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| # Copyright (c) 2022 Insightface Team | |
| # Copyright (c) 2025 Bytedance Ltd. and/or its affiliates | |
| # This file has been modified by Bytedance Ltd. and/or its affiliates on September 15, 2025. | |
| # SPDX-License-Identifier: Apache-2.0 | |
| # Original file (insightface) was released under MIT License: | |
| # Permission is hereby granted, free of charge, to any person obtaining a copy | |
| # of this software and associated documentation files (the "Software"), to deal | |
| # in the Software without restriction, including without limitation the rights | |
| # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | |
| # copies of the Software, and to permit persons to whom the Software is | |
| # furnished to do so, subject to the following conditions: | |
| # The above copyright notice and this permission notice shall be included in all | |
| # copies or substantial portions of the Software. | |
| # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | |
| # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | |
| # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | |
| # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | |
| # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | |
| # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE | |
| # SOFTWARE. | |
| import numpy as np | |
| import cv2 | |
| from PIL import Image | |
| def estimate_norm(lmk, image_size=112, arcface_dst=None): | |
| from skimage import transform as trans | |
| assert lmk.shape == (5, 2) | |
| assert image_size%112==0 or image_size%128==0 | |
| if image_size%112==0: | |
| ratio = float(image_size)/112.0 | |
| diff_x = 0 | |
| else: | |
| ratio = float(image_size)/128.0 | |
| diff_x = 8.0*ratio | |
| dst = arcface_dst * ratio | |
| dst[:,0] += diff_x | |
| tform = trans.SimilarityTransform() | |
| tform.estimate(lmk, dst) | |
| M = tform.params[0:2, :] | |
| return M | |
| def get_arcface_dst(extend_face_crop=False, extend_ratio=0.8): | |
| arcface_dst = np.array( | |
| [[38.2946, 51.6963], [73.5318, 51.5014], [56.0252, 71.7366], | |
| [41.5493, 92.3655], [70.7299, 92.2041]], dtype=np.float32) | |
| if extend_face_crop: | |
| arcface_dst[:,1] = arcface_dst[:,1] + 10 | |
| arcface_dst = (arcface_dst - 112/2) * extend_ratio + 112/2 | |
| return arcface_dst | |
| def align_face(image_pil, face_kpts, extend_face_crop=False, extend_ratio=0.8, face_size=112): | |
| arcface_dst = get_arcface_dst(extend_face_crop, extend_ratio) | |
| M = estimate_norm(face_kpts, face_size, arcface_dst) | |
| image_cv2 = cv2.cvtColor(np.array(image_pil), cv2.COLOR_RGB2BGR) | |
| face_image_cv2 = cv2.warpAffine(image_cv2, M, (face_size, face_size), borderValue=0.0) | |
| face_image = Image.fromarray(cv2.cvtColor(face_image_cv2, cv2.COLOR_BGR2RGB)) | |
| return face_image |