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decision_simple_waypoint.py

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+"""
+BOOTCAMPERS TO COMPLETE.
+
+Travel to designated waypoint.
+"""
+
+from .. import commands
+from .. import drone_report
+
+# Disable for bootcamp use
+# pylint: disable-next=unused-import
+from .. import drone_status
+from .. import location
+from ..private.decision import base_decision
+
+
+# Disable for bootcamp use
+# No enable
+# pylint: disable=duplicate-code,unused-argument
+
+
+class DecisionSimpleWaypoint(base_decision.BaseDecision):
+    """
+    Travel to the designed waypoint.
+    """
+
+    def __init__(self, waypoint: location.Location, acceptance_radius: float) -> None:
+        """
+        Initialize all persistent variables here with self.
+        """
+        self.waypoint = waypoint
+        print(f"Waypoint: {waypoint}")
+
+        self.acceptance_radius = acceptance_radius
+
+        # ============
+        # ↓ BOOTCAMPERS MODIFY BELOW THIS COMMENT ↓
+        # ============
+
+        self.waypoint_found =False
+        self.landing_pad_found = False
+
+
+        # ============
+        # ↑ BOOTCAMPERS MODIFY ABOVE THIS COMMENT ↑
+        # ============
+
+    def run(
+        self, report: drone_report.DroneReport, landing_pad_locations: "list[location.Location]"
+    ) -> commands.Command:
+        """
+        Make the drone fly to the waypoint.
+
+        You are allowed to create as many helper methods as you want,
+        as long as you do not change the __init__() and run() signatures.
+
+        This method will be called in an infinite loop, something like this:
+
+        ```py
+        while True:
+            report, landing_pad_locations = get_input()
+            command = Decision.run(report, landing_pad_locations)
+            put_output(command)
+        ```
+        """
+        # Default command
+        command = commands.Command.create_null_command()
+
+        # ============
+        # ↓ BOOTCAMPERS MODIFY BELOW THIS COMMENT ↓
+        # ============
+
+        status = report.status
+        current_position = report.position
+        waypoint = self.waypoint
+
+        destination_x = waypoint.location_x - current_position.location_x
+        destination_y = waypoint.location_y - current_position.location_y
+        distance_squared = destination_x**2 + destination_y**2
+
+        if status == drone_status.DroneStatus.HALTED:
+            if distance_squared < self.acceptance_radius**2:
+                command = commands.Command.create_land_command()
+                print("The drone is within the accepted range")
+            else: command = commands.Command.create_set_relative_destination_command(
+                destination_x, destination_y
+            )
+            print(f"Moving to waypoint{waypoint.location_x},{waypoint.location_y}")
+
+        # ============
+        # ↑ BOOTCAMPERS MODIFY ABOVE THIS COMMENT ↑
+        # ============
+
+        return command

decision_waypoint_landing_pads.py

@@ -0,0 +1,97 @@
+"""
+BOOTCAMPERS TO COMPLETE.
+
+Travel to designated waypoint and then land at a nearby landing pad.
+"""
+
+from .. import commands
+from .. import drone_report
+
+# Disable for bootcamp use
+# pylint: disable-next=unused-import
+from .. import drone_status
+from .. import location
+from ..private.decision import base_decision
+
+
+# Disable for bootcamp use
+# No enable
+# pylint: disable=duplicate-code,unused-argument
+
+
+class DecisionWaypointLandingPads(base_decision.BaseDecision):
+    """
+    Travel to the designed waypoint and then land at the nearest landing pad.
+    """
+
+    def __init__(self, waypoint: location.Location, acceptance_radius: float) -> None:
+        """
+        Initialize all persistent variables here with self.
+        """
+        self.waypoint = waypoint
+        print(f"Waypoint: {waypoint}")
+
+        self.acceptance_radius = acceptance_radius
+
+        # ============
+        # ↓ BOOTCAMPERS MODIFY BELOW THIS COMMENT ↓
+        # ============
+
+        print(str(waypoint.location_x)+str(waypoint.location_y))
+
+        self.has_sent_landing_command = False
+
+        self.find_nearest_landing_pad = False
+
+        self.reached_waypoint = False
+
+        self.moving_to_landing_pad = False
+
+        self.counter = 0
+
+    def at_point(self, current_x: float, current_y: float):
+
+
+        distance_squared = (self.waypoint.location_x - current_x) ** 2 + (
+                self.waypoint.location_y - current_y
+        ) ** 2
+        return distance_squared <= self.acceptance_radius ** 2
+
+
+
+        # ============
+        # ↑ BOOTCAMPERS MODIFY ABOVE THIS COMMENT ↑
+        # ============
+
+    def run(
+        self, report: drone_report.DroneReport, landing_pad_locations: "list[location.Location]"
+    ) -> commands.Command:
+        """
+        Make the drone fly to the waypoint and then land at the nearest landing pad.
+
+        You are allowed to create as many helper methods as you want,
+        as long as you do not change the __init__() and run() signatures.
+
+        This method will be called in an infinite loop, something like this:
+
+        ```py
+        while True:
+            report, landing_pad_locations = get_input()
+            command = Decision.run(report, landing_pad_locations)
+            put_output(command)
+        ```
+        """
+        # Default command
+        command = commands.Command.create_null_command()
+
+        # ============
+        # ↓ BOOTCAMPERS MODIFY BELOW THIS COMMENT ↓
+        # ============
+
+        # Do something based on the report and the state of this class...
+
+        # ============
+        # ↑ BOOTCAMPERS MODIFY ABOVE THIS COMMENT ↑
+        # ============
+
+        return command

detect_landing_pad.py

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+"""
+BOOTCAMPERS TO COMPLETE.
+
+Detects landing pads.
+"""
+
+import pathlib
+
+import numpy as np
+import torch
+import ultralytics
+
+from .. import bounding_box
+
+
+# ============
+# ↓ 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 ↑
+# ============
+
+
+class DetectLandingPad:
+    """
+    Contains the YOLOv8 model for prediction.
+    """
+
+    __create_key = object()
+
+    # ============
+    # ↓ BOOTCAMPERS MODIFY BELOW THIS COMMENT ↓
+    # ============
+
+    # Chooses the GPU if it exists, otherwise runs on the CPU
+    # If you have a CUDA capable GPU but want to force it to
+    # run on the CPU instead, replace the right side with "cpu"
+    __DEVICE = 0 if torch.cuda.is_available() else "cpu"
+
+    # ============
+    # ↑ BOOTCAMPERS MODIFY ABOVE THIS COMMENT ↑
+    # ============
+
+    __MODEL_NAME = "best-2n.pt"
+
+    @classmethod
+    def create(cls, model_directory: pathlib.Path) -> "tuple[bool, DetectLandingPad | None]":
+        """
+        model_directory: Directory to models.
+        """
+        if not model_directory.is_dir():
+            return False, None
+
+        model_path = pathlib.PurePosixPath(
+            model_directory,
+            cls.__MODEL_NAME,
+        )
+
+        try:
+            model = ultralytics.YOLO(str(model_path))
+        # Library can throw any exception
+        # pylint: disable-next=broad-exception-caught
+        except Exception:
+            return False, None
+
+        return True, DetectLandingPad(cls.__create_key, model)
+
+    def __init__(self, class_private_create_key: object, model: ultralytics.YOLO) -> None:
+        """
+        Private constructor, use create() method.
+        """
+        assert class_private_create_key is DetectLandingPad.__create_key, "Use create() method"
+
+        self.__model = model
+
+    def run(self, image: np.ndarray) -> "tuple[list[bounding_box.BoundingBox], np.ndarray]":
+        """
+        Converts an image into a list of bounding boxes.
+
+        image: The image to run on.
+
+        Return: A tuple of (list of bounding boxes, annotated image) .
+            The list of bounding boxes can be empty.
+        """
+        # ============
+        # ↓ BOOTCAMPERS MODIFY BELOW THIS COMMENT ↓
+        # ============
+
+        # Ultralytics has documentation and examples
+
+        # Use the model's predict() method to run inference
+        # Parameters of interest:
+        # * source
+        # * conf
+        # * device
+        # * verbose
+        # conf threshold is around 0.7
+        predictions = self.__model.predict(
+            source=image, conf=0.7, device=self.__DEVICE, verbose =False
+        )
+
+        # Get the Result object
+        prediction = predictions[0]
+
+        # Plot the annotated image from the Result object
+        # Include the confidence value
+        image_annotated = prediction.plot(boxes=True, 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.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 = ...
+        for boxes_xyxy in boxes_cpu:
+            success, box = bounding_box.BoundingBox.create(boxes_xyxy)
+            if not success:
+                continue
+
+        return [], image_annotated
+
+
+        # ============
+        # ↑ BOOTCAMPERS MODIFY ABOVE THIS COMMENT ↑
+        # ============