made changes indicated in review by ashum68

modules/bootcamp/decision_waypoint_landing_pads.py

@@ -7,7 +7,6 @@
 # Disable for bootcamp use
 # pylint: disable=unused-import
 
-import sys
 from .. import commands
 from .. import drone_report
 from .. import drone_status
@@ -58,13 +57,18 @@ def find_closest_landing_pad(
         self,
         current_position: location.Location,
         landing_pad_locations: "list[location.Location]",
-    ) -> location.Location:
+    ) -> location.Location | None:
         """
         Find the closest landing pad
         """
+
+        # Handle case when no landing pads present
+        if len(landing_pad_locations) == 0:
+            return None
+
         closest_pad = None
         # Setting default to maximum integer value for easy troubleshooting
-        min_distance = sys.maxsize
+        min_distance = float("inf")
 
         # Trivial sorter to determine the closest landing pad
         for lander in landing_pad_locations:
@@ -118,9 +122,9 @@ def run(
 
         # Do something based on the report and the state of this class...
 
+        # Removed redundant variable current_position
         if not self.arrived:
-            current_position = report.position
-            squared_distance_to_waypoint = self.squared_distance(current_position, self.waypoint)
+            squared_distance_to_waypoint = self.squared_distance(report.position, self.waypoint)
 
             # Check acceptance radius
             if squared_distance_to_waypoint <= self.acceptance_radius**2:
@@ -136,6 +140,10 @@ def run(
         if self.arrived:
             closest_pad = self.find_closest_landing_pad(report.position, landing_pad_locations)
 
+            # Keep simulation running if no landing pad found
+            if closest_pad is None:
+                return commands.Command.create_null_command()
+
             # Calculate squared distance to the closest landing pad
             squared_distance_to_pad = self.squared_distance(report.position, closest_pad)
 

modules/bootcamp/detect_landing_pad.py

@@ -38,7 +38,7 @@ class DetectLandingPad:
     __MODEL_NAME = "best-2n.pt"
 
     @classmethod
-    def create(cls, model_directory: pathlib.Path) -> tuple:
+    def create(cls, model_directory: pathlib.Path) -> "tuple[bool, DetectLandingPad | None]":
         """
         model_directory: Directory to models.
         """
@@ -97,23 +97,21 @@ def run(self, image: np.ndarray) -> "tuple[list[bounding_box.BoundingBox], np.nd
 
         # Plot the annotated image from the Result object
         # Include the confidence value
-        image_annotated = prediction.plot()
+        image_annotated = prediction.plot(conf=True)
 
         # Get the xyxy-boxes list from the Boxes object in the Result object
         boxes_xyxy = prediction.boxes.xyxy
 
         # Detach the xyxy boxes to make a copy,
         # move the copy into CPU space,
         # and convert to a numpy array
-        boxes_cpu = boxes_xyxy.cpu().numpy()
+        boxes_cpu = boxes_xyxy.detach().cpu().numpy()
 
         # Loop over the boxes list and create a list of bounding boxes
         bounding_boxes = []
 
-        # Use a range-based for-loop based on number of elements
-        for i in range(boxes_cpu.shape[0]):
-            box = boxes_cpu[i, :]
-            result, box = bounding_box.BoundingBox.create(boxes_cpu[i])
+        for box in boxes_cpu:
+            result, box = bounding_box.BoundingBox.create(box)
             if result:
                 bounding_boxes.append(box)
         # Hint: .shape gets the dimensions of the numpy array