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import fiftyone as fo
import fiftyone.utils.hf_hub as fouh
import datasets
class FashionpediaLoader(fouh.BaseHuggingFaceLoader):
def load(self):
if isinstance(self.hf_dataset, datasets.DatasetDict):
split_names = list(self.hf_dataset.keys())
self.hf_dataset = self.hf_dataset[split_names[0]]
if "name" in self.kwargs:
self.kwargs.pop("name")
dataset = fo.Dataset(name="fashionopedia-val", **self.kwargs)
label_classes = self.hf_dataset.features['objects'].feature['category'].names
samples = []
download_dir = fouh._get_download_dir(self.repo_id)
for i, item in enumerate(self.hf_dataset):
image = item['image']
basename = f"image_{i}"
save_path = fouh._save_PIL_image_to_disk(image, download_dir, basename)
width, height = item['width'], item['height']
objs = item['objects']
categories = objs['category']
bboxes = objs['bbox']
dets = []
for cat, bbox in zip(categories, bboxes):
x0, y0, x1, y1 = bbox
x0n, y0n, x1n, y1n = x0/width, y0/height, x1/width, y1/height
fo_bbox = [x0n, y0n, x1n-x0n, y1n-y0n]
label = label_classes[cat]
dets.append(fo.Detection(label=label, bounding_box=fo_bbox))
detections = fo.Detections(detections=dets)
samples.append(fo.Sample(filepath=save_path, objs=detections))
dataset.add_samples(samples)
return dataset
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