6 changed files with 507 additions and 60 deletions
@ -1,66 +1,161 @@ |
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import os |
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import subprocess |
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from tqdm import tqdm |
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import numpy as np |
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import torch |
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import argparse |
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from skimage import measure |
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import time |
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import trimesh |
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|
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# 使用一个 c++ 程序处理，这里只是调用，注意要在docker里面运行。宿主机编译失败 |
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def create_grid(depth, box_size): |
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""" |
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创建三维网格点 |
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:param depth: 网格深度（决定分辨率） |
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:param box_size: 边界框大小（边长） |
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:return: 网格点数组和坐标网格 |
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""" |
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grid_size = 2**depth + 1 |
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start = -box_size / 2 |
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end = box_size / 2 |
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x = np.linspace(start, end, grid_size) |
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y = np.linspace(start, end, grid_size) |
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z = np.linspace(start, end, grid_size) |
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xx, yy, zz = np.meshgrid(x, y, z, indexing='ij') |
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points = np.stack([xx.ravel(), yy.ravel(), zz.ravel()], axis=1) |
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return points, xx, yy, zz |
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|
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def predict_sdf(model, points, device): |
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""" |
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使用模型预测SDF值 |
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:param model: PyTorch模型 |
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:param points: 输入点坐标 (N, 3) |
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:param device: 设备（CPU/GPU） |
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:return: SDF值数组 (N,) |
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""" |
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points_t = torch.from_numpy(points).float().to(device) |
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|
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with torch.no_grad(): |
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sdf = model(points_t).cpu().numpy().flatten() |
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return sdf |
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|
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def extract_surface(sdf, xx, yy, zz, method='MC'): |
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""" |
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提取零表面 |
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:param sdf: SDF值三维数组 |
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:param xx/yy/zz: 网格坐标 |
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:param method: 提取方法（MC: Marching Cubes） |
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:return: 顶点和面片 |
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""" |
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if method == 'MC': |
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verts, faces, _, _ = measure.marching_cubes(sdf, level=0) |
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else: |
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raise NotImplementedError("仅支持Marching Cubes方法") |
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return verts, faces |
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|
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def save_ply(vertices, faces, filename): |
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""" |
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保存顶点和面片为PLY文件 |
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:param vertices: 顶点数组 (N, 3) |
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:param faces: 面片数组 (M, 3) |
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:param filename: 输出文件名 |
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""" |
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with open(filename, 'w') as f: |
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f.write("ply\n") |
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f.write("format ascii 1.0\n") |
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f.write(f"element vertex {len(vertices)}\n") |
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f.write("property float x\n") |
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f.write("property float y\n") |
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f.write("property float z\n") |
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f.write(f"element face {len(faces)}\n") |
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f.write("property list uchar int vertex_indices\n") |
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f.write("end_header\n") |
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for v in vertices: |
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f.write(f"{v[0]} {v[1]} {v[2]}\n") |
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for face in faces: |
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f.write(f"3 {face[0]} {face[1]} {face[2]}\n") |
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|
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def compute_sdf_error(model, gt_mesh, res, device): |
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""" |
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计算预测SDF与GT网格的误差 |
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:param model: PyTorch模型 |
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:param gt_mesh: GT网格（Trimesh格式） |
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:param res: 误差计算分辨率 |
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:param device: 设备 |
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:return: 平均误差和最大误差 |
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""" |
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# 生成均匀采样点 |
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box_size = max(gt_mesh.extents) |
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start = -box_size / 2 |
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end = box_size / 2 |
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x = np.linspace(start, end, res) |
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y = np.linspace(start, end, res) |
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z = np.linspace(start, end, res) |
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points = np.array(np.meshgrid(x, y, z)).T.reshape(-1, 3) |
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|
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# 预测SDF |
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pred_sdf = predict_sdf(model, points, device) |
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|
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# 计算GT距离 |
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distances = gt_mesh.nearest.on_surface(points)[1] |
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gt_sdf = np.abs(distances) |
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|
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# 计算误差 |
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abs_error = np.abs(pred_sdf - gt_sdf) |
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rel_error = abs_error / (np.abs(gt_sdf) + 1e-9) |
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avg_abs = np.mean(abs_error) |
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avg_rel = np.mean(rel_error) |
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max_abs = np.max(abs_error) |
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max_rel = np.max(rel_error) |
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return avg_abs, avg_rel, max_abs, max_rel |
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|
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def main(): |
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# 定义 STEP 文件目录和名称列表文件路径 |
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output_data_root_dir = "/workspace/home/wch/brep2sdf/data/output_data" |
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name_list_path = "/workspace/home/wch/brep2sdf/data/name_list.txt" |
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|
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# 读取名称列表 |
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try: |
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with open(name_list_path, 'r') as f: |
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names = [line.strip() for line in f if line.strip()] # 去除空行 |
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except FileNotFoundError: |
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print(f"Error: File '{name_list_path}' not found.") |
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return |
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except Exception as e: |
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print(f"Error reading file '{name_list_path}': {e}") |
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return |
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|
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# 遍历名称列表并处理每个 STEP 文件 |
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for name in tqdm(names, desc="ISOsurfing pt files"): |
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pt_file = os.path.join(output_data_root_dir, f"{name}.pt") |
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|
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if not pt_file: |
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print(f"Warning: No pt files found in directory '{output_data_root_dir}'. Skipping...") |
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continue |
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|
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# ./ISG_console_pytorch -i ./test/teaser.pt -o outputmesh.ply -v -0.01 -d 8 |
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# 构造子进程命令 |
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command = [ |
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"python", "/workspace/home/wch/brep2sdf/data/scripts/IsoSurfacing/build/App/console_pytorch/ISG_console_pytorch", |
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"-i", pt_file, # 使用当前遍历的pt文件 |
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"-o", os.path.join(output_data_root_dir, f"{name}_outputmesh.ply"), # 动态生成输出文件路径 |
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"-v", "-0.01", "-d", "8" |
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] |
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|
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# 调用子进程运行命令 |
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try: |
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result = subprocess.run( |
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command, |
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capture_output=True, |
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text=True, |
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check=True # 如果返回非零退出码，则抛出 CalledProcessError |
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parser = argparse.ArgumentParser(description='IsoSurface Generator') |
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parser.add_argument('-i', '--input', type=str, required=True, help='Input model file (.pt)') |
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parser.add_argument('-o', '--output', type=str, required=True, help='Output mesh file (.ply)') |
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parser.add_argument('--depth', type=int, default=7, help='网格深度（分辨率）') |
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parser.add_argument('--box_size', type=float, default=2.0, help='边界框大小') |
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parser.add_argument('--method', type=str, default='MC', choices=['MC'], help='表面提取方法') |
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parser.add_argument('--use-gpu', action='store_true', help='使用GPU') |
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parser.add_argument('--compare', type=str, help='GT网格文件（.ply）') |
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parser.add_argument('--compres', type=int, default=32, help='误差计算分辨率') |
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args = parser.parse_args() |
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|
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# 设置设备 |
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device = torch.device("cuda" if args.use_gpu and torch.cuda.is_available() else "cpu") |
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print(f"Using device: {device}") |
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model = torch.jit.load(args.input).to(device) |
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#model = torch.load(args.input).to(device) |
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model.eval() |
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|
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# 创建网格并预测SDF |
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points, xx, yy, zz = create_grid(args.depth, args.box_size) |
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sdf = predict_sdf(model, points, device) |
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print(points.shape) |
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print(sdf.shape) |
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print(sdf) |
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sdf_grid = sdf.reshape(xx.shape) |
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|
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# 提取表面 |
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print("Extracting surface...") |
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start_time = time.time() |
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verts, faces = extract_surface(sdf_grid, xx, yy, zz, args.method) |
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print(f"Surface extraction took {time.time() - start_time:.2f} seconds") |
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|
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# 保存网格 |
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save_ply(verts, faces, args.output) |
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print(f"Mesh saved to {args.output}") |
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|
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# 误差评估（可选） |
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if args.compare: |
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print("Computing SDF error...") |
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gt_mesh = trimesh.load(args.compare) |
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avg_abs, avg_rel, max_abs, max_rel = compute_sdf_error( |
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model, gt_mesh, args.compres, device |
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) |
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print(f"Successfully processed '{name}'") |
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print("STDOUT:", result.stdout) |
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print("STDERR:", result.stderr) |
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except subprocess.CalledProcessError as e: |
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print(f"Error processing '{name}': Command failed with return code {e.returncode}") |
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print(f"Command: {e.cmd}") |
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print(f"Error type: {type(e).__name__}") |
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print("STDOUT:", e.stdout) |
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print("STDERR:", e.stderr) |
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print("Traceback:", e.__traceback__) |
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except Exception as e: |
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print(f"Unexpected error processing '{name}': {str(e)}") |
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print(f"Command: {command}") |
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print("Traceback:", traceback.format_exc()) |
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if __name__ == '__main__': |
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print(f"Average Absolute Error: {avg_abs:.4f}") |
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print(f"Average Relative Error: {avg_rel:.4f}") |
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print(f"Max Absolute Error: {max_abs:.4f}") |
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print(f"Max Relative Error: {max_rel:.4f}") |
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|
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if __name__ == "__main__": |
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main() |
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@ -0,0 +1,67 @@ |
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import os |
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import subprocess |
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from tqdm import tqdm |
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|
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|
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def main(): |
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# 定义 STEP 文件目录和名称列表文件路径 |
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step_root_dir = "/home/wch/brep2sdf/data/step" |
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name_list_path = "/home/wch/brep2sdf/data/name_list.txt" |
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|
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# 读取名称列表 |
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try: |
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with open(name_list_path, 'r') as f: |
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names = [line.strip() for line in f if line.strip()] # 去除空行 |
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except FileNotFoundError: |
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print(f"Error: File '{name_list_path}' not found.") |
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return |
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except Exception as e: |
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print(f"Error reading file '{name_list_path}': {e}") |
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return |
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|
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# 遍历名称列表并处理每个 STEP 文件 |
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for name in tqdm(names, desc="Processing STEP files"): |
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step_dir = os.path.join(step_root_dir, name) |
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|
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# 动态生成 STEP 文件路径（假设只有一个文件） |
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step_files = [ |
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os.path.join(step_dir, f) |
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for f in os.listdir(step_dir) |
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if f.endswith(".step") and f.startswith(name) |
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] |
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|
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if not step_files: |
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print(f"Warning: No STEP files found in directory '{step_dir}'. Skipping...") |
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continue |
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|
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# 假设我们只处理第一个匹配的文件 |
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input_step = step_files[0] |
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|
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# 构造子进程命令 |
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command = [ |
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"python", "train.py", |
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"--use-normal", |
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"-i", input_step, # 输入文件路径 |
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] |
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|
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# 调用子进程运行命令 |
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try: |
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result = subprocess.run( |
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command, |
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capture_output=True, |
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text=True, |
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check=True # 如果返回非零退出码，则抛出 CalledProcessError |
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) |
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print(f"Processed '{input_step}' successfully.") |
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print("STDOUT:", result.stdout) |
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print("STDERR:", result.stderr) |
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except subprocess.CalledProcessError as e: |
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print(f"Error processing '{input_step}': {e}") |
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print("STDOUT:", e.stdout) |
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print("STDERR:", e.stderr) |
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except Exception as e: |
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print(f"Unexpected error processing '{input_step}': {e}") |
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|
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|
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if __name__ == '__main__': |
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main() |
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@ -0,0 +1,280 @@ |
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import os |
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import sys |
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from brep2sdf.utils.logger import logger |
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# 导入日志系统 |
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from brep2sdf.utils.logger import logger |
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import numpy as np |
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from scipy.spatial import cKDTree |
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from scipy.spatial.distance import directed_hausdorff |
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import trimesh |
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import pandas as pd |
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import csv |
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import math |
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import pickle |
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import argparse |
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project_dir = "/home/wch/brep2sdf" |
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# parse args first and set gpu id |
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parser = argparse.ArgumentParser() |
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parser.add_argument('--gt_path', type=str, |
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default=os.path.join(project_dir, 'data/gt_point'), |
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help='ground truth data path') |
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parser.add_argument('--pred_path', type=str, |
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default=os.path.join(project_dir, 'data/output_data'), |
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help='converted data path') |
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parser.add_argument('--name_list', type=str, default='name_list.txt', help='names of models to be evaluated, if you want to evaluate the whole dataset, please set it as all_names.txt') |
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parser.add_argument('--nsample', type=int, default=50000, help='point batch size') |
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parser.add_argument('--regen', default = False, action="store_true", help = 'regenerate feature curves') |
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parser.add_argument('--csv_name', type=str, default='eval_results.csv', help='csv file name') |
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args = parser.parse_args() |
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|
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def distance_p2p(points_src, normals_src, points_tgt, normals_tgt): |
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''' Computes minimal distances of each point in points_src to points_tgt. |
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|
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Args: |
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points_src (numpy array [N, 3]): source points |
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normals_src (numpy array [N, 3]): source normals |
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points_tgt (numpy array [M, 3]): target points |
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normals_tgt (numpy array [M, 3]): target |
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Returns: |
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dist (numpy array [N]): minimal distances of each point in points_src to points_tgt |
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normals_dot_product (numpy array [N]): dot product of normals of points_src and points_tgt |
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''' |
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kdtree = cKDTree(points_tgt) |
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dist, idx = kdtree.query(points_src) |
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|
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if normals_src is not None and normals_tgt is not None: |
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normals_src = \ |
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normals_src / np.linalg.norm(normals_src, axis=-1, keepdims=True) |
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normals_tgt = \ |
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normals_tgt / np.linalg.norm(normals_tgt, axis=-1, keepdims=True) |
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normals_dot_product = (normals_tgt[idx] * normals_src).sum(axis=-1) |
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# Handle normals that point into wrong direction gracefully |
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# (mostly due to mehtod not caring about this in generation) |
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normals_dot_product = np.abs(normals_dot_product) |
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return dist, normals_dot_product |
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|
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def distance_feature2mesh(points, mesh): |
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prox = trimesh.proximity.ProximityQuery(mesh) |
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signed_distance = prox.signed_distance(points) |
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return np.abs(signed_distance) |
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|
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def distance_p2mesh(points_src, normals_src, mesh): |
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points_tgt, idx = mesh.sample(args.nsample, return_index=True) |
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points_tgt = points_tgt.astype(np.float32) |
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normals_tgt = mesh.face_normals[idx] |
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cd1, nc1 = distance_p2p(points_src, normals_src, points_tgt, normals_tgt) #pred2gt |
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hd1 = cd1.max() |
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cd1 = cd1.mean() |
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|
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nc1 = np.clip(nc1, -1.0, 1.0) |
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angles1 = np.arccos(nc1) / math.pi * 180.0 |
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angles1_mean = angles1.mean() |
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angles1_std = np.std(angles1) |
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cd2, nc2 = distance_p2p(points_tgt, normals_tgt, points_src, normals_src) #gt2pred |
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hd2 = cd2.max() |
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cd2 = cd2.mean() |
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nc2 = np.clip(nc2, -1.0, 1.0) |
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angles2 = np.arccos(nc2)/ math.pi * 180.0 |
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angles2_mean = angles2.mean() |
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angles2_std = np.std(angles2) |
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cd = 0.5 * (cd1 + cd2) |
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hd = max(hd1, hd2) |
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angles_mean = 0.5 * (angles1_mean + angles2_mean) |
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angles_std = 0.5 * (angles1_std + angles2_std) |
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return cd, hd, angles_mean, angles_std, hd1, hd2 |
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|
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|
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def distance_fea(gt_pa, pred_pa): |
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"""计算特征点之间的距离和角度差异 |
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Args: |
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gt_pa: 真实特征点和角度 [N, 4] |
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pred_pa: 预测特征点和角度 [N, 4] |
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Returns: |
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dfg2p: 真实到预测的距离 |
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dfp2g: 预测到真实的距离 |
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fag2p: 真实到预测的角度差 |
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fap2g: 预测到真实的角度差 |
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""" |
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gt_points = gt_pa[:,:3] |
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pred_points = pred_pa[:,:3] |
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gt_angle = gt_pa[:,3] |
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pred_angle = pred_pa[:,3] |
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dfg2p = 0.0 |
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dfp2g = 0.0 |
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fag2p = 0.0 |
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fap2g = 0.0 |
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pred_kdtree = cKDTree(pred_points) |
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dist1, idx1 = pred_kdtree.query(gt_points) |
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dfg2p = dist1.mean() |
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assert(idx1.shape[0] == gt_points.shape[0]) |
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fag2p = np.abs(gt_angle - pred_angle[idx1]) |
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gt_kdtree = cKDTree(gt_points) |
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dist2, idx2 = gt_kdtree.query(pred_points) |
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dfp2g = dist2.mean() |
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fap2g = np.abs(pred_angle - gt_angle[idx2]) |
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fag2p = fag2p.mean() |
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fap2g = fap2g.mean() |
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return dfg2p, dfp2g, fag2p, fap2g |
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|
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def load_and_process_single_model(line, gt_path, pred_mesh_path, args): |
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"""处理单个模型的评估 |
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Args: |
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line (str): 模型名称 |
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gt_path (str): 真值路径 |
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pred_mesh_path (str): 预测网格路径 |
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args: 参数配置 |
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Returns: |
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dict: 包含该模型所有评估指标的字典 |
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""" |
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try: |
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#line = line.strip()[:-4] # 不用去 _50k |
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result = {'name': line} |
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|
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# 加载点云数据 |
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test_xyz = os.path.join(gt_path, line+'_50k.xyz') |
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try: |
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ptnormal = np.loadtxt(test_xyz) |
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except FileNotFoundError: |
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logger.error(f"XYZ file not found: {test_xyz}") |
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return None |
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except IOError as e: |
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logger.error(f"Error reading XYZ file {test_xyz}: {str(e)}") |
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return None |
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except ValueError as e: |
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logger.error(f"Invalid data format in XYZ file {test_xyz}: {str(e)}") |
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return None |
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except Exception as e: |
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logger.error(f"Unexpected error loading {test_xyz}: {str(e)}") |
|||
return None |
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logger.debug("successfully load gt points.") |
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|
|||
# 加载预测网格 |
|||
meshfile = os.path.join(pred_mesh_path, '{}.ply'.format(line)) |
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if not os.path.exists(meshfile): |
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logger.warning(f'File not exists: {meshfile}, try to generate it...') |
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pt_file = os.path.join(pred_mesh_path, '{}.pt'.format(line)) |
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try: |
|||
# 记录开始执行命令 |
|||
logger.debug(f"Executing IsoSurfacing: python ./IsoSurfacing.py -i {pt_file} -o {meshfile} --use-gpu") |
|||
|
|||
# 执行命令并检查返回值 |
|||
ret = os.system(f"python ./IsoSurfacing.py -i {pt_file} -o {meshfile} --use-gpu") |
|||
|
|||
if ret != 0: |
|||
raise RuntimeError(f"IsoSurfacing failed with return code {ret}") |
|||
|
|||
# 检查输出文件是否生成 |
|||
if not os.path.exists(meshfile): |
|||
raise FileNotFoundError(f"Output mesh file not created: {meshfile}") |
|||
|
|||
logger.debug("IsoSurfacing completed successfully") |
|||
|
|||
except FileNotFoundError as e: |
|||
logger.error(f"IsoSurfacing input file not found: {str(e)}") |
|||
return None |
|||
except RuntimeError as e: |
|||
logger.error(f"IsoSurfacing execution failed: {str(e)}") |
|||
return None |
|||
except Exception as e: |
|||
logger.error(f"Unexpected error in IsoSurfacing: {str(e)}") |
|||
return None |
|||
|
|||
# 检查缓存 |
|||
stat_file = meshfile + "_stat" |
|||
if not args.regen and os.path.exists(stat_file) and os.path.getsize(stat_file) > 0: |
|||
with open(stat_file, 'rb') as f: |
|||
return pickle.load(f) |
|||
|
|||
# 计算网格距离指标 |
|||
mesh = trimesh.load(meshfile) |
|||
logger.debug("successfully load pred mesh.") |
|||
cd, hd, adm, ads, hd_pred2gt, hd_gt2pred = distance_p2mesh( |
|||
ptnormal[:,:3], ptnormal[:,3:6], mesh) |
|||
|
|||
result.update({ |
|||
'CD': cd, 'HD': hd, 'HDpred2gt': hd_pred2gt, |
|||
'HDgt2pred': hd_gt2pred, 'AngleDiffMean': adm, |
|||
'AngleDiffStd': ads |
|||
}) |
|||
|
|||
# 计算特征点指标 |
|||
gt_ptangle = np.loadtxt(os.path.join(gt_path, line + '.ptangle')) |
|||
pred_ptangle_path = meshfile[:-4]+'.ptangle' |
|||
|
|||
if not os.path.exists(pred_ptangle_path) or args.regen: |
|||
os.system('/home/wch/brep2sdf/data/scripts/MeshFeatureSample/build/SimpleSample -i {} -o {} -s 4e-3'.format(meshfile, pred_ptangle_path)) |
|||
|
|||
pred_ptangle = np.loadtxt(pred_ptangle_path).reshape(-1,4) |
|||
|
|||
# 处理特征点结果 |
|||
if len(gt_ptangle) == 0 or len(pred_ptangle) == 0: |
|||
result.update({ |
|||
'FeaDfgt2pred': 0.0, 'FeaDfpred2gt': 0.0, |
|||
'FeaAnglegt2pred': 0.0, 'FeaAnglepred2gt': 0.0, |
|||
'FeaDf': 0.0, 'FeaAngle': 0.0 |
|||
}) |
|||
else: |
|||
dfg2p, dfp2g, fag2p, fap2g = distance_fea(gt_ptangle, pred_ptangle) |
|||
result.update({ |
|||
'FeaDfgt2pred': dfg2p, 'FeaDfpred2gt': dfp2g, |
|||
'FeaAnglegt2pred': fag2p, 'FeaAnglepred2gt': fap2g, |
|||
'FeaDf': (dfg2p + dfp2g) / 2.0, |
|||
'FeaAngle': (fag2p + fap2g) / 2.0 |
|||
}) |
|||
|
|||
# 保存缓存 |
|||
with open(stat_file, "wb") as f: |
|||
pickle.dump(result, f) |
|||
|
|||
return result |
|||
except Exception as e: |
|||
logger.error(f"Error processing {line}: {str(e)}") |
|||
return None |
|||
|
|||
def compute_all(): |
|||
"""计算所有模型的评估指标""" |
|||
try: |
|||
# 初始化结果字典 |
|||
results = [] |
|||
|
|||
# 读取模型列表 |
|||
with open(os.path.join(project_dir, 'data', args.name_list), 'r') as f: |
|||
lines = f.readlines() |
|||
print(lines) |
|||
# 处理每个模型 |
|||
for line in lines: |
|||
result = load_and_process_single_model(line, args.gt_path, args.pred_path, args) |
|||
if result: |
|||
results.append(result) |
|||
logger.info(result) |
|||
# 计算平均值 |
|||
mean_result = {'name': 'mean'} |
|||
for key in results[0].keys(): |
|||
if key != 'name': |
|||
mean_result[key] = sum(r[key] for r in results) / len(results) |
|||
results.append(mean_result) |
|||
|
|||
# 保存结果 |
|||
df = pd.DataFrame(results) |
|||
df.to_csv(args.csv_name, index=False) |
|||
|
|||
logger.info(f"Evaluation completed. Results saved to {os.path.abspath(args.csv_name)}") |
|||
|
|||
except Exception as e: |
|||
logger.error(f"Error in compute_all: {str(e)}") |
|||
raise |
|||
|
|||
if __name__ == '__main__': |
|||
compute_all() |
|||
Loading…
Reference in new issue