Added callback for validating parameters

detection_ws/src/fruit_detection/fruit_detection/fruit_detection_node.py

@@ -21,6 +21,8 @@
     QoSProfile,
     ReliabilityPolicy,
 )
+from rclpy.parameter import Parameter
+from rcl_interfaces.msg import SetParametersResult
 from sensor_msgs.msg import CompressedImage, Image
 from std_msgs.msg import Header
 from vision_msgs.msg import (
@@ -77,8 +79,12 @@ class FruitDetectionNode(Node):
     RECT_COLOR = (0, 0, 255)
     SCORE_THRESHOLD = 0.7
     LOGGING_THROTTLE = 1
-    MINIMUM_BOX_SIZE_X = 50
-    MINIMUM_BOX_SIZE_Y = 50
+    DEFAULT_BBOX_SIZE_X = 50
+    DEFAULT_BBOX_SIZE_Y = 50
+    MINIMUM_BBOX_SIZE_X = 0
+    MINIMUM_BBOX_SIZE_Y = 0
+    MAXIMUM_BBOX_SIZE_X = 640
+    MAXIMUM_BBOX_SIZE_Y = 480
 
     def __init__(self) -> None:
         """Initialize the node."""
@@ -90,13 +96,15 @@ def __init__(self) -> None:
         )
         self.declare_parameter(
             "bbox_min_x",
-            FruitDetectionNode.MINIMUM_BOX_SIZE_X,
+            FruitDetectionNode.DEFAULT_BBOX_SIZE_X,
         )
         self.declare_parameter(
             "bbox_min_y",
-            FruitDetectionNode.MINIMUM_BOX_SIZE_Y,
+            FruitDetectionNode.DEFAULT_BBOX_SIZE_Y,
         )
 
+        self.add_on_set_parameters_callback(self.validate_parameters)
+
         self.__model_path = (
             self.get_parameter("model_path").get_parameter_value().string_value
         )
@@ -143,6 +151,34 @@ def __init__(self) -> None:
         self.get_logger().info("ingestion;inference;plot;detection;publish;")
         self.ingest_transform = get_transform()
 
+    def validate_parameters(self, params):
+        """
+        Validate parameter changes.
+
+        :param params: list of parameters.
+
+        :return: SetParametersResult.
+        """
+        parameters_are_valid = True
+        for param in params:
+            if param.name == "bbox_min_x":
+                if param.type_ != Parameter.Type.INTEGER or not (
+                    FruitDetectionNode.MINIMUM_BBOX_SIZE_X
+                    <= param.value
+                    <= FruitDetectionNode.MAXIMUM_BBOX_SIZE_X
+                ):
+                    parameters_are_valid = False
+                    break
+            elif param.name == "bbox_min_y":
+                if param.type_ != Parameter.Type.INTEGER or not (
+                    FruitDetectionNode.MINIMUM_BBOX_SIZE_Y
+                    <= param.value
+                    <= FruitDetectionNode.MAXIMUM_BBOX_SIZE_Y
+                ):
+                    parameters_are_valid = False
+                    break
+        return SetParametersResult(successful=parameters_are_valid)
+
     def load_model(self):
         """Load the torch model."""
         self.model = fasterrcnn_resnet50_fpn(weights=None)
@@ -202,16 +238,22 @@ def score_frame(self, frame):
             for i, (box, score, label) in enumerate(
                 zip(output["boxes"], output["scores"], output["labels"])
             ):
+                bbox_min_x = (
+                    self.get_parameter("bbox_min_x")
+                    .get_parameter_value()
+                    .integer_value  # Minimum bbox x size
+                )
+                bbox_min_y = (
+                    self.get_parameter("bbox_min_y")
+                    .get_parameter_value()
+                    .integer_value  # Minimum bbox y size
+                )
                 if (
                     score >= FruitDetectionNode.SCORE_THRESHOLD
                     and self.bbox_has_minimum_size(
                         box,
-                        self.get_parameter("bbox_min_x")
-                        .get_parameter_value()
-                        .integer_value,
-                        self.get_parameter("bbox_min_y")
-                        .get_parameter_value()
-                        .integer_value,
+                        bbox_min_x,
+                        bbox_min_y,
                     )
                 ):
                     results.append(