Classification of milk based on dried drop images of milks
Image processing tools:
- OpenCV
- Scikit-image
- Pillow
├── 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
Project based on the cookiecutter data science project template. #cookiecutterdatascience
- Find one circle in pancake and two circles (outside and inside) doughnut images by using the Hough circle transform.
- Notebooks
- 1.0-bl-initial-data-exploration: read and display the images given.
- 2.0-bl-detect-circle: detect circles from images
- 3.0-Bruce-Inner-cycle and 04-bruce_inner_cycle: try to detect the inner circle from Doughnut images.
- 5.0-Bruce-extract-circles and 5.0-Bruce-extract-circles-auto: extract the pixels from the circled images.
- 6.0_auto_two_circles: extract pixels from two circled images
- 7.0-original-lbp-histogram: utilize LBP technology to extract image features.
- 7.0_auto_outside_circle_lbp_histogram: LBP to extracted features from circled images
- Notebooks
- Notebooks
- 9.0-image-classification: KNN and GradientBoosting are to classify PC and DN. The notebook calls the pipeline function:
- multiple_cls_test: from src.models.train_model import multiple_cls_test
- 9.0-image-classification: KNN and GradientBoosting are to classify PC and DN. The notebook calls the pipeline function:
- Given the center and extract five rings from images.
- Notebooks:
- 11-new-images-lbp: utilize LBP methods to extract features from different 5 rings which centers are same. The radis of five rings increase by 100 pixels from innter to outside. Classification
- 12-new-images-lbp-classification: three classifiers are to identify three catagories by lbp features of each ring, merged rings.
- Notebook: 17-combining-rings-lbp-classification-merge-central-ring