import cv2 import numpy as np from sklearn import linear_model def detect_horizontal_track_edge(image, observe=False, delay=1000): observe = False # TSET """ 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) # 添加形态学操作以改善掩码 kernel = np.ones((3, 3), np.uint8) mask = cv2.dilate(mask, kernel, iterations=1) if observe: print("步骤2: 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3: 提取黄色部分") cv2.imshow("只保留黄色", yellow_only) cv2.waitKey(delay) # 裁剪掩码到搜索区域 search_mask = mask[top_bound:bottom_bound, left_bound:right_bound] # 找到掩码在搜索区域中最底部的非零点位置 bottom_points = [] non_zero_cols = np.where(np.any(search_mask, axis=0))[0] # 寻找每列的最底部点 for col in non_zero_cols: col_points = np.where(search_mask[:, col] > 0)[0] if len(col_points) > 0: bottom_row = np.max(col_points) bottom_points.append((left_bound + col, top_bound + bottom_row)) if len(bottom_points) < 3: # 如果找不到足够的底部点，使用canny+霍夫变换 edges = cv2.Canny(mask, 50, 150, apertureSize=3) if observe: print("步骤3.1: 边缘检测") cv2.imshow("边缘检测", edges) cv2.waitKey(delay) # 使用霍夫变换检测直线 - 调低阈值以检测短线段 lines = cv2.HoughLinesP(edges, 1, np.pi/180, threshold=30, minLineLength=width*0.1, maxLineGap=30) if lines is None or len(lines) == 0: if observe: print("未检测到直线") return None, None if observe: print(f"步骤4: 检测到 {len(lines)} 条直线") lines_img = img.copy() for line in lines: x1, y1, x2, y2 = line[0] cv2.line(lines_img, (x1, y1), (x2, y2), (0, 255, 0), 2) cv2.imshow("检测到的直线", lines_img) cv2.waitKey(delay) # 筛选水平线，但放宽斜率条件 horizontal_lines = [] for line in lines: x1, y1, x2, y2 = line[0] # 计算斜率 (避免除零错误) if abs(x2 - x1) < 5: # 几乎垂直的线 continue slope = (y2 - y1) / (x2 - x1) # 筛选接近水平的线 (斜率接近0)，但容许更大的倾斜度 if abs(slope) < 0.3: # 确保线在搜索区域内 if ((left_bound <= x1 <= right_bound and top_bound <= y1 <= bottom_bound) or (left_bound <= x2 <= right_bound and top_bound <= y2 <= bottom_bound)): # 计算线的中点y坐标 mid_y = (y1 + y2) / 2 line_length = np.sqrt((x2-x1)**2 + (y2-y1)**2) # 保存线段、其y坐标和长度 horizontal_lines.append((line[0], mid_y, slope, line_length)) if not horizontal_lines: if observe: print("未检测到水平线") return None, None if observe: print(f"步骤4.1: 找到 {len(horizontal_lines)} 条水平线") h_lines_img = img.copy() for line_info in horizontal_lines: line, _, slope, _ = line_info x1, y1, x2, y2 = line cv2.line(h_lines_img, (x1, y1), (x2, y2), (0, 255, 255), 2) # 显示斜率 cv2.putText(h_lines_img, f"{slope:.2f}", ((x1+x2)//2, (y1+y2)//2), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 1) cv2.imshow("水平线", h_lines_img) cv2.waitKey(delay) # 按y坐标排序 (从大到小，底部的线排在前面) horizontal_lines.sort(key=lambda x: x[1], reverse=True) # 取最靠近底部且足够长的线作为横向赛道线 selected_line = None selected_slope = 0 for line_info in horizontal_lines: line, _, slope, length = line_info if length > width * 0.1: # 确保线足够长 selected_line = line selected_slope = slope break if selected_line is None and horizontal_lines: # 如果没有足够长的线，就取最靠近底部的线 selected_line = horizontal_lines[0][0] selected_slope = horizontal_lines[0][2] if selected_line is None: if observe: print("无法选择合适的线段") return None, None x1, y1, x2, y2 = selected_line else: # 使用底部点拟合直线 if observe: bottom_points_img = img.copy() for point in bottom_points: cv2.circle(bottom_points_img, point, 3, (0, 255, 0), -1) cv2.imshow("底部边缘点", bottom_points_img) cv2.waitKey(delay) # 使用RANSAC拟合直线以去除异常值 x_points = np.array([p[0] for p in bottom_points]).reshape(-1, 1) y_points = np.array([p[1] for p in bottom_points]) # 如果点过少或分布不够宽，返回None if len(bottom_points) < 3 or np.max(x_points) - np.min(x_points) < width * 0.1: if observe: print("底部点太少或分布不够宽") return None, None ransac = linear_model.RANSACRegressor(residual_threshold=5.0) ransac.fit(x_points, y_points) # 获取拟合参数 selected_slope = ransac.estimator_.coef_[0] intercept = ransac.estimator_.intercept_ # 检查斜率是否在合理范围内 if abs(selected_slope) > 0.3: if observe: print(f"拟合斜率过大: {selected_slope:.4f}") return None, None # 使用拟合的直线参数计算线段端点 x1 = left_bound y1 = int(selected_slope * x1 + intercept) x2 = right_bound y2 = int(selected_slope * x2 + intercept) if observe: fitted_line_img = img.copy() cv2.line(fitted_line_img, (x1, y1), (x2, y2), (0, 255, 255), 2) cv2.imshow("拟合线段", fitted_line_img) cv2.waitKey(delay) # 确保x1 < x2 if x1 > x2: x1, x2 = x2, x1 y1, y2 = y2, y1 # 找到线上y值最大的点作为边缘点(最靠近相机的点) if y1 > y2: bottom_edge_point = (x1, y1) else: bottom_edge_point = (x2, y2) # 获取线上的更多点 selected_points = [] step = 5 # 每5个像素取一个点 for x in range(max(left_bound, int(min(x1, x2))), min(right_bound, int(max(x1, x2)) + 1), step): y = int(selected_slope * (x - x1) + y1) if top_bound <= y <= bottom_bound: selected_points.append((x, y)) if observe: print(f"步骤5: 找到边缘点 {bottom_edge_point}") edge_img = img.copy() # 画线 cv2.line(edge_img, (x1, y1), (x2, y2), (0, 255, 0), 2) # 绘制所有点 for point in selected_points: cv2.circle(edge_img, point, 3, (255, 0, 0), -1) # 标记边缘点 cv2.circle(edge_img, bottom_edge_point, 10, (0, 0, 255), -1) cv2.imshow("选定的横向线和边缘点", edge_img) cv2.waitKey(delay) # 计算这个点到中线的距离 distance_to_center = bottom_edge_point[0] - center_x # 计算中线与检测到的横向线的交点 # 横向线方程: y = slope * (x - x1) + y1 # 中线方程: x = center_x # 解这个方程组得到交点坐标 intersection_x = center_x intersection_y = selected_slope * (center_x - x1) + y1 intersection_point = (int(intersection_x), int(intersection_y)) # 计算交点到图像底部的距离（以像素为单位） distance_to_bottom = height - intersection_y if observe: slope_img = img.copy() # 画出检测到的线 cv2.line(slope_img, (x1, y1), (x2, y2), (0, 255, 0), 2) # 标记边缘点 cv2.circle(slope_img, bottom_edge_point, 10, (0, 0, 255), -1) # 画出中线 cv2.line(slope_img, (center_x, 0), (center_x, height), (0, 0, 255), 2) # 标记中线与横向线的交点 cv2.circle(slope_img, intersection_point, 12, (255, 0, 255), -1) cv2.circle(slope_img, intersection_point, 5, (255, 255, 255), -1) # 画出交点到底部的距离线 cv2.line(slope_img, intersection_point, (intersection_x, height), (255, 255, 0), 2) cv2.putText(slope_img, f"Slope: {selected_slope:.4f}", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2) cv2.putText(slope_img, f"Distance to center: {distance_to_center}px", (10, 70), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2) cv2.putText(slope_img, f"Distance to bottom: {distance_to_bottom:.1f}px", (10, 110), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2) cv2.putText(slope_img, f"中线交点: ({intersection_point[0]}, {intersection_point[1]})", (10, 150), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2) 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": selected_slope, "is_horizontal": abs(selected_slope) < 0.05, # 判断边缘是否接近水平 "points_count": len(selected_points), # 该组中点的数量 "intersection_point": intersection_point, # 中线与横向线的交点 "distance_to_bottom": distance_to_bottom, # 交点到图像底部的距离 "points": selected_points # 添加选定的点组 } return bottom_edge_point, edge_info # 用法示例 if __name__ == "__main__": pass