Script modification for testing.

diff --git a/Documentation/ma_autocount/plasmodium-autocount-0.1.py b/Documentation/ma_autocount/plasmodium-autocount-0.1.py
index d97e059..14ae21d 100644 (file)
--- a/Documentation/ma_autocount/plasmodium-autocount-0.1.py
+++ b/Documentation/ma_autocount/plasmodium-autocount-0.1.py
@@ -58,7 +58,8 @@ N_processors = 1
 #
 # What to show in diagnostic output images
 
-Show_edges = True
+Show_edges = False
+Show_dark_stain = False
 Show_stain = False
 Show_infection = False
 
@@ -93,9 +94,9 @@ Resize_height = 972 #512
 #
 # gaussian_blur(image, a) - b * gaussian_blur(image, c)
 
-Enhance_a = 1.0
-Enhance_b = 0.75
-Enhance_c = 20.0
+Enhance_a = 2.0  # base: 1.0,  50x: 2.0, 100x: 4.0
+Enhance_b = 0.75 # base: 0.75
+Enhance_c = 40.0 # base: 20.0, 50x: 40.0, 100x: 80.0
 
 # Larger = enlarge background area, smaller = enlarge foreground area
 Background_weight = 0.915 #&median
@@ -120,8 +121,8 @@ Infection_pixels_required = 1
 # Cell size parameters
 
 # Size of cells, for Hough transform
-Hough_min_radius = 5  
-Hough_max_radius = 32 # 20
+Hough_min_radius = 20 # 50x: 20, 100x: 40
+Hough_max_radius = 45 # 50x: 45, 100x: 90
 
 # Portion of cell used for classification as infected/weird
 # (may be abutting an infected cell, so be cautious)
@@ -129,7 +130,7 @@ Hough_max_radius = 32 # 20
 Cell_inside_radius = 0.9
 
 # Do not allow another cell center to be detected within this radius
-Cell_suppression_radius = 1.05 # 1.25
+Cell_suppression_radius = 0.8 # 1.05
 
 
 # =======================
@@ -139,8 +140,8 @@ Cell_suppression_radius = 1.05 # 1.25
 Max_dark_stain_proportion = 0.728
 
 # Minimum/maximum radius (as determined by Hough transform)
-Min_cell_radius = 11 # 9
-Max_cell_radius = 32 # 20
+Min_cell_radius = 25 # Base: 9,  50x: 25, 100x: 50
+Max_cell_radius = 40 # Base: 20, 50x: 40, 100x: 80
 
 # Proportion of radius that center of mass can be away from Hough determined center
 Max_offcenter = 0.5
@@ -233,7 +234,7 @@ def gaussian_blur(array, amount):
     """
     output = numpy.empty(array.shape, array.dtype)
     for i in xrange(array.shape[2]):
-        ndimage.gaussian_filter(array[:,:,i],amount,output=output[:,:,i])
+        ndimage.gaussian_filter(array[:,:,i],amount, output=output[:,:,i])
     return output
 
 
@@ -272,6 +273,7 @@ def process(filename_in, filename_out, filename_coords, name):
     # Denoise/unsharp mask
     # [GB] (Concerne les trois channels RGB).
     a_enhanced = gaussian_blur(a, Enhance_a) - Enhance_b * gaussian_blur(a, Enhance_c)
+        
     
     # Split into foreground and background using k-medians
     status('fg/bg')
@@ -323,8 +325,7 @@ def process(filename_in, filename_out, filename_coords, name):
         (a_enhanced[:,:,1] < color_fg[1]) & # [GB] Green channel
         fg
     )
-    
-    
+
     fg_float = fg.astype('float64')
     green = a[:,:,1] * fg_float
     green_local_bg = ndimage.gaussian_filter(green, Stain_bg_blur)
@@ -350,6 +351,8 @@ def process(filename_in, filename_out, filename_coords, name):
         a_output[stain,2] += 128
     if Show_infection:
         a_output[infection,0] += 128
+    if Show_dark_stain:
+        a_output[infection,0] += 128
         
     #a_output[stain, 1] += 128
         
@@ -409,7 +412,7 @@ def process(filename_in, filename_out, filename_coords, name):
         # (more blurring allows greater deviation from circularity)
         # (if you alter this you will also need to alter thresh, below)
         
-        hough = ndimage.gaussian_filter(hough, 1.0)
+        hough = ndimage.gaussian_filter(hough, 1.5) # 1.0
         
         return hough
 
@@ -426,15 +429,14 @@ def process(filename_in, filename_out, filename_coords, name):
     #a_output[:,:,:] = hough[:,:,None]*20
 
     # Find and classify peaks in Hough transform image
-    thresh = 1.5 #Hmm
+    thresh = 0.8 #Hmm 1.5
     candidates = [ ]
     for y in xrange(height):
         for x in xrange(width):
             if hough[y, x] > thresh:
                 candidates.append((hough[y,x], y, x))
     candidates.sort(reverse = True)
-    hough
-    
+        
     
     status('classify')
     
@@ -568,6 +570,10 @@ def process(filename_in, filename_out, filename_coords, name):
     i2 = Image.fromarray(a_output.astype('uint8'))    
     i2.save(filename_out)
     
+    thresh_output = numpy.clip(fg, 0, 255)
+    i3 = Image.fromarray(thresh_output.astype('uint8'))    
+    i3.save(filename_out + " - thresholded.tiff")
+    
 
 
 def read_ac(filename):