def matches_exist(template, image, tolerance=1):
# just taken from convolution def
expected = numpy.count_nonzero(template)
ih, iw = image.shape
th, tw = template.shape
# Pad image to even dimensions
if ih % 2 or iw % 2:
if ih % 2:
ih += 1
if iw % 2:
iw += 1
bin_image = pad_bin_image_to_shape(image, (ih, iw))
if expected == 0:
return []
# Calculate the convolution of the FFT's of the image & template
convolution_freqs = rfft2(image) * rfft2(template[::-1, ::-1],
image.shape)
convolution_image = irfft2(convolution_freqs)
found_bitmap = convolution_image > (expected - tolerance)
if True in found_bitmap:
return True
else:
return False
def test_same_size(self):
image = np.array([[0, 0, 0, 0, 0],
[0, 0, 0, 1, 0],
[0, 0, 1, 0, 0],
[0, 1, 0, 0, 0],
[0, 0, 0, 0, 0]])
expected = image
actual = pad_bin_image_to_shape(image, image.shape)
assert_array_equal(expected, actual)
def test_padding(self):
image = np.array([[0, 0, 0, 0, 0],
[0, 0, 0, 1, 0],
[0, 0, 1, 0, 0],
[0, 1, 0, 0, 0],
[0, 0, 0, 0, 0]])
expected = np.array([[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, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0]])
actual = pad_bin_image_to_shape(image, (6, 8))
assert_array_equal(expected, actual)
def matches_exist(template, image, tolerance=1):
# just taken from convolution def
expected = numpy.count_nonzero(template)
ih, iw = image.shape
th, tw = template.shape
# Pad image to even dimensions
if ih % 2 or iw % 2:
if ih % 2:
ih += 1
if iw % 2:
iw += 1
bin_image = pad_bin_image_to_shape(image, (ih, iw))
if expected == 0:
return []
# Calculate the convolution of the FFT's of the image & template
convolution_freqs = rfft2(image) * rfft2(template[::-1, ::-1], image.shape)
convolution_image = irfft2(convolution_freqs)
found_bitmap = convolution_image > (expected - tolerance)
if True in found_bitmap:
return True
else:
return False
