Refactor: Logging Cleanup

REFACTOR_PLAN.md

@@ -400,7 +400,33 @@ core/
   - [ ] Test stream interruption handling
   - [ ] Test concurrent subscription management
 
-### 6.5 Final Cleanup
+### 6.5 Logging Optimization & Cleanup ✅
+- ✅ **Removed Debug Frame Saving**
+  - ✅ Removed hard-coded debug frame saving in `core/detection/pipeline.py`
+  - ✅ Removed hard-coded debug frame saving in `core/detection/branches.py`
+  - ✅ Eliminated absolute debug paths for production use
+
+- ✅ **Eliminated Test/Mock Functionality**
+  - ✅ Removed `save_frame_for_testing` function from `core/streaming/buffers.py`
+  - ✅ Removed `save_test_frames` configuration from `StreamConfig`
+  - ✅ Cleaned up test frame saving calls in stream manager
+  - ✅ Updated module exports to remove test functions
+
+- ✅ **Reduced Verbose Logging**
+  - ✅ Commented out verbose frame storage logging (every frame)
+  - ✅ Converted debug-level info logs to proper debug level
+  - ✅ Reduced repetitive frame dimension logging
+  - ✅ Maintained important model results and detection confidence logging
+  - ✅ Kept critical pipeline execution and error messages
+
+- ✅ **Production-Ready Logging**
+  - ✅ Clean startup and initialization messages
+  - ✅ Clear model loading and pipeline status
+  - ✅ Preserved detection results with confidence scores
+  - ✅ Maintained session management and tracking messages
+  - ✅ Kept important error and warning messages
+
+### 6.6 Final Cleanup
 - [ ] Remove any remaining duplicate code
 - [ ] Optimize imports across all modules
 - [ ] Clean up temporary files and debugging code

core/communication/websocket.py

@@ -393,7 +393,6 @@ class WebSocketHandler:
                 snapshot_url=payload.get('snapshotUrl'),
                 snapshot_interval=payload.get('snapshotInterval', 5000),
                 max_retries=3,
-                save_test_frames=False  # Disable frame saving, focus on tracking
             )
 
             # Add subscription to StreamManager with tracking

core/detection/branches.py

@@ -441,19 +441,6 @@ class BranchProcessor:
             logger.info(f"[INFERENCE START] {branch_id}: Running inference on {'cropped' if input_frame is not frame else 'full'} frame "
                        f"({input_frame.shape[1]}x{input_frame.shape[0]}) with confidence={min_confidence}")
 
-            # Save input frame for debugging
-            import os
-            import cv2
-            debug_dir = "/Users/ziesorx/Documents/Work/Adsist/Bangchak/worker/python-detector-worker/debug_frames"
-            timestamp = detection_context.get('timestamp', 'unknown')
-            session_id = detection_context.get('session_id', 'unknown')
-            debug_filename = f"{debug_dir}/{branch_id}_{session_id}_{timestamp}_input.jpg"
-
-            try:
-                cv2.imwrite(debug_filename, input_frame)
-                logger.info(f"[DEBUG] Saved inference input frame: {debug_filename} ({input_frame.shape[1]}x{input_frame.shape[0]})")
-            except Exception as e:
-                logger.warning(f"[DEBUG] Failed to save debug frame: {e}")
 
             # Use .predict() method for both detection and classification models
             inference_start = time.time()

core/detection/pipeline.py

@@ -503,17 +503,6 @@ class DetectionPipeline:
                 'filename': f"{uuid.uuid4()}.jpg"
             }
 
-            # Save full frame for debugging
-            import cv2
-            debug_dir = "/Users/ziesorx/Documents/Work/Adsist/Bangchak/worker/python-detector-worker/debug_frames"
-            timestamp = detection_context.get('timestamp', 'unknown')
-            session_id = detection_context.get('session_id', 'unknown')
-            debug_filename = f"{debug_dir}/pipeline_full_frame_{session_id}_{timestamp}.jpg"
-            try:
-                cv2.imwrite(debug_filename, frame)
-                logger.info(f"[DEBUG PIPELINE] Saved full input frame: {debug_filename} ({frame.shape[1]}x{frame.shape[0]})")
-            except Exception as e:
-                logger.warning(f"[DEBUG PIPELINE] Failed to save debug frame: {e}")
 
             # Run inference on single snapshot using .predict() method
             detection_results = self.detection_model.model.predict(

core/streaming/__init__.py

@@ -3,7 +3,7 @@ Streaming system for RTSP and HTTP camera feeds.
 Provides modular frame readers, buffers, and stream management.
 """
 from .readers import RTSPReader, HTTPSnapshotReader
-from .buffers import FrameBuffer, CacheBuffer, shared_frame_buffer, shared_cache_buffer, save_frame_for_testing
+from .buffers import FrameBuffer, CacheBuffer, shared_frame_buffer, shared_cache_buffer
 from .manager import StreamManager, StreamConfig, SubscriptionInfo, shared_stream_manager
 
 __all__ = [
@@ -16,7 +16,6 @@ __all__ = [
     'CacheBuffer',
     'shared_frame_buffer',
     'shared_cache_buffer',
-    'save_frame_for_testing',
 
     # Manager
     'StreamManager',

core/streaming/buffers.py

@@ -67,8 +67,9 @@ class FrameBuffer:
                 'size_mb': frame.nbytes / (1024 * 1024)
             }
 
-            logger.debug(f"Stored {stream_type.value} frame for camera {camera_id}: "
+            # Commented out verbose frame storage logging
-                        f"{frame.shape[1]}x{frame.shape[0]}, {frame.nbytes / (1024 * 1024):.2f}MB")
+            # logger.debug(f"Stored {stream_type.value} frame for camera {camera_id}: "
+            #              f"{frame.shape[1]}x{frame.shape[0]}, {frame.nbytes / (1024 * 1024):.2f}MB")
 
     def get_frame(self, camera_id: str) -> Optional[np.ndarray]:
         """Get the latest frame for the given camera ID."""
@@ -400,31 +401,3 @@ shared_frame_buffer = FrameBuffer(max_age_seconds=5)
 shared_cache_buffer = CacheBuffer(max_age_seconds=10)
 
 
-def save_frame_for_testing(camera_id: str, frame: np.ndarray, test_dir: str = "test_frames"):
-    """Save frame to test directory for verification purposes."""
-    import os
-
-    try:
-        os.makedirs(test_dir, exist_ok=True)
-        timestamp = int(time.time() * 1000)  # milliseconds
-        filename = f"{camera_id}_{timestamp}.jpg"
-        filepath = os.path.join(test_dir, filename)
-
-        # Use appropriate quality based on frame size
-        h, w = frame.shape[:2]
-        if w >= 2000:  # High resolution
-            quality = 95
-        else:  # Standard resolution
-            quality = 90
-
-        encode_params = [cv2.IMWRITE_JPEG_QUALITY, quality]
-        success = cv2.imwrite(filepath, frame, encode_params)
-
-        if success:
-            size_kb = os.path.getsize(filepath) / 1024
-            logger.info(f"Saved test frame: {filepath} ({w}x{h}, {size_kb:.1f}KB)")
-        else:
-            logger.error(f"Failed to save test frame: {filepath}")
-
-    except Exception as e:
-        logger.error(f"Error saving test frame for camera {camera_id}: {e}")

core/streaming/manager.py

@@ -10,7 +10,7 @@ from dataclasses import dataclass
 from collections import defaultdict
 
 from .readers import RTSPReader, HTTPSnapshotReader
-from .buffers import shared_cache_buffer, save_frame_for_testing, StreamType
+from .buffers import shared_cache_buffer, StreamType
 from ..tracking.integration import TrackingPipelineIntegration
 
 
@@ -25,7 +25,6 @@ class StreamConfig:
     snapshot_url: Optional[str] = None
     snapshot_interval: int = 5000  # milliseconds
     max_retries: int = 3
-    save_test_frames: bool = False
 
 
 @dataclass
@@ -184,13 +183,6 @@ class StreamManager:
             # Store frame in shared buffer with stream type
             shared_cache_buffer.put_frame(camera_id, frame, stream_type)
 
-            # Save test frames if enabled for any subscription
-            with self._lock:
-                for subscription_id in self._camera_subscribers[camera_id]:
-                    subscription_info = self._subscriptions[subscription_id]
-                    if subscription_info.stream_config.save_test_frames:
-                        save_frame_for_testing(camera_id, frame)
-                        break  # Only save once per frame
 
             # Process tracking for subscriptions with tracking integration
             self._process_tracking_for_camera(camera_id, frame)
@@ -349,7 +341,6 @@ class StreamManager:
                 snapshot_url=payload.get('snapshotUrl'),
                 snapshot_interval=payload.get('snapshotInterval', 5000),
                 max_retries=3,
-                save_test_frames=True  # Enable for testing
             )
 
             return self.add_subscription(

core/tracking/integration.py

@@ -200,11 +200,11 @@ class TrackingPipelineIntegration:
                     raw_detections = len(tracking_results.detections)
                     if raw_detections > 0:
                         class_names = [detection.class_name for detection in tracking_results.detections]
-                        logger.info(f"[DEBUG] Raw detections: {raw_detections}, classes: {class_names}")
+                        logger.debug(f"Raw detections: {raw_detections}, classes: {class_names}")
                     else:
-                        logger.debug(f"[DEBUG] No raw detections found")
+                        logger.debug(f"No raw detections found")
                 else:
-                    logger.debug(f"[DEBUG] No tracking results or detections attribute")
+                    logger.debug(f"No tracking results or detections attribute")
 
                 # Process tracking results
                 tracked_vehicles = self.tracker.process_detections(

core/tracking/validator.py

@@ -73,7 +73,8 @@ class StableCarValidator:
         """Update frame dimensions for zone calculations."""
         self.frame_width = width
         self.frame_height = height
-        logger.debug(f"Updated frame dimensions: {width}x{height}")
+        # Commented out verbose frame dimension logging
+        # logger.debug(f"Updated frame dimensions: {width}x{height}")
 
     def validate_vehicle(self, vehicle: TrackedVehicle, frame_shape: Optional[Tuple] = None) -> ValidationResult:
         """