frames = pipeline.wait_for_frames()
color_frame = frames.get_color_frame()
bgr_image = np.asanyarray(color_frame.get_data())
gaze.refresh(bgr_image)
bgr_image = gaze.annotated_frame()
text = ""
if gaze.is_right():
text = "Looking mono chino de pelo morado"
elif gaze.is_left():
text = "Looking mona china"
elif gaze.is_up():
text = "Looking mono chino rubio intenso"
elif gaze.is_down():
text = "Looking logo"
elif gaze.is_center():
text = "Looking mono chino de pelo verde"
cv2.putText(bgr_image, text, (30, 30), cv2.FONT_HERSHEY_DUPLEX, 1,
(147, 58, 31), 2)
left_pupil = gaze.pupil_left_coords()
right_pupil = gaze.pupil_right_coords()
gray_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2GRAY)
rgb_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2RGB)
faces = detector(rgb_image)
for face_coordinates in faces:
def run(models):
#카메라 열기
cap = cv2.VideoCapture(0)
gaze = GazeTracking()
cheat_cnt = 0
right_cnt = 0
left_cnt = 0
up_cnt = 0
down_cnt = 0
name = ""
while True:
#카메라로 부터 사진 한장 읽기
ret, frame = cap.read()
gaze.refresh(frame)
# 얼굴 검출 시도
image, face = face_detector(frame)
try:
min_score = 999 #가장 낮은 점수로 예측된 사람의 점수
min_score_name = "" #가장 높은 점수로 예측된 사람의 이름
#검출된 사진을 흑백으로 변환
face = cv2.cvtColor(face, cv2.COLOR_BGR2GRAY)
#위에서 학습한 모델로 예측시도
for key, model in models.items():
result = model.predict(face)
if min_score > result[1]:
min_score = result[1]
min_score_name = key
#min_score 신뢰도이고 0에 가까울수록 자신과 같다는 뜻이다.
if min_score < 500:
confidence = int(100 * (1 - (min_score) / 300))
# 유사도 화면에 표시
display_string = str(
confidence) + '% Confidence it is ' + min_score_name
name = min_score_name
cv2.putText(image, display_string, (90, 170),
cv2.FONT_HERSHEY_COMPLEX, 1, (250, 120, 255), 2)
#75 보다 크면 동일 인물로 간주해 Match!
if confidence > 75:
cv2.putText(image, "Match : " + min_score_name, (250, 450),
cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 2)
cv2.imshow('Face Detection', image)
frame = gaze.annotated_frame()
text = ""
if gaze.is_blinking():
text = "Blinking"
elif gaze.is_right():
text = "Looking right"
right_cnt += 1
elif gaze.is_left():
text = "Looking left"
left_cnt += 1
elif gaze.is_up():
text = "Looking up"
up_cnt += 1
elif gaze.is_down():
text = "Looking down"
down_cnt += 1
elif gaze.is_center():
text = "Looking center"
cv2.putText(frame, text, (90, 60), cv2.FONT_HERSHEY_DUPLEX,
1.6, (147, 58, 31), 2)
left_pupil = gaze.pupil_left_coords()
right_pupil = gaze.pupil_right_coords()
cv2.putText(frame, "Left pupil: " + str(left_pupil),
(90, 100), cv2.FONT_HERSHEY_DUPLEX, 0.9,
(147, 58, 31), 1)
cv2.putText(frame, "Right pupil: " + str(right_pupil),
(90, 135), cv2.FONT_HERSHEY_DUPLEX, 0.9,
(147, 58, 31), 1)
cv2.imshow("Face Detection", frame)
if left_pupil is None and right_pupil is None:
cheat_cnt += 1
elif right_cnt > 5:
cheat_cnt += 1
print('Too much looking right')
elif left_cnt > 5:
cheat_cnt += 1
print('Too much looking left')
elif up_cnt > 5:
cheat_cnt += 1
print('Too much looking up')
elif down_cnt > 5:
cheat_cnt += 1
print('Too much looking down')
else:
#75 이하면 Unmatch
cv2.putText(image, "Unmatch", (250, 450),
cv2.FONT_HERSHEY_COMPLEX, 1, (0, 0, 255), 2)
cv2.imshow('Face Detection', image)
except:
#얼굴 검출 안됨
cv2.putText(image, "Face Not Found", (250, 450),
cv2.FONT_HERSHEY_COMPLEX, 1, (255, 0, 0), 2)
cv2.imshow('Face Detection', image)
pass
if cheat_cnt > 100:
print(name + " Cheating Probability is high")
break
if cv2.waitKey(1) == 13:
break
cap.release()
cv2.destroyAllWindows()
