File size: 3,426 Bytes
aa35aae 515876c aa35aae 515876c aa35aae 88f79d1 aa35aae 515876c aa35aae 515876c aa35aae c63764d aa35aae |
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 |
import collections
import json
import os
import datasets
ANNOTATION_FILENAME = "_annotations.coco.json"
class AHv3Config(datasets.BuilderConfig):
def __init__(self, data_urls, **kwargs):
super(AHv3Config, self).__init__(**kwargs)
self.data_urls = data_urls
class AHv3(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
AHv3Config(name="With-augmentation", data_urls={"train": "https://huggingface.co/datasets/nyuuzyou/AnimeHeadsv3/resolve/main/AHv3-AUG/train.zip", "validation": "https://huggingface.co/datasets/nyuuzyou/AnimeHeadsv3/resolve/main/AHv3-AUG/valid.zip", "test": "https://huggingface.co/datasets/nyuuzyou/AnimeHeadsv3/resolve/main/AHv3-AUG/test.zip"}),
AHv3Config(name="Without-augmentation", data_urls={"train": "https://huggingface.co/datasets/nyuuzyou/AnimeHeadsv3/resolve/main/AHv3-NA/train.zip", "validation": "https://huggingface.co/datasets/nyuuzyou/AnimeHeadsv3/resolve/main/AHv3-NA/valid.zip", "test": "https://huggingface.co/datasets/nyuuzyou/AnimeHeadsv3/resolve/main/AHv3-NA/test.zip"}),
]
def _info(self):
features = datasets.Features({"image_id": datasets.Value("int64"), "image": datasets.Image(), "width": datasets.Value("int32"), "height": datasets.Value("int32"), "objects": datasets.Sequence({"id": datasets.Value("int64"), "area": datasets.Value("int64"), "bbox": datasets.Sequence(datasets.Value("float32"), length=4), "category": datasets.Value("string")})})
return datasets.DatasetInfo(features=features)
def _split_generators(self, dl_manager):
data_files = dl_manager.download_and_extract(self.config.data_urls)
return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"folder_dir": data_files["train"]}), datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"folder_dir": data_files["validation"]}), datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"folder_dir": data_files["test"]})]
def _generate_examples(self, folder_dir):
def process_annot(annot, category_id_to_category):
return {"id": annot["id"], "area": annot["area"], "bbox": annot["bbox"], "category": category_id_to_category[annot["category_id"]]}
image_id_to_image = {}
idx = 0
annotation_filepath = os.path.join(folder_dir, ANNOTATION_FILENAME)
with open(annotation_filepath, "r") as f:
annotations = json.load(f)
category_id_to_category = {category["id"]: category["name"] for category in annotations["categories"]}
image_id_to_annotations = collections.defaultdict(list)
for annot in annotations["annotations"]:
image_id_to_annotations[annot["image_id"]].append(annot)
filename_to_image = {image["file_name"]: image for image in annotations["images"]}
for filename in os.listdir(folder_dir):
filepath = os.path.join(folder_dir, filename)
if filename in filename_to_image:
image = filename_to_image[filename]
with open(filepath, "rb") as f:
image_bytes = f.read()
objects = [process_annot(annot, category_id_to_category) for annot in image_id_to_annotations[image["id"]]]
yield idx, {"image_id": image["id"], "image": {"path": filepath, "bytes": image_bytes}, "height": image["height"], "width": image["width"], "objects": objects}
idx += 1
|