Merge branch 'main' of ssh://120.27.199.238:222/Havoc420mac/mi-task into main
This commit is contained in:
commit
5c3e858029
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logs/robot_2025-05-28.log
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logs/robot_2025-05-28.log
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2025-05-28 18:10:21 | DEBUG | utils.log_helper - 🐞 步骤1: 原始图像已加载
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2025-05-28 18:10:23 | DEBUG | utils.log_helper - 🐞 步骤2: 创建黄色掩码
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2025-05-28 18:10:23 | DEBUG | utils.log_helper - 🐞 步骤3: 提取黄色部分
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2025-05-28 18:10:24 | DEBUG | utils.log_helper - 🐞 正在处理底部边缘点
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2025-05-28 18:10:25 | DEBUG | utils.log_helper - 🐞 显示拟合线段
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2025-05-28 18:10:26 | DEBUG | utils.log_helper - 👁️ 步骤5: 找到边缘点 (320, 764)
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2025-05-28 18:10:27 | DEBUG | utils.log_helper - 🐞 显示边缘斜率和中线交点
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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
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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
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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)}
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2025-05-28 18:10:48 | DEBUG | utils.log_helper - 🐞 步骤1: 原始图像已加载
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2025-05-28 18:10:49 | DEBUG | utils.log_helper - 🐞 步骤2: 创建黄色掩码
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2025-05-28 18:10:49 | DEBUG | utils.log_helper - 🐞 步骤3: 提取黄色部分
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2025-05-28 18:10:50 | DEBUG | utils.log_helper - 🐞 正在处理底部边缘点
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2025-05-28 18:10:51 | DEBUG | utils.log_helper - 🐞 显示拟合线段
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2025-05-28 18:10:52 | DEBUG | utils.log_helper - 👁️ 步骤5: 找到边缘点 (320, 765)
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2025-05-28 18:10:53 | DEBUG | utils.log_helper - 🐞 显示边缘斜率和中线交点
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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
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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
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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)}
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2025-05-28 18:11:06 | DEBUG | utils.log_helper - 🐞 步骤1: 原始图像已加载
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2025-05-28 18:11:07 | DEBUG | utils.log_helper - 🐞 步骤2: 创建黄色掩码
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2025-05-28 18:11:08 | DEBUG | utils.log_helper - 🐞 步骤3: 提取黄色部分
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2025-05-28 18:11:08 | DEBUG | utils.log_helper - 🐞 正在处理底部边缘点
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2025-05-28 18:11:09 | DEBUG | utils.log_helper - 🐞 显示拟合线段
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2025-05-28 18:11:10 | DEBUG | utils.log_helper - 👁️ 步骤5: 找到边缘点 (1600, 1079)
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2025-05-28 18:11:11 | DEBUG | utils.log_helper - 🐞 显示边缘斜率和中线交点
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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
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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
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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)}
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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
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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
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2025-05-28 18:13:52 | DEBUG | utils.log_helper - 🐞 步骤1: 原始图像已加载
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2025-05-28 18:13:53 | DEBUG | utils.log_helper - 🐞 步骤2: 创建黄色掩码
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2025-05-28 18:13:54 | DEBUG | utils.log_helper - 🐞 步骤3: 提取黄色部分
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2025-05-28 18:13:55 | DEBUG | utils.log_helper - 🐞 检测底部和顶部边缘点
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2025-05-28 18:13:55 | DEBUG | utils.log_helper - 🐞 步骤4: 边缘检测
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2025-05-28 18:13:56 | DEBUG | utils.log_helper - 🐞 步骤5: 检测到 14 条直线
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2025-05-28 18:13:57 | DEBUG | utils.log_helper - 🐞 步骤5.1: 合并后剩余 7 条线
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2025-05-28 18:13:58 | ERROR | utils.log_helper - ❌ 未检测到合格的水平线
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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
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2025-05-28 18:19:56 | DEBUG | utils.log_helper - 🐞 步骤1: 原始图像已加载
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2025-05-28 18:19:57 | DEBUG | utils.log_helper - 🐞 步骤2: 创建黄色掩码
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2025-05-28 18:19:58 | DEBUG | utils.log_helper - 🐞 步骤3: 提取黄色部分
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2025-05-28 18:19:59 | DEBUG | utils.log_helper - 🐞 步骤4: 检测边缘点
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2025-05-28 18:20:00 | DEBUG | utils.log_helper - 🐞 步骤5: 边缘检测
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2025-05-28 18:20:00 | DEBUG | utils.log_helper - 🐞 步骤6: 检测到 16 条直线
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2025-05-28 18:20:01 | DEBUG | utils.log_helper - 🐞 步骤7: 合并后剩余 7 条线
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2025-05-28 18:20:02 | DEBUG | utils.log_helper - 🐞 步骤8: 找到 1 条水平线
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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
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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
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2025-05-28 18:20:04 | DEBUG | utils.log_helper - 🐞 步骤9: 显示最终结果
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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
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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}
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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
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2025-05-28 18:20:27 | DEBUG | utils.log_helper - 🐞 步骤1: 原始图像已加载
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2025-05-28 18:20:28 | DEBUG | utils.log_helper - 🐞 步骤2: 创建黄色掩码
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2025-05-28 18:20:28 | DEBUG | utils.log_helper - 🐞 步骤3: 提取黄色部分
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2025-05-28 18:20:29 | DEBUG | utils.log_helper - 🐞 步骤4: 检测边缘点
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2025-05-28 18:20:30 | DEBUG | utils.log_helper - 🐞 步骤5: 边缘检测
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2025-05-28 18:20:31 | DEBUG | utils.log_helper - 🐞 步骤6: 检测到 15 条直线
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2025-05-28 18:20:32 | DEBUG | utils.log_helper - 🐞 步骤7: 合并后剩余 8 条线
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2025-05-28 18:20:33 | DEBUG | utils.log_helper - 🐞 步骤8: 找到 1 条水平线
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2025-05-28 18:20:33 | DEBUG | utils.log_helper - 🐞 步骤9: 显示最终结果
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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
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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}
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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
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2025-05-28 18:20:50 | DEBUG | utils.log_helper - 🐞 步骤1: 原始图像已加载
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2025-05-28 18:20:51 | DEBUG | utils.log_helper - 🐞 步骤2: 创建黄色掩码
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2025-05-28 18:20:52 | DEBUG | utils.log_helper - 🐞 步骤3: 提取黄色部分
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2025-05-28 18:20:52 | DEBUG | utils.log_helper - 🐞 步骤4: 检测边缘点
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2025-05-28 18:20:53 | DEBUG | utils.log_helper - 🐞 步骤5: 边缘检测
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2025-05-28 18:20:54 | DEBUG | utils.log_helper - 🐞 步骤6: 检测到 15 条直线
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2025-05-28 18:20:55 | DEBUG | utils.log_helper - 🐞 步骤7: 合并后剩余 8 条线
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2025-05-28 18:20:56 | DEBUG | utils.log_helper - 🐞 步骤8: 找到 1 条水平线
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2025-05-28 18:20:57 | DEBUG | utils.log_helper - 🐞 步骤9: 显示最终结果
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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
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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}
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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
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2025-05-28 18:21:01 | DEBUG | utils.log_helper - 🐞 步骤1: 原始图像已加载
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2025-05-28 18:21:02 | DEBUG | utils.log_helper - 🐞 步骤2: 创建黄色掩码
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2025-05-28 18:21:02 | DEBUG | utils.log_helper - 🐞 步骤3: 提取黄色部分
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2025-05-28 18:21:03 | DEBUG | utils.log_helper - 🐞 步骤4: 检测边缘点
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2025-05-28 18:21:04 | DEBUG | utils.log_helper - 🐞 步骤5: 边缘检测
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2025-05-28 18:21:05 | DEBUG | utils.log_helper - 🐞 步骤6: 检测到 15 条直线
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2025-05-28 18:21:06 | DEBUG | utils.log_helper - 🐞 步骤7: 合并后剩余 8 条线
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2025-05-28 18:21:06 | DEBUG | utils.log_helper - 🐞 步骤8: 找到 1 条水平线
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2025-05-28 18:21:07 | DEBUG | utils.log_helper - 🐞 步骤9: 显示最终结果
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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
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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}
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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
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2025-05-28 18:21:26 | DEBUG | utils.log_helper - 🐞 步骤1: 原始图像已加载
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2025-05-28 18:21:27 | DEBUG | utils.log_helper - 🐞 步骤2: 创建黄色掩码
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2025-05-28 18:21:28 | DEBUG | utils.log_helper - 🐞 步骤3: 提取黄色部分
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2025-05-28 18:21:29 | DEBUG | utils.log_helper - 🐞 步骤4: 检测边缘点
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2025-05-28 18:21:30 | DEBUG | utils.log_helper - 🐞 步骤5: 边缘检测
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2025-05-28 18:21:30 | DEBUG | utils.log_helper - 🐞 步骤6: 检测到 16 条直线
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2025-05-28 18:21:31 | DEBUG | utils.log_helper - 🐞 步骤7: 合并后剩余 7 条线
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2025-05-28 18:21:32 | DEBUG | utils.log_helper - 🐞 步骤8: 找到 1 条水平线
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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
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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
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2025-05-28 18:21:34 | DEBUG | utils.log_helper - 🐞 步骤9: 显示最终结果
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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
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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}
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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
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2025-05-28 18:33:44 | DEBUG | utils.log_helper - 🐞 步骤1: 原始图像已加载
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2025-05-28 18:33:45 | DEBUG | utils.log_helper - 🐞 步骤2: 创建黄色掩码
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2025-05-28 18:33:45 | DEBUG | utils.log_helper - 🐞 步骤3: 提取黄色部分
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2025-05-28 18:33:46 | DEBUG | utils.log_helper - 🐞 步骤4: 检测边缘点
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2025-05-28 18:33:47 | DEBUG | utils.log_helper - 🐞 步骤5: 边缘检测
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2025-05-28 18:33:48 | DEBUG | utils.log_helper - 🐞 步骤6: 检测到 16 条直线
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2025-05-28 18:33:49 | DEBUG | utils.log_helper - 🐞 步骤7: 合并后剩余 7 条线
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2025-05-28 18:33:50 | DEBUG | utils.log_helper - 🐞 步骤8: 找到 3 条水平线
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2025-05-28 18:33:50 | DEBUG | utils.log_helper - 🐞 步骤9: 显示最终结果
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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
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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}
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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
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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
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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])}
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2025-05-28 18:34:28 | DEBUG | utils.log_helper - 🐞 步骤1: 原始图像已加载
|
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2025-05-28 18:34:29 | DEBUG | utils.log_helper - 🐞 步骤2: 创建黄色掩码
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2025-05-28 18:34:30 | DEBUG | utils.log_helper - 🐞 步骤3: 提取黄色部分
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||||
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
|
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2025-05-28 18:34:32 | DEBUG | utils.log_helper - 🐞 步骤5: 检测到 16 条直线
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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)
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2025-05-28 18:34:34 | DEBUG | utils.log_helper - 🐞 选择下方水平线
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2025-05-28 18:34:34 | DEBUG | utils.log_helper - 🐞 显示边缘斜率和中线交点
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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
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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
|
||||
@ -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')
|
||||
|
||||
578
utils/detect_furthest_intersection.py
Normal file
578
utils/detect_furthest_intersection.py
Normal file
@ -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")
|
||||
@ -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)
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user