Awesome

CS-Flow

This is the code to the WACV 2022 paper "Fully Convolutional Cross-Scale-Flows for Image-based Defect Detection" by Marco Rudolph, Tom Wehrbein, Bodo Rosenhahn and Bastian Wandt.

Getting Started

You will need Python 3.6 and the packages specified in requirements.txt. We recommend setting up a virtual environment with pip and installing the packages there.

Install packages with:

$ pip install -r requirements.txt

Configure and Run

All configurations concerning data, model, training, visualization etc. can be made in config.py. The default configuration will run a training with paper-given parameters on the provided dummy dataset. This dataset contains images of 4 squares as normal examples and 4 circles as anomaly.

To extract features, run extract_features.py (this was already done here for the dummy dataset, features were extracted to data/features). To start the training, just run main.py! Please report us if you have issues when using the code.

Data

The given dummy dataset shows how the implementation expects the construction of a dataset. Coincidentally, the MVTec AD dataset is constructed in this way.

Set the variables dataset_path and class_name in config.py to run experiments on a dataset of your choice. The expected structure of the data is as follows:

train data:

        dataset_path/class_name/train/good/any_filename.png
        dataset_path/class_name/train/good/another_filename.tif
        dataset_path/class_name/train/good/xyz.png
        [...]

test data:

    'normal data' = non-anomalies

        dataset_path/class_name/test/good/name_the_file_as_you_like_as_long_as_there_is_an_image_extension.webp
        dataset_path/class_name/test/good/did_you_know_the_image_extension_webp?.png
        dataset_path/class_name/test/good/did_you_know_that_filenames_may_contain_question_marks????.png
        dataset_path/class_name/test/good/dont_know_how_it_is_with_windows.png
        dataset_path/class_name/test/good/just_dont_use_windows_for_this.png
        [...]

    anomalies - assume there are anomaly classes 'crack' and 'curved'

        dataset_path/class_name/test/crack/dat_crack_damn.png
        dataset_path/class_name/test/crack/let_it_crack.png
        dataset_path/class_name/test/crack/writing_docs_is_fun.png
        [...]

        dataset_path/class_name/test/curved/wont_make_a_difference_if_you_put_all_anomalies_in_one_class.png
        dataset_path/class_name/test/curved/but_this_code_is_practicable_for_the_mvtec_dataset.png
        [...]

Credits

Some code of an old version of the FrEIA framework was used for the implementation of Normalizing Flows. Follow their tutorial if you need more documentation about it.

Citation

Please cite our paper in your publications if it helps your research. Even if it does not, you are welcome to cite us.

    @inproceedings { RudWeh2022,
    author = {Marco Rudolph and Tom Wehrbein and Bodo Rosenhahn and Bastian Wandt},
    title = {Fully Convolutional Cross-Scale-Flows for Image-based Defect Detection},
    booktitle = {Winter Conference on Applications of Computer Vision (WACV)},
    year = {2022},
    url = {arxiv},
    month = jan
    }

    

License

This project is licensed under the MIT License.