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_run_length_encoding_normal_array(self):
data_in = numpy.array([1, 1, 3, 2, 3, 3, 3, 2, 2, 1, 4, 4])
data_out = ImagePreprocessor.run_length_encoding(data_in)
expected = numpy.array([[2, 1], [1, 3], [1, 2], [3, 3], [2, 2], [1, 1],
[2, 4]])
numpy.testing.assert_array_equal(data_out, expected)
def test_run_length_encoding_empty(self):
data_in = numpy.array([])
data_out = ImagePreprocessor.run_length_encoding(data_in)
expected = numpy.array([])
numpy.testing.assert_array_equal(data_out, expected)