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HDRTV1K

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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 "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1594, in _prepare_split_single
                  writer.write(example)
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 682, in write
                  self.write_examples_on_file()
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 655, in write_examples_on_file
                  self.write_batch(batch_examples=batch_examples)
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 747, in write_batch
                  self.write_table(pa_table, writer_batch_size)
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 762, in write_table
                  pa_table = embed_table_storage(pa_table)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2249, in embed_table_storage
                  embed_array_storage(table[name], feature, token_per_repo_id=token_per_repo_id)
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 1795, in wrapper
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2124, in embed_array_storage
                  return feature.embed_storage(array, token_per_repo_id=token_per_repo_id)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/image.py", line 312, in embed_storage
                  storage = pa.StructArray.from_arrays([bytes_array, path_array], ["bytes", "path"], mask=bytes_array.is_null())
                            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "pyarrow/array.pxi", line 4259, in pyarrow.lib.StructArray.from_arrays
                File "pyarrow/array.pxi", line 4929, 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 "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1607, in _prepare_split_single
                  num_examples, num_bytes = writer.finalize()
                                            ^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 770, in finalize
                  self.write_examples_on_file()
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 655, in write_examples_on_file
                  self.write_batch(batch_examples=batch_examples)
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 747, in write_batch
                  self.write_table(pa_table, writer_batch_size)
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 762, in write_table
                  pa_table = embed_table_storage(pa_table)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2249, in embed_table_storage
                  embed_array_storage(table[name], feature, token_per_repo_id=token_per_repo_id)
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 1795, in wrapper
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2124, in embed_array_storage
                  return feature.embed_storage(array, token_per_repo_id=token_per_repo_id)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/image.py", line 312, in embed_storage
                  storage = pa.StructArray.from_arrays([bytes_array, path_array], ["bytes", "path"], mask=bytes_array.is_null())
                            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "pyarrow/array.pxi", line 4259, in pyarrow.lib.StructArray.from_arrays
                File "pyarrow/array.pxi", line 4929, 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 1342, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 907, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1438, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1616, 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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HDRTV1K

HDRTV1K is a dataset for SDRTV-to-HDRTV conversion and related HDR imaging research, hosted on Hugging Face:

  • Hugging Face Dataset: chxy95/HDRTV1K

This dataset is associated with the HDRTVNet / HDRTVNet++ project line for SDRTV-to-HDRTV conversion. It contains HDR and SDR image data for training and testing, and is suitable for tasks such as HDR reconstruction, SDR-to-HDR translation, inverse tone mapping, and other low-level vision applications. According to the Hugging Face repository page, the dataset modality is Image and the license is MIT. (github.com)

Related Links

Dataset

  • Hugging Face: chxy95/HDRTV1K

GitHub Repositories

  • HDRTVNet (ICCV 2021): https://github.com/chxy95/HDRTVNet
  • HDRTVNet-plus / HDRTVNet++ (extended version): https://github.com/xiaom233/HDRTVNet-plus

Papers

  • A New Journey From SDRTV to HDRTV
    ICCV 2021: https://arxiv.org/abs/2108.07978

  • Towards Efficient SDRTV-to-HDRTV by Learning from Image Formation
    TMM: https://ieeexplore.ieee.org/document/11146495

Overview

HDRTV1K is designed for research on converting SDR TV content to HDR TV content.
It provides paired or corresponding SDR and HDR image data derived from HDR10 videos.
The dataset is useful for:

  • SDRTV-to-HDRTV conversion
  • HDR reconstruction
  • SDR-to-HDR translation
  • Tone mapping / inverse tone mapping
  • HDR image enhancement
  • Low-level vision research

According to the HDRTVNet repository, the dataset is built from 4K-resolution HDR10 videos and their SDR counterparts.
The paper further describes HDRTV1K as a dataset containing 1235 training images and 117 testing images. (github.com)

Dataset Structure

Based on the current Hugging Face repository file tree, the dataset is organized as follows:

HDRTV1K/
├── train_hdr/              # HDR training images
│   ├── black_001.png
│   ├── black_002.png
│   ├── ...
│   └── ...
├── train_sdr/              # SDR training images
│   ├── black_001.png
│   ├── black_002.png
│   ├── ...
│   └── ...
└── test_set/
    ├── test_hdr/           # HDR test images
    │   ├── 001.png
    │   ├── 002.png
    │   ├── ...
    │   └── ...
    └── test_sdr/           # SDR test images
        ├── 001.png
        ├── 002.png
        ├── ...
        └── ...
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Paper for chxy95/HDRTV1K