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Prepare_Neg_test.py

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+# -*- coding: utf-8 -*-
+import h5py
+import numpy as np
+import scipy.io as sio
+import scipy.misc as sc
+import glob
+
+# Parameters
+height = 256
+width  = 256
+channels = 3
+
+############################################################# Prepare NormalSkin data set #################################################
+
+
+Tr_list = glob.glob("Normal(Healthy skin)"+'/*.png')
+Data_train_2018    = np.zeros([200, height, width, channels])
+Label_train_2018   = np.zeros([200, height, width])
+
+print(len(Tr_list))
+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]
+    b = b[len(b)-8: len(b)-4]
+    add = ("masks_black/" + b +'.png')
+    img2 = sc.imread(add)
+    img2 = np.double(sc.imresize(img2, [height, width], interp='bilinear'))
+    Label_train_2018[idx, :,:] = img2    
+
+print('finished')
+
+Test_img       = Data_train_2018[0:200,:,:,:]
+
+Test_mask       = Label_train_2018[0:200,:,:]
+
+
+np.save('data_test' , Test_img)
+
+np.save('mask_test' , Test_mask)
+
+

generate_black.py

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+import os
+from PIL import Image
+import numpy as np
+
+folder = 'masks_black'
+if not os.path.exists(folder):
+    os.makedirs(folder)
+
+for i in range(200):
+    image = np.zeros((256,256), dtype=np.uint8)
+    img = Image.fromarray(image)
+    name = os.path.join(folder, f'{i:04}.png')
+    img.save(name)
+
+print(f'Generated 200 images at {folder}')