# texton = create_texton_feature(sat_image, ((25, 25), (50, 50), (100, 100)), image_name, n_clusters=n_clusters, cached=True)
plt.close('all')
print("Running feature set {}, image {}".format(
feature_set, image_name))
results_path = '{root}/results/jaccard/{fs}'.format(
root=get_project_root(), fs=str(feature_set))
try:
os.makedirs(os.path.dirname(results_path + '/'), exist_ok=True)
except OSError:
pass
X_test = get_x_matrix(test_image_loaded,
image_name=image_name,
feature_set=feature_set,
window_size=main_window_size,
cached=cached)
y_test, real_mask = get_y_vector(mask_full_path,
main_window_size,
percentage_threshold,
cached=False)
# X, y = balance_dataset(X, y, class_ratio=class_ratio)
# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=42, stratify=None)
X_train, y_train, real_mask, groups_train = create_models(
train_images,
feature_set,
base_path,
main_window_size=main_window_size,
# sift = create_sift_feature(sat_image, ((25, 25), (50, 50), (100, 100)), image_name, n_clusters=n_clusters,
# cached=True)
# lacunarity = create_lacunarity(sat_image, image_name, windows=((25, 25),), cached=True)
lacunarity = Lacunarity(windows=lac_window_size, box_sizes=(lac_box_size,))
feature_set.add(lacunarity, "LACUNARITY")
# feature_set.add(pantex, "PANTEX")
# feature_set.add(sift, "SIFT")
classifier = RF_classifier()
# del sat_image # Free-up memory
X = get_x_matrix(sat_image, image_name=image_name, feature_set=feature_set, window_size=main_window_size,
cached=True)
print(X.shape)
X = X[:, 1:]
# X = np.mean(X, axis=3)
ds, img, bands = load_from_file(image_file, WORLDVIEW3)
img = normalize_image(img, bands)
rgb_img = get_rgb_bands(img, bands)
plt.figure()
plt.imshow(rgb_img)
plt.savefig(results_path + "/lacunarity_heatmap_image_{image_name}.png".format(
image_name=image_name
))