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@@ -1,27 +1,43 @@
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-# IPFML
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+IPFML
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+=====
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Image Processing For Machine Learning package.
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-## Modules
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+How to use ?
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+------------
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+
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+To use, simply do::
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+
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+ >>> from PIL import Image
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+ >>> import ipfml as iml
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+ >>> img = Image.open('path/to/image.png')
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+ >>> s = iml.metrics.get_SVD_s(img)
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+
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+
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+Modules
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+-------
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This project contains modules.
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-### **img_processing** : *PIL image processing part*
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+- **img_processing** : *PIL image processing part*
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- fig2data(fig): *Convert a Matplotlib figure to a 3D numpy array with RGB channels and return it*
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- fig2img(fig): *Convert a Matplotlib figure to a PIL Image in RGB format and return it*
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-### **metrics** : *Metrics computation of PIL image*
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+- **metrics** : *Metrics computation of PIL image*
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- get_SVD(image): *Transforms PIL Image into SVD*
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- get_SVD_s(image): *Transforms PIL Image into SVD and returns only 's' part*
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- get_SVD_U(image): *Transforms PIL Image into SVD and returns only 'U' part*
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- get_SVD_V(image): *Transforms PIL Image into SVD and returns only 'V' part*
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-### **ts_model_helper** : *contains helpful function to save or display model information and performance of tensorflow model*
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+- **ts_model_helper** : *contains helpful function to save or display model information and performance of tensorflow model*
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- save(history, filename): *Function which saves data from neural network model*
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- show(history, filename): *Function which shows data from neural network model*
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All these modules will be enhanced during development of the project
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-## How to contribute
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+How to contribute
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+-----------------
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+
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+This git project uses git-flow_ implementation. You are free to contribute to it.
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-This git project uses [git-flow](https://danielkummer.github.io/git-flow-cheatsheet/) implementation. You are free to contribute to it.
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+.. _git-flow : https://danielkummer.github.io/git-flow-cheatsheet/
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