improved trap binarizer : it now uses a manual threshold value to allow the user to adjust it.

As a result, both sides of the trap are now detected

src/lipase/imageengine.py

@@ -73,6 +73,12 @@ class IImage(ABC):
         """
         pass
 
+    @abc.abstractmethod
+    def get_value_range(self):
+        """
+        :rtype (float, float): the min and the max value
+        """
+        pass
     
 
 class IHyperStack(ABC):
@@ -372,6 +378,13 @@ class IImageEngine(ABC):
         :return list(Match): maxima
         """
 
+    @abc.abstractmethod
+    def threshold(self, src_image, threshold_value):
+        """
+        :param float threshold_value:
+        :rtype IImage:
+        """
+
     @abc.abstractmethod
     def auto_threshold(self, src_image):
         """

src/lipase/imagej/ijimageengine.py

@@ -74,6 +74,16 @@ class IJImage(IImage):
         dst_processor = self.ij_image.getProcessor()
         dst_processor.copyBits(src_processor, x, y, Blitter.COPY)
 
+    def get_value_range(self):
+        processor = self.ij_image.getProcessor()
+        # processor.getMin() and processor.getMax() give a higher min and a lower max, probably because they only consider a subpart of the image (this is badly documented)
+        # so we use processor.getStats instead
+        image_stats = processor.getStats()  # :type ImageStatistics is:
+        min_value = image_stats.min
+        max_value = image_stats.max
+        return (min_value, max_value)
+
+
 class IJHyperStack(IHyperStack):
 
     def __init__(self, image_engine, width, height, num_channels, num_slices, num_frames, pixel_type):
@@ -367,6 +377,12 @@ class IJImageEngine(IImageEngine):
             matches.append(match)
         return matches
 
+    def threshold(self, src_image, threshold_value):
+        binary_image = src_image.clone()
+        IJ.setThreshold(binary_image.ij_image, threshold_value, 1.e30)  # TODO:remove hardcoded value (1.0e30 is the value imagej chooses when the user moves the threshold slider to the right)
+        IJ.run(binary_image.ij_image, "Convert to Mask", "")
+        return binary_image
+
     def auto_threshold(self, src_image):
         # Image/Adjust/Threshold, choose auto, then click apply
         # IJ.setAutoThreshold(imp, "Default dark");

src/lipase/traps_detector.py

@@ -27,7 +27,12 @@ class TrapBinarizer(object):
         # after find edges, the walls are dark lines surrounded by 2 lines of clear pixels.
         # Image/Adjust/Threshold, choose auto, then click apply
         # IJ.setAutoThreshold(imp, "Default dark");
-        is_edge = ie.auto_threshold( edge_image )
+        (min_value, max_value) = edge_image.get_value_range()
+        print('min_value = %f' % min_value)
+        print('max_value = %f' % max_value)
+        threshold_value = 0.20  # TODO: remove hardcoded value
+        is_edge = ie.threshold( edge_image, max_value * threshold_value)
+        # is_edge = ie.auto_threshold( edge_image)
         ie.save_as_tiff(is_edge, './is_trap_edge.tiff')
         # convert image to mask
         # at this point, the walls are black lines surrounded by 2 thick lines of white pixels