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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 1766, in _prepare_split_single writer.write(example, key) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 500, in write self.write_examples_on_file() File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 458, 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 572, 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 587, in write_table pa_table = embed_table_storage(pa_table) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2280, in embed_table_storage arrays = [ File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2281, 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 1802, 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 1802, 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 2154, in embed_array_storage return feature.embed_storage(array) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/image.py", line 283, 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 1775, 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 599, in finalize self.write_examples_on_file() File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 458, 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 572, 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 587, in write_table pa_table = embed_table_storage(pa_table) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2280, in embed_table_storage arrays = [ File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2281, 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 1802, 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 1802, 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 2154, in embed_array_storage return feature.embed_storage(array) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/image.py", line 283, 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 1524, 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 1099, in stream_convert_to_parquet builder._prepare_split( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1627, 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 1784, 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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BlueNeg: A 35mm Negative Film Dataset for Restoring Channel-Heterogeneous Deterioration
Description
This set of images is collected for studying the research problem of restoring the corrupted negative films. Due to the physical nature of negative film, the red, green, and blue light-sensitive layers are located differently inside the negative film. Therefore, the rates of deterioration of these three layers are different. Tien-Tsin Wong found that the blue channel is relatively vulnerable compared to the other two channels. Because the blue light-sensitive layer is on the outermost layer on the emulsion side. That is, the deterioration rates are heterogeneous. This characteristic is inherited from its ancestor, i.e., storing the three color negatives (R, G, B) on three separate glass plates by Prokudin-Gorsky.
Since the blue channel is more vulnerable and the other two channels are relatively well-preserved, this means we can restore the blue channel by exploiting the retained information from the red and green channels to restore the color photograph. This is especially sound with the latest AI technologies. Unfortunately, most existing photo restoration techniques are developed based on printed photographs, in which the nature of deterioration is different from that of the negatives. This is why this dataset is created.
Data Information
Meta Information
The meta information is stored in the meta.json
and transformations.pkl
. The meta information includes the following fields:
meta.json
- filename
- partition
- is_testset
- date
- roll_id
- number_in_roll
- location
- geo_location
- film_type
- negative_path
- printed_path
- preview_path
- pseudogt_path
- scene_property
- is_indoor
- is_daytime
transformations.pkl
: a python dict using filename (without extension) as key, and the value is a dict with the following fields:- matrix: the perspective projection matrix to warp the printed photo to the negative preview
- bbox: the bounding box of the warped printed photo in the negative preview; the bounding box is in the format of [x0, y0, x1, y1], preview[y0:y1, x0:x1, :] has the same size as pseudo ground truth
Naming Convention
YYYYMMDD[R]-NN-NAME.EXT
YYYYMMDD
: The date when the photo was taken (might not always be accurate, but roughly correct).[R]
: the roll number, if availableNN
: the photo number in the rollNAME
: the photo's name, usually the location or the subject of the photo.EXT
: the file extension, e.g.,.dng
,.tif
,.preview.png
,.pseudogt.png
, etc.
License
Our license shares the same spirit with the CC-BY 4.0. However, please note that our license is not CC-BY 4.0. Please check the LICENSE file for more details.
All photos are owned and copyrighted by Tien-Tsin Wong. You are
automatically granted with permission to use the images for academic
and commerical usages, provided that the image credit
"Copyrighted by Tien-Tsin Wong" is included in any forms of
publication, reproduction, redistribution, or derivatives of the images.
Citation
If you find the dataset is useful to you, please also cite our publication below in your work/publication.
- Hanyuan Liu, Chengze Li, Minshan Xie, Zhenni Wang, Jiawen Liang, Chi-Sing Leung, and Tien-Tsin Wong, "BlueNeg: BlueNeg: A 35mm Negative Film Dataset for Restoring Channel-Heterogeneous Deterioration", arXiv preprint, 2024.
@misc{blueneg,
title={BlueNeg: A 35mm Negative Film Dataset for Restoring Channel-Heterogeneous Deterioration},
author={Hanyuan Liu and Chengze Li and Minshan Xie and Zhenni Wang and Jiawen Liang and Chi-Sing Leung and and Tien-Tsin Wong},
year={2024},
eprint={to be updated},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
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