This Tensorflow implementation of Deep Convolutional Generative Adversarial Networks was originally modified to generate artwork. The code was further modified to incorporate BatchNorm- SELU layers to generate Computer Tomography (CT) images in HD quality. The submitted EMBC conference abstract is provided under the /assets folder. An example of a pediatric CT image generated from the BS-DCGAN network is shown below:
- Python 3.3+
- Tensorflow 0.12.1 or Tensorflow GPU
- SciPy
- pillow
First, put all the processed images (in png/ jpg format) within the /processed folder (please create your own in the root directory).
Then run the following command to begin training:
python main.py --data_dir=./processed --input_fname_pattern=*.png --batch_size=4 --input_height=512 --input_width=512 --output_height=512 --output_width=512 --generate_test_images=106 --dataset=nifty_ct --epoch=250 --train
Test images are generated at the end of training. The number of test images can be specified with the flag generate_test_images.
All the generated train/ test samples are within the /samples folder. Run python debatch.py to create single images from batches (you may have to change the directories and parameters within debatch.py accordingly).
Original Authors: (DCGAN) Taehoon Kim / @carpedm20 (Scraper, artDCGAN) Robbie Barrat / @robbiebarrat / (BS-DCGAN) Chi Nok Enoch Kan [@enochk22] (kanxx030@gmail.com)