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prepare_dataset_hdf5_for_3D.py
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prepare_dataset_hdf5_for_3D.py
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#==========================================================
#
# This prepare the hdf5 datasets of the DRIVE_new database
#
#============================================================
import os
import h5py
from PIL import Image
import numpy as np
import os
import pylab as py
import matplotlib.pyplot as plt
plt.switch_backend('agg')
def write_hdf5(arr,outfile):
with h5py.File(outfile,"w") as f:
f.create_dataset("image", data=arr, dtype=arr.dtype)
f.close()
##3------------Path of the images 3D--------------------------------------------------------------
original_imgs_test = "/home/a/ori_retinal_vessel_pretraining/final_experiments/new_test_data/##3D/"
Nimgs_test = 37
channels = 1
height = 512
width = 512
frame=4
dataset_path = "./3D_new_datasets_training_testing/"
def get_datasets(imgs_dir):
imgs_test = np.empty((Nimgs_test,frame, height, width))
for item in range(Nimgs_test):
ori_img_name1=imgs_dir+'ori_'+str(item+1)+'_1.png'
ori_img_name2 = imgs_dir + 'ori_' + str(item + 1) + '_2.png'
ori_img_name3 = imgs_dir + 'ori_' + str(item + 1) + '_3.png'
ori_img_name4 = imgs_dir + 'ori_' + str(item + 1) + '_4.png'
img1=Image.open(ori_img_name1)
img2 = Image.open(ori_img_name2)
img3 = Image.open(ori_img_name3)
img4 = Image.open(ori_img_name4)
img1_=np.array(img1)/np.max(np.array(img1))
img2_=np.array(img2)/np.max(np.array(img2))
img3_=np.array(img3)/np.max(np.array(img3))
img4_=np.array(img4)/np.max(np.array(img4))
imgs_test[item,0,:,:] = img1_
imgs_test[item, 1, :, :] = img2_
imgs_test[item, 2, :, :] = img3_
imgs_test[item, 3, :, :] = img4_
#imgs_test=imgs_test/np.max(imgs_test)
print("imgs1 max: " + str(np.max(img1_)))
print("imgs1 min: " + str(np.min(img1_)))
print("imgs2 max: " + str(np.max(img2_)))
print("imgs2 min: " + str(np.min(img2_)))
print("imgs3 max: " + str(np.max(img3_)))
print("imgs3 min: " + str(np.min(img3_)))
print("imgs4 max: " + str(np.max(img4_)))
print("imgs4 min: " + str(np.min(img4_)))
imgs1 = np.reshape(imgs_test, (Nimgs_test, 1,frame, height, width))
return imgs1
if not os.path.exists(dataset_path):
os.makedirs(dataset_path)
#getting the testing datasets
imgs_1 = get_datasets(original_imgs_test)
print ("saving test datasets")
print (imgs_1.shape)
print(np.max(imgs_1))
print(np.min(imgs_1))
write_hdf5(imgs_1,dataset_path + "dataset_imgs_train.hdf5")