Added parameters to filter bounding boxes based on size. (#49)

* Added parameters to filter bounding boxes based on size.

* Added callback for validating parameters

README.md

@@ -190,6 +190,10 @@ docker compose -f docker/docker-compose.yml --profile simulated_pipeline up
 docker compose -f docker/docker-compose.yml --profile simulated_pipeline down
 ```
 
+### Minimum bounding box tuning
+
+There are two parameters (`bbox_min_x` and `bbox_min_y`, both measured in pixels) available in the detection node, that can be used to filter the inferences based on the size of the bounding boxes generated. They can be modified via rqt_gui (Plugins -> Configuration -> Dynamic Reconfigure).
+
 ## Test
 
 ### Detection stack

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 (
@@ -77,6 +79,12 @@ class FruitDetectionNode(Node):
     RECT_COLOR = (0, 0, 255)
     SCORE_THRESHOLD = 0.7
     LOGGING_THROTTLE = 1
+    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."""
@@ -86,6 +94,16 @@ def __init__(self) -> None:
         self.declare_parameter(
             "olive_camera_topic", "/olive/camera/id02/image/compressed"
         )
+        self.declare_parameter(
+            "bbox_min_x",
+            FruitDetectionNode.DEFAULT_BBOX_SIZE_X,
+        )
+        self.declare_parameter(
+            "bbox_min_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
@@ -133,6 +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)
@@ -159,6 +205,19 @@ def cv2_to_torch_frame(self, img):
         """Prepare cv2 image for inference."""
         return self.image_to_tensor(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
 
+    def bbox_has_minimum_size(self, box, min_x, min_y):
+        """
+        Check if a box is bigger than a minimum size.
+
+        :param box: bounding box from inference.
+        :param min_x: minimum horizontal length of the bounding box.
+        :param min_y: minimum vertical length of the bounding box.
+
+        :return: True if the box has the minimum size.
+        """
+        x1, y1, x2, y2 = int(box[0]), int(box[1]), int(box[2]), int(box[3])
+        return (min_x <= (x2 - x1)) and (min_y <= (y2 - y1))
+
     def score_frame(self, frame):
         """
         Score each frame of the video and return results.
@@ -179,7 +238,24 @@ def score_frame(self, frame):
             for i, (box, score, label) in enumerate(
                 zip(output["boxes"], output["scores"], output["labels"])
             ):
-                if score >= FruitDetectionNode.SCORE_THRESHOLD:
+                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,
+                        bbox_min_x,
+                        bbox_min_y,
+                    )
+                ):
                     results.append(
                         {
                             "box": box,