Add files using upload-large-folder tool

README.md

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+---
+license: apache-2.0
+---

hf.py

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+import glob
+import json
+import time
+from pathlib import Path
+
+import datasets
+from datasets.table import embed_table_storage
+from tqdm import tqdm
+
+
+def extract_number(s):
+    # 移除 `.png` 后缀部分
+    if s.endswith(".png"):
+        s = s.rsplit('.', 1)[0]  # 删除文件扩展名
+
+    # 查找最后一个 "_" 后的数字部分
+    last_underscore_pos = s.rfind("_")
+    if last_underscore_pos == -1:
+        return float("inf")  # 没有找到 "_" 时，返回一个较大的值，表示该项排最后
+
+    number_part = s[last_underscore_pos + 1:]
+
+    # 尝试将数字部分转换为整数，如果失败则返回 float('inf')
+    try:
+        return int(number_part)
+    except ValueError:
+        return float("inf")
+
+def sort_strings(strings):
+    # 使用 sorted 排序，根据数字部分排序
+    return sorted(strings, key=extract_number)
+
+
+def embed_images(dataset: datasets.Dataset) -> datasets.Dataset:
+    format = dataset.format
+    dataset = dataset.with_format("arrow")
+    dataset = dataset.map(embed_table_storage, batched=False)
+    dataset = dataset.with_format(**format)
+    return dataset
+
+def read_jsonl(file_path):
+    with open(file_path, 'r') as f:
+        content = f.read()
+
+    # 用来存储完整的 JSON 对象
+    json_objects = []
+
+    # 变量用于跟踪大括号的嵌套层级
+    brace_count = 0
+    start = 0
+
+    # 遍历文件内容
+    for idx, char in enumerate(content):
+        if char == '{':
+            # 增加嵌套层级
+            if brace_count == 0:
+                start = idx  # 记录开始位置
+            brace_count += 1
+        elif char == '}':
+            # 减少嵌套层级
+            brace_count -= 1
+            if brace_count == 0:
+                # 当嵌套层级归零时，意味着我们找到了一个完整的 JSON 对象
+                json_str = content[start:idx+1]
+                try:
+                    # 将字符串解析为字典
+                    json_objects.append(json.loads(json_str))
+                except json.JSONDecodeError as e:
+                    print(f"Error parsing JSON: {e}")
+                    continue
+    action_dicts = [d for d in json_objects if 'action' in d]
+
+    return action_dicts
+
+
+features = datasets.Features(
+    {
+        "env_id": datasets.Value("string"),
+        "traj_id": datasets.Value("string"),
+        "image_id": datasets.Value("string"),
+        "frame_index": datasets.Value("int32"),
+        "image": datasets.Image(),
+        "pos": datasets.Sequence(length=3, feature=datasets.Value("float64")),
+        "yaw": datasets.Value("float64"),
+        "action_type": datasets.Value("string"),
+        "action_value": datasets.Value("int32"),
+    }
+)
+
+
+json_path = '/cpfs01/shared/optimal/Datasets/Openfly/OpenFly/Annotation/unseen.json'
+
+with open(json_path, 'r') as file:
+    data = json.load(file)
+
+for item in tqdm(data, ncols=100, position=0, desc="Progress"):
+    path = '/cpfs01/shared/optimal/Datasets/Openfly/OpenFly/Image/'+str(item['image_path']) + '/'
+    json_path = path + 'pose.jsonl'
+    path += '*.png'  # 假设图片格式为png，可以根据实际情况修改
+    print(json_path)
+    # 获取所有匹配的文件
+    png_files = sort_strings(glob.glob(path))
+    action_list = read_jsonl(json_path)  # 假设read_jsonl函数已经定义，用于读取jsonl文件
+    action_list.sort(key=lambda x: int(x['action']['imageid']))  # 根据帧号排序action_list
+    
+    image_path = Path(item['image_path'])
+    env_id_item = image_path.parent
+    traj_id_item = image_path.stem
+    img_id = [Path(item).stem for item in png_files]
+    num = len(png_files)
+    assert num == len(action_list), f"Number of images and actions do not match: {num} != {len(action_list)}"
+    data_dict = {
+        "env_id" : [env_id_item] * num,
+        "traj_id" : [traj_id_item] * num,
+        "image_id" : img_id,
+        "frame_index" : [i for i in range(num)],
+        "image" : ['/cpfs01/shared/optimal/Datasets/Openfly/OpenFly/Image/'+str(image_path) + '/' + img_id[i] + '.png'  for i in range(num)],
+        "pos": [action_list[i]['action']['pos'][:3] for i in range(num)],
+        "yaw": [action_list[i]['action']['yaw'] for i in range(num)],
+        "action_type": [action_list[i]['action']['type'] for i in range(num)],
+        "action_value": [action_list[i]['action']['value'] for i in range(num)]
+    }
+        
+
+    path = Path("./hf")
+    episode_dict = {key: data_dict[key] for key in features}
+    ep_dataset = datasets.Dataset.from_dict(episode_dict, features=features, split="train")
+    ep_dataset = embed_images(ep_dataset)
+    ep_data_path = (path / data_dict["env_id"][0] / data_dict["traj_id"][0]).with_suffix(".parquet")
+    ep_data_path.parent.mkdir(parents=True, exist_ok=True)
+    ep_dataset.to_parquet(ep_data_path)
+#     break
+
+# data = datasets.load_dataset('parquet', data_files=str(ep_data_path), split='train')
+# print(data)
+
+
+
+
+# import datasets
+
+# path = '/cpfs01/shared/optimal/Datasets/Openfly/OpenFly/hf/env_airsim_16/astar_data/high_average/2025-1-9_16-50-28_2007905771.parquet'
+
+# data = datasets.load_dataset('parquet', data_files=path, split='train')
+# print(data[1])
+
+
+

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