Metadata-Version: 2.1
Name: pyimgaug3d
Version: 0.43
Summary: Python image augmentation
Home-page: UNKNOWN
Author: Siyu Liu
Author-email: siyu.liu2@uqconnect.edu.au
License: UNKNOWN
Description: # Pyimgaug3d
        A 3D *GPU* augmentation library. This library is work-in-progress and will be constantly updated with more augmentation methods. The current supported ones are grid warp 3D, flip  3D and identity. As all the augmentation methods are implemented in TensorFlow, a Cuda compatible GPU is required to take advantage of increases speeds.
        
        # Installation
        ```
        pip install pyimgaug3d
        ```
        
        # Example Usage
        ```
        from pyimgaug3d.augmentation import GridWarp, Flip, Identity
        from pyimgaug3d.augmenters import ImageSegmentationAugmenter
        
        img = load_img... # shape=(H,W,D,C)
        seg = load_seg... # shape=(H,W,D,C)(one hot encoded)
        
        # This augmenter automatically rounds the segmentation mask
        aug = ImageSegmentationAugmenter()
        aug.add_augmentation(GridWarp(grid=(4, 5, 5), max_shift=10))
        aug.add_augmentation(Flip(0))
        aug.add_augmentation(Identity())
        
        aug_img, aug_seg = aug([img, seg]) # call to perform augmentation, each time an augmentation method is sampled at random.
        ```
        # Citation
        This library is published along with the following paper
        ```
        S.  Liu,  W.  Dai,  C.  Engstrom,  J.  Fripp,  P.  B.  Greer,  S.  Crozier,J. A. Dowling, 
        and S. S. Chandra, “Fabric Image Representation Encoding Networks for Large-scale 3D Medical 
        Image Analysis,”arXiv e-prints, p. arXiv:2006.15578, Jun. 2020.
        ```
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.5, <4
Description-Content-Type: text/markdown
