managed to compute the image mean value

lipase.py

@@ -7,7 +7,8 @@
 # the 'greeting' output parameter, based on its type.
 from ij import IJ  # pylint: disable=import-error
 from ij import ImagePlus  # pylint: disable=import-error
-
+from ij.process import ImageStatistics
+from ij.plugin import ImageCalculator
 
 # greeting = "Hello, " + name + "!"
 
@@ -176,13 +177,20 @@ class ImageCatalog(object):
 
 
 def get_image_median_value(src_image):
-    return 0
+    '''
+    :param ImageProcessor src_image:
+    '''
+    # https://imagej.nih.gov/ij/developer/api/ij/process/ImageStatistics.html
+    stats = ImageStatistics.getStatistics(src_image, ImageStatistics.MEDIAN, None)
+    print(stats)
+    print(stats.pixelCount)
+    return stats.median
 
 
 def test_get_image_median_value():
     image_file_path = '/Users/graffy/ownCloud/ipr/lipase/raw-images/res_soleil2018/GGH/GGH_2018_cin2_phiG_I_327_vis_-40_1/Pos0/img_000000000_DM300_327-353_fluo_000.tif'
     image = IJ.openImage(image_file_path)
-    median_value = get_image_median_value(image)
+    median_value = get_image_median_value(image.getProcessor())
     print('median value : %d' % median_value)
 
 # double median( cv::Mat channel )
@@ -213,12 +221,28 @@ def test_get_image_median_value():
 def find_depth_index(src_image, white_sequence):
     ''' finds in the image sequence white_sequence the image that correlates the best to src_image
 
-    :param IJ.ImagePlus src_image:
+    :param IJ.ImageProcessor src_image:
     :param Sequence white_sequence:
     '''
+    src_median_value = get_image_median_value(src_image)
+    normalized_src = src_image.convertToFloat()
+    normalized_src.multiply(1.0 / src_median_value)
     for z_index in range(white_sequence.num_slices):
         white_image_file_path = white_sequence.get_image_file_path(channel_index=0, frame_index=0, z_index=z_index)
         white_image = IJ.openImage(white_image_file_path)
+        white_median_value = get_image_median_value(white_image.getProcessor())
+        # white_to_src_factor = 1.0 / float(white_median_value)
+        normalized_white = white_image.getProcessor().convertToFloat()
+        normalized_white.multiply(1.0 / float(white_median_value))
+
+        imp1 = IJ.openImage("http://imagej.nih.gov/ij/images/boats.gif")
+        print(imp1)
+        imp2 = IJ.openImage("http://imagej.nih.gov/ij/images/bridge.gif")
+        print(imp2)
+        ic = ImageCalculator()
+        imp3 = ic.run("sub create float", imp1, imp2)
+        imp3.show()
+
         # imp2mat = ImagePlusMatConverter()
         # white_mat = imp2mat.toMat(white_image.getProcessor())
         # white_mat = imread(white_image_file_path)
@@ -235,7 +259,7 @@ def process_sequence(sequence):
     src_image_file_path = sequence.get_image_file_path(channel_index=0, frame_index=0, z_index=0)
     src_image = IJ.openImage(src_image_file_path)
     # src_image = IJ.openImage(src_image_file_path)
-    find_depth_index(src_image, white_seq)
+    find_depth_index(src_image.getProcessor(), white_seq)
 
 
 def run_script():
@@ -261,5 +285,5 @@ def run_script():
 # If a Jython script is run, the variable __name__ contains the string '__main__'.
 # If a script is loaded as module, __name__ has a different value.
 if __name__ in ['__builtin__', '__main__']:
-    # run_script()
+    run_script()
-    test_get_image_median_value()
+    # test_get_image_median_value()