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Bootcamp Complete - Tejas Srikanth #49

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Bootcamp Complete - Tejas Srikanth #49

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159ad4b
finish boot camp
tejas-srikanth Apr 7, 2024
ce300a6
fix decision and rename variables
tejas-srikanth Apr 8, 2024
1dbf7dd
use result
tejas-srikanth Apr 8, 2024
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18 changes: 14 additions & 4 deletions modules/bootcamp/decision_simple_waypoint.py
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Original file line number Diff line number Diff line change
Expand Up @@ -37,7 +37,13 @@ def __init__(self, waypoint: location.Location, acceptance_radius: float):
# ↓ BOOTCAMPERS MODIFY BELOW THIS COMMENT ↓
# ============

# Add your own
self.command_index = 0
self.commands = [
commands.Command.create_set_relative_destination_command( 4, 1),
commands.Command.create_set_relative_destination_command( 0.0, -2),
commands.Command.create_set_relative_destination_command(-4, 1),
]
Comment on lines +41 to +45
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@TongguangZhang TongguangZhang Apr 17, 2024

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let's not hardcode the coordinates - use self.waypoint instead

self.has_sent_landing_command = False

# ============
# ↑ BOOTCAMPERS MODIFY ABOVE THIS COMMENT ↑
Expand Down Expand Up @@ -68,10 +74,14 @@ def run(self,
# ↓ BOOTCAMPERS MODIFY BELOW THIS COMMENT ↓
# ============

# Do something based on the report and the state of this class...
if report.status == drone_report.drone_status.DroneStatus.HALTED and self.command_index<len(self.commands):
command = self.commands[self.command_index]
self.command_index += 1

# Remove this when done
raise NotImplementedError
elif report.status == drone_report.drone_status.DroneStatus.HALTED and not self.has_sent_landing_command:
self.has_sent_landing_command = True
command = commands.Command.create_land_command()
Comment on lines +77 to +83
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@TongguangZhang TongguangZhang Apr 17, 2024

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make sure we're using the acceptance radius here - if the drone is too far from the waypoint, we don't want to land there



# ============
# ↑ BOOTCAMPERS MODIFY ABOVE THIS COMMENT ↑
Expand Down
41 changes: 35 additions & 6 deletions modules/bootcamp/decision_waypoint_landing_pads.py
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Expand Up @@ -32,17 +32,26 @@ def __init__(self, waypoint: location.Location, acceptance_radius: float):
print("Waypoint: " + str(waypoint))

self.acceptance_radius = acceptance_radius


# ============
# ↓ 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.landed_at_waypoint = False
self.has_sent_landing_command = False
self.landing_pad_cache = None

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

def getL2Norm(self, l1: location.Location, l2: location.Location) -> int:
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technically this doesn't give us the L2 norm - it gives the square of the L2 norm, let's rename the function to be more descriptive (btw, we try to use snake_case instead of camelCase)

return (l2.location_x - l1.location_x) * (l2.location_x - l1.location_x) + (l2.location_y - l1.location_y) * (l2.location_y - l1.location_y)
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let's use ** 2 to make it explicit that we're squaring and not multiplying


def run(self,
report: drone_report.DroneReport,
landing_pad_locations: "list[location.Location]") -> commands.Command:
Expand All @@ -68,10 +77,30 @@ def run(self,
# ↓ BOOTCAMPERS MODIFY BELOW THIS COMMENT ↓
# ============

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

# Remove this when done
raise NotImplementedError
if report.status == drone_report.drone_status.DroneStatus.HALTED and self.command_index<len(self.commands):
command = self.commands[self.command_index]
self.command_index += 1

elif report.status == drone_report.drone_status.DroneStatus.HALTED and not self.landed_at_waypoint:

self.landed_at_waypoint = True
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we're not actually landing at the waypoint here, just stopped, let's rename this to be more descriptive

min_landing_distance = 10000000
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let's try to not use arbitrary values or magic numbers - set this to infinity instead with min_landing_distance = float('inf')

for landing_pad in landing_pad_locations:
distance_to_lp = self.getL2Norm(landing_pad, report.position)
if distance_to_lp < min_landing_distance:
self.landing_pad_cache = landing_pad
min_landing_distance = distance_to_lp

location_delta_x = self.landing_pad_cache.location_x - report.position.location_x
location_delta_y = self.landing_pad_cache.location_y - report.position.location_y
self.commands.append(commands.Command.create_set_relative_destination_command(location_delta_x, location_delta_y))
command = self.commands[self.command_index]

self.command_index += 1

elif report.status == drone_report.drone_status.DroneStatus.HALTED and not self.has_sent_landing_command:
self.has_sent_landing_command = True
command = commands.Command.create_land_command()
Comment on lines +80 to +103
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let's use the acceptance radius here too - we don't want to land if the drone is too far from the waypoint


# ============
# ↑ BOOTCAMPERS MODIFY ABOVE THIS COMMENT ↑
Expand Down
23 changes: 13 additions & 10 deletions modules/bootcamp/detect_landing_pad.py
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Expand Up @@ -86,32 +86,35 @@ 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, show_conf=True)
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Take a look at the parameters of interest above - we want to set the source, conf, device, and verbose (but not the show_conf)


# 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.orig_img
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We're looking for the annotated image (with the confidence threshold), not the original image


# 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, ...):
for i in range(0, boxes_cpu.shape[0]):
# Create BoundingBox object and append to list
# result, box = ...
Comment on lines 108 to -111
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let's remove this commented out code since we're not using it (the if statement and result, box =) we can go back for it in the git history if we ever need it


# Remove this when done
raise NotImplementedError
result, box = bounding_box.BoundingBox.create(boxes_cpu[i])

if not result:
return ([], image_annotated)
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let's remove unnecessary brackets, return [], image annotated will work here


bounding_boxes.append(box)
return bounding_boxes, image_annotated

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