CNN based Autoencoders :

Implementation of CNN based autoencoders.

Author: Ravin Kumar

Written in Tensorflow: 1.12

Directory Structure :

|
|--input   (dir)
|
|--pros    (dir)
|
|--restmp  (dir)
|
|--test    (dir)
|
|--testres (dir)
|
|--script.py
|
|--run.py

Description of various directories and files :

• input directory contains input images.
• test directory contains test images.
• pros directory will contain processed images, that then feed to the encoder side of autoencders.
• restmp directory contains intermediate results during training phase.
• testres directory will contain the output over the images at inference time.
• script.py this is a module based file in python, where cnn based autoencoders are implimented.
• run.py file is automated version of the process.
• Note: lets say at training time input image name is "abc.jpg", then please save it's desired output image by "abcr.jpg" in the input directory.

Steps required :

• We can import script.py file and than proceed :

import script
script.preprocess() # it will convert all input dir images to fixed size of 256 x 256.
# processed images are saved to pros directory.
script.generate_more_data(90) # it will generate more data from processed images of pros directory.
# by rotating them. here images are rotated by 90 degree to produce more data.
script.run(1000) #  1000 defines the number of epoches for training.

• To automate the process of training and testing :

>>> python3 run.py 1000

Note: you do have to resize the images of test directory manually or by using the script code separately.

Working Project Videos

A sample working demonstration of Autoencoders is available at Youtube.

Copyright (c) 2018 Ravin Kumar
Website: https://mr-ravin.github.io

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