Added script that runs a given classifier and savesthe faulty classified images.

.gitignore

@@ -15,3 +15,4 @@ images/BBB
 images/Images
 images/Infos
 images/licenseplates
+images/faulty

src/run_classifier.py

@@ -0,0 +1,63 @@
+#!/usr/bin/python
+from cPickle import dump, load
+from sys import argv, exit
+from pylab import imsave
+import os
+
+from Classifier import Classifier
+
+if len(argv) < 3:
+    print 'Usage: python %s NEIGHBOURS BLUR_SCALE' % argv[0]
+    exit(1)
+
+neighbours = int(argv[1])
+blur_scale = float(argv[2])
+suffix = '_%s_%s' % (blur_scale, neighbours)
+
+test_set_file = 'test_set%s.dat' % suffix
+classifier_file = 'classifier%s.dat' % suffix
+
+print 'Loading classifier...'
+classifier = Classifier(filename=classifier_file)
+classifier.neighbours = neighbours
+
+print 'Loading test set...'
+test_set = load(file(test_set_file, 'r'))
+l = len(test_set)
+matches = 0
+classified = {}
+
+for i, char in enumerate(test_set):
+    prediction = classifier.classify(char, char.value)
+
+    if char.value != prediction:
+        key = '%s_as_%s' % (char.value, prediction)
+
+        if key not in classified:
+            classified[key] = [char]
+        else:
+            classified[key].append(char)
+
+        print '"%s" was classified as "%s"' \
+                % (char.value, prediction)
+    else:
+        matches += 1
+
+    print '%d of %d (%d%% done)' % (i + 1, l, int(100 * (i + 1) / l))
+
+print '\n%d matches (%d%%), %d fails' % (matches, \
+        int(100 * matches / l), \
+        len(test_set) - matches)
+
+print 'Saving faulty classified characters...'
+folder = '../images/faulty/'
+
+if not os.path.exists(folder):
+    os.mkdir(folder)
+
+for filename, l in classified.iteritems():
+    if len(l) == 1:
+        imsave('%s%s' % (folder, filename), char.image.data, cmap='gray')
+    else:
+        for i, char in enumerate(l):
+            imsave('%s%s_%d' % (folder, filename, i), char.image.data, cmap='gray')