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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 argparse
import numpy as np
import cv2 as cv
# Check OpenCV version
opencv_python_version = lambda str_version: tuple(map(int, (str_version.split("."))))
assert opencv_python_version(cv.__version__) >= opencv_python_version("4.10.0"), \
"Please install latest opencv-python for benchmark: python3 -m pip install --upgrade opencv-python"
from yunet import YuNet
# 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='YuNet: A Fast and Accurate CNN-based Face Detector (https://github.com/ShiqiYu/libfacedetection).')
parser.add_argument('--input', '-i', type=str,
help='Usage: Set input to a certain image, omit if using camera.')
parser.add_argument('--model', '-m', type=str, default='face_detection_yunet_2023mar.onnx',
help="Usage: Set model type, defaults to 'face_detection_yunet_2023mar.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('--conf_threshold', type=float, default=0.9,
help='Usage: Set the minimum needed confidence for the model to identify a face, defauts to 0.9. Smaller values may result in faster detection, but will limit accuracy. Filter out faces of confidence < conf_threshold.')
parser.add_argument('--nms_threshold', type=float, default=0.3,
help='Usage: Suppress bounding boxes of iou >= nms_threshold. Default = 0.3.')
parser.add_argument('--top_k', type=int, default=5000,
help='Usage: Keep top_k bounding boxes before NMS.')
parser.add_argument('--save', '-s', action='store_true',
help='Usage: Specify to save file with results (i.e. bounding box, confidence level). Invalid in case of camera input.')
parser.add_argument('--vis', '-v', action='store_true',
help='Usage: Specify to open a new window to show results. Invalid in case of camera input.')
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:
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__':
backend_id = backend_target_pairs[args.backend_target][0]
target_id = backend_target_pairs[args.backend_target][1]
# Instantiate YuNet
model = YuNet(modelPath=args.model,
inputSize=[320, 320],
confThreshold=args.conf_threshold,
nmsThreshold=args.nms_threshold,
topK=args.top_k,
backendId=backend_id,
targetId=target_id)
# 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()
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