def test_calc_template_match_compare_cv2_score_CCORR(self):
image_1 = np.full((200, 200, 3), [0, 200, 0], dtype=np.uint8)
image_2 = image_1
image_3 = np.full((200, 200, 3), [200, 0, 0], dtype=np.uint8)
matching_result = TSEImageUtils.calc_template_match_compare_cv2_score(
image_1, image_2, cv2.cv.CV_TM_CCORR_NORMED)
non_matching_result = TSEImageUtils.calc_template_match_compare_cv2_score(
image_1, image_3, cv2.cv.CV_TM_CCORR_NORMED)
# For CCORR, we would expect that a perfectly matching image will score HIGHER than a non-matching image
assert_true(matching_result > non_matching_result)
def test_calc_template_match_compare_cv2_score_SQDIFF(self):
image_1 = np.zeros((200, 200, 3), dtype=np.uint8)
image_2 = image_1
image_3 = 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_template_match_compare_cv2_score(
image_1, image_2, cv2.cv.CV_TM_SQDIFF), 0)
# Check that for non-matching images, we get a score > 0.
assert_true(
TSEImageUtils.calc_template_match_compare_cv2_score(
image_1, image_3, cv2.cv.CV_TM_SQDIFF) > 0)
def scan_search_window(self,
template_patch,
template_patch_origin,
match_method,
force_cont_search=False):
image_height, image_width = self._hsv_img2.shape[:2]
template_patch_height, template_patch_width = template_patch.shape[:2]
localised_window = self._hsv_img2[template_patch_origin.y:image_height,
template_patch_origin.x:(
template_patch_origin.x +
template_patch_width)]
localised_window_height, localised_window_width = localised_window.shape[:
2]
best_score = -1
best_position = 0
stop = False
for i in range(0, (localised_window_height - template_patch_height)):
current_window = localised_window[i:(i + template_patch_height),
0:template_patch_width]
score = 0
if match_method.match_type == tse_match_methods.DISTANCE_ED:
score = TSEImageUtils.calc_euclidean_distance_cv2_norm(
template_patch, current_window)
elif match_method.match_type == tse_match_methods.DISTANCE:
score = TSEImageUtils.calc_template_match_compare_cv2_score(
template_patch, current_window, match_method.match_id)
elif match_method.match_type == tse_match_methods.HIST:
score = TSEImageUtils.calc_compare_hsv_histogram(
template_patch, current_window, match_method.match_id)
# If lower score means better match, then the method is a 'reverse' method.
if match_method.reverse_score:
if best_score == -1 or score < best_score:
best_score = score
best_position = i
else:
stop = True
else:
if best_score == -1 or score > best_score:
best_score = score
best_position = i
else:
stop = True
if (force_cont_search is False) and (stop is True):
break
# We need to return the 'Y' with the best score (i.e. the displacement)
return best_position