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import cv2 | |
from utils.detect import create_mtcnn_net, MtcnnDetector | |
from utils.vision import vis_face | |
import argparse | |
MIN_FACE_SIZE = 3 | |
def parse_args(): | |
parser = argparse.ArgumentParser(description='Test MTCNN', | |
formatter_class=argparse.ArgumentDefaultsHelpFormatter) | |
parser.add_argument('--net', default='onet', help='which net to show', type=str) | |
parser.add_argument('--pnet_path', default="./model_store/pnet_epoch_20.pt",help='path to pnet model', type=str) | |
parser.add_argument('--rnet_path', default="./model_store/rnet_epoch_20.pt",help='path to rnet model', type=str) | |
parser.add_argument('--onet_path', default="./model_store/onet_epoch_20.pt",help='path to onet model', type=str) | |
parser.add_argument('--path', default="./img/mid.png",help='path to image', type=str) | |
parser.add_argument('--min_face_size', default=MIN_FACE_SIZE,help='min face size', type=int) | |
parser.add_argument('--use_cuda', default=False,help='use cuda', type=bool) | |
parser.add_argument('--thresh', default='[0.1, 0.1, 0.1]',help='thresh', type=str) | |
parser.add_argument('--save_name', default="result.jpg",help='save name', type=str) | |
parser.add_argument('--input_mode', default=1,help='image or video', type=int) | |
args = parser.parse_args() | |
return args | |
if __name__ == '__main__': | |
args = parse_args() | |
thresh = [float(i) for i in (args.thresh).split('[')[1].split(']')[0].split(',')] | |
pnet, rnet, onet = create_mtcnn_net(p_model_path=args.pnet_path, r_model_path=args.rnet_path,o_model_path=args.onet_path, use_cuda=args.use_cuda) | |
mtcnn_detector = MtcnnDetector(pnet=pnet, rnet=rnet, onet=onet, min_face_size=args.min_face_size,threshold=thresh) | |
if args.input_mode == 1: | |
img = cv2.imread(args.path) | |
img_bg = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) | |
p_bboxs, r_bboxs, bboxs, landmarks = mtcnn_detector.detect_face(img) | |
# print box_align | |
save_name = args.save_name | |
if args.net == 'pnet': | |
vis_face(img_bg, p_bboxs, landmarks, MIN_FACE_SIZE, save_name) | |
elif args.net == 'rnet': | |
vis_face(img_bg, r_bboxs, landmarks, MIN_FACE_SIZE, save_name) | |
elif args.net == 'onet': | |
vis_face(img_bg, bboxs, landmarks, MIN_FACE_SIZE, save_name) | |
elif args.input_mode == 0: | |
cap=cv2.VideoCapture(0) | |
fourcc = cv2.VideoWriter_fourcc(*'XVID') | |
out = cv2.VideoWriter('out.mp4' ,fourcc,10,(640,480)) | |
while True: | |
t1=cv2.getTickCount() | |
ret,frame = cap.read() | |
if ret == True: | |
boxes_c,landmarks = mtcnn_detector.detect_face(frame) | |
t2=cv2.getTickCount() | |
t=(t2-t1)/cv2.getTickFrequency() | |
fps=1.0/t | |
for i in range(boxes_c.shape[0]): | |
bbox = boxes_c[i, :4] | |
score = boxes_c[i, 4] | |
corpbbox = [int(bbox[0]), int(bbox[1]), int(bbox[2]), int(bbox[3])] | |
#画人脸框 | |
cv2.rectangle(frame, (corpbbox[0], corpbbox[1]), | |
(corpbbox[2], corpbbox[3]), (255, 0, 0), 1) | |
#画置信度 | |
cv2.putText(frame, '{:.2f}'.format(score), | |
(corpbbox[0], corpbbox[1] - 2), | |
cv2.FONT_HERSHEY_SIMPLEX, | |
0.5,(0, 0, 255), 2) | |
#画fps值 | |
cv2.putText(frame, '{:.4f}'.format(t) + " " + '{:.3f}'.format(fps), (10, 20), | |
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 255), 2) | |
#画关键点 | |
for i in range(landmarks.shape[0]): | |
for j in range(len(landmarks[i])//2): | |
cv2.circle(frame, (int(landmarks[i][2*j]),int(int(landmarks[i][2*j+1]))), 2, (0,0,255)) | |
a = out.write(frame) | |
cv2.imshow("result", frame) | |
if cv2.waitKey(1) & 0xFF == ord('q'): | |
break | |
else: | |
break | |
cap.release() | |
out.release() | |
cv2.destroyAllWindows() | |