codes/pointcloud_extract/ann2pc.py

# -*- coding: utf-8 -*-
"""
将标注文件 (semantic.npy) 转换为带语义标签的PLY点云真值
基于 visualize_semantic_labels.py 的标注逻辑
"""

import os
import glob
import numpy as np
import cv2
import yaml
from tqdm import tqdm
from concurrent.futures import ProcessPoolExecutor, as_completed
import multiprocessing

SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))

def load_config(config_path=None):
    if config_path is None:
        config_path = os.path.join(SCRIPT_DIR, 'config.yaml')
    with open(config_path, 'r', encoding='utf-8') as f:
        return yaml.safe_load(f)

CONFIG = load_config()

# 相机和深度参数
IMG_H, IMG_W = 192, 256
BIN_TO_M = 299_792_458.0 * CONFIG['common']['dt_ps'] * 1e-12 / 2.0


def save_ply_with_label(pts, path):
    """保存带语义标签的PLY文件: x y z label"""
    with open(path, "wb") as f:
        header = f"ply\nformat ascii 1.0\nelement vertex {len(pts)}\n"
        header += "property float x\nproperty float y\nproperty float z\nproperty int label\nend_header\n"
        f.write(header.encode())
        np.savetxt(f, pts, fmt='%.6f %.6f %.6f %d')


def process_single_ann(args):
    """处理单个标注文件，转换为PLY点云"""
    npy_path, cam_config, out_dir = args
    
    # 从文件名提取基础名 (去掉 _semantic.npy)
    basename = os.path.basename(npy_path)
    if basename.endswith('_semantic.npy'):
        basename = basename[:-13]  # 去掉 '_semantic.npy'
    
    out_path = os.path.join(out_dir, f"{basename}_gt.ply")
    
    try:
        # 加载标注数据 (H*W, num_bins)
        sem_bins = np.load(npy_path)
        num_pos, num_bins = sem_bins.shape
        
        # 预计算相机参数
        K = np.array([[cam_config['fx'], 0, cam_config['cx']], 
                      [0, cam_config['fy'], cam_config['cy']], 
                      [0, 0, 1]], dtype=np.float64)
        D = np.array([cam_config['k1'], cam_config['k2'], cam_config['p1'], cam_config['p2']], dtype=np.float64)
        
        points = []
        
        # 遍历每个像素，提取标注的峰值
        for idx in range(num_pos):
            row = sem_bins[idx]
            nz = np.flatnonzero(row > 0)  # 找到有标注的bin
            if nz.size == 0:
                continue
            
            # 找到连续的标注段
            breaks = np.flatnonzero(np.diff(nz) != 1)
            run_starts = np.concatenate(([0], breaks + 1))
            run_ends = np.concatenate((breaks, [nz.size - 1]))
            
            for rs, re in zip(run_starts, run_ends):
                b0 = int(nz[rs])
                b1 = int(nz[re])
                cid = int(row[b0])  # 语义类别
                if cid <= 0:
                    continue
                
                # 使用标注段的中心bin作为深度
                peak_bin = (b0 + b1) // 2
                depth = peak_bin * BIN_TO_M
                
                # 深度范围过滤
                if depth < CONFIG['common']['min_range_m'] or depth > CONFIG['common']['max_range_m']:
                    continue
                
                # 计算3D坐标
                v, u = idx // IMG_W, idx % IMG_W
                
                if CONFIG['common']['undistort']:
                    uv = np.array([[[u, v]]], dtype=np.float32)
                    uv_norm = cv2.undistortPoints(uv, K, D)
                    x_n, y_n = uv_norm[0, 0, 0], uv_norm[0, 0, 1]
                else:
                    x_n = (u - cam_config['cx']) / cam_config['fx']
                    y_n = (v - cam_config['cy']) / cam_config['fy']
                
                if CONFIG['common']['depth_is_range']:
                    ray = np.array([x_n, y_n, 1.0])
                    ray_unit = ray / np.linalg.norm(ray)
                    xyz = ray_unit * depth
                else:
                    xyz = np.array([x_n * depth, y_n * depth, depth])
                
                points.append([xyz[0], xyz[1], xyz[2], cid])
        
        if len(points) == 0:
            return basename, False, 0
        
        pts = np.array(points, dtype=np.float32)
        pts[:, 3] = pts[:, 3].astype(np.int32)  # 确保label是整数
        save_ply_with_label(pts, out_path)
        
        return basename, True, len(pts)
    
    except Exception as e:
        print(f"Error processing {npy_path}: {e}")
        return basename, False, 0


def main():
    config = load_config()
    datasets = config['datasets']
    
    ann_root = os.path.join(SCRIPT_DIR, 'ann')
    output_root = os.path.join(SCRIPT_DIR, 'output_denoised', 'gt')
    
    num_workers = min(config['common'].get('num_workers', 8), multiprocessing.cpu_count())
    
    print(f"[INFO] Converting annotations to PLY ground truth")
    print(f"[INFO] Ann root: {ann_root}")
    print(f"[INFO] Output root: {output_root}")
    print(f"[INFO] Workers: {num_workers}")
    
    # 遍历数据集 (p1, p2)
    for dataset_name, cam_config in datasets.items():
        dataset_ann_path = os.path.join(ann_root, dataset_name)
        if not os.path.isdir(dataset_ann_path):
            print(f"[Skip] {dataset_name} not found in ann/")
            continue
        
        print(f"\n[Dataset] {dataset_name}")
        
        # 遍历序列目录
        seq_dirs = [d for d in os.listdir(dataset_ann_path) 
                    if os.path.isdir(os.path.join(dataset_ann_path, d))]
        
        for seq_name in seq_dirs:
            seq_path = os.path.join(dataset_ann_path, seq_name)
            out_dir = os.path.join(output_root, seq_name)
            os.makedirs(out_dir, exist_ok=True)
            
            # 查找所有标注文件
            npy_files = sorted(glob.glob(os.path.join(seq_path, "*_semantic.npy")))
            if not npy_files:
                continue
            
            # 准备任务
            tasks = [(f, cam_config, out_dir) for f in npy_files]
            
            success_count = 0
            total_pts = 0
            
            with ProcessPoolExecutor(max_workers=num_workers) as executor:
                futures = [executor.submit(process_single_ann, t) for t in tasks]
                
                pbar = tqdm(as_completed(futures), total=len(npy_files), 
                           desc=f"  {seq_name}", leave=False)
                for future in pbar:
                    basename, ok, pts_count = future.result()
                    if ok:
                        success_count += 1
                        total_pts += pts_count
            
            print(f"  {seq_name}: {success_count}/{len(npy_files)} files, {total_pts:,} pts")
    
    print(f"\n[Done] Ground truth PLY saved to: {output_root}")


if __name__ == "__main__":
    main()