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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() |