continue
elif len(right_eye) == []:
print('Right eye not detected')
continue
#HeadPoseEstimation
head_inf, output_hp = net3.predict(fd_image, hp_shape)
p, r, y, hp_vis = HeadPoseEstimation.preprocess_output(output_hp, frame.copy())
head_time.append(head_inf)
if args['hpv']:
cv2.imshow('FD Vis', cv2.resize(hp_vis, (700, 500)))
cv2.waitKey(1)
head_pose_angles = np.array([[y, p, r]])
#GazeEstimation
out, gaze_inf = net4.ge_predict(head_pose_angles, left_eye, right_eye, hp_shape)
gaze_time.append(gaze_inf)
mouse.move(out[0][0], out[0][1])
avg_face_time = sum(face_time) / len(face_time)
avg_land_time = sum(land_time) / len(land_time)
avg_head_time = sum(head_time) / len(head_time)
avg_gaze_time = sum(gaze_time) / len(gaze_time)
# Write load time, inference time, and fps to txt file
with open(f"output/{args['output']}.txt", "w") as f:
f.write(str(avg_face_time)+'\n') #Average Face Detection Inference Time
f.write(str(1/avg_face_time)+'\n') #Face Detection FPS
f.write(str(sum(face_time))+'\n') #Total Face Detection Inference Time
