face_detect.py
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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('--input', '-i', type=str, help='Path to the input image.')
parser.add_argument('--model', '-m', type=str, default='yunet.onnx', help='Path to the model. Download the model at https://github.com/ShiqiYu/libfacedetection.train/tree/master/tasks/task1/onnx.')
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.')
parser.add_argument('--vis', '-v', type=str2bool, default=True, help='Set true to open a window for result visualization. This flag is invalid when using camera.')
args = parser.parse_args()
def visualize(input, faces, thickness=2):
output = input.copy()
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(output, (coords[0], coords[1]), (coords[0]+coords[2], coords[1]+coords[3]), (0, 255, 0), 2)
cv.circle(output, (coords[4], coords[5]), 2, (255, 0, 0), 2)
cv.circle(output, (coords[6], coords[7]), 2, (0, 0, 255), 2)
cv.circle(output, (coords[8], coords[9]), 2, (0, 255, 0), 2)
cv.circle(output, (coords[10], coords[11]), 2, (255, 0, 255), 2)
cv.circle(output, (coords[12], coords[13]), 2, (0, 255, 255), 2)
return output
if __name__ == '__main__':
# Instantiate FaceDetectorYN
detector = cv.FaceDetectorYN.create(
args.model,
"",
(320, 320),
args.score_threshold,
args.nms_threshold,
args.top_k
)
# If input is an image
if args.input is not None:
image = cv.imread(args.input)
# Set input size before inference
detector.setInputSize((image.shape[1], image.shape[0]))
# Inference
faces = detector.detect(image)
# Draw results on the input image
result = visualize(image, faces)
# Save results if save is true
if args.save:
print('Resutls saved to result.jpg\n')
cv.imwrite('result.jpg', result)
# Visualize results in a new window
if args.vis:
cv.namedWindow(args.input, cv.WINDOW_AUTOSIZE)
cv.imshow(args.input, result)
cv.waitKey(0)
else: # Omit input to call default camera
deviceId = 0
cap = cv.VideoCapture(deviceId)
frameWidth = int(cap.get(cv.CAP_PROP_FRAME_WIDTH))
frameHeight = int(cap.get(cv.CAP_PROP_FRAME_HEIGHT))
detector.setInputSize([frameWidth, frameHeight])
tm = cv.TickMeter()
while cv.waitKey(1) < 0:
hasFrame, frame = cap.read()
if not hasFrame:
print('No frames grabbed!')
break
# Inference
tm.start()
faces = detector.detect(frame) # faces is a tuple
tm.stop()
# Draw results on the input image
frame = visualize(frame, faces)
cv.putText(frame, 'FPS: {}'.format(tm.getFPS()), (0, 15), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0))
# Visualize results in a new Window
cv.imshow('Live', frame)
tm.reset()