scripts/filter/filter.py

import os
import json
import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm
import time

# 假设你的 utils 都在 action_state.utils 里
from action_state.utils import (
    CO3DDataLoader, 
    get_camera_center, 
    get_view_direction,
    get_sequence_geometry
)

# ==========================================
# 配置区域
# ==========================================
ROOT_PATH = "/run/determined/NAS1/public/lixinyuan/interleaved-co3d"

# 设置为 None 则处理所有类别，否则处理列表中的类别，例如 ["bench"]
CATEGORY = None
# CATEGORY = ["bench", "hydrant"] 

# 输出目录配置
IMAGE_OUTPUT_DIR = "./debug/traj/"
JSON_OUTPUT_DIR = "./data/filter_log/"

# ==========================================
# 1. 核心筛选算法 (V3版本: Sinuosity + Strict Mono)
# ==========================================

def get_pca_axis_ratio(coords):
    if len(coords) < 3: return 999.0
    centered = coords - np.mean(coords, axis=0)
    cov = np.cov(centered.T)
    eigenvalues, _ = np.linalg.eig(cov)
    eigenvalues = np.sort(eigenvalues)[::-1]
    if eigenvalues[1] < 1e-6: return 999.0
    return np.sqrt(eigenvalues[0]) / np.sqrt(eigenvalues[1])

def analyze_trajectory_robust(seq_data, mean_center):
    """V3: 包含弯曲度(Sinuosity)和严格单调性检查"""
    frame_indices = sorted(list(seq_data.keys()))
    if len(frame_indices) < 10:
        return {'valid': False, 'reason': 'too_few_frames'}

    coords_xz = []
    for fid in frame_indices:
        R = seq_data[fid]['R']
        T = seq_data[fid]['T']
        C = -R.T @ T 
        dx = C[0] - mean_center[0]
        dz = C[2] - mean_center[2]
        coords_xz.append([dx, dz])
    coords_xz = np.array(coords_xz)
    
    # 几何指标
    azimuths = np.arctan2(coords_xz[:, 1], coords_xz[:, 0])
    azimuths_unwrapped = np.unwrap(azimuths)
    sweep_rad = np.max(azimuths_unwrapped) - np.min(azimuths_unwrapped)
    sweep_deg = np.degrees(sweep_rad)
    
    # 单调性
    diffs = np.diff(azimuths_unwrapped)
    valid_diffs = diffs[np.abs(diffs) > np.radians(0.5)]
    if len(valid_diffs) == 0:
        monotonicity = 0.0
    else:
        monotonicity = max(np.sum(valid_diffs > 0), np.sum(valid_diffs < 0)) / len(valid_diffs)

    # PCA & 半径
    axis_ratio = get_pca_axis_ratio(coords_xz)
    radii = np.linalg.norm(coords_xz, axis=1)
    mean_radius = np.mean(radii)
    r_min = np.percentile(radii, 5)
    r_max = np.percentile(radii, 95)
    radius_ratio = r_min / (r_max + 1e-6)
    
    # 跳变 & 弯曲度
    steps = np.linalg.norm(np.diff(coords_xz, axis=0), axis=1)
    jump_factor = np.max(steps) / (np.median(steps) + 1e-6)
    
    ideal_path_length = sweep_rad * mean_radius
    total_path_length = np.sum(steps)
    sinuosity = total_path_length / (ideal_path_length + 1e-3) if ideal_path_length > 1e-3 else 999.0

    return {
        'valid': True,
        'sweep_deg': sweep_deg,
        'monotonicity': monotonicity,
        'axis_ratio': axis_ratio,
        'radius_ratio': radius_ratio,
        'jump_factor': jump_factor,
        'sinuosity': sinuosity,
        'num_frames': len(frame_indices)
    }

def check_if_sequence_is_good(metrics):
    """V3筛选阈值"""
    if not metrics['valid']: return False, metrics['reason']

    # 阈值配置
    MIN_SWEEP_DEG = 120.0
    MIN_MONOTONICITY = 0.70  # 严格单调性
    MAX_AXIS_RATIO = 6.0
    MIN_RADIUS_RATIO = 0.3
    MAX_JUMP_FACTOR = 5.0
    MAX_SINUOSITY = 2.0      # 严格弯曲度

    # 一票否决
    if metrics['jump_factor'] > MAX_JUMP_FACTOR: return False, f"Jump ({metrics['jump_factor']:.1f})"
    if metrics['radius_ratio'] < MIN_RADIUS_RATIO: return False, f"Unstable Radius ({metrics['radius_ratio']:.2f})"
    if metrics['sweep_deg'] > 60.0 and metrics['sinuosity'] > MAX_SINUOSITY: return False, f"Jittery/Wavy ({metrics['sinuosity']:.2f})"

    # 分级判断
    if metrics['sweep_deg'] > 270.0:
        if metrics['monotonicity'] < MIN_MONOTONICITY: return False, f"Messy Loop ({metrics['monotonicity']:.2f})"
        return True, "Full Loop"
    elif metrics['sweep_deg'] > MIN_SWEEP_DEG:
        if metrics['axis_ratio'] > MAX_AXIS_RATIO: return False, f"Linear ({metrics['axis_ratio']:.1f})"
        if metrics['monotonicity'] < MIN_MONOTONICITY: return False, f"Messy Semi ({metrics['monotonicity']:.2f})"
        return True, "Semi Loop"
    else:
        return False, f"Small Angle ({metrics['sweep_deg']:.1f})"

# ==========================================
# 2. 绘图函数
# ==========================================

def plot_sequence_trajectory(loader, sequence_name, output_path, metrics):
    """绘制并保存轨迹图"""
    try:
        frame_ids = sorted(loader.get_frames(sequence_name))
        seq_data = loader.seq_data[sequence_name]
        mean_center, _, aligned_seq_data = get_sequence_geometry(seq_data, align_to_standard=True)
        
        camera_centers = []
        for fid in frame_ids:
            info = aligned_seq_data[fid]
            C = get_camera_center(info['R'], info['T'])
            camera_centers.append(C)
        camera_centers = np.array(camera_centers)
        
        x_coords = camera_centers[:, 0] - mean_center[0]
        z_coords = camera_centers[:, 2] - mean_center[2]
        
        fig, ax = plt.subplots(1, 1, figsize=(10, 8))
        ax.plot(x_coords, z_coords, c='lightgray', alpha=0.5, linestyle='--')
        sc = ax.scatter(x_coords, z_coords, c=frame_ids, cmap='viridis', s=30, zorder=5)
        ax.scatter(0, 0, c='black', marker='X', s=200, label='Center', zorder=10)
        
        title = (f"Seq: {sequence_name}\n"
                 f"Sweep={metrics['sweep_deg']:.0f}°, Mono={metrics['monotonicity']:.2f}, "
                 f"Sinuosity={metrics['sinuosity']:.2f}")
        ax.set_title(title, fontsize=12)
        ax.axis('equal')
        
        # 确保目录存在
        os.makedirs(os.path.dirname(output_path), exist_ok=True)
        plt.savefig(output_path, dpi=100, bbox_inches='tight')
        plt.close(fig)
    except Exception as e:
        print(f"Error plotting {sequence_name}: {e}")

# ==========================================
# 3. 流程控制
# ==========================================

def process_category(category_name, global_stats):
    """处理单个类别"""
    print(f"\nProcessing Category: {category_name}")
    
    # 1. 加载数据
    try:
        loader = CO3DDataLoader(ROOT_PATH, category_name)
        sequences = loader.get_sequences()
    except Exception as e:
        print(f"Failed to load category {category_name}: {e}")
        return

    # 2. 准备输出路径
    cat_img_dir = os.path.join(IMAGE_OUTPUT_DIR, category_name)
    cat_json_dir = os.path.join(JSON_OUTPUT_DIR, category_name)
    os.makedirs(cat_json_dir, exist_ok=True)
    
    # 3. 初始化统计
    keep_list = []
    stats = {
        'total': len(sequences),
        'kept': 0,
        'rejected': 0,
        'reasons': {}
    }
    
    # 4. 遍历序列
    for seq_name in tqdm(sequences, desc=f"Filtering {category_name}", leave=False):
        try:
            # 获取数据 & 计算指标
            seq_data = loader.seq_data[seq_name]
            mean_center, _, aligned_seq_data = get_sequence_geometry(seq_data, align_to_standard=True)
            metrics = analyze_trajectory_robust(aligned_seq_data, mean_center)
            
            # 判定
            is_good, reason = check_if_sequence_is_good(metrics)
            
            if is_good:
                keep_list.append(seq_name)
                stats['kept'] += 1
                
                # 保存可视化图片 (仅保存 Keep 的)
                img_path = os.path.join(cat_img_dir, f"{seq_name}.png")
                plot_sequence_trajectory(loader, seq_name, img_path, metrics)
            else:
                stats['rejected'] += 1
                # 记录拒绝原因
                base_reason = reason.split('(')[0].strip() # 简化原因统计
                stats['reasons'][base_reason] = stats['reasons'].get(base_reason, 0) + 1
                
        except Exception as e:
            print(f"Error processing {seq_name}: {e}")
            stats['rejected'] += 1
            stats['reasons']['Error'] = stats['reasons'].get('Error', 0) + 1

    # 5. 保存 keep.json
    keep_json_path = os.path.join(cat_json_dir, "keep.json")
    with open(keep_json_path, 'w') as f:
        json.dump(keep_list, f, indent=2)
    
    # 6. 更新全局统计
    global_stats[category_name] = stats
    
    print(f"  -> Kept: {stats['kept']}/{stats['total']} ({stats['kept']/stats['total']*100:.1f}%)")
    print(f"  -> Saved keep list to: {keep_json_path}")

def main():
    start_time = time.time()
    print(f"{'='*60}")
    print(f"CO3D Trajectory Filtering Pipeline (V3)")
    print(f"Root Path: {ROOT_PATH}")
    print(f"Output Images: {IMAGE_OUTPUT_DIR}")
    print(f"Output JSONs:  {JSON_OUTPUT_DIR}")
    print(f"{'='*60}")

    # 1. 确定要处理的类别列表
    if CATEGORY:
        categories_to_process = CATEGORY if isinstance(CATEGORY, list) else [CATEGORY]
    else:
        # 自动扫描所有类别
        data_root = os.path.join(ROOT_PATH, 'data', 'original')
        if os.path.exists(data_root):
            categories_to_process = sorted([d for d in os.listdir(data_root) 
                                          if os.path.isdir(os.path.join(data_root, d))])
        else:
            print(f"Error: Data root {data_root} not found.")
            return

    print(f"Found {len(categories_to_process)} categories to process.")
    
    # 2. 全局统计容器
    global_stats = {}

    # 3. 循环处理
    for cat in tqdm(categories_to_process, desc="Total Progress"):
        process_category(cat, global_stats)

    # 4. 保存全局统计信息
    os.makedirs(JSON_OUTPUT_DIR, exist_ok=True)
    stats_path = os.path.join(JSON_OUTPUT_DIR, "statistics.json")
    
    # 添加汇总信息
    total_seqs = sum(s['total'] for s in global_stats.values())
    total_kept = sum(s['kept'] for s in global_stats.values())
    
    final_report = {
        'summary': {
            'total_categories': len(global_stats),
            'total_sequences': total_seqs,
            'total_kept': total_kept,
            'overall_pass_rate': total_kept / total_seqs if total_seqs > 0 else 0
        },
        'details': global_stats
    }
    
    with open(stats_path, 'w') as f:
        json.dump(final_report, f, indent=2)

    print(f"\n{'='*60}")
    print(f"Pipeline Completed in {time.time()-start_time:.1f}s")
    print(f"Global Statistics saved to: {stats_path}")
    print(f"Overall Pass Rate: {final_report['summary']['overall_pass_rate']*100:.1f}%")
    print(f"{'='*60}")

if __name__ == "__main__":
    main()