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Bootcamp Submission - Emily Qi #135

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Bootcamp Submission - Emily Qi #135

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6d5173b
tasks 1, 2, 3, 4
emlyqi Jan 1, 2025
2ff5bdd
task fixes
emlyqi Jan 6, 2025
3a10adf
task4
emlyqi Jan 6, 2025
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28 changes: 27 additions & 1 deletion modules/bootcamp/decision_simple_waypoint.py
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Original file line number Diff line number Diff line change
Expand Up @@ -37,7 +37,9 @@ def __init__(self, waypoint: location.Location, acceptance_radius: float) -> Non
# ↓ BOOTCAMPERS MODIFY BELOW THIS COMMENT ↓
# ============

# Add your own
self.has_sent_landing_command = False
self.min_flight_boundary = -60
self.max_flight_boundary = 60

# ============
# ↑ BOOTCAMPERS MODIFY ABOVE THIS COMMENT ↑
Expand Down Expand Up @@ -70,6 +72,30 @@ def run(

# Do something based on the report and the state of this class...

if (
self.waypoint.location_x >= self.min_flight_boundary
and self.waypoint.location_x <= self.max_flight_boundary
and self.waypoint.location_y >= self.min_flight_boundary
and self.waypoint.location_y <= self.max_flight_boundary
):
distance_from_waypoint = (
self.waypoint.location_x - report.position.location_x
) ** 2 + (self.waypoint.location_y - report.position.location_y) ** 2

if (
report.status == drone_status.DroneStatus.HALTED
and not self.has_sent_landing_command
):

if distance_from_waypoint <= self.acceptance_radius**2:
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we generally say that an acceptance radius is strictly <, not <=

command = commands.Command.create_land_command()
self.has_sent_landing_command = True

elif distance_from_waypoint > self.acceptance_radius**2:
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do you need the elif or can it just be an else?

command = commands.Command.create_set_relative_destination_command(
self.waypoint.location_x, self.waypoint.location_y
)

# ============
# ↑ BOOTCAMPERS MODIFY ABOVE THIS COMMENT ↑
# ============
Expand Down
59 changes: 58 additions & 1 deletion modules/bootcamp/decision_waypoint_landing_pads.py
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Expand Up @@ -37,12 +37,25 @@ def __init__(self, waypoint: location.Location, acceptance_radius: float) -> Non
# ↓ BOOTCAMPERS MODIFY BELOW THIS COMMENT ↓
# ============

# Add your own
self.has_sent_landing_command = False
self.min_flight_boundary = -60
self.max_flight_boundary = 60

self.ready_to_land = False

# ============
# ↑ BOOTCAMPERS MODIFY ABOVE THIS COMMENT ↑
# ============

def distance_between_locations(self, other_location: location.Location) -> float:
"""
Calculate the squared distance between two Location objects
"""
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since this is the distance squared, name it accordingly


return (self.waypoint.location_x - other_location.location_x) ** 2 + (
self.waypoint.location_y - other_location.location_y
) ** 2

def run(
self, report: drone_report.DroneReport, landing_pad_locations: "list[location.Location]"
) -> commands.Command:
Expand Down Expand Up @@ -70,6 +83,50 @@ def run(

# Do something based on the report and the state of this class...

if (
self.waypoint.location_x >= self.min_flight_boundary
and self.waypoint.location_x <= self.max_flight_boundary
and self.waypoint.location_y >= self.min_flight_boundary
and self.waypoint.location_y <= self.max_flight_boundary
):
distance_from_waypoint = (
self.waypoint.location_x - report.position.location_x
) ** 2 + (self.waypoint.location_y - report.position.location_y) ** 2
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consider using your defined function with report.position as the input


if report.status == drone_status.DroneStatus.HALTED:

if self.ready_to_land:
command = commands.Command.create_land_command()
self.has_sent_landing_command = True

elif (
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we generally say that an acceptance radius is strictly <, not <=

distance_from_waypoint <= self.acceptance_radius**2
and not self.has_sent_landing_command
):
smallest_distance = 999999999
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it's generally bad practice to use just a very large number like this - try looking for how to represent infinity in python

smallest_distance_x = 0
smallest_distance_y = 0
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consider storing the waypoint object instead of the x and y, this also makes is so you don't need to check ready_to_land to avoid landing the drone and (0, 0)


for landing_pad in landing_pad_locations:
if self.distance_between_locations(landing_pad) < smallest_distance:
smallest_distance = self.distance_between_locations(landing_pad)
smallest_distance_x = landing_pad.location_x - self.waypoint.location_x
smallest_distance_y = landing_pad.location_y - self.waypoint.location_y

command = commands.Command.create_set_relative_destination_command(
smallest_distance_x, smallest_distance_y
)

self.ready_to_land = True

elif (
distance_from_waypoint > self.acceptance_radius**2
and not self.has_sent_landing_command
):
command = commands.Command.create_set_relative_destination_command(
self.waypoint.location_x, self.waypoint.location_y
)

# ============
# ↑ BOOTCAMPERS MODIFY ABOVE THIS COMMENT ↑
# ============
Expand Down
26 changes: 15 additions & 11 deletions modules/bootcamp/detect_landing_pad.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,9 +17,7 @@
# ↓ 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 ↑
# ============
Expand Down Expand Up @@ -98,31 +96,37 @@ def run(self, image: np.ndarray) -> "tuple[list[bounding_box.BoundingBox], np.nd
# * conf
# * device
# * verbose
predictions = ...
predictions = self.__model.predict(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()

# 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 = ...
for i in range(0, np.shape(boxes_cpu)[0]):

# Create BoundingBox object and append to list
(result, box) = bounding_box.BoundingBox.create(boxes_cpu[i])

if result:
bounding_boxes.append(box)
Comment on lines +126 to +127
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instead of skipping failed boxes, we want to return no boxes if any of then fail. This is because in data science, when we have corrupted data, we skip the whole data point instead of partial data - in this case, the image is the data point and the boxes are parts of the data


return [], image_annotated
return bounding_boxes, image_annotated
# ============
# ↑ BOOTCAMPERS MODIFY ABOVE THIS COMMENT ↑
# ============
2 changes: 1 addition & 1 deletion modules/bootcamp/tests/run_decision_example.py
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Expand Up @@ -21,7 +21,7 @@
# to reach the 1st command
# Increase the step size if your computer is lagging
# Larger step size is smaller FPS
TIME_STEP_SIZE = 0.1 # seconds
TIME_STEP_SIZE = 0.4 # seconds

# OpenCV ignores your display settings,
# so if the window is too small or too large,
Expand Down
2 changes: 1 addition & 1 deletion modules/bootcamp/tests/run_decision_waypoint_landing_pads.py
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Expand Up @@ -21,7 +21,7 @@
# to reach the 1st command
# Increase the step size if your computer is lagging
# Larger step size is smaller FPS
TIME_STEP_SIZE = 0.1 # seconds
TIME_STEP_SIZE = 0.4 # seconds

# OpenCV ignores your display settings,
# so if the window is too small or too large,
Expand Down
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