File size: 5,167 Bytes
c61acba | 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 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 | 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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