ccj5351 · June 11, 2021 13:06 · Mar 5, 2019 · Mar 5, 2019 · Mar 5, 2019 · Mar 5, 2019
diff --git a/pascal-voc-label-color-map.jpg b/pascal-voc-label-color-map.jpg
diff --git a/VOClabelcolormap.py b/VOClabelcolormap.py
@@ -28,7 +28,7 @@ def bitget(byteval, idx):
     return cmap
 
 
-def color_map_viz():
+def color_map_viz_1_column():
     labels = ['background', 'aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor', 'void']
     nclasses = 21
     row_size = 50

diff --git a/color_map_info.py b/color_map_info.py
@@ -0,0 +1,31 @@
+""" added by CCJ """
+def color_map_info(palette):
+    labels = [
+          'background', #0
+          'aeroplane', #1
+          'bicycle', #2
+          'bird', #3
+          'boat', #4
+          'bottle', #5
+          'bus', #6
+          'car', #7
+          'cat', #8
+          'chair', #9
+          'cow', #10
+          'diningtable', #11
+          'dog', #12
+          'horse', #13
+          'motorbike', #14
+          'person', #15
+          'pottedplant', #16
+          'sheep', #17
+          'sofa', #18
+          'train', #19
+          'tv/monitor', #20
+          "void/unlabelled", #255
+          ] 
+    print 'class colormap and palette = {r,g,b}'
+    for i in range(0,21*3,3):
+        print '# {:>3d}: {:<20} (R,G,B) = {},{},{}'.format(i/3, labels[i/3], palette[i], palette[i+1],palette[i+2])
+    i = 255*3
+    print '# {:>3d}: {:<20} (R,G,B) = {},{},{}'.format(i/3, labels[21], palette[i], palette[i+1],palette[i+2])
diff --git a/color_map_viz.py b/color_map_viz.py
@@ -0,0 +1,43 @@
+# added by CCJ:
+""" arrange these 21 classes to 2D matrix with 3 rows and 7 columns"""
+def color_map_viz(fname = None):
+    labels = ['B-ground', 'Aero plane', 'Bicycle', 'Bird', 'Boat', 'Bottle', 
+            'Bus', 'Car', 'Cat', 'Chair', 'Cow', 'Dining-Table', 'Dog', 'Horse',
+            'Motorbike', 'Person', 'Potted-Plant', 'Sheep', 'Sofa', 'Train', 
+            'TV/Monitor', 'Void/Unlabelled']
+
+    nclasses = 21
+    row_size = 80
+    col_size = 250
+    cmap = color_map()
+
+    """ arrange these 21 classes to 2D matrix with 3 rows and 7 columns"""
+    r = 3
+    c = 7
+    delta = 10
+    array = np.empty((row_size*(r+1), col_size*c, cmap.shape[1]), dtype=cmap.dtype)
+    fig=plt.figure()
+    for r_idx in range(0,r):
+        for c_idx in range(0,c):
+            i = r_idx *c + c_idx
+            array[r_idx*row_size:(r_idx+1)*row_size, c_idx*col_size: (c_idx+1)*col_size, :] = cmap[i]
+            x = c_idx*col_size + delta
+            y = r_idx*row_size + row_size/2
+            s = labels[i]
+            plt.text(x, y,s, fontsize=9, color='white')
+            print "write {} at pixel (r={},c={})".format(labels[i], y,x)
+
+    array[r*row_size:(r+1)*row_size, :] = cmap[-1]
+    x = 3*col_size + delta
+    y = r*row_size + row_size/2
+    s = labels[-1]
+    plt.text(x, y,s, fontsize=9, color='black')
+    print "write {} at pixel (r={},c={})".format(labels[i], y,x)
+    plt.title("PASCAL VOC Label Color Map")
+    imshow(array)
+    axis = plt.subplot(1, 1, 1)
+    plt.axis('off')
+    if fname:
+        plt.savefig(fname, dpi=300,bbox_inches='tight', pad_inches=0.1)
+    else:
+        plt.show()
diff --git a/VOClabelcolormap.m b/VOClabelcolormap.m
@@ -1,3 +1,6 @@
+%Official Matlab version can be found in the PASCAL VOC devkit 
+%http://host.robots.ox.ac.uk/pascal/VOC/voc2012/index.html#devkit
+
 % VOCLABELCOLORMAP Creates a label color map such that adjacent indices have different
 % colors.  Useful for reading and writing index images which contain large indices,
 % by encoding them as RGB images.

diff --git a/VOClabelcolormap.m b/VOClabelcolormap.m
@@ -0,0 +1,22 @@
+% VOCLABELCOLORMAP Creates a label color map such that adjacent indices have different
+% colors.  Useful for reading and writing index images which contain large indices,
+% by encoding them as RGB images.
+%
+% CMAP = VOCLABELCOLORMAP(N) creates a label color map with N entries.
+function cmap = labelcolormap(N)
+
+if nargin==0
+    N=256
+end
+cmap = zeros(N,3);
+for i=1:N
+    id = i-1; r=0;g=0;b=0;
+    for j=0:7
+        r = bitor(r, bitshift(bitget(id,1),7 - j));
+        g = bitor(g, bitshift(bitget(id,2),7 - j));
+        b = bitor(b, bitshift(bitget(id,3),7 - j));
+        id = bitshift(id,-3);
+    end
+    cmap(i,1)=r; cmap(i,2)=g; cmap(i,3)=b;
+end
+cmap = cmap / 255;
diff --git a/VOClabelcolormap.py b/VOClabelcolormap.py
@@ -29,7 +29,7 @@ def bitget(byteval, idx):
 
 
 def color_map_viz():
-    labels = ['void', 'aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor', 'border']
+    labels = ['background', 'aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor', 'void']
     nclasses = 21
     row_size = 50
     col_size = 500

diff --git a/VOClabelcolormap.py b/VOClabelcolormap.py
@@ -32,8 +32,9 @@ def color_map_viz():
     labels = ['void', 'aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor', 'border']
     nclasses = 21
     row_size = 50
+    col_size = 500
     cmap = color_map()
-    array = np.empty((row_size*(nclasses+1), 500, cmap.shape[1]), dtype=cmap.dtype)
+    array = np.empty((row_size*(nclasses+1), col_size, cmap.shape[1]), dtype=cmap.dtype)
     for i in range(nclasses):
         array[i*row_size:i*row_size+row_size, :] = cmap[i]
     array[nclasses*row_size:nclasses*row_size+row_size, :] = cmap[-1]

diff --git a/VOClabelcolormap.py b/VOClabelcolormap.py
@@ -3,15 +3,14 @@
 Official Matlab version can be found in the PASCAL VOC devkit 
 http://host.robots.ox.ac.uk/pascal/VOC/voc2012/index.html#devkit
 """
-
 import numpy as np
-
-
-def bitget(byteval, idx):
-    return ((byteval & (1 << idx)) != 0)
-
+from skimage.io import imshow
+import matplotlib.pyplot as plt
 
 def color_map(N=256, normalized=False):
+    def bitget(byteval, idx):
+        return ((byteval & (1 << idx)) != 0)
+
     dtype = 'float32' if normalized else 'uint8'
     cmap = np.zeros((N, 3), dtype=dtype)
     for i in range(N):
@@ -26,4 +25,20 @@ def color_map(N=256, normalized=False):
         cmap[i] = np.array([r, g, b])
 
     cmap = cmap/255 if normalized else cmap
-    return cmap
+    return cmap
+
+
+def color_map_viz():
+    labels = ['void', 'aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor', 'border']
+    nclasses = 21
+    row_size = 50
+    cmap = color_map()
+    array = np.empty((row_size*(nclasses+1), 500, cmap.shape[1]), dtype=cmap.dtype)
+    for i in range(nclasses):
+        array[i*row_size:i*row_size+row_size, :] = cmap[i]
+    array[nclasses*row_size:nclasses*row_size+row_size, :] = cmap[-1]
+
+    imshow(array)
+    plt.yticks([row_size*i+row_size/2 for i in range(nclasses+1)], labels)
+    plt.xticks([])
+    plt.show()
diff --git a/VOClabelcolormap.py b/VOClabelcolormap.py
@@ -1,3 +1,9 @@
+""" 
+Python implementation of the color map function for the PASCAL VOC data set. 
+Official Matlab version can be found in the PASCAL VOC devkit 
+http://host.robots.ox.ac.uk/pascal/VOC/voc2012/index.html#devkit
+"""
+
 import numpy as np
 
 

diff --git a/VOClabelcolormap.py b/VOClabelcolormap.py
@@ -0,0 +1,23 @@
+import numpy as np
+
+
+def bitget(byteval, idx):
+    return ((byteval & (1 << idx)) != 0)
+
+
+def color_map(N=256, normalized=False):
+    dtype = 'float32' if normalized else 'uint8'
+    cmap = np.zeros((N, 3), dtype=dtype)
+    for i in range(N):
+        r = g = b = 0
+        c = i
+        for j in range(8):
+            r = r | (bitget(c, 0) << 7-j)
+            g = g | (bitget(c, 1) << 7-j)
+            b = b | (bitget(c, 2) << 7-j)
+            c = c >> 3
+
+        cmap[i] = np.array([r, g, b])
+
+    cmap = cmap/255 if normalized else cmap
+    return cmap
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