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from typing import Tuple
import numpy
from facefusion import inference_manager
from facefusion.download import conditional_download_hashes, conditional_download_sources
from facefusion.face_helper import warp_face_by_translation
from facefusion.filesystem import resolve_relative_path
from facefusion.thread_helper import conditional_thread_semaphore
from facefusion.typing import BoundingBox, InferencePool, ModelOptions, ModelSet, VisionFrame
MODEL_SET : ModelSet =\
{
'gender_age':
{
'hashes':
{
'gender_age':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/gender_age.hash',
'path': resolve_relative_path('../.assets/models/gender_age.hash')
}
},
'sources':
{
'gender_age':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/gender_age.onnx',
'path': resolve_relative_path('../.assets/models/gender_age.onnx')
}
}
}
}
def get_inference_pool() -> InferencePool:
model_sources = get_model_options().get('sources')
return inference_manager.get_inference_pool(__name__, model_sources)
def clear_inference_pool() -> None:
inference_manager.clear_inference_pool(__name__)
def get_model_options() -> ModelOptions:
return MODEL_SET.get('gender_age')
def pre_check() -> bool:
download_directory_path = resolve_relative_path('../.assets/models')
model_hashes = get_model_options().get('hashes')
model_sources = get_model_options().get('sources')
return conditional_download_hashes(download_directory_path, model_hashes) and conditional_download_sources(download_directory_path, model_sources)
def detect_gender_age(temp_vision_frame : VisionFrame, bounding_box : BoundingBox) -> Tuple[int, int]:
gender_age = get_inference_pool().get('gender_age')
bounding_box = bounding_box.reshape(2, -1)
scale = 64 / numpy.subtract(*bounding_box[::-1]).max()
translation = 48 - bounding_box.sum(axis = 0) * scale * 0.5
crop_vision_frame, affine_matrix = warp_face_by_translation(temp_vision_frame, translation, scale, (96, 96))
crop_vision_frame = crop_vision_frame[:, :, ::-1].transpose(2, 0, 1).astype(numpy.float32)
crop_vision_frame = numpy.expand_dims(crop_vision_frame, axis = 0)
with conditional_thread_semaphore():
prediction = gender_age.run(None,
{
'input': crop_vision_frame
})[0][0]
gender = int(numpy.argmax(prediction[:2]))
age = int(numpy.round(prediction[2] * 100))
return gender, age
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