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convert_npz.py
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convert_npz.py
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import numpy as np
from numpy import asarray,savez_compressed
from keras.preprocessing.image import img_to_array
from keras.preprocessing.image import load_img
from keras.utils import to_categorical
import argparse
def convert_npz(imgpath,maskpath, size=(512,512),crops=50,n_images=17):
src_list, tar_list = list(), list()
for i in range(n_images):
for j in range(crops):
# load and resize the image
filename = str(i+1)+"_"+str(j+1)+".png"
mask_name = str(i+1)+"_mask_" + str(j+1)+".png"
img = load_img(imgpath + filename, target_size=size)
fundus_img = img_to_array(img)
mask = load_img(maskpath + mask_name, target_size=size,color_mode="grayscale")
angio_img = img_to_array(mask)
# split into satellite and map
src_list.append(fundus_img)
tar_list.append(angio_img)
return [asarray(src_list), asarray(tar_list)]
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--input_dim', type=int, default=512)
parser.add_argument('--n_crops', type=int, default=50)
parser.add_argument('--datadir', type=str, required=True, help='path/to/data_directory',default='data')
parser.add_argument('--outfile_name', type=str, default='vtgan')
parser.add_argument('--n_images', type=int, default=17)
args = parser.parse_args()
# dataset path
imgpath = args.datadir+'/Images/'
maskpath = args.datadir+'/Masks/'
[src_images, tar_images] = convert_npz(imgpath,maskpath,size=(args.input_dim,args.input_dim),crops=args.n_crops,n_images=args.n_images)
print('Loaded: ', src_images.shape, tar_images.shape)
# labels
a = np.zeros((350,1))
b = np.ones((500, 1))
labels = np.vstack((a,b))
labels = to_categorical(labels)
# save as compressed numpy array
filename = args.outfile_name+'.npz'
savez_compressed(filename, src_images, tar_images, labels)
print('Saved dataset: ', filename)