File size: 4,689 Bytes
42310ef 8ac861c 42310ef 8ac861c 42310ef 3af1dea 42310ef 9d96bb5 42310ef |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 |
# 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 argparse
import numpy as np
import cv2 as cv
from yunet import YuNet
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(description='YuNet: A Fast and Accurate CNN-based Face Detector (https://github.com/ShiqiYu/libfacedetection).')
parser.add_argument('--input', '-i', type=str, help='Path to the input image. Omit for using default camera.')
parser.add_argument('--model', '-m', type=str, default='face_detection_yunet_2021dec.onnx', help='Path to the model.')
parser.add_argument('--conf_threshold', type=float, default=0.9, help='Filter out faces of confidence < conf_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=str, 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(image, results, box_color=(0, 255, 0), text_color=(0, 0, 255), fps=None):
output = image.copy()
landmark_color = [
(255, 0, 0), # right eye
( 0, 0, 255), # left eye
( 0, 255, 0), # nose tip
(255, 0, 255), # right mouth corner
( 0, 255, 255) # left mouth corner
]
if fps is not None:
cv.putText(output, 'FPS: {:.2f}'.format(fps), (0, 15), cv.FONT_HERSHEY_SIMPLEX, 0.5, text_color)
for det in (results if results is not None else []):
bbox = det[0:4].astype(np.int32)
cv.rectangle(output, (bbox[0], bbox[1]), (bbox[0]+bbox[2], bbox[1]+bbox[3]), box_color, 2)
conf = det[-1]
cv.putText(output, '{:.4f}'.format(conf), (bbox[0], bbox[1]+12), cv.FONT_HERSHEY_DUPLEX, 0.5, text_color)
landmarks = det[4:14].astype(np.int32).reshape((5,2))
for idx, landmark in enumerate(landmarks):
cv.circle(output, landmark, 2, landmark_color[idx], 2)
return output
if __name__ == '__main__':
# Instantiate YuNet
model = YuNet(modelPath=args.model,
inputSize=[320, 320],
confThreshold=args.conf_threshold,
nmsThreshold=args.nms_threshold,
topK=args.top_k)
# If input is an image
if args.input is not None:
image = cv.imread(args.input)
h, w, _ = image.shape
# Inference
model.setInputSize([w, h])
results = model.infer(image)
# Print results
print('{} faces detected.'.format(results.shape[0]))
for idx, det in enumerate(results):
print('{}: {:.0f} {:.0f} {:.0f} {:.0f} {:.0f} {:.0f} {:.0f} {:.0f} {:.0f} {:.0f} {:.0f} {:.0f} {:.0f} {:.0f}'.format(
idx, *det[:-1])
)
# Draw results on the input image
image = visualize(image, results)
# Save results if save is true
if args.save:
print('Resutls saved to result.jpg\n')
cv.imwrite('result.jpg', image)
# Visualize results in a new window
if args.vis:
cv.namedWindow(args.input, cv.WINDOW_AUTOSIZE)
cv.imshow(args.input, image)
cv.waitKey(0)
else: # Omit input to call default camera
deviceId = 0
cap = cv.VideoCapture(deviceId)
w = int(cap.get(cv.CAP_PROP_FRAME_WIDTH))
h = int(cap.get(cv.CAP_PROP_FRAME_HEIGHT))
model.setInputSize([w, h])
tm = cv.TickMeter()
while cv.waitKey(1) < 0:
hasFrame, frame = cap.read()
if not hasFrame:
print('No frames grabbed!')
break
# Inference
tm.start()
results = model.infer(frame) # results is a tuple
tm.stop()
# Draw results on the input image
frame = visualize(frame, results, fps=tm.getFPS())
# Visualize results in a new Window
cv.imshow('YuNet Demo', frame)
tm.reset() |