Update app.py

app.py

@@ -1,8 +1,7 @@
 import os
 import cv2
-import dlib
-import numpy as np
 import face_recognition
+import numpy as np
 import time
 import pickle
 import gradio as gr
@@ -11,7 +10,7 @@ from googleapiclient.http import MediaIoBaseDownload, MediaFileUpload
 from io import BytesIO
 
 # === CONFIGURATION ===
-CCTVFEED_IDS = ['1KJRkSf2SKEZ1mXS9_si65IwMBtjs6p4n']  # Replace with actual folder IDs
+CCTVFEED_IDS = ['1KJRkSf2SKEZ1mXS9_si65IwMBtjs6p4n']
 REG_FOLDER_ID = '1qkcR7nQTEtiMH9OFUv2bGxVn08E3dKjF'
 INTRUDER_FOLDER_ID = '1PPAUWU-wMx7fek73p-hqPqYQypYtG8Ob'
 TOKEN_PICKLE = 'token.pickle'
@@ -38,9 +37,9 @@ def list_files(service, folder_id, mime_type='video/mp4'):
 
 def load_known_faces(service):
     known_encodings = []
-    reg_files = list_files(service, REG_FOLDER_ID, mime_type='image/jpeg')
-    print(f"[INFO] Found {len(reg_files)} registered face images.")
-    for f in reg_files:
+    files = list_files(service, REG_FOLDER_ID, mime_type='image/jpeg')
+    print(f"[INFO] Found {len(files)} registered images.")
+    for f in files:
         fname = f"{f['id']}.jpg"
         download_file(service, f['id'], fname)
         image = face_recognition.load_image_file(fname)
@@ -55,25 +54,26 @@ def upload_intruder(service, img_path):
     media = MediaFileUpload(img_path, mimetype='image/jpeg')
     service.files().create(body=file_metadata, media_body=media, fields='id').execute()
 
-def is_match(face_encoding, known_encodings, tolerance=0.45):
-    matches = face_recognition.compare_faces(known_encodings, face_encoding, tolerance=tolerance)
-    return any(matches)
+def is_match(encoding, known_encodings, tolerance=0.45):
+    results = face_recognition.compare_faces(known_encodings, encoding, tolerance)
+    return any(results)
 
 def process_video(video_path, known_encodings, service):
-    detector = dlib.get_frontal_face_detector()
-    video_capture = cv2.VideoCapture(video_path)
-    frame_count, detections, uploaded = 0, 0, []
+    cap = cv2.VideoCapture(video_path)
+    frame_count, detections = 0, 0
+    uploaded = []
 
     while True:
-        ret, frame = video_capture.read()
+        ret, frame = cap.read()
         if not ret:
             break
         if frame_count % 30 == 0:
-            rgb_frame = frame[:, :, ::-1]
-            face_locations = face_recognition.face_locations(rgb_frame)
-            face_encodings = face_recognition.face_encodings(rgb_frame, face_locations)
-            for face_encoding in face_encodings:
-                if not is_match(face_encoding, known_encodings):
+            rgb = frame[:, :, ::-1]
+            locations = face_recognition.face_locations(rgb)
+            encodings = face_recognition.face_encodings(rgb, locations)
+            print(f"[FRAME {frame_count}] Found {len(encodings)} face(s).")
+            for encoding in encodings:
+                if not is_match(encoding, known_encodings):
                     ts = int(time.time())
                     fname = f"intruder_{ts}_{frame_count}.jpg"
                     cv2.imwrite(fname, frame)
@@ -83,30 +83,30 @@ def process_video(video_path, known_encodings, service):
                     detections += 1
         frame_count += 1
 
-    video_capture.release()
-    print(f"[INFO] Processed {frame_count} frames from video: {video_path}")
+    cap.release()
+    print(f"[INFO] Done with {video_path}, {frame_count} frames processed.")
     return detections, uploaded
 
 def run_detection():
     try:
-        drive = init_gdrive()
-        known_encodings = load_known_faces(drive)
-        total_detections, intruder_imgs = 0, []
-        total_videos = 0
+        service = init_gdrive()
+        known_encodings = load_known_faces(service)
+        total_detected, total_videos, intruder_imgs = 0, 0, []
 
         for folder_id in CCTVFEED_IDS:
-            videos = list_files(drive, folder_id)
+            videos = list_files(service, folder_id)
             total_videos += len(videos)
+            print(f"[INFO] Found {len(videos)} video(s) in {folder_id}.")
             for v in videos:
-                video_path = f"{v['id']}.mp4"
-                print(f"[INFO] Downloading: {v['name']}")
-                download_file(drive, v['id'], video_path)
-                detections, uploads = process_video(video_path, known_encodings, drive)
-                total_detections += detections
-                intruder_imgs.extend(uploads)
-                os.remove(video_path)
+                path = f"{v['id']}.mp4"
+                download_file(service, v['id'], path)
+                detected, found = process_video(path, known_encodings, service)
+                total_detected += detected
+                intruder_imgs.extend(found)
+                os.remove(path)
 
-        return f"✅ Processed {total_videos} video(s). {total_detections} intruder(s) detected.", [f for f in intruder_imgs]
+        summary = f"✅ Processed {total_videos} video(s). {total_detected} intruder(s) detected."
+        return summary, intruder_imgs
     except Exception as e:
         return f"❌ Error: {str(e)}", []
 
@@ -114,6 +114,6 @@ gr.Interface(
     fn=run_detection,
     inputs=[],
     outputs=["text", "gallery"],
-    title="CCTV Intruder Detection with Face Recognition",
-    description="Click to scan CCTV feed and detect unrecognized faces. Uses dlib + face_recognition."
+    title="CCTV Intruder Detector (GPU Ready)",
+    description="Detects intruders by comparing faces in CCTV footage with registered staff/guest photos."
 ).launch()