73 lines
2.4 KiB
Python
73 lines
2.4 KiB
Python
import os.path
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from concurrent.futures import ProcessPoolExecutor
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from multiprocessing import get_context
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def _load_tf_and_return_tags(pil_image, threshold):
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import deepdanbooru as dd
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import tensorflow as tf
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import numpy as np
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this_folder = os.path.dirname(__file__)
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model_path = os.path.join(this_folder, '..', 'models', 'deepbooru', 'deepdanbooru-v3-20211112-sgd-e28')
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model_good = False
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for path_candidate in [model_path, os.path.dirname(model_path)]:
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if os.path.exists(os.path.join(path_candidate, 'project.json')):
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model_path = path_candidate
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model_good = True
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if not model_good:
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return ("Download https://github.com/KichangKim/DeepDanbooru/releases/download/v3-20211112-sgd-e28/"
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"deepdanbooru-v3-20211112-sgd-e28.zip unpack and put into models/deepbooru")
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tags = dd.project.load_tags_from_project(model_path)
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model = dd.project.load_model_from_project(
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model_path, compile_model=True
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)
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width = model.input_shape[2]
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height = model.input_shape[1]
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image = np.array(pil_image)
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image = tf.image.resize(
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image,
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size=(height, width),
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method=tf.image.ResizeMethod.AREA,
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preserve_aspect_ratio=True,
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)
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image = image.numpy() # EagerTensor to np.array
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image = dd.image.transform_and_pad_image(image, width, height)
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image = image / 255.0
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image_shape = image.shape
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image = image.reshape((1, image_shape[0], image_shape[1], image_shape[2]))
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y = model.predict(image)[0]
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result_dict = {}
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for i, tag in enumerate(tags):
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result_dict[tag] = y[i]
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result_tags_out = []
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result_tags_print = []
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for tag in tags:
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if result_dict[tag] >= threshold:
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if tag.startswith("rating:"):
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continue
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result_tags_out.append(tag)
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result_tags_print.append(f'{result_dict[tag]} {tag}')
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print('\n'.join(sorted(result_tags_print, reverse=True)))
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return ', '.join(result_tags_out).replace('_', ' ').replace(':', ' ')
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def subprocess_init_no_cuda():
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import os
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os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
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def get_deepbooru_tags(pil_image, threshold=0.5):
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context = get_context('spawn')
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with ProcessPoolExecutor(initializer=subprocess_init_no_cuda, mp_context=context) as executor:
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f = executor.submit(_load_tf_and_return_tags, pil_image, threshold, )
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ret = f.result() # will rethrow any exceptions
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return ret |