app.py

import streamlit as st
import cv2
import numpy as np
import requests
import tempfile
import mediapipe as mp
import os
import pickle
from googleapiclient.discovery import build
from googleapiclient.http import MediaFileUpload
from PIL import Image

# === CONFIGURATION ===
API_KEY = "AIzaSyDojJrpauA0XZtCCDUuo9xeQHZQamYKsC4"
CCTVFEED_IDS = ['1KJRkSf2SKEZ1mXS9_si65IwMBtjs6p4n']
REG_FOLDER_ID = '1qkcR7nQTEtiMH9OFUv2bGxVn08E3dKjF'
INTRUDER_FOLDER_ID = '1PPAUWU-wMx7fek73p-hqPqYQypYtG8Ob'

mp_face_detection = mp.solutions.face_detection
face_detector = mp_face_detection.FaceDetection(model_selection=0, min_detection_confidence=0.5)

# === INTRUDER UPLOAD ===
def get_drive_service():
    try:
        with open("token.pickle", "rb") as token:
            creds = pickle.load(token)
        return build("drive", "v3", credentials=creds)
    except Exception as e:
        print(f"❌ Drive auth failed: {e}")
        return None

def upload_intruder_face(image_path):
    service = get_drive_service()
    if service is None:
        print("🔒 Upload skipped (auth failed)")
        return

    metadata = {"name": os.path.basename(image_path), "parents": [INTRUDER_FOLDER_ID]}
    media = MediaFileUpload(image_path, mimetype="image/jpeg")
    try:
        service.files().create(body=metadata, media_body=media, fields="id").execute()
        print(f"✅ Uploaded to INTRUDER folder: {image_path}")
    except Exception as e:
        print(f"❌ Upload failed: {e}")

# === HELPERS ===
def get_drive_files(folder_id, allowed_mime):
    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(url)
    files = response.json().get("files", [])
    return [(f["name"], f["id"]) for f in files if f["mimeType"] in allowed_mime]

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_face_embedding(image):
    rgb = cv2.cvtColor(image, 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, _ = image.shape
            x1 = int(bbox.xmin * w)
            y1 = int(bbox.ymin * h)
            x2 = int((bbox.xmin + bbox.width) * w)
            y2 = int((bbox.ymin + bbox.height) * h)
            face_crop = image[y1:y2, x1:x2]
            return cv2.resize(face_crop, (128, 128))
    return None

def is_match(face1, face2):
    if face1 is None or face2 is None:
        return False
    diff = np.linalg.norm(face1.astype("float") - face2.astype("float"))
    return diff < 30.0

# === STREAMLIT UI ===
st.set_page_config(page_title="Intruder Detection", layout="wide")
st.title("🔐 Intruder Detection from CCTV Feed")
st.write("Streaming videos from Google Drive, detecting and matching faces.")

sidebar_faces = []

with st.spinner("Loading registered faces..."):
    registered = []
    reg_images = get_drive_files(REG_FOLDER_ID, ["image/jpeg", "image/png"])
    for name, fid in reg_images:
        img_path = download_file(fid, ".jpg")
        img = cv2.imread(img_path)
        emb = extract_face_embedding(img)
        if emb is not None:
            registered.append((name, emb))

with st.spinner("Scanning CCTV feed..."):
    total_intruders = 0
    for folder_id in CCTVFEED_IDS:
        videos = get_drive_files(folder_id, ["video/mp4", "video/x-msvideo", "video/avi"])
        for vname, fid in videos:
            st.subheader(f"🎥 {vname}")
            vid_path = download_file(fid, ".mp4")
            cap = cv2.VideoCapture(vid_path)
            frame_count = 0
            matched_faces = 0
            unmatched_faces = 0

            while cap.isOpened():
                ret, frame = cap.read()
                if not ret:
                    break
                face_img = extract_face_embedding(frame)
                if face_img is not None:
                    match_found = any(is_match(face_img, emb) for _, emb in registered)
                    if match_found:
                        matched_faces += 1
                    else:
                        unmatched_faces += 1
                        temp = tempfile.NamedTemporaryFile(delete=False, suffix=".jpg")
                        cv2.imwrite(temp.name, face_img)
                        upload_intruder_face(temp.name)
                        sidebar_faces.append(temp.name)
                frame_count += 1
            cap.release()

            st.success(f"🎯 Processed {frame_count} frames — Matched: {matched_faces}, Unmatched: {unmatched_faces}")
            total_intruders += unmatched_faces

st.info(f"🎯 Completed processing all videos. Intruders detected: {total_intruders}")

with st.sidebar:
    st.markdown("## 🚨 Intruder Snapshots")
    for face_path in sidebar_faces:
        st.image(face_path, width=160)