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demo.py
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demo.py
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import cv2
import time
import numpy as np
import os
import argparse
from Skps import FaceAna
def video(video_path_or_cam):
facer = FaceAna()
vide_capture=cv2.VideoCapture(video_path_or_cam)
while 1:
ret, image = vide_capture.read()
if ret:
pattern = np.zeros_like(image)
img_show = image.copy()
star=time.time()
result = facer.run(image)
duration=time.time()-star
#print('one iamge cost %f s'%(duration))
fps=1/duration
cv2.putText(img_show, "X: " + "{:7.2f}".format(fps), (20, 20), cv2.FONT_HERSHEY_SIMPLEX,
0.75, (0, 0, 0), thickness=2)
for face_index in range(len(result)):
#######head pose need develop
#reprojectdst, euler_angle=get_head_pose(landmarks[face_index],img_show)
cur_face_kps=result[face_index]['kps']
cur_face_kps_score=result[face_index]['scores']
for landmarks_index in range(cur_face_kps.shape[0]):
x_y = cur_face_kps[landmarks_index]
score=cur_face_kps_score[landmarks_index]
# color = (255, 255, 255)
if score>0.8:
color=(255,255,255)
else:
color = (0, 0, 255)
cv2.circle(img_show, (int(x_y[0]), int(x_y[1])),
color=color, radius=1, thickness=2)
cv2.namedWindow("capture", 0)
cv2.imshow("capture", img_show)
if args.mask:
cv2.namedWindow("masked", 0)
cv2.imshow("masked", image*pattern)
key=cv2.waitKey(1)
if key==ord('q'):
return
def images(image_dir):
facer = FaceAna()
image_list=os.listdir(image_dir)
image_list=[x for x in image_list if 'jpg' in x or 'png' in x]
image_list.sort()
for image_name in image_list:
image=cv2.imread(os.path.join(image_dir,image_name))
pattern = np.zeros_like(image)
img_show = image.copy()
star=time.time()
result = facer.run(image)
###no track
facer.reset()
duration=time.time()-star
print('one iamge cost %f s'%(duration))
for face_index in range(len(result)):
#######head pose
#reprojectdst, euler_angle=get_head_pose(landmarks[face_index],img_show)
cur_face_kps = result[face_index]['kps']
cur_face_kps_score = result[face_index]['scores']
for landmarks_index in range(cur_face_kps.shape[0]):
x_y = cur_face_kps[landmarks_index]
score = cur_face_kps_score[landmarks_index]
# color = (255, 255, 255)
if score > 0.8:
color = (255, 255, 255)
else:
color = (0, 0, 255)
cv2.circle(img_show, (int(x_y[0]), int(x_y[1])),
color=color, radius=1, thickness=2)
cv2.namedWindow("capture", 0)
cv2.imshow("capture", img_show)
key=cv2.waitKey(0)
if key==ord('q'):
return
def build_argparse():
parser = argparse.ArgumentParser(description='Start train.')
parser.add_argument('--video', dest='video', type=str, default=None, \
help='the camera id (default: 0)')
parser.add_argument('--cam_id', dest='cam_id', type=int, default=0, \
help='the camera to use')
parser.add_argument('--img_dir', dest='img_dir', type=str, default=None, \
help='the images dir to use')
parser.add_argument('--mask', dest='mask', type=bool, default=False, \
help='mask the face or not')
args = parser.parse_args()
return args
if __name__=='__main__':
args=build_argparse()
if args.img_dir is not None:
images(args.img_dir)
elif args.video is not None:
video(args.video)
else:
video(args.cam_id)