125 lines
3.8 KiB
Python
125 lines
3.8 KiB
Python
|
#!/usr/bin/env python
|
||
|
|
|
||
|
|
from __future__ import annotations
|
||
|
|
|
||
|
|
import argparse
|
||
|
|
import functools
|
||
|
|
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
|
||
|
|
|
||
|
|
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("./model-resnet_custom_v3.h5")
|
||
|
|
return model
|
||
|
|
|
||
|
|
|
||
|
|
def load_labels() -> list[str]:
|
||
|
|
# path = huggingface_hub.hf_hub_download(MODEL_REPO,
|
||
|
|
# LABEL_FILENAME,
|
||
|
|
# use_auth_token=HF_TOKEN)
|
||
|
|
|
||
|
|
path = "./tags.txt"
|
||
|
|
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]) -> dict[str, float]:
|
||
|
|
_, 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
|
||
|
|
return res
|
||
|
|
|
||
|
|
|
||
|
|
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'),
|
||
|
|
#examples=examples,
|
||
|
|
#title=TITLE,
|
||
|
|
#description=DESCRIPTION,
|
||
|
|
#article=ARTICLE,
|
||
|
|
allow_flagging='never',
|
||
|
|
).launch(
|
||
|
|
enable_queue=True,
|
||
|
|
server_name = "0.0.0.0",
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
if __name__ == '__main__':
|
||
|
|
main()
|