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e9f7abc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 | 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()
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