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| #!/usr/bin/env python | |
| from __future__ import annotations | |
| import deepdanbooru as dd | |
| import huggingface_hub | |
| import numpy as np | |
| import PIL.Image | |
| import tensorflow as tf | |
| def load_model() -> tf.keras.Model: | |
| path = huggingface_hub.hf_hub_download('public-data/DeepDanbooru', | |
| 'model-resnet_custom_v3.h5') | |
| model = tf.keras.models.load_model(path) | |
| return model | |
| def load_labels() -> list[str]: | |
| path = huggingface_hub.hf_hub_download('public-data/DeepDanbooru', | |
| 'tags.txt') | |
| with open(path) as f: | |
| labels = [line.strip() for line in f.readlines()] | |
| return labels | |
| model = load_model() | |
| labels = load_labels() | |
| def genTag(image: PIL.Image.Image, score_threshold: 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) | |
| indices = np.argsort(probs)[::-1] | |
| result_all = dict() | |
| result_threshold = dict() | |
| result_html = '' | |
| for index in indices: | |
| label = labels[index] | |
| prob = probs[index] | |
| result_all[label] = prob | |
| if prob < score_threshold: | |
| break | |
| result_threshold[label] = prob | |
| return result_threshold | |