Merge branch 'master' of vo20.nl:/git/uva

improc/ass3/back_projection.py

@@ -66,9 +66,7 @@ def hbp(image, environment, bins, model, radius, mask=None):
     b = zeros(environment.shape[:2])
     use = environment.astype(float) * map(lambda x: x - 1, bins)
 
-    if model == 'rgb':
-        use /= 255
-    elif model == 'hsv':
+    if model == 'hsv':
         for p in domainIterator(image):
             use[p] = rgb_to_hsv(*use[p].tolist())
 
@@ -92,16 +90,16 @@ def exclude_color(color, image):
 
 if __name__ == '__main__':
     print 'Reading images...'
-    waldo = imread('waldo.tiff')
-    env = imread('waldo_env.tiff')
+    waldo = imread('waldo.tiff') / 255.
+    env = imread('waldo_env.tiff') / 255.
 
     # Use a mask to ignore the white background in the Waldo image
     mask = exclude_color([255] * 3, waldo)
 
     # Execute the back projection algorithm
-    import pickle
+    #import pickle
     b = hbp(waldo, env, [64] * 3, 'rgb', 15, mask)
-    pickle.dump(b, open('projection.dat', 'w'))
+    #pickle.dump(b, open('projection.dat', 'w'))
     #b = pickle.load(open('projection.dat', 'r'))
 
     w, h = waldo.shape[:2]

improc/ass3/intersect.py

 #!/usr/bin/env python
 from numpy import zeros
 from matplotlib.pyplot import imread
+from colorsys import rgb_to_hsv
 
 def col2bin(color):
     """Get the histogram bin coordinates of a color."""
@@ -21,18 +22,17 @@ def domainIterator(image, dim=2):
 def colHist(image, bins, model, mask=None):
     """Create the color histogram of an image."""
     h = zeros(bins, dtype=int)
+    use = image.astype(float)
 
-    # Multiply with the number of bins to normalize bin coordinates
-    use = image.astype(float) * map(lambda x: x - 1, bins)
-
-    if model == 'rgb':
-        use /= 255
-    elif model == 'hsv':
-        # TODO: implement HSV color model
-        pass
-    else:
+    if model == 'hsv':
+        for p in domainIterator(image):
+            use[p] = rgb_to_hsv(*use[p].tolist())
+    elif model != 'rgb':
         raise ValueError('Color model "%s" is not supported.' % model)
 
+    # Multiply with the number of bins to normalize bin coordinates
+    use *= map(lambda x: x - 1, bins)
+
     for p in domainIterator(image):
         # Optionally use a mask over the image
         if mask is None or mask[p]:
@@ -55,12 +55,22 @@ def histogramIntersect(h1, h2):
     return float(match) / h2.sum()
 
 if __name__ == '__main__':
+    from sys import argv
+
+    count = 20
+    model = 'rgb'
     bins = [64] * 3
 
+    # Usage: python intersect.py [ count [ model ] ]
+    if len(argv) > 1:
+        count = int(argv[1])
+
+        if len(argv) > 2:
+            model = argv[2]
+
     # Create table of intersections of images in the database
-    print 'Creating histograms...'
-    hist = [colHist(imread('database/%d.jpg' % i), \
-            bins, 'rgb') for i in xrange(1, 4)]
+    hist = [colHist(imread('database/%d.jpg' % i) / 255., \
+            bins, model) for i in xrange(1, count + 1)]
 
     table = zeros((len(hist), len(hist)), dtype=int)
 
@@ -68,7 +78,6 @@ if __name__ == '__main__':
         table[i, i] = 100
 
         for j in xrange(i + 1, len(hist)):
-            print 'comparing %d, %d' % (i, j)
             table[i, j] = table[j, i] \
                     = round(histogramIntersect(hist[i], hist[j]) * 100)
 

improc/ass3/results_hsv.txt

+[[100  36  50  22  25  35  20  28  38  29  32  37  17  38  47  28  39  34  16  30]
+ [ 36 100  48  49  43  48  34  61  52  12  37  55  12  51  50  47  52  57  11  49]
+ [ 50  48 100  30  33  52  18  45  49  37  32  58  22  38  48  30  51  42  19  45]
+ [ 22  49  30 100  41  34  33  40  36   5  32  36   7  30  37  26  27  54  10  28]
+ [ 25  43  33  41 100  42  25  43  38   4  36  39   4  37  35  31  30  40   5  34]
+ [ 35  48  52  34  42 100  22  48  42  13  34  53  12  42  43  33  41  46  10  40]
+ [ 20  34  18  33  25  22 100  25  22   4  25  22   4  25  24  27  21  32  15  24]
+ [ 28  61  45  40  43  48  25 100  41   4  33  56   5  41  40  36  44  47   6  43]
+ [ 38  52  49  36  38  42  22  41 100  17  35  46  12  44  40  35  48  35   7  43]
+ [ 29  12  37   5   4  13   4   4  17 100   6  17  45  14  20   5  18  11  42   7]
+ [ 32  37  32  32  36  34  25  33  35   6 100  35   4  33  34  27  31  35   5  37]
+ [ 37  55  58  36  39  53  22  56  46  17  35 100  12  48  55  37  51  46   9  48]
+ [ 17  12  22   7   4  12   4   5  12  45   4  12 100  12  12   4  16  12  58   5]
+ [ 38  51  38  30  37  42  25  41  44  14  33  48  12 100  46  45  44  40  13  40]
+ [ 47  50  48  37  35  43  24  40  40  20  34  55  12  46 100  35  44  47  14  38]
+ [ 28  47  30  26  31  33  27  36  35   5  27  37   4  45  35 100  49  35   5  41]
+ [ 39  52  51  27  30  41  21  44  48  18  31  51  16  44  44  49 100  39  13  52]
+ [ 34  57  42  54  40  46  32  47  35  11  35  46  12  40  47  35  39 100  14  36]
+ [ 16  11  19  10   5  10  15   6   7  42   5   9  58  13  14   5  13  14 100   5]
+ [ 30  49  45  28  34  40  24  43  43   7  37  48   5  40  38  41  52  36   5 100]]