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The dataset generation failed
Error code:   DatasetGenerationError
Exception:    TypeError
Message:      Mask must be a pyarrow.Array of type boolean
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1626, in _prepare_split_single
                  writer.write(example, key)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 552, in write
                  self.write_examples_on_file()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 510, in write_examples_on_file
                  self.write_batch(batch_examples=batch_examples)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 630, in write_batch
                  self.write_table(pa_table, writer_batch_size)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 645, in write_table
                  pa_table = embed_table_storage(pa_table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2271, in embed_table_storage
                  arrays = [
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2272, in <listcomp>
                  embed_array_storage(table[name], feature) if require_storage_embed(feature) else table[name]
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1796, in wrapper
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1796, in <listcomp>
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2141, in embed_array_storage
                  return feature.embed_storage(array)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/image.py", line 282, in embed_storage
                  storage = pa.StructArray.from_arrays([bytes_array, path_array], ["bytes", "path"], mask=bytes_array.is_null())
                File "pyarrow/array.pxi", line 3257, in pyarrow.lib.StructArray.from_arrays
                File "pyarrow/array.pxi", line 3697, in pyarrow.lib.c_mask_inverted_from_obj
              TypeError: Mask must be a pyarrow.Array of type boolean
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1635, in _prepare_split_single
                  num_examples, num_bytes = writer.finalize()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 657, in finalize
                  self.write_examples_on_file()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 510, in write_examples_on_file
                  self.write_batch(batch_examples=batch_examples)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 630, in write_batch
                  self.write_table(pa_table, writer_batch_size)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 645, in write_table
                  pa_table = embed_table_storage(pa_table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2271, in embed_table_storage
                  arrays = [
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2272, in <listcomp>
                  embed_array_storage(table[name], feature) if require_storage_embed(feature) else table[name]
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1796, in wrapper
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1796, in <listcomp>
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2141, in embed_array_storage
                  return feature.embed_storage(array)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/image.py", line 282, in embed_storage
                  storage = pa.StructArray.from_arrays([bytes_array, path_array], ["bytes", "path"], mask=bytes_array.is_null())
                File "pyarrow/array.pxi", line 3257, in pyarrow.lib.StructArray.from_arrays
                File "pyarrow/array.pxi", line 3697, in pyarrow.lib.c_mask_inverted_from_obj
              TypeError: Mask must be a pyarrow.Array of type boolean
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1436, in compute_config_parquet_and_info_response
                  parquet_operations = convert_to_parquet(builder)
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1053, in convert_to_parquet
                  builder.download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 925, in download_and_prepare
                  self._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1649, in _download_and_prepare
                  super()._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1001, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1487, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1644, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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0gt
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1input50
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1input50
End of preview.

Description

This dataset contains a paired real hyperspectral image (HSI) denoising dataset, which consists of shortexposure noisy HSIs and the corresponding long-exposure clean HSIs.

The dataset employs a SOC710-VP hyperspectral camera, manufactured by Surface Optics Corporation (SOC), USA. The SOC710-VP hyperspectral camera is equipped with a silicon-based charge-coupled device (CCD) and an integrated scanning system. With standard settings, the SOC710-VP can capture HSI with 696 × 520 pixels in spatial resolution and 256 spectral bands from 376.76 nm to 1075.80 nm at 2.7 nm interval.

The dataset contains both indoor and outdoor scenes. Indoor images under incandescent lamp to lighten the scene. The outdoor images are generally captured on sunny days between around 9am and 5pm.

34 bands were selected around 400nm to 700nm in the visible spectral range. The dynamic range of the captured HSI is 12 bit, and the spectral value ranges from 0 to 4095.

The dataset contains 62 image pairs, separated as follows into two folders:

  • gt: where the camera settings, such as aperture, focus and exposure time, are adjusted to maximize the quality.
  • input50: short-exposure image, where the exposure time was intentionally decreased by a factor of 50 with respect to the reference.

Credits

Dataset originally collected at this Google drive link: https://drive.google.com/file/d/1cKtSF42HiDQdW48Ccjq3vSEq5zcxq1HK/view?usp=sharing

Associated repository: https://github.com/ColinTaoZhang/HSIDwRD

If this dataset is used for research, please ensure to cite this paper:

@InProceedings{Zhang_2021_ICCV,
    author    = {Zhang, Tao and Fu, Ying and Li, Cheng},
    title     = {Hyperspectral Image Denoising With Realistic Data},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {2248-2257}
}
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