diff --git a/logs/robot_2025-05-28.log b/logs/robot_2025-05-28.log new file mode 100644 index 0000000..ecb48cc --- /dev/null +++ b/logs/robot_2025-05-28.log @@ -0,0 +1,260 @@ +2025-05-28 18:10:21 | DEBUG | utils.log_helper - 🐞 步骤1: 原始图像已加载 +2025-05-28 18:10:23 | DEBUG | utils.log_helper - 🐞 步骤2: 创建黄色掩码 +2025-05-28 18:10:23 | DEBUG | utils.log_helper - 🐞 步骤3: 提取黄色部分 +2025-05-28 18:10:24 | DEBUG | utils.log_helper - 🐞 正在处理底部边缘点 +2025-05-28 18:10:25 | DEBUG | utils.log_helper - 🐞 显示拟合线段 +2025-05-28 18:10:26 | DEBUG | utils.log_helper - 👁️ 步骤5: 找到边缘点 (320, 764) +2025-05-28 18:10:27 | DEBUG | utils.log_helper - 🐞 显示边缘斜率和中线交点 +2025-05-28 18:10:27 | INFO | utils.log_helper - ℹ️ 保存原始图像到: logs/image/origin_horizontal_edge_20250528_181027_948631.jpg +2025-05-28 18:10:27 | INFO | utils.log_helper - ℹ️ 保存横向边缘检测结果图像到: logs/image/horizontal_edge_20250528_181027_948631.jpg +2025-05-28 18:10:27 | INFO | utils.log_helper - ℹ️ 横向边缘检测结果: {'timestamp': '20250528_181027_948631', 'edge_point': (320, 764), 'distance_to_center': -640, 'slope': -0.024199460833261684, 'distance_to_bottom': 331.48765493328744, 'intersection_point': (960, 748)} +2025-05-28 18:10:48 | DEBUG | utils.log_helper - 🐞 步骤1: 原始图像已加载 +2025-05-28 18:10:49 | DEBUG | utils.log_helper - 🐞 步骤2: 创建黄色掩码 +2025-05-28 18:10:49 | DEBUG | utils.log_helper - 🐞 步骤3: 提取黄色部分 +2025-05-28 18:10:50 | DEBUG | utils.log_helper - 🐞 正在处理底部边缘点 +2025-05-28 18:10:51 | DEBUG | utils.log_helper - 🐞 显示拟合线段 +2025-05-28 18:10:52 | DEBUG | utils.log_helper - 👁️ 步骤5: 找到边缘点 (320, 765) +2025-05-28 18:10:53 | DEBUG | utils.log_helper - 🐞 显示边缘斜率和中线交点 +2025-05-28 18:10:54 | INFO | utils.log_helper - ℹ️ 保存原始图像到: logs/image/origin_horizontal_edge_20250528_181054_002691.jpg +2025-05-28 18:10:54 | INFO | utils.log_helper - ℹ️ 保存横向边缘检测结果图像到: logs/image/horizontal_edge_20250528_181054_002691.jpg +2025-05-28 18:10:54 | INFO | utils.log_helper - ℹ️ 横向边缘检测结果: {'timestamp': '20250528_181054_002691', 'edge_point': (320, 765), 'distance_to_center': -640, 'slope': -0.025730022186332406, 'distance_to_bottom': 331.4672141992528, 'intersection_point': (960, 748)} +2025-05-28 18:11:06 | DEBUG | utils.log_helper - 🐞 步骤1: 原始图像已加载 +2025-05-28 18:11:07 | DEBUG | utils.log_helper - 🐞 步骤2: 创建黄色掩码 +2025-05-28 18:11:08 | DEBUG | utils.log_helper - 🐞 步骤3: 提取黄色部分 +2025-05-28 18:11:08 | DEBUG | utils.log_helper - 🐞 正在处理底部边缘点 +2025-05-28 18:11:09 | DEBUG | utils.log_helper - 🐞 显示拟合线段 +2025-05-28 18:11:10 | DEBUG | utils.log_helper - 👁️ 步骤5: 找到边缘点 (1600, 1079) +2025-05-28 18:11:11 | DEBUG | utils.log_helper - 🐞 显示边缘斜率和中线交点 +2025-05-28 18:11:12 | INFO | utils.log_helper - ℹ️ 保存原始图像到: logs/image/origin_horizontal_edge_20250528_181112_191153.jpg +2025-05-28 18:11:12 | INFO | utils.log_helper - ℹ️ 保存横向边缘检测结果图像到: logs/image/horizontal_edge_20250528_181112_191153.jpg +2025-05-28 18:11:12 | INFO | utils.log_helper - ℹ️ 横向边缘检测结果: {'timestamp': '20250528_181112_191153', 'edge_point': (1600, 1079), 'distance_to_center': 640, 'slope': 0.00019855956489055974, 'distance_to_bottom': 1.8729218784701516, 'intersection_point': (960, 1078)} +2025-05-28 18:13:40 | INFO | utils.log_helper - ℹ️ 保存原始图像到: logs/image/origin_horizontal_edge_20250528_181340_008182.jpg +2025-05-28 18:13:52 | INFO | utils.log_helper - ℹ️ 保存原始图像到: logs/image/origin_horizontal_edge_20250528_181352_540296.jpg +2025-05-28 18:13:52 | DEBUG | utils.log_helper - 🐞 步骤1: 原始图像已加载 +2025-05-28 18:13:53 | DEBUG | utils.log_helper - 🐞 步骤2: 创建黄色掩码 +2025-05-28 18:13:54 | DEBUG | utils.log_helper - 🐞 步骤3: 提取黄色部分 +2025-05-28 18:13:55 | DEBUG | utils.log_helper - 🐞 检测底部和顶部边缘点 +2025-05-28 18:13:55 | DEBUG | utils.log_helper - 🐞 步骤4: 边缘检测 +2025-05-28 18:13:56 | DEBUG | utils.log_helper - 🐞 步骤5: 检测到 14 条直线 +2025-05-28 18:13:57 | DEBUG | utils.log_helper - 🐞 步骤5.1: 合并后剩余 7 条线 +2025-05-28 18:13:58 | ERROR | utils.log_helper - ❌ 未检测到合格的水平线 +2025-05-28 18:19:56 | INFO | utils.log_helper - ℹ️ 保存原始图像到: logs/image/origin_horizontal_edge_20250528_181956_608652.jpg +2025-05-28 18:19:56 | DEBUG | utils.log_helper - 🐞 步骤1: 原始图像已加载 +2025-05-28 18:19:57 | DEBUG | utils.log_helper - 🐞 步骤2: 创建黄色掩码 +2025-05-28 18:19:58 | DEBUG | utils.log_helper - 🐞 步骤3: 提取黄色部分 +2025-05-28 18:19:59 | DEBUG | utils.log_helper - 🐞 步骤4: 检测边缘点 +2025-05-28 18:20:00 | DEBUG | utils.log_helper - 🐞 步骤5: 边缘检测 +2025-05-28 18:20:00 | DEBUG | utils.log_helper - 🐞 步骤6: 检测到 16 条直线 +2025-05-28 18:20:01 | DEBUG | utils.log_helper - 🐞 步骤7: 合并后剩余 7 条线 +2025-05-28 18:20:02 | DEBUG | utils.log_helper - 🐞 步骤8: 找到 1 条水平线 +2025-05-28 18:20:03 | DEBUG | utils.log_helper - 🐞 线段质量得分过低: 0.32，尝试使用边缘点拟合 +2025-05-28 18:20:03 | DEBUG | utils.log_helper - 🐞 使用拟合直线，斜率: 0.0131, 内点比例: 0.63 +2025-05-28 18:20:04 | DEBUG | utils.log_helper - 🐞 步骤9: 显示最终结果 +2025-05-28 18:20:04 | INFO | utils.log_helper - ℹ️ 保存横向边缘检测结果图像到: logs/image/horizontal_edge_20250528_182004_958627.jpg +2025-05-28 18:20:04 | INFO | utils.log_helper - ℹ️ 横向边缘检测结果: {'timestamp': '20250528_182004_958627', 'edge_point': (1440, 747), 'distance_to_center': 480, 'slope': 0.013122411913480955, 'distance_to_bottom': 339.7012422815292, 'intersection_point': (960, 740), 'score': 0.3160988129039916} +2025-05-28 18:20:27 | INFO | utils.log_helper - ℹ️ 保存原始图像到: logs/image/origin_horizontal_edge_20250528_182027_171258.jpg +2025-05-28 18:20:27 | DEBUG | utils.log_helper - 🐞 步骤1: 原始图像已加载 +2025-05-28 18:20:28 | DEBUG | utils.log_helper - 🐞 步骤2: 创建黄色掩码 +2025-05-28 18:20:28 | DEBUG | utils.log_helper - 🐞 步骤3: 提取黄色部分 +2025-05-28 18:20:29 | DEBUG | utils.log_helper - 🐞 步骤4: 检测边缘点 +2025-05-28 18:20:30 | DEBUG | utils.log_helper - 🐞 步骤5: 边缘检测 +2025-05-28 18:20:31 | DEBUG | utils.log_helper - 🐞 步骤6: 检测到 15 条直线 +2025-05-28 18:20:32 | DEBUG | utils.log_helper - 🐞 步骤7: 合并后剩余 8 条线 +2025-05-28 18:20:33 | DEBUG | utils.log_helper - 🐞 步骤8: 找到 1 条水平线 +2025-05-28 18:20:33 | DEBUG | utils.log_helper - 🐞 步骤9: 显示最终结果 +2025-05-28 18:20:34 | INFO | utils.log_helper - ℹ️ 保存横向边缘检测结果图像到: logs/image/horizontal_edge_20250528_182034_689922.jpg +2025-05-28 18:20:34 | INFO | utils.log_helper - ℹ️ 横向边缘检测结果: {'timestamp': '20250528_182034_689922', 'edge_point': (586, 727), 'distance_to_center': -374, 'slope': -0.020338983050847456, 'distance_to_bottom': 360.60677966101696, 'intersection_point': (960, 719), 'score': 0.7896090284152656} +2025-05-28 18:20:50 | INFO | utils.log_helper - ℹ️ 保存原始图像到: logs/image/origin_horizontal_edge_20250528_182050_376838.jpg +2025-05-28 18:20:50 | DEBUG | utils.log_helper - 🐞 步骤1: 原始图像已加载 +2025-05-28 18:20:51 | DEBUG | utils.log_helper - 🐞 步骤2: 创建黄色掩码 +2025-05-28 18:20:52 | DEBUG | utils.log_helper - 🐞 步骤3: 提取黄色部分 +2025-05-28 18:20:52 | DEBUG | utils.log_helper - 🐞 步骤4: 检测边缘点 +2025-05-28 18:20:53 | DEBUG | utils.log_helper - 🐞 步骤5: 边缘检测 +2025-05-28 18:20:54 | DEBUG | utils.log_helper - 🐞 步骤6: 检测到 15 条直线 +2025-05-28 18:20:55 | DEBUG | utils.log_helper - 🐞 步骤7: 合并后剩余 8 条线 +2025-05-28 18:20:56 | DEBUG | utils.log_helper - 🐞 步骤8: 找到 1 条水平线 +2025-05-28 18:20:57 | DEBUG | utils.log_helper - 🐞 步骤9: 显示最终结果 +2025-05-28 18:20:57 | INFO | utils.log_helper - ℹ️ 保存横向边缘检测结果图像到: logs/image/horizontal_edge_20250528_182057_885573.jpg +2025-05-28 18:20:57 | INFO | utils.log_helper - ℹ️ 横向边缘检测结果: {'timestamp': '20250528_182057_885573', 'edge_point': (586, 727), 'distance_to_center': -374, 'slope': -0.020338983050847456, 'distance_to_bottom': 360.60677966101696, 'intersection_point': (960, 719), 'score': 0.7896090284152656} +2025-05-28 18:21:01 | INFO | utils.log_helper - ℹ️ 保存原始图像到: logs/image/origin_horizontal_edge_20250528_182101_045147.jpg +2025-05-28 18:21:01 | DEBUG | utils.log_helper - 🐞 步骤1: 原始图像已加载 +2025-05-28 18:21:02 | DEBUG | utils.log_helper - 🐞 步骤2: 创建黄色掩码 +2025-05-28 18:21:02 | DEBUG | utils.log_helper - 🐞 步骤3: 提取黄色部分 +2025-05-28 18:21:03 | DEBUG | utils.log_helper - 🐞 步骤4: 检测边缘点 +2025-05-28 18:21:04 | DEBUG | utils.log_helper - 🐞 步骤5: 边缘检测 +2025-05-28 18:21:05 | DEBUG | utils.log_helper - 🐞 步骤6: 检测到 15 条直线 +2025-05-28 18:21:06 | DEBUG | utils.log_helper - 🐞 步骤7: 合并后剩余 8 条线 +2025-05-28 18:21:06 | DEBUG | utils.log_helper - 🐞 步骤8: 找到 1 条水平线 +2025-05-28 18:21:07 | DEBUG | utils.log_helper - 🐞 步骤9: 显示最终结果 +2025-05-28 18:21:08 | INFO | utils.log_helper - ℹ️ 保存横向边缘检测结果图像到: logs/image/horizontal_edge_20250528_182108_558237.jpg +2025-05-28 18:21:08 | INFO | utils.log_helper - ℹ️ 横向边缘检测结果: {'timestamp': '20250528_182108_558237', 'edge_point': (586, 727), 'distance_to_center': -374, 'slope': -0.020338983050847456, 'distance_to_bottom': 360.60677966101696, 'intersection_point': (960, 719), 'score': 0.7896090284152656} +2025-05-28 18:21:26 | INFO | utils.log_helper - ℹ️ 保存原始图像到: logs/image/origin_horizontal_edge_20250528_182126_619927.jpg +2025-05-28 18:21:26 | DEBUG | utils.log_helper - 🐞 步骤1: 原始图像已加载 +2025-05-28 18:21:27 | DEBUG | utils.log_helper - 🐞 步骤2: 创建黄色掩码 +2025-05-28 18:21:28 | DEBUG | utils.log_helper - 🐞 步骤3: 提取黄色部分 +2025-05-28 18:21:29 | DEBUG | utils.log_helper - 🐞 步骤4: 检测边缘点 +2025-05-28 18:21:30 | DEBUG | utils.log_helper - 🐞 步骤5: 边缘检测 +2025-05-28 18:21:30 | DEBUG | utils.log_helper - 🐞 步骤6: 检测到 16 条直线 +2025-05-28 18:21:31 | DEBUG | utils.log_helper - 🐞 步骤7: 合并后剩余 7 条线 +2025-05-28 18:21:32 | DEBUG | utils.log_helper - 🐞 步骤8: 找到 1 条水平线 +2025-05-28 18:21:33 | DEBUG | utils.log_helper - 🐞 线段质量得分过低: 0.32，尝试使用边缘点拟合 +2025-05-28 18:21:33 | DEBUG | utils.log_helper - 🐞 使用拟合直线，斜率: 0.0162, 内点比例: 0.62 +2025-05-28 18:21:34 | DEBUG | utils.log_helper - 🐞 步骤9: 显示最终结果 +2025-05-28 18:21:34 | INFO | utils.log_helper - ℹ️ 保存横向边缘检测结果图像到: logs/image/horizontal_edge_20250528_182134_957893.jpg +2025-05-28 18:21:34 | INFO | utils.log_helper - ℹ️ 横向边缘检测结果: {'timestamp': '20250528_182134_957893', 'edge_point': (1440, 749), 'distance_to_center': 480, 'slope': 0.016224570257057244, 'distance_to_bottom': 339.2122062766125, 'intersection_point': (960, 740), 'score': 0.3160988129039916} +2025-05-28 18:33:44 | INFO | utils.log_helper - ℹ️ 保存原始图像到: logs/image/origin_horizontal_edge_20250528_183344_087438.jpg +2025-05-28 18:33:44 | DEBUG | utils.log_helper - 🐞 步骤1: 原始图像已加载 +2025-05-28 18:33:45 | DEBUG | utils.log_helper - 🐞 步骤2: 创建黄色掩码 +2025-05-28 18:33:45 | DEBUG | utils.log_helper - 🐞 步骤3: 提取黄色部分 +2025-05-28 18:33:46 | DEBUG | utils.log_helper - 🐞 步骤4: 检测边缘点 +2025-05-28 18:33:47 | DEBUG | utils.log_helper - 🐞 步骤5: 边缘检测 +2025-05-28 18:33:48 | DEBUG | utils.log_helper - 🐞 步骤6: 检测到 16 条直线 +2025-05-28 18:33:49 | DEBUG | utils.log_helper - 🐞 步骤7: 合并后剩余 7 条线 +2025-05-28 18:33:50 | DEBUG | utils.log_helper - 🐞 步骤8: 找到 3 条水平线 +2025-05-28 18:33:50 | DEBUG | utils.log_helper - 🐞 步骤9: 显示最终结果 +2025-05-28 18:33:51 | INFO | utils.log_helper - ℹ️ 保存横向边缘检测结果图像到: logs/image/horizontal_edge_20250528_183351_666893.jpg +2025-05-28 18:33:51 | INFO | utils.log_helper - ℹ️ 横向边缘检测结果: {'timestamp': '20250528_183351_666893', 'edge_point': (574, 705), 'distance_to_center': -386, 'slope': 0.08121827411167512, 'distance_to_bottom': 343.6497461928934, 'intersection_point': (960, 736), 'score': 0.4539862637807487} +2025-05-28 18:34:09 | INFO | utils.log_helper - ℹ️ 保存原始图像到: logs/image/origin_horizontal_edge_20250528_183409_503808.jpg +2025-05-28 18:34:09 | INFO | utils.log_helper - ℹ️ 保存横向边缘检测结果图像到: logs/image/horizontal_edge_20250528_183409_503808.jpg +2025-05-28 18:34:09 | INFO | utils.log_helper - ℹ️ 横向边缘检测结果: {'timestamp': '20250528_183409_503808', 'edge_point': (986, 713), 'distance_to_center': 26, 'slope': 0.01699029126213592, 'distance_to_bottom': 367.44174757281553, 'intersection_point': (960, 712), 'score': 0.4415637632646397, 'valid': True, 'reason': '', 'is_lower_line': array([ True, True, True, True])} +2025-05-28 18:34:28 | DEBUG | utils.log_helper - 🐞 步骤1: 原始图像已加载 +2025-05-28 18:34:29 | DEBUG | utils.log_helper - 🐞 步骤2: 创建黄色掩码 +2025-05-28 18:34:30 | DEBUG | utils.log_helper - 🐞 步骤3: 提取黄色部分 +2025-05-28 18:34:31 | DEBUG | utils.log_helper - 🐞 检测底部和顶部边缘点 +2025-05-28 18:34:32 | DEBUG | utils.log_helper - 🐞 步骤4: 边缘检测 +2025-05-28 18:34:32 | DEBUG | utils.log_helper - 🐞 步骤5: 检测到 16 条直线 +2025-05-28 18:34:33 | DEBUG | utils.log_helper - 🐞 步骤6: 找到 1 条水平线 (下方: 1, 上方: 0) +2025-05-28 18:34:34 | DEBUG | utils.log_helper - 🐞 选择下方水平线 +2025-05-28 18:34:34 | DEBUG | utils.log_helper - 🐞 显示边缘斜率和中线交点 +2025-05-28 18:34:35 | INFO | utils.log_helper - ℹ️ 保存原始图像到: logs/image/origin_horizontal_edge_20250528_183435_284173.jpg +2025-05-28 18:34:35 | INFO | utils.log_helper - ℹ️ 保存横向边缘检测结果图像到: logs/image/horizontal_edge_20250528_183435_284173.jpg +2025-05-28 18:34:35 | INFO | utils.log_helper - ℹ️ 横向边缘检测结果: {'timestamp': '20250528_183435_284173', 'edge_point': (986, 713), 'distance_to_center': 26, 'slope': 0.01699029126213592, 'distance_to_bottom': 367.44174757281553, 'intersection_point': (960, 712), 'score': 0.4415637632646397, 'valid': True, 'reason': '', 'is_lower_line': array([ True, True, True, True])} +2025-05-28 19:06:36 | DEBUG | utils.log_helper - 🐞 步骤1: 原始图像已加载 +2025-05-28 19:06:37 | DEBUG | utils.log_helper - 🐞 步骤2: 创建黄色掩码 +2025-05-28 19:06:38 | DEBUG | utils.log_helper - 🐞 步骤3: 提取黄色部分 +2025-05-28 19:06:39 | DEBUG | utils.log_helper - 🐞 检测底部和顶部边缘点 +2025-05-28 19:06:40 | DEBUG | utils.log_helper - 🐞 步骤4: 边缘检测 +2025-05-28 19:06:41 | DEBUG | utils.log_helper - 🐞 步骤5: 检测到 16 条直线 +2025-05-28 19:06:41 | DEBUG | utils.log_helper - 🐞 步骤6: 找到 1 条水平线 (下方: 1, 上方: 0) +2025-05-28 19:06:42 | DEBUG | utils.log_helper - 🐞 选择下方水平线 +2025-05-28 19:06:42 | DEBUG | utils.log_helper - 🐞 显示边缘斜率和中线交点 +2025-05-28 19:06:43 | INFO | utils.log_helper - ℹ️ 保存原始图像到: logs/image/origin_horizontal_edge_20250528_190643_597364.jpg +2025-05-28 19:06:43 | INFO | utils.log_helper - ℹ️ 保存横向边缘检测结果图像到: logs/image/horizontal_edge_20250528_190643_597364.jpg +2025-05-28 19:06:43 | INFO | utils.log_helper - ℹ️ 横向边缘检测结果: {'timestamp': '20250528_190643_597364', 'edge_point': (986, 713), 'distance_to_center': 26, 'slope': 0.01699029126213592, 'distance_to_bottom': 367.44174757281553, 'intersection_point': (960, 712), 'score': 0.4415637632646397, 'valid': True, 'reason': '', 'is_lower_line': array([ True, True, True, True])} +2025-05-28 19:06:55 | DEBUG | utils.log_helper - 🐞 步骤1: 原始图像已加载 +2025-05-28 19:06:56 | DEBUG | utils.log_helper - 🐞 步骤2: 创建黄色掩码 +2025-05-28 19:06:57 | DEBUG | utils.log_helper - 🐞 步骤3: 提取黄色部分 +2025-05-28 19:06:58 | DEBUG | utils.log_helper - 🐞 检测底部和顶部边缘点 +2025-05-28 19:06:59 | DEBUG | utils.log_helper - 🐞 步骤4: 边缘检测 +2025-05-28 19:06:59 | DEBUG | utils.log_helper - 🐞 步骤5: 检测到 16 条直线 +2025-05-28 19:07:00 | DEBUG | utils.log_helper - 🐞 步骤6: 找到 1 条水平线 (下方: 1, 上方: 0) +2025-05-28 19:07:01 | DEBUG | utils.log_helper - 🐞 选择下方水平线 +2025-05-28 19:07:01 | DEBUG | utils.log_helper - 🐞 显示边缘斜率和中线交点 +2025-05-28 19:07:02 | INFO | utils.log_helper - ℹ️ 保存原始图像到: logs/image/origin_horizontal_edge_20250528_190702_335947.jpg +2025-05-28 19:07:02 | INFO | utils.log_helper - ℹ️ 保存横向边缘检测结果图像到: logs/image/horizontal_edge_20250528_190702_335947.jpg +2025-05-28 19:07:02 | INFO | utils.log_helper - ℹ️ 横向边缘检测结果: {'timestamp': '20250528_190702_335947', 'edge_point': (986, 713), 'distance_to_center': 26, 'slope': 0.01699029126213592, 'distance_to_bottom': 367.44174757281553, 'intersection_point': (960, 712), 'score': 0.4415637632646397, 'valid': True, 'reason': '', 'is_lower_line': array([ True, True, True, True])} +2025-05-28 19:07:16 | DEBUG | utils.log_helper - 🐞 步骤1: 原始图像已加载 +2025-05-28 19:07:17 | DEBUG | utils.log_helper - 🐞 步骤2: 创建黄色掩码 +2025-05-28 19:07:17 | DEBUG | utils.log_helper - 🐞 步骤3: 提取黄色部分 +2025-05-28 19:07:18 | DEBUG | utils.log_helper - 🐞 检测底部和顶部边缘点 +2025-05-28 19:07:19 | DEBUG | utils.log_helper - 🐞 步骤4: 边缘检测 +2025-05-28 19:07:20 | DEBUG | utils.log_helper - 🐞 步骤5: 检测到 19 条直线 +2025-05-28 19:07:21 | ERROR | utils.log_helper - ❌ 未检测到合格的水平线 +2025-05-28 19:07:26 | DEBUG | utils.log_helper - 🐞 步骤1: 原始图像已加载 +2025-05-28 19:07:27 | DEBUG | utils.log_helper - 🐞 步骤2: 创建黄色掩码 +2025-05-28 19:07:28 | DEBUG | utils.log_helper - 🐞 步骤3: 提取黄色部分 +2025-05-28 19:07:29 | DEBUG | utils.log_helper - 🐞 检测底部和顶部边缘点 +2025-05-28 19:07:30 | DEBUG | utils.log_helper - 🐞 步骤4: 边缘检测 +2025-05-28 19:07:31 | DEBUG | utils.log_helper - 🐞 步骤5: 检测到 19 条直线 +2025-05-28 19:07:31 | ERROR | utils.log_helper - ❌ 未检测到合格的水平线 +2025-05-28 19:07:36 | DEBUG | utils.log_helper - 🐞 步骤1: 原始图像已加载 +2025-05-28 19:07:37 | DEBUG | utils.log_helper - 🐞 步骤2: 创建黄色掩码 +2025-05-28 19:07:38 | DEBUG | utils.log_helper - 🐞 步骤3: 提取黄色部分 +2025-05-28 19:07:39 | DEBUG | utils.log_helper - 🐞 检测底部和顶部边缘点 +2025-05-28 19:07:40 | DEBUG | utils.log_helper - 🐞 步骤4: 边缘检测 +2025-05-28 19:07:40 | DEBUG | utils.log_helper - 🐞 步骤5: 检测到 19 条直线 +2025-05-28 19:07:41 | ERROR | utils.log_helper - ❌ 未检测到合格的水平线 +2025-05-28 19:07:51 | DEBUG | utils.log_helper - 🐞 步骤1: 原始图像已加载 +2025-05-28 19:07:52 | DEBUG | utils.log_helper - 🐞 步骤2: 创建黄色掩码 +2025-05-28 19:07:52 | DEBUG | utils.log_helper - 🐞 步骤3: 提取黄色部分 +2025-05-28 19:07:53 | DEBUG | utils.log_helper - 🐞 检测底部和顶部边缘点 +2025-05-28 19:07:54 | DEBUG | utils.log_helper - 🐞 步骤4: 边缘检测 +2025-05-28 19:07:55 | DEBUG | utils.log_helper - 🐞 步骤5: 检测到 19 条直线 +2025-05-28 19:07:56 | ERROR | utils.log_helper - ❌ 未检测到合格的水平线 +2025-05-28 19:08:18 | INFO | utils.log_helper - ℹ️ 保存原始图像到: logs/image/origin_horizontal_edge_20250528_190818_294767.jpg +2025-05-28 19:08:18 | DEBUG | utils.log_helper - 🐞 步骤1: 原始图像已加载 +2025-05-28 19:08:19 | DEBUG | utils.log_helper - 🐞 步骤2: 创建黄色掩码 +2025-05-28 19:08:20 | DEBUG | utils.log_helper - 🐞 步骤3: 提取黄色部分 +2025-05-28 19:08:20 | DEBUG | utils.log_helper - 🐞 检测底部和顶部边缘点 +2025-05-28 19:08:21 | DEBUG | utils.log_helper - 🐞 步骤4: 边缘检测 +2025-05-28 19:08:22 | DEBUG | utils.log_helper - 🐞 步骤5: 检测到 15 条直线 +2025-05-28 19:08:23 | DEBUG | utils.log_helper - 🐞 步骤5.1: 合并后剩余 8 条线 +2025-05-28 19:08:24 | DEBUG | utils.log_helper - 🐞 步骤6: 找到 1 条水平线 (下方: 1, 上方: 0) +2025-05-28 19:08:25 | DEBUG | utils.log_helper - 🐞 没有合适的上方线，选择下方水平线 +2025-05-28 19:08:25 | WARNING | utils.log_helper - ⚠️ 检测结果不合理: 边缘点y坐标超出有效范围; +2025-05-28 19:08:25 | DEBUG | utils.log_helper - 🐞 显示边缘斜率和中线交点 +2025-05-28 19:08:25 | INFO | utils.log_helper - ℹ️ 保存原始图像到: logs/image/origin_horizontal_edge_20250528_190825_827218.jpg +2025-05-28 19:08:25 | INFO | utils.log_helper - ℹ️ 保存横向边缘检测结果图像到: logs/image/horizontal_edge_20250528_190825_827218.jpg +2025-05-28 19:08:25 | INFO | utils.log_helper - ℹ️ 横向边缘检测结果: {'timestamp': '20250528_190825_827218', 'edge_point': (1171, 779), 'distance_to_center': 211, 'slope': 0.0994263862332696, 'distance_to_bottom': 321.9789674952199, 'intersection_point': (960, 758), 'score': 0.5319744957227472, 'valid': False, 'reason': '边缘点y坐标超出有效范围; ', 'is_upper_line': False} +2025-05-28 19:38:09 | INFO | utils.log_helper - ℹ️ 保存原始图像到: logs/image/origin_intersection_20250528_193809_784190.jpg +2025-05-28 19:38:09 | DEBUG | utils.log_helper - 🐞 步骤1: 原始图像已加载 +2025-05-28 19:39:20 | INFO | utils.log_helper - ℹ️ 保存原始图像到: logs/image/origin_intersection_20250528_193920_198287.jpg +2025-05-28 19:39:20 | DEBUG | utils.log_helper - 🐞 步骤1: 原始图像已加载 +2025-05-28 19:39:21 | DEBUG | utils.log_helper - 🐞 步骤2: 创建黄色掩码 +2025-05-28 19:39:21 | DEBUG | utils.log_helper - 🐞 步骤3: 提取黄色部分 +2025-05-28 19:39:22 | DEBUG | utils.log_helper - 🐞 检测顶部边缘点 +2025-05-28 19:39:23 | DEBUG | utils.log_helper - 🐞 步骤4: 边缘检测 +2025-05-28 19:39:24 | DEBUG | utils.log_helper - 🐞 步骤5: 检测到 17 条直线 +2025-05-28 19:39:25 | DEBUG | utils.log_helper - 🐞 步骤5.1: 合并后剩余 8 条线 +2025-05-28 19:39:26 | DEBUG | utils.log_helper - 🐞 步骤6: 找到 1 条水平线 +2025-05-28 19:39:26 | DEBUG | utils.log_helper - 🐞 显示交点结果 +2025-05-28 19:39:27 | INFO | utils.log_helper - ℹ️ 保存最远交点检测结果图像到: logs/image/furthest_intersection_20250528_193927_755148.jpg +2025-05-28 19:39:27 | INFO | utils.log_helper - ℹ️ 最远交点检测结果: {'timestamp': '20250528_193927_755148', 'intersection_point': (960, 720), 'distance_to_bottom': 359.25714285714287, 'slope': -0.01904761904761905, 'score': 0.5227506599361932, 'valid': True, 'reason': ''} +2025-05-28 19:40:27 | INFO | utils.log_helper - ℹ️ 保存原始图像到: logs/image/origin_intersection_20250528_194027_029530.jpg +2025-05-28 19:40:27 | DEBUG | utils.log_helper - 🐞 步骤1: 原始图像已加载 +2025-05-28 19:40:27 | DEBUG | utils.log_helper - 🐞 步骤2: 创建黄色掩码 +2025-05-28 19:40:28 | DEBUG | utils.log_helper - 🐞 步骤3: 提取黄色部分 +2025-05-28 19:40:29 | DEBUG | utils.log_helper - 🐞 检测顶部边缘点 +2025-05-28 19:40:30 | DEBUG | utils.log_helper - 🐞 步骤4: 边缘检测 +2025-05-28 19:40:31 | DEBUG | utils.log_helper - 🐞 步骤5: 检测到 14 条直线 +2025-05-28 19:40:32 | DEBUG | utils.log_helper - 🐞 步骤5.1: 合并后剩余 7 条线 +2025-05-28 19:40:32 | ERROR | utils.log_helper - ❌ 未检测到合格的水平线 +2025-05-28 19:40:43 | INFO | utils.log_helper - ℹ️ 保存原始图像到: logs/image/origin_intersection_20250528_194043_341472.jpg +2025-05-28 19:40:43 | DEBUG | utils.log_helper - 🐞 步骤1: 原始图像已加载 +2025-05-28 19:40:44 | DEBUG | utils.log_helper - 🐞 步骤2: 创建黄色掩码 +2025-05-28 19:40:45 | DEBUG | utils.log_helper - 🐞 步骤3: 提取黄色部分 +2025-05-28 19:40:45 | DEBUG | utils.log_helper - 🐞 检测顶部边缘点 +2025-05-28 19:40:46 | DEBUG | utils.log_helper - 🐞 步骤4: 边缘检测 +2025-05-28 19:40:47 | DEBUG | utils.log_helper - 🐞 步骤5: 检测到 14 条直线 +2025-05-28 19:40:48 | DEBUG | utils.log_helper - 🐞 步骤5.1: 合并后剩余 7 条线 +2025-05-28 19:40:49 | ERROR | utils.log_helper - ❌ 未检测到合格的水平线 +2025-05-28 19:40:51 | INFO | utils.log_helper - ℹ️ 保存原始图像到: logs/image/origin_intersection_20250528_194051_844002.jpg +2025-05-28 19:40:51 | DEBUG | utils.log_helper - 🐞 步骤1: 原始图像已加载 +2025-05-28 19:40:52 | DEBUG | utils.log_helper - 🐞 步骤2: 创建黄色掩码 +2025-05-28 19:40:53 | DEBUG | utils.log_helper - 🐞 步骤3: 提取黄色部分 +2025-05-28 19:40:54 | DEBUG | utils.log_helper - 🐞 检测顶部边缘点 +2025-05-28 19:40:55 | DEBUG | utils.log_helper - 🐞 步骤4: 边缘检测 +2025-05-28 19:40:56 | DEBUG | utils.log_helper - 🐞 步骤5: 检测到 14 条直线 +2025-05-28 19:40:56 | DEBUG | utils.log_helper - 🐞 步骤5.1: 合并后剩余 7 条线 +2025-05-28 19:40:57 | ERROR | utils.log_helper - ❌ 未检测到合格的水平线 +2025-05-28 19:52:43 | INFO | utils.log_helper - ℹ️ 保存原始图像到: logs/image/origin_intersection_20250528_195243_206199.jpg +2025-05-28 19:52:43 | DEBUG | utils.log_helper - 🐞 步骤1: 原始图像已加载 +2025-05-28 19:52:44 | DEBUG | utils.log_helper - 🐞 步骤1.5: 增强对比度 +2025-05-28 19:52:45 | DEBUG | utils.log_helper - 🐞 步骤2: 创建黄色掩码 +2025-05-28 19:52:45 | DEBUG | utils.log_helper - 🐞 步骤3: 提取黄色部分 +2025-05-28 19:52:46 | DEBUG | utils.log_helper - 🐞 检测顶部边缘点 +2025-05-28 19:52:47 | DEBUG | utils.log_helper - 🐞 从顶部边缘点直接拟合出横线 +2025-05-28 19:53:07 | INFO | utils.log_helper - ℹ️ 保存原始图像到: logs/image/origin_intersection_20250528_195307_891312.jpg +2025-05-28 19:53:07 | DEBUG | utils.log_helper - 🐞 步骤1: 原始图像已加载 +2025-05-28 19:53:08 | DEBUG | utils.log_helper - 🐞 步骤1.5: 增强对比度 +2025-05-28 19:53:09 | DEBUG | utils.log_helper - 🐞 步骤2: 创建黄色掩码 +2025-05-28 19:53:10 | DEBUG | utils.log_helper - 🐞 步骤3: 提取黄色部分 +2025-05-28 19:53:11 | DEBUG | utils.log_helper - 🐞 检测顶部边缘点 +2025-05-28 19:53:12 | DEBUG | utils.log_helper - 🐞 从顶部边缘点直接拟合出横线 +2025-05-28 19:53:18 | INFO | utils.log_helper - ℹ️ 保存原始图像到: logs/image/origin_intersection_20250528_195318_404574.jpg +2025-05-28 19:53:18 | DEBUG | utils.log_helper - 🐞 步骤1: 原始图像已加载 +2025-05-28 19:53:19 | DEBUG | utils.log_helper - 🐞 步骤1.5: 增强对比度 +2025-05-28 19:53:20 | DEBUG | utils.log_helper - 🐞 步骤2: 创建黄色掩码 +2025-05-28 19:53:21 | DEBUG | utils.log_helper - 🐞 步骤3: 提取黄色部分 +2025-05-28 19:53:21 | DEBUG | utils.log_helper - 🐞 检测顶部边缘点 +2025-05-28 19:53:22 | DEBUG | utils.log_helper - 🐞 从顶部边缘点直接拟合出横线 diff --git a/test/task-path-track/yellow_track_demo.py b/test/task-path-track/yellow_track_demo.py index 07cc434..f4c857d 100644 --- a/test/task-path-track/yellow_track_demo.py +++ b/test/task-path-track/yellow_track_demo.py @@ -9,7 +9,7 @@ current_dir = os.path.dirname(os.path.abspath(__file__)) project_root = os.path.dirname(os.path.dirname(current_dir)) sys.path.append(project_root) -from utils.detect_track import detect_horizontal_track_edge, detect_horizontal_track_edge_v2 +from utils.detect_track import detect_horizontal_track_edge_v3 def process_image(image_path, save_dir=None, show_steps=False): """处理单张图像""" @@ -17,7 +17,7 @@ def process_image(image_path, save_dir=None, show_steps=False): # 检测赛道并估算距离 start_time = time.time() - edge_point, edge_info = detect_horizontal_track_edge(image_path, observe=show_steps, save_log=True, delay=800) + edge_point, edge_info = detect_horizontal_track_edge_v3(image_path, observe=show_steps, save_log=True, delay=800) processing_time = time.time() - start_time # 输出结果 @@ -44,7 +44,7 @@ def process_image(image_path, save_dir=None, show_steps=False): def main(): parser = argparse.ArgumentParser(description='黄色赛道检测演示程序') - parser.add_argument('--input', type=str, default='res/path/image_20250513_162556.png', help='输入图像或视频的路径') + parser.add_argument('--input', type=str, default='res/path/task-5/origin_horizontal_edge_20250528_100604_158105.jpg', help='输入图像或视频的路径') parser.add_argument('--output', type=str, default='res/path/test-v2/2-end.jpg', help='输出结果的保存路径') parser.add_argument('--type', type=str, choices=['image', 'video'], help='输入类型，不指定会自动检测') parser.add_argument('--show', default=True, action='store_true', help='显示处理步骤') diff --git a/utils/detect_furthest_intersection.py b/utils/detect_furthest_intersection.py new file mode 100644 index 0000000..3fb331a --- /dev/null +++ b/utils/detect_furthest_intersection.py @@ -0,0 +1,578 @@ +import cv2 +import numpy as np +import os +import datetime +from sklearn import linear_model +from utils.log_helper import get_logger, debug, info, warning, error, success + +def detect_furthest_horizontal_intersection(image, observe=False, delay=1000, save_log=True): + """ + 检测正前方x轴中间线与最远横向黄色赛道线的交点 + + 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) + save_log: 是否保存日志和图像 + 返回: + intersection_point: x轴中线与最远横线的交点坐标 (x, y) + intersection_info: 交点信息字典 + """ + # 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: + error("无法加载图像", "失败") + return None, None + + if save_log: + timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S_%f") + origin_image_path = os.path.join("logs/image", f"origin_intersection_{timestamp}.jpg") + os.makedirs("logs/image", exist_ok=True) + cv2.imwrite(origin_image_path, img) + info(f"保存原始图像到: {origin_image_path}", "日志") + + # 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 * 0.8) # 扩大搜索区域宽度为图像宽度的80% + 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 + + # 定义合理的值范围 - 更宽松的参数以检测更远的横线 + valid_y_range = (height * 0.05, height * 0.6) # 扩大有效的y坐标范围 + max_slope = 0.3 # 增加允许的最大斜率 + min_line_length = width * 0.1 # 减小最小线长度要求 + + if observe: + debug("步骤1: 原始图像已加载", "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) + + # 图像预处理 - 增强对比度以便更好地提取黄色部分 + img_enhanced = img.copy() + # 将图像转换为LAB颜色空间 + lab = cv2.cvtColor(img_enhanced, cv2.COLOR_BGR2LAB) + # 分离L通道 + l, a, b = cv2.split(lab) + # 应用CLAHE（对比度受限自适应直方图均衡化） + clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8, 8)) + cl = clahe.apply(l) + # 合并通道 + limg = cv2.merge((cl, a, b)) + # 转回BGR颜色空间 + img_enhanced = cv2.cvtColor(limg, cv2.COLOR_LAB2BGR) + + if observe: + debug("步骤1.5: 增强对比度", "处理") + cv2.imshow("增强对比度", img_enhanced) + cv2.waitKey(delay) + + # 转换到HSV颜色空间以便更容易提取黄色 + hsv = cv2.cvtColor(img_enhanced, cv2.COLOR_BGR2HSV) + + # 黄色的HSV范围 - 扩大范围以适应不同光照条件下的黄色 + lower_yellow = np.array([15, 70, 70]) # 降低饱和度和亮度阈值 + upper_yellow = np.array([35, 255, 255]) # 扩大色调范围 + + # 创建黄色的掩码 + mask = cv2.inRange(hsv, lower_yellow, upper_yellow) + + # 添加形态学操作以改善掩码 + kernel = np.ones((5, 5), np.uint8) # 增大内核大小 + mask = cv2.dilate(mask, kernel, iterations=2) # 增加膨胀次数 + 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) + + # 应用掩码，只保留黄色部分 + yellow_only = cv2.bitwise_and(img_enhanced, img_enhanced, mask=mask) + + if observe: + debug("步骤3: 提取黄色部分", "处理") + cv2.imshow("只保留黄色", yellow_only) + cv2.waitKey(delay) + + # 裁剪掩码到搜索区域 + search_mask = mask[top_bound:bottom_bound, left_bound:right_bound] + + # 寻找每列的最顶部点（最远的边缘点） + top_points = [] + non_zero_cols = np.where(np.any(search_mask, axis=0))[0] + + for col in non_zero_cols: + col_points = np.where(search_mask[:, col] > 0)[0] + if len(col_points) > 0: + top_row = np.min(col_points) + top_points.append((left_bound + col, top_bound + top_row)) + + if observe and top_points: + debug("检测顶部边缘点", "处理") + edge_points_img = img.copy() + for point in top_points: + cv2.circle(edge_points_img, point, 3, (255, 0, 255), -1) + cv2.imshow("顶部边缘点", edge_points_img) + cv2.waitKey(delay) + + # 尝试直接从顶部边缘点拟合直线 + if len(top_points) >= 10: # 如果有足够多的顶部边缘点 + try: + # 使用RANSAC拟合直线 + x_points = np.array([p[0] for p in top_points]).reshape(-1, 1) + y_points = np.array([p[1] for p in top_points]) + + ransac = linear_model.RANSACRegressor(residual_threshold=5.0) + ransac.fit(x_points, y_points) + + # 获取拟合的斜率和截距 + direct_fitted_slope = ransac.estimator_.coef_[0] + direct_intercept = ransac.estimator_.intercept_ + + # 如果斜率在合理范围内 + if abs(direct_fitted_slope) < max_slope: + # 计算线段端点 + direct_x1 = left_bound + direct_y1 = int(direct_fitted_slope * direct_x1 + direct_intercept) + direct_x2 = right_bound + direct_y2 = int(direct_fitted_slope * direct_x2 + direct_intercept) + + # 计算交点 + direct_intersection_x = center_x + direct_intersection_y = direct_fitted_slope * (center_x - direct_x1) + direct_y1 + direct_intersection_point = (int(direct_intersection_x), int(direct_intersection_y)) + + # 如果交点在合理范围内 + if 0 <= direct_intersection_y < height * 0.7: + # 创建直接拟合的线的信息 + direct_fitted_info = { + "x": direct_intersection_point[0], + "y": direct_intersection_point[1], + "distance_to_bottom": height - direct_intersection_y, + "slope": direct_fitted_slope, + "is_horizontal": abs(direct_fitted_slope) < 0.05, + "score": 0.9, # 给予较高的分数 + "valid": True, + "fitted_from_edge_points": True + } + + if observe: + debug("从顶部边缘点直接拟合出横线", "处理") + direct_fit_img = img.copy() + cv2.line(direct_fit_img, (int(direct_x1), int(direct_y1)), + (int(direct_x2), int(direct_y2)), (0, 255, 0), 2) + cv2.circle(direct_fit_img, direct_intersection_point, 10, (255, 0, 255), -1) + cv2.imshow("从边缘点直接拟合的线", direct_fit_img) + cv2.waitKey(delay) + + return direct_intersection_point, direct_fitted_info + except Exception as e: + if observe: + warning(f"从边缘点直接拟合线失败: {str(e)}", "警告") + + # 边缘检测 - 使用更适合检测远处横线的参数 + edges = cv2.Canny(mask, 30, 120, apertureSize=3) # 降低阈值以检测更多边缘 + + if observe: + debug("步骤4: 边缘检测", "处理") + cv2.imshow("边缘检测", edges) + cv2.waitKey(delay) + + # 使用霍夫变换检测直线，使用更宽松的参数 + lines = cv2.HoughLinesP(edges, 1, np.pi/180, threshold=20, # 降低阈值 + minLineLength=width*0.08, maxLineGap=40) # 减少最小长度，增加最大间隙 + + if lines is None or len(lines) == 0: + # 如果找不到线，尝试使用更宽松的参数 + lines = cv2.HoughLinesP(edges, 1, np.pi/180, threshold=15, + minLineLength=width*0.05, maxLineGap=50) + + if lines is None or len(lines) == 0: + if observe: + 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 i, line in enumerate(lines): + x1, y1, x2, y2 = line[0] + # 使用HSV颜色空间生成不同的颜色 + hue = (i * 30) % 180 # 每30度一个颜色 + color = cv2.cvtColor(np.uint8([[[hue, 255, 255]]]), cv2.COLOR_HSV2BGR)[0][0] + color = (int(color[0]), int(color[1]), int(color[2])) + cv2.line(lines_img, (x1, y1), (x2, y2), color, 2) + cv2.imshow("检测到的直线", lines_img) + cv2.waitKey(delay) + + # 过滤和合并相似的线段 + filtered_lines = [] + for line in lines: + x1, y1, x2, y2 = line[0] + # 确保x1 < x2 + if x1 > x2: + x1, x2 = x2, x1 + y1, y2 = y2, y1 + filtered_lines.append([x1, y1, x2, y2]) + + # 合并相似线段 + merged_lines = [] + used_indices = set() + + for i, line1 in enumerate(filtered_lines): + if i in used_indices: + continue + + x1, y1, x2, y2 = line1 + similar_lines = [line1] + used_indices.add(i) + + # 查找与当前线段相似的其他线段 + for j, line2 in enumerate(filtered_lines): + if j in used_indices or i == j: + continue + + x3, y3, x4, y4 = line2 + + # 计算两条线段的斜率 + slope1 = (y2 - y1) / (x2 - x1) if abs(x2 - x1) > 5 else 100 + slope2 = (y4 - y3) / (x4 - x3) if abs(x4 - x3) > 5 else 100 + + # 计算两条线段的中点 + mid1_x, mid1_y = (x1 + x2) / 2, (y1 + y2) / 2 + mid2_x, mid2_y = (x3 + x4) / 2, (y3 + y4) / 2 + + # 计算中点之间的距离 + mid_dist = np.sqrt((mid2_x - mid1_x)**2 + (mid2_y - mid1_y)**2) + + # 计算线段端点之间的最小距离 + end_dists = [ + np.sqrt((x1-x3)**2 + (y1-y3)**2), + np.sqrt((x1-x4)**2 + (y1-y4)**2), + np.sqrt((x2-x3)**2 + (y2-y3)**2), + np.sqrt((x2-x4)**2 + (y2-y4)**2) + ] + min_end_dist = min(end_dists) + + # 判断两条线段是否相似：满足以下条件之一 + # 1. 斜率接近且中点距离不太远 + # 2. 斜率接近且端点之间距离很近（可能是连接的线段） + # 3. 端点非常接近（几乎连接），且斜率差异不太大 + if (abs(slope1 - slope2) < 0.15 and mid_dist < height * 0.15) or \ + (abs(slope1 - slope2) < 0.1 and min_end_dist < height * 0.05) or \ + (min_end_dist < height * 0.03 and abs(slope1 - slope2) < 0.25): + similar_lines.append(line2) + used_indices.add(j) + + # 如果找到相似线段，合并它们 + if len(similar_lines) > 1: + # 合并所有相似线段的端点 + all_points = [] + for line in similar_lines: + all_points.append((line[0], line[1])) # 起点 + all_points.append((line[2], line[3])) # 终点 + + # 找出x坐标的最小值和最大值 + min_x = min(p[0] for p in all_points) + max_x = max(p[0] for p in all_points) + + # 使用所有点拟合一条直线 + x_points = np.array([p[0] for p in all_points]).reshape(-1, 1) + y_points = np.array([p[1] for p in all_points]) + + # 使用RANSAC拟合更稳定的直线 + ransac = linear_model.RANSACRegressor(residual_threshold=5.0) + ransac.fit(x_points, y_points) + + # 获取拟合的斜率和截距 + merged_slope = ransac.estimator_.coef_[0] + merged_intercept = ransac.estimator_.intercept_ + + # 计算新的端点 + y_min = int(merged_slope * min_x + merged_intercept) + y_max = int(merged_slope * max_x + merged_intercept) + + # 添加合并后的线段 + merged_lines.append([min_x, y_min, max_x, y_max]) + else: + # 如果没有相似线段，直接添加原线段 + merged_lines.append(line1) + + # 将合并后的线段转换为霍夫变换的格式 + merged_hough_lines = [] + for line in merged_lines: + merged_hough_lines.append(np.array([[line[0], line[1], line[2], line[3]]])) + + if observe: + debug(f"步骤5.1: 合并后剩余 {len(merged_hough_lines)} 条线", "处理") + merged_img = img.copy() + for i, line in enumerate(merged_hough_lines): + x1, y1, x2, y2 = line[0] + # 使用HSV颜色空间生成不同的颜色 + hue = (i * 50) % 180 # 每50度一个颜色 + color = cv2.cvtColor(np.uint8([[[hue, 255, 255]]]), cv2.COLOR_HSV2BGR)[0][0] + color = (int(color[0]), int(color[1]), int(color[2])) + cv2.line(merged_img, (x1, y1), (x2, y2), color, 3) + cv2.imshow("合并后的线段", merged_img) + cv2.waitKey(delay) + + # 使用合并后的线段继续处理 + lines = merged_hough_lines + + # 筛选水平线 + horizontal_lines = [] + + for line in lines: + x1, y1, x2, y2 = line[0] + + # 计算斜率 (避免除零错误) + if abs(x2 - x1) < 5: # 几乎垂直的线 + continue + + slope = (y2 - y1) / (x2 - x1) + + # 筛选接近水平的线 (斜率接近0) + if abs(slope) < max_slope: + # 确保线在搜索区域内 + if ((left_bound <= x1 <= right_bound and top_bound <= y1 <= bottom_bound) or + (left_bound <= x2 <= right_bound and top_bound <= y2 <= bottom_bound)): + # 计算线的中点y坐标 + mid_y = (y1 + y2) / 2 + line_length = np.sqrt((x2-x1)**2 + (y2-y1)**2) + + # 优先选择更靠近图像上方的线段（y值更小） + position_score = 1.0 - (mid_y / height) + + # 计算长度得分（越长越好） + length_score = min(1.0, line_length / (width * 0.5)) + + # 计算斜率得分（越水平越好） + slope_score = max(0.0, 1.0 - abs(slope) / max_slope) + + # 计算线段位于图像中央的程度 + mid_x = (x1 + x2) / 2 + center_score = max(0.0, 1.0 - abs(mid_x - center_x) / (width * 0.4)) + + # 计算综合得分，优先考虑高位置和水平度 + quality_score = position_score * 0.6 + length_score * 0.15 + slope_score * 0.2 + center_score * 0.05 + + # 保存线段、其y坐标、斜率、长度和质量得分 + horizontal_lines.append((line[0], mid_y, slope, line_length, quality_score)) + + # 如果没有找到水平线，尝试使用顶部边缘点拟合 + if not horizontal_lines and len(top_points) >= 5: + if observe: + debug("未检测到水平线，尝试使用顶部边缘点拟合", "处理") + + # 筛选上半部分的点 + upper_points = [p for p in top_points if p[1] < height * 0.5] + + if len(upper_points) >= 5: + try: + # 使用RANSAC拟合直线 + x_points = np.array([p[0] for p in upper_points]).reshape(-1, 1) + y_points = np.array([p[1] for p in upper_points]) + + ransac = linear_model.RANSACRegressor(residual_threshold=8.0) # 增大残差阈值 + ransac.fit(x_points, y_points) + + # 获取拟合的斜率和截距 + fitted_slope = ransac.estimator_.coef_[0] + intercept = ransac.estimator_.intercept_ + + # 如果斜率在合理范围内 + if abs(fitted_slope) < max_slope: + # 计算线段端点 + x1 = left_bound + y1 = int(fitted_slope * x1 + intercept) + x2 = right_bound + y2 = int(fitted_slope * x2 + intercept) + + # 计算中点y坐标和线长 + mid_y = (y1 + y2) / 2 + line_length = np.sqrt((x2-x1)**2 + (y2-y1)**2) + + # 计算得分 + position_score = 1.0 - (mid_y / height) + quality_score = position_score * 0.7 + 0.3 # 边缘点拟合的线给予高分 + + # 添加到水平线列表 + horizontal_lines.append((np.array([x1, y1, x2, y2]), mid_y, fitted_slope, line_length, quality_score)) + + if observe: + debug(f"从边缘点成功拟合出水平线，斜率: {fitted_slope:.4f}", "处理") + fitted_line_img = img.copy() + cv2.line(fitted_line_img, (x1, y1), (x2, y2), (0, 255, 255), 2) + for point in upper_points: + cv2.circle(fitted_line_img, point, 3, (0, 255, 0), -1) + cv2.imshow("拟合的水平线", fitted_line_img) + cv2.waitKey(delay) + except Exception as e: + if observe: + error(f"拟合水平线失败: {str(e)}", "失败") + + if not horizontal_lines: + if observe: + error("未检测到合格的水平线", "失败") + return None, None + + # 根据质量得分排序水平线（得分高的排前面） + horizontal_lines.sort(key=lambda x: x[4], reverse=True) + + if observe: + debug(f"步骤6: 找到 {len(horizontal_lines)} 条水平线", "处理") + h_lines_img = img.copy() + + # 绘制所有水平线 + for i, line_info in enumerate(horizontal_lines): + line, mid_y, slope, length, score = line_info + if isinstance(line, np.ndarray) and line.shape[0] == 4: + x1, y1, x2, y2 = line + else: + x1, y1, x2, y2 = line + # 根据得分调整线的颜色，得分越高越绿 + color = (int(255 * (1-score)), int(255 * score), 0) + cv2.line(h_lines_img, (int(x1), int(y1)), (int(x2), int(y2)), color, 2) + # 显示斜率和得分 + cv2.putText(h_lines_img, f"{i}:{score:.2f}", ((int(x1)+int(x2))//2, (int(y1)+int(y2))//2), + cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 1) + cv2.imshow("水平线", h_lines_img) + cv2.waitKey(delay) + + # 选择得分最高的线作为最远横线 + selected_line = horizontal_lines[0][0] + selected_slope = horizontal_lines[0][2] + selected_score = horizontal_lines[0][4] + + # 提取线段端点 + if isinstance(selected_line, np.ndarray) and selected_line.shape[0] == 4: + x1, y1, x2, y2 = selected_line + else: + x1, y1, x2, y2 = selected_line + + # 确保x1 < x2 + if x1 > x2: + x1, x2 = x2, x1 + y1, y2 = y2, y1 + + # 计算中线与检测到的横向线的交点 + # 横向线方程: y = slope * (x - x1) + y1 + # 中线方程: x = center_x + # 解这个方程组得到交点坐标 + intersection_x = center_x + intersection_y = selected_slope * (center_x - x1) + y1 + intersection_point = (int(intersection_x), int(intersection_y)) + + # 计算交点到图像底部的距离（以像素为单位） + distance_to_bottom = height - intersection_y + + # 检查交点是否在合理范围内 + valid_result = True + reason = "" + if intersection_y < 0: + valid_result = False + reason += "交点y坐标超出图像上边界; " + elif intersection_y > height * 0.95: # 允许交点在靠近底部但不太接近底部的位置 + valid_result = False + reason += "交点y坐标过于接近图像底部; " + + # 可视化结果 + result_img = None + if observe or save_log: + result_img = img.copy() + # 画出检测到的线 + line_color = (0, 255, 0) if valid_result else (0, 0, 255) + cv2.line(result_img, (int(x1), int(y1)), (int(x2), int(y2)), line_color, 2) + # 画出中线 + cv2.line(result_img, (center_x, 0), (center_x, height), (0, 0, 255), 2) + # 标记中线与横向线的交点 + cv2.circle(result_img, intersection_point, 12, (255, 0, 255), -1) + cv2.circle(result_img, intersection_point, 5, (255, 255, 255), -1) + + cv2.putText(result_img, f"Slope: {selected_slope:.4f}", (10, 30), + cv2.FONT_HERSHEY_SIMPLEX, 1, line_color, 2) + cv2.putText(result_img, f"Intersection: ({intersection_point[0]}, {intersection_point[1]})", (10, 70), + cv2.FONT_HERSHEY_SIMPLEX, 1, line_color, 2) + cv2.putText(result_img, f"Distance to bottom: {distance_to_bottom:.1f}px", (10, 110), + cv2.FONT_HERSHEY_SIMPLEX, 1, line_color, 2) + cv2.putText(result_img, f"Score: {selected_score:.2f}", (10, 150), + cv2.FONT_HERSHEY_SIMPLEX, 1, line_color, 2) + + if not valid_result: + cv2.putText(result_img, f"Warning: {reason}", (10, 190), + cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 255), 2) + + if observe: + debug("显示交点结果", "显示") + cv2.imshow("交点结果", 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) + + if result_img is not None: + img_path = os.path.join(log_dir, f"furthest_intersection_{timestamp}.jpg") + cv2.imwrite(img_path, result_img) + info(f"保存最远交点检测结果图像到: {img_path}", "日志") + + # 保存文本日志信息 + log_info = { + "timestamp": timestamp, + "intersection_point": intersection_point, + "distance_to_bottom": distance_to_bottom, + "slope": selected_slope, + "score": selected_score, + "valid": valid_result, + "reason": reason if not valid_result else "" + } + info(f"最远交点检测结果: {log_info}", "日志") + + # 即使结果无效也返回，方便调试 + # 创建交点信息字典 + intersection_info = { + "x": intersection_point[0], + "y": intersection_point[1], + "distance_to_bottom": distance_to_bottom, + "slope": selected_slope, + "is_horizontal": abs(selected_slope) < 0.05, # 判断是否接近水平 + "score": selected_score, # 线段质量得分 + "valid": valid_result, # 添加有效性标志 + "reason": reason if not valid_result else "" # 添加无效原因 + } + + # 即使交点可能无效，也返回计算结果，由调用者决定是否使用 + return intersection_point, intersection_info + +# 测试代码，仅在直接运行该文件时执行 +if __name__ == "__main__": + import sys + + if len(sys.argv) > 1: + image_path = sys.argv[1] + else: + image_path = "res/path/task-5/origin_horizontal_edge_20250528_100447_858352.jpg" # 默认测试图像 + + intersection_point, intersection_info = detect_furthest_horizontal_intersection(image_path, observe=True, delay=800) + + if intersection_point is not None: + print(f"检测到的最远交点: {intersection_point}") + print(f"交点信息: {intersection_info}") + else: + print("未检测到有效的最远交点") \ No newline at end of file diff --git a/utils/detect_track.py b/utils/detect_track.py index 8a0c113..c834383 100644 --- a/utils/detect_track.py +++ b/utils/detect_track.py @@ -362,7 +362,7 @@ def detect_horizontal_track_edge_v2(image, observe=False, delay=1000, save_log=T edge_point: 赛道前方边缘点的坐标 (x, y) edge_info: 边缘信息字典 """ - observe = False # TEST + # observe = False # TEST # 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) @@ -817,7 +817,6 @@ def detect_horizontal_track_edge_v3(image, observe=False, delay=1000, save_log=T edge_point: 赛道前方边缘点的坐标 (x, y) edge_info: 边缘信息字典 """ - observe = False # TEST # 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) @@ -1088,7 +1087,7 @@ def detect_horizontal_track_edge_v3(image, observe=False, delay=1000, save_log=T continue slope = (y2 - y1) / (x2 - x1) - + # 筛选接近水平的线 (斜率接近0)，但容许更大的倾斜度 if abs(slope) < max_slope: # 确保线在搜索区域内 @@ -1325,9 +1324,6 @@ def detect_horizontal_track_edge_v3(image, observe=False, delay=1000, save_log=T cv2.line(slope_img, intersection_point, (intersection_x, height), (255, 255, 0), 2) # 画出上下分界线 cv2.line(slope_img, (0, int(lower_upper_boundary)), (width, int(lower_upper_boundary)), (255, 0, 255), 1) - # 画出有效高度范围 - cv2.line(slope_img, (0, int(valid_y_range[0])), (width, int(valid_y_range[0])), (255, 255, 0), 1) - cv2.line(slope_img, (0, int(valid_y_range[1])), (width, int(valid_y_range[1])), (255, 255, 0), 1) cv2.putText(slope_img, f"Slope: {selected_slope:.4f}", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, line_color, 2) @@ -1352,7 +1348,7 @@ def detect_horizontal_track_edge_v3(image, observe=False, delay=1000, save_log=T result_img = slope_img # 保存日志图像 - if save_log: + 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)