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import cv2 |
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import numpy as np |
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import torch |
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from face_feature.irn50_pytorch import irn50_pytorch |
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from face_util.faceutil import align_vertical |
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import ctypes |
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model_feature = irn50_pytorch("./face_recognition/face_feature/irn50_pytorch.npy") |
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model_feature.eval() |
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feature_align_image = np.zeros([128, 128, 3], dtype=np.uint8) |
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def get_face_feature(image, landmark): |
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landmark_vec = (ctypes.c_float * len(landmark))(*landmark) |
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align_vertical(image, image.shape[1], image.shape[0], feature_align_image, 128, 128, 3, landmark_vec, 48, 64, 40) |
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feature_align_image_proc = feature_align_image / 256 |
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feature_align_image_proc = torch.from_numpy(feature_align_image_proc.astype(np.float32)) |
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feature_align_image_proc = feature_align_image_proc.permute(2, 0, 1) |
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feature_align_image_proc = feature_align_image_proc.unsqueeze(0) |
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feature_out = model_feature(feature_align_image_proc) |
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feature_out = torch.nn.functional.normalize(feature_out)[0, :] |
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return feature_align_image, feature_out.data.numpy() |
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