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- from __future__ import division
- import numpy as np
- import scipy.io as sio
- import scipy.misc as sc
- import glob
-
- # Parameters
- height = 256
- width = 256
- channels = 3
-
- ############################################################# Prepare ISIC 2018 data set #################################################
- Dataset_add = 'dataset_isic18/'
- Tr_add = 'ISIC2018_Task1-2_Training_Input'
-
- Tr_list = glob.glob(Dataset_add+ Tr_add+'/*.jpg')
- # It contains 2594 training samples
- Data_train_2018 = np.zeros([2594, height, width, channels])
- Label_train_2018 = np.zeros([2594, height, width])
-
- print('Reading ISIC 2018')
- for idx in range(len(Tr_list)):
- print(idx+1)
- img = sc.imread(Tr_list[idx])
- img = np.double(sc.imresize(img, [height, width, channels], interp='bilinear', mode = 'RGB'))
- Data_train_2018[idx, :,:,:] = img
-
- b = Tr_list[idx]
- a = b[0:len(Dataset_add)]
- b = b[len(b)-16: len(b)-4]
- add = (a+ 'ISIC2018_Task1_Training_GroundTruth/' + b +'_segmentation.png')
- img2 = sc.imread(add)
- img2 = np.double(sc.imresize(img2, [height, width], interp='bilinear'))
- Label_train_2018[idx, :,:] = img2
-
- print('Reading ISIC 2018 finished')
-
- ################################################################ Make the train and test sets ########################################
- # We consider 1815 samples for training, 259 samples for validation and 520 samples for testing
-
- Train_img = Data_train_2018[0:1815,:,:,:]
- Validation_img = Data_train_2018[1815:1815+259,:,:,:]
- Test_img = Data_train_2018[1815+259:2594,:,:,:]
-
- Train_mask = Label_train_2018[0:1815,:,:]
- Validation_mask = Label_train_2018[1815:1815+259,:,:]
- Test_mask = Label_train_2018[1815+259:2594,:,:]
-
-
- np.save('data_train', Train_img)
- np.save('data_test' , Test_img)
- np.save('data_val' , Validation_img)
-
- np.save('mask_train', Train_mask)
- np.save('mask_test' , Test_mask)
- np.save('mask_val' , Validation_mask)
-
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