Bootcamp Complete - Tejas Srikanth #49
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| # * 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)
| # 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
| # 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 = ... |
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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
| result, box = bounding_box.BoundingBox.create(boxes_cpu[i]) | ||
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| if not result: | ||
| return ([], image_annotated) |
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let's remove unnecessary brackets, return [], image annotated will work here
| 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), | ||
| ] |
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let's not hardcode the coordinates - use self.waypoint instead
| # ↑ BOOTCAMPERS MODIFY ABOVE THIS COMMENT ↑ | ||
| # ============ | ||
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| 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)
| # ============ | ||
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| def getL2Norm(self, l1: location.Location, l2: location.Location) -> int: | ||
| 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
| elif report.status == drone_report.drone_status.DroneStatus.HALTED and not self.landed_at_waypoint: | ||
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| self.landed_at_waypoint = True | ||
| 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')
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| elif report.status == drone_report.drone_status.DroneStatus.HALTED and not self.landed_at_waypoint: | ||
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| 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
| 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 | ||
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| elif report.status == drone_report.drone_status.DroneStatus.HALTED and not self.landed_at_waypoint: | ||
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| self.landed_at_waypoint = True | ||
| min_landing_distance = 10000000 | ||
| 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 | ||
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| 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] | ||
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| self.command_index += 1 | ||
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| 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() |
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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
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