README.rst 1.4 KB

12345678910111213141516171819202122232425262728293031323334353637383940414243
  1. IPFML
  2. =====
  3. Image Processing For Machine Learning package.
  4. How to use ?
  5. ------------
  6. To use, simply do::
  7. >>> from PIL import Image
  8. >>> import ipfml as iml
  9. >>> img = Image.open('path/to/image.png')
  10. >>> s = iml.metrics.get_SVD_s(img)
  11. Modules
  12. -------
  13. This project contains modules.
  14. - **img_processing** : *PIL image processing part*
  15. - fig2data(fig): *Convert a Matplotlib figure to a 3D numpy array with RGB channels and return it*
  16. - fig2img(fig): *Convert a Matplotlib figure to a PIL Image in RGB format and return it*
  17. - **metrics** : *Metrics computation of PIL image*
  18. - get_SVD(image): *Transforms PIL Image into SVD*
  19. - get_SVD_s(image): *Transforms PIL Image into SVD and returns only 's' part*
  20. - get_SVD_U(image): *Transforms PIL Image into SVD and returns only 'U' part*
  21. - get_SVD_V(image): *Transforms PIL Image into SVD and returns only 'V' part*
  22. - **ts_model_helper** : *contains helpful function to save or display model information and performance of tensorflow model*
  23. - save(history, filename): *Function which saves data from neural network model*
  24. - show(history, filename): *Function which shows data from neural network model*
  25. All these modules will be enhanced during development of the project
  26. How to contribute
  27. -----------------
  28. This git project uses git-flow_ implementation. You are free to contribute to it.
  29. .. _git-flow : https://danielkummer.github.io/git-flow-cheatsheet/