understand this code and say yes.

```
import cv2
import numpy as np
import tensorflow as tf
from collections import deque
category\_class\_names = ['Family', 'Friend', 'Inhabitant', 'None']
target\_size = (128, 128)

def load\_model():
 # Load the TFLite model
 interpreter = tf.lite.Interpreter(model\_path='/home/rufaida/Desktop/SmartHomeSecurity/SmartHomeSecuritySystem/Models/shs\_face\_recognition\_mobilenetv2.tflite')
 interpreter.allocate\_tensors()
 
 # Get the input and output tensor details
 input\_details = interpreter.get\_input\_details()
 output\_details = interpreter.get\_output\_details()
 
 # Set the tensor data for the input and output details
 interpreter.tensor(input\_details[0]['index'])()[0][:] = np.zeros(input\_details[0]['shape'], dtype=np.float32)
 interpreter.tensor(output\_details[0]['index'])()[0][:] = np.zeros(output\_details[0]['shape'], dtype=np.float32)
 
 return interpreter, input\_details, output\_details
def face\_detection\_recognition(interpreter, input\_details, output\_details):
 # Load the face cascade classifier
 face\_cascade = cv2.CascadeClassifier('/home/rufaida/Desktop/SmartHomeSecurity/SmartHomeSecuritySystem/Models/haarcascade\_frontalface\_default.xml')
 # Start video capture
 cap = cv2.VideoCapture(0)
 
 # Define the codec and create VideoWriter object
 fourcc = cv2.VideoWriter\_fourcc(\*'mp4v')
 out = cv2.VideoWriter('output.mp4', fourcc, 20.0, (640, 480))
 
 # Create a deque to hold the category names with a maximum length of 100
 category\_names = deque(maxlen=100)
 
 # Loop through the frames
 while True:
 # Read a frame from the stream
 ret, frame = cap.read()
 print("Capture Image")
 # Convert the frame to grayscale for face detection
 gray = cv2.cvtColor(frame, cv2.COLOR\_BGR2GRAY)
 # Detect faces in the grayscale frame
 faces = face\_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
 print("Faces printed")
 # Loop through the faces and classify them
 for (x, y, w, h) in faces:
 # Extract the face from the frame
 face = frame[y:y+h, x:x+w]
 print("Faces extracted")
 # Preprocess the face
 face\_resized = preprocess\_image(face, target\_size)
 print("Faces resized")
 # Run inference
 interpreter.set\_tensor(input\_details[0]['index'], face\_resized)
 interpreter.invoke()
 prediction = interpreter.get\_tensor(output\_details[0]['index'])
 # Get prediction result
 category\_index = np.argmax(prediction[0])
 category\_accuracy = round(np.max(prediction[0]) \* 100, 2)
 print("Faces")
 if category\_accuracy < 80:
 category\_name = 'None'
 else:
 category\_name = category\_class\_names[category\_index]
 print("Faces recognised")
 # Append the category name to the deque
 category\_names.append(category\_name)
 print("category\_name", category\_name)
 # Display the frame with the face rectangle and name
 cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
 cv2.putText(frame, f"{category\_name}: ({category\_accuracy}%):", (10, 20), cv2.FONT\_HERSHEY\_SIMPLEX, 0.3, (0, 255, 0), 1, cv2.LINE\_AA)
 print("Dispaly faces")
 cv2.imshow('SHSS', frame)
 # Write the frame to the output video
 out.write(frame)
 if cv2.waitKey(1) & 0xFF == ord('q'):
 break
 # Release the video capture and writer objects and close all windows
 cap.release()
 out.release()
 cv2.destroyAllWindows()
 # Return the list of category names
 return category\_names

def preprocess\_image(image, target\_size):
 img = cv2.resize(image, target\_size)
 img = cv2.cvtColor(img, cv2.COLOR\_BGR2RGB)
 img = np.expand\_dims(img, axis=0)
 img = tf.keras.applications.mobilenet\_v2.preprocess\_input(img)
 return img

# Load the model
interpreter, input\_details, output\_details = load\_model()
# Start face detection and recognition
predicted\_categories = face\_detection\_recognition(interpreter, input\_details, output\_details)
print(predicted\_categories)
# Count the number of occurrences of each category in the deque
count\_category1 = predicted\_categories.count(category\_class\_names[0]) # Family
count\_category2 = predicted\_categories.count(category\_class\_names[1]) # Friend
count\_category3 = predicted\_categories.count(category\_class\_names[2]) # Inhabitant
count\_category4 = predicted\_categories.count(category\_class\_names[3]) # None
# Check for possibilities based on the number of categories present
if count\_category1 > 0 and count\_category2 > 0 and count\_category3 > 0 and count\_category4 > 0:
 # Do something when all categories are present
 print('All categories found!')
elif (count\_category1 > 0 and count\_category2 > 0 and count\_category3 > 0) or (count\_category1 > 0 and count\_category2 > 0 and count\_category4 > 0) or (count\_category1 > 0 and count\_category3 > 0 and count\_category4 > 0) or (count\_category2 > 0 and count\_category3 > 0 and count\_category4 > 0):
 # Do something when 3 categories are present
 print('3 categories found!')
elif (count\_category1 > 0 and count\_category2 > 0) or (count\_category1 > 0 and count\_category3 > 0) or (count\_category1 > 0 and count\_category4 > 0) or (count\_category2 > 0 and count\_category3 > 0) or (count\_category2 > 0 and count\_category4 > 0) or (count\_category3 > 0 and count\_category4 > 0):
 # Do something when 2 categories are present
 print('2 categories found!')
elif count\_category1 > 0 or count\_category2 > 0 or count\_category3 > 0 or count\_category4 > 0:
 # Do something when 1 category is present
 print('1 category found!')
else:
 # Do something when no categories are present
 print('Null category found...')
```
이 코드를 이해하고 '예'라고 답하세요.```import cv2import numpy as npimport tensorflow as tf컬렉션에서 deque를 가져옵니다.category\_class\_names = ['가족', '친구', '거주자', '없음']target\_size = (128, 128)def load\_model(): # TFLite 모델을 로드합니다. interpreter = tf.lite.Interpreter(model\_path='/home/rufaida/Desktop/SmartHomeSecurity/SmartHomeSecuritySystem/Models/shs\_face\_recognition\_mobilenetv2.tflite') interpreter.allocate\_tensors()  # 입력 및 출력 텐서 세부 정보 가져오기 input\_details = interpreter.get\_input\_details() output\_details = interpreter.get\_output\_details()  # 입력 및 출력 세부 정보에 대한 텐서 데이터를 설정합니다. interpreter.tensor(input\_details[0]['index'])()[0][:] = np.zeros(input\_details[0]['shape'], dtype=np.float32) interpreter.tensor(output\_details[0]['index'])()[0][:] = np.zeros(output\_details[0]['shape'], dtype=np.float32)  반환 인터프리터, 입력\_details, 출력\_detailsdef face\_detection\_recognition(인터프리터, 입력\_details, 출력\_details): # 얼굴 캐스케이드 분류기 로드 face\_cascade = cv2.CascadeClassifier('/home/rufaida/Desktop/SmartHomeSecurity/SmartHomeSecuritySystem/Models/haarcascade\_frontalface\_default.xml') # 비디오 캡처 시작 cap = cv2.VideoCapture(0)  # 코덱을 정의하고 VideoWriter 객체를 생성합니다. fourcc = cv2.VideoWriter\_fourcc(\*'mp4v') out = cv2.VideoWriter('output.mp4', fourcc, 20.0, (640, 480))  # 최대 길이가 100인 카테고리 이름을 저장할 deque를 생성합니다. category\_names = deque(maxlen=100)  # 프레임을 반복합니다. 동안 반복합니다: # 스트림에서 프레임 읽기 ret, frame = cap.read() print("이미지 캡처") # 얼굴 감지를 위해 프레임을 회색조로 변환합니다. gray = cv2.cvtColor(frame, cv2.COLOR\_BGR2GRAY) # 회색조 프레임에서 얼굴을 감지합니다. faces = face\_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30)) print("면이 인쇄되었습니다") # 면을 반복하고 분류합니다. for (x, y, w, h) in faces: # 프레임에서 얼굴 추출 face = frame[y:y+h, x:x+w] print("얼굴 추출 완료") # 얼굴 전처리 face\_resized = preprocess\_image(face, target\_size) print("얼굴 크기 조정됨") # 추론 실행 interpreter.set\_tensor(input\_details[0]['index'], face\_resized) 인터프리터.호출() prediction = interpreter.get\_tensor(output\_details[0]['index']) # 예측 결과 가져오기 category\_index = np.argmax(prediction[0]) category\_accuracy = round(np.max(prediction[0]) \* 100, 2) print("얼굴") if category\_accuracy < 80: category\_name = 'None' else: category\_name = category\_class\_names[category\_index] print("얼굴 인식") # 덱에 카테고리 이름을 추가합니다. category\_names.append(category\_name) print("카테고리\_이름", 카테고리\_이름) # 얼굴 사각형과 이름이 포함된 프레임을 표시합니다. cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2) cv2.putText(frame, f"{category\_name}: ({category\_accuracy}%):", (10, 20), cv2.FONT\_HERSHEY\_SIMPLEX, 0.3, (0, 255, 0), 1, cv2.LINE\_AA) print("Dispaly 얼굴") cv2.imshow('SHSS', frame) # 출력 비디오에 프레임 쓰기 out.write(frame) cv2.waitKey(1) & 0xFF == ord('q'): break # 비디오 캡처 및 쓰기 객체를 해제하고 모든 창을 닫습니다. cap.release() out.release() cv2.destroyAllWindows() # 카테고리 이름 목록을 반환합니다. 반환 카테고리\_이름def preprocess\_image(image, target\_size): img = cv2.resize(image, target\_size) img = cv2.cvtColor(img, cv2.COLOR\_BGR2RGB) 이미지 = np.expand\_dims(이미지, 축=0) img = tf.keras.applications.mobilenet\_v2.preprocess\_input(img) 반환 이미지# 모델 로드인터프리터, 입력\_details, 출력\_details = load\_model()# 얼굴 감지 및 인식 시작predicted\_categories = face\_detection\_recognition(인터프리터, 입력\_details, 출력\_details)print(predicted\_categories)# 덱에서 각 카테고리의 발생 횟수를 계산합니다.count\_category1 = predicted\_categories.count(category\_class\_names[0]) # 계열count\_category2 = predicted\_categories.count(category\_class\_names[1]) # 친구count\_category3 = predicted\_categories.count(category\_class\_names[2]) # 거주자count\_category4 = predicted\_categories.count(category\_class\_names[3]) # 없음# 존재하는 카테고리 수에 따라 가능성을 확인합니다.count\_category1 > 0, count\_category2 > 0, count\_category3 > 0, count\_category4 > 0인 경우: # 모든 카테고리가 존재할 때 무언가를 수행합니다. print('모든 카테고리가 발견되었습니다!')elif (count\_category1 > 0 and count\_category2 > 0 and count\_category3 > 0) or (count\_category1 > 0 and count\_category2 > 0 and count\_category4 > 0) or (count\_category1 > 0 and count\_category3 > 0 and count\_category4 > 0) or (count\_category2 > 0 and count\_category3 > 0 and count\_category4 > 0): # 3개의 카테고리가 존재할 때 무언가를 수행합니다. print('3개의 카테고리가 발견되었습니다!')elif (count\_category1 > 0 and count\_category2 > 0) or (count\_category1 > 0 and count\_category3 > 0) or (co