Firsi Commit

Author: shubham1166

Dice_loss.py

@@ -0,0 +1,112 @@
+import numpy as np
+from _23May2019_Unet.UNET_ import U_Net
+from _23May2019_Unet.VGG_segmentation import VGG_
+from keras import backend as K
+import keras
+from tqdm import tqdm
+from final_post_processing.Algorithm import algorithm
+# import mxnet as nd
+
+########################################################################
+
+width=512
+height=512
+depth=3
+classes=2
+data_shape=width*height
+########################################################################
+x_train=np.load('E:/Cell_data/x_train.npy')
+x_test=np.load('E:/Cell_data/x_test.npy')
+y_test=np.load('E:/Cell_data/y_test.npy')
+y_train=np.load('E:/Cell_data/y_train.npy')
+y_test = np.reshape(y_test,(len(y_test),data_shape,classes))
+y_train = np.reshape(y_train,(len(y_train),data_shape,classes))
+y=np.load('E:/Cell_data/label_cell.npy')
+y=np.reshape(y,(y.shape[0],y.shape[1]*y.shape[2],y.shape[3]))
+x_whitened=np.load('E:/Cell_data/data_cell.npy')
+
+ 
+########################################################################
+def dice_loss(y_true,y_pred):
+    y_true=y_true.astype('float32')
+    y_pred=y_pred.astype('float32')
+    smooth=1
+    intersection=2*sum(y_true*y_pred)
+    union=sum(y_true)+sum(y_pred)
+    if np.mean(union)==0 and np.mean(intersection)==0:
+        dice_coef=0
+    else:
+        dice_coef=np.mean((intersection+smooth)/(union+smooth))
+    dice_loss_=1-dice_coef
+    return dice_loss_
+   
+   
+   
+   
+   
+   
+# def give_y(net,x):
+#     z=[]
+#     z.append(x)
+#     a=net.predict(np.array(z))
+#     a=np.reshape(a,(data_shape,classes))
+#     a=a.astype('uint8')
+#     return a
+#    
+# def give_dice(x_test_whitened,y_test,net):
+#     l=len(y_test)
+#     dice=[]
+#     for i in tqdm(range(l)):
+#         y=give_y(net,x_test_whitened[i])
+# #         print('giv_y is : ',y)
+#         d=dice_loss(y, y_test[i])
+#         dice.append(d)
+#     return dice
+         
+ 
+# #############################################################################
+# #UNet
+# pathtoweight1='E:/Cell_data/U-Net/'
+# K.set_image_data_format('channels_first')
+# net = U_Net(width, height, depth, classes,weightsPath=pathtoweight1+'Cell.h5_0183-0.9988.h5')
+# print('here')  
+# dice_unet=give_dice(x_whitened, y, net)
+# # 
+# mean_dice_unet=np.mean(dice_unet)
+# print('mean_dice_unet : ',mean_dice_unet)
+# # #########################################################################
+# #VGG
+# pathtoweight2='E:/Cell_data/VGG/'
+# model=VGG_(weightsPath=pathtoweight2+'Cell.h5_0008-0.9979.h5')
+#       
+# dice_vgg=give_dice(x_whitened, y, model)
+# mean_dice_vgg=np.mean(dice_vgg)
+# print('mean_dice_vgg : ',mean_dice_vgg)
+#  
+# ###########################################################################
+# #POST-PROCESSED
+# y_processed=np.load('E:/Cell_data/U-Net/Post-processing/final/images_categorical.npy')
+# dice_processed=[]
+# for i in tqdm(range(len(y_processed))):
+#     d=dice_loss(y[i],y_processed[i])
+#     dice_processed.append(d)
+# mean_dice_processed=np.mean(np.array(dice_processed))
+# print('mean_dice_processed : ',mean_dice_processed)
+###############################################################################
+#SUPER-PIXELS
+y_superpixels=np.load('E:/Cell_data/Superpixels/images_categorical.npy')
+dice_superpixels=[]
+for i in tqdm(range(len(y_superpixels))):
+#     print(i)
+    d=dice_loss(y[i],y_superpixels[i])
+    dice_superpixels.append(d)
+mean_dice_superpixels=np.mean(np.array(dice_superpixels))
+print('mean_dice_superpixels : ',mean_dice_superpixels)
+print('done')  
+     
+
+      
+  
+  
+  
+