diff --git a/brep2sdf/data/data.py b/brep2sdf/data/data.py index 51f2782..5065e9b 100644 --- a/brep2sdf/data/data.py +++ b/brep2sdf/data/data.py @@ -3,10 +3,8 @@ import torch from torch.utils.data import Dataset import numpy as np import pickle -from brep2sdf.utils.logger import setup_logger -from .utils import process_brep_data -# 设置日志记录器 -logger = setup_logger('dataset') +from brep2sdf.utils.logger import logger +from brep2sdf.data.utils import process_brep_data @@ -22,10 +20,16 @@ class BRepSDFDataset(Dataset): sdf_dir: npz文件目录 split: 数据集分割('train', 'val', 'test') """ + super().__init__() self.brep_dir = os.path.join(brep_dir, split) self.sdf_dir = os.path.join(sdf_dir, split) self.split = split + # 设置固定参数 + self.max_face = 70 + self.max_edge = 70 + self.bbox_scaled = 1.0 + # 检查目录是否存在 if not os.path.exists(self.brep_dir): raise ValueError(f"B-rep directory not found: {self.brep_dir}") @@ -59,112 +63,187 @@ class BRepSDFDataset(Dataset): def __getitem__(self, idx): """获取单个数据样本""" - brep_path = self.brep_data_list[idx] - sdf_path = self.sdf_data_list[idx] - try: - # 获取文件名(不含扩展名)作为sample name + brep_path = self.brep_data_list[idx] + sdf_path = self.sdf_data_list[idx] name = os.path.splitext(os.path.basename(brep_path))[0] # 加载B-rep和SDF数据 - brep_data = self._load_brep_file(brep_path) + with open(brep_path, 'rb') as f: + brep_raw = pickle.load(f) sdf_data = self._load_sdf_file(sdf_path) - - # 修改返回格式，将sdf_data作为一个键值对添加 - return { - 'name': name, - **brep_data, # 解包B-rep特征 - 'sdf': sdf_data # 添加SDF数据作为一个键 - } - + + try: + # 处理B-rep数据 + brep_features = process_brep_data( + data=brep_raw, + max_face=self.max_face, + max_edge=self.max_edge, + bbox_scaled=self.bbox_scaled + ) + + # 检查返回值的类型和数量 + if not isinstance(brep_features, tuple): + logger.error(f"process_brep_data returned {type(brep_features)}, expected tuple") + raise ValueError("Invalid return type from process_brep_data") + + if len(brep_features) != 6: + logger.error(f"Expected 6 features, got {len(brep_features)}") + logger.error("Features returned:") + for i, feat in enumerate(brep_features): + if isinstance(feat, torch.Tensor): + logger.error(f" {i}: Tensor of shape {feat.shape}") + else: + logger.error(f" {i}: {type(feat)}") + raise ValueError(f"Incorrect number of features: {len(brep_features)}") + + # 解包处理后的特征 + edge_ncs, edge_pos, edge_mask, surf_ncs, surf_pos, vertex_pos = brep_features + + # 构建返回字典 + return { + 'name': name, + 'edge_ncs': edge_ncs, # [max_face, max_edge, 10, 3] + 'edge_pos': edge_pos, # [max_face, max_edge, 6] + 'edge_mask': edge_mask, # [max_face, max_edge] + 'surf_ncs': surf_ncs, # [max_face, 100, 3] + 'surf_pos': surf_pos, # [max_face, 6] + 'vertex_pos': vertex_pos, # [max_face, max_edge, 6] + 'sdf': sdf_data # [N, 4] + } + + except Exception as e: + logger.error(f"\nError processing B-rep data for file: {brep_path}") + logger.error(f"Error type: {type(e).__name__}") + logger.error(f"Error message: {str(e)}") + + # 打印原始数据的结构 + logger.error("\nRaw data structure:") + for key, value in brep_raw.items(): + if isinstance(value, list): + logger.error(f" {key}: list of length {len(value)}") + if value: + logger.error(f" First element type: {type(value[0])}") + if hasattr(value[0], 'shape'): + logger.error(f" First element shape: {value[0].shape}") + elif hasattr(value, 'shape'): + logger.error(f" {key}: shape {value.shape}") + else: + logger.error(f" {key}: {type(value)}") + raise + except Exception as e: logger.error(f"Error loading sample from {brep_path}: {str(e)}") logger.error("Data structure:") - if 'brep_data' in locals(): - for key, value in brep_data.items(): - if isinstance(value, np.ndarray): - logger.error(f" {key}: type={type(value)}, dtype={value.dtype}, shape={value.shape}") raise def _load_brep_file(self, brep_path): """加载B-rep特征文件""" try: + # 1. 加载原始数据 with open(brep_path, 'rb') as f: - brep_data = pickle.load(f) + raw_data = pickle.load(f) brep_data = {} - # 1. 处理几何数据（不等长序列） - for key in ['surf_wcs', 'surf_ncs', 'edge_wcs', 'edge_ncs']: - if key in brep_data: + # 2. 处理几何数据（不等长序列） + geom_keys = ['surf_wcs', 'surf_ncs', 'edge_wcs', 'edge_ncs'] + for key in geom_keys: + if key in raw_data: try: - brep_data[key] = [ - torch.from_numpy(np.array(x, dtype=np.float32)) - for x in brep_data[key] - ] + # 确保数据是列表 + if not isinstance(raw_data[key], list): + raise ValueError(f"{key} is not a list") + + # 转换每个元素为张量 + tensors = [] + for i, x in enumerate(raw_data[key]): + try: + # 先转换为numpy数组 + arr = np.array(x, dtype=np.float32) + # 再转换为张量 + tensor = torch.from_numpy(arr) + tensors.append(tensor) + except Exception as e: + logger.error(f"Error converting {key}[{i}]:") + logger.error(f" Data type: {type(x)}") + if isinstance(x, np.ndarray): + logger.error(f" Shape: {x.shape}") + logger.error(f" dtype: {x.dtype}") + raise ValueError(f"Failed to convert {key}[{i}]: {str(e)}") + + brep_data[key] = tensors + except Exception as e: - logger.error(f"Error converting {key}:") - logger.error(f" Type: {type(brep_data[key])}") - if isinstance(brep_data[key], list): - logger.error(f" List length: {len(brep_data[key])}") - if len(brep_data[key]) > 0: - logger.error(f" First element type: {type(brep_data[key][0])}") - logger.error(f" First element shape: {brep_data[key][0].shape}") - logger.error(f" First element dtype: {brep_data[key][0].dtype}") - raise ValueError(f"Failed to convert {key}: {str(e)}") + logger.error(f"Error processing {key}:") + logger.error(f" Raw data type: {type(raw_data[key])}") + raise ValueError(f"Failed to process {key}: {str(e)}") - # 2. 处理固定形状的数据 - for key in ['corner_wcs', 'corner_unique', 'surf_bbox_wcs', 'edge_bbox_wcs']: - if key in brep_data: + # 3. 处理固定形状的数据 + fixed_keys = ['corner_wcs', 'corner_unique', 'surf_bbox_wcs', 'edge_bbox_wcs'] + for key in fixed_keys: + if key in raw_data: try: - data = np.array(brep_data[key], dtype=np.float32) - brep_data[key] = torch.from_numpy(data) + # 直接从原始数据转换 + arr = np.array(raw_data[key], dtype=np.float32) + brep_data[key] = torch.from_numpy(arr) except Exception as e: - logger.error(f"Error converting {key}:") - logger.error(f" Type: {type(brep_data[key])}") - if isinstance(brep_data[key], np.ndarray): - logger.error(f" Shape: {brep_data[key].shape}") - logger.error(f" dtype: {brep_data[key].dtype}") + logger.error(f"Error converting fixed shape data {key}:") + logger.error(f" Raw data type: {type(raw_data[key])}") + if isinstance(raw_data[key], np.ndarray): + logger.error(f" Shape: {raw_data[key].shape}") + logger.error(f" dtype: {raw_data[key].dtype}") raise ValueError(f"Failed to convert {key}: {str(e)}") - # 3. 处理邻接矩阵 - for key in ['edgeFace_adj', 'edgeCorner_adj', 'faceEdge_adj']: - if key in brep_data: + # 4. 处理邻接矩阵 + adj_keys = ['edgeFace_adj', 'edgeCorner_adj', 'faceEdge_adj'] + for key in adj_keys: + if key in raw_data: try: - data = np.array(brep_data[key], dtype=np.int32) - brep_data[key] = torch.from_numpy(data) + # 转换为整型数组 + arr = np.array(raw_data[key], dtype=np.int32) + brep_data[key] = torch.from_numpy(arr) except Exception as e: - logger.error(f"Error converting {key}:") - logger.error(f" Type: {type(brep_data[key])}") - if isinstance(brep_data[key], np.ndarray): - logger.error(f" Shape: {brep_data[key].shape}") - logger.error(f" dtype: {brep_data[key].dtype}") + logger.error(f"Error converting adjacency matrix {key}:") + logger.error(f" Raw data type: {type(raw_data[key])}") + if isinstance(raw_data[key], np.ndarray): + logger.error(f" Shape: {raw_data[key].shape}") + logger.error(f" dtype: {raw_data[key].dtype}") raise ValueError(f"Failed to convert {key}: {str(e)}") - feature_embedder = process_brep_data(brep_data, 70,70,1,) - - return feature_embedder + # 5. 验证必要的键是否存在 + required_keys = {'surf_wcs', 'edge_wcs', 'corner_wcs'} + missing_keys = required_keys - set(brep_data.keys()) + if missing_keys: + raise ValueError(f"Missing required keys: {missing_keys}") + + # 6. 使用process_brep_data处理数据 + try: + features = process_brep_data( + data=brep_data, + max_face=self.max_face, + max_edge=self.max_edge, + bbox_scaled=self.bbox_scaled + ) + return features + + except Exception as e: + logger.error("Error in process_brep_data:") + logger.error(f" Error message: {str(e)}") + # 打印数据形状信息 + logger.error("\nInput data shapes:") + for key, value in brep_data.items(): + if isinstance(value, list): + shapes = [t.shape for t in value] + logger.error(f" {key}: list of tensors with shapes {shapes}") + elif isinstance(value, torch.Tensor): + logger.error(f" {key}: tensor of shape {value.shape}") + raise + except Exception as e: logger.error(f"\nError loading B-rep file: {brep_path}") logger.error(f"Error message: {str(e)}") - - # 打印完整的数据结构信息 - if 'brep_data' in locals(): - logger.error("\nComplete data structure:") - for key, value in brep_data.items(): - logger.error(f"\n{key}:") - logger.error(f" Type: {type(value)}") - if isinstance(value, np.ndarray): - logger.error(f" Shape: {value.shape}") - logger.error(f" dtype: {value.dtype}") - elif isinstance(value, list): - logger.error(f" List length: {len(value)}") - if len(value) > 0: - logger.error(f" First element type: {type(value[0])}") - if isinstance(value[0], np.ndarray): - logger.error(f" First element shape: {value[0].shape}") - logger.error(f" First element dtype: {value[0].dtype}") raise def _load_sdf_file(self, sdf_path): @@ -190,6 +269,52 @@ class BRepSDFDataset(Dataset): logger.error(f"Error type: {type(e).__name__}") logger.error(f"Error message: {str(e)}") raise + @staticmethod + def collate_fn(batch): + """自定义批处理函数""" + # 收集所有样本的名称 + names = [item['name'] for item in batch] + + # 处理固定大小的张量数据 + tensor_keys = ['edge_ncs', 'edge_pos', 'edge_mask', + 'surf_ncs', 'surf_pos', 'vertex_pos'] + tensors = { + key: torch.stack([item[key] for item in batch]) + for key in tensor_keys + } + + # 处理变长的SDF数据 + sdf_data = [item['sdf'] for item in batch] + max_sdf_len = max(sdf.size(0) for sdf in sdf_data) + + # 填充SDF数据 + padded_sdfs = [] + sdf_masks = [] + for sdf in sdf_data: + pad_len = max_sdf_len - sdf.size(0) + if pad_len > 0: + padding = torch.zeros(pad_len, sdf.size(1), + dtype=sdf.dtype, device=sdf.device) + padded_sdf = torch.cat([sdf, padding], dim=0) + mask = torch.cat([ + torch.ones(sdf.size(0), dtype=torch.bool), + torch.zeros(pad_len, dtype=torch.bool) + ]) + else: + padded_sdf = sdf + mask = torch.ones(sdf.size(0), dtype=torch.bool) + padded_sdfs.append(padded_sdf) + sdf_masks.append(mask) + + # 合并所有数据 + batch_data = { + 'name': names, + 'sdf': torch.stack(padded_sdfs), + 'sdf_mask': torch.stack(sdf_masks), + **tensors + } + + return batch_data def test_dataset(): """测试数据集功能""" diff --git a/brep2sdf/data/utils.py b/brep2sdf/data/utils.py index d9106a0..164c68c 100644 --- a/brep2sdf/data/utils.py +++ b/brep2sdf/data/utils.py @@ -8,6 +8,8 @@ import argparse from chamferdist import ChamferDistance from mpl_toolkits.mplot3d.art3d import Poly3DCollection from typing import List, Optional, Tuple, Union +from brep2sdf.utils.logger import logger + from OCC.Core.gp import gp_Pnt, gp_Pnt from OCC.Core.TColgp import TColgp_Array2OfPnt @@ -986,8 +988,16 @@ def process_brep_data( - data_class: (可选) 类别标签 [1] """ # 解包数据 - _, _, surf_ncs, edge_ncs, corner_wcs, _, _, faceEdge_adj, surf_pos, edge_pos, _, _ = data.values() - + #_, _, surf_ncs, edge_ncs, corner_wcs, _, _, faceEdge_adj, surf_pos, edge_pos, _ = data.values() + # 直接获取需要的数据 + surf_ncs = data['surf_ncs'] # (num_faces,) -> 每个元素形状 (N, 3) + edge_ncs = data['edge_ncs'] # (num_edges, 100, 3) + corner_wcs = data['corner_wcs'] # (num_corners, 3) + faceEdge_adj = data['faceEdge_adj'] # (num_faces, num_edges) + edgeCorner_adj = data['edgeCorner_adj'] # (num_edges, 2) 每条边连接2个顶点 + surf_pos = data['surf_bbox_wcs'] # (num_faces, 6) + edge_pos = data['edge_bbox_wcs'] # (num_edges, 6) + # 数据增强 random_num = np.random.rand() if random_num > 0.5 and aug: @@ -1002,29 +1012,38 @@ def process_brep_data( surfpos_corners = rotate_axis(surfpos_corners, angle, axis, normalized=True) edgepos_corners = rotate_axis(edgepos_corners, angle, axis, normalized=True) corner_wcs = rotate_axis(corner_wcs, angle, axis, normalized=True) - surf_ncs = rotate_axis(surf_ncs, angle, axis, normalized=False) + + # 对每个面的点云进行旋转 + for i in range(len(surf_ncs)): + surf_ncs[i] = rotate_axis(surf_ncs[i], angle, axis, normalized=False) edge_ncs = rotate_axis(edge_ncs, angle, axis, normalized=False) + # 重新计算边界框 surf_pos = get_bbox(surfpos_corners) # [num_faces, 2, 3] surf_pos = surf_pos.reshape(len(surf_pos), 6) # [num_faces, 6] edge_pos = get_bbox(edgepos_corners) # [num_edges, 2, 3] edge_pos = edge_pos.reshape(len(edge_pos), 6) # [num_edges, 6] - + # 缩放值范围 surf_pos = surf_pos * bbox_scaled # [num_faces, 6] edge_pos = edge_pos * bbox_scaled # [num_edges, 6] corner_wcs = corner_wcs * bbox_scaled # [num_edges, 2, 3] # 特征复制 - edge_pos_duplicated: List[np.ndarray] = [] # 每个元素形状: [num_edges_per_face, 6] - vertex_pos_duplicated: List[np.ndarray] = [] # 每个元素形状: [num_edges_per_face, 6] - edge_ncs_duplicated: List[np.ndarray] = [] # 每个元素形状: [num_edges_per_face, 10, 3] + edge_pos_duplicated: List[np.ndarray] = [] # 每个元素形状: [num_edges, 6] + vertex_pos_duplicated: List[np.ndarray] = [] # 每个元素形状: [num_edges, 6] + edge_ncs_duplicated: List[np.ndarray] = [] # 每个元素形状: [num_edges, 10, 3] for adj in faceEdge_adj: # [num_faces, num_edges] - edge_ncs_duplicated.append(edge_ncs[adj]) # [num_edges_per_face, 10, 3] - edge_pos_duplicated.append(edge_pos[adj]) # [num_edges_per_face, 6] - corners = corner_wcs[adj] # [num_edges_per_face, 2, 3] + edge_ncs_duplicated.append(edge_ncs[adj]) # [num_edges, 10, 3] + edge_pos_duplicated.append(edge_pos[adj]) # [num_edges, 6] + #corners = corner_wcs[adj] # [num_vertax, 3] FIXME + edge_indices = np.where(adj)[0] # 获取当前面的边索引 + corner_indices = edgeCorner_adj[edge_indices] # 获取这些边对应的顶点索引 + corners = corner_wcs[corner_indices] # 获取顶点坐标 + logger.debug(corners) + corners_sorted = [] for corner in corners: # [2, 3] sorted_indices = np.lexsort((corner[:, 2], corner[:, 1], corner[:, 0])) @@ -1062,7 +1081,10 @@ def process_brep_data( random_indices = np.random.permutation(surf_pos.shape[0]) surf_pos = surf_pos[random_indices] # [num_faces, 6] edge_pos = edge_pos[random_indices] # [num_faces, max_edge, 6] - surf_ncs = surf_ncs[random_indices] # [num_faces, 100, 3] + # 修改这里：surf_ncs是对象数组，需要特殊处理 + surf_ncs_new = np.array([surf_ncs[i] for i in random_indices]) # 重新排列对象数组 + surf_ncs = surf_ncs_new + #surf_ncs = surf_ncs[random_indices] # [num_faces, 100, 3] edge_ncs = edge_ncs[random_indices] # [num_faces, max_edge, 10, 3] edge_mask = edge_mask[random_indices] # [num_faces, max_edge] vertex_pos = vertex_pos[random_indices] # [num_faces, max_edge, 6] diff --git a/brep2sdf/scripts/process_brep.py b/brep2sdf/scripts/process_brep.py index 1380b72..6a9472f 100644 --- a/brep2sdf/scripts/process_brep.py +++ b/brep2sdf/scripts/process_brep.py @@ -184,32 +184,27 @@ def get_bbox(shape, subshape): def parse_solid(step_path): - """Parse the surface, curve, face, edge, vertex in a CAD solid using OCC.""" """ - 返回值：一个dict，包括 - 几何关系 - # 面片数据 - 'surf_wcs': surfs_wcs, # list of np.array(N, 3), 每个面片的点云坐标 - 'surf_ncs': surfs_ncs, # list of np.array(N, 3), 每个面片的法向量 + 解析STEP文件中的CAD模型数据 + + 返回: + dict: 包含以下键值对的字典: + # 几何数据 + 'surf_wcs': np.ndarray(dtype=object) # 形状为(N,)的数组，每个元素是形状为(M, 3)的float32数组，表示面的点云坐标 + 'edge_wcs': np.ndarray(dtype=object) # 形状为(N,)的数组，每个元素是形状为(100, 3)的float32数组，表示边的采样点坐标 + 'surf_ncs': np.ndarray(dtype=object) # 形状为(N,)的数组，每个元素是形状为(M, 3)的float32数组，表示归一化后的面点云 + 'edge_ncs': np.ndarray(dtype=object) # 形状为(N,)的数组，每个元素是形状为(100, 3)的float32数组，表示归一化后的边采样点 + 'corner_wcs': np.ndarray(dtype=float32) # 形状为(K, 3)的数组，表示所有顶点的坐标 + 'corner_unique': np.ndarray(dtype=float32) # 形状为(L, 3)的数组，表示去重后的顶点坐标 - # 边数据 (num_samples=100) - 'edge_wcs': edges_wcs, # list of np.array(100, 3), 每条边的采样点坐标 - 'edge_ncs': edges_ncs, # list of np.array(100, 3), 每条边的法向量 + # 拓扑关系 + 'edgeFace_adj': np.ndarray(dtype=int32) # 形状为(num_edges, num_faces)的数组，表示边-面邻接关系 + 'edgeCorner_adj': np.ndarray(dtype=int32) # 形状为(num_edges, 2)的数组，表示边-顶点邻接关系 + 'faceEdge_adj': np.ndarray(dtype=int32) # 形状为(num_faces, num_edges)的数组，表示面-边邻接关系 - # 顶点数据 - 'corner_wcs': corner_wcs.astype(np.float32), # np.array(N, 3), 所有顶点坐标 - 'corner_unique': np.unique(corner_wcs, axis=0).astype(np.float32), # np.array(M, 3), 去重后的顶点坐标 - 拓扑关系 - # 邻接矩阵，都是int32类型 - 'edgeFace_adj': edgeFace_adj, # np.array(num_edges, num_faces), 边-面邻接关系 - 'edgeCorner_adj': edgeCorner_adj, # np.array(num_edges, 2), 边-顶点邻接关系 - 'faceEdge_adj': faceEdge_adj, # np.array(num_faces, num_edges), 面-边邻接关系 - 包围盒数据 - # 包围盒坐标，float32类型 - 'surf_bbox_wcs': surf_bbox_wcs, # np.array(num_faces, 6), 每个面的包围盒 [xmin,ymin,zmin,xmax,ymax,zmax] - 'edge_bbox_wcs': edge_bbox_wcs, # np.array(num_edges, 6), 每条边的包围盒 [xmin,ymin,zmin,xmax,ymax,zmax] - - + # 包围盒数据 + 'surf_bbox_wcs': np.ndarray(dtype=float32) # 形状为(num_faces, 6)的数组，表示每个面的包围盒[xmin,ymin,zmin,xmax,ymax,zmax] + 'edge_bbox_wcs': np.ndarray(dtype=float32) # 形状为(num_edges, 6)的数组，表示每条边的包围盒[xmin,ymin,zmin,xmax,ymax,zmax] """ # Load STEP file reader = STEPControl_Reader() diff --git a/brep2sdf/scripts/read_pkl.py b/brep2sdf/scripts/read_pkl.py index 7fa35dc..03d36ac 100644 --- a/brep2sdf/scripts/read_pkl.py +++ b/brep2sdf/scripts/read_pkl.py @@ -8,7 +8,6 @@ def inspect_data(pkl_file): """检查并显示pickle文件中的数据结构""" with open(pkl_file, 'rb') as f: data = pickle.load(f) - print("数据结构概览:") print("=" * 50) @@ -36,5 +35,5 @@ def inspect_data(pkl_file): print(f"值: {value}") if __name__ == "__main__": - pkl_file = "./bathtub_0011.pkl" # 替换为你的文件路径 + pkl_file = "/home/wch/brep2sdf/test_data/pkl/train/bathtub_0004.pkl" # 替换为你的文件路径 inspect_data(pkl_file) \ No newline at end of file diff --git a/brep2sdf/utils/logger.py b/brep2sdf/utils/logger.py index ff9ac73..5691eb9 100644 --- a/brep2sdf/utils/logger.py +++ b/brep2sdf/utils/logger.py @@ -1,46 +1,82 @@ import os +import sys import logging +import traceback from datetime import datetime -def setup_logger(name, log_dir='logs'): - """ - 设置日志记录器 +class BRepLogger: + _instance = None - 参数: - name: 日志记录器名称 - log_dir: 日志文件存储目录 - - 返回: - logger: 配置好的日志记录器 - """ - # 创建logs目录 - os.makedirs(log_dir, exist_ok=True) - - # 创建日志记录器 - logger = logging.getLogger(name) - logger.setLevel(logging.INFO) + def __new__(cls): + if cls._instance is None: + cls._instance = super().__new__(cls) + cls._instance._initialize_logger() + return cls._instance - # 如果logger已经有处理器，则不添加 - if logger.handlers: - return logger + def _initialize_logger(self): + """初始化日志记录器""" + # 创建logs目录 + log_dir = 'logs' + os.makedirs(log_dir, exist_ok=True) + + # 创建logger + self.logger = logging.getLogger('BRepLogger') + self.logger.setLevel(logging.DEBUG) + + # 如果logger已经有处理器，则返回 + if self.logger.handlers: + return + + # 创建格式化器 + formatter = logging.Formatter( + '%(asctime)s - %(levelname)s - ' + '%(filename)s:%(lineno)d in %(funcName)s - %(message)s' + ) + + # 创建文件处理器 + current_time = datetime.now().strftime('%Y%m%d_%H%M%S') + log_file = os.path.join(log_dir, f'brep2sdf_{current_time}.log') + file_handler = logging.FileHandler(log_file, encoding='utf-8') + file_handler.setLevel(logging.DEBUG) + file_handler.setFormatter(formatter) - # 创建格式化器 - formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') + # 创建控制台处理器 + console_handler = logging.StreamHandler(sys.stdout) + console_handler.setLevel(logging.DEBUG) + console_handler.setFormatter(formatter) + + # 添加处理器 + self.logger.addHandler(file_handler) + self.logger.addHandler(console_handler) + + # 记录初始信息 + self.logger.info("="*50) + self.logger.info("BRep Logger initialized") + self.logger.info(f"Log file: {log_file}") + self.logger.info("="*50) - # 创建文件处理器 - current_time = datetime.now().strftime('%Y%m%d_%H%M%S') - log_file = os.path.join(log_dir, f'{name}_{current_time}.log') - file_handler = logging.FileHandler(log_file, encoding='utf-8') - file_handler.setLevel(logging.INFO) - file_handler.setFormatter(formatter) + def debug(self, msg): + """带调用位置的调试信息""" + caller = traceback.extract_stack()[-2] + filename = os.path.basename(caller.filename) + self.logger.debug(f"{msg} (in {filename})") - # 添加文件处理器到日志记录器 - logger.addHandler(file_handler) + def info(self, msg): + self.logger.info(msg) - # 记录初始信息 - logger.info("="*50) - logger.info(f"Logger initialized: {name}") - logger.info(f"Log file: {log_file}") - logger.info("="*50) + def warning(self, msg): + self.logger.warning(msg) - return logger \ No newline at end of file + def error(self, msg, include_trace=True): + """错误信息，可选是否包含调用栈""" + if include_trace: + self.logger.error(msg, exc_info=True, stack_info=True) + else: + self.logger.error(msg) + + def exception(self, msg): + """异常信息，总是包含异常堆栈""" + self.logger.exception(msg) + +# 创建全局logger实例 +logger = BRepLogger() \ No newline at end of file