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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 | |
from yunet import YuNet | |
# Check OpenCV version | |
assert cv.__version__ >= "4.8.0", \ | |
"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='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 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__': | |
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() | |