This repository is an implementation on the U-Net Paper.

• The script/getdataset.sh downloads the The Inria Aerial Image Labeling.This is not the best data set if you want to do aerial image segmentation on your own computer, because of computational limits because of 5k Images.
• This Repo contains a hot encoder which encodes the dataset of n classes to a one hot encoded matrix of n* height* width for every class. The encoder can be seen at utils/ .
• To use train it, put the Images and the gt file locations in the config.json folder and execute train.py, a .pth file will be saved for the following model in the working directory.
• Ch/ foler includes the example for creating a N class hot encoded image

Working

• git clone https://github.com/madhavkhoslaa/Pytorch-U-Net-Segmentation
• Open the config.json file and put the location of your dataset in the json files and put the resolution of the Images(you cannot train model with different sized images and a single batch size because of PyTorch collate)
• Then edit the train.py file and esit the file extensions.
• Train the model, by running train.py

Using just the hot encoder

• img= skimage.io.imread("someimage.jpeg")

Load your Image

• encoder= HotEncoder(dir= '', extension="tif", is_binary= False)

Make an encoder object for you dataset.

• classes= encoder.gen_colors()

This generates the number of classes from the number of colors in your annotated data.

• ClassMatrix= encoder.PerPixelClassMatrix(image)

This method returns a martrix of the same size height and width but single channel, depecting class of each pixel in the Image.

• encoded= encoder.HotEncode(ClassMatrix)

This encodes your Image to n channel matrix of same height and width You also might want to view each channel depecting every class for an Image, to split the n channel matrix to n single channel matrices.

• ch= encoder.channelSplit(encoded)

This returns n number of matrices depecting each class in an Image. This method is also used to encode the Image in the dataloader.

Known Issues

• Single Channel tif files cannot be hot encoded right now. If you have a single channel tif file for the annotation, convert it into a png or a 3 channel tif file
• Predict.py not tested

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If you feel any need to contribute don’t feel shy to do a pull request or contact me:D

To Understand Repo structure

Credits:

milesial's UNET Repository for the U-Net Model for the model file