def test_calc_ed_template_match_score_scaled_compiled_slow(self):
# Create a sample test image that is empty.
original_image = np.full((400, 400, 3), [0, 200, 0], dtype=np.uint8)
# Calculate the scale factor (MAKING SURE TO SUBTRACT '1' from the max height/width to account for array index out of bounds issue)
scale_factor_width = TSEGeometry.calc_measure_scale_factor(
200, (400 - 1))
# Calculate the scaled indices to identify the pixels in the larger image that we will want to make GREEN to provide evidence for the test succeeding.
original_image_scaled_indices = np.rint(
(np.arange(0, 200) * scale_factor_width)).astype(int)
rows_cols_cartesian_product = np.hsplit(
TSEDataUtils.calc_cartesian_product(
[original_image_scaled_indices,
original_image_scaled_indices]), 2)
rows_to_extract = rows_cols_cartesian_product[0].astype(int)
cols_to_extract = rows_cols_cartesian_product[1].astype(int)
# We now want to set each fo the pixels THAT WE EXPECT TO BE EXTRACTED BY THE TEST to GREEN to show that the test has passed.
original_image[rows_to_extract, cols_to_extract] = [0, 200, 0]
# Once we have performed the pixel extraction, we expect that all of the pixels returned will be GREEN (based ont he setup above)
matching_image = np.full((200, 200, 3), [0, 200, 0], dtype=np.uint8)
non_matching_image = np.full((200, 200, 3), [200, 0, 0],
dtype=np.uint8)
# Check that for perfectly matching images, we get a score of exactly 0.
assert_equal(
TSEImageUtils.calc_ed_template_match_score_scaled_compiled_slow(
matching_image, original_image), 0)
# Check that for non-matching images, we get a score > 0.
assert_true(
TSEImageUtils.calc_ed_template_match_score_scaled_compiled_slow(
non_matching_image, original_image) > 0)
def test_calc_ed_template_match_score_scaled_compiled_slow_fix_96(self):
original_image_1 = np.zeros((400, 400, 3), dtype=np.uint8)
original_image_2 = np.full((400, 400, 3), [200, 0, 0], dtype=np.uint8)
original_image_3 = np.full((400, 400, 3), [0, 200, 0], dtype=np.uint8)
original_image_4 = np.full((400, 400, 3), [0, 0, 200], dtype=np.uint8)
# Notice template patch is half the size of the original. We can therefore scale it up.
matching_image_1 = np.zeros((200, 200, 3), dtype=np.uint8)
matching_image_2 = np.full((200, 200, 3), [200, 0, 0], dtype=np.uint8)
matching_image_3 = np.full((200, 200, 3), [0, 200, 0], dtype=np.uint8)
matching_image_4 = np.full((200, 200, 3), [0, 0, 200], dtype=np.uint8)
non_matching_image = np.full((200, 200, 3), [0, 0, 200],
dtype=np.uint8)
# Check that for perfectly matching images, we get a score of exactly 0.
assert_equal(
TSEImageUtils.calc_ed_template_match_score_scaled_compiled_slow(
matching_image_1, original_image_1), 0)
assert_equal(
TSEImageUtils.calc_ed_template_match_score_scaled_compiled_slow(
matching_image_2, original_image_2), 0)
assert_equal(
TSEImageUtils.calc_ed_template_match_score_scaled_compiled_slow(
matching_image_3, original_image_3), 0)
assert_equal(
TSEImageUtils.calc_ed_template_match_score_scaled_compiled_slow(
matching_image_4, original_image_4), 0)
# Check that for non-matching images, we get a score > 0.
assert_true(
TSEImageUtils.calc_ed_template_match_score_scaled_compiled_slow(
non_matching_image, original_image_1) > 0)
assert_true(
TSEImageUtils.calc_ed_template_match_score_scaled_compiled_slow(
non_matching_image, original_image_2) > 0)
assert_true(
TSEImageUtils.calc_ed_template_match_score_scaled_compiled_slow(
non_matching_image, original_image_3) > 0)
# As the "non-matching" image has the same pixel value as "original_image_4", we WOULD EXPECT A MATCH.
assert_equal(
TSEImageUtils.calc_ed_template_match_score_scaled_compiled_slow(
non_matching_image, original_image_4), 0)