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    "info": {
        "author": "Pavel Yakubovskiy",
        "author_email": "qubvel@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: Implementation :: CPython",
            "Programming Language :: Python :: Implementation :: PyPy"
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        "description": "\n# TTA wrapper\nTest time augmnentation wrapper for keras image segmentation and classification models.\n\n## Description\n\n### How it works?\n\nWrapper add augmentation layers to your Keras model like this:\n\n```\n          Input\n            |           # input image; shape 1, H, W, C\n       / / / \\ \\ \\      # duplicate image for augmentation; shape N, H, W, C\n      | | |   | | |     # apply augmentations (flips, rotation, shifts)\n     your Keras model\n      | | |   | | |     # reverse transformations\n       \\ \\ \\ / / /      # merge predictions (mean, max, gmean)\n            |           # output mask; shape 1, H, W, C\n          Output\n```\n\n### Arguments\n\n  - `h_flip` - bool, horizontal flip augmentation\n  - `v_flip` - bool, vertical flip augmentation\n  - `rotataion` - list, allowable angles - 90, 180, 270\n  - `h_shift` - list of int, horizontal shift augmentation in pixels\n  - `v_shift` - list of int, vertical shift augmentation in pixels\n  - `add` - list of int/float, additive factor (aug_image = image + factor)\n  - `mul` - list of int/float, additive factor (aug_image = image * factor)\n  - `contrast` - list of int/float, contrast adjustment factor (aug_image = (image - mean) * factor + mean)\n  - `merge` - one of 'mean', 'gmean' and 'max' - mode of merging augmented predictions together\n\n### Constraints\n  1) model has to have 1 `input` and 1 `output`\n  2) inference `batch_size == 1`\n  3) image `height == width` if `rotation_angles` augmentation is used\n\n\n## Example\n```python\nfrom keras.models import load_model\nfrom tta_wrapper import tta_segmentation\n\nmodel = load_model('path/to/model.h5')\ntta_model = tta_segmentation(model, h_flip=True, rotation_angles=(90, 270), \n                             h_shifts=(-5, 5), merge='mean')\ny = tta_model.predict(x)\n```\n\n\n",
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        "license": "MIT",
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        "name": "tta-wrapper",
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        "summary": "Test time image augmentation for Keras models.",
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