some modifs

brick/component/extension.py

@@ -252,11 +252,11 @@ def __HysterisisImage__(image: array_t, low: float, high: float) -> array_t:
     # high = 0.04
     nonzero_sites = (image > 0).nonzero()
     nonzero_values = image[nonzero_sites]
-    # print(nonzero_values.min(), image.max())
+    print(nonzero_values.min(), image.max())
 
     low = low * nonzero_values.min()
     high = high * image.max()
-    # print("low=", low, "   high=", high)
+    print("low=", low, "   high=", high)
     # lowt = low*(x_image_f-min_image_f)+max_image_f
     # hight = high*(max_image_f- min_image_f)+min_image_f
     # lowt = (image_f >lowt).astype(int)

brick/processing/input.py

@@ -106,6 +106,7 @@ def FindVoxelDimensionInMicron(data_path: str, size_voxel_in_micron: list = None
     '''
     #
     if size_voxel_in_micron is not None:
+        print('VOXEL DIM: [X Y Z] =', size_voxel_in_micron, 'micron.')
         return np_.array(size_voxel_in_micron)
 
     else:
@@ -131,7 +132,7 @@ def FindVoxelDimensionInMicron(data_path: str, size_voxel_in_micron: list = None
             return voxel_size_micron
 
         except:
-            print('/!\ Unable to find the voxel dimensions in micron in the metadata. Please specify it in the parameters.')
+            raise('/!\ Unable to find the voxel dimensions in micron in the metadata. Please specify it in the parameters.')
 
 
 def ToPixel(micron: float, voxel_size_micron: array_t, dimension: tuple = (0,), decimals: int = None) -> int:

nutrimorph.py

@@ -81,6 +81,7 @@ soma_high_c = None
 soma_selem_micron_c = None
 soma_min_area_c = None
 soma_max_area_c = None
+adapt_hist_equalization = None
 ext_low_c = None
 ext_high_c = None
 ext_selem_micron_c = None
@@ -132,7 +133,7 @@ image = in_.ImageVerification(image, channel)
 # iv_.image_verification(image, channel)  # -> PySide2 user interface  # TODO: must return the modified image!
 # /!\ conflicts between some versions of PySide2 and Python3
 
-# image = image[:, 512:, 512:]  # 512 # 562 # Just for development
+image = image[:, :512, :512]  # 512 # 562 # Just for development
 img_shape = image.shape
 
 #
@@ -164,7 +165,7 @@ som_nfo["map_"] = soma_t.Map(image_for_soma, soma_low_c, soma_high_c, soma_selem
 som_nfo["map_"], som_lmp_ = soma_t.FilteredMap_(som_nfo["map_"], soma_min_area_c)  # Deletion of too small
 som_nfo["map"], som_lmp = soma_t.FilteredMap(som_nfo["map_"], soma_max_area_c)  # and too big somas
 som_nfo["lmp"], n_somas = ms_.label(som_nfo["map"], return_num=True)  # Labelling of somas
-# po_.MaximumIntensityProjectionZ(som_nfo["lmp"])
+po_.MaximumIntensityProjectionZ(som_nfo["lmp"])
 
 # Use relabel instead of label to optimize the algorithm. But BUG.
 # som_nfo["lmp"] = relabel_sequential(som_lmp)[0]
@@ -204,14 +205,14 @@ if ext_selem_micron_c == 0:
 else:
     ext_selem_pixel_c = mp_.disk(in_.ToPixel(ext_selem_micron_c, size_voxel_in_micron))
 
-scale_range_pixel = []
+# scale_range_pixel = []
+# #
+# for value in scale_range:
+#     value_in_pixel = in_.ToPixel(value, size_voxel_in_micron, decimals=1)
+#     scale_range_pixel.append(value_in_pixel)
+# scale_range = tuple(scale_range_pixel)
 #
-for value in scale_range:
-    value_in_pixel = in_.ToPixel(value, size_voxel_in_micron, decimals=1)
-    scale_range_pixel.append(value_in_pixel)
-scale_range = tuple(scale_range_pixel)
-#
-scale_step = in_.ToPixel(scale_step, size_voxel_in_micron, decimals=1)
+# scale_step = in_.ToPixel(scale_step, size_voxel_in_micron, decimals=1)
 
 # Perform frangi feature enhancement (via python or c - faster - implementation)
 enhanced_ext, ext_scales = extension_t.EnhancedForDetection(
@@ -231,16 +232,22 @@ print(f"Elapsed Time={tm_.strftime('%Hh %Mm %Ss', elapsed_time)}\n")
 # po_.MaximumIntensityProjectionZ(enhanced_ext, cmap="OrRd")
 
 # Creation of the enhanced maps
-enhanced_ext = ex_.adjust_log(enhanced_ext, 1)  # Not necessary, log contrast adjustment
-enhanced_ext = ex_.equalize_hist(enhanced_ext)  # necessary, histogram equalisation (global)
-# po_.MaximumIntensityProjectionZ(enhanced_ext, cmap="OrRd")
+# enhanced_ext = ex_.adjust_log(enhanced_ext, 1)  # Not necessary, log contrast adjustment
+if adapt_hist_equalization:
+    enhanced_ext = ex_.equalize_adapthist(enhanced_ext)  # necessary, histogram equalisation (global)
+else:
+    enhanced_ext = ex_.equalize_hist(enhanced_ext)  # necessary, histogram equalisation (global)
+po_.MaximumIntensityProjectionZ(enhanced_ext, cmap="OrRd")
 enhanced_ext = ex_.rescale_intensity(enhanced_ext, out_range=(0, 255))  # necessary, rescaling by stretching the values btw 0 and 255
 ext_nfo["coarse_map"] = extension_t.CoarseMap(enhanced_ext, ext_low_c, ext_high_c, ext_selem_pixel_c) # seuillage
+po_.MaximumIntensityProjectionZ(ext_nfo["coarse_map"])
 ext_nfo["coarse_map"], ext_lmp = extension_t.FilteredCoarseMap(ext_nfo["coarse_map"], ext_min_area_c)  # min
+po_.MaximumIntensityProjectionZ(ext_nfo["coarse_map"])
 ext_nfo["map"] = extension_t.FineMapFromCoarseMap(ext_nfo["coarse_map"]) # skeleton
 # ext_nfo["map"] = extension_t.FilteredSkeletonMap(ext_nfo["coarse_map"], ext_max_length_c) # TODO delete too long extensions
-# po_.MaximumIntensityProjectionZ(ext_nfo["map"])
+po_.MaximumIntensityProjectionZ(ext_nfo["map"])
 ext_nfo["map"][som_nfo["map"] > 0] = 0
+po_.MaximumIntensityProjectionZ(ext_nfo["map"])
 ext_nfo["lmp"], n_extensions = ms_.label(ext_nfo["map"], return_num=True)
 # Use relabel instead of label to optimize the algorithm. BUT PROBLEM WITH THE NUMBER OF EXTENSIONS DETECTED !
 # ext_nfo["lmp"] = relabel_sequential(ext_lmp)[0]
@@ -258,8 +265,6 @@ print(f"\nElapsed Time={tm_.strftime('%Hh %Mm %Ss', elapsed_time)}")
 if with_plot:
     fb_.PlotExtensions(extensions, ext_nfo, img_shape)
 
-# Delete too big somas
-
 
 # -- Preparation for Connections
 dijkstra_costs = in_.DijkstraCosts(image, som_nfo["map"], ext_nfo["map"])

parameters.ini

@@ -49,8 +49,7 @@ parameter3                  :   None
 ; then your parameter has the value string: 'None', and not None
 
 [Input]
-data_path                   :   D:\MorganeNadal\IBA1-Microglies\CHO_1H_D_1.70_3.3\CHO_1H_D_1.70_3.3_8.tif
-# D:\MorganeNadal\IBA1-Microglies\standard conditions\020620_ST8_20W_1_2.lif - Position 3.tif
+data_path                   :   D:\MorganeNadal\IBA1-Microglies\standard conditions\020620_ST8_20W_1_2.lif - Position 3.tif
 ; direct data path to the image
 file_path
 # D:\\MorganeNadal\\IBA1-Microglies
@@ -63,7 +62,7 @@ conditions
 channel
 ; Can take the values R, G or B.
 ; Can also be set to None (cf. README above).
-size_voxel_in_micron
+size_voxel_in_micron       :    [0.071, 0.071, 0.4997]
 ; size_voxel_in_micron ->  [X,Y,Z]
 ; Can also be set to None (cf. README above).
 save_images
@@ -79,6 +78,7 @@ soma_min_area_c            :    58
 soma_max_area_c            :    580
 
 [Extensions]
+adapt_hist_equalization    :    True
 ; for hysteresis
 ext_low_c                  :    10
 #7.5e-8
@@ -96,11 +96,11 @@ max_straight_sq_dist_c     :    216.45
 max_weighted_length_c      :    4.81
 
 [Frangi]
-scale_range                :    (0.024,0.745)
-; Stop at max 3.1 (<=> 0.745) for number of Z stacks < 18
+scale_range                :    (0.1,6.1)
+; Stop at max 3.1 for number of Z stacks < 18
 ; write min,max scale range
-scale_step                 :    0.24
-#  1.0
+scale_step                 :    2.5
+#  1.0 for Z stacks < 18
 alpha                      :    0.8
 # 0.8
 beta                       :    0.5

search_parameters.py

@@ -63,6 +63,7 @@ soma_min_area_c = IfNotFloat('Somas', 'soma_min_area_c')
 soma_max_area_c = IfNotFloat('Somas', 'soma_max_area_c')
 
 # [Extensions]
+adapt_hist_equalization = parameters.getboolean('Extensions', 'adapt_hist_equalization')
 ext_low_c = IfNotFloat('Extensions', 'ext_low_c')
 ext_high_c = IfNotFloat('Extensions', 'ext_high_c')
 ext_selem_micron_c = IfNotFloat('Extensions', 'ext_selem_micron_c')