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import argparse
import numpy as np
import cv2 as cv
def str2bool(v):
if v.lower() in ['on', 'yes', 'true', 'y', 't']:
return True
elif v.lower() in ['off', 'no', 'false', 'n', 'f']:
return False
else:
raise NotImplementedError
parser = argparse.ArgumentParser()
parser.add_argument('--image1', '-i1', type=str, help='Path to the input image1. Omit for detecting on default camera.')
parser.add_argument('--image2', '-i2', type=str, help='Path to the input image2. When image1 and image2 parameters given then the program try to find a face on both images and runs face recognition algorithm.')
parser.add_argument('--video', '-v', type=str, help='Path to the input video.')
parser.add_argument('--scale', '-sc', type=float, default=1.0, help='Scale factor used to resize input video frames.')
parser.add_argument('--face_detection_model', '-fd', type=str, default='face_detection_yunet_2021dec.onnx', help='Path to the face detection model. Download the model at https://github.com/opencv/opencv_zoo/tree/master/models/face_detection_yunet')
parser.add_argument('--face_recognition_model', '-fr', type=str, default='face_recognition_sface_2021dec.onnx', help='Path to the face recognition model. Download the model at https://github.com/opencv/opencv_zoo/tree/master/models/face_recognition_sface')
parser.add_argument('--score_threshold', type=float, default=0.9, help='Filtering out faces of score < score_threshold.')
parser.add_argument('--nms_threshold', type=float, default=0.3, help='Suppress bounding boxes of iou >= nms_threshold.')
parser.add_argument('--top_k', type=int, default=5000, help='Keep top_k bounding boxes before NMS.')
parser.add_argument('--save', '-s', type=str2bool, default=False, help='Set true to save results. This flag is invalid when using camera.')
args = parser.parse_args()
def visualize(input, faces, fps, thickness=2):
if faces[1] is not None:
for idx, face in enumerate(faces[1]):
print('Face {}, top-left coordinates: ({:.0f}, {:.0f}), box width: {:.0f}, box height {:.0f}, score: {:.2f}'.format(idx, face[0], face[1], face[2], face[3], face[-1]))
coords = face[:-1].astype(np.int32)
cv.rectangle(input, (coords[0], coords[1]), (coords[0]+coords[2], coords[1]+coords[3]), (0, 255, 0), thickness)
cv.circle(input, (coords[4], coords[5]), 2, (255, 0, 0), thickness)
cv.circle(input, (coords[6], coords[7]), 2, (0, 0, 255), thickness)
cv.circle(input, (coords[8], coords[9]), 2, (0, 255, 0), thickness)
cv.circle(input, (coords[10], coords[11]), 2, (255, 0, 255), thickness)
cv.circle(input, (coords[12], coords[13]), 2, (0, 255, 255), thickness)
cv.putText(input, 'FPS: {:.2f}'.format(fps), (1, 16), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
if __name__ == '__main__':
## [initialize_FaceDetectorYN]
detector = cv.FaceDetectorYN.create(
args.face_detection_model,
"",
(320, 320),
args.score_threshold,
args.nms_threshold,
args.top_k
)
## [initialize_FaceDetectorYN]
tm = cv.TickMeter()
# If input is an image
if args.image1 is not None:
img1 = cv.imread(cv.samples.findFile(args.image1))
img1Width = int(img1.shape[1]*args.scale)
img1Height = int(img1.shape[0]*args.scale)
img1 = cv.resize(img1, (img1Width, img1Height))
tm.start()
## [inference]
# Set input size before inference
detector.setInputSize((img1Width, img1Height))
faces1 = detector.detect(img1)
## [inference]
tm.stop()
assert faces1[1] is not None, 'Cannot find a face in {}'.format(args.image1)
# Draw results on the input image
visualize(img1, faces1, tm.getFPS())
# Save results if save is true
if args.save:
print('Results saved to result.jpg\n')
cv.imwrite('result.jpg', img1)
# Visualize results in a new window
cv.imshow("image1", img1)
if args.image2 is not None:
img2 = cv.imread(cv.samples.findFile(args.image2))
tm.reset()
tm.start()
detector.setInputSize((img2.shape[1], img2.shape[0]))
faces2 = detector.detect(img2)
tm.stop()
assert faces2[1] is not None, 'Cannot find a face in {}'.format(args.image2)
visualize(img2, faces2, tm.getFPS())
cv.imshow("image2", img2)
## [initialize_FaceRecognizerSF]
recognizer = cv.FaceRecognizerSF.create(
args.face_recognition_model,"")
## [initialize_FaceRecognizerSF]
## [facerecognizer]
# Align faces
face1_align = recognizer.alignCrop(img1, faces1[1][0])
face2_align = recognizer.alignCrop(img2, faces2[1][0])
# Extract features
face1_feature = recognizer.feature(face1_align)
face2_feature = recognizer.feature(face2_align)
## [facerecognizer]
cosine_similarity_threshold = 0.363
l2_similarity_threshold = 1.128
## [match]
cosine_score = recognizer.match(face1_feature, face2_feature, cv.FaceRecognizerSF_FR_COSINE)
l2_score = recognizer.match(face1_feature, face2_feature, cv.FaceRecognizerSF_FR_NORM_L2)
## [match]
msg = 'different identities'
if cosine_score >= cosine_similarity_threshold:
msg = 'the same identity'
print('They have {}. Cosine Similarity: {}, threshold: {} (higher value means higher similarity, max 1.0).'.format(msg, cosine_score, cosine_similarity_threshold))
msg = 'different identities'
if l2_score <= l2_similarity_threshold:
msg = 'the same identity'
print('They have {}. NormL2 Distance: {}, threshold: {} (lower value means higher similarity, min 0.0).'.format(msg, l2_score, l2_similarity_threshold))
cv.waitKey(0)
else: # Omit input to call default camera
if args.video is not None:
deviceId = args.video
else:
deviceId = 0
cap = cv.VideoCapture(deviceId)
frameWidth = int(cap.get(cv.CAP_PROP_FRAME_WIDTH)*args.scale)
frameHeight = int(cap.get(cv.CAP_PROP_FRAME_HEIGHT)*args.scale)
detector.setInputSize([frameWidth, frameHeight])
while cv.waitKey(1) < 0:
hasFrame, frame = cap.read()
if not hasFrame:
print('No frames grabbed!')
break
frame = cv.resize(frame, (frameWidth, frameHeight))
# Inference
tm.start()
faces = detector.detect(frame) # faces is a tuple
tm.stop()
# Draw results on the input image
visualize(frame, faces, tm.getFPS())
# Visualize results
cv.imshow('Live', frame)
cv.destroyAllWindows()
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