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from __future__ import annotations |
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import argparse |
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import functools |
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import os |
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import pathlib |
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import subprocess |
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import tarfile |
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command = 'pip install -U gradio==2.7.0' |
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subprocess.call(command.split()) |
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import deepdanbooru as dd |
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import gradio as gr |
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import huggingface_hub |
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import numpy as np |
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import PIL.Image |
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import tensorflow as tf |
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TOKEN = os.environ['TOKEN'] |
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MODEL_REPO = 'hysts/DeepDanbooru' |
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MODEL_FILENAME = 'model-resnet_custom_v3.h5' |
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LABEL_FILENAME = 'tags.txt' |
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def parse_args() -> argparse.Namespace: |
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parser = argparse.ArgumentParser() |
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parser.add_argument('--score-slider-step', type=float, default=0.05) |
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parser.add_argument('--score-threshold', type=float, default=0.5) |
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parser.add_argument('--theme', type=str, default='dark-grass') |
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parser.add_argument('--live', action='store_true') |
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parser.add_argument('--share', action='store_true') |
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parser.add_argument('--port', type=int) |
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parser.add_argument('--disable-queue', |
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dest='enable_queue', |
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action='store_false') |
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parser.add_argument('--allow-flagging', type=str, default='never') |
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parser.add_argument('--allow-screenshot', action='store_true') |
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return parser.parse_args() |
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def load_sample_image_paths() -> list[pathlib.Path]: |
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image_dir = pathlib.Path('images') |
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if not image_dir.exists(): |
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dataset_repo = 'hysts/sample-images-TADNE' |
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path = huggingface_hub.hf_hub_download(dataset_repo, |
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'images.tar.gz', |
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repo_type='dataset', |
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use_auth_token=TOKEN) |
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with tarfile.open(path) as f: |
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f.extractall() |
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return sorted(image_dir.glob('*')) |
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def load_model() -> tf.keras.Model: |
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path = huggingface_hub.hf_hub_download(MODEL_REPO, |
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MODEL_FILENAME, |
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use_auth_token=TOKEN) |
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model = tf.keras.models.load_model(path) |
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return model |
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def load_labels() -> list[str]: |
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path = huggingface_hub.hf_hub_download(MODEL_REPO, |
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LABEL_FILENAME, |
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use_auth_token=TOKEN) |
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with open(path) as f: |
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labels = [line.strip() for line in f.readlines()] |
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return labels |
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def predict(image: PIL.Image.Image, score_threshold: float, |
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model: tf.keras.Model, labels: list[str]) -> dict[str, float]: |
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_, height, width, _ = model.input_shape |
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image = np.asarray(image) |
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image = tf.image.resize(image, |
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size=(height, width), |
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method=tf.image.ResizeMethod.AREA, |
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preserve_aspect_ratio=True) |
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image = image.numpy() |
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image = dd.image.transform_and_pad_image(image, width, height) |
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image = image / 255. |
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probs = model.predict(image[None, ...])[0] |
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probs = probs.astype(float) |
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res = dict() |
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for prob, label in zip(probs, labels): |
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if prob < score_threshold: |
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continue |
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res[label] = prob |
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return res |
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def main(): |
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gr.close_all() |
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args = parse_args() |
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image_paths = load_sample_image_paths() |
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examples = [[path.as_posix(), args.score_threshold] |
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for path in image_paths] |
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model = load_model() |
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labels = load_labels() |
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func = functools.partial(predict, model=model, labels=labels) |
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func = functools.update_wrapper(func, predict) |
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repo_url = 'https://github.com/KichangKim/DeepDanbooru' |
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title = 'KichangKim/DeepDanbooru' |
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description = f'A demo for {repo_url}' |
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article = None |
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gr.Interface( |
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func, |
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[ |
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gr.inputs.Image(type='pil', label='Input'), |
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gr.inputs.Slider(0, |
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1, |
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step=args.score_slider_step, |
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default=args.score_threshold, |
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label='Score Threshold'), |
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], |
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gr.outputs.Label(label='Output'), |
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theme=args.theme, |
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title=title, |
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description=description, |
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article=article, |
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examples=examples, |
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allow_screenshot=args.allow_screenshot, |
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allow_flagging=args.allow_flagging, |
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live=args.live, |
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).launch( |
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enable_queue=args.enable_queue, |
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server_port=args.port, |
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share=args.share, |
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) |
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if __name__ == '__main__': |
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main() |
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