1026 lines
39 KiB
Python
1026 lines
39 KiB
Python
import cv2
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import numpy as np
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from sklearn.cluster import KMeans
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from sklearn.metrics import silhouette_score
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from sklearn import linear_model
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def preprocess_image(image):
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"""
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预处理图像以便进行赛道检测
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参数:
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image: 输入图像，可以是文件路径或者已加载的图像数组
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返回:
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edges: 处理后的边缘图像
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"""
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# 如果输入是字符串（文件路径），则加载图像
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if isinstance(image, str):
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img = cv2.imread(image)
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else:
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img = image.copy()
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if img is None:
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print("无法加载图像")
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return None
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# 使用提供的预处理步骤
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gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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blurred = cv2.GaussianBlur(gray, (5, 5), 0)
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edges = cv2.Canny(blurred, 50, 150) # 调整阈值以适应不同光照条件
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cv2.imshow("edges", edges)
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key = cv2.waitKey(0)
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if key == ord('q'): # 按q键退出
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cv2.destroyAllWindows()
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else:
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cv2.destroyAllWindows()
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return edges
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def detect_yellow_track(image, observe=False, delay=1500):
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"""
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从图像中提取黄色赛道并估算距离
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参数:
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image: 输入图像，可以是文件路径或者已加载的图像数组
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observe: 是否输出中间状态信息和可视化结果，默认为False
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delay: 展示每个步骤的等待时间(毫秒)，默认为1500ms
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返回:
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distance: 估算的赛道到摄像机的距离
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path_info: 赛道路径信息字典
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"""
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# 如果输入是字符串（文件路径），则加载图像
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if isinstance(image, str):
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img = cv2.imread(image)
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else:
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img = image.copy()
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if img is None:
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print("无法加载图像")
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return None, None
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if observe:
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print("步骤1: 原始图像已加载")
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cv2.imshow("原始图像", img)
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cv2.waitKey(delay)
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# 转换到HSV颜色空间以便更容易提取黄色
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hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
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if observe:
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print("步骤2: 转换到HSV颜色空间")
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cv2.imshow("HSV图像", hsv)
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cv2.waitKey(delay)
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# 黄色的HSV范围
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# 调整这些值以匹配图像中黄色的具体色调
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lower_yellow = np.array([20, 100, 100])
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upper_yellow = np.array([30, 255, 255])
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# 创建黄色的掩码
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mask = cv2.inRange(hsv, lower_yellow, upper_yellow)
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if observe:
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print("步骤3: 创建黄色掩码")
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cv2.imshow("黄色掩码", mask)
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cv2.waitKey(delay)
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# 应用掩码，只保留黄色部分
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yellow_only = cv2.bitwise_and(img, img, mask=mask)
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if observe:
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print("步骤4: 提取黄色部分")
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cv2.imshow("只保留黄色", yellow_only)
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cv2.waitKey(delay)
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# 将掩码转为灰度图
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gray = mask.copy()
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# 查找轮廓
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contours, _ = cv2.findContours(gray, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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# 如果没有找到轮廓，返回None
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if not contours:
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if observe:
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print("未找到轮廓")
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return None, None
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if observe:
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print(f"步骤5: 找到 {len(contours)} 个轮廓")
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contour_img = img.copy()
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cv2.drawContours(contour_img, contours, -1, (0, 255, 0), 2)
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cv2.imshow("所有轮廓", contour_img)
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cv2.waitKey(delay)
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# 获取图像尺寸
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height, width = img.shape[:2]
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# 在图像上绘制三条竖线并计算与轮廓的交点
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vertical_lines = [
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width // 2, # 中线
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int(width * 2 / 5), # 2/5位置的线
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int(width * 3 / 5) # 3/5位置的线
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]
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intersection_points = []
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vertical_line_images = []
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if observe:
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print("步骤5.1: 绘制三条竖线并计算与轮廓的交点")
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for i, x in enumerate(vertical_lines):
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# 为每条竖线创建单独的图像用于可视化
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line_img = img.copy()
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cv2.line(line_img, (x, 0), (x, height), (0, 0, 255), 2)
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# 记录每条线与轮廓的交点
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line_intersections = []
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# 对于每个轮廓
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for contour in contours:
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# 遍历轮廓中的所有点对
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for j in range(len(contour) - 1):
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pt1 = contour[j][0]
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pt2 = contour[j+1][0]
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# 检查两点是否在竖线两侧
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if (pt1[0] <= x and pt2[0] >= x) or (pt1[0] >= x and pt2[0] <= x):
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# 计算交点的y坐标（线性插值）
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if pt2[0] == pt1[0]: # 避免除以零
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y_intersect = pt1[1]
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else:
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slope = (pt2[1] - pt1[1]) / (pt2[0] - pt1[0])
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y_intersect = pt1[1] + slope * (x - pt1[0])
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# 添加交点
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line_intersections.append((x, int(y_intersect)))
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# 在每条线上标记所有交点
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for point in line_intersections:
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cv2.circle(line_img, point, 5, (255, 0, 0), -1)
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vertical_line_images.append(line_img)
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# 寻找最底部的交点（y坐标最大）
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if line_intersections:
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bottom_most = max(line_intersections, key=lambda p: p[1])
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intersection_points.append(bottom_most)
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else:
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intersection_points.append(None)
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if observe:
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for i, line_img in enumerate(vertical_line_images):
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cv2.imshow(f"竖线 {i+1} 与轮廓的交点", line_img)
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cv2.waitKey(delay)
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# 找出底部最近的点（y坐标最大的点）
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valid_intersections = [p for p in intersection_points if p is not None]
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if not valid_intersections:
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if observe:
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print("未找到任何竖线与轮廓的交点")
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return None, None
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bottom_most_point = max(valid_intersections, key=lambda p: p[1])
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bottom_most_index = intersection_points.index(bottom_most_point)
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# 计算目标线的斜率和中线距离
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target_line_x = vertical_lines[bottom_most_index]
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center_line_x = vertical_lines[0] # 中线x坐标
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# 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）
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distance_to_center = target_line_x - center_line_x
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# 计算目标线的斜率
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# 为了计算斜率，我们需要在目标线上找到两个点
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# 首先找到与目标线相同轮廓上的所有点
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target_contour_points = []
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for contour in contours:
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for point in contour:
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# 检查点是否接近目标线（允许一定误差）
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if abs(point[0][0] - target_line_x) < 5: # 5像素的误差范围
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target_contour_points.append((point[0][0], point[0][1]))
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# 如果找到了足够的点，计算斜率
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slope = 0 # 默认斜率为0（水平线）
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if len(target_contour_points) >= 2:
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# 使用线性回归计算斜率
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x_coords = np.array([p[0] for p in target_contour_points])
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y_coords = np.array([p[1] for p in target_contour_points])
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if np.std(x_coords) > 0: # 避免除以零
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slope, _ = np.polyfit(x_coords, y_coords, 1)
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if observe:
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result_img = img.copy()
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# 标记底部最近的点
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cv2.circle(result_img, bottom_most_point, 10, (255, 255, 0), -1)
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# 绘制三条竖线
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for x in vertical_lines:
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cv2.line(result_img, (x, 0), (x, height), (0, 0, 255), 2)
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# 显示目标线到中线的距离和斜率
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cv2.putText(result_img, f"Distance to center: {distance_to_center}px", (10, 30),
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cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
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cv2.putText(result_img, f"Slope: {slope:.4f}", (10, 70),
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cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
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cv2.imshow("目标线分析", result_img)
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cv2.waitKey(delay)
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# 更新路径信息字典，包含新的目标线信息
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path_info = {
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"target_line_x": target_line_x,
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"distance_to_center": distance_to_center,
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"target_line_slope": slope,
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"bottom_most_point": bottom_most_point
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}
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# 合并所有轮廓或选择最大的轮廓
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# 在这个场景中，我们可能有多个赛道段落，所以合并它们
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all_contours = np.vstack([contours[i] for i in range(len(contours))])
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all_contours = all_contours.reshape((-1, 1, 2))
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# 使用多边形近似轮廓
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epsilon = 0.01 * cv2.arcLength(all_contours, False)
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approx = cv2.approxPolyDP(all_contours, epsilon, False)
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if observe:
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print(f"步骤6: 多边形近似，顶点数: {len(approx)}")
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approx_img = img.copy()
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cv2.drawContours(approx_img, [approx], -1, (255, 0, 0), 2)
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cv2.imshow("多边形近似", approx_img)
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cv2.waitKey(delay)
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# 获取图像尺寸
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height, width = img.shape[:2]
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# 提取赛道底部的点（靠近摄像机的点）
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bottom_points = [p[0] for p in approx if p[0][1] > height * 0.7]
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if not bottom_points:
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if observe:
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print("未找到靠近摄像机的赛道点")
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return None, None
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# 计算底部点的平均x坐标，作为赛道中心线
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bottom_center_x = sum(p[0] for p in bottom_points) / len(bottom_points)
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# 提取赛道顶部的点（远离摄像机的点）
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top_points = [p[0] for p in approx if p[0][1] < height * 0.3]
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if not top_points:
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if observe:
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print("未找到远离摄像机的赛道点")
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return None, None
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# 计算顶部点的平均x坐标
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top_center_x = sum(p[0] for p in top_points) / len(top_points)
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# 计算赛道方向（从底部到顶部的角度）
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delta_x = top_center_x - bottom_center_x
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track_angle = np.arctan2(height * 0.4, delta_x) * 180 / np.pi
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# 估算距离
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# 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
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yellow_area = cv2.countNonZero(mask)
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total_area = height * width
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area_ratio = yellow_area / total_area
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# 假设：区域比例与距离成反比（实际应用中需要标定）
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# 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
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estimated_distance = 1.0 / (area_ratio + 0.01) # 避免除以零
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# 将距离标准化到一个合理的范围（例如0-10米）
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normalized_distance = min(10.0, max(0.0, estimated_distance / 100.0))
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# 创建路径信息字典
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path_info = {
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"bottom_center_x": bottom_center_x,
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"top_center_x": top_center_x,
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"track_angle": track_angle,
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"area_ratio": area_ratio,
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"is_straight": abs(track_angle) < 10, # 判断赛道是否笔直
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"turn_direction": "left" if delta_x < 0 else "right" if delta_x > 0 else "straight",
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"target_line_x": target_line_x,
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"distance_to_center": distance_to_center,
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"target_line_slope": slope,
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"bottom_most_point": bottom_most_point
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}
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if observe:
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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']}")
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result_img = img.copy()
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# 绘制底部中心点
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cv2.circle(result_img, (int(bottom_center_x), int(height * 0.8)), 10, (0, 0, 255), -1)
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# 绘制顶部中心点
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cv2.circle(result_img, (int(top_center_x), int(height * 0.2)), 10, (0, 0, 255), -1)
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# 绘制路径线
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cv2.line(result_img, (int(bottom_center_x), int(height * 0.8)),
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(int(top_center_x), int(height * 0.2)), (0, 255, 0), 2)
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# 添加信息文本
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cv2.putText(result_img, f"Distance: {normalized_distance:.2f}m", (10, 30),
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cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
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cv2.putText(result_img, f"Angle: {track_angle:.2f}°", (10, 70),
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cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
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cv2.putText(result_img, f"Direction: {path_info['turn_direction']}", (10, 110),
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cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
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cv2.imshow("赛道分析结果", result_img)
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cv2.waitKey(delay)
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return normalized_distance, path_info
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def visualize_track_detection(image, save_path=None, observe=False, delay=500):
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"""
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可视化赛道检测过程，显示中间结果和最终分析
|
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参数:
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image: 输入图像，可以是文件路径或者已加载的图像数组
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save_path: 保存结果图像的路径（可选）
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observe: 是否输出中间状态信息和可视化结果，默认为False
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delay: 展示每个步骤的等待时间(毫秒)，默认为500ms
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"""
|
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# 如果输入是字符串（文件路径），则加载图像
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if isinstance(image, str):
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img = cv2.imread(image)
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else:
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img = image.copy()
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if img is None:
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print("无法加载图像")
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return
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# 获取图像尺寸
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height, width = img.shape[:2]
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# 创建输出图像
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output = img.copy()
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# 转换到HSV颜色空间
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hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
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# 黄色的HSV范围
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lower_yellow = np.array([20, 100, 100])
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upper_yellow = np.array([30, 255, 255])
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# 创建黄色的掩码
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mask = cv2.inRange(hsv, lower_yellow, upper_yellow)
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# 应用掩码，只保留黄色部分
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yellow_only = cv2.bitwise_and(img, img, mask=mask)
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# 进行边缘检测
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edges = preprocess_image(img)
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# 组合黄色掩码和边缘检测
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combined_mask = cv2.bitwise_and(mask, edges) if edges is not None else mask
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# 查找轮廓
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contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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# 如果找到轮廓，绘制并分析
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if contours:
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# 绘制所有轮廓
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cv2.drawContours(output, contours, -1, (0, 255, 0), 2)
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# 绘制三条竖线
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vertical_lines = [
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width // 2, # 中线
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int(width * 2 / 5), # 2/5位置的线
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int(width * 3 / 5) # 3/5位置的线
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]
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# 找出与各条竖线的交点
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intersection_points = []
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for x in vertical_lines:
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# 绘制竖线
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cv2.line(output, (x, 0), (x, height), (0, 0, 255), 2)
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# 记录与当前竖线的交点
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line_intersections = []
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# 对于每个轮廓
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for contour in contours:
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# 遍历轮廓中的所有点对
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for j in range(len(contour) - 1):
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pt1 = contour[j][0]
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pt2 = contour[j+1][0]
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# 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):
|
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# 计算交点的y坐标
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||
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']}")
|
||
|