Metadata-Version: 2.1
Name: splitraster
Version: 0.1.0
Summary: Provide good support for deep learning and computer vision tasks by creating a tiled output from an input raster dataset.
Home-page: https://github.com/cuicaihao/split_raster
Author: Chris Cui
Author-email: 
License: UNKNOWN
Description: # Split Raster
        
        Provide good support for deep learning and computer vision tasks by creating a tiled output from an input raster dataset.'
        
        ## Use the packages
        
        ```bash
        pip install splitpackage
        ```
        
        ## Try Sample code
        
        ```python
        
        from splitraster import io
        
        input_image_path = "../data/raw/RGB.png"
        gt_image_path = "../data/raw/GT.png"
        
        save_path = "../data/processed/RGB"
        crop_size = 256
        repetition_rate = 0.5
        overwrite = False
        
        n = io.split_image(input_image_path, save_path, crop_size,
                           repetition_rate=repetition_rate, overwrite=overwrite)
        print(f"{n} tiles sample of {input_image_path} are added at {save_path}")
        
        save_path_gt = "../data/processed/GT"
        n = io.split_image(gt_image_path, save_path_gt, crop_size,
                           repetition_rate=repetition_rate, overwrite=overwrite)
        print(f"{n} tiles sample of {gt_image_path} are added at {save_path_gt}")
        
        
        ```
        
        ## Project Organization
        
            ├── LICENSE
            ├── Makefile           <- Makefile with commands like `make data` or `make train`
            ├── README.md          <- The top-level README for developers using this project.
            ├── data
            │   ├── external       <- Data from third party sources.
            │   ├── interim        <- Intermediate data that has been transformed.
            │   ├── processed      <- The final, canonical data sets for modeling.
            │   └── raw            <- The original, immutable data dump.
            │
            ├── docs               <- A default Sphinx project; see sphinx-doc.org for details
            │
            ├── models             <- Trained and serialized models, model predictions, or model summaries
            │
            ├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
            │                         the creator's initials, and a short `-` delimited description, e.g.
            │                         `1.0-jqp-initial-data-exploration`.
            │
            ├── references         <- Data dictionaries, manuals, and all other explanatory materials.
            │
            ├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
            │   └── figures        <- Generated graphics and figures to be used in reporting
            │
            ├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
            │                         generated with `pip freeze > requirements.txt`
            │
            ├── setup.py           <- makes project pip installable (pip install -e .) so src can be imported
            ├── src                <- Source code for use in this project.
            │   ├── __init__.py    <- Makes src a Python module
            │   │
            │   ├── data           <- Scripts to download or generate data
            │   │   └── make_dataset.py
            │   │
            │   ├── features       <- Scripts to turn raw data into features for modeling
            │   │   └── build_features.py
            │   │
            │   ├── models         <- Scripts to train models and then use trained models to make
            │   │   │                 predictions
            │   │   ├── predict_model.py
            │   │   └── train_model.py
            │   │
            │   └── visualization  <- Scripts to create exploratory and results oriented visualizations
            │       └── visualize.py
            │
            └── tox.ini            <- tox file with settings for running tox; see tox.readthedocs.io
        
        ---
        
        <p><small>Project based on the <a target="_blank" href="https://drivendata.github.io/cookiecutter-data-science/">cookiecutter data science project template</a>. #cookiecutterdatascience</small></p>
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
