Update app.py

app.py

@@ -1,74 +1,92 @@
-
-import os
+import streamlit as st
 import cv2
+import requests
+import numpy as np
+import tempfile
+import time
 import mediapipe as mp
-import gradio as gr
-import shutil
-from datetime import datetime
 
-# Create output directory
-OUTPUT_DIR = "output_faces"
-if os.path.exists(OUTPUT_DIR):
-    shutil.rmtree(OUTPUT_DIR)
-os.makedirs(OUTPUT_DIR)
+# === CONFIGURATION ===
+CCTVFEED_IDS = ['1KJRkSf2SKEZ1mXS9_si65IwMBtjs6p4n']  # List of folder IDs (Google Drive)
+SUPPORTED_EXTENSIONS = ['.mp4', '.avi']
 
+# === INITIALIZE FACE DETECTION ===
 mp_face_detection = mp.solutions.face_detection
-mp_drawing = mp.solutions.drawing_utils
+face_detection = mp_face_detection.FaceDetection(model_selection=0, min_detection_confidence=0.5)
+
+# === HELPER FUNCTIONS ===
+
+def list_gdrive_folder_files(folder_id):
+    """Returns list of (filename, download_url) from public Google Drive folder."""
+    api_url = f"https://drive.google.com/embeddedfolderview?id={folder_id}#grid"
+    resp = requests.get(api_url)
+    file_lines = resp.text.splitlines()
+    links = []
+    for line in file_lines:
+        if 'data-id=' in line and any(ext in line for ext in SUPPORTED_EXTENSIONS):
+            try:
+                file_id = line.split('data-id="')[1].split('"')[0]
+                file_name = line.split('data-title="')[1].split('"')[0]
+                download_url = f"https://drive.google.com/uc?id={file_id}"
+                links.append((file_name, download_url))
+            except Exception:
+                continue
+    return links
+
+def stream_and_process_video(video_url, filename):
+    """Streams video frame-by-frame and runs face detection."""
+    st.markdown(f"### 🎥 Processing `{filename}`")
+    vid_bytes = requests.get(video_url, stream=True).content
+    tfile = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4")
+    tfile.write(vid_bytes)
+    cap = cv2.VideoCapture(tfile.name)
 
-def extract_faces_from_video(video_path):
-    video_cap = cv2.VideoCapture(video_path)
-    basename = os.path.basename(video_path)
-    video_name = os.path.splitext(basename)[0]
     frame_count = 0
-    saved_count = 0
-
-    with mp_face_detection.FaceDetection(model_selection=0, min_detection_confidence=0.5) as face_detection:
-        while video_cap.isOpened():
-            success, frame = video_cap.read()
-            if not success:
-                break
-
-            frame_count += 1
-            rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-            results = face_detection.process(rgb_frame)
-
-            if results.detections:
-                for i, detection in enumerate(results.detections):
-                    bbox = detection.location_data.relative_bounding_box
-                    h, w, _ = frame.shape
-                    x_min = int(bbox.xmin * w)
-                    y_min = int(bbox.ymin * h)
-                    box_w = int(bbox.width * w)
-                    box_h = int(bbox.height * h)
-
-                    # Ensure bounding box within frame
-                    x_min = max(0, x_min)
-                    y_min = max(0, y_min)
-                    x_max = min(w, x_min + box_w)
-                    y_max = min(h, y_min + box_h)
-
-                    cropped_face = frame[y_min:y_max, x_min:x_max]
-                    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-                    filename = f"{OUTPUT_DIR}/{video_name}_frame{frame_count}_face{i+1}_{timestamp}.jpg"
-                    cv2.imwrite(filename, cropped_face)
-                    saved_count += 1
-
-    video_cap.release()
-    return f"✅ Processed {frame_count} frames. {saved_count} face(s) saved from {video_name}."
-
-def process_videos(videos):
-    logs = []
-    for video in videos:
-        logs.append(extract_faces_from_video(video))
-    return "\n".join(logs)
-
-demo = gr.Interface(
-    fn=process_videos,
-    inputs=gr.File(file_types=[".mp4", ".avi"], label="Upload video files", file_count="multiple"),
-    outputs="text",
-    title="Face Detection using MediaPipe",
-    description="Upload MP4 or AVI videos. Faces will be saved to `output_faces/` folder."
-)
-
-if __name__ == "__main__":
-    demo.launch()
+    face_count = 0
+
+    frame_display = st.empty()
+    stats_display = st.empty()
+
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            break
+        frame_count += 1
+
+        # Convert BGR to RGB
+        image_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        results = face_detection.process(image_rgb)
+
+        # Draw faces
+        if results.detections:
+            for detection in results.detections:
+                bboxC = detection.location_data.relative_bounding_box
+                ih, iw, _ = frame.shape
+                x, y, w, h = int(bboxC.xmin * iw), int(bboxC.ymin * ih), int(bboxC.width * iw), int(bboxC.height * ih)
+                cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
+                face_count += 1
+
+        # Resize for display
+        display_frame = cv2.resize(frame, (640, 360))
+        frame_display.image(display_frame, channels="BGR")
+
+        stats_display.markdown(f"**Frames Processed:** {frame_count} | **Faces Detected So Far:** {face_count}")
+        time.sleep(1 / 24.0)  # Approx. 24 FPS
+
+    cap.release()
+    st.success(f"✅ Done! `{filename}`: {frame_count} frames, {face_count} faces detected.")
+
+# === MAIN UI ===
+
+st.title("🚨 CCTV Face Detection from Google Drive")
+st.markdown("Detects faces in `.mp4` and `.avi` videos from your public Google Drive folder.")
+
+for folder_id in CCTVFEED_IDS:
+    videos = list_gdrive_folder_files(folder_id)
+    if not videos:
+        st.warning(f"No valid videos found in folder ID: {folder_id}")
+        continue
+
+    for name, url in videos:
+        if st.button(f"▶️ Run Face Detection on `{name}`"):
+            stream_and_process_video(url, name)