def template_segmentation_image(source, color_radius, color_neighbors, object_radius, object_neighbors, noise_size):
data = read_image(source);
dbscan_instance = dbscan(data, color_radius, color_neighbors, True);
print("Segmentation: '", source, "', Dimensions:", len(data[0]));
dbscan_instance.process();
clusters = dbscan_instance.get_clusters();
real_clusters = [cluster for cluster in clusters if len(cluster) > noise_size];
print("Draw allocated color segments (back mask representation)...");
draw_image_mask_segments(source, real_clusters);
print("Draw allocated color segments (color segment representation)...");
draw_image_color_segments(source, real_clusters);
if (object_radius is None):
return;
# continue analysis
pointer_image = Image.open(source);
image_size = pointer_image.size;
object_colored_clusters = [];
for cluster in clusters:
coordinates = [];
for index in cluster:
y = int(floor(index / image_size[0]));
x = index - y * image_size[0];
coordinates.append([x, y]);
# perform clustering analysis of the colored objects
if (len(coordinates) < noise_size):
continue;
dbscan_instance = dbscan(coordinates, object_radius, object_neighbors, True);
dbscan_instance.process();
object_clusters = dbscan_instance.get_clusters();
# decode it
real_description_clusters = [];
for object_cluster in object_clusters:
real_description = [];
for index_object in object_cluster:
real_description.append(cluster[index_object]);
real_description_clusters.append(real_description);
if (len(real_description) > noise_size):
object_colored_clusters.append(real_description);
print("Draw allocated object segments (back mask representation)...");
draw_image_mask_segments(source, object_colored_clusters);
print("Draw allocated object segments (color segment representation)...");
draw_image_color_segments(source, object_colored_clusters);
def template_segmentation_image(source, color_radius, color_neighbors, object_radius, object_neighbors, noise_size):
data = read_image(source);
dbscan_instance = dbscan(data, color_radius, color_neighbors, True);
print("Segmentation: '", source, "', Dimensions:", len(data[0]));
dbscan_instance.process();
clusters = dbscan_instance.get_clusters();
real_clusters = [cluster for cluster in clusters if len(cluster) > noise_size];
print("Draw allocated color segments (back mask representation)...");
draw_image_mask_segments(source, real_clusters);
print("Draw allocated color segments (color segment representation)...");
draw_image_color_segments(source, real_clusters);
if (object_radius is None):
return;
# continue analysis
pointer_image = Image.open(source);
image_size = pointer_image.size;
object_colored_clusters = [];
for cluster in clusters:
coordinates = [];
for index in cluster:
y = int(floor(index / image_size[0]));
x = index - y * image_size[0];
coordinates.append([x, y]);
# perform clustering analysis of the colored objects
if (len(coordinates) < noise_size):
continue;
dbscan_instance = dbscan(coordinates, object_radius, object_neighbors, True);
dbscan_instance.process();
object_clusters = dbscan_instance.get_clusters();
# decode it
real_description_clusters = [];
for object_cluster in object_clusters:
real_description = [];
for index_object in object_cluster:
real_description.append(cluster[index_object]);
real_description_clusters.append(real_description);
if (len(real_description) > noise_size):
object_colored_clusters.append(real_description);
print("Draw allocated object segments (back mask representation)...");
draw_image_mask_segments(source, object_colored_clusters);
print("Draw allocated object segments (color segment representation)...");
draw_image_color_segments(source, object_colored_clusters);
def testDrawSegmentationResultNoFailure(self):
data = utils.read_image(IMAGE_SIMPLE_SAMPLES.IMAGE_SIMPLE01);
kmeans_instance = kmeans(data, [[255, 0, 0], [0, 0, 255], [180, 136, 0], [255, 255, 255]]);
kmeans_instance.process();
clusters = kmeans_instance.get_clusters();
utils.draw_image_mask_segments(IMAGE_SIMPLE_SAMPLES.IMAGE_SIMPLE01, clusters);
utils.draw_image_color_segments(IMAGE_SIMPLE_SAMPLES.IMAGE_SIMPLE01, clusters);
