Fixed manual gamma filtering. this refs #9

NeuNorm/__init__.py

@@ -1,4 +1,4 @@
-__version__ = "1.5.1"
+__version__ = "1.5.2"
 
 
 class DataType:

NeuNorm/exporter.py

@@ -7,6 +7,7 @@ def make_tif(data=[], metadata=[], file_name=''):
     new_image = Image.fromarray(data)
     new_image.save(file_name, tiffinfo=metadata)
 
+
 def make_fits(data=[], file_name=''):
     '''create fits file'''
     fits.writeto(file_name, data, overwrite=True)

NeuNorm/loader.py

@@ -33,7 +33,8 @@ def load_fits(file_name):
         return tmp
     except OSError:
         raise OSError("Unable to read the FITS file provided!")
-
+
+
 def load_tiff(file_name):
     '''load tiff image
     

NeuNorm/normalization.py

@@ -4,6 +4,7 @@
 import copy
 import time
 from scipy.ndimage import convolve
+from scipy.ndimage import median_filter
 
 from NeuNorm.loader import load_hdf, load_tiff, load_fits
 from NeuNorm.exporter import make_fits, make_tif
@@ -356,7 +357,7 @@ def _auto_gamma_filtering(self, data=None):
 
         return data_gamma_filtered
 
-    def _manual_gamma_filtering(self, data=None, manual_gamma_threshold=0.1):
+    def _manual_gamma_filtering(self, data=None, manual_gamma_threshold=0.95):
         """perform manual gamma filtering on the data
 
         This algoritm uses the manual_gamma_threshold value to estimate if a pixel is a gamma or not.
@@ -379,13 +380,11 @@ def _manual_gamma_filtering(self, data=None, manual_gamma_threshold=0.1):
             raise ValueError("Data array is empty!")
 
         data_gamma_filtered = np.copy(data)
-        mean_counts = np.mean(data_gamma_filtered)
-        gamma_indexes = np.where(manual_gamma_threshold * data_gamma_filtered > mean_counts)
 
-        mean_kernel = np.array([[1, 1, 1], [1, 0, 1], [1, 1, 1]]) / 8.0
-        convolved_data = convolve(data_gamma_filtered, mean_kernel, mode='constant')
-
-        data_gamma_filtered[gamma_indexes] = convolved_data[gamma_indexes]
+        radius = 2
+        median_counts = median_filter(data_gamma_filtered, radius, mode='grid-wrap')
+        gamma_indexes = np.where(manual_gamma_threshold * data_gamma_filtered > median_counts)
+        data_gamma_filtered[gamma_indexes] = median_counts[gamma_indexes]
 
         return data_gamma_filtered
 

tests/NeuNorm/loading_test.py

@@ -253,11 +253,15 @@ def test_gamma_filtered_works(self):
         o_norm = Normalization()
         o_norm.load(folder=path, data_type='sample', manual_gamma_filter=True, auto_gamma_filter=False)
         _expected_sample = np.ones((5, 5))
-        _expected_sample[0, 0] = 0.375
+        _expected_sample[0, 0] = 4
         _expected_sample[:, 2] = 2
         _expected_sample[:, 3] = 3
         _expected_sample[:, 4] = 4
         _returned_sample = o_norm.data['sample']['data']
+
+        print(f"_expected_sample: {_expected_sample}")
+        print(f"_returned_sample: {_returned_sample}")
+
         self.assertTrue((_expected_sample == _returned_sample).all())
 
         # gamma filter is False