{
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        "author": "Andrew Khalel",
        "author_email": "andrewekhalel@gmail.com",
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        "classifiers": [
            "Development Status :: 2 - Pre-Alpha",
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.6",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.1",
            "Programming Language :: Python :: 3.2",
            "Programming Language :: Python :: 3.3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6"
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        "description": "# Edafa\n![GitHub](https://img.shields.io/github/license/mashape/apistatus.svg) [![contributions welcome](https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat)](https://github.com/andrewekhalel/edafa/issues) [![HitCount](http://hits.dwyl.io/andrewekhalel/edafa.svg)](http://hits.dwyl.io/andrewekhalel/edafa)<br/>\nEdafa is a simple wrapper that implements Test Time Augmentations (TTA) on images for computer vision problems like: segmentation, classification, super-resolution, Pansharpening, etc. TTAs guarantees better results in most of the tasks.\n\n### Test Time Augmentation (TTA)\n\nApplying different transformations to test images and then average for more robust results.\n\n![pipeline](https://preview.ibb.co/kH61v0/pipeline.png)\n\n### Installation\n```shell\npip install edafa\n```\n\n### Getting started\nThe easiest way to get up and running is to follow [example notebooks](https://github.com/andrewekhalel/edafa/tree/master/examples) for segmentation and classification showing TTA effect on performance.\n\n### How to use Edafa\nThe whole process can be done in 4 steps:\n1.  Import Predictor class based on your task category: Segmentation (`SegPredictor`) or Classification (`ClassPredictor`) \n```python\nfrom edafa import SegPredictor\n```\n2. Inherit Predictor class and implement the main function \n\t* `predict_patches(self,patches)` : where your model takes image patches (numpy.ndarray) and return prediction (numpy.ndarray)\n\n```python\nclass myPredictor(SegPredictor):\n    def __init__(self,model,*args,**kwargs):\n        super().__init__(*args,**kwargs)\n        self.model = model\n\n    def predict_patches(self,patches):\n        return self.model.predict(patches)\n```\n3. Create an instance of you class\n```python\np = myPredictor(model,patch_size,model_output_channels,conf_file_path)\n```\n4.  Call `predict_images()` to run the prediction process \n```python\np.predict_images(images,overlap=0)\n```\n### Configuration file\nConfiguration file is a json file containing two pieces of information\n1. Augmentations to apply (**augs**). Supported augmentations:\n\t* **NO** : No augmentation\n\t* **ROT90** : Rotate 90 degrees\n\t* **ROT180** : Rotate 180 degrees\n\t* **ROT270** : Rotate 270 degrees\n\t* **FLIP_UD** : Flip upside-down\n\t* **FLIP_LR** : Flip left-right\n\t* **BRIGHT** : Change image brightness randomly\n\t* **CONTRAST** : Change image contrast randomly\n\t* **GAUSSIAN** : Add random gaussian noise\n\t* **GAMMA** : Perform gamma correction with random gamma\n2. Combination of the results (**mean**). Supported mean types:\n\t* **ARITH** : Arithmetic mean\n\t* **GEO** : Geometric mean\n3. Number of bits image (default is 8-bits) (**bits**).\n\nExample of a conf file\n```json\n{\n\"augs\":[\"NO\",\n\"FLIP_UD\",\n\"FLIP_LR\"],\n\"mean\":\"ARITH\",\n\"bits\":8\n}\n```\nYou can either pass file path or the actual json text to `conf` parameter.\n\n## Contribution\nAll contributions are welcomed. Please make sure that all tests passed before pull request. To run tests\n```shell\nnosetests\n```\n\n\n",
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        "keywords": "augmentation classification segmentation super-resolution pansharpening keras tensorflow pytorch",
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