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Test time augmnentation wrapper for keras image segmentation and classification models.

Wrapper add augmentation layers to your Keras model like this:

          Input            |           # input image; shape 1, H, W, C       / / / \ \ \      # duplicate image for augmentation; shape N, H, W, C      | | |   | | |     # apply augmentations (flips, rotation, shifts)     your Keras model      | | |   | | |     # reverse transformations       \ \ \ / / /      # merge predictions (mean, max, gmean)            |           # output mask; shape 1, H, W, C          Output

• h_flip - bool, horizontal flip augmentation
• v_flip - bool, vertical flip augmentation
• rotataion - list, allowable angles - 90, 180, 270
• h_shift - list of int, horizontal shift augmentation in pixels
• v_shift - list of int, vertical shift augmentation in pixels
• add - list of int/float, additive factor (aug_image = image + factor)
• mul - list of int/float, additive factor (aug_image = image * factor)
• contrast - list of int/float, contrast adjustment factor (aug_image = (image - mean) * factor + mean)
• merge - one of 'mean', 'gmean' and 'max' - mode of merging augmented predictions together

1. model has to have 1input and 1output
2. inferencebatch_size == 1
3. imageheight == width ifrotation augmentation is used

1. PyPI package:

$ pip install tta-wrapper

2. Latest version:

$ pip install git+https://github.com/qubvel/tta_wrapper/

fromkeras.modelsimportload_modelfromtta_wrapperimporttta_segmentationmodel=load_model('path/to/model.h5')tta_model=tta_segmentation(model,h_flip=True,rotation=(90,270),h_shift=(-5,5),merge='mean')y=tta_model.predict(x)