CoLeafLabels / coleaf_dataset.py
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Upload folder using huggingface_hub
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import os
from PIL import Image
from datasets import Dataset
from huggingface_hub import HfApi
# Set the path to the CoLeaf dataset directory
coleaf_dir = "/home/goya/CV_Plant_Disease/Datasets/CoLeaf_dataset"
# Set the name and description for the Huggingface dataset
dataset_name = "bhugxer/CoLeafLabels"
dataset_description = "CoLeaf dataset for fine-tuning Stable Diffusion"
# Create lists to store the image paths and labels
image_paths = []
labels = []
api_token = os.environ.get("HUGGINGFACE_API_TOKEN")
# Iterate over the CoLeaf dataset directory
for label in os.listdir(coleaf_dir):
label_dir = os.path.join(coleaf_dir, label)
if os.path.isdir(label_dir):
for image_file in os.listdir(label_dir):
image_path = os.path.join(label_dir, image_file)
image_paths.append(image_path)
labels.append(label)
# Create a Huggingface dataset
dataset = Dataset.from_dict({"image_path": image_paths, "label": labels})
# Define the image loading function
def load_image(example):
with open(example["image_path"], "rb") as f:
image_data = f.read()
return {"image": image_data}
# Apply the image loading function to the dataset
dataset = dataset.map(load_image, batched=False, num_proc=4)
# Remove the "image_path" column from the dataset
dataset = dataset.remove_columns("image_path")
# Push the dataset to the Huggingface Hub
api = HfApi(token=api_token)
api.upload_folder(
folder_path=".",
repo_id=dataset_name,
repo_type="dataset",
ignore_patterns=["**/.*", "**/__pycache__"],
use_auth_token=api_token,
)
# Create a dataset card
dataset_card = f"""
# {dataset_name}
{dataset_description}
## Dataset Structure
- `image`: The image data.
- `label`: The label or text description of the image.
## Dataset Info
- Number of examples: {len(dataset)}
- Image format: Various (PNG, JPEG, etc.)
## License
[Insert license information here]
## Citation
[Insert citation information here]
"""
# Push the dataset card to the Huggingface Hub
with open("README.md", "w") as f:
f.write(dataset_card)
api.upload_file(
path_or_fileobj="README.md",
path_in_repo="README.md",
repo_id=dataset_name,
repo_type="dataset",
use_auth_token=api_token,
)