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| #!/usr/bin/env python | |
| from __future__ import annotations | |
| import argparse | |
| import functools | |
| import os | |
| import pathlib | |
| import tarfile | |
| import tempfile | |
| import deepdanbooru as dd | |
| import gradio as gr | |
| import huggingface_hub | |
| import numpy as np | |
| import PIL.Image | |
| import tensorflow as tf | |
| TITLE = 'KichangKim/DeepDanbooru' | |
| DESCRIPTION = 'This is an unofficial demo for https://github.com/KichangKim/DeepDanbooru.' | |
| ARTICLE = '<center><img src="https://visitor-badge.glitch.me/badge?page_id=hysts.deepdanbooru" alt="visitor badge"/></center>' | |
| HF_TOKEN = os.environ['HF_TOKEN'] | |
| MODEL_REPO = 'hysts/DeepDanbooru' | |
| MODEL_FILENAME = 'model-resnet_custom_v3.h5' | |
| LABEL_FILENAME = 'tags.txt' | |
| def parse_args() -> argparse.Namespace: | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument('--score-slider-step', type=float, default=0.05) | |
| parser.add_argument('--score-threshold', type=float, default=0.5) | |
| parser.add_argument('--share', action='store_true') | |
| return parser.parse_args() | |
| def load_sample_image_paths() -> list[pathlib.Path]: | |
| image_dir = pathlib.Path('images') | |
| if not image_dir.exists(): | |
| dataset_repo = 'hysts/sample-images-TADNE' | |
| path = huggingface_hub.hf_hub_download(dataset_repo, | |
| 'images.tar.gz', | |
| repo_type='dataset', | |
| use_auth_token=HF_TOKEN) | |
| with tarfile.open(path) as f: | |
| f.extractall() | |
| return sorted(image_dir.glob('*')) | |
| def load_model() -> tf.keras.Model: | |
| path = huggingface_hub.hf_hub_download(MODEL_REPO, | |
| MODEL_FILENAME, | |
| use_auth_token=HF_TOKEN) | |
| model = tf.keras.models.load_model(path) | |
| return model | |
| def load_labels() -> list[str]: | |
| path = huggingface_hub.hf_hub_download(MODEL_REPO, | |
| LABEL_FILENAME, | |
| use_auth_token=HF_TOKEN) | |
| with open(path) as f: | |
| labels = [line.strip() for line in f.readlines()] | |
| return labels | |
| def predict(image: PIL.Image.Image, score_threshold: float, | |
| model: tf.keras.Model, | |
| labels: list[str]) -> tuple[dict[str, float], str]: | |
| _, height, width, _ = model.input_shape | |
| image = np.asarray(image) | |
| image = tf.image.resize(image, | |
| size=(height, width), | |
| method=tf.image.ResizeMethod.AREA, | |
| preserve_aspect_ratio=True) | |
| image = image.numpy() | |
| image = dd.image.transform_and_pad_image(image, width, height) | |
| image = image / 255. | |
| probs = model.predict(image[None, ...])[0] | |
| probs = probs.astype(float) | |
| res = dict() | |
| for prob, label in zip(probs.tolist(), labels): | |
| if prob < score_threshold: | |
| continue | |
| res[label] = prob | |
| sorted_preds = sorted(res.items(), key=lambda x: -x[1]) | |
| out_path = tempfile.NamedTemporaryFile(suffix='.txt', delete=False) | |
| with open(out_path.name, 'w') as f: | |
| for key, _ in sorted_preds: | |
| f.write(f'{key}\n') | |
| return res, out_path.name | |
| def main(): | |
| args = parse_args() | |
| image_paths = load_sample_image_paths() | |
| examples = [[path.as_posix(), args.score_threshold] | |
| for path in image_paths] | |
| model = load_model() | |
| labels = load_labels() | |
| func = functools.partial(predict, model=model, labels=labels) | |
| gr.Interface( | |
| func, | |
| [ | |
| gr.Image(type='pil', label='Input'), | |
| gr.Slider(0, | |
| 1, | |
| step=args.score_slider_step, | |
| value=args.score_threshold, | |
| label='Score Threshold'), | |
| ], | |
| [ | |
| gr.Label(label='Output'), | |
| gr.File(label='Tag List'), | |
| ], | |
| examples=examples, | |
| title=TITLE, | |
| description=DESCRIPTION, | |
| article=ARTICLE, | |
| allow_flagging='never', | |
| ).launch( | |
| enable_queue=True, | |
| share=args.share, | |
| ) | |
| if __name__ == '__main__': | |
| main() | |