def create_row_patterns(img, step=10):
"""
Extracts pixel rows from the image
:param img: Source images
:type img: Binary numpy array
:param step: Pixel distance between each row
:return: An array of row patterns from top to bottom
"""
h, w = img.shape
row_patterns = []
for i in range(0, h, step):
rle_data = ImagePreprocessor.run_length_encoding(img[i])
detailed_pattern = ImagePreprocessor.cleanup_row_noise(rle_data)
row_patterns.append(detailed_pattern[:, 1])
return row_patterns
def test_cleanup_row_noise_invalid_suffix(self):
data_in = numpy.array([[7, 1], [8, 0], [12, 1], [9, 0], [4, 1]])
data_out = ImagePreprocessor.cleanup_row_noise(data_in, 6)
expected = numpy.array([[7, 1], [8, 0], [12, 1], [13, 0]])
numpy.testing.assert_array_equal(data_out, expected)
def test_cleanup_row_noise_double_invalid_middle(self):
data_in = numpy.array([[7, 1], [2, 0], [3, 1], [9, 0], [12, 1]])
data_out = ImagePreprocessor.cleanup_row_noise(data_in, 6)
expected = numpy.array([[12, 1], [9, 0], [12, 1]])
numpy.testing.assert_array_equal(data_out, expected)