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from datasets import load_dataset, DatasetDict |
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import polars as pl |
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import numpy as np |
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import matplotlib.pyplot as plt |
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from matplotlib.animation import FuncAnimation |
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from scipy.io import loadmat |
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def draw_animation(id, rgb, depth, radar): |
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fig, ax = plt.subplots(1, 3, figsize=(15, 5)) |
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fig.suptitle(f"ID: {id}") |
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def update(frame): |
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ax[0].clear() |
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ax[0].set_title("Depth") |
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ax[0].imshow(depth[frame]) |
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ax[1].clear() |
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ax[1].set_title("RGB") |
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ax[1].imshow(rgb[frame]) |
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radar_data = radar[frame]["spec_db_slice"][::-1] |
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radar_data = (radar_data - radar_data.min()) / ( |
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radar_data.max() - radar_data.min() |
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) |
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ax[2].clear() |
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ax[2].set_title("Radar") |
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ax[2].imshow(radar_data, aspect="auto", cmap="jet") |
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ani = FuncAnimation(fig, update, frames=len(depth), interval=15) |
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plt.show() |
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if __name__ == "__main__": |
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datasets = load_dataset("parquet", data_files="./train.parquet") |
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df_polars = datasets["train"].to_polars() |
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ids = df_polars["id"].unique().to_list() |
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for id in ids: |
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frames = df_polars.filter( |
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pl.col("id").eq(id) |
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& pl.col("selected_range_bin").eq(76) |
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& pl.col("sub_index_frame").eq(1) |
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).sort("index_frame") |
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print( |
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frames.select( |
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"file_path_radar", |
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"file_path_depth", |
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"file_path_rgb", |
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"index_frame", |
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"id", |
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) |
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) |
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rgb = [np.load(f) for f in frames["file_path_rgb"].to_list()] |
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depth = [np.load(f) for f in frames["file_path_depth"].to_list()] |
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radar = [loadmat(f) for f in frames["file_path_radar"].to_list()] |
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draw_animation(id, rgb, depth, radar) |
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break |
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