men and women faces to folders

from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.models import load_model
import numpy as np
import cv2
import cvlib as cv

# load model
model = load_model('gender_detection.model')
classes = ['man', 'woman']

img = cv2.imread("faces.jpg")
# img = cv2.resize(img,(1800,1800))
# apply face detection
face, confidence = cv.detect_face(img)

def save_imgs(img,name, bbox, width=350,height=400):
        x, y, w, h = bbox
        # imgCrop = img[y:y + h, x:x + w]
        img = img[y:h, x:w]
        # img = cv2.resize(img, (width, height))
        cv2.imwrite(name+".jpg", img)


men_path ='D:/pythonProject2021/Facial Recognition/men/'
women_path ='D:/pythonProject2021/Facial Recognition/women/'

fit = 5
# loop through detected faces
counter =0
counter2 =0
for idx, f in enumerate(face):
    (startX, startY) = f[0], f[1]
    (endX, endY) = f[2], f[3]
    face_crop = np.copy(img[startY:endY, startX:endX])
    # if (face_crop.shape[0]) < 10 or (face_crop.shape[1]) < 10:
    #     continue
    # preprocessing for gender detection model
    face_crop = cv2.resize(face_crop, (96, 96))
    face_crop = face_crop.astype("float") / 255.0
    face_crop = img_to_array(face_crop)
    face_crop = np.expand_dims(face_crop, axis=0)
    # apply gender detection on face
    conf = model.predict(face_crop)[0]  # model.predict return a 2D matrix, ex: [[9.9993384e-01 7.4850512e-05]]

    # get label with max accuracy
    idx = np.argmax(conf)
    label = classes[idx]

    Y = startY - 10 if startY - 10 > 10 else startY + 10
    percent = conf[idx] * 100
    if label == "man" and percent > 80.0:
        counter +=1
        save_imgs(img, men_path+ label+str(counter), (startX - fit, startY - fit, endX + fit, endY + fit))
    elif label == "woman" and percent > 70.0:
        counter2 += 1
        save_imgs(img, women_path+ label+str(counter2), (startX - fit, startY - fit, endX + fit, endY + fit))
    cv2.putText(img, label, (startX, Y), cv2.FONT_HERSHEY_SIMPLEX,0.6, (0, 255, 0), 2)
    cv2.rectangle(img, (startX, startY), (endX, endY), (0, 255, 0), 2)
print("done saving---","men = ",counter,"---- women = ",counter2)

# display output
img = cv2.resize(img,(800,800))
cv2.imshow("gender detection", img)
cv2.waitKey(0)
cv2.destroyAllWindows()