Metadata-Version: 1.1
Name: tfds-defect-detection
Version: 1.0.0
Summary: TensorFlow Datasets for Defect Detection
Home-page: https://github.com/thetoby9944/tfds_defect_detection
Author: thetoby9944
Author-email: thetoby@web.de
License: UNKNOWN
Description: 
        .. figure:: tfds_defect_detection/assets/images/logo.png
           :align: center
           :alt:
           :scale: 50 %
        
        
        .. image:: https://readthedocs.org/projects/tfds-defect-detection/badge/?version=latest
            :target: https://tfds-defect-detection.readthedocs.io/en/latest/README.html
            :alt: Documentation Status
        .. image:: https://img.shields.io/pypi/v/tfds_defect_detection
           :target: https://pypi.org/project/tfds-defect-detection/
        .. image:: https://img.shields.io/pypi/pyversions/tfds_defect_detection
           :alt: PyPI - Python Version
        
        ========================================
        TensorFlow Datasets for Defect Detection
        ========================================
        
        To directly jump into the code look at the sample notebook
        
        .. class:: center
        
        |Open in Colab|
        
        .. |Open in Colab| image:: https://img.shields.io/badge/Open%20In-Colab-orange?style=for-the-badge&logo=data:image/png;base64,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
           :target: https://colab.research.google.com/drive/1_0diKQAHBX2q8iCEI7bmv0TnnmaWZR1M?usp=sharing
        
        .. admonition:: Features
        
            - tensorflow.data.Dataset builder for defect segmentation
            - Comes with unsupervised / self-supervised SotA datasets
                - MVTEC
                - VISA
            - Artificial defect generator
            - Evaluation data with hand labelled images
        
        
        Install
        -------
        
        Create a new python=3.9 env and install `tfds_defect_detection` from pip
        
        .. code-block:: bash
        
            pip install tfds_defect_detection
        
        
        
        Examples
        -----------
        
        .. code-block:: python
        
            import tfds_defect_detection as tfd
            tfd.load()
        
        
        
        
        Usage
        -----------
        
        All parmeters
        
        .. code-block:: python
        
            import tfds_defect_detection as tfd
            impor albumentations as A
        
            ds = tfd.load(
                names = ("mvtec", "visa"),
                data_dir=Path("."),
                pairing_mode = "result_with_contrastive_pair",  # "result_only", "result_with_original"
                create_artificial_anomalies=True,
                validation_split=0.2,
                subset_mode = "training",                       # "validation", "test", "holdout", None
                drop_masks=False,
                width=256,
                height=256,
                repeat=True,
                anomaly_size = None,
        
                global_transform=A.Compose([
                  A.RandomBrightnessContrast(),
                  A.HueSaturationValue(),
                ]),
        
                process_deviation=A.Compose([
                  A.ShiftScaleRotate(
                    shift_limit=0.01,
                    scale_limit=0.0,
                    rotate_limit=1.5,
                    p=1
                  ),
                  A.Blur(blur_limit=3),
                  A.RandomBrightnessContrast(),
                  A.RandomGamma(),
                  A.HueSaturationValue(),
                ]),
        
                anomaly_composition=A.Compose([
                  A.RandomRotate90(),
                  A.Transpose(),
                  A.ShiftScaleRotate(
                    shift_limit=0.0625,
                    scale_limit=0.50,
                    rotate_limit=45, p=1
                  ),
                  A.RandomGamma(),
                  A.OpticalDistortion(),
                  A.GridDistortion(),
                  A.RandomContrast(0.5, p=1),
                ]),
        
                batch_size=9,
                seed=123,
                shuffle=True,
                peek=True,
                image_validation=False,
                delete_tmp=True,
                crop_to_aspect_ratio=True
            )
        
        
        .. figure:: tfds_defect_detection/assets/images/example.png
           :align: center
           :alt:
           :scale: 50 %
        
        
        
        .. admonition:: Docs
        
            FOR API Reference see
        
            https://tfds-defect-detection.readthedocs.io/en/latest/autoapi/tfds_defect_detection/index.html
        
        
        .. admonition:: Cite
        
            If this project helped you during your work:
            Until a publication is available, please cite as
        
            Tobias Schiele. (2022). TFDS DD - Datasets for Defect Detection. https://github.com/thetoby9944/tfds_defect_detection.
        
        
            .. code-block:: latex
        
                @misc{Schiele2019,
                    author = {Tobias Schiele},
                    title = {TFDS DD - Datasets for Defect Detection},
                    year = {2022},
                    publisher = {GitHub},
                    journal = {GitHub repository},
                    howpublished = {\url{https://github.com/thetoby9944/tfds_defect_detection}},
                }
        
        
        
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: License :: OSI Approved :: GNU Affero General Public License v3 or later (AGPLv3+)
