app.py

#!/usr/bin/env python

from __future__ import annotations

import argparse
import functools
import io
import os
import pathlib
import tarfile

import deepdanbooru as dd
import gradio as gr
import huggingface_hub
import numpy as np
import PIL.Image
import tensorflow as tf
from huggingface_hub import hf_hub_download

TITLE = 'TADNE Image Search with DeepDanbooru'
DESCRIPTION = '''The original TADNE site is https://thisanimedoesnotexist.ai/.

This app shows images similar to the query image from images generated
by the TADNE model with seed 0-99999.
Here, image similarity is measured by the L2 distance of the intermediate
features by the [DeepDanbooru](https://github.com/KichangKim/DeepDanbooru)
model.

The resolution of the output images in this app is 128x128, but you can
check the original 512x512 images from URLs like
https://thisanimedoesnotexist.ai/slider.html?seed=10000 using the output seeds.

Expected execution time on Hugging Face Spaces: 25s

Related Apps:
- [TADNE](https://huggingface.co/spaces/hysts/TADNE)
- [TADNE Image Viewer](https://huggingface.co/spaces/hysts/TADNE-image-viewer)
- [TADNE Image Selector](https://huggingface.co/spaces/hysts/TADNE-image-selector)
- [TADNE Interpolation](https://huggingface.co/spaces/hysts/TADNE-interpolation)
'''
ARTICLE = None

TOKEN = os.environ['TOKEN']


def parse_args() -> argparse.Namespace:
    parser = argparse.ArgumentParser()
    parser.add_argument('--theme', type=str)
    parser.add_argument('--live', action='store_true')
    parser.add_argument('--share', action='store_true')
    parser.add_argument('--port', type=int)
    parser.add_argument('--disable-queue',
                        dest='enable_queue',
                        action='store_false')
    parser.add_argument('--allow-flagging', type=str, default='never')
    return parser.parse_args()


def download_image_tarball(size: int, dirname: str) -> pathlib.Path:
    path = hf_hub_download('hysts/TADNE-sample-images',
                           f'{size}/{dirname}.tar',
                           repo_type='dataset',
                           use_auth_token=TOKEN)
    return path


def load_deepdanbooru_predictions(dirname: str) -> np.ndarray:
    path = hf_hub_download(
        'hysts/TADNE-sample-images',
        f'prediction_results/deepdanbooru/intermediate_features/{dirname}.npy',
        repo_type='dataset',
        use_auth_token=TOKEN)
    return np.load(path)


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=TOKEN)
        with tarfile.open(path) as f:
            f.extractall()
    return sorted(image_dir.glob('*'))


def create_model() -> tf.keras.Model:
    path = huggingface_hub.hf_hub_download('hysts/DeepDanbooru',
                                           'model-resnet_custom_v3.h5',
                                           use_auth_token=TOKEN)
    model = tf.keras.models.load_model(path)
    model = tf.keras.Model(model.input, model.layers[-4].output)
    layer = tf.keras.layers.GlobalAveragePooling2D()
    model = tf.keras.Sequential([model, layer])
    return model


def predict(image: PIL.Image.Image, model: tf.keras.Model) -> np.ndarray:
    _, 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.
    features = model.predict(image[None, ...])[0]
    features = features.astype(float)
    return features


def run(
    image: PIL.Image.Image,
    nrows: int,
    ncols: int,
    image_size: int,
    dirname: str,
    tarball_path: pathlib.Path,
    deepdanbooru_predictions: np.ndarray,
    model: tf.keras.Model,
) -> tuple[np.ndarray, np.ndarray]:
    features = predict(image, model)
    distances = ((deepdanbooru_predictions - features)**2).sum(axis=1)

    image_indices = np.argsort(distances)

    seeds = []
    images = []
    with tarfile.TarFile(tarball_path) as tar_file:
        for index in range(nrows * ncols):
            image_index = image_indices[index]
            seeds.append(image_index)
            member = tar_file.getmember(f'{dirname}/{image_index:07d}.jpg')
            with tar_file.extractfile(member) as f:
                data = io.BytesIO(f.read())
            image = PIL.Image.open(data)
            image = np.asarray(image)
            images.append(image)
    res = np.asarray(images).reshape(nrows, ncols, image_size, image_size,
                                     3).transpose(0, 2, 1, 3, 4).reshape(
                                         nrows * image_size,
                                         ncols * image_size, 3)
    seeds = np.asarray(seeds).reshape(nrows, ncols)

    return res, seeds


def main():
    args = parse_args()

    image_size = 128
    dirname = '0-99999'
    tarball_path = download_image_tarball(image_size, dirname)
    deepdanbooru_predictions = load_deepdanbooru_predictions(dirname)

    model = create_model()

    image_paths = load_sample_image_paths()
    examples = [[path.as_posix(), 2, 5] for path in image_paths]

    func = functools.partial(
        run,
        image_size=image_size,
        dirname=dirname,
        tarball_path=tarball_path,
        deepdanbooru_predictions=deepdanbooru_predictions,
        model=model,
    )
    func = functools.update_wrapper(func, run)

    gr.Interface(
        func,
        [
            gr.inputs.Image(type='pil', label='Input'),
            gr.inputs.Slider(1, 10, step=1, default=2, label='Number of Rows'),
            gr.inputs.Slider(
                1, 10, step=1, default=5, label='Number of Columns'),
        ],
        [
            gr.outputs.Image(type='numpy', label='Output'),
            gr.outputs.Dataframe(type='numpy', label='Seed'),
        ],
        examples=examples,
        title=TITLE,
        description=DESCRIPTION,
        article=ARTICLE,
        theme=args.theme,
        allow_flagging=args.allow_flagging,
        live=args.live,
    ).launch(
        enable_queue=args.enable_queue,
        server_port=args.port,
        share=args.share,
    )


if __name__ == '__main__':
    main()