# from ~69% to 75% in one test.
#if char in 'Iil':
# c /= 2.0
return c
def rankings(self, limit=None, ignore_case=True):
r = sorted(self._sums, key=self.certainty, reverse=True)
return self.filter_rankings(r, limit, ignore_case)
def test(image, char=None):
classifications = MaskClassifications()
m1 = make_mask(image, width=mask_table.width, height=mask_table.height)
for c, m2 in mask_table.masks.items():
s = cmp_masks(m1, m2)
classifications.add(c, s)
return classifications
if __name__ == '__main__':
import sys
args = sys.argv[1:]
if len(args) == 1:
i = cv.LoadImageM(sys.argv[1], cv.CV_LOAD_IMAGE_GRAYSCALE)
classifications = test(i)
rankings = classifications.rankings(limit=4)
for r in rankings:
print "guess = %s; certainty = %.4f" % (r, classifications.certainty(r))
else:
score_files(args, test)
def rankings(self, limit=None, ignore_case=True):
r = sorted(self._certainties, key=self.certainty, reverse=True)
return self.filter_rankings(r, limit, ignore_case)
class Combiner(object):
def __init__(self, tests):
self._tests = tests
def test(self, image, char=None):
classifications = CombinedClassifications()
for test in self._tests:
classifications.add(test(image, char=char))
return classifications
if __name__ == '__main__':
import sys
args = sys.argv[1:]
knn_test = KNNTest(data_path)
c = Combiner([cont_test, mask_test, knn_test.test])
if len(args) == 1:
i = cv.LoadImageM(sys.argv[1], cv.CV_LOAD_IMAGE_GRAYSCALE)
classifications = c.test(i)
rankings = classifications.rankings(limit=4)
for r in rankings:
print "guess = %s; certainty = %.4f" % (r, classifications.certainty(r))
else:
score_files(args, c.test)