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# 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 sys
import argparse
import numpy as np
import cv2 as cv
from sface import SFace
sys.path.append('../face_detection_yunet')
from yunet import YuNet
# Check OpenCV version
assert cv.__version__ >= "4.7.0", \
"Please install latest opencv-python to try this demo: python3 -m pip install --upgrade opencv-python"
# Valid combinations of backends and targets
backend_target_pairs = [
[cv.dnn.DNN_BACKEND_OPENCV, cv.dnn.DNN_TARGET_CPU],
[cv.dnn.DNN_BACKEND_CUDA, cv.dnn.DNN_TARGET_CUDA],
[cv.dnn.DNN_BACKEND_CUDA, cv.dnn.DNN_TARGET_CUDA_FP16],
[cv.dnn.DNN_BACKEND_TIMVX, cv.dnn.DNN_TARGET_NPU],
[cv.dnn.DNN_BACKEND_CANN, cv.dnn.DNN_TARGET_NPU]
]
parser = argparse.ArgumentParser(
description="SFace: Sigmoid-Constrained Hypersphere Loss for Robust Face Recognition (https://ieeexplore.ieee.org/document/9318547)")
parser.add_argument('--input1', '-i1', type=str,
help='Usage: Set path to the input image 1 (original face).')
parser.add_argument('--input2', '-i2', type=str,
help='Usage: Set path to the input image 2 (comparison face).')
parser.add_argument('--model', '-m', type=str, default='face_recognition_sface_2021dec.onnx',
help='Usage: Set model path, defaults to face_recognition_sface_2021dec.onnx.')
parser.add_argument('--backend_target', '-bt', type=int, default=0,
help='''Choose one of the backend-target pair to run this demo:
{:d}: (default) OpenCV implementation + CPU,
{:d}: CUDA + GPU (CUDA),
{:d}: CUDA + GPU (CUDA FP16),
{:d}: TIM-VX + NPU,
{:d}: CANN + NPU
'''.format(*[x for x in range(len(backend_target_pairs))]))
parser.add_argument('--dis_type', type=int, choices=[0, 1], default=0,
help='Usage: Distance type. \'0\': cosine, \'1\': norm_l1. Defaults to \'0\'')
args = parser.parse_args()
if __name__ == '__main__':
backend_id = backend_target_pairs[args.backend_target][0]
target_id = backend_target_pairs[args.backend_target][1]
# Instantiate SFace for face recognition
recognizer = SFace(modelPath=args.model,
disType=args.dis_type,
backendId=backend_id,
targetId=target_id)
# Instantiate YuNet for face detection
detector = YuNet(modelPath='../face_detection_yunet/face_detection_yunet_2023mar.onnx',
inputSize=[320, 320],
confThreshold=0.9,
nmsThreshold=0.3,
topK=5000,
backendId=backend_id,
targetId=target_id)
img1 = cv.imread(args.input1)
img2 = cv.imread(args.input2)
# Detect faces
detector.setInputSize([img1.shape[1], img1.shape[0]])
face1 = detector.infer(img1)
assert face1.shape[0] > 0, 'Cannot find a face in {}'.format(args.input1)
detector.setInputSize([img2.shape[1], img2.shape[0]])
face2 = detector.infer(img2)
assert face2.shape[0] > 0, 'Cannot find a face in {}'.format(args.input2)
# Match
result = recognizer.match(img1, face1[0][:-1], img2, face2[0][:-1])
print('Result: {}.'.format('same identity' if result else 'different identities'))
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