Add files

.gitignore

@@ -0,0 +1 @@
+images

README.md

@@ -4,7 +4,7 @@ emoji: 🌖
 colorFrom: red
 colorTo: yellow
 sdk: gradio
-sdk_version: 2.9.2
+sdk_version: 2.9.3
 app_file: app.py
 pinned: false
 ---

app.py

@@ -0,0 +1,197 @@
+#!/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.
+
+Known issues:
+- The `Seed` table in the output doesn't refresh properly in gradio 2.9.1.
+  https://github.com/gradio-app/gradio/issues/921
+'''
+ARTICLE = None
+
+TOKEN = os.environ['TOKEN']
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--theme', type=str, default='dark-grass')
+    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')
+    parser.add_argument('--allow-screenshot', action='store_true')
+    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():
+    gr.close_all()
+
+    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_screenshot=args.allow_screenshot,
+        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()

requirements.txt

@@ -0,0 +1,3 @@
+pillow==9.1.0
+tensorflow==2.8.0
+git+https://github.com/KichangKim/DeepDanbooru@v3-20200915-sgd-e30#egg=deepdanbooru