Update app.py

app.py

@@ -1,6 +1,5 @@
 import os
 import cv2
-import face_recognition
 import numpy as np
 import time
 import pickle
@@ -8,11 +7,13 @@ from googleapiclient.discovery import build
 from googleapiclient.http import MediaIoBaseDownload, MediaFileUpload
 from io import BytesIO
 
+
 # === CONFIGURATION ===
-CCTVFEED_IDS = ['1KJRkSf2SKEZ1mXS9_si65IwMBtjs6p4n']  # Replace with actual folder IDs
+CCTVFEED_IDS = ['1KJRkSf2SKEZ1mXS9_si65IwMBtjs6p4n'] # Replace with actual folder IDs
 REG_FOLDER_ID = '1qkcR7nQTEtiMH9OFUv2bGxVn08E3dKjF'
-INTRUDER_FOLDER_ID = '1PPAUWU-wMx7fek73p-hqPqYQypYtG8Ob'
+INTRUDER_FOLDER_ID = '1PPAUWU-wMx7fek73p-hqPqYQypYtG8Ob' 
 TOKEN_PICKLE = 'token.pickle'
+CASCADE_PATH = cv2.data.haarcascades + 'haarcascade_frontalface_default.xml'
 
 # === GOOGLE DRIVE AUTH ===
 def init_gdrive():
@@ -37,43 +38,49 @@ def list_files(service, folder_id, mime_type='video/mp4'):
     result = service.files().list(q=query, fields="files(id, name)").execute()
     return result.get('files', [])
 
-# === KNOWN FACES ===
+# === LOAD KNOWN FACE IMAGES ===
 def load_known_faces(service):
     known_faces = []
-    known_names = []
     reg_files = list_files(service, REG_FOLDER_ID, mime_type='image/jpeg')
     for f in reg_files:
         fname = f"{f['id']}.jpg"
         download_file(service, f['id'], fname)
-        img = face_recognition.load_image_file(fname)
-        encoding = face_recognition.face_encodings(img)
-        if encoding:
-            known_faces.append(encoding[0])
-            known_names.append(f['name'])
+        img = cv2.imread(fname, cv2.IMREAD_GRAYSCALE)
+        if img is not None:
+            img = cv2.resize(img, (100, 100))
+            known_faces.append(img)
         os.remove(fname)
-    return known_faces, known_names
+    return known_faces
 
-# === INTRUDER REPORTING ===
+# === UPLOAD INTRUDER IMAGE ===
 def upload_intruder(service, img_path):
     file_metadata = {'name': os.path.basename(img_path), 'parents': [INTRUDER_FOLDER_ID]}
     media = MediaFileUpload(img_path, mimetype='image/jpeg')
     service.files().create(body=file_metadata, media_body=media, fields='id').execute()
 
-# === VIDEO PROCESSING ===
-def process_video(video_path, known_faces, known_names, service):
+# === BASIC FACE COMPARISON ===
+def is_match(face_img, known_faces, threshold=2000):
+    face_img = cv2.resize(face_img, (100, 100))
+    errors = [np.mean((face_img - kf) ** 2) for kf in known_faces]
+    return any(err < threshold for err in errors)
+
+# === VIDEO ANALYSIS ===
+def process_video(video_path, known_faces, service):
+    face_cascade = cv2.CascadeClassifier(CASCADE_PATH)
     video_capture = cv2.VideoCapture(video_path)
     frame_count = 0
+
     while True:
         ret, frame = video_capture.read()
         if not ret:
             break
         if frame_count % 30 == 0:
-            rgb = frame[:, :, ::-1]
-            faces = face_recognition.face_locations(rgb)
-            encodings = face_recognition.face_encodings(rgb, faces)
-            for face_encoding in encodings:
-                matches = face_recognition.compare_faces(known_faces, face_encoding)
-                if not any(matches):
+            gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+            faces = face_cascade.detectMultiScale(gray, 1.3, 5)
+
+            for (x, y, w, h) in faces:
+                face_img = gray[y:y+h, x:x+w]
+                if not is_match(face_img, known_faces):
                     ts = int(time.time())
                     fname = f"intruder_{ts}.jpg"
                     cv2.imwrite(fname, frame)
@@ -85,15 +92,15 @@ def process_video(video_path, known_faces, known_names, service):
 # === MAIN ===
 def main():
     drive = init_gdrive()
-    known_faces, known_names = load_known_faces(drive)
+    known_faces = load_known_faces(drive)
 
     for folder_id in CCTVFEED_IDS:
         videos = list_files(drive, folder_id)
         for v in videos:
             video_path = f"{v['id']}.mp4"
             download_file(drive, v['id'], video_path)
-            process_video(video_path, known_faces, known_names, drive)
+            process_video(video_path, known_faces, drive)
             os.remove(video_path)
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     main()