DNN-based face recognizer.
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#include <opencv2/objdetect/face.hpp>
◆ DisType
Definition of distance used for calculating the distance between two face features.
Enumerator |
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FR_COSINE | |
FR_NORM_L2 | |
◆ ~FaceRecognizerSF()
virtual cv::FaceRecognizerSF::~FaceRecognizerSF |
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inlinevirtual |
◆ alignCrop()
Python: |
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| cv.FaceRecognizerSF.alignCrop( | src_img, face_box[, aligned_img] | ) -> | aligned_img |
Aligning image to put face on the standard position.
- Parameters
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src_img | input image |
face_box | the detection result used for indicate face in input image |
aligned_img | output aligned image |
◆ create()
Python: |
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| cv.FaceRecognizerSF.create( | model, config[, backend_id[, target_id]] | ) -> | retval |
| cv.FaceRecognizerSF_create( | model, config[, backend_id[, target_id]] | ) -> | retval |
Creates an instance of this class with given parameters.
- Parameters
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model | the path of the onnx model used for face recognition |
config | the path to the config file for compability, which is not requested for ONNX models |
backend_id | the id of backend |
target_id | the id of target device |
◆ feature()
Python: |
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| cv.FaceRecognizerSF.feature( | aligned_img[, face_feature] | ) -> | face_feature |
Extracting face feature from aligned image.
- Parameters
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aligned_img | input aligned image |
face_feature | output face feature |
◆ match()
Python: |
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| cv.FaceRecognizerSF.match( | face_feature1, face_feature2[, dis_type] | ) -> | retval |
Calculating the distance between two face features.
- Parameters
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face_feature1 | the first input feature |
face_feature2 | the second input feature of the same size and the same type as face_feature1 |
dis_type | defining the similarity with optional values "FR_OSINE" or "FR_NORM_L2" |
The documentation for this class was generated from the following file: