Python Cinsiyet Algılama (Python Gender Detection)

import cv2
import numpy as np

# Load pre-trained face detection model
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')

# Load pre-trained gender classification model
gender_model = cv2.dnn.readNetFromCaffe("deploy_gender.prototxt", "gender_net.caffemodel")

# Load labels for gender classification
gender_labels = ['Male', 'Female']

# Initialize video capture
cap = cv2.VideoCapture(0)

while True:
    # Read frame from video capture
    _, frame = cap.read()

    # Convert frame to gray scale for face detection
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    # Detect faces in the frame
    faces = face_cascade.detectMultiScale(gray, 1.3, 5)

    for (x, y, w, h) in faces:
        # Extract face region of interest
        roi = frame[y:y + h, x:x + w]

        # Preprocess face for gender classification
        blob = cv2.dnn.blobFromImage(roi, 1, (227, 227), (78.4263377603, 87.7689143744, 114.895847746), swapRB=False)

        # Pass the face through gender classification model
        gender_model.setInput(blob)
        gender_preds = gender_model.forward()

        # Get predicted gender label
        gender_idx = np.argmax(gender_preds)
        gender_label = gender_labels[gender_idx]

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

        # Put predicted gender label and probability on the frame
        label = f'{gender_label}: {gender_preds[0][gender_idx] * 100:.2f}%'
        cv2.putText(frame, label, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (255, 0, 0), 2)

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

    # Quit if ESC is pressed
    if cv2.waitKey(1) == 27:
        break