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import os
from io import BytesIO
from multiprocessing import Pool, cpu_count
import fiftyone as fo
from datasets import load_dataset
from PIL import Image
# Load the dataset
imagenet_hard_dataset = load_dataset('taesiri/imagenet-hard', split='validation')
os.makedirs("dataset", exist_ok=True)
def process_image(i):
image = imagenet_hard_dataset[i]["image"].convert("RGB")
image_path = f"dataset/{i}.JPEG"
image.save(image_path, "JPEG", quality=80)
return {
"file_path": image_path,
"labels": imagenet_hard_dataset[i]["english_label"],
"origin": imagenet_hard_dataset[i]["origin"],
}
def create_fiftyone_sample(sample):
origin_label = fo.Classification(label=str(sample["origin"]))
english_label = fo.Classification(label=str(sample["labels"]))
return fo.Sample(
filepath=sample["file_path"],
labels=fo.Classifications(classifications=[origin_label, english_label]),
)
if __name__ == "__main__":
# Process images in parallel and get the list of images with their labels
with Pool(cpu_count()) as pool:
samples_data = pool.map(process_image, range(len(imagenet_hard_dataset)))
# Create a FiftyOne dataset
dataset = fo.Dataset(name="imagenet-hard")
# Add images and labels to the FiftyOne dataset
samples = [create_fiftyone_sample(sample_data) for sample_data in samples_data]
dataset.add_samples(samples)
session = fo.launch_app(dataset, port=8888, remote=True, address="0.0.0.0")
session.wait()
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