mask_detection.py

import cv2
import numpy as np
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.applications.mobilenet_v2 import preprocess_input

model = load_model("mask_detector_final_model.keras")

face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
cap = cv2.VideoCapture(0)

while True:
    ret, frame = cap.read()
    if not ret:
        break

    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray, 1.1, 4)

    for (x, y, w, h) in faces:
        face = frame[y:y + h, x:x + w]
        face = cv2.resize(face, (224, 224))
        face = img_to_array(face)
        face = preprocess_input(face)
        face = np.expand_dims(face, axis=0)

        (mask, withoutMask) = model.predict(face)[0]
        confidence = max(mask, withoutMask)
        print(f"Probabilities: Mask={mask:.4f}, No Mask={withoutMask:.4f}")

        if confidence > 0.9:
            label = "Mask" if mask > withoutMask else "No Mask"
            color = (0, 255, 0) if label == "Mask" else (0, 0, 255)
            cv2.putText(frame, f"{label}", (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.45, color, 2)
            cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2)

    cv2.imshow("Face Mask Detector", frame)

    if cv2.waitKey(1) & 0xFF == ord('q') or cv2.getWindowProperty("Face Mask Detector", cv2.WND_PROP_VISIBLE) < 1:
        break

cap.release()
cv2.destroyAllWindows()