--- a
+++ b/Preprocessing/bayesnoiseremoval.py
@@ -0,0 +1,110 @@
+import cv2
+import numpy as np
+from nilearn import plotting
+import nibabel as nib
+import matplotlib.pyplot as plt
+
+class BayesPreprocessor:
+    def __init__(self, image):
+        # store target image width, height and interpolation
+        # inter is an optional argument
+        self.image = image
+        # self.height = height
+        # self.kernel = kernel
+        # self.inter = inter
+    @staticmethod
+    def bayes_noise_removal(self,o):
+
+        # implement image processing
+        S1=self.image
+        S2 = 255 - S1
+        S3 = S1 + S2
+        dv = self.calculate_params(S1, S2, S3, o)
+
+        dv[dv < 0] = 0
+
+        return dv
+
+    def calculate_params(self, S1, S2, S3, o):
+        try:
+            w = S2 / S3
+        except ZeroDivisionError:
+            w = 0
+        w = np.divide(S2, S3)
+        p = np.divide(S3, w)
+        try:
+            p = S3 / w
+        except ZeroDivisionError:
+            p = 0
+
+        dv = np.log(np.i0((p * np.sinc(w) * S1) / o ** 2)) + np.log(np.i0((p * np.sinc(1 - w) * S2) / o ** 2)) - p * (
+                ((np.sinc(w)) ** 2 + (np.sinc(1 - w)) ** 2) / (2 * o ** 2))
+        return dv
+
+# plt.imsave('slice.png',slice_2l)
+
+
+# cv2.imshow('dw.jpg',slice_2l)
+# cv2.waitKey()
+
+img = nib.load('../../mr_train/mr_train_1020_image.nii.gz')
+label=nib.load('../../mr_train/mr_train_1001_label.nii.gz')
+
+# display = plotting.plot_anat(img)
+# plotting.show()
+# plotting.plot_img(img)
+# plotting.show()
+#
+img_data=img.get_fdata()
+shape_0=int((img_data.shape[0]-1)/2)
+shape_1=int((img_data.shape[1]-1)/2)
+shape_2=int((img_data.shape[2]-1)/2)
+
+
+slice_1=img_data[shape_0,:,:]
+image=cv2.imread('../tmp/pred_0.png')
+image = cv2.resize(image, (128,512), interpolation=cv2.INTER_AREA)
+cv2.imshow('daw',image)
+cv2.waitKey(0)
+# bayes=BayesPreprocessor(image)
+
+# # image = image / 255
+# cv2.imshow('dfdw',bayes.bayes_noise_removal(90))
+# # plt.imshow(slice_1)
+# # plt.show()
+# cv2.waitKey(0)
+
+
+##img=cv2.imread("mri.png",0);
+#
+# # plt.figure(1)
+# S1=slice_1
+# # plt.figure(2)
+# S2=255-S1
+# S3=S1+S2
+#
+#
+# # plt.figure(3)
+# try:
+#     w = S2/S3
+# except ZeroDivisionError:
+#     w = 0
+# w=np.divide(S2,S3)
+# p=np.divide(S3,w)
+# try:
+#     p = S3/w
+# except ZeroDivisionError:
+#     p = 0
+#
+#
+#
+# # cv2.imshow("pikseli",np.uint8(p))
+# # cv2.imwrite("pikseli.png",np.uint8(p))
+#
+# o=40
+#
+# dv=np.log(np.i0((p*np.sinc(w)*S1)/o**2))+np.log(np.i0((p*np.sinc(1-w)*S2)/o**2))-p*(((np.sinc(w))**2+(np.sinc(1-w))**2)/(2*o**2))
+# # plt.figure(4)
+# dv[dv<0]=0
+# cv2.imshow("popravljena.png",np.uint8(dv))
+# cv2.waitKey(0)