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