Completed Bootcamp

modules/bootcamp/decision_simple_waypoint.py

@@ -43,6 +43,16 @@ def __init__(self, waypoint: location.Location, acceptance_radius: float) -> Non
         # ↑ BOOTCAMPERS MODIFY ABOVE THIS COMMENT ↑
         # ============
 
+    def check_proximity(self, position: location.Location) -> bool:
+        """
+        Returns True if position is within self's acceptance radius of the waypoint.
+        """
+        return (
+            abs(position.location_x - self.waypoint.location_x) ** 2
+            + abs(position.location_y - self.waypoint.location_y) ** 2
+            < self.acceptance_radius**2
+        )
+
     def run(
         self, report: drone_report.DroneReport, landing_pad_locations: "list[location.Location]"
     ) -> commands.Command:
@@ -69,6 +79,17 @@ def run(
         # ============
 
         # Do something based on the report and the state of this class...
+        if report.status.name == "HALTED":
+            if self.check_proximity(report.position):
+                command = commands.Command.create_land_command()
+            else:
+                command = commands.Command.create_set_relative_destination_command(
+                    self.waypoint.location_x - report.position.location_x,
+                    self.waypoint.location_y - report.position.location_y,
+                )
+        elif report.status.name == "MOVING":
+            if self.check_proximity(report.position):
+                command = commands.Command.create_halt_command()
 
         # ============
         # ↑ BOOTCAMPERS MODIFY ABOVE THIS COMMENT ↑

modules/bootcamp/decision_waypoint_landing_pads.py

@@ -38,11 +38,36 @@ def __init__(self, waypoint: location.Location, acceptance_radius: float) -> Non
         # ============
 
         # Add your own
+        self.closest_landing_pad = None
+        self.reached_waypoint = False
 
         # ============
         # ↑ BOOTCAMPERS MODIFY ABOVE THIS COMMENT ↑
         # ============
 
+    def is_same(self, position1: location.Location, position2: location.Location) -> bool:
+        """
+        Returns True if the two position differ by less than the acceptance radius.
+        """
+        return (
+            abs(position1.location_x - position2.location_x) ** 2
+            + abs(position1.location_y - position2.location_y) ** 2
+            < self.acceptance_radius**2
+        )
+
+    def find_closest(self, landing_pads: "list[location.Location]") -> None:
+        """
+        Finds the closest landing pad to the waypoint and stores it in self.
+        """
+        min_distance = float("inf")
+        for pad in landing_pads:
+            new_distance = (pad.location_x - self.waypoint.location_x) ** 2 + (
+                pad.location_y - self.waypoint.location_y
+            ) ** 2
+            if new_distance < min_distance:
+                self.closest_landing_pad = pad
+                min_distance = new_distance
+
     def run(
         self, report: drone_report.DroneReport, landing_pad_locations: "list[location.Location]"
     ) -> commands.Command:
@@ -69,6 +94,33 @@ def run(
         # ============
 
         # Do something based on the report and the state of this class...
+        if report.status.name == "HALTED":
+            if not self.reached_waypoint:
+                # At waypoint.
+                if self.is_same(self.waypoint, report.position):
+                    print("reached waypoint")
+                    self.reached_waypoint = True
+                    self.find_closest(landing_pad_locations)
+                    print(self.closest_landing_pad)
+                    command = commands.Command.create_set_relative_destination_command(
+                        self.closest_landing_pad.location_x - report.position.location_x,
+                        self.closest_landing_pad.location_y - report.position.location_y,
+                    )
+                # At origin.
+                else:
+                    print("should not be here")
+                    print(self.waypoint)
+                    print(report.position)
+                    print(self.is_same(self.waypoint, report.position))
+                    command = commands.Command.create_set_relative_destination_command(
+                        self.waypoint.location_x - report.position.location_x,
+                        self.waypoint.location_y - report.position.location_y,
+                    )
+            # At landing pad.
+            elif self.is_same(self.closest_landing_pad, report.position):
+                command = commands.Command.create_land_command()
+        elif report.status.name == "MOVING":
+            pass
 
         # ============
         # ↑ BOOTCAMPERS MODIFY ABOVE THIS COMMENT ↑

modules/bootcamp/detect_landing_pad.py

@@ -17,9 +17,6 @@
 # ↓ BOOTCAMPERS MODIFY BELOW THIS COMMENT ↓
 # ============
 # Bootcampers remove the following lines:
-# Allow linters and formatters to pass for bootcamp maintainers
-# No enable
-# pylint: disable=unused-argument,unused-private-member,unused-variable
 # ============
 # ↑ BOOTCAMPERS MODIFY ABOVE THIS COMMENT ↑
 # ============
@@ -98,31 +95,36 @@ def run(self, image: np.ndarray) -> "tuple[list[bounding_box.BoundingBox], np.nd
         # * conf
         # * device
         # * verbose
-        predictions = ...
+        predictions = self.__model.predict(
+            source=image, conf=0.7, device=self.__DEVICE, verbose=False
+        )
 
         # Get the Result object
-        prediction = ...
+        prediction = predictions[0]
 
         # Plot the annotated image from the Result object
         # Include the confidence value
-        image_annotated = ...
+        image_annotated = prediction.plot(conf=True)
 
         # Get the xyxy boxes list from the Boxes object in the Result object
-        boxes_xyxy = ...
+        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_cpu = boxes_xyxy.detach().cpu().numpy()
 
         # Loop over the boxes list and create a list of bounding boxes
         bounding_boxes = []
         # Hint: .shape gets the dimensions of the numpy array
-        # for i in range(0, ...):
-        #     # Create BoundingBox object and append to list
-        #     result, box = ...
-
-        return [], image_annotated
+        for i in range(0, boxes_cpu.shape[0]):
+            #     # Create BoundingBox object and append to list
+            result, box = bounding_box.BoundingBox.create(boxes_cpu[i])
+            if not result:
+                return [], None
+            bounding_boxes.append(box)
+
+        return bounding_boxes, image_annotated
         # ============
         # ↑ BOOTCAMPERS MODIFY ABOVE THIS COMMENT ↑
         # ============