Yuantao Feng
Update to OpenCV APIs (YuNet -> FaceDetectorYN, SFace -> FaceRecognizerSF) (#6)
3af1dea
| # This file is part of OpenCV Zoo project. | |
| # It is subject to the license terms in the LICENSE file found in the same directory. | |
| # | |
| # Copyright (C) 2021, Shenzhen Institute of Artificial Intelligence and Robotics for Society, all rights reserved. | |
| # Third party copyrights are property of their respective owners. | |
| import numpy as np | |
| import cv2 as cv | |
| from _testcapi import FLT_MIN | |
| class SFace: | |
| def __init__(self, modelPath, disType=0, backendId=0, targetId=0): | |
| self._modelPath = modelPath | |
| self._backendId = backendId | |
| self._targetId = targetId | |
| self._model = cv.FaceRecognizerSF.create( | |
| model=self._modelPath, | |
| config="", | |
| backend_id=self._backendId, | |
| target_id=self._targetId) | |
| self._disType = disType # 0: cosine similarity, 1: Norm-L2 distance | |
| assert self._disType in [0, 1], "0: Cosine similarity, 1: norm-L2 distance, others: invalid" | |
| self._threshold_cosine = 0.363 | |
| self._threshold_norml2 = 1.128 | |
| def name(self): | |
| return self.__class__.__name__ | |
| def setBackend(self, backendId): | |
| self._backendId = backendId | |
| self._model = cv.FaceRecognizerSF.create( | |
| model=self._modelPath, | |
| config="", | |
| backend_id=self._backendId, | |
| target_id=self._targetId) | |
| def setTarget(self, targetId): | |
| self._targetId = targetId | |
| self._model = cv.FaceRecognizerSF.create( | |
| model=self._modelPath, | |
| config="", | |
| backend_id=self._backendId, | |
| target_id=self._targetId) | |
| def _preprocess(self, image, bbox): | |
| return self._model.alignCrop(image, bbox) | |
| def infer(self, image, bbox): | |
| # Preprocess | |
| inputBlob = self._preprocess(image, bbox) | |
| # Forward | |
| features = self._model.feature(inputBlob) | |
| return features | |
| def match(self, image1, face1, image2, face2): | |
| feature1 = self.infer(image1, face1) | |
| feature2 = self.infer(image2, face2) | |
| if self._disType == 0: # COSINE | |
| cosine_score = self._model.match(feature1, feature2, self._disType) | |
| return 1 if cosine_score >= self._threshold_cosine else 0 | |
| else: # NORM_L2 | |
| norml2_distance = self._model.match(feature1, feature2, self._disType) | |
| return 1 if norml2_distance <= self._threshold_norml2 else 0 |