Merge branch 'shuo_fixed_colors'

Author: binarybottle

colors_script/calc_colormap.py

@@ -0,0 +1,69 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+
+import os
+import argparse
+import numpy as np
+
+from mindboggle.mio.colors import distinguishable_colors, label_adjacency_matrix
+
+
+if __name__ == "__main__":
+
+    description = ('calculate colormap for labeled image;'
+                   'calculated result is stored in output_dirname/colors.npy')
+    parser = argparse.ArgumentParser(description=description)
+    parser.add_argument('label_filename', help='path to the label image')
+    parser.add_argument('output_dirname', help='path to the folder storing '
+                                               'temporary files and result')
+    parser.add_argument('-v', '--verbose', action='store_true', default=False)
+    args = parser.parse_args()
+
+    if not os.path.isdir(args.output_dirname):
+        os.makedirs(args.output_dirname)
+        
+    matrix_filename = os.path.join(args.output_dirname, 'matrix.npy')
+    colormap_filename = os.path.join(args.output_dirname, 'colormap.npy')
+    labels_filename = os.path.join(args.output_dirname, 'labels.npy')
+    colors_filename = os.path.join(args.output_dirname, 'colors.npy')
+
+    if args.verbose:
+        print('finding adjacency maps...')
+
+    if not os.path.isfile(matrix_filename) or \
+            not os.path.isfile(labels_filename):
+        labels, matrix = label_adjacency_matrix(args.label_filename,
+                                                out_dir=args.output_dirname)[:2]
+        matrix = matrix.as_matrix()[:, 1:]
+        np.save(matrix_filename, matrix)
+        np.save(labels_filename, labels)
+    else:
+        labels = np.load(labels_filename)
+        matrix = np.load(matrix_filename)
+
+    if args.verbose:
+        print('finding colormap...')
+
+    if not os.path.isfile(colormap_filename):
+        num_colors = len(labels)
+        colormap = distinguishable_colors(ncolors=num_colors,
+                                          plot_colormap=False,
+                                          save_csv=False,
+                                          out_dir=args.output_dirname)
+        np.save(colormap_filename, colormap)
+    else:
+        colormap = np.load(colormap_filename)
+
+    if args.verbose:
+        print('finding label colors')
+
+    if not os.path.isfile(colors_filename):
+        label_colors = colors.group_colors(colormap, 
+                                           args.label_filename, 
+                                           IDs=labels,
+                                           adjacency_matrix=matrix, 
+                                           out_dir=args.output_dirname,
+                                           plot_colors=False,
+                                           plot_graphs=False)
+        np.save(colors_filename, label_colors)

colors_script/convert_to_mipav_lut.py

@@ -0,0 +1,41 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+import numpy as np
+import argparse
+
+def parse_inputs():
+    des = 'convert colormap to mipav lut file.'
+    parser = argparse.ArgumentParser(description=des)
+    parser.add_argument('colormap_filename')
+    parser.add_argument('ids_filename')
+    parser.add_argument('output_filename')
+    args = parser.parse_args()
+    return args
+
+def main(args):
+
+    labels = np.load(args.ids_filename)
+    colors = np.load(args.colormap_filename) * 255
+
+    contents = list()
+    contents.append('<LUT>')
+    contents.append('256\t# Size of LUT Arrays')
+
+    for i in range(256):
+        if i in labels:
+            idx = np.where(labels == i)[0][0]
+            c = colors[idx].tolist()
+        else:
+            c = [0.0, 0.0, 0.0]
+        c_str = [str(cc) for cc in c]
+        line = '\t'.join([str(i), '1.0', *c_str])
+        contents.append(line)
+
+    with open(args.output_filename, 'w') as file:
+        file.write('\n'.join(contents))
+
+
+if __name__ == "__main__":
+    args = parse_inputs()
+    main(args)

mindboggle/mio/colors.py

@@ -9,9 +9,11 @@
 
 """
 
+import os
 
 def distinguishable_colors(ncolors, backgrounds=[[0,0,0],[1,1,1]],
-                           save_csv=True, plot_colormap=True, verbose=True):
+                           save_csv=True, plot_colormap=True, verbose=True,
+                           out_dir='.'):
     """
     Create a colormap of perceptually distinguishable colors.
 
@@ -74,6 +76,7 @@ def distinguishable_colors(ncolors, backgrounds=[[0,0,0],[1,1,1]],
     from colormath.color_diff import delta_e_cie2000
 
     filename = "colormap_of_{0}_distinguishable_colors".format(ncolors)
+    filename = os.path.join(out_dir, filename)
 
     # ------------------------------------------------------------------------
     # Generate a sizable number of RGB triples. This represents our space of
@@ -158,7 +161,7 @@ def distinguishable_colors(ncolors, backgrounds=[[0,0,0],[1,1,1]],
             if verbose:
                 print(rgb)
             plt.barh(0, 50, 1, 0, color=rgb)
-            plt.savefig(filename + ".png")
+        plt.savefig(filename + ".png")
         if verbose:
             print("Colormap image saved to {0}".format(filename + ".png"))
 
@@ -176,7 +179,7 @@ def distinguishable_colors(ncolors, backgrounds=[[0,0,0],[1,1,1]],
 
 def label_adjacency_matrix(label_file, ignore_values=[-1, 999], add_value=0,
                            save_table=True, output_format='csv',
-                           verbose=True):
+                           verbose=True, out_dir='.'):
     """
     Extract surface or volume label boundaries, find unique label pairs,
     and write adjacency matrix (useful for constructing a colormap).
@@ -280,6 +283,8 @@ def label_adjacency_matrix(label_file, ignore_values=[-1, 999], add_value=0,
     else:
         raise IOError("Use appropriate input file type.")
 
+    output_table = os.path.join(out_dir, output_table)
+
     # Find unique pairs (or first two of each list):
     pairs = []
     for pair in label_pairs:
@@ -1188,4 +1193,4 @@ def viridis_colormap():
         [0.983868, 0.904867, 0.136897],
         [0.993248, 0.906157, 0.143936]]
 
-    return viridis
+    return viridis

mindboggle/version.py

@@ -1 +1 @@
-__version__ = '1.2.0'
+__version__ = '1.2.2'

setup.py

@@ -69,12 +69,12 @@ def main(**extra_args):
           package_data={'mindboggle': [pjoin('data', '*.nii.gz'),
                                        pjoin('data', '*.txt')]},
           scripts=[pjoin('mindboggle', 'mindboggle'),
-                   pjoin('mindboggle', 'mindboggle123')],
+                   pjoin('mindboggle', 'mindboggle123'),
+                   pjoin('colors_script', 'calc_colormap.py'),
+                   pjoin('colors_script', 'convert_to_mipav_lut.py')],
           **extra_args
          )
 
 if __name__ == "__main__":
     main()
     #main(**extra_setuptools_args)
-
-