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2025-05-31 09:49:18 | INFO | utils.log_helper - ℹ️ 保存双轨迹线检测结果图像到: logs/image/dual_track_20250531_094918_337104.jpg
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2025-05-31 09:49:18 | INFO | utils.log_helper - ℹ️ 保存原始图像到: logs/image/dual_track_orig_20250531_094918_337104.jpg
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2025-05-31 09:49:18 | INFO | utils.log_helper - ℹ️ 双轨迹线检测结果: {'timestamp': '20250531_094918_337104', 'center_point': (944, 1080), 'deviation': -15.5, 'left_track_mid_x': 624.0, 'right_track_mid_x': 1219.0, 'track_width': 595.0, 'center_slope': 0.02132675438596272, 'stone_path_mode': False}
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2025-05-31 10:29:14 | DEBUG | utils.log_helper - 🐞 步骤1: 创建黄色掩码
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2025-05-31 10:29:16 | DEBUG | utils.log_helper - 🐞 步骤1.5: 底部区域掩码
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2025-05-31 10:29:18 | DEBUG | utils.log_helper - 🐞 步骤2: 边缘检测
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2025-05-31 10:29:20 | DEBUG | utils.log_helper - 🐞 步骤3: 检测到 49 条直线
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2025-05-31 10:29:22 | DEBUG | utils.log_helper - 🐞 步骤4: 找到 19 条垂直线
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2025-05-31 10:29:24 | DEBUG | utils.log_helper - 🐞 步骤4.5: 合并后找到 10 条垂直线
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2025-05-31 10:29:26 | DEBUG | utils.log_helper - 🐞 左侧候选线数量: 5, 右侧候选线数量: 5
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2025-05-31 10:29:26 | DEBUG | utils.log_helper - 🐞 选择最佳线对，评分: 0.89
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2025-05-31 10:29:28 | INFO | utils.log_helper - ℹ️ 保存双轨迹线检测结果图像到: logs/image/dual_track_20250531_102928_778495.jpg
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2025-05-31 10:29:28 | INFO | utils.log_helper - ℹ️ 保存原始图像到: logs/image/dual_track_orig_20250531_102928_778495.jpg
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2025-05-31 10:29:28 | INFO | utils.log_helper - ℹ️ 双轨迹线检测结果: {'timestamp': '20250531_102928_778495', 'center_point': (952, 1080), 'deviation': -1.1368683772161603e-13, 'left_track_mid_x': 528.0, 'right_track_mid_x': 1388.5, 'track_width': 860.5, 'center_slope': -0.0016446763600527115}
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2025-05-31 10:29:37 | DEBUG | utils.log_helper - 🐞 步骤1: 创建黄色掩码
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2025-05-31 10:29:39 | DEBUG | utils.log_helper - 🐞 步骤1.5: 底部区域掩码
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2025-05-31 10:29:41 | DEBUG | utils.log_helper - 🐞 步骤2: 边缘检测
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2025-05-31 10:29:44 | DEBUG | utils.log_helper - 🐞 步骤3: 检测到 54 条直线
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2025-05-31 10:29:46 | DEBUG | utils.log_helper - 🐞 步骤4: 找到 24 条垂直线
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2025-05-31 10:29:48 | DEBUG | utils.log_helper - 🐞 左侧候选线数量: 12, 右侧候选线数量: 12
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2025-05-31 10:29:48 | DEBUG | utils.log_helper - 🐞 选择最佳线对，评分: 1.02
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2025-05-31 10:29:50 | INFO | utils.log_helper - ℹ️ 保存双轨迹线检测结果图像到: logs/image/dual_track_20250531_102950_067699.jpg
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2025-05-31 10:29:50 | INFO | utils.log_helper - ℹ️ 保存原始图像到: logs/image/dual_track_orig_20250531_102950_067699.jpg
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2025-05-31 10:29:50 | INFO | utils.log_helper - ℹ️ 双轨迹线检测结果: {'timestamp': '20250531_102950_067699', 'center_point': (944, 1080), 'deviation': -1.1368683772161603e-13, 'left_track_mid_x': 565.0, 'right_track_mid_x': 1335.5, 'track_width': 770.5, 'center_slope': 0.01738261738261669, 'stone_path_mode': False}
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2025-05-31 10:32:18 | DEBUG | utils.log_helper - 🐞 步骤1: 创建黄色掩码
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2025-05-31 10:32:20 | DEBUG | utils.log_helper - 🐞 步骤1.5: 底部区域掩码
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2025-05-31 10:32:22 | DEBUG | utils.log_helper - 🐞 步骤2: 边缘检测
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2025-05-31 10:32:24 | DEBUG | utils.log_helper - 🐞 步骤3: 检测到 54 条直线
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2025-05-31 10:32:26 | DEBUG | utils.log_helper - 🐞 步骤4: 找到 24 条垂直线
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2025-05-31 10:32:28 | DEBUG | utils.log_helper - 🐞 左侧候选线数量: 12, 右侧候选线数量: 12
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2025-05-31 10:32:28 | DEBUG | utils.log_helper - 🐞 选择最佳线对，评分: 1.02
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2025-05-31 10:32:30 | INFO | utils.log_helper - ℹ️ 保存双轨迹线检测结果图像到: logs/image/dual_track_20250531_103230_706359.jpg
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2025-05-31 10:32:30 | INFO | utils.log_helper - ℹ️ 保存原始图像到: logs/image/dual_track_orig_20250531_103230_706359.jpg
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2025-05-31 10:32:30 | INFO | utils.log_helper - ℹ️ 双轨迹线检测结果: {'timestamp': '20250531_103230_706359', 'center_point': (944, 1080), 'deviation': -1.1368683772161603e-13, 'left_track_mid_x': 565.0, 'right_track_mid_x': 1335.5, 'track_width': 770.5, 'center_slope': 0.01738261738261669}
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2025-05-31 10:54:05 | DEBUG | utils.log_helper - 🐞 步骤1: 创建黄色掩码
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2025-05-31 10:54:06 | DEBUG | utils.log_helper - 🐞 步骤1.5: 底部区域掩码
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2025-05-31 10:54:06 | DEBUG | utils.log_helper - 🐞 步骤2: 边缘检测
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2025-05-31 10:54:07 | DEBUG | utils.log_helper - 🐞 步骤3: 检测到 54 条直线
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2025-05-31 10:54:08 | DEBUG | utils.log_helper - 🐞 步骤4: 找到 24 条垂直线
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2025-05-31 10:54:09 | DEBUG | utils.log_helper - 🐞 左侧候选线数量: 12, 右侧候选线数量: 12
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2025-05-31 10:54:09 | DEBUG | utils.log_helper - 🐞 选择最佳线对，评分: 1.01
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2025-05-31 10:54:10 | INFO | utils.log_helper - ℹ️ 保存双轨迹线检测结果图像到: logs/image/dual_track_20250531_105410_128420.jpg
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2025-05-31 10:54:10 | INFO | utils.log_helper - ℹ️ 保存原始图像到: logs/image/dual_track_orig_20250531_105410_128420.jpg
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2025-05-31 10:54:10 | INFO | utils.log_helper - ℹ️ 双轨迹线检测结果: {'timestamp': '20250531_105410_128420', 'center_point': (944, 1080), 'deviation': -1.1368683772161603e-13, 'left_track_mid_x': 565.0, 'right_track_mid_x': 1335.5, 'track_width': 770.5, 'center_slope': 0.01738261738261669}
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2025-05-31 10:56:14 | DEBUG | utils.log_helper - 🐞 步骤1: 创建黄色掩码
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2025-05-31 10:56:15 | DEBUG | utils.log_helper - 🐞 步骤1.5: 底部区域掩码
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2025-05-31 10:56:16 | DEBUG | utils.log_helper - 🐞 步骤2: 边缘检测
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2025-05-31 10:56:16 | DEBUG | utils.log_helper - 🐞 步骤3: 检测到 54 条直线
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2025-05-31 10:56:17 | DEBUG | utils.log_helper - 🐞 步骤4: 找到 24 条垂直线
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2025-05-31 10:56:18 | DEBUG | utils.log_helper - 🐞 左侧候选线数量: 12, 右侧候选线数量: 12
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2025-05-31 10:56:18 | DEBUG | utils.log_helper - 🐞 选择最佳线对，评分: 1.01
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2025-05-31 10:56:19 | INFO | utils.log_helper - ℹ️ 保存双轨迹线检测结果图像到: logs/image/dual_track_20250531_105619_406431.jpg
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2025-05-31 10:56:19 | INFO | utils.log_helper - ℹ️ 保存原始图像到: logs/image/dual_track_orig_20250531_105619_406431.jpg
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2025-05-31 10:56:19 | INFO | utils.log_helper - ℹ️ 双轨迹线检测结果: {'timestamp': '20250531_105619_406431', 'center_point': (944, 1080), 'deviation': -1.1368683772161603e-13, 'left_track_mid_x': 565.0, 'right_track_mid_x': 1335.5, 'track_width': 770.5, 'center_slope': 0.01738261738261669}
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2025-05-31 10:59:46 | DEBUG | utils.log_helper - 🐞 步骤1: 创建黄色掩码
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2025-05-31 10:59:47 | DEBUG | utils.log_helper - 🐞 步骤1.5: 底部区域掩码
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2025-05-31 10:59:48 | DEBUG | utils.log_helper - 🐞 步骤2: 边缘检测
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2025-05-31 10:59:49 | DEBUG | utils.log_helper - 🐞 步骤3: 检测到 54 条直线
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2025-05-31 10:59:50 | DEBUG | utils.log_helper - 🐞 步骤4: 找到 24 条垂直线
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2025-05-31 10:59:51 | DEBUG | utils.log_helper - 🐞 左侧候选线数量: 12, 右侧候选线数量: 12
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2025-05-31 10:59:51 | DEBUG | utils.log_helper - 🐞 选择最佳线对，评分: 1.01
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2025-05-31 10:59:51 | INFO | utils.log_helper - ℹ️ 保存双轨迹线检测结果图像到: logs/image/dual_track_20250531_105951_966275.jpg
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2025-05-31 10:59:51 | INFO | utils.log_helper - ℹ️ 保存原始图像到: logs/image/dual_track_orig_20250531_105951_966275.jpg
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2025-05-31 10:59:51 | INFO | utils.log_helper - ℹ️ 双轨迹线检测结果: {'timestamp': '20250531_105951_966275', 'center_point': (944, 1080), 'deviation': -1.1368683772161603e-13, 'left_track_mid_x': 565.0, 'right_track_mid_x': 1335.5, 'track_width': 770.5, 'center_slope': 0.01738261738261669}
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2025-05-31 11:09:05 | DEBUG | utils.log_helper - 🐞 步骤1: 创建黄色掩码
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2025-05-31 11:09:06 | DEBUG | utils.log_helper - 🐞 步骤1.5: 底部区域掩码
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2025-05-31 11:09:07 | DEBUG | utils.log_helper - 🐞 步骤2: 边缘检测
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2025-05-31 11:09:08 | DEBUG | utils.log_helper - 🐞 步骤3: 检测到 54 条直线
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2025-05-31 11:09:09 | DEBUG | utils.log_helper - 🐞 步骤4: 找到 24 条垂直线
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2025-05-31 11:09:10 | DEBUG | utils.log_helper - 🐞 左侧候选线数量: 12, 右侧候选线数量: 12
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2025-05-31 11:09:10 | DEBUG | utils.log_helper - 🐞 选择最佳线对，评分: 1.01
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2025-05-31 11:09:10 | INFO | utils.log_helper - ℹ️ 保存双轨迹线检测结果图像到: logs/image/dual_track_20250531_110910_854647.jpg
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2025-05-31 11:09:10 | INFO | utils.log_helper - ℹ️ 保存原始图像到: logs/image/dual_track_orig_20250531_110910_854647.jpg
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2025-05-31 11:09:10 | INFO | utils.log_helper - ℹ️ 双轨迹线检测结果: {'timestamp': '20250531_110910_854647', 'center_point': (944, 1080), 'deviation': -1.1368683772161603e-13, 'left_track_mid_x': 565.0, 'right_track_mid_x': 1335.5, 'track_width': 770.5, 'center_slope': 0.01738261738261669}
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2025-05-31 11:09:24 | DEBUG | utils.log_helper - 🐞 步骤1: 创建黄色掩码
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2025-05-31 11:09:26 | DEBUG | utils.log_helper - 🐞 步骤1.5: 底部区域掩码
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2025-05-31 11:09:28 | DEBUG | utils.log_helper - 🐞 步骤2: 边缘检测
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2025-05-31 11:09:30 | DEBUG | utils.log_helper - 🐞 步骤3: 检测到 54 条直线
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2025-05-31 11:09:32 | DEBUG | utils.log_helper - 🐞 步骤4: 找到 24 条垂直线
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2025-05-31 11:09:34 | DEBUG | utils.log_helper - 🐞 左侧候选线数量: 12, 右侧候选线数量: 12
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2025-05-31 11:09:34 | DEBUG | utils.log_helper - 🐞 选择最佳线对，评分: 1.01
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2025-05-31 11:09:36 | INFO | utils.log_helper - ℹ️ 保存双轨迹线检测结果图像到: logs/image/dual_track_20250531_110936_844379.jpg
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2025-05-31 11:09:36 | INFO | utils.log_helper - ℹ️ 保存原始图像到: logs/image/dual_track_orig_20250531_110936_844379.jpg
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2025-05-31 11:09:36 | INFO | utils.log_helper - ℹ️ 双轨迹线检测结果: {'timestamp': '20250531_110936_844379', 'center_point': (944, 1080), 'deviation': -1.1368683772161603e-13, 'left_track_mid_x': 565.0, 'right_track_mid_x': 1335.5, 'track_width': 770.5, 'center_slope': 0.01738261738261669}
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2025-05-31 11:14:39 | DEBUG | utils.log_helper - 🐞 步骤1: 创建黄色掩码
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2025-05-31 11:14:41 | DEBUG | utils.log_helper - 🐞 步骤1.5: 底部区域掩码
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2025-05-31 11:14:43 | DEBUG | utils.log_helper - 🐞 步骤2: 边缘检测
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2025-05-31 11:14:45 | DEBUG | utils.log_helper - 🐞 步骤3: 检测到 54 条直线
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2025-05-31 11:14:47 | DEBUG | utils.log_helper - 🐞 步骤4: 找到 24 条垂直线
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2025-05-31 11:14:49 | DEBUG | utils.log_helper - 🐞 左侧候选线数量: 12, 右侧候选线数量: 12
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2025-05-31 11:14:49 | DEBUG | utils.log_helper - 🐞 选择最佳线对，评分: 1.01
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2025-05-31 11:14:51 | INFO | utils.log_helper - ℹ️ 保存双轨迹线检测结果图像到: logs/image/dual_track_20250531_111451_964924.jpg
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2025-05-31 11:14:51 | INFO | utils.log_helper - ℹ️ 保存原始图像到: logs/image/dual_track_orig_20250531_111451_964924.jpg
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2025-05-31 11:14:51 | INFO | utils.log_helper - ℹ️ 双轨迹线检测结果: {'timestamp': '20250531_111451_964924', 'center_point': (944, 1080), 'deviation': -1.1368683772161603e-13, 'left_track_mid_x': 565.0, 'right_track_mid_x': 1335.5, 'track_width': 770.5, 'center_slope': 0.01738261738261669}
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2025-05-31 11:14:56 | DEBUG | utils.log_helper - 🐞 步骤1: 创建黄色掩码
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2025-05-31 11:14:58 | DEBUG | utils.log_helper - 🐞 步骤1.5: 底部区域掩码
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2025-05-31 11:15:00 | DEBUG | utils.log_helper - 🐞 步骤2: 边缘检测
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2025-05-31 11:15:02 | DEBUG | utils.log_helper - 🐞 步骤3: 检测到 54 条直线
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2025-05-31 11:15:04 | DEBUG | utils.log_helper - 🐞 步骤4: 找到 24 条垂直线
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2025-05-31 11:15:06 | DEBUG | utils.log_helper - 🐞 左侧候选线数量: 12, 右侧候选线数量: 12
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2025-05-31 11:15:06 | DEBUG | utils.log_helper - 🐞 选择最佳线对，评分: 1.01
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2025-05-31 11:15:08 | INFO | utils.log_helper - ℹ️ 保存双轨迹线检测结果图像到: logs/image/dual_track_20250531_111508_434244.jpg
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2025-05-31 11:15:08 | INFO | utils.log_helper - ℹ️ 保存原始图像到: logs/image/dual_track_orig_20250531_111508_434244.jpg
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2025-05-31 11:15:08 | INFO | utils.log_helper - ℹ️ 双轨迹线检测结果: {'timestamp': '20250531_111508_434244', 'center_point': (944, 1080), 'deviation': -1.1368683772161603e-13, 'left_track_mid_x': 565.0, 'right_track_mid_x': 1335.5, 'track_width': 770.5, 'center_slope': 0.01738261738261669}
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2025-05-31 11:15:23 | DEBUG | utils.log_helper - 🐞 步骤1: 创建黄色掩码
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2025-05-31 11:15:24 | DEBUG | utils.log_helper - 🐞 步骤1.5: 底部区域掩码
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2025-05-31 11:15:25 | DEBUG | utils.log_helper - 🐞 步骤2: 边缘检测
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2025-05-31 11:15:26 | DEBUG | utils.log_helper - 🐞 步骤3: 检测到 54 条直线
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2025-05-31 11:15:27 | DEBUG | utils.log_helper - 🐞 步骤4: 找到 24 条垂直线
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2025-05-31 11:15:28 | DEBUG | utils.log_helper - 🐞 左侧候选线数量: 12, 右侧候选线数量: 12
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2025-05-31 11:15:28 | DEBUG | utils.log_helper - 🐞 选择最佳线对，评分: 1.01
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2025-05-31 11:15:29 | INFO | utils.log_helper - ℹ️ 保存双轨迹线检测结果图像到: logs/image/dual_track_20250531_111529_709703.jpg
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2025-05-31 11:15:29 | INFO | utils.log_helper - ℹ️ 保存原始图像到: logs/image/dual_track_orig_20250531_111529_709703.jpg
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2025-05-31 11:15:29 | INFO | utils.log_helper - ℹ️ 双轨迹线检测结果: {'timestamp': '20250531_111529_709703', 'center_point': (944, 1080), 'deviation': -1.1368683772161603e-13, 'left_track_mid_x': 565.0, 'right_track_mid_x': 1335.5, 'track_width': 770.5, 'center_slope': 0.01738261738261669}
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2025-05-31 11:15:35 | DEBUG | utils.log_helper - 🐞 步骤1: 创建黄色掩码
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2025-05-31 11:15:36 | DEBUG | utils.log_helper - 🐞 步骤1.5: 底部区域掩码
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2025-05-31 11:15:37 | DEBUG | utils.log_helper - 🐞 步骤2: 边缘检测
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2025-05-31 11:15:38 | DEBUG | utils.log_helper - 🐞 步骤3: 检测到 54 条直线
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2025-05-31 11:15:39 | DEBUG | utils.log_helper - 🐞 步骤4: 找到 27 条垂直线
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2025-05-31 11:15:40 | DEBUG | utils.log_helper - 🐞 左侧候选线数量: 15, 右侧候选线数量: 12
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2025-05-31 11:15:40 | DEBUG | utils.log_helper - 🐞 选择最佳线对，评分: 1.01
|
||||
2025-05-31 11:15:41 | INFO | utils.log_helper - ℹ️ 保存双轨迹线检测结果图像到: logs/image/dual_track_20250531_111541_404961.jpg
|
||||
2025-05-31 11:15:41 | INFO | utils.log_helper - ℹ️ 保存原始图像到: logs/image/dual_track_orig_20250531_111541_404961.jpg
|
||||
2025-05-31 11:15:41 | INFO | utils.log_helper - ℹ️ 双轨迹线检测结果: {'timestamp': '20250531_111541_404961', 'center_point': (944, 1080), 'deviation': -1.1368683772161603e-13, 'left_track_mid_x': 565.0, 'right_track_mid_x': 1335.5, 'track_width': 770.5, 'center_slope': 0.01738261738261669}
|
||||
2025-05-31 11:18:59 | DEBUG | utils.log_helper - 🐞 步骤1: 创建黄色掩码
|
||||
2025-05-31 11:19:00 | DEBUG | utils.log_helper - 🐞 步骤1.5: 底部区域掩码
|
||||
2025-05-31 11:19:01 | DEBUG | utils.log_helper - 🐞 步骤2: 边缘检测
|
||||
2025-05-31 11:19:03 | DEBUG | utils.log_helper - 🐞 步骤3: 检测到 54 条直线
|
||||
2025-05-31 11:19:04 | DEBUG | utils.log_helper - 🐞 步骤4: 找到 27 条垂直线
|
||||
2025-05-31 11:19:05 | DEBUG | utils.log_helper - 🐞 左侧候选线数量: 15, 右侧候选线数量: 12
|
||||
2025-05-31 11:19:05 | DEBUG | utils.log_helper - 🐞 选择最佳线对，评分: 1.01
|
||||
2025-05-31 11:19:06 | INFO | utils.log_helper - ℹ️ 保存双轨迹线检测结果图像到: logs/image/dual_track_20250531_111906_059419.jpg
|
||||
2025-05-31 11:19:06 | INFO | utils.log_helper - ℹ️ 保存原始图像到: logs/image/dual_track_orig_20250531_111906_059419.jpg
|
||||
2025-05-31 11:19:06 | INFO | utils.log_helper - ℹ️ 双轨迹线检测结果: {'timestamp': '20250531_111906_059419', 'center_point': (944, 1080), 'deviation': -1.1368683772161603e-13, 'left_track_mid_x': 565.0, 'right_track_mid_x': 1335.5, 'track_width': 770.5, 'center_slope': 0.01738261738261669}
|
||||
2025-05-31 11:20:17 | DEBUG | utils.log_helper - 🐞 步骤1: 创建黄色掩码
|
||||
2025-05-31 11:20:19 | DEBUG | utils.log_helper - 🐞 步骤1.5: 底部区域掩码
|
||||
2025-05-31 11:20:20 | DEBUG | utils.log_helper - 🐞 步骤2: 边缘检测
|
||||
2025-05-31 11:20:21 | DEBUG | utils.log_helper - 🐞 步骤3: 检测到 54 条直线
|
||||
2025-05-31 11:20:22 | DEBUG | utils.log_helper - 🐞 步骤4: 找到 27 条垂直线
|
||||
2025-05-31 11:20:23 | DEBUG | utils.log_helper - 🐞 左侧候选线数量: 15, 右侧候选线数量: 12
|
||||
2025-05-31 11:20:24 | DEBUG | utils.log_helper - 🐞 选择最佳线对，评分: 1.01
|
||||
2025-05-31 11:20:25 | INFO | utils.log_helper - ℹ️ 保存双轨迹线检测结果图像到: logs/image/dual_track_20250531_112025_176560.jpg
|
||||
2025-05-31 11:20:25 | INFO | utils.log_helper - ℹ️ 保存原始图像到: logs/image/dual_track_orig_20250531_112025_176560.jpg
|
||||
2025-05-31 11:20:25 | INFO | utils.log_helper - ℹ️ 双轨迹线检测结果: {'timestamp': '20250531_112025_176560', 'center_point': (944, 1080), 'deviation': -1.1368683772161603e-13, 'left_track_mid_x': 565.0, 'right_track_mid_x': 1335.5, 'track_width': 770.5, 'center_slope': 0.01738261738261669}
|
||||
2025-05-31 11:20:36 | DEBUG | utils.log_helper - 🐞 步骤1: 创建黄色掩码
|
||||
2025-05-31 11:20:37 | DEBUG | utils.log_helper - 🐞 步骤1.5: 底部区域掩码
|
||||
2025-05-31 11:20:38 | DEBUG | utils.log_helper - 🐞 步骤2: 边缘检测
|
||||
2025-05-31 11:20:39 | DEBUG | utils.log_helper - 🐞 步骤3: 检测到 54 条直线
|
||||
2025-05-31 11:20:40 | DEBUG | utils.log_helper - 🐞 步骤4: 找到 27 条垂直线
|
||||
2025-05-31 11:20:41 | DEBUG | utils.log_helper - 🐞 左侧候选线数量: 15, 右侧候选线数量: 12
|
||||
2025-05-31 11:20:42 | DEBUG | utils.log_helper - 🐞 选择最佳线对，评分: 1.01
|
||||
2025-05-31 11:20:43 | INFO | utils.log_helper - ℹ️ 保存双轨迹线检测结果图像到: logs/image/dual_track_20250531_112043_362415.jpg
|
||||
2025-05-31 11:20:43 | INFO | utils.log_helper - ℹ️ 保存原始图像到: logs/image/dual_track_orig_20250531_112043_362415.jpg
|
||||
2025-05-31 11:20:43 | INFO | utils.log_helper - ℹ️ 双轨迹线检测结果: {'timestamp': '20250531_112043_362415', 'center_point': (944, 1080), 'deviation': -1.1368683772161603e-13, 'left_track_mid_x': 565.0, 'right_track_mid_x': 1335.5, 'track_width': 770.5, 'center_slope': 0.01738261738261669}
|
||||
2025-05-31 11:47:40 | DEBUG | utils.log_helper - 🐞 步骤1: 创建黄色掩码
|
||||
2025-05-31 11:47:42 | DEBUG | utils.log_helper - 🐞 步骤1.5: 底部区域掩码
|
||||
2025-05-31 11:47:43 | DEBUG | utils.log_helper - 🐞 步骤2: 边缘检测
|
||||
2025-05-31 11:47:44 | DEBUG | utils.log_helper - 🐞 步骤2.5: 初始检测到 54 条直线，尝试合并
|
||||
2025-05-31 11:47:44 | DEBUG | utils.log_helper - 🐞 合并后剩余 31 条直线
|
||||
2025-05-31 11:47:44 | DEBUG | utils.log_helper - 🐞 步骤3: 检测到 31 条直线
|
||||
2025-05-31 11:47:45 | DEBUG | utils.log_helper - 🐞 步骤4: 找到 15 条垂直线
|
||||
2025-05-31 11:47:46 | DEBUG | utils.log_helper - 🐞 左侧候选线数量: 10, 右侧候选线数量: 5
|
||||
2025-05-31 11:47:47 | DEBUG | utils.log_helper - 🐞 选择最佳线对，评分: 0.95
|
||||
2025-05-31 11:47:48 | INFO | utils.log_helper - ℹ️ 保存双轨迹线检测结果图像到: logs/image/dual_track_20250531_114748_278587.jpg
|
||||
2025-05-31 11:47:48 | INFO | utils.log_helper - ℹ️ 保存原始图像到: logs/image/dual_track_orig_20250531_114748_278587.jpg
|
||||
2025-05-31 11:47:48 | INFO | utils.log_helper - ℹ️ 双轨迹线检测结果: {'timestamp': '20250531_114748_278587', 'center_point': (945, 1080), 'deviation': 3.410605131648481e-13, 'left_track_mid_x': 608.5, 'right_track_mid_x': 1219.0, 'track_width': 610.5, 'center_slope': 0.02351973684210257}
|
||||
2025-05-31 11:47:50 | DEBUG | utils.log_helper - 🐞 步骤1: 创建黄色掩码
|
||||
2025-05-31 11:47:51 | DEBUG | utils.log_helper - 🐞 步骤1.5: 底部区域掩码
|
||||
2025-05-31 11:47:52 | DEBUG | utils.log_helper - 🐞 步骤2: 边缘检测
|
||||
2025-05-31 11:47:53 | DEBUG | utils.log_helper - 🐞 步骤2.5: 初始检测到 54 条直线，尝试合并
|
||||
2025-05-31 11:47:53 | DEBUG | utils.log_helper - 🐞 合并后剩余 31 条直线
|
||||
2025-05-31 11:47:53 | DEBUG | utils.log_helper - 🐞 步骤3: 检测到 31 条直线
|
||||
2025-05-31 11:47:54 | DEBUG | utils.log_helper - 🐞 步骤4: 找到 15 条垂直线
|
||||
2025-05-31 11:47:55 | DEBUG | utils.log_helper - 🐞 左侧候选线数量: 10, 右侧候选线数量: 5
|
||||
2025-05-31 11:47:56 | DEBUG | utils.log_helper - 🐞 选择最佳线对，评分: 0.95
|
||||
2025-05-31 11:47:57 | INFO | utils.log_helper - ℹ️ 保存双轨迹线检测结果图像到: logs/image/dual_track_20250531_114757_837088.jpg
|
||||
2025-05-31 11:47:57 | INFO | utils.log_helper - ℹ️ 保存原始图像到: logs/image/dual_track_orig_20250531_114757_837088.jpg
|
||||
2025-05-31 11:47:57 | INFO | utils.log_helper - ℹ️ 双轨迹线检测结果: {'timestamp': '20250531_114757_837088', 'center_point': (945, 1080), 'deviation': 3.410605131648481e-13, 'left_track_mid_x': 608.5, 'right_track_mid_x': 1219.0, 'track_width': 610.5, 'center_slope': 0.02351973684210257}
|
||||
2025-05-31 11:49:15 | DEBUG | utils.log_helper - 🐞 步骤1: 创建黄色掩码
|
||||
2025-05-31 11:49:16 | DEBUG | utils.log_helper - 🐞 步骤1.5: 底部区域掩码
|
||||
2025-05-31 11:49:17 | DEBUG | utils.log_helper - 🐞 步骤2: 边缘检测
|
||||
2025-05-31 11:49:18 | DEBUG | utils.log_helper - 🐞 步骤2.5: 初始检测到 54 条直线，尝试合并
|
||||
2025-05-31 11:49:18 | DEBUG | utils.log_helper - 🐞 合并后剩余 31 条直线
|
||||
2025-05-31 11:49:18 | DEBUG | utils.log_helper - 🐞 步骤3: 检测到 31 条直线
|
||||
2025-05-31 11:49:19 | DEBUG | utils.log_helper - 🐞 步骤4: 找到 15 条垂直线
|
||||
2025-05-31 11:49:20 | DEBUG | utils.log_helper - 🐞 左侧候选线数量: 10, 右侧候选线数量: 5
|
||||
2025-05-31 11:49:21 | DEBUG | utils.log_helper - 🐞 选择最佳线对，评分: 0.95
|
||||
2025-05-31 11:49:22 | INFO | utils.log_helper - ℹ️ 保存双轨迹线检测结果图像到: logs/image/dual_track_20250531_114922_309402.jpg
|
||||
2025-05-31 11:49:22 | INFO | utils.log_helper - ℹ️ 保存原始图像到: logs/image/dual_track_orig_20250531_114922_309402.jpg
|
||||
2025-05-31 11:49:22 | INFO | utils.log_helper - ℹ️ 双轨迹线检测结果: {'timestamp': '20250531_114922_309402', 'center_point': (945, 1080), 'deviation': 3.410605131648481e-13, 'left_track_mid_x': 608.5, 'right_track_mid_x': 1219.0, 'track_width': 610.5, 'center_slope': 0.02351973684210257}
|
||||
2025-05-31 11:54:18 | DEBUG | utils.log_helper - 🐞 步骤1: 创建黄色掩码
|
||||
2025-05-31 11:54:19 | DEBUG | utils.log_helper - 🐞 步骤1.5: 底部区域掩码
|
||||
2025-05-31 11:54:20 | DEBUG | utils.log_helper - 🐞 步骤2: 边缘检测
|
||||
2025-05-31 11:54:21 | DEBUG | utils.log_helper - 🐞 步骤2.5: 初始检测到 54 条直线，尝试合并
|
||||
2025-05-31 11:54:21 | DEBUG | utils.log_helper - 🐞 合并后剩余 31 条直线
|
||||
2025-05-31 11:54:21 | DEBUG | utils.log_helper - 🐞 步骤3: 检测到 31 条直线
|
||||
2025-05-31 11:54:22 | DEBUG | utils.log_helper - 🐞 步骤4: 找到 15 条垂直线
|
||||
2025-05-31 11:54:23 | DEBUG | utils.log_helper - 🐞 左侧候选线数量: 10, 右侧候选线数量: 5
|
||||
2025-05-31 11:54:24 | DEBUG | utils.log_helper - 🐞 选择最佳线对，评分: 0.95
|
||||
2025-05-31 11:54:25 | INFO | utils.log_helper - ℹ️ 保存双轨迹线检测结果图像到: logs/image/dual_track_20250531_115425_535559.jpg
|
||||
2025-05-31 11:54:25 | INFO | utils.log_helper - ℹ️ 保存原始图像到: logs/image/dual_track_orig_20250531_115425_535559.jpg
|
||||
2025-05-31 11:54:25 | INFO | utils.log_helper - ℹ️ 双轨迹线检测结果: {'timestamp': '20250531_115425_535559', 'center_point': (945, 1080), 'deviation': 3.410605131648481e-13, 'left_track_mid_x': 608.5, 'right_track_mid_x': 1219.0, 'track_width': 610.5, 'center_slope': 0.02351973684210257}
|
||||
2025-05-31 11:54:31 | DEBUG | utils.log_helper - 🐞 步骤1: 创建黄色掩码
|
||||
2025-05-31 11:54:32 | DEBUG | utils.log_helper - 🐞 步骤1.5: 底部区域掩码
|
||||
2025-05-31 11:54:33 | DEBUG | utils.log_helper - 🐞 步骤2: 边缘检测
|
||||
2025-05-31 11:54:34 | DEBUG | utils.log_helper - 🐞 步骤2.5: 初始检测到 54 条直线，尝试合并
|
||||
2025-05-31 11:54:34 | DEBUG | utils.log_helper - 🐞 合并后剩余 31 条直线
|
||||
2025-05-31 11:54:34 | DEBUG | utils.log_helper - 🐞 步骤3: 检测到 31 条直线
|
||||
2025-05-31 11:54:35 | DEBUG | utils.log_helper - 🐞 步骤4: 找到 15 条垂直线
|
||||
2025-05-31 11:54:36 | DEBUG | utils.log_helper - 🐞 左侧候选线数量: 10, 右侧候选线数量: 5
|
||||
2025-05-31 11:54:37 | DEBUG | utils.log_helper - 🐞 选择最佳线对，评分: 0.95
|
||||
2025-05-31 11:54:38 | INFO | utils.log_helper - ℹ️ 保存双轨迹线检测结果图像到: logs/image/dual_track_20250531_115438_968316.jpg
|
||||
2025-05-31 11:54:38 | INFO | utils.log_helper - ℹ️ 保存原始图像到: logs/image/dual_track_orig_20250531_115438_968316.jpg
|
||||
2025-05-31 11:54:38 | INFO | utils.log_helper - ℹ️ 双轨迹线检测结果: {'timestamp': '20250531_115438_968316', 'center_point': (945, 1080), 'deviation': 3.410605131648481e-13, 'left_track_mid_x': 608.5, 'right_track_mid_x': 1219.0, 'track_width': 610.5, 'center_slope': 0.02351973684210257}
|
||||
2025-05-31 11:54:42 | DEBUG | utils.log_helper - 🐞 步骤1: 创建黄色掩码
|
||||
2025-05-31 11:54:43 | DEBUG | utils.log_helper - 🐞 步骤1.5: 底部区域掩码
|
||||
2025-05-31 11:54:44 | DEBUG | utils.log_helper - 🐞 步骤2: 边缘检测
|
||||
2025-05-31 11:54:45 | DEBUG | utils.log_helper - 🐞 步骤2.5: 初始检测到 54 条直线，尝试合并
|
||||
2025-05-31 11:54:45 | DEBUG | utils.log_helper - 🐞 合并后剩余 31 条直线
|
||||
2025-05-31 11:54:45 | DEBUG | utils.log_helper - 🐞 步骤3: 检测到 31 条直线
|
||||
2025-05-31 11:54:46 | DEBUG | utils.log_helper - 🐞 步骤4: 找到 15 条垂直线
|
||||
2025-05-31 11:54:47 | DEBUG | utils.log_helper - 🐞 左侧候选线数量: 10, 右侧候选线数量: 5
|
||||
2025-05-31 11:54:48 | DEBUG | utils.log_helper - 🐞 选择最佳线对，评分: 0.95
|
||||
2025-05-31 11:54:49 | INFO | utils.log_helper - ℹ️ 保存双轨迹线检测结果图像到: logs/image/dual_track_20250531_115449_602349.jpg
|
||||
2025-05-31 11:54:49 | INFO | utils.log_helper - ℹ️ 保存原始图像到: logs/image/dual_track_orig_20250531_115449_602349.jpg
|
||||
2025-05-31 11:54:49 | INFO | utils.log_helper - ℹ️ 双轨迹线检测结果: {'timestamp': '20250531_115449_602349', 'center_point': (945, 1080), 'deviation': 3.410605131648481e-13, 'left_track_mid_x': 608.5, 'right_track_mid_x': 1219.0, 'track_width': 610.5, 'center_slope': 0.02351973684210257}
|
||||
2025-05-31 11:55:50 | DEBUG | utils.log_helper - 🐞 步骤1: 创建黄色掩码
|
||||
2025-05-31 11:55:51 | DEBUG | utils.log_helper - 🐞 步骤1.5: 底部区域掩码
|
||||
2025-05-31 11:55:52 | DEBUG | utils.log_helper - 🐞 步骤2: 边缘检测
|
||||
2025-05-31 11:55:53 | DEBUG | utils.log_helper - 🐞 步骤2.5: 初始检测到 54 条直线，尝试合并
|
||||
2025-05-31 11:55:53 | DEBUG | utils.log_helper - 🐞 合并后剩余 31 条直线
|
||||
2025-05-31 11:55:53 | DEBUG | utils.log_helper - 🐞 步骤3: 检测到 31 条直线
|
||||
2025-05-31 11:55:54 | DEBUG | utils.log_helper - 🐞 步骤4: 找到 15 条垂直线
|
||||
2025-05-31 11:55:55 | DEBUG | utils.log_helper - 🐞 左侧候选线数量: 10, 右侧候选线数量: 5
|
||||
2025-05-31 11:55:56 | DEBUG | utils.log_helper - 🐞 选择最佳线对，评分: 0.95
|
||||
2025-05-31 11:55:57 | INFO | utils.log_helper - ℹ️ 保存双轨迹线检测结果图像到: logs/image/dual_track_20250531_115557_917521.jpg
|
||||
2025-05-31 11:55:57 | INFO | utils.log_helper - ℹ️ 保存原始图像到: logs/image/dual_track_orig_20250531_115557_917521.jpg
|
||||
2025-05-31 11:55:57 | INFO | utils.log_helper - ℹ️ 双轨迹线检测结果: {'timestamp': '20250531_115557_917521', 'center_point': (945, 1080), 'deviation': 0.10552257197241488, 'left_track_mid_x': 608.5, 'right_track_mid_x': 1219.0, 'track_width': 610.5, 'center_slope': 0.02638064299316605}
|
||||
2025-05-31 11:56:12 | DEBUG | utils.log_helper - 🐞 步骤1: 创建黄色掩码
|
||||
2025-05-31 11:56:13 | DEBUG | utils.log_helper - 🐞 步骤1.5: 底部区域掩码
|
||||
2025-05-31 11:56:14 | DEBUG | utils.log_helper - 🐞 步骤2: 边缘检测
|
||||
2025-05-31 11:57:45 | DEBUG | utils.log_helper - 🐞 步骤1: 创建黄色掩码
|
||||
2025-05-31 11:57:46 | DEBUG | utils.log_helper - 🐞 步骤1.5: 底部区域掩码
|
||||
2025-05-31 11:57:47 | DEBUG | utils.log_helper - 🐞 步骤2: 边缘检测
|
||||
2025-05-31 11:57:48 | DEBUG | utils.log_helper - 🐞 步骤2.5: 初始检测到 54 条直线，尝试合并
|
||||
2025-05-31 11:57:48 | DEBUG | utils.log_helper - 🐞 合并后剩余 31 条直线
|
||||
2025-05-31 11:57:48 | DEBUG | utils.log_helper - 🐞 步骤3: 检测到 31 条直线
|
||||
2025-05-31 11:57:49 | DEBUG | utils.log_helper - 🐞 步骤4: 找到 15 条垂直线
|
||||
2025-05-31 11:57:50 | DEBUG | utils.log_helper - 🐞 左侧候选线数量: 10, 右侧候选线数量: 5
|
||||
2025-05-31 11:57:51 | DEBUG | utils.log_helper - 🐞 选择最佳线对，评分: 0.95
|
||||
2025-05-31 11:57:52 | INFO | utils.log_helper - ℹ️ 保存双轨迹线检测结果图像到: logs/image/dual_track_20250531_115752_281641.jpg
|
||||
2025-05-31 11:57:52 | INFO | utils.log_helper - ℹ️ 保存原始图像到: logs/image/dual_track_orig_20250531_115752_281641.jpg
|
||||
2025-05-31 11:57:52 | INFO | utils.log_helper - ℹ️ 双轨迹线检测结果: {'timestamp': '20250531_115752_281641', 'center_point': (945, 1080), 'deviation': 0.10552257197241488, 'left_track_mid_x': 608.5, 'right_track_mid_x': 1219.0, 'track_width': 610.5, 'center_slope': 0.02638064299316605}
|
||||
2025-05-31 11:58:36 | DEBUG | utils.log_helper - 🐞 步骤1: 创建黄色掩码
|
||||
2025-05-31 11:58:37 | DEBUG | utils.log_helper - 🐞 步骤1.5: 底部区域掩码
|
||||
2025-05-31 11:58:38 | DEBUG | utils.log_helper - 🐞 步骤2: 边缘检测
|
||||
2025-05-31 11:58:39 | DEBUG | utils.log_helper - 🐞 步骤2.5: 初始检测到 54 条直线，尝试合并
|
||||
2025-05-31 11:58:39 | DEBUG | utils.log_helper - 🐞 合并后剩余 31 条直线
|
||||
2025-05-31 11:58:39 | DEBUG | utils.log_helper - 🐞 步骤3: 检测到 31 条直线
|
||||
2025-05-31 11:58:40 | DEBUG | utils.log_helper - 🐞 步骤4: 找到 7 条垂直线
|
||||
2025-05-31 11:58:41 | DEBUG | utils.log_helper - 🐞 左侧候选线数量: 4, 右侧候选线数量: 3
|
||||
2025-05-31 11:58:42 | DEBUG | utils.log_helper - 🐞 选择最佳线对，评分: 0.95
|
||||
2025-05-31 11:58:43 | INFO | utils.log_helper - ℹ️ 保存双轨迹线检测结果图像到: logs/image/dual_track_20250531_115843_399705.jpg
|
||||
2025-05-31 11:58:43 | INFO | utils.log_helper - ℹ️ 保存原始图像到: logs/image/dual_track_orig_20250531_115843_399705.jpg
|
||||
2025-05-31 11:58:43 | INFO | utils.log_helper - ℹ️ 双轨迹线检测结果: {'timestamp': '20250531_115843_399705', 'center_point': (945, 1080), 'deviation': 0.10552257197241488, 'left_track_mid_x': 608.5, 'right_track_mid_x': 1219.0, 'track_width': 610.5, 'center_slope': 0.02638064299316605}
|
||||
2025-05-31 12:14:32 | DEBUG | utils.log_helper - 🐞 步骤1: 创建黄色掩码
|
||||
2025-05-31 12:14:33 | DEBUG | utils.log_helper - 🐞 步骤1.5: 底部区域掩码
|
||||
2025-05-31 12:14:35 | DEBUG | utils.log_helper - 🐞 步骤2: 边缘检测
|
||||
2025-05-31 12:14:36 | DEBUG | utils.log_helper - 🐞 步骤2.5: 初始检测到 54 条直线，尝试合并
|
||||
2025-05-31 12:14:36 | DEBUG | utils.log_helper - 🐞 合并后剩余 31 条直线
|
||||
2025-05-31 12:14:36 | DEBUG | utils.log_helper - 🐞 步骤3: 检测到 31 条直线
|
||||
2025-05-31 12:14:37 | DEBUG | utils.log_helper - 🐞 步骤4: 找到 7 条垂直线
|
||||
2025-05-31 12:14:38 | DEBUG | utils.log_helper - 🐞 左侧候选线数量: 4, 右侧候选线数量: 3
|
||||
2025-05-31 12:14:39 | DEBUG | utils.log_helper - 🐞 选择最佳线对，评分: 0.95
|
||||
2025-05-31 12:14:40 | INFO | utils.log_helper - ℹ️ 保存双轨迹线检测结果图像到: logs/image/dual_track_20250531_121440_131832.jpg
|
||||
2025-05-31 12:14:40 | INFO | utils.log_helper - ℹ️ 保存原始图像到: logs/image/dual_track_orig_20250531_121440_131832.jpg
|
||||
2025-05-31 12:14:40 | INFO | utils.log_helper - ℹ️ 双轨迹线检测结果: {'timestamp': '20250531_121440_131832', 'center_point': (945, 1080), 'deviation': 3.410605131648481e-13, 'left_track_mid_x': 608.5, 'right_track_mid_x': 1219.0, 'track_width': 610.5, 'center_slope': 0.02351973684210257}
|
||||
2025-05-31 12:18:29 | DEBUG | utils.log_helper - 🐞 步骤1: 创建黄色掩码
|
||||
2025-05-31 12:18:30 | DEBUG | utils.log_helper - 🐞 步骤1.5: 底部区域掩码
|
||||
2025-05-31 12:18:31 | DEBUG | utils.log_helper - 🐞 步骤2: 边缘检测
|
||||
2025-05-31 12:18:32 | DEBUG | utils.log_helper - 🐞 步骤2.5: 初始检测到 54 条直线，尝试合并
|
||||
2025-05-31 12:18:32 | DEBUG | utils.log_helper - 🐞 合并后剩余 31 条直线
|
||||
2025-05-31 12:18:32 | DEBUG | utils.log_helper - 🐞 步骤3: 检测到 31 条直线
|
||||
2025-05-31 12:18:33 | DEBUG | utils.log_helper - 🐞 步骤4: 找到 7 条垂直线
|
||||
2025-05-31 12:18:34 | DEBUG | utils.log_helper - 🐞 左侧候选线数量: 4, 右侧候选线数量: 3
|
||||
2025-05-31 12:18:35 | DEBUG | utils.log_helper - 🐞 选择最佳线对，评分: 0.95
|
||||
2025-05-31 12:18:37 | INFO | utils.log_helper - ℹ️ 保存双轨迹线检测结果图像到: logs/image/dual_track_20250531_121837_013535.jpg
|
||||
2025-05-31 12:18:37 | INFO | utils.log_helper - ℹ️ 保存原始图像到: logs/image/dual_track_orig_20250531_121837_013535.jpg
|
||||
2025-05-31 12:18:37 | INFO | utils.log_helper - ℹ️ 双轨迹线检测结果: {'timestamp': '20250531_121837_013535', 'center_point': (945, 1080), 'deviation': 3.410605131648481e-13, 'left_track_mid_x': 608.5, 'right_track_mid_x': 1219.0, 'track_width': 610.5, 'center_slope': 0.02351973684210257}
|
||||
2025-05-31 12:18:46 | DEBUG | utils.log_helper - 🐞 步骤1: 创建黄色掩码
|
||||
2025-05-31 12:18:47 | DEBUG | utils.log_helper - 🐞 步骤1.5: 底部区域掩码
|
||||
2025-05-31 12:18:48 | DEBUG | utils.log_helper - 🐞 步骤2: 边缘检测
|
||||
2025-05-31 12:18:49 | DEBUG | utils.log_helper - 🐞 步骤2.5: 初始检测到 54 条直线，尝试合并
|
||||
2025-05-31 12:18:49 | DEBUG | utils.log_helper - 🐞 合并后剩余 31 条直线
|
||||
2025-05-31 12:18:49 | DEBUG | utils.log_helper - 🐞 步骤3: 检测到 31 条直线
|
||||
2025-05-31 12:18:50 | DEBUG | utils.log_helper - 🐞 步骤4: 找到 7 条垂直线
|
||||
2025-05-31 12:18:51 | DEBUG | utils.log_helper - 🐞 左侧候选线数量: 4, 右侧候选线数量: 3
|
||||
2025-05-31 12:18:52 | DEBUG | utils.log_helper - 🐞 选择最佳线对，评分: 0.95
|
||||
2025-05-31 12:18:53 | INFO | utils.log_helper - ℹ️ 保存双轨迹线检测结果图像到: logs/image/dual_track_20250531_121853_427060.jpg
|
||||
2025-05-31 12:18:53 | INFO | utils.log_helper - ℹ️ 保存原始图像到: logs/image/dual_track_orig_20250531_121853_427060.jpg
|
||||
2025-05-31 12:18:53 | INFO | utils.log_helper - ℹ️ 双轨迹线检测结果: {'timestamp': '20250531_121853_427060', 'center_point': (945, 1080), 'deviation': 3.410605131648481e-13, 'left_track_mid_x': 608.5, 'right_track_mid_x': 1219.0, 'track_width': 610.5, 'center_slope': 0.02351973684210257}
|
||||
2025-05-31 12:19:01 | DEBUG | utils.log_helper - 🐞 步骤1: 创建黄色掩码
|
||||
2025-05-31 12:19:02 | DEBUG | utils.log_helper - 🐞 步骤1.5: 底部区域掩码
|
||||
2025-05-31 12:19:03 | DEBUG | utils.log_helper - 🐞 步骤2: 边缘检测
|
||||
2025-05-31 12:19:04 | DEBUG | utils.log_helper - 🐞 步骤2.5: 初始检测到 54 条直线，尝试合并
|
||||
2025-05-31 12:19:04 | DEBUG | utils.log_helper - 🐞 合并后剩余 31 条直线
|
||||
2025-05-31 12:19:04 | DEBUG | utils.log_helper - 🐞 步骤3: 检测到 31 条直线
|
||||
2025-05-31 12:19:05 | DEBUG | utils.log_helper - 🐞 步骤4: 找到 7 条垂直线
|
||||
2025-05-31 12:19:06 | DEBUG | utils.log_helper - 🐞 左侧候选线数量: 4, 右侧候选线数量: 3
|
||||
2025-05-31 12:19:07 | DEBUG | utils.log_helper - 🐞 选择最佳线对，评分: 0.95
|
||||
2025-05-31 12:19:08 | INFO | utils.log_helper - ℹ️ 保存双轨迹线检测结果图像到: logs/image/dual_track_20250531_121908_570358.jpg
|
||||
2025-05-31 12:19:08 | INFO | utils.log_helper - ℹ️ 保存原始图像到: logs/image/dual_track_orig_20250531_121908_570358.jpg
|
||||
2025-05-31 12:19:08 | INFO | utils.log_helper - ℹ️ 双轨迹线检测结果: {'timestamp': '20250531_121908_570358', 'center_point': (945, 1080), 'deviation': 3.410605131648481e-13, 'left_track_mid_x': 608.5, 'right_track_mid_x': 1219.0, 'track_width': 610.5, 'center_slope': 0.02351973684210257}
|
||||
2025-05-31 12:25:05 | DEBUG | utils.log_helper - 🐞 步骤1: 创建黄色掩码
|
||||
2025-05-31 12:25:07 | DEBUG | utils.log_helper - 🐞 步骤1.5: 底部区域掩码
|
||||
2025-05-31 12:25:08 | DEBUG | utils.log_helper - 🐞 步骤2: 边缘检测
|
||||
2025-05-31 12:25:09 | DEBUG | utils.log_helper - 🐞 步骤3: 检测到 54 条直线
|
||||
2025-05-31 12:25:10 | DEBUG | utils.log_helper - 🐞 步骤4: 找到 7 条垂直线
|
||||
2025-05-31 12:31:54 | DEBUG | utils.log_helper - 🐞 步骤1: 创建黄色掩码
|
||||
2025-05-31 12:31:55 | DEBUG | utils.log_helper - 🐞 步骤1.5: 底部区域掩码
|
||||
2025-05-31 12:31:56 | DEBUG | utils.log_helper - 🐞 步骤2: 边缘检测
|
||||
2025-05-31 12:31:57 | DEBUG | utils.log_helper - 🐞 步骤3: 检测到 54 条直线
|
||||
2025-05-31 12:31:58 | DEBUG | utils.log_helper - 🐞 步骤4: 找到 7 条垂直线
|
||||
2025-05-31 12:32:08 | DEBUG | utils.log_helper - 🐞 步骤1: 创建黄色掩码
|
||||
2025-05-31 12:32:09 | DEBUG | utils.log_helper - 🐞 步骤1.5: 底部区域掩码
|
||||
2025-05-31 12:32:10 | DEBUG | utils.log_helper - 🐞 步骤2: 边缘检测
|
||||
2025-05-31 12:32:11 | DEBUG | utils.log_helper - 🐞 步骤3: 检测到 54 条直线
|
||||
2025-05-31 12:32:12 | DEBUG | utils.log_helper - 🐞 步骤4: 找到 7 条垂直线
|
||||
2025-05-31 12:33:06 | DEBUG | utils.log_helper - 🐞 步骤1: 创建黄色掩码
|
||||
2025-05-31 12:33:07 | DEBUG | utils.log_helper - 🐞 步骤1.5: 底部区域掩码
|
||||
2025-05-31 12:33:09 | DEBUG | utils.log_helper - 🐞 步骤2: 边缘检测
|
||||
2025-05-31 12:33:10 | DEBUG | utils.log_helper - 🐞 步骤3: 检测到 54 条直线
|
||||
2025-05-31 12:33:11 | DEBUG | utils.log_helper - 🐞 步骤4: 找到 11 条垂直线
|
||||
2025-05-31 12:33:12 | DEBUG | utils.log_helper - 🐞 左侧候选线数量: 6, 右侧候选线数量: 5
|
||||
2025-05-31 12:33:13 | DEBUG | utils.log_helper - 🐞 选择最佳线对，评分: 1.01
|
||||
2025-05-31 12:33:14 | INFO | utils.log_helper - ℹ️ 保存双轨迹线检测结果图像到: logs/image/dual_track_20250531_123314_086361.jpg
|
||||
2025-05-31 12:33:14 | INFO | utils.log_helper - ℹ️ 保存原始图像到: logs/image/dual_track_orig_20250531_123314_086361.jpg
|
||||
2025-05-31 12:33:14 | INFO | utils.log_helper - ℹ️ 双轨迹线检测结果: {'timestamp': '20250531_123314_086361', 'center_point': (944, 1080), 'deviation': -1.1368683772161603e-13, 'left_track_mid_x': 565.0, 'right_track_mid_x': 1335.5, 'track_width': 770.5, 'center_slope': 0.01738261738261669}
|
||||
|
||||
@ -22,9 +22,8 @@ print("正在使用普通模式检测...")
|
||||
center_info, left_track_info, right_track_info = detect_dual_track_lines(
|
||||
image_path,
|
||||
observe=True, # 设置为True以查看处理过程和结果
|
||||
delay=2000, # 增加显示时间以便观察
|
||||
delay=1000, # 增加显示时间以便观察
|
||||
save_log=True,
|
||||
stone_path_mode=False
|
||||
)
|
||||
|
||||
if center_info:
|
||||
|
||||
166
utils/base_line_handler.py
Normal file
166
utils/base_line_handler.py
Normal file
@ -0,0 +1,166 @@
|
||||
import numpy as np
|
||||
import cv2
|
||||
|
||||
def _are_lines_mergeable(line1_pts, line2_pts,
|
||||
max_angle_diff_deg,
|
||||
max_ep_gap_abs, max_ep_gap_factor,
|
||||
max_p_dist_abs, max_p_dist_factor):
|
||||
"""
|
||||
判断两条线段是否可以合并。
|
||||
lineX_pts: [x1, y1, x2, y2]
|
||||
max_angle_diff_deg: 最大角度差 (度)
|
||||
max_ep_gap_abs: 端点间最大绝对间隙
|
||||
max_ep_gap_factor: 端点间最大相对间隙 (基于较短线段长度的因子)
|
||||
max_p_dist_abs: 最大绝对垂直距离
|
||||
max_p_dist_factor: 最大相对垂直距离 (基于较短线段长度的因子)
|
||||
"""
|
||||
x1a, y1a, x2a, y2a = line1_pts
|
||||
x1b, y1b, x2b, y2b = line2_pts
|
||||
|
||||
p1a, p2a = np.array([x1a, y1a]), np.array([x2a, y2a])
|
||||
p1b, p2b = np.array([x1b, y1b]), np.array([x2b, y2b])
|
||||
|
||||
len_a = np.linalg.norm(p2a - p1a)
|
||||
len_b = np.linalg.norm(p2b - p1b)
|
||||
|
||||
if len_a < 1e-3 or len_b < 1e-3: return False # 避免零长度线段
|
||||
shorter_len = min(len_a, len_b)
|
||||
|
||||
# 1. 角度相似性
|
||||
angle_a = np.arctan2(y2a - y1a, x2a - x1a)
|
||||
angle_b = np.arctan2(y2b - y1b, x2b - x1b)
|
||||
angle_diff = np.degrees(angle_a - angle_b)
|
||||
|
||||
while angle_diff <= -180: angle_diff += 360
|
||||
while angle_diff > 180: angle_diff -= 360
|
||||
|
||||
is_angle_similar = abs(angle_diff) < max_angle_diff_deg or \
|
||||
abs(abs(angle_diff) - 180) < max_angle_diff_deg
|
||||
if not is_angle_similar:
|
||||
return False
|
||||
|
||||
# 2. 共线性: 检查一个线段的端点到另一个线段（无限延长）的垂直距离
|
||||
current_max_allowed_perp_dist = max(max_p_dist_abs, max_p_dist_factor * shorter_len)
|
||||
|
||||
def get_perp_dist_point_to_inf_line(p, line_p1, line_p2, line_len):
|
||||
if line_len < 1e-3: return np.linalg.norm(p - line_p1) # 线是点
|
||||
return abs((line_p2[1]-line_p1[1])*p[0] - (line_p2[0]-line_p1[0])*p[1] + \
|
||||
line_p2[0]*line_p1[1] - line_p2[1]*line_p1[0]) / line_len
|
||||
|
||||
dist_b1_to_a_inf = get_perp_dist_point_to_inf_line(p1b, p1a, p2a, len_a)
|
||||
dist_b2_to_a_inf = get_perp_dist_point_to_inf_line(p2b, p1a, p2a, len_a)
|
||||
dist_a1_to_b_inf = get_perp_dist_point_to_inf_line(p1a, p1b, p2b, len_b)
|
||||
dist_a2_to_b_inf = get_perp_dist_point_to_inf_line(p2a, p1b, p2b, len_b)
|
||||
|
||||
# 如果两条线都很短，那么允许的垂直距离也应该小
|
||||
# 如果一条线的端点离另一条无限延长的线太远，则不合并
|
||||
if min(dist_b1_to_a_inf, dist_b2_to_a_inf) > current_max_allowed_perp_dist and \
|
||||
min(dist_a1_to_b_inf, dist_a2_to_b_inf) > current_max_allowed_perp_dist:
|
||||
return False
|
||||
|
||||
# 3. 邻近性/重叠性: 端点之间的最小距离
|
||||
min_ep_dist = min(np.linalg.norm(p1a-p1b), np.linalg.norm(p1a-p2b),
|
||||
np.linalg.norm(p2a-p1b), np.linalg.norm(p2a-p2b))
|
||||
|
||||
current_max_allowed_ep_gap = max(max_ep_gap_abs, max_ep_gap_factor * shorter_len)
|
||||
if min_ep_dist > current_max_allowed_ep_gap:
|
||||
return False
|
||||
|
||||
# 附加检查：确保线段确实是"连接的"或"重叠的"
|
||||
# 计算一个端点到另一个 *线段* (非无限线) 的距离
|
||||
def dist_point_to_segment(p, seg_a, seg_b):
|
||||
ap = p - seg_a
|
||||
ab = seg_b - seg_a
|
||||
ab_len_sq = np.dot(ab, ab)
|
||||
if ab_len_sq < 1e-6: return np.linalg.norm(ap)
|
||||
|
||||
t = np.dot(ap, ab) / ab_len_sq
|
||||
if t < 0.0: closest_point = seg_a
|
||||
elif t > 1.0: closest_point = seg_b
|
||||
else: closest_point = seg_a + t * ab
|
||||
return np.linalg.norm(p - closest_point)
|
||||
|
||||
dist_p1a_to_seg_b = dist_point_to_segment(p1a, p1b, p2b)
|
||||
dist_p2a_to_seg_b = dist_point_to_segment(p2a, p1b, p2b)
|
||||
dist_p1b_to_seg_a = dist_point_to_segment(p1b, p1a, p2a)
|
||||
dist_p2b_to_seg_a = dist_point_to_segment(p2b, p1a, p2a)
|
||||
|
||||
if min(dist_p1a_to_seg_b, dist_p2a_to_seg_b, dist_p1b_to_seg_a, dist_p2b_to_seg_a) > current_max_allowed_ep_gap:
|
||||
return False
|
||||
|
||||
return True
|
||||
|
||||
def _merge_two_lines(line1_pts, line2_pts):
|
||||
points = np.array([
|
||||
line1_pts[:2], line1_pts[2:],
|
||||
line2_pts[:2], line2_pts[2:]
|
||||
], dtype=np.float32)
|
||||
|
||||
vx, vy, x0, y0 = cv2.fitLine(points.reshape(-1,1,2), cv2.DIST_L2, 0, 0.01, 0.01)
|
||||
vx, vy, x0, y0 = vx[0], vy[0], x0[0], y0[0]
|
||||
|
||||
projected_params = []
|
||||
line_origin = np.array([x0, y0])
|
||||
line_direction = np.array([vx, vy])
|
||||
|
||||
for i in range(4):
|
||||
pt_vec = points[i] - line_origin
|
||||
param = np.dot(pt_vec, line_direction)
|
||||
projected_params.append(param)
|
||||
|
||||
min_param = min(projected_params)
|
||||
max_param = max(projected_params)
|
||||
|
||||
p_start = line_origin + min_param * line_direction
|
||||
p_end = line_origin + max_param * line_direction
|
||||
|
||||
return [int(round(p_start[0])), int(round(p_start[1])),
|
||||
int(round(p_end[0])), int(round(p_end[1]))]
|
||||
|
||||
def _merge_collinear_lines_iterative(lines_from_hough,
|
||||
min_initial_len,
|
||||
max_angle_diff_deg,
|
||||
max_ep_gap_abs, max_ep_gap_factor,
|
||||
max_p_dist_abs, max_p_dist_factor):
|
||||
if lines_from_hough is None:
|
||||
return None
|
||||
|
||||
# 将HoughLinesP的输出转换为Python列表 [[x1,y1,x2,y2], ...]
|
||||
py_lines = [list(line[0]) for line in lines_from_hough]
|
||||
|
||||
# 过滤掉初始太短的线
|
||||
current_lines = [line for line in py_lines if np.linalg.norm(np.array(line[:2]) - np.array(line[2:])) >= min_initial_len]
|
||||
|
||||
if not current_lines or len(current_lines) < 2:
|
||||
return np.array([[line] for line in current_lines]) if current_lines else np.array([])
|
||||
|
||||
made_change_in_pass = True
|
||||
while made_change_in_pass:
|
||||
made_change_in_pass = False
|
||||
i = 0
|
||||
while i < len(current_lines):
|
||||
j = i + 1
|
||||
merged_line_i = False
|
||||
while j < len(current_lines):
|
||||
if _are_lines_mergeable(current_lines[i], current_lines[j],
|
||||
max_angle_diff_deg,
|
||||
max_ep_gap_abs, max_ep_gap_factor,
|
||||
max_p_dist_abs, max_p_dist_factor):
|
||||
|
||||
merged_line = _merge_two_lines(current_lines[i], current_lines[j])
|
||||
current_lines[i] = merged_line # 更新第i条线
|
||||
current_lines.pop(j) # 移除第j条线
|
||||
made_change_in_pass = True
|
||||
merged_line_i = True
|
||||
# 由于current_lines[i]已改变，且列表长度已改变，
|
||||
# 最安全的是从头开始或至少重新评估current_lines[i]
|
||||
# 但对于这种贪心策略，继续尝试将（新的）current_lines[i]与后续线合并是可以的
|
||||
# j的位置不需要改变，因为它指向被删除元素后的下一个元素
|
||||
continue # j 现在指向下一条未检查的线，相对于更新后的i
|
||||
j += 1
|
||||
|
||||
# 如果 line_i 在内部循环中被修改过，则外部的 i 也应该重新评估
|
||||
# 但由于我们是迭代地进行整个过程(while made_change_in_pass), 最终会收敛
|
||||
i += 1
|
||||
|
||||
return np.array([[line] for line in current_lines]) if current_lines else np.array([])
|
||||
@ -3,9 +3,14 @@ import numpy as np
|
||||
import os
|
||||
import datetime
|
||||
|
||||
from utils.base_line_handler import _merge_collinear_lines_iterative
|
||||
from utils.log_helper import LogHelper, get_logger, section, info, debug, warning, error, success, timing
|
||||
|
||||
def detect_dual_track_lines(image, observe=False, delay=1000, save_log=True, stone_path_mode=False):
|
||||
def detect_dual_track_lines(image, observe=False, delay=1000, save_log=True,
|
||||
max_slope_threshold=2,
|
||||
min_slope_threshold=0.5,
|
||||
min_line_length=0.05,
|
||||
max_line_gap=40):
|
||||
"""
|
||||
检测左右两条平行的黄色轨道线，优化后能够更准确处理各种路况
|
||||
|
||||
@ -14,7 +19,11 @@ def detect_dual_track_lines(image, observe=False, delay=1000, save_log=True, sto
|
||||
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
|
||||
stone_path_mode: 石板路模式，针对石板路上的黄线进行特殊处理
|
||||
|
||||
max_slope_threshold: 最大斜率阈值
|
||||
min_slope_threshold: 最小斜率阈值
|
||||
min_line_length: 最小线段长度
|
||||
max_line_gap: 最大线段间距
|
||||
|
||||
返回:
|
||||
tuple: (中心线信息, 左轨迹线信息, 右轨迹线信息)
|
||||
@ -44,57 +53,24 @@ def detect_dual_track_lines(image, observe=False, delay=1000, save_log=True, sto
|
||||
# 转换到HSV颜色空间以便更容易提取黄色
|
||||
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
|
||||
|
||||
# 根据是否为石板路模式选择不同的参数
|
||||
if stone_path_mode:
|
||||
# 石板路上的黄线通常对比度更低，需要更宽松的颜色范围
|
||||
lower_yellow = np.array([8, 50, 50]) # 更宽松的黄色下限
|
||||
upper_yellow = np.array([45, 255, 255]) # 更宽松的黄色上限
|
||||
else:
|
||||
# 标准黄色的HSV范围
|
||||
lower_yellow = np.array([15, 80, 80])
|
||||
upper_yellow = np.array([35, 255, 255])
|
||||
# 标准黄色的HSV范围
|
||||
lower_yellow = np.array([15, 80, 80])
|
||||
upper_yellow = np.array([35, 255, 255])
|
||||
|
||||
# 创建黄色的掩码
|
||||
mask = cv2.inRange(hsv, lower_yellow, upper_yellow)
|
||||
|
||||
# 应用对比度增强
|
||||
if stone_path_mode:
|
||||
# 在处理掩码前先对原图像进行预处理
|
||||
# 对原图进行自适应直方图均衡化增强对比度
|
||||
lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)
|
||||
l, a, b = cv2.split(lab)
|
||||
clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8, 8))
|
||||
l = clahe.apply(l)
|
||||
lab = cv2.merge((l, a, b))
|
||||
enhanced_img = cv2.cvtColor(lab, cv2.COLOR_LAB2BGR)
|
||||
|
||||
# 用增强后的图像重新检测黄色
|
||||
enhanced_hsv = cv2.cvtColor(enhanced_img, cv2.COLOR_BGR2HSV)
|
||||
enhanced_mask = cv2.inRange(enhanced_hsv, lower_yellow, upper_yellow)
|
||||
|
||||
# 组合原始掩码和增强掩码
|
||||
mask = cv2.bitwise_or(mask, enhanced_mask)
|
||||
|
||||
if observe:
|
||||
debug("增强对比度和颜色检测", "处理")
|
||||
cv2.imshow("增强对比度", enhanced_img)
|
||||
cv2.imshow("增强后的黄色掩码", enhanced_mask)
|
||||
cv2.waitKey(delay)
|
||||
|
||||
# 形态学操作以改善掩码
|
||||
if stone_path_mode:
|
||||
# 石板路上的线条可能更细更断续，使用更大的膨胀核和更多的迭代次数
|
||||
kernel = np.ones((7, 7), np.uint8)
|
||||
mask = cv2.dilate(mask, kernel, iterations=2)
|
||||
mask = cv2.erode(mask, np.ones((3, 3), np.uint8), iterations=1)
|
||||
else:
|
||||
kernel = np.ones((5, 5), np.uint8)
|
||||
mask = cv2.dilate(mask, kernel, iterations=1)
|
||||
mask = cv2.erode(mask, np.ones((3, 3), np.uint8), iterations=1)
|
||||
kernel = np.ones((5, 5), np.uint8)
|
||||
mask = cv2.dilate(mask, kernel, iterations=1)
|
||||
mask = cv2.erode(mask, np.ones((3, 3), np.uint8), iterations=1)
|
||||
|
||||
if observe:
|
||||
debug("步骤1: 创建黄色掩码", "处理")
|
||||
cv2.imshow("黄色掩码", mask)
|
||||
mask_display = cv2.cvtColor(mask, cv2.COLOR_GRAY2BGR)
|
||||
cv2.putText(mask_display, "Step 1: Yellow Mask", (10, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
|
||||
cv2.putText(mask_display, f"Lower: {lower_yellow}", (10, 40), cv2.FONT_HERSHEY_SIMPLEX, 0.4, (0, 255, 0), 1)
|
||||
cv2.putText(mask_display, f"Upper: {upper_yellow}", (10, 55), cv2.FONT_HERSHEY_SIMPLEX, 0.4, (0, 255, 0), 1)
|
||||
cv2.imshow("黄色掩码", mask_display)
|
||||
cv2.waitKey(delay)
|
||||
|
||||
# 裁剪底部区域重点关注近处的黄线
|
||||
@ -103,28 +79,26 @@ def detect_dual_track_lines(image, observe=False, delay=1000, save_log=True, sto
|
||||
|
||||
if observe:
|
||||
debug("步骤1.5: 底部区域掩码", "处理")
|
||||
cv2.imshow("底部区域掩码", bottom_roi)
|
||||
bottom_roi_display = cv2.cvtColor(bottom_roi, cv2.COLOR_GRAY2BGR)
|
||||
cv2.putText(bottom_roi_display, "Step 1.5: Bottom ROI", (10, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
|
||||
cv2.putText(bottom_roi_display, f"ROI Height: {bottom_roi_height}px ({bottom_roi_height/height*100:.0f}%)", (10, 40), cv2.FONT_HERSHEY_SIMPLEX, 0.4, (0, 255, 0), 1)
|
||||
cv2.imshow("底部区域掩码", bottom_roi_display)
|
||||
cv2.waitKey(delay)
|
||||
|
||||
# 边缘检测 - 针对石板路调整参数
|
||||
if stone_path_mode:
|
||||
edges = cv2.Canny(mask, 20, 100, apertureSize=3) # 进一步降低阈值以捕捉更弱的边缘
|
||||
else:
|
||||
edges = cv2.Canny(mask, 50, 150, apertureSize=3)
|
||||
# INFO 边缘检测
|
||||
edges = cv2.Canny(mask, 50, 150, apertureSize=3)
|
||||
|
||||
if observe:
|
||||
debug("步骤2: 边缘检测", "处理")
|
||||
cv2.imshow("边缘检测", edges)
|
||||
edges_display = cv2.cvtColor(edges, cv2.COLOR_GRAY2BGR)
|
||||
cv2.putText(edges_display, "Step 2: Edge Detection (Canny)", (10, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
|
||||
cv2.putText(edges_display, "Thresholds: (50, 150)", (10, 40), cv2.FONT_HERSHEY_SIMPLEX, 0.4, (0, 255, 0), 1)
|
||||
cv2.imshow("边缘检测", edges_display)
|
||||
cv2.waitKey(delay)
|
||||
|
||||
# 霍夫变换检测直线 - 根据是否为石板路调整参数
|
||||
if stone_path_mode:
|
||||
# 石板路上的线段可能更短更断续，使用更宽松的参数
|
||||
lines = cv2.HoughLinesP(edges, 1, np.pi/180, threshold=15,
|
||||
minLineLength=width*0.02, maxLineGap=60)
|
||||
else:
|
||||
lines = cv2.HoughLinesP(edges, 1, np.pi/180, threshold=25,
|
||||
minLineLength=width*0.05, maxLineGap=40)
|
||||
# INFO 霍夫变换检测直线
|
||||
lines = cv2.HoughLinesP(edges, 1, np.pi/180, threshold=25,
|
||||
minLineLength=width*min_line_length, maxLineGap=max_line_gap)
|
||||
|
||||
if lines is None or len(lines) == 0:
|
||||
error("未检测到直线", "失败")
|
||||
@ -133,6 +107,8 @@ def detect_dual_track_lines(image, observe=False, delay=1000, save_log=True, sto
|
||||
if observe:
|
||||
debug(f"步骤3: 检测到 {len(lines)} 条直线", "处理")
|
||||
lines_img = img.copy()
|
||||
cv2.putText(lines_img, "Step 3: Hough Lines", (10, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)
|
||||
cv2.putText(lines_img, f"Th:25, MinLen:{width*0.05:.1f}, MaxGap:{max_line_gap}", (10, 40), cv2.FONT_HERSHEY_SIMPLEX, 0.4, (0, 0, 255), 1)
|
||||
for line in lines:
|
||||
x1, y1, x2, y2 = line[0]
|
||||
cv2.line(lines_img, (x1, y1), (x2, y2), (0, 255, 0), 2)
|
||||
@ -154,11 +130,8 @@ def detect_dual_track_lines(image, observe=False, delay=1000, save_log=True, sto
|
||||
else:
|
||||
slope = (y2 - y1) / (x2 - x1)
|
||||
|
||||
# 根据是否为石板路调整斜率阈值
|
||||
min_slope_threshold = 0.4 if stone_path_mode else 0.75
|
||||
|
||||
# 筛选接近垂直的线 (斜率较大)，但允许更多倾斜度
|
||||
if abs(slope) > min_slope_threshold:
|
||||
if max_slope_threshold > abs(slope) > min_slope_threshold:
|
||||
line_length = np.sqrt((x2-x1)**2 + (y2-y1)**2)
|
||||
# 计算线的中点x坐标
|
||||
mid_x = (x1 + x2) / 2
|
||||
@ -167,30 +140,15 @@ def detect_dual_track_lines(image, observe=False, delay=1000, save_log=True, sto
|
||||
# 保存线段、其坐标、斜率和长度
|
||||
vertical_lines.append((line[0], mid_x, mid_y, slope, line_length))
|
||||
|
||||
# vertical_lines = _merge_collinear_lines_iterative(vertical_lines,
|
||||
# min_initial_len=5.0,
|
||||
# max_angle_diff_deg=10.0,
|
||||
# max_ep_gap_abs=max_line_gap / 2.0,
|
||||
# max_ep_gap_factor=0.25,
|
||||
# max_p_dist_abs=max_line_gap / 4.0,
|
||||
# max_p_dist_factor=0.1)
|
||||
|
||||
if len(vertical_lines) < 2:
|
||||
# 石板路模式下，尝试放宽斜率条件重新筛选线段
|
||||
if stone_path_mode:
|
||||
vertical_lines = []
|
||||
for line in lines:
|
||||
x1, y1, x2, y2 = line[0]
|
||||
|
||||
# 仍然优先选择图像底部的线
|
||||
if y1 < height * 0.6 and y2 < height * 0.6:
|
||||
continue # 忽略上部分的线
|
||||
|
||||
# 计算斜率 (避免除零错误)
|
||||
if abs(x2 - x1) < 5: # 几乎垂直的线
|
||||
slope = 100
|
||||
else:
|
||||
slope = (y2 - y1) / (x2 - x1)
|
||||
|
||||
# 使用更宽松的斜率阈值
|
||||
if abs(slope) > 0.2: # 进一步放宽斜率阈值
|
||||
line_length = np.sqrt((x2-x1)**2 + (y2-y1)**2)
|
||||
mid_x = (x1 + x2) / 2
|
||||
mid_y = (y1 + y2) / 2
|
||||
vertical_lines.append((line[0], mid_x, mid_y, slope, line_length))
|
||||
|
||||
if len(vertical_lines) < 2:
|
||||
error("未检测到足够的垂直线", "失败")
|
||||
return None, None, None
|
||||
@ -198,6 +156,8 @@ def detect_dual_track_lines(image, observe=False, delay=1000, save_log=True, sto
|
||||
if observe:
|
||||
debug(f"步骤4: 找到 {len(vertical_lines)} 条垂直线", "处理")
|
||||
v_lines_img = img.copy()
|
||||
cv2.putText(v_lines_img, "Step 4: Vertical Lines Filtered", (10, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)
|
||||
cv2.putText(v_lines_img, f"Min Slope Abs: {min_slope_threshold:.2f}", (10, 40), cv2.FONT_HERSHEY_SIMPLEX, 0.4, (0, 0, 255), 1)
|
||||
for line_info in vertical_lines:
|
||||
line, _, _, slope, _ = line_info
|
||||
x1, y1, x2, y2 = line
|
||||
@ -211,76 +171,6 @@ def detect_dual_track_lines(image, observe=False, delay=1000, save_log=True, sto
|
||||
# 优先选择更接近图像底部的线 - 根据y坐标均值排序
|
||||
vertical_lines.sort(key=lambda x: x[2], reverse=True) # 按mid_y从大到小排序
|
||||
|
||||
# 石板路模式下，可能需要处理断续的线段或合并相近的线段
|
||||
if stone_path_mode and len(vertical_lines) >= 3:
|
||||
# 尝试合并相近的线段
|
||||
merged_lines = []
|
||||
processed = [False] * len(vertical_lines)
|
||||
|
||||
for i in range(len(vertical_lines)):
|
||||
if processed[i]:
|
||||
continue
|
||||
|
||||
current_line = vertical_lines[i]
|
||||
_, current_mid_x, current_mid_y, current_slope, current_length = current_line
|
||||
|
||||
# 查找相近的线段
|
||||
similar_lines = [current_line]
|
||||
processed[i] = True
|
||||
|
||||
for j in range(i+1, len(vertical_lines)):
|
||||
if processed[j]:
|
||||
continue
|
||||
|
||||
candidate_line = vertical_lines[j]
|
||||
_, candidate_mid_x, candidate_mid_y, candidate_slope, candidate_length = candidate_line
|
||||
|
||||
# 如果x坐标接近且斜率相似，认为是同一条线的不同部分
|
||||
x_diff = abs(current_mid_x - candidate_mid_x)
|
||||
slope_diff = abs(current_slope - candidate_slope)
|
||||
y_diff = abs(current_mid_y - candidate_mid_y)
|
||||
|
||||
# 放宽相似线段的判断条件
|
||||
if ((x_diff < width * 0.08 and slope_diff < 0.4) or # 更宽松的x差异和斜率差异
|
||||
(x_diff < width * 0.05 and y_diff < height * 0.2)): # 或者x和y都比较接近
|
||||
similar_lines.append(candidate_line)
|
||||
processed[j] = True
|
||||
|
||||
# 如果找到多条相近的线，合并它们
|
||||
if len(similar_lines) > 1:
|
||||
# 按线长度加权合并
|
||||
total_weight = sum(line[4] for line in similar_lines)
|
||||
merged_x1 = sum(line[0][0] * line[4] for line in similar_lines) / total_weight
|
||||
merged_y1 = sum(line[0][1] * line[4] for line in similar_lines) / total_weight
|
||||
merged_x2 = sum(line[0][2] * line[4] for line in similar_lines) / total_weight
|
||||
merged_y2 = sum(line[0][3] * line[4] for line in similar_lines) / total_weight
|
||||
|
||||
merged_line = (np.array([int(merged_x1), int(merged_y1),
|
||||
int(merged_x2), int(merged_y2)]),
|
||||
(merged_x1 + merged_x2) / 2,
|
||||
(merged_y1 + merged_y2) / 2,
|
||||
(merged_y2 - merged_y1) / (merged_x2 - merged_x1 + 1e-6),
|
||||
np.sqrt((merged_x2-merged_x1)**2 + (merged_y2-merged_y1)**2))
|
||||
|
||||
merged_lines.append(merged_line)
|
||||
else:
|
||||
merged_lines.append(current_line)
|
||||
|
||||
vertical_lines = merged_lines
|
||||
|
||||
if observe:
|
||||
debug(f"步骤4.5: 合并后找到 {len(vertical_lines)} 条垂直线", "处理")
|
||||
merged_img = img.copy()
|
||||
for line_info in vertical_lines:
|
||||
line, _, _, slope, _ = line_info
|
||||
x1, y1, x2, y2 = line
|
||||
cv2.line(merged_img, (int(x1), int(y1)), (int(x2), int(y2)), (255, 0, 255), 2)
|
||||
# 显示斜率
|
||||
cv2.putText(merged_img, f"{slope:.2f}", (int((x1+x2)//2), int((y1+y2)//2)),
|
||||
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 1)
|
||||
cv2.imshow("合并后的垂直线", merged_img)
|
||||
cv2.waitKey(delay)
|
||||
|
||||
# 按x坐标将线分为左右两组
|
||||
left_lines = [line for line in vertical_lines if line[1] < center_x]
|
||||
right_lines = [line for line in vertical_lines if line[1] > center_x]
|
||||
@ -301,10 +191,40 @@ def detect_dual_track_lines(image, observe=False, delay=1000, save_log=True, sto
|
||||
if observe:
|
||||
debug(f"左侧候选线数量: {len(left_lines)}, 右侧候选线数量: {len(right_lines)}", "线候选")
|
||||
|
||||
# 创建左右线可视化图像
|
||||
left_right_img = img.copy()
|
||||
cv2.putText(left_right_img, "Left and Right Candidate Lines", (10, 20),
|
||||
cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)
|
||||
|
||||
# 绘制左侧候选线（蓝色）
|
||||
for line_info in left_lines:
|
||||
line = line_info[0]
|
||||
x1, y1, x2, y2 = line
|
||||
cv2.line(left_right_img, (x1, y1), (x2, y2), (255, 0, 0), 2)
|
||||
# 显示斜率
|
||||
mid_x = (x1 + x2) // 2
|
||||
mid_y = (y1 + y2) // 2
|
||||
cv2.putText(left_right_img, f"L:{line_info[3]:.2f}", (mid_x, mid_y),
|
||||
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 1)
|
||||
|
||||
# 绘制右侧候选线（红色）
|
||||
for line_info in right_lines:
|
||||
line = line_info[0]
|
||||
x1, y1, x2, y2 = line
|
||||
cv2.line(left_right_img, (x1, y1), (x2, y2), (0, 0, 255), 2)
|
||||
# 显示斜率
|
||||
mid_x = (x1 + x2) // 2
|
||||
mid_y = (y1 + y2) // 2
|
||||
cv2.putText(left_right_img, f"R:{line_info[3]:.2f}", (mid_x, mid_y),
|
||||
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1)
|
||||
|
||||
cv2.imshow("左右候选线", left_right_img)
|
||||
cv2.waitKey(delay)
|
||||
|
||||
# 优化说明：在默认模式下，评分函数和线对选择都优先考虑更靠近图像中心的线段
|
||||
# 这有助于减少对图像边缘可能出现的干扰线的选择，提高轨道线检测的准确性
|
||||
|
||||
# 改进的评分函数 - 同时考虑斜率、位置、长度和在图像中的位置
|
||||
# INFO 改进的评分函数 - 同时考虑斜率、位置、长度和在图像中的位置
|
||||
def score_line(line_info, is_left):
|
||||
_, mid_x, mid_y, slope, length = line_info
|
||||
line_points = line_info[0] # 获取线段的端点坐标
|
||||
@ -325,14 +245,8 @@ def detect_dual_track_lines(image, observe=False, delay=1000, save_log=True, sto
|
||||
length_score = min(1.0, length_ratio * 1.2) # 适当提高长线段的权重
|
||||
|
||||
# 位置评分 - 线段位于预期位置得分高
|
||||
# 根据是否为石板路模式调整预期位置
|
||||
center_x = width / 2
|
||||
if stone_path_mode:
|
||||
# 石板路上的轨道宽度可能不同
|
||||
expected_track_width = width * 0.5 # 石板路轨道宽度估计
|
||||
else:
|
||||
# 普通轨道的期望位置 - 默认模式下更靠近中心
|
||||
expected_track_width = width * 0.4 # 普通轨道宽度估计，更窄以接近中心
|
||||
expected_track_width = width * 0.4 # 普通轨道宽度估计，更窄以接近中心
|
||||
|
||||
# 计算预期的线位置（基于图像中心和轨道宽度）
|
||||
expected_x = center_x - expected_track_width * 0.5 if is_left else center_x + expected_track_width * 0.5
|
||||
@ -391,27 +305,16 @@ def detect_dual_track_lines(image, observe=False, delay=1000, save_log=True, sto
|
||||
bottom_reach_score = 1.0 if reaches_bottom else 0.5
|
||||
|
||||
# 综合评分 - 调整权重
|
||||
if stone_path_mode:
|
||||
# 石板路模式下更关注位置和底部接近程度
|
||||
final_score = (
|
||||
y_score * 0.25 + # 底部接近度
|
||||
length_score * 0.15 + # 线段长度
|
||||
x_score * 0.15 + # 中点位置
|
||||
bottom_x_score * 0.2 + # 底部点位置
|
||||
slope_score * 0.15 + # 斜率合适性
|
||||
bottom_reach_score * 0.1 # 是否到达底部
|
||||
)
|
||||
else:
|
||||
# 普通轨道模式下更关注中心接近性
|
||||
final_score = (
|
||||
y_score * 0.15 + # 底部接近度
|
||||
length_score * 0.15 + # 线段长度
|
||||
x_score * 0.15 + # 中点位置
|
||||
center_proximity_score * 0.2 + # 与中心的接近度 (新增)
|
||||
bottom_x_score * 0.15 + # 底部点位置
|
||||
slope_score * 0.1 + # 斜率合适性
|
||||
bottom_reach_score * 0.1 # 是否到达底部
|
||||
)
|
||||
# 普通轨道模式下更关注中心接近性
|
||||
final_score = (
|
||||
y_score * 0.15 + # 底部接近度
|
||||
length_score * 0.05 + # 线段长度
|
||||
x_score * 0.15 + # 中点位置
|
||||
center_proximity_score * 0.3 + # 与中心的接近度 (新增)
|
||||
bottom_x_score * 0.15 + # 底部点位置
|
||||
slope_score * 0.1 + # 斜率合适性
|
||||
bottom_reach_score * 0.1 # 是否到达底部
|
||||
)
|
||||
|
||||
return final_score
|
||||
|
||||
@ -465,12 +368,8 @@ def detect_dual_track_lines(image, observe=False, delay=1000, save_log=True, sto
|
||||
width_score = 0.0
|
||||
else:
|
||||
# 轨道宽度应该在合理范围内 - 调整范围更加精确
|
||||
if stone_path_mode:
|
||||
expected_width = width * 0.5 # 石板路可能更宽一些
|
||||
allowed_deviation = width * 0.3 # 允许的偏差范围
|
||||
else:
|
||||
expected_width = width * 0.4 # 普通轨道相对窄一些，更靠近中心
|
||||
allowed_deviation = width * 0.2 # 允许的偏差范围，更精确
|
||||
expected_width = width * 0.4 # 普通轨道相对窄一些，更靠近中心
|
||||
allowed_deviation = width * 0.2 # 允许的偏差范围，更精确
|
||||
|
||||
width_diff = abs(track_width - expected_width)
|
||||
width_score = max(0, 1.0 - width_diff / allowed_deviation)
|
||||
@ -509,25 +408,22 @@ def detect_dual_track_lines(image, observe=False, delay=1000, save_log=True, sto
|
||||
|
||||
# 综合评分 - 调整权重
|
||||
# 更重视平行性和底部宽度
|
||||
if stone_path_mode:
|
||||
pair_score = (left_score + right_score) * 0.3 + parallel_score * 0.25 + width_score * 0.2 + symmetry_score * 0.1 + bottom_width_score * 0.15
|
||||
else:
|
||||
# 默认模式下，增加对中心接近性的权重
|
||||
# 计算左右线到中心的距离
|
||||
left_to_center = abs(left_x - center_x)
|
||||
right_to_center = abs(right_x - center_x)
|
||||
|
||||
# 标准化距离（与图像宽度相关）
|
||||
left_center_ratio = left_to_center / (width * 0.5)
|
||||
right_center_ratio = right_to_center / (width * 0.5)
|
||||
|
||||
# 接近度得分 - 越接近中心分数越高
|
||||
left_center_score = max(0, 1.0 - left_center_ratio)
|
||||
right_center_score = max(0, 1.0 - right_center_ratio)
|
||||
center_proximity_score = (left_center_score + right_center_score) / 2
|
||||
|
||||
# 给予中心接近性更高的权重
|
||||
pair_score = (left_score + right_score) * 0.25 + parallel_score * 0.2 + width_score * 0.15 + symmetry_score * 0.1 + bottom_width_score * 0.1 + center_proximity_score * 0.2
|
||||
# 默认模式下，增加对中心接近性的权重
|
||||
# 计算左右线到中心的距离
|
||||
left_to_center = abs(left_x - center_x)
|
||||
right_to_center = abs(right_x - center_x)
|
||||
|
||||
# 标准化距离（与图像宽度相关）
|
||||
left_center_ratio = left_to_center / (width * 0.5)
|
||||
right_center_ratio = right_to_center / (width * 0.5)
|
||||
|
||||
# 接近度得分 - 越接近中心分数越高
|
||||
left_center_score = max(0, 1.0 - left_center_ratio)
|
||||
right_center_score = max(0, 1.0 - right_center_ratio)
|
||||
center_proximity_score = (left_center_score + right_center_score) / 2
|
||||
|
||||
# 给予中心接近性更高的权重
|
||||
pair_score = (left_score + right_score) * 0.25 + parallel_score * 0.2 + width_score * 0.15 + symmetry_score * 0.1 + bottom_width_score * 0.1 + center_proximity_score * 0.2
|
||||
|
||||
if pair_score > best_pair_score:
|
||||
best_pair_score = pair_score
|
||||
@ -575,6 +471,26 @@ def detect_dual_track_lines(image, observe=False, delay=1000, save_log=True, sto
|
||||
right_x1, right_x2 = right_x2, right_x1
|
||||
right_y1, right_y2 = right_y2, right_y1
|
||||
|
||||
|
||||
# 尝试延长线段到图像底部，处理被石板路部分遮挡的情况
|
||||
left_extended_y2 = height
|
||||
if abs(left_x2 - left_x1) < 5: # 几乎垂直
|
||||
left_extended_x2 = left_x2
|
||||
else:
|
||||
left_slope = (left_y2 - left_y1) / (left_x2 - left_x1)
|
||||
left_extended_x2 = left_x1 + (left_extended_y2 - left_y1) / left_slope
|
||||
|
||||
right_extended_y2 = height
|
||||
if abs(right_x2 - right_x1) < 5: # 几乎垂直
|
||||
right_extended_x2 = right_x2
|
||||
else:
|
||||
right_slope = (right_y2 - right_y1) / (right_x2 - right_x1)
|
||||
right_extended_x2 = right_x1 + (right_extended_y2 - right_y1) / right_slope
|
||||
|
||||
# 更新线段端点为延长后的坐标
|
||||
left_x2, left_y2 = int(left_extended_x2), left_extended_y2
|
||||
right_x2, right_y2 = int(right_extended_x2), right_extended_y2
|
||||
|
||||
# 尝试延长线段到图像底部，处理被石板路部分遮挡的情况
|
||||
left_extended_y2 = height
|
||||
if abs(left_x2 - left_x1) < 5: # 几乎垂直
|
||||
@ -893,11 +809,16 @@ def detect_dual_track_lines(image, observe=False, delay=1000, save_log=True, sto
|
||||
# 显示偏差信息
|
||||
cv2.putText(result_img, f"Deviation: {deviation:.1f}px", (10, 30),
|
||||
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
|
||||
cv2.putText(result_img, "Final Result", (10, 60), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
|
||||
if 'best_pair_score' in locals() and best_pair_score != -1:
|
||||
cv2.putText(result_img, f"Pair Score: {best_pair_score:.2f}", (10, 85), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)
|
||||
current_y_offset = 105
|
||||
else:
|
||||
current_y_offset = 85
|
||||
|
||||
# 显示是否为石板路模式
|
||||
if stone_path_mode:
|
||||
cv2.putText(result_img, "Stone Path Mode", (10, 60),
|
||||
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 255), 2)
|
||||
cv2.putText(result_img, f"L-Slope: {left_line[3]:.2f}, R-Slope: {right_line[3]:.2f}", (10, current_y_offset), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)
|
||||
current_y_offset += 20
|
||||
cv2.putText(result_img, f"Track Width: {right_line[1] - left_line[1]:.1f}px", (10, current_y_offset), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)
|
||||
|
||||
if observe:
|
||||
cv2.imshow("轨迹线检测结果", result_img)
|
||||
@ -929,7 +850,6 @@ def detect_dual_track_lines(image, observe=False, delay=1000, save_log=True, sto
|
||||
"right_track_mid_x": float(right_line[1]),
|
||||
"track_width": float(right_line[1] - left_line[1]),
|
||||
"center_slope": float(center_slope),
|
||||
"stone_path_mode": stone_path_mode
|
||||
}
|
||||
info(f"双轨迹线检测结果: {log_info}", "日志")
|
||||
|
||||
@ -952,7 +872,6 @@ def detect_dual_track_lines(image, observe=False, delay=1000, save_log=True, sto
|
||||
"slope": float(center_slope),
|
||||
"is_vertical": abs(center_slope) > 5.0, # 判断是否接近垂直
|
||||
"track_width": float(right_line[1] - left_line[1]), # 两轨迹线之间的距离
|
||||
"stone_path_mode": stone_path_mode # 记录是否使用了石板路模式
|
||||
}
|
||||
|
||||
return center_info, left_track_info, right_track_info
|
||||
@ -1013,7 +932,11 @@ def detect_center_based_dual_track_lines(image, observe=False, delay=1000, save_
|
||||
|
||||
if observe:
|
||||
debug("步骤1: 创建黄色掩码", "处理")
|
||||
cv2.imshow("黄色掩码", combined_mask)
|
||||
mask_display = cv2.cvtColor(combined_mask, cv2.COLOR_GRAY2BGR)
|
||||
cv2.putText(mask_display, "Step 1: Yellow Mask (Center Based)", (10, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
|
||||
cv2.putText(mask_display, f"Lower: {lower_yellow} + Darker Var.", (10, 40), cv2.FONT_HERSHEY_SIMPLEX, 0.4, (0, 255, 0), 1)
|
||||
cv2.putText(mask_display, f"Upper: {upper_yellow} + Darker Var.", (10, 55), cv2.FONT_HERSHEY_SIMPLEX, 0.4, (0, 255, 0), 1)
|
||||
cv2.imshow("黄色掩码", mask_display)
|
||||
cv2.waitKey(delay)
|
||||
|
||||
# 裁剪底部区域重点关注近处的黄线
|
||||
@ -1022,7 +945,10 @@ def detect_center_based_dual_track_lines(image, observe=False, delay=1000, save_
|
||||
|
||||
if observe:
|
||||
debug("步骤1.5: 底部区域掩码", "处理")
|
||||
cv2.imshow("底部区域掩码", bottom_roi)
|
||||
bottom_roi_display = cv2.cvtColor(bottom_roi, cv2.COLOR_GRAY2BGR)
|
||||
cv2.putText(bottom_roi_display, "Step 1.5: Bottom ROI (Center Based)", (10, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
|
||||
cv2.putText(bottom_roi_display, f"ROI Height: {bottom_roi_height}px ({bottom_roi_height/height*100:.0f}%)", (10, 40), cv2.FONT_HERSHEY_SIMPLEX, 0.4, (0, 255, 0), 1)
|
||||
cv2.imshow("底部区域掩码", bottom_roi_display)
|
||||
cv2.waitKey(delay)
|
||||
|
||||
# 边缘检测
|
||||
@ -1030,7 +956,10 @@ def detect_center_based_dual_track_lines(image, observe=False, delay=1000, save_
|
||||
|
||||
if observe:
|
||||
debug("步骤2: 边缘检测", "处理")
|
||||
cv2.imshow("边缘检测", edges)
|
||||
edges_display = cv2.cvtColor(edges, cv2.COLOR_GRAY2BGR)
|
||||
cv2.putText(edges_display, "Step 2: Edge Detection (Canny - Center Based)", (10, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
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cv2.putText(edges_display, "Thresholds: (50, 150)", (10, 40), cv2.FONT_HERSHEY_SIMPLEX, 0.4, (0, 255, 0), 1)
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cv2.imshow("边缘检测", edges_display)
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cv2.waitKey(delay)
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# 霍夫变换检测直线
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@ -1038,18 +967,9 @@ def detect_center_based_dual_track_lines(image, observe=False, delay=1000, save_
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minLineLength=width*0.05, maxLineGap=40)
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if lines is None or len(lines) == 0:
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error("未检测到直线", "失败")
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error("未检测到直线 (或合并后无直线 - Center Based)", "失败")
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return None, None, None
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if observe:
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debug(f"步骤3: 检测到 {len(lines)} 条直线", "处理")
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lines_img = img.copy()
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for line in lines:
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x1, y1, x2, y2 = line[0]
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cv2.line(lines_img, (x1, y1), (x2, y2), (0, 255, 0), 2)
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cv2.imshow("检测到的直线", lines_img)
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cv2.waitKey(delay)
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# 筛选近似垂直的线
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vertical_lines = []
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for line in lines:
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@ -1065,7 +985,7 @@ def detect_center_based_dual_track_lines(image, observe=False, delay=1000, save_
|
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else:
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slope = (y2 - y1) / (x2 - x1)
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# 筛选接近垂直的线 (斜率较大)
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# 筛选接近垂直的线 (斜率较大)，但允许更多倾斜度
|
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if abs(slope) > 0.5: # 降低斜率阈值以捕获更多候选线
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line_length = np.sqrt((x2-x1)**2 + (y2-y1)**2)
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# 计算线的中点x坐标
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@ -1081,18 +1001,41 @@ def detect_center_based_dual_track_lines(image, observe=False, delay=1000, save_
|
||||
|
||||
if observe:
|
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debug(f"步骤4: 找到 {len(vertical_lines)} 条垂直线", "处理")
|
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v_lines_img = img.copy()
|
||||
v_lines_img_display = img.copy()
|
||||
cv2.putText(v_lines_img_display, "Step 4: Vertical Lines Filtered (Center Based)", (10, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)
|
||||
cv2.putText(v_lines_img_display, "Min Slope Abs: 0.50", (10, 40), cv2.FONT_HERSHEY_SIMPLEX, 0.4, (0, 0, 255), 1) # Slope threshold is 0.5 here
|
||||
for line_info in vertical_lines:
|
||||
line, _, _, slope, _ = line_info
|
||||
x1, y1, x2, y2 = line
|
||||
cv2.line(v_lines_img, (x1, y1), (x2, y2), (0, 255, 255), 2)
|
||||
cv2.line(v_lines_img_display, (x1, y1), (x2, y2), (0, 255, 255), 2)
|
||||
# 显示斜率
|
||||
cv2.putText(v_lines_img, f"{slope:.2f}", ((x1+x2)//2, (y1+y2)//2),
|
||||
cv2.putText(v_lines_img_display, f"{slope:.2f}", ((x1+x2)//2, (y1+y2)//2),
|
||||
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 1)
|
||||
cv2.imshow("垂直线", v_lines_img)
|
||||
cv2.imshow("垂直线", v_lines_img_display)
|
||||
cv2.waitKey(delay)
|
||||
|
||||
# 评分函数 - 用于找到最可能的中心线
|
||||
debug(f"步骤2.5 (Center Based): 初始检测到 {len(vertical_lines)} 条垂直线，尝试合并", "处理")
|
||||
|
||||
# 合并参数设置 (可以根据需要调整，这里使用与 detect_dual_track_lines 类似的设置)
|
||||
# 注意：这里的 max_line_gap 是 HoughLinesP 本身的参数，不是函数参数
|
||||
# 如果需要独立于 detect_dual_track_lines 中的 max_line_gap，需要单独定义或传递
|
||||
current_max_line_gap = 40 # 来自 HoughLinesP
|
||||
min_len_for_merge_cb = 5.0
|
||||
angle_thresh_deg_cb = 10.0
|
||||
ep_gap_abs_cb = current_max_line_gap / 2.0
|
||||
ep_gap_factor_cb = 0.25
|
||||
p_dist_abs_cb = current_max_line_gap / 4.0
|
||||
p_dist_factor_cb = 0.1
|
||||
|
||||
# vertical_lines = _merge_collinear_lines_iterative(vertical_lines,
|
||||
# min_initial_len=min_len_for_merge_cb,
|
||||
# max_angle_diff_deg=angle_thresh_deg_cb,
|
||||
# max_ep_gap_abs=ep_gap_abs_cb,
|
||||
# max_ep_gap_factor=ep_gap_factor_cb,
|
||||
# max_p_dist_abs=p_dist_abs_cb,
|
||||
# max_p_dist_factor=p_dist_factor_cb)
|
||||
|
||||
# INFO 评分函数 - 用于找到最可能的中心线
|
||||
def score_center_line(line_info):
|
||||
_, mid_x, mid_y, slope, length = line_info
|
||||
|
||||
@ -1129,6 +1072,20 @@ def detect_center_based_dual_track_lines(image, observe=False, delay=1000, save_
|
||||
|
||||
# 找到可能的中心线
|
||||
center_candidates = sorted(vertical_lines, key=score_center_line, reverse=True)
|
||||
|
||||
if observe:
|
||||
debug(f"步骤3.5: 找到 {len(center_candidates)} 条可能的中心线", "处理")
|
||||
center_candidates_img_display = img.copy()
|
||||
cv2.putText(center_candidates_img_display, "Step 3: Center Candidates (Center Based)", (10, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)
|
||||
for line_info in center_candidates:
|
||||
line, _, _, slope, _ = line_info
|
||||
x1, y1, x2, y2 = line
|
||||
cv2.line(center_candidates_img_display, (x1, y1), (x2, y2), (0, 255, 255), 2)
|
||||
# 显示斜率
|
||||
cv2.putText(center_candidates_img_display, f"{slope:.2f}", ((x1+x2)//2, (y1+y2)//2),
|
||||
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 1)
|
||||
cv2.imshow("可能的中心线", center_candidates_img_display)
|
||||
cv2.waitKey(delay)
|
||||
|
||||
# 如果有足够的候选线，取前3个进行评估
|
||||
if len(center_candidates) >= 3:
|
||||
@ -1345,7 +1302,7 @@ def detect_center_based_dual_track_lines(image, observe=False, delay=1000, save_
|
||||
bottom_center_x = max(0, min(width - 1, bottom_center_x))
|
||||
deviation = bottom_center_x - center_x
|
||||
|
||||
# 创建结果图像
|
||||
# INFO 创建结果图像
|
||||
result_img = None
|
||||
if observe or save_log:
|
||||
result_img = img.copy()
|
||||
@ -1368,6 +1325,18 @@ def detect_center_based_dual_track_lines(image, observe=False, delay=1000, save_
|
||||
# 显示偏差信息
|
||||
cv2.putText(result_img, f"Deviation: {deviation:.1f}px", (10, 30),
|
||||
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
|
||||
cv2.putText(result_img, "Final Result (Center Based)", (10, 60), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
|
||||
if 'best_score' in locals() and best_score != -1:
|
||||
cv2.putText(result_img, f"Pair Score: {best_score:.2f}", (10, 85), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)
|
||||
current_y_offset = 105
|
||||
else:
|
||||
current_y_offset = 85
|
||||
|
||||
cv2.putText(result_img, f"Center Slope: {center_slope:.2f}", (10, current_y_offset), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)
|
||||
current_y_offset += 20
|
||||
cv2.putText(result_img, f"L-Slope: {left_line[3]:.2f}, R-Slope: {right_line[3]:.2f}", (10, current_y_offset), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)
|
||||
current_y_offset += 20
|
||||
cv2.putText(result_img, f"Track Width: {right_line[1] - left_line[1]:.1f}px", (10, current_y_offset), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)
|
||||
|
||||
if observe:
|
||||
cv2.imshow("改进的中心线检测结果", result_img)
|
||||
@ -1446,7 +1415,7 @@ def auto_detect_dual_track_lines(image, observe=False, delay=1000, save_log=True
|
||||
result = detect_center_based_dual_track_lines(image, observe, delay, save_log)
|
||||
else:
|
||||
info("使用传统检测方法", "检测")
|
||||
result = detect_dual_track_lines(image, observe, delay, save_log, stone_path_mode=False)
|
||||
result = detect_dual_track_lines(image, observe, delay, save_log)
|
||||
|
||||
# 检查结果是否成功
|
||||
if result[0] is not None:
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user