added some visualization tools for lidar pointclouds
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67
tools/util.py
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67
tools/util.py
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from pointcloudset import Dataset
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from pathlib import Path
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from argparse import ArgumentTypeError
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from subprocess import run
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from datetime import timedelta
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from matplotlib.colors import Colormap
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from matplotlib import colormaps
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def load_dataset_from_bag(bag_file_path: Path, pointcloud_topic: str = "/ouster/points") -> Dataset:
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return Dataset.from_file(bag_file_path, topic=pointcloud_topic)
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def calculate_average_frame_rate(dataset: Dataset):
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timestamps = dataset.timestamps
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time_deltas = [timestamps[i + 1] - timestamps[i] for i in range(len(timestamps) - 1)]
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average_delta = sum(time_deltas, timedelta()) / len(time_deltas)
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average_frame_rate = 1 / average_delta.total_seconds()
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return average_frame_rate
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def existing_file(path_string: str) -> Path:
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path = Path(path_string)
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if not path.exists():
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raise ArgumentTypeError(f"{path} does not exist!")
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if not path.is_file():
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raise ArgumentTypeError(f"{path} is not a valid file!")
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return path
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def existing_folder(path_string: str) -> Path:
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path = Path(path_string)
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if not path.exists():
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raise ArgumentTypeError(f"{path} does not exist!")
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if not path.is_folder():
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raise ArgumentTypeError(f"{path} is not a valid folder!")
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return path
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def get_colormap_with_special_missing_color(
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colormap_name: str, missing_data_color: str = "black", reverse: bool = False
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) -> Colormap:
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colormap = colormaps[colormap_name] if not reverse else colormaps[f"{colormap_name}_r"]
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colormap.set_bad(missing_data_color)
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return colormap
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def create_video_from_images(input_images_pattern: str, output_file: Path, frame_rate: int) -> None:
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# Construct the ffmpeg command
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command = [
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"ffmpeg",
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"-y",
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"-framerate",
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str(frame_rate),
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"-i",
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input_images_pattern,
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"-c:v",
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"libx264",
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"-profile:v",
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"high",
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"-crf",
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"20",
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"-pix_fmt",
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"yuv420p",
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output_file.as_posix(),
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]
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run(command, check=True)
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