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import os
import time
import cv2
import av
import numpy as np
import streamlit as st
from deepface import DeepFace
from streamlit_webrtc import webrtc_streamer, WebRtcMode, RTCConfiguration
# ---------------------------------------------
# 🌐 Streamlit Page Config
# ---------------------------------------------
st.set_page_config(page_title="AI Facial Interview Monitor", layout="wide")
st.title(":blue[MOCKVIEWER - Face Monitoring System]")
# ---------------------------------------------
# 📦 Load Haar Cascade Models
# ---------------------------------------------
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
eye_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_eye.xml")
# ---------------------------------------------
# 🖼️ Upload Reference Image
# ---------------------------------------------
uploaded_image = st.file_uploader("Upload your reference face image", type=["jpg", "jpeg", "png"])
user_ref_img = None
if uploaded_image:
user_ref_img = cv2.imdecode(np.frombuffer(uploaded_image.read(), np.uint8), cv2.IMREAD_COLOR)
st.image(user_ref_img, caption="Reference Image", use_column_width=True)
# ---------------------------------------------
# ⏱️ Global State Variables
# ---------------------------------------------
face_detected_time = time.time()
last_verified_time = 0
# ---------------------------------------------
# ⚙️ Configurations
# ---------------------------------------------
FACE_TIMEOUT = 60 # seconds of face absence before cancelling
VERIFY_INTERVAL = 30 # seconds between identity checks
# ---------------------------------------------
# 👁️ Confidence Heuristic
# ---------------------------------------------
def is_confident_pose(face_roi):
"""Check if eyes are visible and head is upright."""
gray = cv2.cvtColor(face_roi, cv2.COLOR_BGR2GRAY)
eyes = eye_cascade.detectMultiScale(gray, 1.1, 4)
return len(eyes) >= 1
# ---------------------------------------------
# 🧬 Identity Verification
# ---------------------------------------------
def match_identity(live_face, ref_img):
try:
result = DeepFace.verify(
live_face, ref_img,
enforce_detection=False,
model_name='Facenet',
detector_backend='opencv'
)
return result["verified"]
except Exception as e:
print("Verification error:", e)
return False
# ---------------------------------------------
# 📹 Streamlit Webcam Video Processor
# ---------------------------------------------
class VideoProcessor:
def __init__(self):
self.last_check = time.time()
self.last_verified = time.time()
self.face_missing = False
def recv(self, frame):
global face_detected_time, last_verified_time, user_ref_img
img = frame.to_ndarray(format="bgr24")
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.1, 4)
if len(faces) == 0:
if time.time() - face_detected_time > FACE_TIMEOUT:
cv2.putText(img, "❌ Interview Cancelled: Face not visible!", (30, 30),
cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 255), 2)
else:
cv2.putText(img, "⚠️ Face not visible. You have 60 seconds.", (30, 30),
cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 255), 2)
else:
face_detected_time = time.time()
for (x, y, w, h) in faces:
face_roi = img[y:y+h, x:x+w]
# Eye/Posture Check
confident = is_confident_pose(face_roi)
status = "✅ Confident Pose" if confident else "⚠️ Look Straight!"
color = (0, 255, 0) if confident else (0, 255, 255)
cv2.putText(img, status, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.7, color, 2)
cv2.rectangle(img, (x, y), (x + w, y + h), color, 2)
# Identity Verification
if uploaded_image and (time.time() - last_verified_time > VERIFY_INTERVAL):
matched = match_identity(face_roi, user_ref_img)
last_verified_time = time.time()
if matched:
cv2.putText(img, "✅ Identity Verified", (x, y + h + 30),
cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2)
else:
cv2.putText(img, "❌ Identity mismatch!", (x, y + h + 30),
cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 255), 2)
return av.VideoFrame.from_ndarray(img, format="bgr24")
# ---------------------------------------------
# 🔌 Activate Webcam Stream
# ---------------------------------------------
webrtc_streamer(
key="monitor",
video_processor_factory=VideoProcessor,
mode=WebRtcMode.RECVONLY,
rtc_configuration=RTCConfiguration(iceServers=[])
)
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