Changed linear to radial blur in SVM.

src/Classifier.py

@@ -1,5 +1,5 @@
 from svmutil import svm_train, svm_problem, svm_parameter, svm_predict, \
-        LINEAR, svm_save_model, svm_load_model
+        svm_save_model, svm_load_model
 from cPickle import dump, load
 
 
@@ -13,7 +13,7 @@ class Classifier:
             f.close()
         else:
             self.param = svm_parameter()
-            self.param.kernel_type = LINEAR
+            self.param.kernel_type = 2
             self.param.C = c
             self.character_map = {}
             self.model = None

src/ClassifierTest.py

@@ -3,63 +3,63 @@ from LicensePlate import LicensePlate
 from Classifier import Classifier
 from cPickle import dump, load
 
-#chars = []
-#
-#for i in range(9):
-#    for j in range(100):
-#        try:
-#            filename = '%04d/00991_%04d%02d.info' % (i, i, j)
-#            print 'loading file "%s"' % filename
-#            plate = LicensePlate(i, j)
-#
-#            if hasattr(plate, 'characters'):
-#                chars.extend(plate.characters)
-#        except:
-#            print 'epic fail'
-#
-#print 'loaded %d chars' % len(chars)
-#
+chars = []
+
+for i in range(9):
+    for j in range(100):
+        try:
+            filename = '%04d/00991_%04d%02d.info' % (i, i, j)
+            print 'loading file "%s"' % filename
+            plate = LicensePlate(i, j)
+
+            if hasattr(plate, 'characters'):
+                chars.extend(plate.characters)
+        except:
+            print 'epic fail'
+
+print 'loaded %d chars' % len(chars)
+
 #dump(chars, file('chars', 'w+'))
 #----------------------------------------------------------------
-#chars = load(file('chars', 'r'))
-#learned = []
-#learning_set = []
-#test_set = []
-#
-#for char in chars:
-#    if learned.count(char.value) > 80:
-#        test_set.append(char)
-#    else:
-#        learning_set.append(char)
-#        learned.append(char.value)
-#
-#dump(learning_set, file('learning_set', 'w+'))
-#dump(test_set, file('test_set', 'w+'))
+chars = load(file('chars', 'r'))
+learned = []
+learning_set = []
+test_set = []
+
+for char in chars:
+    if learned.count(char.value) > 80:
+        test_set.append(char)
+    else:
+        learning_set.append(char)
+        learned.append(char.value)
+
+dump(learning_set, file('learning_set', 'w+'))
+dump(test_set, file('test_set', 'w+'))
 #----------------------------------------------------------------
 learning_set = load(file('learning_set', 'r'))
 
 # Train the classifier with the learning set
-classifier = Classifier(c=3)
+classifier = Classifier(c=30)
 classifier.train(learning_set)
-#classifier.save('classifier')
+classifier.save('classifier')
 #----------------------------------------------------------------
-#classifier = Classifier(filename='classifier')
-#test_set = load(file('test_set', 'r'))
-#l = len(test_set)
-#matches = 0
-#
-#for i, char in enumerate(test_set):
-#    prediction = classifier.classify(char)
-#
-#    if char.value == prediction:
-#        print ':) ------> Successfully recognized "%s"' % char.value
-#        matches += 1
-#    else:
-#        print ':( Expected character "%s", got "%s"' \
-#                % (char.value, prediction),
-#
-#    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 / len(test_set)), \
-#        len(test_set) - matches)
+classifier = Classifier(filename='classifier')
+test_set = load(file('test_set', 'r'))
+l = len(test_set)
+matches = 0
+
+for i, char in enumerate(test_set):
+    prediction = classifier.classify(char)
+
+    if char.value == prediction:
+        print ':) ------> Successfully recognized "%s"' % char.value,
+        matches += 1
+    else:
+        print ':( Expected character "%s", got "%s"' \
+                % (char.value, prediction),
+
+    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 / len(test_set)), \
+        len(test_set) - matches)