Update app.py

app.py

@@ -6,55 +6,55 @@
 # In[1]:
 
 
-# import keras 
-# get_ipython().run_line_magic('env', 'SM_FRAMEWORK=tf.keras')
-# import segmentation_models as sm
-# import glob
-# import matplotlib.pyplot as plt
-# from scipy import ndimage
-# from scipy.ndimage import label, generate_binary_structure
-# import radiomics
-# import cv2
-# import SimpleITK as sitk
-# import six
-# import numpy as np
-# import pandas as pd
-# import seaborn as sns
-# import matplotlib.pyplot as plt
-# from sklearn.model_selection import train_test_split
-# get_ipython().run_line_magic('matplotlib', 'inline')
-# from sklearn.feature_selection import SelectKBest
-# from sklearn.feature_selection import mutual_info_classif
-# from sklearn.ensemble import RandomForestClassifier
-# from sklearn.feature_selection import RFE
-# from sklearn.metrics import roc_auc_score
-# from sklearn.feature_selection import SelectFromModel
-# from sklearn.linear_model import Lasso, LogisticRegression
-# from sklearn.preprocessing import StandardScaler
-# from sklearn.decomposition import PCA 
-# from sklearn.svm import LinearSVC
-# from sklearn.datasets import load_iris
-# from sklearn.feature_selection import SelectFromModel
-# from sklearn.feature_selection import mutual_info_classif as MIC
-# from sklearn.feature_selection import mutual_info_classif, mutual_info_regression
-# from sklearn.feature_selection import SelectKBest, SelectPercentile
-# from sklearn.feature_selection import SequentialFeatureSelector
-# from sklearn.neighbors import KNeighborsClassifier
-# from sklearn.linear_model import LogisticRegression
-# from sklearn.ensemble import RandomForestClassifier
-# from sklearn.svm import SVC
-# from sklearn.metrics import f1_score
-# from sklearn.model_selection import cross_validate
-# from sklearn.tree import DecisionTreeClassifier 
-# from sklearn.linear_model import Perceptron
-# from sklearn.model_selection import RandomizedSearchCV
-# from sklearn.model_selection import GridSearchCV
-
-# import warnings
-# warnings.filterwarnings('ignore')
-# import os
-
-# os.environ["CUDA_VISIBLE_DEVICES"] = "1"
+import keras 
+get_ipython().run_line_magic('env', 'SM_FRAMEWORK=tf.keras')
+import segmentation_models as sm
+import glob
+import matplotlib.pyplot as plt
+from scipy import ndimage
+from scipy.ndimage import label, generate_binary_structure
+import radiomics
+import cv2
+import SimpleITK as sitk
+import six
+import numpy as np
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+from sklearn.model_selection import train_test_split
+get_ipython().run_line_magic('matplotlib', 'inline')
+from sklearn.feature_selection import SelectKBest
+from sklearn.feature_selection import mutual_info_classif
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.feature_selection import RFE
+from sklearn.metrics import roc_auc_score
+from sklearn.feature_selection import SelectFromModel
+from sklearn.linear_model import Lasso, LogisticRegression
+from sklearn.preprocessing import StandardScaler
+from sklearn.decomposition import PCA 
+from sklearn.svm import LinearSVC
+from sklearn.datasets import load_iris
+from sklearn.feature_selection import SelectFromModel
+from sklearn.feature_selection import mutual_info_classif as MIC
+from sklearn.feature_selection import mutual_info_classif, mutual_info_regression
+from sklearn.feature_selection import SelectKBest, SelectPercentile
+from sklearn.feature_selection import SequentialFeatureSelector
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.linear_model import LogisticRegression
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.svm import SVC
+from sklearn.metrics import f1_score
+from sklearn.model_selection import cross_validate
+from sklearn.tree import DecisionTreeClassifier 
+from sklearn.linear_model import Perceptron
+from sklearn.model_selection import RandomizedSearchCV
+from sklearn.model_selection import GridSearchCV
+
+import warnings
+warnings.filterwarnings('ignore')
+import os
+
+os.environ["CUDA_VISIBLE_DEVICES"] = "1"
 
 
 # # Defining the path to Model and Images
@@ -72,15 +72,15 @@ path_base_model = './/models//'
 
 
 BACKBONE = 'efficientnetb0'
-model1 = segmentation_models.Unet(BACKBONE, input_shape = (IMAGE_SIZE[0],IMAGE_SIZE[1],IMAGE_SIZE[2]),classes=1, activation='sigmoid',encoder_weights='imagenet')
-model2 = segmentation_models.Unet(BACKBONE, input_shape = (IMAGE_SIZE[0],IMAGE_SIZE[1],IMAGE_SIZE[2]),classes=1, activation='sigmoid',encoder_weights='imagenet')
-model3 = segmentation_models.Unet(BACKBONE, input_shape = (IMAGE_SIZE[0],IMAGE_SIZE[1],IMAGE_SIZE[2]),classes=1, activation='sigmoid',encoder_weights='imagenet')
+model1 = sm.Unet(BACKBONE, input_shape = (IMAGE_SIZE[0],IMAGE_SIZE[1],IMAGE_SIZE[2]),classes=1, activation='sigmoid',encoder_weights='imagenet')
+model2 = sm.Unet(BACKBONE, input_shape = (IMAGE_SIZE[0],IMAGE_SIZE[1],IMAGE_SIZE[2]),classes=1, activation='sigmoid',encoder_weights='imagenet')
+model3 = sm.Unet(BACKBONE, input_shape = (IMAGE_SIZE[0],IMAGE_SIZE[1],IMAGE_SIZE[2]),classes=1, activation='sigmoid',encoder_weights='imagenet')
 
 BACKBONE = 'efficientnetb7'
-model4 = segmentation_models.Unet(BACKBONE, input_shape = (IMAGE_SIZE[0],IMAGE_SIZE[1],IMAGE_SIZE[2]),classes=1, activation='sigmoid',encoder_weights='imagenet')
-model5 = segmentation_models.Unet(BACKBONE, input_shape = (IMAGE_SIZE[0],IMAGE_SIZE[1],IMAGE_SIZE[2]),classes=1, activation='sigmoid',encoder_weights='imagenet')
+model4 = sm.Unet(BACKBONE, input_shape = (IMAGE_SIZE[0],IMAGE_SIZE[1],IMAGE_SIZE[2]),classes=1, activation='sigmoid',encoder_weights='imagenet')
+model5 = sm.Unet(BACKBONE, input_shape = (IMAGE_SIZE[0],IMAGE_SIZE[1],IMAGE_SIZE[2]),classes=1, activation='sigmoid',encoder_weights='imagenet')
 
-preprocess_input = segmentation_models.get_preprocessing(BACKBONE)
+preprocess_input = sm.get_preprocessing(BACKBONE)
 
 model1.load_weights(path_base_model + 'model1.hdf5')
 model2.load_weights(path_base_model + 'model2.hdf5')
@@ -96,11 +96,11 @@ model5.load_weights(path_base_model + 'model5.hdf5')
 
 def preprocessing_HE(img_):
     
-    hist, bins = numpy.histogram(img_.flatten(), 256,[0,256])
+    hist, bins = np.histogram(img_.flatten(), 256,[0,256])
     cdf = hist.cumsum()
-    cdf_m = numpy.ma.masked_equal(cdf,0)
+    cdf_m = np.ma.masked_equal(cdf,0)
     cdf_m = (cdf_m - cdf_m.min())*255/(cdf_m.max()-cdf_m.min())
-    cdf = numpy.ma.filled(cdf_m,0).astype('uint8')
+    cdf = np.ma.filled(cdf_m,0).astype('uint8')
 
     img_2 = cdf[img_]