facemaskdetection_video.py

import streamlit as st
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

# Function to detect and predict mask
def detect_and_predict_mask(frame, faceNet, maskNet, confidence_threshold):
    (h, w) = frame.shape[:2]
    blob = cv2.dnn.blobFromImage(frame, 1.0, (300, 300), (104.0, 177.0, 123.0))
    faceNet.setInput(blob)
    detections = faceNet.forward()

    faces = []
    locs = []
    preds = []

    for i in range(detections.shape[2]):
        confidence = detections[0, 0, i, 2]
        if confidence > confidence_threshold:
            box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
            (startX, startY, endX, endY) = box.astype("int")
            (startX, startY) = (max(0, startX), max(0, startY))
            (endX, endY) = (min(w - 1, endX), min(h - 1, endY))

            face = frame[startY:endY, startX:endX]
            if face.shape[0] > 0 and face.shape[1] > 0:
                face = cv2.cvtColor(face, cv2.COLOR_BGR2RGB)
                face = cv2.resize(face, (224, 224))
                face = img_to_array(face)
                face = preprocess_input(face)
                faces.append(face)
                locs.append((startX, startY, endX, endY))

    if len(faces) > 0:
        faces = np.array(faces, dtype="float32")
        preds = maskNet.predict(faces, batch_size=32)

    return (locs, preds)

# Load models
@st.cache_resource
def load_models():
    prototxtPath = "face_detector/deploy.prototxt"
    weightsPath = "face_detector/res10_300x300_ssd_iter_140000.caffemodel"
    faceNet = cv2.dnn.readNet(prototxtPath, weightsPath)
    maskNet = load_model("mask_detector.model")
    return faceNet, maskNet

faceNet, maskNet = load_models()

# Streamlit UI
st.title("Real-Time Face Mask Detection with TensorFlow")
st.text("Turn on your webcam to detect masks in real-time.")

run = st.button("Start Camera")

# Create a Streamlit "Stop" button outside the loop to avoid duplicate key issues
stop_button = st.button("Stop")

if run:
    confidence_threshold = st.slider("Confidence Threshold", 0.1, 1.0, 0.5, 0.1)
    stframe = st.empty()

    cap = cv2.VideoCapture(0)

    while True:
        ret, frame = cap.read()
        if not ret:
            st.error("Failed to access camera.")
            break

        frame = cv2.resize(frame, (800, 600))
        locs, preds = detect_and_predict_mask(frame, faceNet, maskNet, confidence_threshold)

        for (box, pred) in zip(locs, preds):
            (startX, startY, endX, endY) = box
            (mask, withoutMask) = pred
            label = "Mask" if mask > withoutMask else "No Mask"
            color = (0, 255, 0) if label == "Mask" else (0, 0, 255)
            text = f"{label}: {'Allowed' if label == 'Mask' else 'Not Allowed'}"
            cv2.putText(frame, text, (startX, startY - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.7, color, 2)
            cv2.rectangle(frame, (startX, startY), (endX, endY), color, 2)

        stframe.image(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB), channels="RGB")

        # Check if the "Stop" button was clicked
        if stop_button:
            break

    cap.release()
    cv2.destroyAllWindows()