feat: save high-confidence images to sorted directories

Co-authored-by: aider (gemini/gemini-2.5-pro-preview-05-06) <aider@aider.chat>

server.py

@@ -9,7 +9,7 @@ import asyncio
 import aiohttp
 from aiohttp import web
 import io
-from datetime import datetime
+from datetime import datetime, timedelta
 
 import torch
 import torch.nn.functional as F
@@ -32,6 +32,7 @@ UNSURE_CONFIDENCE_THRESHOLD = 0.97
 PREDICTION_HISTORY = []
 PREDICTION_HISTORY_MAX_LENGTH = 3
 PREVIOUS_STATE = "unknown"
+LAST_OPEN_SAVE_TIME = None
 
 # --- Model Inference ---
 def get_prediction(model, image_bytes, device):
@@ -61,6 +62,7 @@ def get_prediction(model, image_bytes, device):
 # --- Background Task ---
 async def monitor_garage_door(app):
     """Periodically fetches an image and logs the garage door status."""
+    global LAST_OPEN_SAVE_TIME
     logging.info("Starting garage door monitoring task.")
     session = app['client_session']
     model = app['model']
@@ -89,11 +91,10 @@ async def monitor_garage_door(app):
                         if len(PREDICTION_HISTORY) > PREDICTION_HISTORY_MAX_LENGTH:
                             PREDICTION_HISTORY.pop(0)
 
+                        timestamp = datetime.now().isoformat().replace(':', '-')
+                        filename = f"{timestamp}.jpg"
+
                         if confidence < UNSURE_CONFIDENCE_THRESHOLD:
-                            # Sanitize timestamp for use in filename
-                            timestamp = datetime.now().isoformat().replace(':', '-')
-                            filename = f"{timestamp}.jpg"
-                            
                             # Construct path and save file
                             unsure_dir = os.path.join('data', 'unsure', prediction)
                             os.makedirs(unsure_dir, exist_ok=True)
@@ -103,6 +104,29 @@ async def monitor_garage_door(app):
                                 f.write(image_bytes)
                             
                             logging.info(f"Low confidence prediction: {prediction} ({confidence:.4f}). Saved for review: {filepath}")
+                        else:
+                            # High confidence, save to sorted
+                            if prediction == 'open':
+                                if LAST_OPEN_SAVE_TIME is None or (datetime.now() - LAST_OPEN_SAVE_TIME) > timedelta(minutes=5):
+                                    sorted_dir = os.path.join('data', 'sorted', 'open')
+                                    os.makedirs(sorted_dir, exist_ok=True)
+                                    filepath = os.path.join(sorted_dir, filename)
+                                    with open(filepath, 'wb') as f:
+                                        f.write(image_bytes)
+                                    LAST_OPEN_SAVE_TIME = datetime.now()
+                                    logging.info(f"Saved high-confidence 'open' image: {filepath}")
+                            elif prediction == 'closed':
+                                open_dir = os.path.join('data', 'sorted', 'open')
+                                closed_dir = os.path.join('data', 'sorted', 'closed')
+                                os.makedirs(open_dir, exist_ok=True)
+                                os.makedirs(closed_dir, exist_ok=True)
+                                num_open = len(os.listdir(open_dir))
+                                num_closed = len(os.listdir(closed_dir))
+                                if num_closed < num_open:
+                                    filepath = os.path.join(closed_dir, filename)
+                                    with open(filepath, 'wb') as f:
+                                        f.write(image_bytes)
+                                    logging.info(f"Saved high-confidence 'closed' image: {filepath}")
                 else:
                     logging.error(f"Failed to fetch image. Status: {response.status}, Reason: {response.reason}")