File size: 2,683 Bytes
e3551e9 586d4ca e3551e9 41781d4 e3551e9 d396904 e3551e9 d396904 e3551e9 41781d4 586d4ca e3551e9 586d4ca e3551e9 d396904 e3551e9 1a63212 d396904 1a63212 e3551e9 d396904 e3551e9 d396904 |
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 |
import os
import random
import requests
import datasets
from tqdm import tqdm
from io import BytesIO
from PIL import Image
_DOMAIN = "https://www.modelscope.cn"
_URL = f"{_DOMAIN}/datasets/Genius-Society/{os.path.basename(__file__)[:-3]}"
class insecta(datasets.GeneratorBasedBuilder):
def _info(self):
return datasets.DatasetInfo(
features=datasets.Features(
{
"image": datasets.Image(),
"label": datasets.Value("string"),
"latin": datasets.Value("string"),
}
),
supervised_keys=("image", "latin"),
homepage=_URL,
license="mit",
version="0.0.1",
)
def _get_files(
self,
root: str,
url=f"{_DOMAIN}/api/v1/datasets/171488/repo/tree",
page_size=500,
):
try:
response = requests.get(
url,
params={
"Revision": "master",
"Root": root,
"PageNumber": 1,
"PageSize": page_size,
},
)
response.raise_for_status()
return response.json()["Data"]["Files"]
except Exception as e:
print(f"{e}, retrying...")
return self._get_files(root)
def _dld_img(self, url: str):
try:
response = requests.get(url)
response.raise_for_status()
return Image.open(BytesIO(response.content))
except Exception as e:
print(f"{e}, retrying...")
return self._dld_img(url)
def _split_generators(self, _):
dataset = []
files = self._get_files("已鉴定")
for file in tqdm(files, desc="Parsing classes"):
imgs = self._get_files(file["Path"])
label, latin = str(file["Name"]).split(" ", 1)
for img in imgs:
dataset.append(
{
"image": f"{_URL}/resolve/master/" + img["Path"],
"label": label.strip(),
"latin": latin.strip(),
}
)
random.shuffle(dataset)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"files": dataset},
)
]
def _generate_examples(self, files):
for i, path in enumerate(files):
yield i, {
"image": self._dld_img(path["image"]),
"label": path["label"],
"latin": path["latin"],
}
|