0, 0, 255), 2) # 记录每条线与轮廓的交点 line_intersections = [] # 对于每个轮廓 for contour in contours: # 遍历轮廓中的所有点对 for j in range(len(contour) - 1): pt1 = contour[j][0] pt2 = contour[j+1][0] # 检查两点是否在竖线两侧 if (pt1[0] <= x and pt2[0] >= x) or (pt1[0] >= x and pt2[0] <= x): # 计算交点的y坐标（线性插值） if pt2[0] == pt1[0]: # 避免除以零 y_intersect = pt1[1] else: slope = (pt2[1] - pt1[1]) / (pt2[0] - pt1[0]) y_intersect = pt1[1] + slope * (x - pt1[0]) # 添加交点 line_intersections.append((x, int(y_intersect))) # 在每条线上标记所有交点 for point in line_intersections: cv2.circle(line_img, point, 5, (255, 0, 0), -1) vertical_line_images.append(line_img) # 寻找最底部的交点（y坐标最大） if line_intersections: bottom_most = max(line_intersections, key=lambda p: p[1]) intersection_points.append(bottom_most) else: intersection_points.append(None) if observe: for i, line_img in enumerate(vertical_line_images): cv2.imshow(f"竖线 {i+1} 与轮廓的交点", line_img) cv2.waitKey(delay) # 找出底部最近的点（y坐标最大的点） valid_intersections = [p for p in intersection_points if p is not None] if not valid_intersections: if observe: print("未找到任何竖线与轮廓的交点") return None, None bottom_most_point = max(valid_intersections, key=lambda p: p[1]) bottom_most_index = intersection_points.index(bottom_most_point) # 计算目标线的斜率和中线距离 target_line_x = vertical_lines[bottom_most_index] center_line_x = vertical_lines[0] # 中线x坐标 # 计算目标线到中线的距离（正值表示在右侧，负值表示在左侧） distance_to_center = target_line_x - center_line_x # 计算目标线的斜率 # 为了计算斜率，我们需要在目标线上找到两个点 # 首先找到与目标线相同轮廓上的所有点 target_contour_points = [] for contour in contours: for point in contour: # 检查点是否接近目标线（允许一定误差） if abs(point[0][0] - target_line_x) < 5: # 5像素的误差范围 target_contour_points.append((point[0][0], point[0][1])) # 如果找到了足够的点，计算斜率 slope = 0 # 默认斜率为0（水平线） if len(target_contour_points) >= 2: # 使用线性回归计算斜率 x_coords = np.array([p[0] for p in target_contour_points]) y_coords = np.array([p[1] for p in target_contour_points]) if np.std(x_coords) > 0: # 避免除以零 slope, _ = np.polyfit(x_coords, y_coords, 1) if observe: result_img = img.copy() # 标记底部最近的点 cv2.circle(result_img, bottom_most_point, 10, (255, 255, 0), -1) # 绘制三条竖线 for x in vertical_lines: cv2.line(result_img, (x, 0), (x, height), (0, 0, 255), 2) # 显示目标线到中线的距离和斜率 cv2.putText(result_img, f"Distance to center: {distance_to_center}px", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2) cv2.putText(result_img, f"Slope: {slope:.4f}", (10, 70), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2) cv2.imshow("目标线分析", result_img) cv2.waitKey(delay) # 更新路径信息字典，包含新的目标线信息 path_info = { "target_line_x": target_line_x, "distance_to_center": distance_to_center, "target_line_slope": slope, "bottom_most_point": bottom_most_point } # 合并所有轮廓或选择最大的轮廓 # 在这个场景中，我们可能有多个赛道段落，所以合并它们 all_contours = np.vstack([contours[i] for i in range(len(contours))]) all_contours = all_contours.reshape((-1, 1, 2)) # 使用多边形近似轮廓 epsilon = 0.01 * cv2.arcLength(all_contours, False) approx = cv2.approxPolyDP(all_contours, epsilon, False) if observe: print(f"步骤6: 多边形近似，顶点数: {len(approx)}") approx_img = img.copy() cv2.drawContours(approx_img, [approx], -1, (255, 0, 0), 2) cv2.imshow("多边形近似", approx_img) cv2.waitKey(delay) # 获取图像尺寸 height, width = img.shape[:2] # 提取赛道底部的点（靠近摄像机的点） bottom_points = [p[0] for p in approx if p[0][1] > height * 0.7] if not bottom_points: if observe: print("未找到靠近摄像机的赛道点") return None, None # 计算底部点的平均x坐标，作为赛道中心线 bottom_center_x = sum(p[0] for p in bottom_points) / len(bottom_points) # 提取赛道顶部的点（远离摄像机的点） top_points = [p[0] for p in approx if p[0][1] < height * 0.3] if not top_points: if observe: print("未找到远离摄像机的赛道点") return None, None # 计算顶部点的平均x坐标 top_center_x = sum(p[0] for p in top_points) / len(top_points) # 计算赛道方向（从底部到顶部的角度） delta_x = top_center_x - bottom_center_x track_angle = np.arctan2(height * 0.4, delta_x) * 180 / np.pi # 估算距离 # 这里使用简化的方法：基于黄色区域在图像中的占比来估算距离 yellow_area = cv2.countNonZero(mask) total_area = height * width area_ratio = yellow_area / total_area # 假设：区域比例与距离成反比（实际应用中需要标定） # 这里使用一个简单的比例关系，实际应用需要根据相机参数和实际测量进行标定 estimated_distance = 1.0 / (area_ratio + 0.01) # 避免除以零 # 将距离标准化到一个合理的范围（例如0-10米） normalized_distance = min(10.0, max(0.0, estimated_distance / 100.0)) # 创建路径信息字典 path_info = { "bottom_center_x": bottom_center_x, "top_center_x": top_center_x, "track_angle": track_angle, "area_ratio": area_ratio, "is_straight": abs(track_angle) < 10, # 判断赛道是否笔直 "turn_direction": "left" if delta_x < 0 else "right" if delta_x > 0 else "straight", "target_line_x": target_line_x, "distance_to_center": distance_to_center, "target_line_slope": slope, "bottom_most_point": bottom_most_point } if observe: print(f"步骤7: 路径分析 - 角度: {track_angle:.2f}°, 距离: {normalized_distance:.2f}m, 方向: {path_info['turn_direction']}") result_img = img.copy() # 绘制底部中心点 cv2.circle(result_img, (int(bottom_center_x), int(height * 0.8)), 10, (0, 0, 255), -1) # 绘制顶部中心点 cv2.circle(result_img, (int(top_center_x), int(height * 0.2)), 10, (0, 0, 255), -1) # 绘制路径线 cv2.line(result_img, (int(bottom_center_x), int(height * 0.8)), (int(top_center_x), int(height * 0.2)), (0, 255, 0), 2) # 添加信息文本 cv2.putText(result_img, f"Distance: {normalized_distance:.2f}m", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2) cv2.putText(result_img, f"Angle: {track_angle:.2f}°", (10, 70), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2) cv2.putText(result_img, f"Direction: {path_info['turn_direction']}", (10, 110), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2) cv2.imshow("赛道分析结果", result_img) cv2.waitKey(delay) return normalized_distance, path_info def visualize_track_detection(image, save_path=None, observe=False, delay=500): """ 可视化赛道检测过程，显示中间结果和最终分析 参数: image: 输入图像，可以是文件路径或者已加载的图像数组 save_path: 保存结果图像的路径（可选） 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)，默认为500ms """ # 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 # 获取图像尺寸 height, width = img.shape[:2] # 创建输出图像 output = img.copy() # 转换到HSV颜色空间 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) # 应用掩码，只保留黄色部分 yellow_only = cv2.bitwise_and(img, img, mask=mask) # 进行边缘检测 edges = preprocess_image(img) # 组合黄色掩码和边缘检测 combined_mask = cv2.bitwise_and(mask, edges) if edges is not None else mask # 查找轮廓 contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) # 如果找到轮廓，绘制并分析 if contours: # 绘制所有轮廓 cv2.drawContours(output, contours, -1, (0, 255, 0), 2) # 绘制三条竖线 vertical_lines = [ width // 2, # 中线 int(width * 2 / 5), # 2/5位置的线 int(width * 3 / 5) # 3/5位置的线 ] # 找出与各条竖线的交点 intersection_points = [] for x in vertical_lines: # 绘制竖线 cv2.line(output, (x, 0), (x, height), (0, 0, 255), 2) # 记录与当前竖线的交点 line_intersections = [] # 对于每个轮廓 for contour in contours: # 遍历轮廓中的所有点对 for j in range(len(contour) - 1): pt1 = contour[j][0] pt2 = contour[j+1][0] # 检查两点是否在竖线两侧 if (pt1[0] <= x and pt2[0] >= x) or (pt1[0] >= x and pt2[0] <= x): # 计算交点的y坐标 if pt2[0] == pt1[0]: # 避免除以零 y_intersect = pt1[1] else: slope = (pt2[1] - pt1[1]) / (pt2[0] - pt1[0]) y_intersect = pt1[1] + slope * (x - pt1[0]) # 添加交点 line_intersections.append((x, int(y_intersect))) # 在图像上标记交点 for point in line_intersections: cv2.circle(output, point, 5, (255, 0, 0), -1) # 寻找最底部的交点 if line_intersections: bottom_most = max(line_intersections, key=lambda p: p[1]) intersection_points.append(bottom_most) else: intersection_points.append(None) # 找出底部最近的点 valid_intersections = [p for p in intersection_points if p is not None] if valid_intersections: bottom_most_point = max(valid_intersections, key=lambda p: p[1]) bottom_most_index = intersection_points.index(bottom_most_point) # 计算目标线信息 target_line_x = vertical_lines[bottom_most_index] center_line_x = vertical_lines[0] distance_to_center = target_line_x - center_line_x # 标记底部最近的点 cv2.circle(output, bottom_most_point, 10, (255, 255, 0), -1) # 计算目标线的斜率 target_contour_points = [] for contour in contours: for point in contour: if abs(point[0][0] - target_line_x) < 5: target_contour_points.append((point[0][0], point[0][1])) slope = 0 if len(target_contour_points) >= 2: x_coords = np.array([p[0] for p in target_contour_points]) y_coords = np.array([p[1] for p in target_contour_points]) if np.std(x_coords) > 0: slope, _ = np.polyfit(x_coords, y_coords, 1) # 在图像上添加目标线信息 cv2.putText(output, f"Distance to center: {distance_to_center}px", (10, 150), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2) cv2.putText(output, f"Slope: {slope:.4f}", (10, 190), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2) # 合并所有轮廓 all_contours = np.vstack([contours[i] for i in range(len(contours))]) all_contours = all_contours.reshape((-1, 1, 2)) # 使用多边形近似轮廓 epsilon = 0.01 * cv2.arcLength(all_contours, False) approx = cv2.approxPolyDP(all_contours, epsilon, False) # 提取赛道底部和顶部的点 bottom_points = [p[0] for p in approx if p[0][1] > height * 0.7] top_points = [p[0] for p in approx if p[0][1] < height * 0.3] # 如果找到了顶部和底部的点，计算中心线和方向 if bottom_points and top_points: bottom_center_x = sum(p[0] for p in bottom_points) / len(bottom_points) top_center_x = sum(p[0] for p in top_points) / len(top_points) # 绘制底部和顶部中心点 cv2.circle(output, (int(bottom_center_x), int(height * 0.8)), 10, (0, 0, 255), -1) cv2.circle(output, (int(top_center_x), int(height * 0.2)), 10, (0, 0, 255), -1) # 绘制路径线 cv2.line(output, (int(bottom_center_x), int(height * 0.8)), (int(top_center_x), int(height * 0.2)), (0, 255, 0), 2) # 计算方向角度 delta_x = top_center_x - bottom_center_x track_angle = np.arctan2(height * 0.6, delta_x) * 180 / np.pi # 估算距离 yellow_area = cv2.countNonZero(mask) total_area = height * width area_ratio = yellow_area / total_area estimated_distance = 1.0 / (area_ratio + 0.01) normalized_distance = min(10.0, max(0.0, estimated_distance / 100.0)) # 确定转向方向 turn_direction = "left" if delta_x < 0 else "right" if delta_x > 0 else "straight" # 在图像上添加信息 cv2.putText(output, f"Distance: {normalized_distance:.2f}m", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2) cv2.putText(output, f"Angle: {track_angle:.2f}°", (10, 70), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2) cv2.putText(output, f"Direction: {turn_direction}", (10, 110), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2) # 如果提供了保存路径，保存结果图像 if save_path: cv2.imwrite(save_path, output) if observe: print(f"结果已保存到: {save_path}") # 创建包含所有处理步骤的结果图像 result = np.hstack((img, yellow_only, output)) # 调整大小以便查看 scale_percent = 50 # 缩放到原来的50% width = int(result.shape[1] * scale_percent / 100) height = int(result.shape[0] * scale_percent / 100) dim = (width, height) resized = cv2.resize(result, dim, interpolation=cv2.INTER_AREA) # 显示结果 cv2.imshow('Track Detection Process', resized) cv2.waitKey(0) cv2.destroyAllWindows() # 距离估算辅助函数 def estimate_distance_to_track(image): """ 估算摄像机到赛道前方的距离 参数: image: 输入图像，可以是文件路径或者已加载的图像数组 返回: distance: 估算的距离（米） path_angle: 赛道角度（度），正值表示向右转，负值表示向左转 target_line_info: 目标线信息字典，包含到中线的距离和斜率 """ distance, path_info = detect_yellow_track(image, observe=False) if distance is None or path_info is None: return None, None, None # 提取目标线信息 target_line_info = { "distance_to_center": path_info["distance_to_center"], "slope": path_info["target_line_slope"] } return distance, path_info["track_angle"], target_line_info 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__": # 替换为实际图像路径 image_path = "path/to/track/image.png" # 检测赛道并估算距离 distance, path_info = detect_yellow_track(image_path, observe=True, delay=1500) if distance is not None: print(f"估算距离: {distance:.2f}米") print(f"赛道角度: {path_info['track_angle']:.2f}°") print(f"转向方向: {path_info['turn_direction']}") print(f"目标线到中线距离: {path_info['distance_to_center']}像素") print(f"目标线斜率: {path_info['target_line_slope']:.4f}") # 可视化检测过程 visualize_track_detection(image_path, observe=True, delay=1500) # 检测横向赛道边缘 edge_point, edge_info = detect_horizontal_track_edge(image_path, observe=True, delay=1500) if edge_point is not None: print(f"边缘点坐标: ({edge_point[0]}, {edge_point[1]})") print(f"到中线距离: {edge_info['distance_to_center']}像素") print(f"边缘斜率: {edge_info['slope']:.4f}") print(f"是否水平: {edge_info['is_horizontal']}")