OpenCV yüz tanıma herkesi Elon Musk olarak gösteriyor

import cv2

# Yüz algılama ve çizgileri çizecek fonksiyon
def highlightFace(net, frame, conf_threshold=0.7):
 frameOpencvDnn = frame.copy()
 frameHeight = frameOpencvDnn.shape[0]
 frameWidth = frameOpencvDnn.shape[1]
 blob = cv2.dnn.blobFromImage(frameOpencvDnn, 1.0, (300, 300), [104, 117, 123], True, False)

 net.setInput(blob)
 detections = net.forward()
 faceBoxes = []
 for i in range(detections.shape[2]):
 confidence = detections[0,0,i,2]
 if confidence > conf_threshold:
 x1 = int(detections[0,0,i,3] * frameWidth)
 y1 = int(detections[0,0,i,4] * frameHeight)
 x2 = int(detections[0,0,i,5] * frameWidth)
 y2 = int(detections[0,0,i,6] * frameHeight)
 faceBoxes.append([x1, y1, x2, y2])
 cv2.rectangle(frameOpencvDnn, (x1, y1), (x2, y2), (0, 255, 0), int(round(frameHeight/150)), 8)
 return frameOpencvDnn, faceBoxes

# Hazır modellerin eklenmesi
faceProto = "opencv_face_detector.pbtxt"
faceModel = "opencv_face_detector_uint8.pb"
ageProto = "age_deploy.prototxt"
ageModel = "age_net.caffemodel"
genderProto = "gender_deploy.prototxt"
genderModel = "gender_net.caffemodel"

MODEL_MEAN_VALUES = (78.4263377603, 87.7689143744, 114.895847746)
ageList = ['(0-2)', '(4-6)', '(8-12)', '(15-20)', '(25-32)', '(38-43)', '(48-53)', '(60-100)']
genderList = ['Erkek', 'Kadin']

faceNet = cv2.dnn.readNet(faceModel, faceProto)
ageNet = cv2.dnn.readNet(ageModel, ageProto)
genderNet = cv2.dnn.readNet(genderModel, genderProto)

database = {"Elon Musk": "elon.jpg"}

# Kamera açma
video = cv2.VideoCapture(0)
padding = 20
while cv2.waitKey(1) < 0:
 hasFrame, frame = video.read()
 if not hasFrame:
 cv2.waitKey()
 break

 # Yüz algılama ve çizim
 resultImg, faceBoxes = highlightFace(faceNet, frame)
 if not faceBoxes:
 print("Yüz algılanamadı")

 for faceBox in faceBoxes:
 # Yüz bölgesini al
 face = frame[max(0, faceBox[1]-padding):min(faceBox[3]+padding, frame.shape[0]-1), max(0, faceBox[0]-padding):min(faceBox[2]+padding, frame.shape[1]-1)]

 # Yüz bölgesini bloba dönüştür
 blob = cv2.dnn.blobFromImage(face, 1.0, (227, 227), MODEL_MEAN_VALUES, swapRB=False)

 # Cinsiyet tahmini yap
 genderNet.setInput(blob)
 genderPreds = genderNet.forward()
 gender = genderList[genderPreds[0].argmax()]
 print(f'Cinsiyet: {gender}')

 # Yaş tahmini yap
 ageNet.setInput(blob)
 agePreds = ageNet.forward()
 age = ageList[agePreds[0].argmax()]
 print(f'Yaş: {age[1:-1]} yaşında')

 # Kişinin adını ve diğer bilgileri ekrana yazdır
 for name, photo_path in database.items():
 if name.lower() in name.lower():
 cv2.putText(resultImg, name, (faceBox[0], faceBox[1] - 80), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 255), 2, cv2.LINE_AA)
 cv2.putText(resultImg, gender, (faceBox[0], faceBox[1] - 50), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 255), 2, cv2.LINE_AA)
 cv2.putText(resultImg, age, (faceBox[0], faceBox[1] -20), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 255), 2, cv2.LINE_AA)

 # Sonuçları göster
 cv2.imshow("Yas ve Cinsiyet Algilama", resultImg)

# Video yakalama nesnesini serbest bırak
video.release()
cv2.destroyAllWindows()