Refactor logging in SDK

depthai_sdk/src/depthai_sdk/classes/packet_handlers.py

@@ -1,4 +1,4 @@
-import logging
+
 import os
 from abc import abstractmethod
 from queue import Queue, Empty
@@ -8,6 +8,7 @@
 
 from depthai_sdk.classes.packets import BasePacket
 from depthai_sdk.components.component import Component, ComponentOutput
+from depthai_sdk.logger import LOGGER
 from depthai_sdk.oak_outputs.fps import FPS
 from depthai_sdk.oak_outputs.syncing import TimestampSync
 from depthai_sdk.oak_outputs.xout.xout_base import XoutBase, ReplayStream
@@ -65,7 +66,7 @@ def configure_syncing(self,
         """
         if enable_sync:
             if len(self.outputs) < 2:
-                logging.error('Syncing requires at least 2 outputs! Skipping syncing.')
+                LOGGER.error('Syncing requires at least 2 outputs! Skipping syncing.')
                 return
             self.sync = TimestampSync(len(self.outputs), threshold_ms)
 

depthai_sdk/src/depthai_sdk/components/camera_component.py

@@ -1,11 +1,11 @@
-import logging
 from typing import Dict
 
 from depthai_sdk.classes.enum import ResizeMode
 from depthai_sdk.components.camera_control import CameraControl
 from depthai_sdk.components.camera_helper import *
 from depthai_sdk.components.component import Component, ComponentOutput
 from depthai_sdk.components.parser import parse_resolution, parse_encode, encoder_profile_to_fourcc
+from depthai_sdk.logger import LOGGER
 from depthai_sdk.oak_outputs.xout.xout_base import XoutBase, StreamXout, ReplayStream
 from depthai_sdk.oak_outputs.xout.xout_frames import XoutFrames
 from depthai_sdk.replay import Replay
@@ -297,7 +297,7 @@ def control_with_nn(self, detection_component: 'NNComponent', auto_focus=True, a
         """
 
         if not auto_focus and not auto_exposure:
-            logging.error('Attempted to control camera with NN, '
+            LOGGER.error('Attempted to control camera with NN, '
                           'but both Auto-Focus and Auto-Exposure were disabled! Attempt ignored.')
             return
 
@@ -337,12 +337,12 @@ def config_color_camera(self,
                             chroma_denoise: Optional[int] = None,
                             ) -> None:
         if not self.is_color():
-            logging.info('Attempted to configure ColorCamera, '
+            LOGGER.info('Attempted to configure ColorCamera, '
                          'but this component doesn\'t have it. Config attempt ignored.')
             return
 
         if self.is_replay():
-            logging.info('Tried configuring ColorCamera, but replaying is enabled. Config attempt ignored.')
+            LOGGER.info('Tried configuring ColorCamera, but replaying is enabled. Config attempt ignored.')
             return
 
         if interleaved is not None: self.node.setInterleaved(interleaved)

depthai_sdk/src/depthai_sdk/components/nn_component.py

@@ -1,5 +1,4 @@
 import json
-import logging
 import warnings
 from collections import defaultdict
 from pathlib import Path
@@ -22,6 +21,7 @@
 from depthai_sdk.classes.enum import ResizeMode
 from depthai_sdk.components.parser import *
 from depthai_sdk.components.stereo_component import StereoComponent
+from depthai_sdk.logger import LOGGER
 from depthai_sdk.visualize.visualizer_helper import depth_to_disp_factor
 from depthai_sdk.oak_outputs.xout.xout_base import StreamXout, XoutBase
 from depthai_sdk.oak_outputs.xout.xout_frames import XoutFrames
@@ -178,7 +178,7 @@ def __init__(self,
                 self.node.input.setBlocking(True)
                 self.node.input.setQueueSize(20)
             else:
-                logging.debug('Using on-host decoding for multi-stage NN')
+                LOGGER.debug('Using on-host decoding for multi-stage NN')
                 # Custom NN
                 self.image_manip.setResize(*self._size)
                 self.image_manip.setMaxOutputFrameSize(self._size[0] * self._size[1] * 3)
@@ -246,7 +246,7 @@ def _parse_model(self, model):
                 model = models[str(model)] / 'config.json'
                 self._parse_config(model)
             elif str(model) in zoo_models:
-                logging.warning(
+                LOGGER.warning(
                     'Models from the OpenVINO Model Zoo do not carry any metadata'
                     ' (e.g., label map, decoding logic). Please keep this in mind when using models from Zoo.'
                 )
@@ -322,7 +322,7 @@ def _parse_config(self, model_config: Union[Path, str, Dict]):
             self._handler = loadModule(model_config.parent / self._config["handler"])
 
             if not callable(getattr(self._handler, "decode", None)):
-                logging.debug("Custom model handler does not contain 'decode' method!")
+                LOGGER.debug("Custom model handler does not contain 'decode' method!")
             else:
                 self._decode_fn = self._handler.decode if self._decode_fn is None else self._decode_fn
 
@@ -410,7 +410,7 @@ def config_multistage_nn(self,
             num_frame_pool (int, optional): Number of frames to pool for inference. If None, will use the default value.
         """
         if not self.is_multi_stage():
-            logging.warning("Input to this model was not a NNComponent, so 2-stage NN inferencing isn't possible!"
+            LOGGER.warning("Input to this model was not a NNComponent, so 2-stage NN inferencing isn't possible!"
                             "This configuration attempt will be ignored.")
             return
 
@@ -520,7 +520,7 @@ def config_yolo(self,
         Configures (Spatial) Yolo Detection Network node.
         """
         if not self.is_yolo():
-            logging.warning('This is not a YOLO detection network! This configuration attempt will be ignored.')
+            LOGGER.warning('This is not a YOLO detection network! This configuration attempt will be ignored.')
             return
 
         if not self.node:
@@ -569,7 +569,7 @@ def config_spatial(self,
             calc_algo (dai.SpatialLocationCalculatorAlgorithm, optional): Specifies spatial location calculator algorithm: Average/Min/Max
         """
         if not self.is_spatial():
-            logging.warning('This is not a Spatial Detection network! This configuration attempt will be ignored.')
+            LOGGER.warning('This is not a Spatial Detection network! This configuration attempt will be ignored.')
             return
 
         if bb_scale_factor is not None:

depthai_sdk/src/depthai_sdk/components/stereo_component.py

@@ -1,4 +1,3 @@
-import logging
 import warnings
 from enum import Enum
 from typing import Optional, Union, Any, Dict, Tuple, List
@@ -12,6 +11,7 @@
 from depthai_sdk.components.parser import parse_median_filter, parse_encode, encoder_profile_to_fourcc
 from depthai_sdk.components.stereo_control import StereoControl
 from depthai_sdk.components.undistort import _get_mesh
+from depthai_sdk.logger import LOGGER
 from depthai_sdk.oak_outputs.xout.xout_base import XoutBase, StreamXout
 from depthai_sdk.oak_outputs.xout.xout_depth import XoutDisparityDepth
 from depthai_sdk.oak_outputs.xout.xout_disparity import XoutDisparity
@@ -115,12 +115,12 @@ def __init__(self,
                 laser = laser if laser is not None else 800
                 if 0 < laser:
                     device.setIrLaserDotProjectorBrightness(laser)
-                    logging.info(f'Setting IR laser dot projector brightness to {laser}mA')
+                    LOGGER.info(f'Setting IR laser dot projector brightness to {laser}mA')
 
                 led = self._args.get('irFloodBrightness', None)
                 if led is not None:
                     device.setIrFloodLightBrightness(int(led))
-                    logging.info(f'Setting IR flood LED brightness to {int(led)}mA')
+                    LOGGER.info(f'Setting IR flood LED brightness to {int(led)}mA')
 
             input_size = self._get_stream_size(self.left)
             if input_size:
@@ -139,7 +139,7 @@ def __init__(self,
                 manip = pipeline.create(dai.node.ImageManip)
                 new_h = int(h * (1280 / w))
                 manip.setResize(1280, new_h)
-                logging.info(f'Input frame size to stereo component was {w}x{h}, added downscalling to 1280x{new_h}')
+                LOGGER.info(f'Input frame size to stereo component was {w}x{h}, added downscalling to 1280x{new_h}')
                 manip.setMaxOutputFrameSize(1280 * new_h)
                 # Stereo works on GRAY8 frames
                 manip.setFrameType(dai.ImgFrame.Type.GRAY8)
@@ -153,7 +153,7 @@ def __init__(self,
                 manip = pipeline.create(dai.node.ImageManip)
                 new_h = int(h * (1280 / w))
                 manip.setResize(1280, new_h)
-                logging.info(f'Input frame size to stereo component was {w}x{h}, added downscalling to 1280x{new_h}')
+                LOGGER.info(f'Input frame size to stereo component was {w}x{h}, added downscalling to 1280x{new_h}')
                 manip.setMaxOutputFrameSize(1280 * new_h)
                 # Stereo works on GRAY8 frames
                 manip.setFrameType(dai.ImgFrame.Type.GRAY8)

depthai_sdk/src/depthai_sdk/components/stereo_control.py

@@ -1,9 +1,7 @@
 import depthai as dai
 from itertools import cycle
-import logging
 from depthai_sdk.components.parser import parse_median_filter
-
-logger = logging.getLogger(__name__)
+from depthai_sdk.logger import LOGGER
 
 LIMITS = {
     'confidence_threshold': (0, 255),
@@ -43,15 +41,15 @@ def switch_median_filter(self):
         Switch between auto white balance modes.
         """
         mode = next(self._cycle_median_filter)
-        logger.info(f'Switching median filter to {mode}')
+        LOGGER.info(f'Switching median filter to {mode}')
         self.send_controls({'postprocessing': {'median': mode}})
 
     def confidence_threshold_up(self, step=10):
         """
         Increase confidence threshold by step
         """
         if LIMITS['confidence_threshold'][1] < self._current_vals['conf_threshold'] + step:
-            logger.error(f'Confidence threshold cannot be greater than {LIMITS["confidence_threshold"][1]}')
+            LOGGER.error(f'Confidence threshold cannot be greater than {LIMITS["confidence_threshold"][1]}')
             return
         self._current_vals['conf_threshold'] += step
         self.send_controls({'cost_matching': {'confidence_threshold': self._current_vals['conf_threshold']}})
@@ -61,7 +59,7 @@ def confidence_threshold_down(self, step=10):
         Decrease confidence threshold by step
         """
         if LIMITS['confidence_threshold'][0] > self._current_vals['conf_threshold'] - step:
-            logger.error(f'Confidence threshold cannot be less than {LIMITS["confidence_threshold"][0]}')
+            LOGGER.error(f'Confidence threshold cannot be less than {LIMITS["confidence_threshold"][0]}')
             return
         self._current_vals['conf_threshold'] -= step
         self.send_controls({'cost_matching': {'confidence_threshold': self._current_vals['conf_threshold']}})
@@ -71,7 +69,7 @@ def dot_projector_up(self, step=50):
         Increase dot projector power by step
         """
         if LIMITS['dot_projector'][1] < self._current_vals['dot_projector'] + step:
-            logger.error(f'Dot projector power cannot be greater than {LIMITS["dot_projector"][1]}')
+            LOGGER.error(f'Dot projector power cannot be greater than {LIMITS["dot_projector"][1]}')
             return
         self._current_vals['dot_projector'] += step
         self.device.setIrFloodLightBrightness(self._current_vals['dot_projector'])
@@ -81,7 +79,7 @@ def dot_projector_down(self, step=50):
         Decrease dot projector power by step
         """
         if LIMITS['dot_projector'][0] > self._current_vals['dot_projector'] - step:
-            logger.error(f'Dot projector power cannot be less than {LIMITS["dot_projector"][0]}')
+            LOGGER.error(f'Dot projector power cannot be less than {LIMITS["dot_projector"][0]}')
             return
         self._current_vals['dot_projector'] -= step
         self.device.setIrFloodLightBrightness(self._current_vals['dot_projector'])
@@ -91,7 +89,7 @@ def illumination_led_up(self, step=50):
         Increase illumination led power by step
         """
         if LIMITS['illumination_led'][1] < self._current_vals['illumination_led'] + step:
-            logger.error(f'Illumination led power cannot be greater than {LIMITS["illumination_led"][1]}')
+            LOGGER.error(f'Illumination led power cannot be greater than {LIMITS["illumination_led"][1]}')
             return
         self._current_vals['illumination_led'] += step
         self.device.setIrLaserDotProjectorBrightness(self._current_vals['illumination_led'])
@@ -101,7 +99,7 @@ def illumination_led_down(self, step=50):
         Decrease illumination led power by step
         """
         if LIMITS['illumination_led'][0] > self._current_vals['illumination_led'] - step:
-            logger.error(f'Illumination led power cannot be less than {LIMITS["illumination_led"][0]}')
+            LOGGER.error(f'Illumination led power cannot be less than {LIMITS["illumination_led"][0]}')
             return
         self._current_vals['illumination_led'] -= step
         self.device.setIrLaserDotProjectorBrightness(self._current_vals['illumination_led'])
@@ -183,15 +181,15 @@ def send_controls(self, controls: dict):
         }
         """
         if self.queue is None:
-            logger.error('Cannot send controls when replaying.')
+            LOGGER.error('Cannot send controls when replaying.')
             return
 
-        logger.info(f'Sending controls to StereoDepth node: {controls}')
+        LOGGER.info(f'Sending controls to StereoDepth node: {controls}')
 
         ctrl = dai.StereoDepthConfig()
 
         if controls.get('reset', None) and controls['reset']:
-            logger.info('Resetting camera controls.')
+            LOGGER.info('Resetting camera controls.')
             self.raw_cfg = ctrl.get()
             ctrl.set(self.raw_cfg)
             self.queue.send(ctrl)

depthai_sdk/src/depthai_sdk/integrations/roboflow.py

@@ -1,10 +1,10 @@
 import json
-import logging
 from pathlib import Path
 from typing import Dict, Optional
 from zipfile import ZipFile
 
 import depthai as dai
+from depthai_sdk.logger import LOGGER
 import requests
 
 ROBOFLOW_MODELS = Path.home() / Path('.cache/roboflow-models')
@@ -48,9 +48,9 @@ def device_update(self, device: dai.Device) -> Path:
             raise ValueError("This Roboflow's model is not from YOLO family!")
 
         if not str(ret['modelType']).endswith('n'):
-            logging.info('We recommend using a lighter version of the model to get a better performance!')
+            LOGGER.info('We recommend using a lighter version of the model to get a better performance!')
 
-        logging.info(f"Downloading '{ret['name']}' model from Roboflow server")
+        LOGGER.info(f"Downloading '{ret['name']}' model from Roboflow server")
 
         zip_file_req = requests.get(ret['model'])
         zip_file_req.raise_for_status()
@@ -61,7 +61,7 @@ def device_update(self, device: dai.Device) -> Path:
         with open(zip_file_path, 'wb') as f:
             f.write(zip_file_req.content)
 
-        logging.info(f"Downloaded the model to {zip_file_path}")
+        LOGGER.info(f"Downloaded the model to {zip_file_path}")
 
         with ZipFile(zip_file_path, 'r') as zObject:  # Extract the zip
             zObject.extractall(str(ROBOFLOW_MODELS / name))

depthai_sdk/src/depthai_sdk/integrations/ros/ros2_streaming.py

@@ -1,11 +1,11 @@
-import logging
 from queue import Queue
 from threading import Thread
 from typing import Dict, Any
 
 import rclpy
 
 from depthai_sdk.integrations.ros.ros_base import RosBase
+from depthai_sdk.logger import LOGGER
 
 
 def ros_thread(queue: Queue):
@@ -18,7 +18,7 @@ def ros_thread(queue: Queue):
         for topic, msg in msgs.items():
             if topic not in publishers:
                 publishers[topic] = node.create_publisher(type(msg), topic, 10)
-                logging.info(f'SDK started publishing ROS messages to {topic}')
+                LOGGER.info(f'SDK started publishing ROS messages to {topic}')
             publishers[topic].publish(msg)
         rclpy.spin_once(node, timeout_sec=0.001)  # 1ms timeout
 

depthai_sdk/src/depthai_sdk/logger.py

@@ -2,21 +2,24 @@
 
 __all__ = ['set_logging_level']
 
+LOGGER = logging.getLogger(__name__)
+"""The DepthAI SDK logger."""
 
 def _configure_logger():
     """
     Configure the logging module.
     """
-    logging.basicConfig(level=logging.INFO,
-                        format='[%(asctime)s] %(levelname)s [%(name)s.%(funcName)s:%(lineno)d] %(message)s',
-                        datefmt='%Y-%m-%d %H:%M:%S')
-
+    handler = logging.StreamHandler()
+    handler.setLevel(logging.INFO)
+    formatter = logging.Formatter('[%(asctime)s] %(levelname)s [%(name)s.%(funcName)s:%(lineno)d] %(message)s', datefmt='%Y-%m-%d %H:%M:%S')
+    handler.setFormatter(formatter)
+    LOGGER.addHandler(handler)
 
 def set_logging_level(level):
     """
-    Set the logging level for the root logger.
+    Set the logging level for the DepthAI SDK logger.
     """
-    logging.getLogger().setLevel(level)
+    LOGGER.setLevel(level)
 
 
 _configure_logger()

depthai_sdk/src/depthai_sdk/oak_camera.py

@@ -1,9 +1,9 @@
-import logging
 import time
 import warnings
 from pathlib import Path
 from typing import Dict, Any, Optional, List, Union, Callable
 
+from depthai_sdk.logger import LOGGER
 from depthai_sdk import CV2_HAS_GUI_SUPPORT
 from depthai_sdk.types import Resolution
 from depthai_sdk.visualize.visualizer import Visualizer
@@ -127,7 +127,7 @@ def __init__(self,
         if replay is not None:
             self.replay = Replay(replay)
             self.replay.initPipeline(self.pipeline)
-            logging.info(f'Available streams from recording: {self.replay.getStreams()}')
+            LOGGER.info(f'Available streams from recording: {self.replay.getStreams()}')
         self._calibration = self._init_calibration()
 
     def camera(self,
@@ -453,7 +453,7 @@ def __exit__(self, exc_type, exc_value, tb):
         self.close()
 
     def close(self):
-        logging.info("Closing OAK camera")
+        LOGGER.info("Closing OAK camera")
         if self.replay:
             self.replay.close()
 
@@ -503,7 +503,7 @@ def start(self, blocking=False):
         # self._polling.append()
         if self._pipeine_graph is not None:
             self._pipeine_graph.create_graph(self.pipeline.serializeToJson()['pipeline'], self.device)
-            logging.info('Pipeline graph process started')
+            LOGGER.info('Pipeline graph process started')
 
         # Call on_pipeline_started() for each component
         for comp in self._components:
@@ -691,5 +691,5 @@ def _init_calibration(self) -> dai.CalibrationHandler:
         else:
             calibration = self.device.readCalibration()
         if calibration is None:
-            logging.warning("No calibration data found on the device or in replay")
+            LOGGER.warning("No calibration data found on the device or in replay")
         return calibration