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import os.path
from concurrent.futures import ProcessPoolExecutor
from multiprocessing import get_context
def _load_tf_and_return_tags(pil_image, threshold):
import deepdanbooru as dd
import tensorflow as tf
import numpy as np
this_folder = os.path.dirname(__file__)
model_path = os.path.abspath(os.path.join(this_folder, '..', 'models', 'deepbooru'))
if not os.path.exists(os.path.join(model_path, 'project.json')):
# there is no point importing these every time
import zipfile
from basicsr.utils.download_util import load_file_from_url
load_file_from_url(r"https://github.com/KichangKim/DeepDanbooru/releases/download/v3-20211112-sgd-e28/deepdanbooru-v3-20211112-sgd-e28.zip",
model_path)
with zipfile.ZipFile(os.path.join(model_path, "deepdanbooru-v3-20211112-sgd-e28.zip"), "r") as zip_ref:
zip_ref.extractall(model_path)
os.remove(os.path.join(model_path, "deepdanbooru-v3-20211112-sgd-e28.zip"))
tags = dd.project.load_tags_from_project(model_path)
model = dd.project.load_model_from_project(
model_path, compile_model=True
)
width = model.input_shape[2]
height = model.input_shape[1]
image = np.array(pil_image)
image = tf.image.resize(
image,
size=(height, width),
method=tf.image.ResizeMethod.AREA,
preserve_aspect_ratio=True,
)
image = image.numpy() # EagerTensor to np.array
image = dd.image.transform_and_pad_image(image, width, height)
image = image / 255.0
image_shape = image.shape
image = image.reshape((1, image_shape[0], image_shape[1], image_shape[2]))
y = model.predict(image)[0]
result_dict = {}
for i, tag in enumerate(tags):
result_dict[tag] = y[i]
result_tags_out = []
result_tags_print = []
for tag in tags:
if result_dict[tag] >= threshold:
if tag.startswith("rating:"):
continue
result_tags_out.append(tag)
result_tags_print.append(f'{result_dict[tag]} {tag}')
print('\n'.join(sorted(result_tags_print, reverse=True)))
return ', '.join(result_tags_out).replace('_', ' ').replace(':', ' ')
def subprocess_init_no_cuda():
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
def get_deepbooru_tags(pil_image, threshold=0.5):
context = get_context('spawn')
with ProcessPoolExecutor(initializer=subprocess_init_no_cuda, mp_context=context) as executor:
f = executor.submit(_load_tf_and_return_tags, pil_image, threshold, )
ret = f.result() # will rethrow any exceptions
return ret
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