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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
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