Change output condensate_masks_color.tif so that three color images are output...

Change output condensate_masks_color.tif so that three color images are output with binary pixel values indicating whether a pixel is in the dense phase (red), dilute phase (green), or background (blue).

processMFLLPS.py

@@ -562,16 +562,19 @@ def main(args):
                         exposure.rescale_intensity(sum_droplet[zplane, :, :], out_range=(0., 1.)))
                 # Get image of same shape as cropped_droplet with non-dilute phase pixels set to 0
                 dilute_color = np.zeros(cropped_droplet.shape, dtype='uint8')
-                dilute_color[dilute_mask] = cropped_droplet[dilute_mask]
+                dilute_color[dilute_mask] = 1
+                # dilute_color[dilute_mask] = cropped_droplet[dilute_mask]
                 dilute_phase_color.append((bbox, dilute_color))
                 # Get image of same shape as cropped_droplet with non-dense phase pixels set to 0
                 dense_color = np.zeros(cropped_droplet.shape, dtype='uint8')
-                dense_color[dense_mask] = cropped_droplet[dense_mask]
+                dense_color[dense_mask] = 1
+                # dense_color[dense_mask] = cropped_droplet[dense_mask]
                 dense_phase_color.append((bbox, dense_color))
                 # Get image of same shape as cropped_droplet with neither dilute nor dense phase pixels set to 0
                 none_color = np.zeros(cropped_droplet.shape, dtype='uint8')
                 none_mask = np.logical_not(np.logical_or(dilute_mask, dense_mask))
-                none_color[none_mask] = cropped_droplet[none_mask]
+                none_color[none_mask] = 1
+                # none_color[none_mask] = cropped_droplet[none_mask]
                 neither_phase_color.append((bbox, none_color))
 
             # Append measured values to output lists
@@ -589,23 +592,29 @@ def main(args):
         if args.writeintermediates:
             # Write the colored dense/dilute phase mask image
             for dilute in dilute_phase_color:
-                # Dimension 2 is blue channel for RGB image
+                # Dimension 1 is green channel for RGB image
                 # The droplet boundary is determined after the watershedding step so adding intensites for each droplet
                 # will not double count pixels
-                img_color_mask[dilute[0][0]:dilute[0][2], dilute[0][1]:dilute[0][3], 2] += dilute[1]
+                img_color_mask[dilute[0][0]:dilute[0][2], dilute[0][1]:dilute[0][3], 1] += dilute[1]
             for neither in neither_phase_color:
-                # Dimension 1 is green channel
+                # Dimension 2 is blue channel
                 # The pixels in each cropped droplet image outside the dilute phase mask get double counted
                 # if the intensities are added. Zeroing out the green pixels if they overlap with red or blue pixels
                 # after building the image seems like the best way to handle this.
-                img_color_mask[neither[0][0]:neither[0][2], neither[0][1]:neither[0][3], 1] = neither[1]
+                img_color_mask[neither[0][0]:neither[0][2], neither[0][1]:neither[0][3], 2] = neither[1]
             for dense in dense_phase_color:
                 # Dimension 0 is red channel The dense phase is strictly contained inside the dilute phase mask so
                 # adding intensities for each droplet will not double count pixels
                 img_color_mask[dense[0][0]:dense[0][2], dense[0][1]:dense[0][3], 0] += dense[1]
-            # Zero out green pixels overlapping with red or blue pixels
-            img_color_mask[img_color_mask[:, :, 0] > 0, 1] = 0
-            img_color_mask[img_color_mask[:, :, 2] > 0, 1] = 0
+            # Zero out blue pixels overlapping with red or green pixels
+            img_color_mask[img_color_mask[:, :, 0] > 0, 2] = 0
+            img_color_mask[img_color_mask[:, :, 1] > 0, 2] = 0
+            for r in range(n_row):
+                for c in range(n_col):
+                    # If all pixels are 0
+                    if np.all(img_color_mask[r, c, :] == 0):
+                        # pixel is background so set blue channel to 1
+                        img_color_mask[r, c, 2] = 1
             step += 1
             img_name = os.path.join(img_base_name, img_base_name + "_" + str(step) + "_condensate_masks_color" +
                                     ".tif")