def test_extract_image_sub_window(self):
test_image = np.zeros((200, 200, 3), dtype=np.uint8)
# Set 50x50px section to Red (top-left corner)
test_image[0:50, 0:50] = [0, 0, 200]
expected_result = np.full((50, 50, 3), [0, 0, 200], dtype=np.uint8)
extracted_slice = TSEImageUtils.extract_image_sub_window(
test_image, TSEPoint(0, 0), TSEPoint(50, 50))
assert_true(np.array_equal(expected_result, extracted_slice))
def scan_search_window_scaling(self,
template_patch,
template_patch_origin,
match_method,
force_cont_search=False):
image_height, image_width = self._hsv_img2.shape[:2]
image_centre_x = math.floor(image_width / 2)
template_patch_height, template_patch_width = template_patch.shape[:2]
new_localised_window_height = image_height - template_patch_height
best_score = -1
best_position = 0
stop = False
last_width = template_patch_width
prev_current_window_scaled_coords = None
for i in range(template_patch_origin.y, new_localised_window_height):
score = 0
if i >= (template_patch_origin.y + 1):
last_width = self._calibration_lookup[i - 1]
calibrated_patch_width = self._calibration_lookup[i]
patch_half_width = math.floor(calibrated_patch_width / 2)
scale_factor = TSEGeometry.calc_measure_scale_factor(
last_width, calibrated_patch_width)
if prev_current_window_scaled_coords is None:
current_window_scaled_coords = TSEImageUtils.scale_image_roi_relative_centre(
((image_centre_x - patch_half_width), i),
((image_centre_x + patch_half_width),
(i + template_patch_height)), scale_factor)
else:
# We add +1 to the 'Y' coordinate as we are moving the search window down the ROI by one pixel each time we increase the width.
current_window_scaled_coords = TSEImageUtils.scale_image_roi_relative_centre(
(prev_current_window_scaled_coords[0].x,
prev_current_window_scaled_coords[0].y + 1),
(prev_current_window_scaled_coords[1].x,
prev_current_window_scaled_coords[1].y + 1), scale_factor)
prev_current_window_scaled_coords = current_window_scaled_coords
scaled_search_window = TSEImageUtils.extract_image_sub_window(
self._hsv_img2, current_window_scaled_coords[0],
current_window_scaled_coords[1])
if match_method.match_type == tse_match_methods.DISTANCE_ED:
score = TSEImageUtils.calc_ed_template_match_score_scaled_compiled(
template_patch, scaled_search_window)
elif match_method.match_type == tse_match_methods.DISTANCE:
score = TSEImageUtils.calc_template_match_compare_cv2_score_scaled(
template_patch, scaled_search_window,
match_method.match_id)
elif match_method.match_type == tse_match_methods.HIST:
scaled_template_patch = TSEImageUtils.scale_image_interpolation_auto(
template_patch, scaled_search_window)
score = TSEImageUtils.calc_compare_hsv_histogram(
scaled_template_patch, scaled_search_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 += 1
else:
stop = True
else:
if best_score == -1 or score > best_score:
best_score = score
best_position += 1
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