def templatesyncsegmSegmentation(self, image_source, radius_color,
radius_object, noise_size,
expected_color_segments,
expected_object_segments,
collect_dynamic):
result_testing = False
for _ in range(0, 5, 1):
algorithm = syncsegm(radius_color, radius_object, noise_size)
analyser = algorithm.process(image_source, collect_dynamic, 0.9995,
0.9995)
color_segments = analyser.allocate_colors()
object_segments = analyser.allocate_objects(0.2)
if ((len(color_segments) != expected_color_segments)
or (len(object_segments) != expected_object_segments)):
continue
if (collect_dynamic is True):
syncsegm_visualizer.show_first_layer_dynamic(analyser)
syncsegm_visualizer.show_second_layer_dynamic(analyser)
result_testing = True
break
assert result_testing
def templateSyncsegmSegmentation(image_source, radius_color, radius_object,
noise_size, expected_color_segments,
expected_object_segments, collect_dynamic,
ccore_flag):
result_testing = False
color_segments, object_segments = [], []
for _ in range(0, 10, 1):
algorithm = syncsegm(radius_color,
radius_object,
noise_size,
ccore=ccore_flag)
analyser = algorithm.process(image_source, collect_dynamic, 0.9995,
0.9995)
color_segments = analyser.allocate_colors()
object_segments = analyser.allocate_objects(0.2)
if (len(color_segments) != expected_color_segments) or (
len(object_segments) != expected_object_segments):
continue
result_testing = True
break
assertion.eq(expected_color_segments, len(color_segments))
assertion.eq(expected_object_segments, len(object_segments))
assertion.true(result_testing)
def templateSyncsegmVisulizationNoFailure(image_source, radius_color, radius_object, noise_size, expected_color_segments, expected_object_segments, collect_dynamic, ccore_flag):
algorithm = syncsegm(radius_color, radius_object, noise_size, ccore=ccore_flag)
analyser = algorithm.process(image_source, collect_dynamic, 0.9995, 0.9995)
color_segments = analyser.allocate_colors(0.01, noise_size)
draw_image_mask_segments(image_source, color_segments)
object_segments = analyser.allocate_objects(0.01, noise_size)
draw_image_mask_segments(image_source, object_segments)
syncsegm_visualizer.show_first_layer_dynamic(analyser)
syncsegm_visualizer.show_second_layer_dynamic(analyser)
def templateSyncsegmVisulizationNoFailure(image_source, radius_color, radius_object, noise_size, expected_color_segments, expected_object_segments, collect_dynamic, ccore_flag):
algorithm = syncsegm(radius_color, radius_object, noise_size, ccore=ccore_flag)
analyser = algorithm.process(image_source, collect_dynamic, 0.9995, 0.9995)
color_segments = analyser.allocate_colors(0.01, noise_size)
draw_image_mask_segments(image_source, color_segments)
object_segments = analyser.allocate_objects(0.01, noise_size)
draw_image_mask_segments(image_source, object_segments)
syncsegm_visualizer.show_first_layer_dynamic(analyser)
syncsegm_visualizer.show_second_layer_dynamic(analyser)
def template_segmentation_image(source, color_radius, object_radius, noise_size, show_dyn):
algorithm = syncsegm(color_radius, object_radius, noise_size, False)
analyser = algorithm.process(source, show_dyn)
color_segments = analyser.allocate_colors(0.01, noise_size)
draw_image_mask_segments(source, color_segments)
if object_radius is not None:
object_segments = analyser.allocate_objects(0.01, noise_size)
draw_image_mask_segments(source, object_segments)
if show_dyn is True:
syncsegm_visualizer.show_first_layer_dynamic(analyser)
syncsegm_visualizer.show_second_layer_dynamic(analyser)
def template_segmentation_image(source, color_radius, object_radius,
noise_size, show_dyn):
algorithm = syncsegm(color_radius, object_radius, noise_size)
analyser = algorithm.process(source, show_dyn)
color_segments = analyser.allocate_colors(0.01, noise_size)
draw_image_mask_segments(source, color_segments)
if (object_radius is not None):
object_segments = analyser.allocate_objects(0.01, noise_size)
draw_image_mask_segments(source, object_segments)
if (show_dyn is True):
syncsegm_visualizer.show_first_layer_dynamic(analyser)
syncsegm_visualizer.show_second_layer_dynamic(analyser)
def templateSyncsegmSegmentation(image_source, radius_color, radius_object, noise_size, expected_color_segments, expected_object_segments, collect_dynamic, ccore_flag):
result_testing = False
color_segments, object_segments = [], []
for _ in range(0, 10, 1):
algorithm = syncsegm(radius_color, radius_object, noise_size, ccore=ccore_flag)
analyser = algorithm.process(image_source, collect_dynamic, 0.9995, 0.9995)
color_segments = analyser.allocate_colors()
object_segments = analyser.allocate_objects(0.2)
if (len(color_segments) != expected_color_segments) or (len(object_segments) != expected_object_segments):
continue
result_testing = True
break
assertion.eq(expected_color_segments, len(color_segments))
assertion.eq(expected_object_segments, len(object_segments))
assertion.true(result_testing)
def templatesyncsegmSegmentation(self, image_source, radius_color, radius_object, noise_size, expected_color_segments, expected_object_segments, collect_dynamic):
result_testing = False;
for _ in range(0, 3, 1):
algorithm = syncsegm(radius_color, radius_object, noise_size);
analyser = algorithm.process(image_source, collect_dynamic, 0.9995, 0.9995);
color_segments = analyser.allocate_colors();
object_segments = analyser.allocate_objects(0.2);
if ( (len(color_segments) != expected_color_segments) or (len(object_segments) != expected_object_segments) ):
continue;
if (collect_dynamic is True):
syncsegm_visualizer.show_first_layer_dynamic(analyser);
syncsegm_visualizer.show_second_layer_dynamic(analyser);
result_testing = True;
break;
assert result_testing;
