Our code has been tested on Tensorflow 0.11.0rc1, python2.7

To run this code, you need to prepare the training data yourself first.
In our code, there are some files which need to be prepared. But you can also change the data as any format as you want.


** img.npy & label.npy & mask.npy & od.npy
This is the training data includes the matrices of images, masks, vessel ground-truths and optical disk locations respectively. The sizes of these matrices are following the size of placeholder accordingly. For example, the size of image matrix is N * img_size * img_size * 3 and of mask matrix is N * img_size * img_size * 1.

The value of all these 4 metrices is between 0 and 1.


** drive_rnn_test_od.mat
This is the test sample for generating images. The data structure is the same as the .npy file but in .mat format.


** rnn_style.mat / rnn_style_stare.mat / rnn_style_drive_test.mat
This is the file used to save the style images. It contains the color retinal images whose size is the same as the training images (i.e. Ns * img_size * img_size * 3)


** imagenet-vgg-verydeep-19.mat
This is the vgg-19 network weights, which can be downloaded from http://www.vlfeat.org/matconvnet/models/beta16/imagenet-vgg-verydeep-19.mat
