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import sys
import os
import json
from pathlib import Path
# 把项目根目录加到 path，方便 import src
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
from src.solver_bridge import TrussSolver
from src.data_loader import BenchmarkDataLoader
def get_difficulty(filename, data):
name = os.path.splitext(filename)[0]
# User explicit overrides (Legacy)
if name == "frame_010": return 5
if name in ["beam_003", "beam_004", "beam_005"]: return 1
if name in ["beam_001", "beam_002"]: return 2
# Heuristics for new files
links = data.get("links", [])
num_links = len(links)
if "beam" in name:
# Check for hinges
has_hinge = False
for l in links:
if l.get("endA") == "hinge" or l.get("endB") == "hinge":
has_hinge = True
break
return 2 if has_hinge else 1
if "truss" in name:
if num_links > 20: return 4
if num_links > 10: return 3
return 2
if "frame" in name:
if num_links >= 14: return 5
if num_links >= 10: return 4
if num_links >= 5: return 3
return 2
return 1 # Fallback
def main():
print("=== Generating Ground Truth Metadata ===")
# 1. 初始化
loader = BenchmarkDataLoader()
solver = TrussSolver("bin/framecalc.wasm") # 确保路径对
raw_models = loader.load_raw_models()
if not raw_models:
print("No raw models found in data/raw_models/")
return
# 确保 meta 目录存在
loader.meta_dir.mkdir(parents=True, exist_ok=True)
count = 0
for model_info in raw_models:
print(f"Processing {model_info['id']}...")
# 读取 5KB 的大 JSON
with open(model_info['path'], 'r', encoding='utf-8') as f:
full_json = json.load(f)
# 跑 Solver 算出真值
# 注意：这里假设 raw json 的格式直接就是 solver 能吃的格式
# 如果 raw json 包含编辑器杂质，需要这里做一次 cleaning
solution, error = solver.solve(full_json)
if error or not solution:
print(f"❌ Failed to solve {model_info['id']}. Error: {error}")
continue
# 智能判断图片后缀
img_name = f"{model_info['id']}.png"
if not (loader.img_dir / img_name).exists():
if (loader.img_dir / f"{model_info['id']}.jpg").exists():
img_name = f"{model_info['id']}.jpg"
# 构造 Meta 数据
meta_data = {
"id": model_info['id'],
"difficulty": get_difficulty(model_info['filename'], full_json),
"image_filename": img_name,
"solution": solution # 缓存正确答案
}
# 写入 Meta 文件
out_path = loader.meta_dir / model_info['filename']
with open(out_path, 'w', encoding='utf-8') as f:
json.dump(meta_data, f, indent=2)
print(f"✅ Saved meta to {out_path} (Diff: {meta_data['difficulty']})")
count += 1
print(f"\nDone. Generated {count} GT files.")
if __name__ == "__main__":
main() |