| |
| |
| """ |
| NYU Depth V2 Eigen test split (654 张) 相对深度精度评估。 |
| |
| 输入： |
| --mat nyu_depth_v2_labeled.mat（GT 直接从这里读，不落盘） |
| --split export_nyu654_test.py 生成的 split.json |
| --pred 一个或多个 <名称>=<NPZ 目录>，NPZ 由 infer 脚本 --dump-npz 生成 |
| |
| 评估协议： |
| 1. 有效像素：rawDepths > 0 且 depths 在 (0, max_depth]； |
| 2. 预测上采样到 GT 分辨率（bilinear）； |
| 3. 相对深度对齐： |
| - disparity（默认，MiDaS 协议）：拟合 a*pred + b ≈ 1/gt，再取倒数得米制深度； |
| - depth：拟合 a*pred + b ≈ gt； |
| 最小二乘在有效像素上求解； |
| 4. 逐图计算 AbsRel / SqRel / RMSE / RMSE_log / δ<1.25 / 1.25² / 1.25³， |
| 对 654 张取平均； |
| 5. 第一个 --pred 作为基准，其余输出相对掉点 (%)。 |
| |
| 注意：未做 Eigen crop，绝对数值不能直接与论文表格对比；同一批图上不同后端的 |
| 相对掉点不受影响。 |
| |
| 用法： |
| ../../venv/bin/python src/tool/evaluate_nyu654_depth.py \ |
| --mat ../../datasets/nyu_depth_v2_labeled.mat \ |
| --split datasets/nyu654_test/split.json \ |
| --pred fp32=results/nyu654_fp32/npz axonnx=results/nyu654_axonnx/npz \ |
| --out results/nyu654_eval |
| """ |
|
|
| import argparse |
| import json |
| from pathlib import Path |
|
|
| import cv2 |
| import h5py |
| import numpy as np |
| from tqdm import tqdm |
|
|
| METRICS = ('AbsRel', 'SqRel', 'RMSE', 'RMSE_log', 'd1', 'd2', 'd3') |
| SIGMA = (1.25, 1.25 ** 2, 1.25 ** 3) |
|
|
|
|
| def solve_scale_shift(pred, target, mask): |
| """在 mask 上最小二乘求解 a, b 使 a*pred + b ≈ target。""" |
| p = pred[mask] |
| t = target[mask] |
| A = np.stack([p, np.ones_like(p)], axis=1) |
| (a, b), *_ = np.linalg.lstsq(A, t, rcond=None) |
| return float(a), float(b) |
|
|
|
|
| def compute_metrics(pred_depth, gt_depth, mask, align, max_depth): |
| clipped = 0.0 |
| if align == 'disparity': |
| gt_target = 1.0 / gt_depth |
| a, b = solve_scale_shift(pred_depth, gt_target, mask) |
| if a <= 0: |
| raise ValueError(f'disparity 对齐斜率为负 (a={a:.4f})，' |
| f'预测不是"越大越近"的视差量') |
| aligned = a * pred_depth + b |
| |
| neg = aligned[mask] <= 0 |
| clipped = float(neg.mean()) |
| aligned = np.maximum(aligned, 1.0 / max_depth) |
| pred = 1.0 / aligned |
| else: |
| a, b = solve_scale_shift(pred_depth, gt_depth, mask) |
| pred = a * pred_depth + b |
|
|
| pred = pred[mask] |
| gt = gt_depth[mask] |
| if np.any(pred <= 0): |
| raise ValueError('对齐后预测深度出现非正值') |
|
|
| diff = pred - gt |
| out = { |
| 'AbsRel': float(np.mean(np.abs(diff) / gt)), |
| 'SqRel': float(np.mean(diff ** 2 / gt)), |
| 'RMSE': float(np.sqrt(np.mean(diff ** 2))), |
| 'RMSE_log': float(np.sqrt(np.mean((np.log(pred) - np.log(gt)) ** 2))), |
| } |
| ratio = np.maximum(pred / gt, gt / pred) |
| for name, s in zip(('d1', 'd2', 'd3'), SIGMA): |
| out[name] = float(np.mean(ratio < s)) |
| return out, a, b, clipped |
|
|
|
|
| def load_predictions(path): |
| """读取预测：汇总 NPZ（names/depths）、分块汇总目录，或旧的逐图 NPZ 目录。""" |
| path = Path(path) |
| if path.is_file(): |
| files = [path] |
| elif path.is_dir(): |
| files = sorted(path.glob('*.npz')) |
| if not files: |
| raise ValueError(f'目录中没有 NPZ: {path}') |
| else: |
| raise FileNotFoundError(f'预测路径不存在: {path}') |
|
|
| preds = {} |
| for fp in files: |
| with np.load(fp, allow_pickle=False) as data: |
| if 'depths' in data and 'names' in data: |
| for name, depth in zip(data['names'], data['depths']): |
| preds[str(name)] = depth |
| elif 'depth' in data: |
| preds[fp.name[:-len('.npz')]] = data['depth'] |
| else: |
| raise ValueError(f'无法识别的 NPZ 格式: {fp}') |
| return preds |
|
|
|
|
| def main(): |
| parser = argparse.ArgumentParser(description='NYU 654 相对深度评测') |
| parser.add_argument('--mat', required=True) |
| parser.add_argument('--split', required=True, help='split.json') |
| parser.add_argument('--pred', nargs='+', required=True, help='名称=NPZ目录，第一个为基准') |
| parser.add_argument('--out', required=True, help='结果目录') |
| parser.add_argument('--align', choices=['disparity', 'depth'], default='disparity') |
| parser.add_argument('--max-depth', type=float, default=10.0) |
| args = parser.parse_args() |
|
|
| pred_sets = [] |
| for item in args.pred: |
| name, _, path = item.partition('=') |
| if not path: |
| raise ValueError(f'--pred 需要 名称=路径 格式: {item}') |
| pred_sets.append((name, load_predictions(path))) |
|
|
| split = json.load(open(args.split)) |
| out_dir = Path(args.out) |
| out_dir.mkdir(parents=True, exist_ok=True) |
|
|
| per_image = {name: [] for name, _ in pred_sets} |
| failed = {name: [] for name, _ in pred_sets} |
| with h5py.File(args.mat, 'r') as f: |
| for record in tqdm(split, desc=f'eval({args.align})', ncols=80): |
| i = record['mat_index'] |
| gt = np.asarray(f['depths'][i], dtype=np.float32) |
| raw = np.asarray(f['rawDepths'][i], dtype=np.float32) |
| mask = (raw > 0) & (gt > 0) & (gt <= args.max_depth) |
|
|
| for name, preds in pred_sets: |
| if record['file'] not in preds: |
| raise KeyError(f'{name} 缺少预测: {record["file"]}') |
| pred = np.asarray(preds[record['file']], dtype=np.float32) |
| if pred.ndim == 3: |
| pred = pred[0] |
| pred = cv2.resize(pred, (gt.shape[1], gt.shape[0]), interpolation=cv2.INTER_LINEAR) |
| try: |
| m, a, b, clipped = compute_metrics(pred, gt, mask, args.align, args.max_depth) |
| except ValueError as error: |
| failed[name].append({'file': record['file'], 'reason': str(error)}) |
| continue |
| m.update({'file': record['file'], 'scale': a, 'shift': b, |
| 'clipped_frac': clipped, 'valid_pixels': int(mask.sum())}) |
| per_image[name].append(m) |
|
|
| results = {} |
| for name, _ in pred_sets: |
| rows = per_image[name] |
| results[name] = {k: float(np.mean([r[k] for r in rows])) for k in METRICS} |
| results[name]['p90_absrel'] = float(np.percentile([r['AbsRel'] for r in rows], 90)) |
| results[name]['clipped_images'] = int(sum(r['clipped_frac'] > 0 for r in rows)) |
| results[name]['clipped_pixel_frac'] = float(np.mean([r['clipped_frac'] for r in rows])) |
| results[name]['evaluated_images'] = len(rows) |
| results[name]['failed_images'] = len(failed[name]) |
|
|
| base = pred_sets[0][0] |
| drops = {} |
| for name, _ in pred_sets[1:]: |
| drops[name] = {} |
| for k in METRICS: |
| b, v = results[base][k], results[name][k] |
| drops[name][k] = float((v - b) / b * 100) |
| b, v = results[base]['p90_absrel'], results[name]['p90_absrel'] |
| drops[name]['p90_absrel'] = float((v - b) / b * 100) |
|
|
| summary = { |
| 'dataset': 'nyu_depth_v2_labeled / eigen test split', |
| 'num_images': len(split), |
| 'align': args.align, |
| 'max_depth': args.max_depth, |
| 'eigen_crop': False, |
| 'results': results, |
| 'baseline': base, |
| 'drop_percent': drops, |
| 'failed': failed, |
| 'per_image': {name: per_image[name] for name, _ in pred_sets}, |
| } |
| with open(out_dir / 'metrics.json', 'w') as fp: |
| json.dump(summary, fp, indent=2) |
|
|
| print(f'\n{"指标":<10}' + ''.join(f'{n:>14}' for n, _ in pred_sets)) |
| for k in METRICS + ('p90_absrel',): |
| print(f'{k:<10}' + ''.join(f'{results[n][k]:>14.4f}' for n, _ in pred_sets)) |
| for name, d in drops.items(): |
| print(f'\n{name} 相对 {base} 掉点 (%): ' + |
| ', '.join(f'{k}={v:+.2f}' for k, v in d.items())) |
| for name, _ in pred_sets: |
| if failed[name]: |
| print(f'\n{name}: {len(failed[name])} 张对齐失败（方向反了或输出异常），未计入均值') |
| print(f'\n结果已保存: {out_dir / "metrics.json"}') |
|
|
|
|
| if __name__ == '__main__': |
| main() |
|
|
