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# Copyright 2025 Bytedance Ltd. and/or its affiliates
# SPDX-License-Identifier: Apache-2.0
import torch
import numpy as np
__all__ = [
"FaceEncoderArcFace",
"get_landmarks_from_image",
]
detector = None
def get_landmarks_from_image(image):
"""
Detect landmarks with insightface.
Args:
image (np.ndarray or PIL.Image):
The input image in RGB format.
Returns:
5 2D keypoints, only one face will be returned.
"""
from insightface.app import FaceAnalysis
global detector
if detector is None:
detector = FaceAnalysis()
detector.prepare(ctx_id=0, det_size=(640, 640))
in_image = np.array(image).copy()
faces = detector.get(in_image)
if len(faces) == 0:
raise ValueError("No face detected in the image")
# Get the largest face
face = max(faces, key=lambda x: (x.bbox[2] - x.bbox[0]) * (x.bbox[3] - x.bbox[1]))
# Return the 5 keypoints directly
keypoints = face.kps # 5 x 2
return keypoints
from facexlib.utils import load_file_from_url
from facexlib.recognition.arcface_arch import Backbone
def init_recognition_model(model_name, half=False, device='cuda', model_rootpath=None):
print("Initializing recognition model:", model_name)
if model_name == 'arcface':
model = Backbone(num_layers=50, drop_ratio=0.6, mode='ir_se').to('cuda').eval()
model_url = 'https://github.com/xinntao/facexlib/releases/download/v0.1.0/recognition_arcface_ir_se50.pth'
else:
raise NotImplementedError(f'{model_name} is not implemented.')
model_path = load_file_from_url(
url=model_url, model_dir='facexlib/weights', progress=True, file_name=None, save_dir=model_rootpath)
print("Loading model from:", model_path)
model.load_state_dict(torch.load(model_path), strict=True)
model.eval()
model = model.to(device)
return model
class FaceEncoderArcFace():
""" Official ArcFace, no_grad-only """
def __repr__(self):
return "ArcFace"
def init_encoder_model(self, device, eval_mode=True):
self.device = device
self.encoder_model = init_recognition_model('arcface', device=device)
if eval_mode:
self.encoder_model.eval()
def __call__(self, in_image):
return self.encoder_model(in_image[:, [2, 1, 0], :, :].contiguous()) # [B, 512], normalized |