|
|
|
|
|
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: 7s |
|
|
|
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) |
|
- [DeepDanbooru](https://huggingface.co/spaces/hysts/DeepDanbooru) |
|
''' |
|
ARTICLE = '<center><img src="https://visitor-badge.glitch.me/badge?page_id=hysts.tadne-image-search-with-deepdanbooru" alt="visitor badge"/></center>' |
|
|
|
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() |
|
|