Added load_parameters() function and updated logger

object_detection/config/params.yaml

@@ -1,12 +1,11 @@
 object_detection:
   ros__parameters:
-    input_img_topic: /kitti/camera/color/left/image_raw
+    input_img_topic: color/image_raw
     output_bb_topic: object_detection/img_bb
     output_img_topic: object_detection/img
-    publish_output_img: 1
     model_params:
       detector_type: YOLOv5
       model_dir_path: /root/percep_ws/models/yolov5
       weight_file_name: yolov5.onnx
-      confidence_threshold : 0.5
-      show_fps : 1
+      confidence_threshold: 0.5
+      show_fps: 1

object_detection/object_detection/ObjectDetection.py

@@ -32,49 +32,63 @@ class ObjectDetection(Node):
     def __init__(self):
         super().__init__('object_detection')
 
-        # create an empty list that will hold the names of all available detector
+        # Create an empty list that will hold the names of all available detector
         self.available_detectors = []
 
+        # Create a logger instance
+        self.logger  = super().get_logger()
+
+        # Declare parameters with default values
         self.declare_parameters(
             namespace='',
             parameters=[
-                ('input_img_topic', ''),
-                ('output_bb_topic', ''),
-                ('output_img_topic', ''),
-                ('model_params.detector_type', ''),
-                ('model_params.model_dir_path', ''),
-                ('model_params.weight_file_name', ''),
-                ('model_params.confidence_threshold', 0.7),
+                ('input_img_topic', 'color/image_raw'),
+                ('output_bb_topic', 'object_detection/img_bb'),
+                ('output_img_topic', 'object_detection/img'),
+                ('model_params.detector_type', 'YOLOv5'),
+                ('model_params.model_dir_path', '/root/percep_ws/models/yolov5'),
+                ('model_params.weight_file_name', 'yolov5.onnx'),
+                ('model_params.confidence_threshold', 0.5),
                 ('model_params.show_fps', 1),
             ]
         )
-
-        # node params
-        self.input_img_topic = self.get_parameter('input_img_topic').value
-        self.output_bb_topic = self.get_parameter('output_bb_topic').value
-        self.output_img_topic = self.get_parameter('output_img_topic').value
-
-        # model params
-        self.detector_type = self.get_parameter('model_params.detector_type').value
-        self.model_dir_path = self.get_parameter('model_params.model_dir_path').value
-        self.weight_file_name = self.get_parameter('model_params.weight_file_name').value
-        self.confidence_threshold = self.get_parameter('model_params.confidence_threshold').value
-        self.show_fps = self.get_parameter('model_params.show_fps').value
+        # Load parameters set by user
+        self.load_parameters()
 
         # Fill available_detectors with the detectors from Detectors directory
         self.discover_detectors()
         # Load the detector specified through the parameters
         self.load_detector()
 
-        # Create a logger instance
-        self.logger  = self.get_logger()
-
         self.img_pub = self.create_publisher(Image, self.output_img_topic, 10)
         self.bb_pub = None
         self.img_sub = self.create_subscription(Image, self.input_img_topic, self.detection_cb, 10)
 
         self.bridge = CvBridge()
 
+        self.logger.info("[OBJECT DETECTION] Initialized Object Detection Node")
+
+    def load_parameters(self):
+        # Node params
+        self.input_img_topic = self.get_parameter('input_img_topic').value
+        self.output_bb_topic = self.get_parameter('output_bb_topic').value
+        self.output_img_topic = self.get_parameter('output_img_topic').value
+
+        self.logger.info("[OBJECT DETECTION] Input image topic set to {}".format(self.input_img_topic))
+        self.logger.info("[OBJECT DETECTION] Publishig output image on topic {}".format(self.output_img_topic) +
+                         " and bounding box data on topic {}".format(self.output_bb_topic))
+
+        # Model params
+        self.detector_type = self.get_parameter('model_params.detector_type').value
+        self.model_dir_path = self.get_parameter('model_params.model_dir_path').value
+        self.weight_file_name = self.get_parameter('model_params.weight_file_name').value
+        self.confidence_threshold = self.get_parameter('model_params.confidence_threshold').value
+        self.show_fps = self.get_parameter('model_params.show_fps').value
+
+        self.logger.info("[OBJECT DETECTION] Detector type set to {} and".format(self.detector_type) +
+                         " using weigth file from {}".format(self.model_dir_path + self.weight_file_name))
+        self.logger.info("[OBJECT DETECTION] Confidence threshold for detection set to: {}".format(self.confidence_threshold))
+
     def discover_detectors(self):
         curr_dir = os.path.dirname(__file__)
         dir_contents = os.listdir(curr_dir + "/Detectors")
@@ -88,7 +102,7 @@ def discover_detectors(self):
     def load_detector(self):
         # Raise an exception if specified detector was not found
         if self.detector_type not in self.available_detectors:
-            self.logger.error("{} Detector was set in parameters but was not found. Check the " +
+            self.logger.error("[OBJECT DETECTION] {} Detector was set in parameters but was not found. Check the " +
                               "Detectors directory for list of available detectors".format(self.detector_type))
             raise ModuleNotFoundError(self.detector_type + " Detector specified in config was not found. " +
                                       "Check the Detectors dir for available detectors.")
@@ -101,15 +115,15 @@ def load_detector(self):
             self.detector.build_model(self.model_dir_path, self.weight_file_name)
             self.detector.load_classes(self.model_dir_path)
 
-            self.logger.info("Your detector: {} has been loaded!".format(self.detector_type))
+            self.logger.info("[OBJECT DETECTION] Your detector: {} has been loaded!".format(self.detector_type))
 
     def detection_cb(self, img_msg):
         cv_image = self.bridge.imgmsg_to_cv2(img_msg, "bgr8")
 
         predictions = self.detector.get_predictions(cv_image=cv_image)
 
         if predictions is None:
-            self.logger.warning("Image input from topic: {} is empty".format(self.input_img_topic), throttle_duration_sec=1)
+            self.logger.warning("[OBJECT DETECTION] Image input from topic: {} is empty".format(self.input_img_topic), throttle_duration_sec=1)
         else:
             for prediction in predictions:
                 confidence = prediction['confidence']