删除临时代码运行文件，更新横向赛道边缘检测功能，改进斜率计算方法，增强可视化效果，添加交点计算的可视化内容。

test/task-path-track/tempCodeRunnerFile.py

@@ -1 +0,0 @@
-True

utils/detect_track.py

@@ -2,6 +2,7 @@ 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):
     """
@@ -558,14 +559,14 @@ def estimate_distance_to_track(image):
     
     return distance, path_info["track_angle"], target_line_info
 
-def detect_horizontal_track_edge(image, observe=False, delay=1500):
+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)，默认为1500ms
+        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)
@@ -828,8 +829,111 @@ def detect_horizontal_track_edge(image, observe=False, delay=1500):
     # 计算这个点到中线的距离
     distance_to_center = bottom_edge_point[0] - center_x
     
-    # 使用选定组的斜率
-    slope = selected_slope
+    # 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
@@ -877,6 +981,7 @@ def detect_horizontal_track_edge(image, observe=False, delay=1500):
         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
@@ -976,6 +1081,27 @@ def visualize_horizontal_track_edge(image, save_path=None, observe=False, delay=
                     cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
         cv2.putText(output, f"中线交点: ({intersection_point[0]}, {intersection_point[1]})", (10, 210), 
                     cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
+        
+        # 绘制交点计算的可视化内容
+        # 用线条显示边缘点到中线的连接（交点计算的辅助线）
+        cv2.line(output, edge_point, (center_x, edge_point[1]), (255, 153, 51), 2)
+        
+        # 在边缘点处标注其y值
+        cv2.putText(output, f"y={edge_point[1]}", (edge_point[0] + 10, edge_point[1] - 10),
+                   cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 153, 51), 2)
+        
+        # 在交点处标注其坐标
+        cv2.putText(output, f"({intersection_point[0]}, {intersection_point[1]})", 
+                   (intersection_point[0] + 10, intersection_point[1] - 10),
+                   cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 0, 255), 2)
+        
+        # 用箭头显示从边缘点到交点的路径（表示计算过程）
+        cv2.arrowedLine(output, edge_point, intersection_point, (102, 204, 255), 2)
+        
+        # 添加交点计算的方程说明
+        equation_text = f"y = {selected_slope:.2f} * (x - {edge_point[0]}) + {edge_point[1]}"
+        cv2.putText(output, equation_text, (10, 240), 
+                   cv2.FONT_HERSHEY_SIMPLEX, 0.7, (102, 204, 255), 2)
     else:
         # 如果没有检测到，添加提示
         cv2.putText(output, "未检测到横向赛道", (width//4, height//2),