include script for converting SBDD from mat to png

data/pascal/convert_sbdd.py

+import os
+import glob
+import numpy as np
+import scipy.io
+
+from PIL import Image
+
+dataset_dir = './sbdd'
+
+def make_palette(num_classes):
+    """
+    Maps classes to colors in the style of PASCAL VOC.
+    Close values are mapped to far colors for segmentation visualization.
+    See http://host.robots.ox.ac.uk/pascal/VOC/voc2012/index.html#devkit
+
+    Takes:
+        num_classes: the number of classes
+    Gives:
+        palette: the colormap as a k x 3 array of RGB colors
+    """
+    palette = np.zeros((num_classes, 3), dtype=np.uint8)
+    for k in xrange(0, num_classes):
+        label = k
+        i = 0
+        while label:
+            palette[k, 0] |= (((label >> 0) & 1) << (7 - i))
+            palette[k, 1] |= (((label >> 1) & 1) << (7 - i))
+            palette[k, 2] |= (((label >> 2) & 1) << (7 - i))
+            label >>= 3
+            i += 1
+    return palette
+palette = make_palette(256).reshape(-1)
+
+for kind in ('cls', 'inst'):
+    # collect the inputs
+    paths = glob.glob('{}/{}/*.mat'.format(dataset_dir, kind))
+    ids = [os.path.basename(p)[:-4] for p in paths]
+    for i, idx in enumerate(ids):
+        if i % 100 == 0:
+            print "Converting {}th annotation...".format(i)
+        # loading the label
+        mat = scipy.io.loadmat('{}/{}/{}.mat'.format(dataset_dir, kind, idx))
+        label_arr = mat['GT{}'.format(kind)][0]['Segmentation'][0].astype(np.uint8)
+        # saving the label
+        label_im = Image.fromarray(label_arr)
+        label_im.putpalette(palette)
+        label_im.save('{}/{}/{}.png'.format(dataset_dir, kind, idx))