Use variable-length color maps to fix previously failing test

tools/colorize_labels/colorize_labels.py

@@ -1,7 +1,7 @@
 import argparse
 
 import giatools.io
-import matplotlib.colors as mpl
+import matplotlib.pyplot as plt
 import networkx as nx
 import numpy as np
 import scipy.ndimage as ndi
@@ -43,6 +43,18 @@ def build_label_adjacency_graph(im, radius, bg_label):
     return G
 
 
+def get_n_unique_mpl_colors(n, colormap='jet', cyclic=False):
+    """
+    Yields `n` unique colors from the given `colormap`.
+
+    Set `cyclic` to `True` if the `colormap` is cyclic.
+    """
+    cmap = plt.get_cmap(colormap)
+    m = n if cyclic else n - 1
+    for i in range(n):
+        yield np.multiply(255, cmap(i / m))
+
+
 if __name__ == '__main__':
 
     parser = argparse.ArgumentParser()
@@ -60,14 +72,13 @@ def build_label_adjacency_graph(im, radius, bg_label):
 
     # Build adjacency graph of the labels
     G = build_label_adjacency_graph(im, args.radius, args.bg_label)
-    print('---')
 
     # Apply greedy coloring
     graph_coloring = nx.greedy_color(G)
     unique_colors = frozenset(graph_coloring.values())
 
     # Assign colors to nodes based on the greedy coloring
-    graph_color_to_mpl_color = dict(zip(unique_colors, mpl.TABLEAU_COLORS.values()))
+    graph_color_to_mpl_color = dict(zip(unique_colors, get_n_unique_mpl_colors(len(unique_colors))))
     node_colors = [graph_color_to_mpl_color[graph_coloring[n]] for n in G.nodes()]
 
     # Render result
@@ -76,7 +87,6 @@ def build_label_adjacency_graph(im, radius, bg_label):
     for label, label_color in zip(G.nodes(), node_colors):
 
         cc = (im == label)
-        label_color = color_hex_to_rgb_tuple(label_color)
         for ch in range(3):
             result[:, :, ch][cc] = label_color[ch]