app.py

import streamlit as st
import cv2
import numpy as np
import requests
import tempfile
import mediapipe as mp
import os
from PIL import Image

# === CONFIGURATION ===
API_KEY = "AIzaSyDojJrpauA0XZtCCDUuo9xeQHZQamYKsC4"
CCTVFEED_IDS = ['1KJRkSf2SKEZ1mXS9_si65IwMBtjs6p4n']
REG_FOLDER_ID = '1qkcR7nQTEtiMH9OFUv2bGxVn08E3dKjF'
INTRUDER_UPLOAD_URL = "https://upload.gdrive.com/your-anon-endpoint-if-any"  # You can change this if needed

# === SETUP ===
mp_face_detection = mp.solutions.face_detection
mp_drawing = mp.solutions.drawing_utils
face_detector = mp_face_detection.FaceDetection(model_selection=0, min_detection_confidence=0.5)

# === UTILS ===
def get_drive_file_links(folder_id, mime_types):
    api_url = f"https://www.googleapis.com/drive/v3/files?q='{folder_id}'+in+parents&key={API_KEY}&fields=files(id,name,mimeType)"
    response = requests.get(api_url)
    files = response.json().get("files", [])
    return [(f['name'], f['id']) for f in files if f['mimeType'] in mime_types]

def download_file(file_id, suffix):
    url = f"https://drive.google.com/uc?id={file_id}&export=download"
    response = requests.get(url, stream=True)
    temp = tempfile.NamedTemporaryFile(delete=False, suffix=suffix)
    for chunk in response.iter_content(chunk_size=1024 * 1024):
        temp.write(chunk)
    temp.close()
    return temp.name

def extract_faces_from_image(img_path):
    img = cv2.imread(img_path)
    rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    results = face_detector.process(rgb)
    faces = []
    if results.detections:
        for det in results.detections:
            bbox = det.location_data.relative_bounding_box
            h, w, _ = rgb.shape
            x, y, bw, bh = int(bbox.xmin * w), int(bbox.ymin * h), int(bbox.width * w), int(bbox.height * h)
            face_crop = rgb[y:y+bh, x:x+bw]
            if face_crop.size > 0:
                face_resized = cv2.resize(face_crop, (100, 100))
                faces.append(face_resized)
    return faces

def mse(img1, img2):
    if img1.shape != img2.shape:
        return np.inf
    return np.mean((img1.astype("float") - img2.astype("float")) ** 2)

def is_intruder(face_img, known_faces, threshold=5000):
    face_gray = cv2.cvtColor(face_img, cv2.COLOR_RGB2GRAY)
    face_gray = cv2.resize(face_gray, (100, 100))
    return all(mse(face_gray, known) > threshold for known in known_faces)

def upload_intruder(img_array, name):
    temp_img = tempfile.NamedTemporaryFile(delete=False, suffix=".jpg")
    Image.fromarray(cv2.cvtColor(img_array, cv2.COLOR_RGB2BGR)).save(temp_img.name)
    # Simulate upload – requires actual implementation if you want to use Google Forms or Apps Script
    st.warning(f"Upload placeholder: {temp_img.name}")
    return temp_img.name

# === APP UI ===
st.title("🔐 Intruder Detection from CCTV Feed")
st.write("Streaming videos from Google Drive, detecting faces using MediaPipe and flagging unknown individuals.")

sidebar_faces = []

with st.spinner("Loading registered faces..."):
    known_faces = []
    for fname, fid in get_drive_file_links(REG_FOLDER_ID, ["image/jpeg", "image/png"]):
        img_path = download_file(fid, ".jpg")
        faces = extract_faces_from_image(img_path)
        for face in faces:
            gray = cv2.cvtColor(face, cv2.COLOR_RGB2GRAY)
            known_faces.append(cv2.resize(gray, (100, 100)))
        os.remove(img_path)
    st.success(f"Loaded {len(known_faces)} registered faces.")

with st.spinner("Processing CCTV videos..."):
    intruder_count = 0
    for folder_id in CCTVFEED_IDS:
        video_files = get_drive_file_links(folder_id, ["video/mp4", "video/x-msvideo", "video/avi"])
        for vname, vid in video_files:
            st.subheader(f"🎥 {vname}")
            video_path = download_file(vid, ".mp4")
            cap = cv2.VideoCapture(video_path)
            frame_id = 0
            while cap.isOpened():
                ret, frame = cap.read()
                if not ret:
                    break
                frame_id += 1
                if frame_id % 30 != 0:
                    continue
                rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
                results = face_detector.process(rgb)
                if results.detections:
                    for det in results.detections:
                        bbox = det.location_data.relative_bounding_box
                        h, w, _ = rgb.shape
                        x, y, bw, bh = int(bbox.xmin * w), int(bbox.ymin * h), int(bbox.width * w), int(bbox.height * h)
                        face_crop = rgb[y:y+bh, x:x+bw]
                        if face_crop.size == 0:
                            continue
                        if is_intruder(face_crop, known_faces):
                            sidebar_faces.append(face_crop)
                            intruder_count += 1
                            upload_intruder(face_crop, f"intruder_{intruder_count}.jpg")
            cap.release()
            os.remove(video_path)

    st.success(f"🎯 Intruders detected: {intruder_count}")

# === SIDEBAR ===
st.sidebar.title("📸 Faces Detected as Intruders")
for idx, face in enumerate(sidebar_faces):
    st.sidebar.image(face, caption=f"Intruder #{idx + 1}", width=100)