Implemented the bootcamp programs

modules/bootcamp/decision_simple_waypoint.py

@@ -37,7 +37,14 @@ def __init__(self, waypoint: location.Location, acceptance_radius: float) -> Non
         # ↓ BOOTCAMPERS MODIFY BELOW THIS COMMENT ↓
         # ============
 
-        # Add your own
+        self.command_index = 0
+        self.commands = [
+            commands.Command.create_set_relative_destination_command(
+                waypoint.location_x, waypoint.location_y
+            )
+        ]
+        self.counter = 0
+        self.has_sent_landing_command = False
 
         # ============
         # ↑ BOOTCAMPERS MODIFY ABOVE THIS COMMENT ↑
@@ -68,8 +75,24 @@ def run(
         # ↓ BOOTCAMPERS MODIFY BELOW THIS COMMENT ↓
         # ============
 
-        # Do something based on the report and the state of this class...
+        if report.status == drone_status.DroneStatus.HALTED and self.command_index < len(
+            self.commands
+        ):
 
+            print(self.counter)
+            print(self.command_index)
+            print(report.status)
+
+            command = self.commands[self.command_index]
+            self.command_index += 1
+
+        elif report.status == drone_status.DroneStatus.HALTED and not self.has_sent_landing_command:
+            print(self.counter)
+            command = commands.Command.create_land_command()
+
+            self.has_sent_landing_command = True
+
+        self.counter += 1
         # ============
         # ↑ BOOTCAMPERS MODIFY ABOVE THIS COMMENT ↑
         # ============

modules/bootcamp/decision_waypoint_landing_pads.py

@@ -37,12 +37,45 @@ def __init__(self, waypoint: location.Location, acceptance_radius: float) -> Non
         # ↓ BOOTCAMPERS MODIFY BELOW THIS COMMENT ↓
         # ============
 
-        # Add your own
+        self.command_index = 0
+        self.commands = [
+            commands.Command.create_set_relative_destination_command(
+                waypoint.location_x, waypoint.location_y
+            )
+        ]
+        self.has_sent_landing_command = False
+        self.has_reached_waypoint = False
+        self.has_caculated_pad = False
 
         # ============
         # ↑ BOOTCAMPERS MODIFY ABOVE THIS COMMENT ↑
         # ============
 
+    def closest_pad(
+        self, report: drone_report.DroneReport, landing_pad_locations: "list[location.Location]"
+    ) -> int:
+        """ "
+        Finds the index of the closest pad
+        """
+        _min = -1.0
+        closest_index = 0
+        index = 0
+
+        for loc in landing_pad_locations:
+            print(f"landing pad locations: {landing_pad_locations}")
+            print(f"location being evaluated: {loc}")
+            x_distance = loc.location_x - report.position.location_x
+            y_distance = loc.location_y - report.position.location_y
+            if x_distance**2 + y_distance**2 < _min or _min == -1.0:
+                closest_index = index
+                _min = x_distance**2 + y_distance**2
+            index += 1
+
+        if _min <= self.acceptance_radius:
+            return -1
+
+        return closest_index
+
     def run(
         self, report: drone_report.DroneReport, landing_pad_locations: "list[location.Location]"
     ) -> commands.Command:
@@ -68,7 +101,38 @@ def run(
         # ↓ BOOTCAMPERS MODIFY BELOW THIS COMMENT ↓
         # ============
 
-        # Do something based on the report and the state of this class...
+        if report.status == drone_status.DroneStatus.HALTED and self.has_reached_waypoint:
+
+            if not self.has_caculated_pad:
+                closest_index = self.closest_pad(report, landing_pad_locations)
+
+                if closest_index != -1:
+                    self.commands.append(
+                        commands.Command.create_set_relative_destination_command(
+                            landing_pad_locations[closest_index].location_x
+                            - report.position.location_x,
+                            landing_pad_locations[closest_index].location_y
+                            - report.position.location_y,
+                        )
+                    )
+
+        if report.status == drone_status.DroneStatus.HALTED and self.command_index < len(
+            self.commands
+        ):
+
+            print(self.command_index)
+            print(report.status)
+
+            command = self.commands[self.command_index]
+            self.command_index += 1
+
+            if self.command_index == len(self.commands):
+                self.has_reached_waypoint = True
+
+        elif report.status == drone_status.DroneStatus.HALTED and not self.has_sent_landing_command:
+            command = commands.Command.create_land_command()
+
+            self.has_sent_landing_command = True
 
         # ============
         # ↑ BOOTCAMPERS MODIFY ABOVE THIS COMMENT ↑

modules/bootcamp/detect_landing_pad.py

@@ -16,10 +16,6 @@
 # ============
 # ↓ 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 ↑
 # ============
@@ -75,7 +71,7 @@ def __init__(self, class_private_create_key: object, model: ultralytics.YOLO) ->
         """
         assert class_private_create_key is DetectLandingPad.__create_key, "Use create() method"
 
-        self.__model = model
+        self._model = model
 
     def run(self, image: np.ndarray) -> "tuple[list[bounding_box.BoundingBox], np.ndarray]":
         """
@@ -98,31 +94,41 @@ def run(self, image: np.ndarray) -> "tuple[list[bounding_box.BoundingBox], np.nd
         # * conf
         # * device
         # * verbose
-        predictions = ...
 
+        predictions = self._model.predict(image, conf=0.75, 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]):
+            # Hint: .shape gets the dimensions of the numpy array
+            # for i in range(0, ...):
+            #     # Create BoundingBox object and append to l   xist
+            box_coordinates = np.array(
+                [boxes_cpu[i][0], boxes_cpu[i][1], boxes_cpu[i][2], boxes_cpu[i][3]]
+            )
+            box = bounding_box.BoundingBox.create(box_coordinates)
+
+            if box[1]:
+                bounding_boxes.append(box[1])
+        #     result, box = ..
+
+        return bounding_boxes, image_annotated
         # ============
         # ↑ BOOTCAMPERS MODIFY ABOVE THIS COMMENT ↑
         # ============