def test_no_match(self):
blank = np.zeros((100, 100))
template = np.array([[0, 1], [1, 0]])
expected = []
actual = best_convolution(template, blank)
self.assertEquals(expected, actual)
def test_match(self):
image = np.array([[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 1, 0, 0],
[0, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0]])
template = np.array([[0, 1], [1, 0]])
expected = [(1, 5), (5, 2), (2, 4)]
actual = best_convolution(template, image)
self.assertEquals(sorted(expected), sorted(actual))
def test_template_too_big(self):
template = np.array([[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 1, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0]])
image = np.array([[0, 1], [1, 0]])
expected = []
actual = best_convolution(template, image)
self.assertEquals(sorted(expected), sorted(actual))
