File size: 2,082 Bytes
6ef1166 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 | #!/usr/bin/env python
"""
预计算 3D-FUTURE 模型的尺寸缓存
运行一次后会生成 _size_cache.json 文件，后续渲染时直接加载缓存，速度快100倍
"""
import os
import sys
import json
import pickle
from tqdm import tqdm
# 添加 ATISS 路径
ATISS_PATH = os.path.join(os.path.dirname(__file__), 'ATISS')
sys.path.insert(0, ATISS_PATH)
def precompute_size_cache(pkl_path, output_path=None):
"""预计算模型尺寸缓存"""
if output_path is None:
output_path = pkl_path.replace('.pkl', '_size_cache.json')
print(f"加载数据集: {pkl_path}")
with open(pkl_path, 'rb') as f:
dataset = pickle.load(f)
if not hasattr(dataset, 'objects'):
print("错误: 数据集没有 objects 属性")
return
objects = dataset.objects
print(f"共 {len(objects)} 个模型")
size_cache = {}
failed = []
for obj in tqdm(objects, desc="计算模型尺寸"):
if not hasattr(obj, 'model_uid'):
continue
try:
# 访问 size 属性会触发计算
size = obj.size
size_cache[obj.model_uid] = [float(s) for s in size]
except Exception as e:
failed.append((obj.model_uid, str(e)))
print(f"\n成功: {len(size_cache)}, 失败: {len(failed)}")
# 保存缓存
print(f"保存缓存到: {output_path}")
with open(output_path, 'w') as f:
json.dump(size_cache, f)
if failed:
print(f"\n失败的模型 (前10个):")
for uid, err in failed[:10]:
print(f" {uid}: {err}")
return size_cache
def main():
import argparse
parser = argparse.ArgumentParser(description="预计算 3D-FUTURE 模型尺寸缓存")
parser.add_argument('--pkl', type=str, required=True, help='Pickle 数据集路径')
parser.add_argument('--output', type=str, default=None, help='输出缓存文件路径')
args = parser.parse_args()
precompute_size_cache(args.pkl, args.output)
if __name__ == '__main__':
main()
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