added storage option

.gitignore

@@ -7,4 +7,6 @@ flagged/
 .DS_Store
 gradio_cached_examples/
 venv/
-__pycache__
+__pycache__
+*.db
+*.sqlite3

app.py

@@ -6,10 +6,27 @@ import random
 from ultralytics import YOLO
 import numpy as np
 from collections import defaultdict
+import sqlite3
+import time
 
 # Import the supervision library
 import supervision as sv
 
+
+# --- Initialize SQLite DB for logging ---
+conn = sqlite3.connect("detection_log.db", check_same_thread=False)
+cursor = conn.cursor()
+cursor.execute('''
+    CREATE TABLE IF NOT EXISTS detections (
+        timestamp REAL,
+        frame_number INTEGER,
+        bin_name TEXT,
+        class_name TEXT,
+        count INTEGER
+    )
+''')
+conn.commit()
+
 # --- File Downloading ---
 # File URLs for sample images and video
 file_urls = [
@@ -84,27 +101,17 @@ def show_preds_image(image_path):
 
 # --- Video Processing Function (with Supervision) ---
 def process_video_with_two_side_bins(video_path):
-
-    if video_path is None:
-        return
-    
     generator = sv.get_video_frames_generator(video_path)
 
     try:
         first_frame = next(generator)
     except StopIteration:
-        print("No frames found in the provided video input.")
-        # Option 1: Return or yield a blank frame or error image
-        # For example, yield a blank black image of fixed size:
         blank_frame = np.zeros((480, 640, 3), dtype=np.uint8)
         yield cv2.cvtColor(blank_frame, cv2.COLOR_BGR2RGB)
         return
     
-    first_frame = next(generator)
     frame_height, frame_width, _ = first_frame.shape
 
-    # Define two bins: recyle and trash sides
-
     bins = [
         {
             "name": "Recycle Bin",
@@ -130,7 +137,7 @@ def process_video_with_two_side_bins(video_path):
 
     box_annotator = sv.BoxAnnotator(thickness=2)
     label_annotator = sv.LabelAnnotator(
-        text_scale=1.2,  # bigger text size
+        text_scale=1.2,
         text_thickness=3,
         text_position=sv.Position.TOP_LEFT,
     )
@@ -140,14 +147,30 @@ def process_video_with_two_side_bins(video_path):
     items_in_bins = {bin_["name"]: set() for bin_ in bins}
     class_counts_per_bin = {bin_["name"]: defaultdict(int) for bin_ in bins}
 
-    for i, frame in enumerate(generator):
+    frame_number = 0
+    BATCH_SIZE = 10
+    LOGGED_OBJECT_TTL_SECONDS = 300  # 5 minutes
+    insert_buffer = []
+    logged_objects = {}
+
+    for frame in generator:
+        frame_number += 1
+        current_time = time.time()
+
+        # Prune old logged objects every BATCH_SIZE frames
+        if frame_number % BATCH_SIZE == 0:
+            keys_to_remove = [key for key, ts in logged_objects.items()
+                            if current_time - ts > LOGGED_OBJECT_TTL_SECONDS]
+            for key in keys_to_remove:
+                del logged_objects[key]
+
         results = model(frame, verbose=False)[0]
         detections = sv.Detections.from_ultralytics(results)
         tracked_detections = tracker.update_with_detections(detections)
 
         annotated_frame = frame.copy()
 
-        # Draw bins and bigger labels
+        # Draw bins and labels
         for bin_ in bins:
             x1, y1, x2, y2 = bin_["coords"]
             color = bin_["color"]
@@ -157,7 +180,7 @@ def process_video_with_two_side_bins(video_path):
                 bin_["name"],
                 (x1 + 5, y1 - 15),
                 cv2.FONT_HERSHEY_SIMPLEX,
-                1.5,  # bigger font
+                1.5,
                 color,
                 3,
                 cv2.LINE_AA,
@@ -167,6 +190,10 @@ def process_video_with_two_side_bins(video_path):
             yield cv2.cvtColor(annotated_frame, cv2.COLOR_BGR2RGB)
             continue
 
+        # Clear counts for this frame
+        for bin_name in class_counts_per_bin:
+            class_counts_per_bin[bin_name].clear()
+
         for box, track_id, class_id in zip(
             tracked_detections.xyxy,
             tracked_detections.tracker_id,
@@ -175,14 +202,32 @@ def process_video_with_two_side_bins(video_path):
             x1, y1, x2, y2 = map(int, box)
             cx = (x1 + x2) // 2
             cy = (y1 + y2) // 2
+            class_name = class_names[class_id]
 
             for bin_ in bins:
                 bx1, by1, bx2, by2 = bin_["coords"]
+                bin_name = bin_["name"]
+
                 if (bx1 <= cx <= bx2) and (by1 <= cy <= by2):
-                    if track_id not in items_in_bins[bin_["name"]]:
-                        items_in_bins[bin_["name"]].add(track_id)
-                        class_name = class_names[class_id]
-                        class_counts_per_bin[bin_["name"]][class_name] += 1
+                    key = (track_id, bin_name, class_name)
+
+                    if track_id not in items_in_bins[bin_name]:
+                        items_in_bins[bin_name].add(track_id)
+                        class_counts_per_bin[bin_name][class_name] += 1
+
+                    if key not in logged_objects:
+                        timestamp = time.time()
+                        insert_buffer.append((timestamp, frame_number, bin_name, class_name, 1))
+                        logged_objects[key] = current_time
+
+        # Batch insert every BATCH_SIZE frames
+        if frame_number % BATCH_SIZE == 0 and insert_buffer:
+            cursor.executemany('''
+                INSERT INTO detections (timestamp, frame_number, bin_name, class_name, count)
+                VALUES (?, ?, ?, ?, ?)
+            ''', insert_buffer)
+            conn.commit()
+            insert_buffer.clear()
 
         labels = [
             f"#{tid} {class_names[cid]}"
@@ -196,7 +241,7 @@ def process_video_with_two_side_bins(video_path):
             scene=annotated_frame, detections=tracked_detections, labels=labels
         )
 
-        # Show counts per bin with bigger font
+        # Display counts per bin
         y_pos = 50
         for bin_name, class_count_dict in class_counts_per_bin.items():
             text = (
@@ -208,7 +253,7 @@ def process_video_with_two_side_bins(video_path):
                 text,
                 (30, y_pos),
                 cv2.FONT_HERSHEY_SIMPLEX,
-                1.1,  # bigger font for counts
+                1.1,
                 (255, 255, 255),
                 3,
                 cv2.LINE_AA,
@@ -217,6 +262,15 @@ def process_video_with_two_side_bins(video_path):
 
         yield cv2.cvtColor(annotated_frame, cv2.COLOR_BGR2RGB)
 
+    # Insert any remaining buffered data at end
+    if insert_buffer:
+        cursor.executemany('''
+            INSERT INTO detections (timestamp, frame_number, bin_name, class_name, count)
+            VALUES (?, ?, ?, ?, ?)
+        ''', insert_buffer)
+        conn.commit()
+        insert_buffer.clear()
+
 
 
 # --- Gradio Interface Setup ---