تشخیص جنسیت با پایتون

import cv2

# Load the pre-trained face detection classifier

face_cascade = cv2.CascadeClassifier(‘haarcascade_frontalface_default.xml’)

# Load the pre-trained gender detection model

gender_net = cv2.dnn.readNetFromCaffe(‘deploy_gender.prototxt’, ‘gender_net.caffemodel’)

# Load the image

img = cv2.imread(‘image.jpg’)

# Convert the image to grayscale

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# Detect faces in the image

faces = face_cascade.detectMultiScale(gray, 1.3, 5)

# Loop through each face and classify its gender

for (x, y, w, h) in faces:

    # Extract the face ROI

    face = img[y:y+h, x:x+w]

    # Preprocess the face image

    blob = cv2.dnn.blobFromImage(face, 1, (227, 227), (78.4263377603, 87.7689143744, 114.895847746), swapRB=False)

    # Pass the face image through the gender detection model

    gender_net.setInput(blob)

    gender_preds = gender_net.forward()

    # Get the gender label

    gender = ‘Male’ if gender_preds[0][0] > gender_preds[0][1] else ‘Female’

    # Draw the gender label on the image

    cv2.putText(img, gender, (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)

# Display the image

cv2.imshow(‘Gender Detection’, img)

cv2.waitKey(0)

cv2.destroyAllWindows()