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import json
import os
from pathlib import Path
class BenchmarkDataLoader:
def __init__(self, data_root="data"):
self.root = Path(data_root)
self.img_dir = self.root / "images"
self.meta_dir = self.root / "ground_truth_meta"
self.raw_dir = self.root / "raw_models"
def load_tasks_for_eval(self):
"""
加载用于评测的任务列表 (只读 meta 和图片)
"""
tasks = []
if not self.meta_dir.exists():
print(f"Warning: {self.meta_dir} does not exist. Please run tools/generate_gt.py first.")
return []
for meta_file in self.meta_dir.glob("*.json"):
try:
with open(meta_file, 'r', encoding='utf-8') as f:
meta = json.load(f)
# 校验图片是否存在
img_name = meta.get("image_filename")
img_path = self.img_dir / img_name
if not img_path.exists():
print(f"Skipping {meta_file.name}: Image not found at {img_path}")
continue
tasks.append({
"id": meta["id"],
"difficulty": meta.get("difficulty", 1),
"image_path": str(img_path),
"gt_solution": meta["solution"] # 里面已经存了算好的正确答案
})
except Exception as e:
print(f"Error loading {meta_file}: {e}")
# 按 ID 排序，保证顺序固定 (e.g. beam_001 先于 beam_002)
tasks.sort(key=lambda x: x['id'])
return tasks
def load_raw_models(self):
"""
加载原始 JSON 模型 (用于 tools/generate_gt.py 生成真值)
"""
models = []
for json_file in self.raw_dir.glob("*.json"):
models.append({
"id": json_file.stem,
"path": str(json_file),
"filename": json_file.name
})
return models
def load_raw_model_by_id(self, task_id):
"""
[Debug模式专用] 根据 Task ID 读取原始的正确 JSON 文件
"""
# 假设文件名规则是 {task_id}.json
# 如果你的 id 是 "frame_001"，文件名也是 "frame_001.json"
json_path = self.raw_dir / f"{task_id}.json"
if not json_path.exists():
# 尝试做一下兼容，有时候 ID 可能不带后缀
return None
try:
with open(json_path, 'r', encoding='utf-8') as f:
return json.load(f)
except Exception as e:
print(f"Error reading raw model {json_path}: {e}")
return None |