924 lines
36 KiB
Python
924 lines
36 KiB
Python
import cv2
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import numpy as np
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import os
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import datetime
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from sklearn import linear_model
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from utils.log_helper import get_logger, debug, info, warning, error, success
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def detect_horizontal_track_edge(image, observe=False, delay=1000, save_log=True):
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"""
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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
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优先检测下方横线，但在遇到下方线截断的情况时会考虑上边缘
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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)
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save_log: 是否保存日志和图像
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返回:
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edge_point: 赛道前方边缘点的坐标 (x, y)
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edge_info: 边缘信息字典
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"""
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# observe = False # TSET
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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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error("无法加载图像", "失败")
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return None, None
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# 获取图像尺寸
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height, width = img.shape[:2]
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# 计算图像中间区域的范围（用于专注于正前方的赛道）
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center_x = width // 2
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search_width = int(width * 2/3) # 搜索区域宽度为图像宽度的2/3
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search_height = height # 搜索区域高度为图像高度的1/1
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left_bound = center_x - search_width // 2
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right_bound = center_x + search_width // 2
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bottom_bound = height
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top_bound = height - search_height
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# 定义合理的值范围
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valid_y_range = (height * 0.5, height) # 有效的y坐标范围（下半部分图像）
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max_slope = 0.15 # 最大允许斜率（接近水平）
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min_line_length = width * 0.2 # 最小线长度
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if observe:
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debug("步骤1: 原始图像已加载", "加载")
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search_region_img = img.copy()
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# 绘制搜索区域
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cv2.rectangle(search_region_img, (left_bound, top_bound), (right_bound, bottom_bound), (255, 0, 0), 2)
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cv2.line(search_region_img, (center_x, 0), (center_x, height), (0, 0, 255), 2) # 中线
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cv2.imshow("搜索区域", search_region_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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# 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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# 添加形态学操作以改善掩码
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kernel = np.ones((3, 3), np.uint8)
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mask = cv2.dilate(mask, kernel, iterations=1)
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if observe:
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debug("步骤2: 创建黄色掩码", "处理")
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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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debug("步骤3: 提取黄色部分", "处理")
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cv2.imshow("只保留黄色", yellow_only)
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cv2.waitKey(delay)
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# 裁剪掩码到搜索区域
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search_mask = mask[top_bound:bottom_bound, left_bound:right_bound]
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# 找到掩码在搜索区域中最底部的非零点位置
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bottom_points = []
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non_zero_cols = np.where(np.any(search_mask, axis=0))[0]
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# 寻找每列的最底部点
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for col in non_zero_cols:
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col_points = np.where(search_mask[:, col] > 0)[0]
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if len(col_points) > 0:
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bottom_row = np.max(col_points)
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bottom_points.append((left_bound + col, top_bound + bottom_row))
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# 寻找每列的最顶部点（上边缘点）
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top_points = []
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for col in non_zero_cols:
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col_points = np.where(search_mask[:, col] > 0)[0]
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if len(col_points) > 0:
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top_row = np.min(col_points)
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top_points.append((left_bound + col, top_bound + top_row))
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if observe:
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debug("检测底部和顶部边缘点", "处理")
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edge_points_img = img.copy()
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for point in bottom_points:
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cv2.circle(edge_points_img, point, 3, (0, 255, 0), -1)
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for point in top_points:
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cv2.circle(edge_points_img, point, 3, (255, 0, 255), -1)
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cv2.imshow("边缘点", edge_points_img)
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cv2.waitKey(delay)
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# 边缘检测
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edges = cv2.Canny(mask, 50, 150, apertureSize=3)
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if observe:
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debug("步骤4: 边缘检测", "处理")
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cv2.imshow("边缘检测", edges)
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cv2.waitKey(delay)
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# 使用霍夫变换检测直线，降低阈值以检测更多线段
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lines = cv2.HoughLinesP(edges, 1, np.pi/180, threshold=25,
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minLineLength=width*0.1, maxLineGap=30)
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if lines is None or len(lines) == 0:
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if observe:
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error("未检测到直线", "失败")
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return None, None
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if observe:
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debug(f"步骤5: 检测到 {len(lines)} 条直线", "处理")
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lines_img = img.copy()
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for line in lines:
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x1, y1, x2, y2 = line[0]
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cv2.line(lines_img, (x1, y1), (x2, y2), (0, 255, 0), 2)
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cv2.imshow("检测到的直线", lines_img)
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cv2.waitKey(delay)
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# 筛选水平线，但放宽斜率条件
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horizontal_lines = []
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for line in lines:
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x1, y1, x2, y2 = line[0]
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# 计算斜率 (避免除零错误)
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if abs(x2 - x1) < 5: # 几乎垂直的线
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continue
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slope = (y2 - y1) / (x2 - x1)
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# 筛选接近水平的线 (斜率接近0)，但容许更大的倾斜度
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if abs(slope) < max_slope:
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# 确保线在搜索区域内
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if ((left_bound <= x1 <= right_bound and top_bound <= y1 <= bottom_bound) or
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(left_bound <= x2 <= right_bound and top_bound <= y2 <= bottom_bound)):
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# 计算线的中点y坐标
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mid_y = (y1 + y2) / 2
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line_length = np.sqrt((x2-x1)**2 + (y2-y1)**2)
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# 过滤掉短线段和太靠近图像上部的线
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if line_length >= min_line_length and mid_y >= valid_y_range[0]:
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# 计算线段在图像中的位置得分（越靠近底部得分越高）
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position_score = min(1.0, (mid_y - valid_y_range[0]) / (valid_y_range[1] - valid_y_range[0]))
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# 计算长度得分（越长越好）
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length_score = min(1.0, line_length / (width * 0.5))
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# 计算斜率得分（越水平越好）
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slope_score = max(0.0, 1.0 - abs(slope) / max_slope)
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# 计算线段位于图像中央的程度
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mid_x = (x1 + x2) / 2
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center_score = max(0.0, 1.0 - abs(mid_x - center_x) / (width * 0.3))
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# 计算综合得分
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quality_score = position_score * 0.4 + length_score * 0.3 + slope_score * 0.2 + center_score * 0.1
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# 保存线段、其y坐标、斜率、长度和质量得分
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horizontal_lines.append((line[0], mid_y, slope, line_length, quality_score))
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if not horizontal_lines:
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if observe:
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error("未检测到合格的水平线", "失败")
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return None, None
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if observe:
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debug(f"步骤6: 找到 {len(horizontal_lines)} 条水平线", "处理")
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h_lines_img = img.copy()
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for line_info in horizontal_lines:
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line, _, slope, _, score = line_info
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x1, y1, x2, y2 = line
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# 根据得分调整线的颜色，得分越高越绿
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color = (int(255 * (1-score)), int(255 * score), 0)
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cv2.line(h_lines_img, (x1, y1), (x2, y2), color, 2)
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# 显示斜率和得分
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cv2.putText(h_lines_img, f"{slope:.2f}|{score:.2f}", ((x1+x2)//2, (y1+y2)//2),
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cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 1)
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cv2.imshow("水平线", h_lines_img)
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cv2.waitKey(delay)
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# 根据质量得分排序水平线
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horizontal_lines.sort(key=lambda x: x[4], reverse=True)
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# 取质量最高的线段作为最终选择
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selected_line = horizontal_lines[0][0]
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selected_slope = horizontal_lines[0][2]
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selected_score = horizontal_lines[0][4]
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# 提取线段端点
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x1, y1, x2, y2 = selected_line
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# 确保x1 < x2
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if x1 > x2:
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x1, x2 = x2, x1
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y1, y2 = y2, y1
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# 找到线上y值最大的点作为边缘点(最靠近相机的点)
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if y1 > y2:
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bottom_edge_point = (x1, y1)
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else:
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bottom_edge_point = (x2, y2)
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# 如果得分过低，可能是错误识别，尝试使用边缘点拟合
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if selected_score < 0.4 and len(bottom_points) >= 5:
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if observe:
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debug(f"线段质量得分过低: {selected_score:.2f}，尝试使用边缘点拟合", "处理")
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# 筛选下半部分的点
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valid_bottom_points = [p for p in bottom_points if p[1] >= valid_y_range[0]]
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if len(valid_bottom_points) >= 5:
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# 使用RANSAC拟合直线以去除异常值
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x_points = np.array([p[0] for p in valid_bottom_points]).reshape(-1, 1)
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y_points = np.array([p[1] for p in valid_bottom_points])
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ransac = linear_model.RANSACRegressor(residual_threshold=5.0)
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ransac.fit(x_points, y_points)
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# 获取拟合参数
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fitted_slope = ransac.estimator_.coef_[0]
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intercept = ransac.estimator_.intercept_
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# 检查斜率是否在合理范围内
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if abs(fitted_slope) < max_slope:
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# 计算拟合线的inliers比例
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inlier_mask = ransac.inlier_mask_
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inlier_ratio = sum(inlier_mask) / len(inlier_mask)
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# 如果有足够的内点，使用拟合的直线
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if inlier_ratio > 0.5:
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# 使用拟合的直线参数计算线段端点
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x1 = left_bound
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y1 = int(fitted_slope * x1 + intercept)
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x2 = right_bound
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y2 = int(fitted_slope * x2 + intercept)
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selected_slope = fitted_slope
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selected_line = [x1, y1, x2, y2]
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# 重新计算边缘点
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if y1 > y2:
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bottom_edge_point = (x1, y1)
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else:
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bottom_edge_point = (x2, y2)
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if observe:
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debug(f"使用拟合直线，斜率: {fitted_slope:.4f}, 内点比例: {inlier_ratio:.2f}", "处理")
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fitted_line_img = img.copy()
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cv2.line(fitted_line_img, (x1, y1), (x2, y2), (0, 255, 255), 2)
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for i, point in enumerate(valid_bottom_points):
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color = (0, 255, 0) if inlier_mask[i] else (0, 0, 255)
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cv2.circle(fitted_line_img, point, 3, color, -1)
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cv2.imshow("拟合线和内点", fitted_line_img)
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cv2.waitKey(delay)
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# 获取线上的更多点
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selected_points = []
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step = 5 # 每5个像素取一个点
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for x in range(max(left_bound, int(min(x1, x2))), min(right_bound, int(max(x1, x2)) + 1), step):
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y = int(selected_slope * (x - x1) + y1)
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if top_bound <= y <= bottom_bound:
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selected_points.append((x, y))
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# 对结果进行合理性检查
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valid_result = True
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reason = ""
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# 检查边缘点是否在有效范围内
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if not (valid_y_range[0] <= bottom_edge_point[1] <= valid_y_range[1]):
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valid_result = False
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reason += "边缘点y坐标超出有效范围; "
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# 检查线段长度
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line_length = np.sqrt((x2-x1)**2 + (y2-y1)**2)
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if line_length < min_line_length:
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valid_result = False
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reason += "线段长度不足; "
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# 检查是否有足够的点
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if len(selected_points) < 5:
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valid_result = False
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reason += "选定点数量不足; "
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# 计算这个点到中线的距离
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distance_to_center = bottom_edge_point[0] - center_x
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# 检查到中心的距离是否合理
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if abs(distance_to_center) > width * 0.8:
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valid_result = False
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reason += "到中心距离过大; "
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# 计算中线与检测到的横向线的交点
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# 横向线方程: y = slope * (x - x1) + y1
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# 中线方程: x = center_x
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# 解这个方程组得到交点坐标
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intersection_x = center_x
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intersection_y = selected_slope * (center_x - x1) + y1
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intersection_point = (int(intersection_x), int(intersection_y))
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# 检查交点的y坐标是否在有效范围内
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if not (valid_y_range[0] <= intersection_y <= valid_y_range[1]):
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valid_result = False
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reason += "交点y坐标超出有效范围; "
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# 计算交点到图像底部的距离（以像素为单位）
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distance_to_bottom = height - intersection_y
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# 如果结果无效，可能需要返回失败
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if not valid_result and observe:
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warning(f"检测结果不合理: {reason}", "警告")
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result_img = None
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if observe or save_log:
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slope_img = img.copy()
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# 画出检测到的线
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line_color = (0, 255, 0) if valid_result else (0, 0, 255)
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cv2.line(slope_img, (x1, y1), (x2, y2), line_color, 2)
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# 标记边缘点
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cv2.circle(slope_img, bottom_edge_point, 10, (0, 0, 255), -1)
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# 画出中线
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cv2.line(slope_img, (center_x, 0), (center_x, height), (0, 0, 255), 2)
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# 标记中线与横向线的交点
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cv2.circle(slope_img, intersection_point, 12, (255, 0, 255), -1)
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cv2.circle(slope_img, intersection_point, 5, (255, 255, 255), -1)
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# 画出交点到底部的距离线
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cv2.line(slope_img, intersection_point, (intersection_x, height), (255, 255, 0), 2)
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cv2.putText(slope_img, f"Slope: {selected_slope:.4f}", (10, 30),
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cv2.FONT_HERSHEY_SIMPLEX, 1, line_color, 2)
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cv2.putText(slope_img, f"Distance to center: {distance_to_center}px", (10, 70),
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cv2.FONT_HERSHEY_SIMPLEX, 1, line_color, 2)
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cv2.putText(slope_img, f"Distance to bottom: {distance_to_bottom:.1f}px", (10, 110),
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cv2.FONT_HERSHEY_SIMPLEX, 1, line_color, 2)
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cv2.putText(slope_img, f"中线交点: ({intersection_point[0]}, {intersection_point[1]})", (10, 150),
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cv2.FONT_HERSHEY_SIMPLEX, 1, line_color, 2)
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cv2.putText(slope_img, f"质量得分: {selected_score:.2f}", (10, 190),
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cv2.FONT_HERSHEY_SIMPLEX, 1, line_color, 2)
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if not valid_result:
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cv2.putText(slope_img, f"警告: {reason}", (10, 230),
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cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 255), 2)
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if observe:
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debug("显示边缘斜率和中线交点", "显示")
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cv2.imshow("边缘斜率和中线交点", slope_img)
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cv2.waitKey(delay)
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result_img = slope_img
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# 保存日志图像
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if save_log and result_img is not None:
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timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S_%f")
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log_dir = "logs/image"
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os.makedirs(log_dir, exist_ok=True)
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img_path = os.path.join(log_dir, f"horizontal_edge_{timestamp}.jpg")
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cv2.imwrite(img_path, result_img)
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info(f"保存横向边缘检测结果图像到: {img_path}", "日志")
|
||
|
||
# 保存文本日志信息
|
||
log_info = {
|
||
"timestamp": timestamp,
|
||
"edge_point": bottom_edge_point,
|
||
"distance_to_center": distance_to_center,
|
||
"slope": selected_slope,
|
||
"distance_to_bottom": distance_to_bottom,
|
||
"intersection_point": intersection_point,
|
||
"score": selected_score,
|
||
"valid": valid_result,
|
||
"reason": reason if not valid_result else ""
|
||
}
|
||
info(f"横向边缘检测结果: {log_info}", "日志")
|
||
|
||
# 如果结果无效，可能需要返回失败
|
||
if not valid_result:
|
||
return None, None
|
||
|
||
# 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
|
||
"score": selected_score, # 线段质量得分
|
||
# "points": selected_points # 添加选定的点组
|
||
}
|
||
|
||
return bottom_edge_point, edge_info
|
||
|
||
# 用法示例
|
||
if __name__ == "__main__":
|
||
pass
|
||
|
||
def detect_dual_track_lines(image, observe=False, delay=1000, save_log=True):
|
||
"""
|
||
检测左右两条平行的黄色轨道线
|
||
|
||
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)
|
||
save_log: 是否保存日志和图像
|
||
|
||
返回:
|
||
tuple: (中心线信息, 左轨迹线信息, 右轨迹线信息)
|
||
"""
|
||
# 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:
|
||
error("无法加载图像", "失败")
|
||
return None, None, None
|
||
|
||
# 获取图像尺寸
|
||
height, width = img.shape[:2]
|
||
|
||
# 计算图像中间区域的范围
|
||
center_x = width // 2
|
||
|
||
# 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 - 扩大范围以更好地捕捉不同光照条件下的黄色
|
||
lower_yellow = np.array([15, 80, 80]) # 更宽松的黄色下限
|
||
upper_yellow = np.array([35, 255, 255]) # 更宽松的黄色上限
|
||
|
||
# 创建黄色的掩码
|
||
mask = cv2.inRange(hsv, lower_yellow, upper_yellow)
|
||
|
||
# 形态学操作以改善掩码
|
||
kernel = np.ones((5, 5), np.uint8) # 增大kernel尺寸
|
||
mask = cv2.dilate(mask, kernel, iterations=1)
|
||
mask = cv2.erode(mask, np.ones((3, 3), np.uint8), iterations=1) # 添加腐蚀操作去除噪点
|
||
|
||
if observe:
|
||
debug("步骤1: 创建黄色掩码", "处理")
|
||
cv2.imshow("黄色掩码", mask)
|
||
cv2.waitKey(delay)
|
||
|
||
# 裁剪底部区域重点关注近处的黄线
|
||
bottom_roi_height = int(height * 0.4) # 关注图像底部40%区域
|
||
bottom_roi = mask[height-bottom_roi_height:, :]
|
||
|
||
if observe:
|
||
debug("步骤1.5: 底部区域掩码", "处理")
|
||
cv2.imshow("底部区域掩码", bottom_roi)
|
||
cv2.waitKey(delay)
|
||
|
||
# 边缘检测
|
||
edges = cv2.Canny(mask, 50, 150, apertureSize=3)
|
||
|
||
if observe:
|
||
debug("步骤2: 边缘检测", "处理")
|
||
cv2.imshow("边缘检测", edges)
|
||
cv2.waitKey(delay)
|
||
|
||
# 霍夫变换检测直线 - 降低minLineLength以检测到较短的线段
|
||
lines = cv2.HoughLinesP(edges, 1, np.pi/180, threshold=25,
|
||
minLineLength=width*0.05, maxLineGap=40) # 更宽松的参数
|
||
|
||
if lines is None or len(lines) == 0:
|
||
error("未检测到直线", "失败")
|
||
return None, None, None
|
||
|
||
if observe:
|
||
debug(f"步骤3: 检测到 {len(lines)} 条直线", "处理")
|
||
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)
|
||
|
||
# 筛选近似垂直的线
|
||
vertical_lines = []
|
||
for line in lines:
|
||
x1, y1, x2, y2 = line[0]
|
||
|
||
# 优先选择图像底部的线
|
||
if y1 < height * 0.5 and y2 < height * 0.5:
|
||
continue # 忽略上半部分的线
|
||
|
||
# 计算斜率 (避免除零错误)
|
||
if abs(x2 - x1) < 5: # 几乎垂直的线
|
||
slope = 100 # 设置一个较大的值表示接近垂直
|
||
else:
|
||
slope = (y2 - y1) / (x2 - x1)
|
||
|
||
# 筛选接近垂直的线 (斜率较大)，但允许更多倾斜度
|
||
if abs(slope) > 0.75: # 降低垂直线的斜率阈值，允许更多倾斜的线
|
||
line_length = np.sqrt((x2-x1)**2 + (y2-y1)**2)
|
||
# 计算线的中点x坐标
|
||
mid_x = (x1 + x2) / 2
|
||
# 计算线的中点y坐标
|
||
mid_y = (y1 + y2) / 2
|
||
# 保存线段、其坐标、斜率和长度
|
||
vertical_lines.append((line[0], mid_x, mid_y, slope, line_length))
|
||
|
||
if len(vertical_lines) < 2:
|
||
error("未检测到足够的垂直线", "失败")
|
||
return None, None, None
|
||
|
||
if observe:
|
||
debug(f"步骤4: 找到 {len(vertical_lines)} 条垂直线", "处理")
|
||
v_lines_img = img.copy()
|
||
for line_info in vertical_lines:
|
||
line, _, _, slope, _ = line_info
|
||
x1, y1, x2, y2 = line
|
||
cv2.line(v_lines_img, (x1, y1), (x2, y2), (0, 255, 255), 2)
|
||
# 显示斜率
|
||
cv2.putText(v_lines_img, f"{slope:.2f}", ((x1+x2)//2, (y1+y2)//2),
|
||
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 1)
|
||
cv2.imshow("垂直线", v_lines_img)
|
||
cv2.waitKey(delay)
|
||
|
||
# 优先选择更接近图像底部的线 - 根据y坐标均值排序
|
||
vertical_lines.sort(key=lambda x: x[2], reverse=True) # 按mid_y从大到小排序
|
||
|
||
# 按x坐标将线分为左右两组
|
||
left_lines = [line for line in vertical_lines if line[1] < center_x]
|
||
right_lines = [line for line in vertical_lines if line[1] > center_x]
|
||
|
||
# 如果任一侧没有检测到线，则放宽左右两侧线的分组条件
|
||
if not left_lines or not right_lines:
|
||
# 按x坐标排序所有垂直线
|
||
vertical_lines.sort(key=lambda x: x[1])
|
||
|
||
# 如果有至少两条线，将最左侧的线作为左轨迹线，最右侧的线作为右轨迹线
|
||
if len(vertical_lines) >= 2:
|
||
left_lines = [vertical_lines[0]]
|
||
right_lines = [vertical_lines[-1]]
|
||
else:
|
||
error("左侧或右侧未检测到轨迹线", "失败")
|
||
return None, None, None
|
||
|
||
# 从左右两组中各选择一条最佳的线
|
||
# 优先选择同时满足：1. 更靠近底部 2. 足够长 3. 更接近中心的线
|
||
def score_line(line_info, is_left):
|
||
_, mid_x, mid_y, _, length = line_info
|
||
# y越大（越靠近底部）分数越高
|
||
y_score = mid_y / height
|
||
# 线越长分数越高
|
||
length_score = min(1.0, length / (height * 0.3))
|
||
# 与预期位置的接近程度
|
||
expected_x = center_x * 0.3 if is_left else center_x * 1.7
|
||
x_score = 1.0 - min(1.0, abs(mid_x - expected_x) / (center_x * 0.5))
|
||
# 综合评分
|
||
return y_score * 0.5 + length_score * 0.3 + x_score * 0.2
|
||
|
||
# 对左右线组进行评分并排序
|
||
left_lines = sorted(left_lines, key=lambda line: score_line(line, True), reverse=True)
|
||
right_lines = sorted(right_lines, key=lambda line: score_line(line, False), reverse=True)
|
||
|
||
left_line = left_lines[0]
|
||
right_line = right_lines[0]
|
||
|
||
# 获取两条线的坐标
|
||
left_x1, left_y1, left_x2, left_y2 = left_line[0]
|
||
right_x1, right_y1, right_x2, right_y2 = right_line[0]
|
||
|
||
# 确保线段的顺序是从上到下
|
||
if left_y1 > left_y2:
|
||
left_x1, left_x2 = left_x2, left_x1
|
||
left_y1, left_y2 = left_y2, left_y1
|
||
|
||
if right_y1 > right_y2:
|
||
right_x1, right_x2 = right_x2, right_x1
|
||
right_y1, right_y2 = right_y2, right_y1
|
||
|
||
# 计算中心线
|
||
center_line_x1 = (left_x1 + right_x1) // 2
|
||
center_line_y1 = (left_y1 + right_y1) // 2
|
||
center_line_x2 = (left_x2 + right_x2) // 2
|
||
center_line_y2 = (left_y2 + right_y2) // 2
|
||
|
||
# 计算中心线的斜率
|
||
if abs(center_line_x2 - center_line_x1) < 5:
|
||
center_slope = 100 # 几乎垂直
|
||
else:
|
||
center_slope = (center_line_y2 - center_line_y1) / (center_line_x2 - center_line_x1)
|
||
|
||
# 计算中心线延伸到图像底部的点
|
||
if abs(center_slope) < 0.01: # 几乎垂直
|
||
bottom_x = center_line_x1
|
||
else:
|
||
bottom_x = int(center_line_x1 + (height - center_line_y1) / center_slope)
|
||
center_point = (bottom_x, height)
|
||
|
||
# 计算中心线与图像中心线的偏差
|
||
deviation = bottom_x - center_x
|
||
|
||
result_img = None
|
||
if observe or save_log:
|
||
result_img = img.copy()
|
||
# 绘制左右轨迹线
|
||
cv2.line(result_img, (left_x1, left_y1), (left_x2, left_y2), (255, 0, 0), 2)
|
||
cv2.line(result_img, (right_x1, right_y1), (right_x2, right_y2), (0, 0, 255), 2)
|
||
# 绘制中心线
|
||
cv2.line(result_img, (center_line_x1, center_line_y1), (center_line_x2, center_line_y2), (0, 255, 0), 2)
|
||
cv2.line(result_img, (center_line_x2, center_line_y2), center_point, (0, 255, 0), 2)
|
||
# 绘制图像中心线
|
||
cv2.line(result_img, (center_x, 0), (center_x, height), (0, 0, 255), 1)
|
||
# 标记中心点
|
||
cv2.circle(result_img, center_point, 10, (255, 0, 255), -1)
|
||
|
||
# 显示偏差信息
|
||
cv2.putText(result_img, f"Deviation: {deviation}px", (10, 30),
|
||
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
|
||
|
||
if observe:
|
||
cv2.imshow("轨迹线检测结果", result_img)
|
||
cv2.waitKey(delay)
|
||
|
||
# 保存日志图像
|
||
if save_log and result_img is not None:
|
||
timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S_%f")
|
||
log_dir = "logs/image"
|
||
os.makedirs(log_dir, exist_ok=True)
|
||
img_path = os.path.join(log_dir, f"dual_track_{timestamp}.jpg")
|
||
cv2.imwrite(img_path, result_img)
|
||
info(f"保存双轨迹线检测结果图像到: {img_path}", "日志")
|
||
|
||
# 保存文本日志信息
|
||
log_info = {
|
||
"timestamp": timestamp,
|
||
"center_point": center_point,
|
||
"deviation": deviation,
|
||
"left_track_mid_x": left_line[1],
|
||
"right_track_mid_x": right_line[1],
|
||
"track_width": right_line[1] - left_line[1],
|
||
"center_slope": center_slope
|
||
}
|
||
info(f"双轨迹线检测结果: {log_info}", "日志")
|
||
|
||
# 创建左右轨迹线和中心线信息
|
||
left_track_info = {
|
||
"line": left_line[0],
|
||
"slope": left_line[3], # 注意：索引更改为3，因为保存结构更改了
|
||
"x_mid": left_line[1]
|
||
}
|
||
|
||
right_track_info = {
|
||
"line": right_line[0],
|
||
"slope": right_line[3], # 注意：索引更改为3
|
||
"x_mid": right_line[1]
|
||
}
|
||
|
||
center_info = {
|
||
"point": center_point,
|
||
"deviation": deviation,
|
||
"slope": center_slope,
|
||
"is_vertical": abs(center_slope) > 5.0, # 判断是否接近垂直
|
||
"track_width": right_line[1] - left_line[1] # 两轨迹线之间的距离
|
||
}
|
||
|
||
return center_info, left_track_info, right_track_info
|
||
|
||
def detect_left_side_track(image, observe=False, delay=1000, save_log=True):
|
||
"""
|
||
检测视野左侧黄色轨道线，用于机器狗左侧靠线移动
|
||
|
||
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)
|
||
save_log: 是否保存日志和图像
|
||
|
||
返回:
|
||
tuple: (线信息字典, 最佳跟踪点)
|
||
"""
|
||
# 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:
|
||
error("无法加载图像", "失败")
|
||
return None, None
|
||
|
||
# 获取图像尺寸
|
||
height, width = img.shape[:2]
|
||
|
||
# 计算图像中间和左侧区域的范围
|
||
center_x = width // 2
|
||
# 主要关注视野的左半部分
|
||
left_region_width = center_x
|
||
left_region_height = height
|
||
left_bound = 0
|
||
right_bound = center_x
|
||
bottom_bound = height
|
||
top_bound = 0
|
||
|
||
if observe:
|
||
debug("步骤1: 原始图像已加载", "加载")
|
||
region_img = img.copy()
|
||
# 绘制左侧搜索区域
|
||
cv2.rectangle(region_img, (left_bound, top_bound), (right_bound, bottom_bound), (255, 0, 0), 2)
|
||
cv2.line(region_img, (center_x, 0), (center_x, height), (0, 0, 255), 2) # 中线
|
||
cv2.imshow("左侧搜索区域", 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 - 扩大范围以更好地捕捉不同光照条件下的黄色
|
||
lower_yellow = np.array([15, 80, 80]) # 更宽松的黄色下限
|
||
upper_yellow = np.array([35, 255, 255]) # 更宽松的黄色上限
|
||
|
||
# 创建黄色的掩码
|
||
mask = cv2.inRange(hsv, lower_yellow, upper_yellow)
|
||
|
||
# 形态学操作以改善掩码
|
||
kernel = np.ones((5, 5), np.uint8) # 增大kernel尺寸
|
||
mask = cv2.dilate(mask, kernel, iterations=1)
|
||
mask = cv2.erode(mask, np.ones((3, 3), np.uint8), iterations=1) # 添加腐蚀操作去除噪点
|
||
|
||
if observe:
|
||
debug("步骤2: 创建黄色掩码", "处理")
|
||
cv2.imshow("黄色掩码", mask)
|
||
cv2.waitKey(delay)
|
||
|
||
# 裁剪左侧区域
|
||
left_region_mask = mask[:, left_bound:right_bound]
|
||
|
||
if observe:
|
||
debug("步骤3: 左侧区域掩码", "处理")
|
||
cv2.imshow("左侧区域掩码", left_region_mask)
|
||
cv2.waitKey(delay)
|
||
|
||
# 边缘检测
|
||
edges = cv2.Canny(mask, 50, 150, apertureSize=3)
|
||
|
||
if observe:
|
||
debug("步骤4: 边缘检测", "处理")
|
||
cv2.imshow("边缘检测", edges)
|
||
cv2.waitKey(delay)
|
||
|
||
# 霍夫变换检测直线
|
||
lines = cv2.HoughLinesP(edges, 1, np.pi/180, threshold=25,
|
||
minLineLength=height*0.15, maxLineGap=50) # 调整参数以检测更长的线段
|
||
|
||
if lines is None or len(lines) == 0:
|
||
error("未检测到直线", "失败")
|
||
return None, None
|
||
|
||
if observe:
|
||
debug(f"步骤5: 检测到 {len(lines)} 条直线", "处理")
|
||
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)
|
||
|
||
# 筛选左侧区域内的近似垂直线
|
||
left_vertical_lines = []
|
||
for line in lines:
|
||
x1, y1, x2, y2 = line[0]
|
||
|
||
# 确保线在左侧区域内
|
||
if not (max(x1, x2) <= right_bound):
|
||
continue
|
||
|
||
# 计算斜率 (避免除零错误)
|
||
if abs(x2 - x1) < 5: # 几乎垂直的线
|
||
slope = 100 # 设置一个较大的值表示接近垂直
|
||
else:
|
||
slope = (y2 - y1) / (x2 - x1)
|
||
|
||
# 筛选接近垂直的线 (斜率较大)
|
||
if abs(slope) > 0.7: # 设置较宽松的垂直线斜率阈值
|
||
line_length = np.sqrt((x2-x1)**2 + (y2-y1)**2)
|
||
# 计算线的中点坐标
|
||
mid_x = (x1 + x2) / 2
|
||
mid_y = (y1 + y2) / 2
|
||
|
||
# 保存线段、其坐标、斜率和长度
|
||
left_vertical_lines.append((line[0], mid_x, mid_y, slope, line_length))
|
||
|
||
if len(left_vertical_lines) == 0:
|
||
error("左侧区域未检测到垂直线", "失败")
|
||
return None, None
|
||
|
||
if observe:
|
||
debug(f"步骤6: 左侧区域找到 {len(left_vertical_lines)} 条垂直线", "处理")
|
||
left_lines_img = img.copy()
|
||
for line_info in left_vertical_lines:
|
||
line, _, _, slope, _ = line_info
|
||
x1, y1, x2, y2 = line
|
||
cv2.line(left_lines_img, (x1, y1), (x2, y2), (0, 255, 255), 2)
|
||
# 显示斜率
|
||
cv2.putText(left_lines_img, f"{slope:.2f}", ((x1+x2)//2, (y1+y2)//2),
|
||
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 1)
|
||
cv2.imshow("左侧垂直线", left_lines_img)
|
||
cv2.waitKey(delay)
|
||
|
||
# 按线段长度和位置进行评分，优先选择更长且更靠近图像左边的线
|
||
def score_left_line(line_info):
|
||
_, mid_x, _, _, length = line_info
|
||
# 线段越长分数越高
|
||
length_score = min(1.0, length / (height * 0.3))
|
||
# 越靠近左边分数越高
|
||
position_score = 1.0 - (mid_x / center_x)
|
||
# 综合评分
|
||
return length_score * 0.7 + position_score * 0.3
|
||
|
||
# 对线段进行评分并排序
|
||
left_vertical_lines = sorted(left_vertical_lines, key=score_left_line, reverse=True)
|
||
|
||
# 选择最佳的左侧线段
|
||
best_left_line = left_vertical_lines[0]
|
||
line, mid_x, mid_y, slope, length = best_left_line
|
||
x1, y1, x2, y2 = line
|
||
|
||
# 确保线段的顺序是从上到下
|
||
if y1 > y2:
|
||
x1, x2 = x2, x1
|
||
y1, y2 = y2, y1
|
||
|
||
# 计算最佳跟踪点 - 选择线段底部较靠近机器人的点
|
||
tracking_point = (x2, y2) if y2 > y1 else (x1, y1)
|
||
|
||
# 计算线与地面的交点
|
||
# 使用线段的方程: (y - y1) = slope * (x - x1)
|
||
# 地面对应图像底部: y = height
|
||
# 解这个方程得到交点的x坐标
|
||
if abs(slope) < 0.01: # 几乎垂直
|
||
ground_intersection_x = x1
|
||
else:
|
||
ground_intersection_x = x1 + (height - y1) / slope
|
||
ground_intersection = (int(ground_intersection_x), height)
|
||
|
||
# 计算线与图像左边界的距离（以像素为单位）
|
||
distance_to_left = mid_x
|
||
|
||
result_img = None
|
||
if observe or save_log:
|
||
result_img = img.copy()
|
||
# 绘制检测到的最佳左侧线
|
||
cv2.line(result_img, (x1, y1), (x2, y2), (255, 0, 0), 2)
|
||
# 绘制图像中线
|
||
cv2.line(result_img, (center_x, 0), (center_x, height), (0, 0, 255), 1)
|
||
# 标记最佳跟踪点和地面交点
|
||
cv2.circle(result_img, tracking_point, 10, (0, 255, 0), -1)
|
||
cv2.circle(result_img, ground_intersection, 10, (0, 0, 255), -1)
|
||
|
||
# 显示信息
|
||
cv2.putText(result_img, f"斜率: {slope:.2f}", (10, 30),
|
||
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
|
||
cv2.putText(result_img, f"距左边界: {distance_to_left:.1f}px", (10, 70),
|
||
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
|
||
cv2.putText(result_img, f"地面交点: ({ground_intersection[0]}, {ground_intersection[1]})", (10, 110),
|
||
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
|
||
|
||
if observe:
|
||
debug("步骤7: 左侧最佳跟踪线和点", "显示")
|
||
cv2.imshow("左侧最佳跟踪线和点", result_img)
|
||
cv2.waitKey(delay)
|
||
|
||
# 保存日志图像
|
||
if save_log and result_img is not None:
|
||
timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S_%f")
|
||
log_dir = "logs/image"
|
||
os.makedirs(log_dir, exist_ok=True)
|
||
img_path = os.path.join(log_dir, f"left_track_{timestamp}.jpg")
|
||
cv2.imwrite(img_path, result_img)
|
||
info(f"保存左侧轨迹线检测结果图像到: {img_path}", "日志")
|
||
|
||
# 保存文本日志信息
|
||
log_info = {
|
||
"timestamp": timestamp,
|
||
"tracking_point": tracking_point,
|
||
"ground_intersection": ground_intersection,
|
||
"distance_to_left": distance_to_left,
|
||
"slope": slope,
|
||
"line_mid_x": mid_x
|
||
}
|
||
info(f"左侧轨迹线检测结果: {log_info}", "日志")
|
||
|
||
# 创建线段信息字典
|
||
track_info = {
|
||
"line": line,
|
||
"slope": slope,
|
||
"tracking_point": tracking_point,
|
||
"ground_intersection": ground_intersection,
|
||
"distance_to_left": distance_to_left,
|
||
"mid_x": mid_x,
|
||
"mid_y": mid_y,
|
||
"is_vertical": abs(slope) > 5.0 # 判断是否接近垂直
|
||
}
|
||
|
||
return track_info, tracking_point
|