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Open Source Computer Vision Library
https://opencv.org/
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57 lines
2.3 KiB
57 lines
2.3 KiB
import argparse |
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import numpy as np |
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import cv2 as cv |
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parser = argparse.ArgumentParser() |
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parser.add_argument('--input1', '-i1', type=str, help='Path to the input image1.') |
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parser.add_argument('--input2', '-i2', type=str, help='Path to the input image2.') |
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parser.add_argument('--face_detection_model', '-fd', type=str, help='Path to the face detection model. Download the model at https://github.com/ShiqiYu/libfacedetection.train/tree/master/tasks/task1/onnx.') |
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parser.add_argument('--face_recognition_model', '-fr', type=str, help='Path to the face recognition model. Download the model at https://drive.google.com/file/d/1ClK9WiB492c5OZFKveF3XiHCejoOxINW/view.') |
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args = parser.parse_args() |
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# Read the input image |
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img1 = cv.imread(args.input1) |
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img2 = cv.imread(args.input2) |
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# Instantiate face detector and recognizer |
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detector = cv.FaceDetectorYN.create( |
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args.face_detection_model, |
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"", |
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(img1.shape[1], img1.shape[0]) |
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) |
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recognizer = cv.FaceRecognizerSF.create( |
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args.face_recognition_model, |
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"" |
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) |
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# Detect face |
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detector.setInputSize((img1.shape[1], img1.shape[0])) |
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face1 = detector.detect(img1) |
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detector.setInputSize((img2.shape[1], img2.shape[0])) |
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face2 = detector.detect(img2) |
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assert face1[1].shape[0] > 0, 'Cannot find a face in {}'.format(args.input1) |
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assert face2[1].shape[0] > 0, 'Cannot find a face in {}'.format(args.input2) |
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# Align faces |
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face1_align = recognizer.alignCrop(img1, face1[1][0]) |
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face2_align = recognizer.alignCrop(img2, face2[1][0]) |
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# Extract features |
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face1_feature = recognizer.faceFeature(face1_align) |
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face2_feature = recognizer.faceFeature(face2_align) |
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# Calculate distance (0: cosine, 1: L2) |
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cosine_similarity_threshold = 0.363 |
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cosine_score = recognizer.faceMatch(face1_feature, face2_feature, 0) |
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msg = 'different identities' |
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if cosine_score >= cosine_similarity_threshold: |
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msg = 'the same identity' |
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print('They have {}. Cosine Similarity: {}, threshold: {} (higher value means higher similarity, max 1.0).'.format(msg, cosine_score, cosine_similarity_threshold)) |
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l2_similarity_threshold = 1.128 |
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l2_score = recognizer.faceMatch(face1_feature, face2_feature, 1) |
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msg = 'different identities' |
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if l2_score <= l2_similarity_threshold: |
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msg = 'the same identity' |
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print('They have {}. NormL2 Distance: {}, threshold: {} (lower value means higher similarity, min 0.0).'.format(msg, l2_score, l2_similarity_threshold)) |