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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_face_landmark_5
from facefusion.filesystem import resolve_relative_path
from facefusion.thread_helper import conditional_thread_semaphore
from facefusion.typing import Embedding, FaceLandmark5, InferencePool, ModelOptions, ModelSet, VisionFrame
MODEL_SET : ModelSet =\
{
'arcface':
{
'hashes':
{
'face_recognizer':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/arcface_w600k_r50.hash',
'path': resolve_relative_path('../.assets/models/arcface_w600k_r50.hash')
}
},
'sources':
{
'face_recognizer':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/arcface_w600k_r50.onnx',
'path': resolve_relative_path('../.assets/models/arcface_w600k_r50.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('arcface')
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 calc_embedding(temp_vision_frame : VisionFrame, face_landmark_5 : FaceLandmark5) -> Tuple[Embedding, Embedding]:
face_recognizer = get_inference_pool().get('face_recognizer')
crop_vision_frame, matrix = warp_face_by_face_landmark_5(temp_vision_frame, face_landmark_5, 'arcface_112_v2', (112, 112))
crop_vision_frame = crop_vision_frame / 127.5 - 1
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():
embedding = face_recognizer.run(None,
{
'input': crop_vision_frame
})[0]
embedding = embedding.ravel()
normed_embedding = embedding / numpy.linalg.norm(embedding)
return embedding, normed_embedding
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