verify this code:

```
import cv2
import numpy as np
import time
import tensorflow as tf
import threading

FACE\_RECOGNITION\_BUFFER\_SIZE = 5
FACE\_RECOGNITION\_THRESHOLD = 0.7

interpreter = tf.lite.Interpreter(model\_path="model\_quant.tflite")
interpreter.allocate\_tensors()

face\_cascade = cv2.CascadeClassifier('haarcascade\_frontalface\_default.xml')

cap = cv2.VideoCapture(0)

face\_recognition\_buffer = deque(maxlen=FACE\_RECOGNITION\_BUFFER\_SIZE)

def recognize\_face(frame, x, y, w, h):
 try:
 gray = cv2.cvtColor(frame, cv2.COLOR\_BGR2GRAY)
 face\_image = gray[y:y+h, x:x+w]
 face\_image = cv2.resize(face\_image, (96, 96))
 face\_image = np.expand\_dims(face\_image, axis=0)
 face\_image = np.expand\_dims(face\_image, axis=3)
 face\_image = face\_image.astype(np.float16)
 face\_image = (face\_image / 255.0).astype(np.float16)

 # Run inference on the quantized TFLite model
 input\_index = interpreter.get\_input\_details()[0]["index"]
 output\_index = interpreter.get\_output\_details()[0]["index"]
 interpreter.set\_tensor(input\_index, face\_image)
 interpreter.invoke()
 output = interpreter.get\_tensor(output\_index)
 output = output[0].astype(np.float16)

 # Add the output to the circular buffer
 face\_recognition\_buffer.append(output)

 # Check if the buffer is full
 if len(face\_recognition\_buffer) == FACE\_RECOGNITION\_BUFFER\_SIZE:
 # Compute the mean output of the buffer
 mean\_output = np.mean(face\_recognition\_buffer, axis=0)

 # Check if the mean output is above the recognition threshold
 if np.max(mean\_output) >= FACE\_RECOGNITION\_THRESHOLD:
 # Display the output with % accuracy on the frame
 label = np.argmax(mean\_output)
 accuracy = mean\_output[label] \* 100
 text = "Face: {:.2f}%".format(accuracy)
 cv2.putText(frame, text, (x, y - 10), cv2.FONT\_HERSHEY\_SIMPLEX, 0.45, (0, 0, 255), 2)

 # Draw a rectangle around the face
 cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)

 except Exception as e:
 print("Error in recognize\_face:", e)

def detect\_face(frame):
 try:
 gray = cv2.cvtColor(frame, cv2.COLOR\_BGR2GRAY)
 faces = face\_cascade.detectMultiScale(gray, 1.3, 5)

 threads = []
 for (x,y,w,h) in faces:
 t = threading.Thread(target=recognize\_face, args=(frame, x, y, w, h))
 t.start()
 threads.append(t)

 for t in threads:
 t.join()

 except Exception as e:
 print("Error in detect\_face:", e)

# Run face detection and recognition every 3 seconds
prev\_time = time.time()
while True:
 try:
 ret, frame = cap.read()

 if not ret:
 break

 # Resize the frame to reduce processing time
 frame = cv2.resize(frame, (640, 480))

 # Detect faces in the frame
 gray = cv2.cvtColor(frame, cv2.COLOR\_BGR2GRAY)
 faces = face\_cascade.detectMultiScale(gray, 1.3, 5)

 threads = []
 for (x,y,w,h) in faces:
 t = threading.Thread(target=recognize\_face, args=(frame, x, y, w, h))
 t.start()
 threads.append(t)

 for t in threads:
 t.join()

 # Display the frame
 cv2.imshow('frame', frame)

 # Wait for 'q' key to exit
 if cv2.waitKey(1) & 0xFF == ord('q'):
 break

 # Wait for 3 seconds
 curr\_time = time.time()
 time\_diff = curr\_time - prev\_time
 if time\_diff < 3:
 time.sleep(3 - time\_diff)

 prev\_time = curr\_time

 except Exception as e:
 print("Error:", e)

# Release resources
cap.release()
cv2.destroyAllWindows()
```
이 코드를 확인합니다:```import cv2numpy를 np로 임포트import time임포트 텐서플로우로 tf스레딩 가져오기face\_recognition\_buffer\_size = 5face\_recognition\_threshold = 0.7interpreter = tf.lite.Interpreter(model\_path="model\_quant.tflite")interpreter.allocate\_tensors()face\_cascade = cv2.CascadeClassifier('haarcascade\_frontalface\_default.xml')cap = cv2.VideoCapture(0)face\_recognition\_buffer = deque(maxlen=FACE\_RECOGNITION\_BUFFER\_SIZE)def recognize\_face(frame, x, y, w, h): try: gray = cv2.cvtColor(frame, cv2.COLOR\_BGR2GRAY) face\_image = gray[y:y+h, x:x+w] face\_image = cv2.resize(face\_image, (96, 96)) face\_image = np.expand\_dims(face\_image, axis=0) face\_image = np.expand\_dims(face\_image, axis=3) face\_image = face\_image.astype(np.float16) face\_image = (face\_image / 255.0).astype(np.float16) # 양자화된 TFLite 모델에서 추론을 실행합니다. input\_index = interpreter.get\_input\_details()[0]["index"] output\_index = interpreter.get\_output\_details()[0]["index"] 인터프리터.set\_tensor(입력\_index, 얼굴\_이미지) 인터프리터.호출() 출력 = 인터프리터.get\_tensor(출력\_index) 출력 = 출력[0].astype(np.float16) # 원형 버퍼에 출력을 추가합니다. face\_recognition\_buffer.append(output) # 버퍼가 꽉 찼는지 확인 len(face\_recognition\_buffer) == FACE\_RECOGNITION\_BUFFER\_SIZE: # 버퍼의 평균 출력을 계산합니다. mean\_output = np.mean(face\_recognition\_buffer, axis=0) # 평균 출력이 인식 임계값 이상인지 확인합니다. np.max(mean\_output) >= FACE\_RECOGNITION\_THRESHOLD: # 프레임에 % 정확도로 출력을 표시합니다. label = np.argmax(mean\_output) 정확도 = 평균\_출력[라벨] \* 100 text = "얼굴: {:.2f}%".format(accuracy) cv2.putText(frame, text, (x, y - 10), cv2.FONT\_HERSHEY\_SIMPLEX, 0.45, (0, 0, 255), 2) # 얼굴 주위에 직사각형을 그립니다. cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2) 예외를 e로 제외합니다: print("인식 중 오류\_face:", e)def detect\_face(frame): try: gray = cv2.cvtColor(frame, cv2.COLOR\_BGR2GRAY) faces = face\_cascade.detectMultiScale(회색, 1.3, 5) threads = [] for (x,y,w,h) in faces: t = threading.Thread(target=recognize\_face, args=(frame, x, y, w, h)) t.start() threads.append(t) 스레드에서 t에 대해: t.join() 예외를 e로 제외합니다: print("Error in detect\_face:", e)# 3초마다 얼굴 감지 및 인식 실행prev\_time = time.time()동안 True: try: ret, frame = cap.read() ret가 아니라면 break # 처리 시간을 줄이기 위해 프레임 크기 조정 frame = cv2.resize(frame, (640, 480)) # 프레임에서 얼굴 감지 회색 = cv2.cvtColor(frame, cv2.COLOR\_BGR2GRAY) faces = face\_cascade.detectMultiScale(회색, 1.3, 5) threads = [] for (x,y,w,h) in faces: t = threading.Thread(target=recognize\_face, args=(frame, x, y, w, h)) t.start() threads.append(t) 스레드에서 t에 대해: t.join() # 프레임 표시 cv2.imshow('frame', frame) # 'q' 키가 종료될 때까지 기다립니다. cv2.waitKey(1) & 0xFF == ord('q'): break # 3초 동안 대기 curr\_time = time.time() time\_diff = curr\_time - prev\_time time\_diff < 3: time.sleep(3 - time\_diff) prev\_time = curr\_time 예외를 e로 제외합니다: print("Error:", e)# 리소스 릴리스cap.release()cv2.destroyAllWindows()```