Age and Gender Detection: MLOPs

pip install opencv-python numpy

import cv2
import numpy as np

# Load model files
AGE_PROTO = "age_deploy.prototxt"
AGE_MODEL = "age_net.caffemodel"
GENDER_PROTO = "gender_deploy.prototxt"
GENDER_MODEL = "gender_net.caffemodel"

# Load models
age_net = cv2.dnn.readNet(AGE_MODEL, AGE_PROTO)
gender_net = cv2.dnn.readNet(GENDER_MODEL, GENDER_PROTO)

# Define model mean values
MODEL_MEAN_VALUES = (78.4263377603, 87.7689143744, 114.895847746)

# Labels
age_list = ['(0-2)', '(4-6)', '(8-12)', '(15-20)',
            '(25-32)', '(38-43)', '(48-53)', '(60-100)']
gender_list = ['Male', 'Female']

# Initialize webcam
cap = cv2.VideoCapture(0)
while True:
    ret, frame = cap.read()
    if not ret:
        break

    # Face detection
    face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray, 1.1, 5)

    for (x, y, w, h) in faces:
        face_img = frame[y:y+h, x:x+w].copy()
        blob = cv2.dnn.blobFromImage(face_img, 1.0, (227, 227), MODEL_MEAN_VALUES, swapRB=False)

        # Predict Gender
        gender_net.setInput(blob)
        gender_preds = gender_net.forward()
        gender = gender_list[gender_preds[0].argmax()]

        # Predict Age
        age_net.setInput(blob)
        age_preds = age_net.forward()
        age = age_list[age_preds[0].argmax()]

        label = f"{gender}, {age}"
        cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 255, 255), 2)
        cv2.putText(frame, label, (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 255), 2)

    cv2.imshow("Age and Gender Detection", frame)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()