|
|
|
|
|
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() |
|
|
