Refactor: Logging Cleanup
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ziesorx
parent
7a9a149955
commit
5176f99ba7
9 changed files with 37 additions and 72 deletions
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@ -400,7 +400,33 @@ core/
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- [ ] Test stream interruption handling
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- [ ] Test concurrent subscription management
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### 6.5 Final Cleanup
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### 6.5 Logging Optimization & Cleanup ✅
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- ✅ **Removed Debug Frame Saving**
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- ✅ Removed hard-coded debug frame saving in `core/detection/pipeline.py`
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- ✅ Removed hard-coded debug frame saving in `core/detection/branches.py`
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- ✅ Eliminated absolute debug paths for production use
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- ✅ **Eliminated Test/Mock Functionality**
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- ✅ Removed `save_frame_for_testing` function from `core/streaming/buffers.py`
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- ✅ Removed `save_test_frames` configuration from `StreamConfig`
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- ✅ Cleaned up test frame saving calls in stream manager
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- ✅ Updated module exports to remove test functions
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- ✅ **Reduced Verbose Logging**
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- ✅ Commented out verbose frame storage logging (every frame)
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- ✅ Converted debug-level info logs to proper debug level
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- ✅ Reduced repetitive frame dimension logging
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- ✅ Maintained important model results and detection confidence logging
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- ✅ Kept critical pipeline execution and error messages
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- ✅ **Production-Ready Logging**
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- ✅ Clean startup and initialization messages
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- ✅ Clear model loading and pipeline status
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- ✅ Preserved detection results with confidence scores
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- ✅ Maintained session management and tracking messages
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- ✅ Kept important error and warning messages
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### 6.6 Final Cleanup
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- [ ] Remove any remaining duplicate code
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- [ ] Optimize imports across all modules
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- [ ] Clean up temporary files and debugging code
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@ -393,7 +393,6 @@ class WebSocketHandler:
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snapshot_url=payload.get('snapshotUrl'),
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snapshot_interval=payload.get('snapshotInterval', 5000),
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max_retries=3,
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save_test_frames=False # Disable frame saving, focus on tracking
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)
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# Add subscription to StreamManager with tracking
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@ -441,19 +441,6 @@ class BranchProcessor:
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logger.info(f"[INFERENCE START] {branch_id}: Running inference on {'cropped' if input_frame is not frame else 'full'} frame "
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f"({input_frame.shape[1]}x{input_frame.shape[0]}) with confidence={min_confidence}")
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# Save input frame for debugging
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import os
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import cv2
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debug_dir = "/Users/ziesorx/Documents/Work/Adsist/Bangchak/worker/python-detector-worker/debug_frames"
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timestamp = detection_context.get('timestamp', 'unknown')
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session_id = detection_context.get('session_id', 'unknown')
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debug_filename = f"{debug_dir}/{branch_id}_{session_id}_{timestamp}_input.jpg"
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try:
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cv2.imwrite(debug_filename, input_frame)
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logger.info(f"[DEBUG] Saved inference input frame: {debug_filename} ({input_frame.shape[1]}x{input_frame.shape[0]})")
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except Exception as e:
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logger.warning(f"[DEBUG] Failed to save debug frame: {e}")
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# Use .predict() method for both detection and classification models
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inference_start = time.time()
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@ -503,17 +503,6 @@ class DetectionPipeline:
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'filename': f"{uuid.uuid4()}.jpg"
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}
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# Save full frame for debugging
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import cv2
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debug_dir = "/Users/ziesorx/Documents/Work/Adsist/Bangchak/worker/python-detector-worker/debug_frames"
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timestamp = detection_context.get('timestamp', 'unknown')
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session_id = detection_context.get('session_id', 'unknown')
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debug_filename = f"{debug_dir}/pipeline_full_frame_{session_id}_{timestamp}.jpg"
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try:
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cv2.imwrite(debug_filename, frame)
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logger.info(f"[DEBUG PIPELINE] Saved full input frame: {debug_filename} ({frame.shape[1]}x{frame.shape[0]})")
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except Exception as e:
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logger.warning(f"[DEBUG PIPELINE] Failed to save debug frame: {e}")
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# Run inference on single snapshot using .predict() method
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detection_results = self.detection_model.model.predict(
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@ -3,7 +3,7 @@ Streaming system for RTSP and HTTP camera feeds.
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Provides modular frame readers, buffers, and stream management.
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"""
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from .readers import RTSPReader, HTTPSnapshotReader
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from .buffers import FrameBuffer, CacheBuffer, shared_frame_buffer, shared_cache_buffer, save_frame_for_testing
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from .buffers import FrameBuffer, CacheBuffer, shared_frame_buffer, shared_cache_buffer
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from .manager import StreamManager, StreamConfig, SubscriptionInfo, shared_stream_manager
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__all__ = [
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@ -16,7 +16,6 @@ __all__ = [
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'CacheBuffer',
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'shared_frame_buffer',
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'shared_cache_buffer',
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'save_frame_for_testing',
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# Manager
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'StreamManager',
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@ -67,8 +67,9 @@ class FrameBuffer:
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'size_mb': frame.nbytes / (1024 * 1024)
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}
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logger.debug(f"Stored {stream_type.value} frame for camera {camera_id}: "
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f"{frame.shape[1]}x{frame.shape[0]}, {frame.nbytes / (1024 * 1024):.2f}MB")
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# Commented out verbose frame storage logging
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# logger.debug(f"Stored {stream_type.value} frame for camera {camera_id}: "
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# f"{frame.shape[1]}x{frame.shape[0]}, {frame.nbytes / (1024 * 1024):.2f}MB")
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def get_frame(self, camera_id: str) -> Optional[np.ndarray]:
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"""Get the latest frame for the given camera ID."""
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@ -400,31 +401,3 @@ shared_frame_buffer = FrameBuffer(max_age_seconds=5)
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shared_cache_buffer = CacheBuffer(max_age_seconds=10)
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def save_frame_for_testing(camera_id: str, frame: np.ndarray, test_dir: str = "test_frames"):
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"""Save frame to test directory for verification purposes."""
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import os
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try:
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os.makedirs(test_dir, exist_ok=True)
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timestamp = int(time.time() * 1000) # milliseconds
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filename = f"{camera_id}_{timestamp}.jpg"
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filepath = os.path.join(test_dir, filename)
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# Use appropriate quality based on frame size
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h, w = frame.shape[:2]
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if w >= 2000: # High resolution
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quality = 95
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else: # Standard resolution
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quality = 90
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encode_params = [cv2.IMWRITE_JPEG_QUALITY, quality]
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success = cv2.imwrite(filepath, frame, encode_params)
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if success:
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size_kb = os.path.getsize(filepath) / 1024
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logger.info(f"Saved test frame: {filepath} ({w}x{h}, {size_kb:.1f}KB)")
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else:
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logger.error(f"Failed to save test frame: {filepath}")
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except Exception as e:
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logger.error(f"Error saving test frame for camera {camera_id}: {e}")
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@ -10,7 +10,7 @@ from dataclasses import dataclass
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from collections import defaultdict
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from .readers import RTSPReader, HTTPSnapshotReader
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from .buffers import shared_cache_buffer, save_frame_for_testing, StreamType
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from .buffers import shared_cache_buffer, StreamType
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from ..tracking.integration import TrackingPipelineIntegration
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@ -25,7 +25,6 @@ class StreamConfig:
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snapshot_url: Optional[str] = None
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snapshot_interval: int = 5000 # milliseconds
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max_retries: int = 3
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save_test_frames: bool = False
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@dataclass
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@ -184,13 +183,6 @@ class StreamManager:
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# Store frame in shared buffer with stream type
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shared_cache_buffer.put_frame(camera_id, frame, stream_type)
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# Save test frames if enabled for any subscription
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with self._lock:
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for subscription_id in self._camera_subscribers[camera_id]:
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subscription_info = self._subscriptions[subscription_id]
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if subscription_info.stream_config.save_test_frames:
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save_frame_for_testing(camera_id, frame)
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break # Only save once per frame
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# Process tracking for subscriptions with tracking integration
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self._process_tracking_for_camera(camera_id, frame)
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@ -349,7 +341,6 @@ class StreamManager:
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snapshot_url=payload.get('snapshotUrl'),
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snapshot_interval=payload.get('snapshotInterval', 5000),
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max_retries=3,
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save_test_frames=True # Enable for testing
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)
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return self.add_subscription(
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@ -200,11 +200,11 @@ class TrackingPipelineIntegration:
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raw_detections = len(tracking_results.detections)
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if raw_detections > 0:
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class_names = [detection.class_name for detection in tracking_results.detections]
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logger.info(f"[DEBUG] Raw detections: {raw_detections}, classes: {class_names}")
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logger.debug(f"Raw detections: {raw_detections}, classes: {class_names}")
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else:
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logger.debug(f"[DEBUG] No raw detections found")
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logger.debug(f"No raw detections found")
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else:
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logger.debug(f"[DEBUG] No tracking results or detections attribute")
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logger.debug(f"No tracking results or detections attribute")
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# Process tracking results
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tracked_vehicles = self.tracker.process_detections(
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@ -73,7 +73,8 @@ class StableCarValidator:
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"""Update frame dimensions for zone calculations."""
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self.frame_width = width
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self.frame_height = height
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logger.debug(f"Updated frame dimensions: {width}x{height}")
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# Commented out verbose frame dimension logging
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# logger.debug(f"Updated frame dimensions: {width}x{height}")
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def validate_vehicle(self, vehicle: TrackedVehicle, frame_shape: Optional[Tuple] = None) -> ValidationResult:
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"""
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