# vectorize_tiff(path_results_nn + "Anomaly/" + loss_folder_nn_t_t1, "/" + "Anomaly_" +image_name_nn_t_t1, ds_anomaly)
# intersection_arr_anomaly = ds_anomaly.GetRasterBand(1).ReadAsArray()
intersection_arr_anomaly_list.append(intersection_arr_anomaly)
ds_anomaly = None
else:
#we deal with the first CM. we cannot correct it, so we just copy it in the new folder
date1, date2 = dates_couples[d]
loss_folder_nn_t_t1, image_name_nn_t_t1, image_array_outliers_nn_t_t1, ds1, H, W, geo, proj, bands_nb = open_image(date1, date2, path_results_nn_t_t1)
create_dir(path_results+loss_folder_nn_t_t1)
create_dir(path_results + "Anomaly/" + loss_folder_nn_t_t1)
ds_anomaly = create_tiff(1, path_results + "Anomaly/" + loss_folder_nn_t_t1 + "Anomaly_" +image_name_nn_t_t1 + ".TIF", W, H,
gdal.GDT_Byte, np.zeros((H,W)), geo,
proj)
vectorize_tiff(path_results + "Anomaly/" + loss_folder_nn_t_t1, "/" + "Anomaly_" +image_name_nn_t_t1, ds_anomaly)
ds_anomaly = None
outliers1 = driver_shp.Open(path_results_nn_t_t1 + loss_folder_nn_t_t1 + "Segments_" + image_name_nn_t_t1 + ".shp", 0)
layer1 = outliers1.GetLayer()
spatialRef1 = layer1.GetSpatialRef()
source_intersection = driver_shp.CreateDataSource(path_results +loss_folder_nn_t_t1 + "/" + image_name_nn_t_t1 + '.shp')
layer_intersection = source_intersection.CreateLayer(image_name_nn_t_t1, spatialRef1, geom_type=ogr.wkbPolygon)
idField = ogr.FieldDefn("value", ogr.OFTInteger)
layer_intersection.CreateField(idField)
new_id1=0
for f1 in range(len(layer1)):
feature1 = layer1[f1]
id1 = feature1.GetField("value")
def main():
gpu = on_gpu()
print("ON GPU is " + str(gpu))
#Parameters
parser = argparse.ArgumentParser(description='train')
parser.add_argument('--patch_size', default=1, type=int)
parser.add_argument('--nb_features',
default=150,
type=int,
help="Number of hidden features in GRU.")
parser.add_argument('--nb_features_final',
default=10,
type=int,
help="Number of final features of the encoder.")
parser.add_argument(
'--nb_clusters',
default=15,
type=int,
help=
"Number of desired clusters. In case if we do not compute for a range of clusters."
)
parser.add_argument('--batch_size', default=50, type=int)
parser.add_argument('--epoch_nb', default=150, type=int)
parser.add_argument('--learning_rate', default=0.001, type=float)
args = parser.parse_args()
#Start time
start_time = time.time()
run_name = "." + str(time.strftime("%Y-%m-%d_%H%M"))
print(run_name)
#Montpellier
path_results_seg_series = os.path.expanduser(
'~/Desktop/Results/Segmentation_outliers_upd_filled/Montpellier_SPOT5_graph_cut_series_2D/'
)
seg_folder_series = "series_sigma_0.3_k_6_min_10_bands_3_threshold_int_0.4/"
folder_encoded = "patch_9_feat_5.2019-09-03_1619_noise1_mean_std"
path_results = path_results_seg_series + seg_folder_series + "Graph_coverage_filtered/"
path_results_final = path_results + "alpha_" + str(alpha) + "_t1_" + str(
t1) + "_t2_" + str(t2) + "_t3_" + str(t3) + "/"
# We open BB file that contains synopses
bb_final_list = np.load(path_results_final + "Graph_list_synopsys_alpha_" +
str(alpha) + "_t1_" + str(t1) + "_t2_" + str(t2) +
"_t3_" + str(t3) + "_" + folder_encoded + ".npy")
for z in range(8):
bb_final_list = np.c_[bb_final_list, np.full(len(bb_final_list), None)]
folder_results = "Synopsys_padding_feat_" + str(
args.nb_features) + "_lr_" + str(args.learning_rate) + run_name
# Folder with the results
path_results_NN = path_results_final + model + "_" + type + "/" + folder_results + "/"
create_dir(path_results_NN)
stats_file = path_results_NN + 'stats.txt'
path_model = path_results_NN + 'model' + run_name + "/"
create_dir(path_model)
# We add new arguments to the parser
print_stats(stats_file, folder_encoded, print_to_console=False)
print_stats(stats_file, str(args), print_to_console=False)
parser.add_argument('--stats_file', default=stats_file)
parser.add_argument('--path_results', default=path_results_NN)
parser.add_argument('--path_model', default=path_model)
parser.add_argument('--run_name', default=run_name)
args = parser.parse_args()
# We open segmentation rasters
segm_array_list = []
date_list = []
image_name_segm_list = np.sort(
list(
filter(
lambda f:
(f.endswith(".TIF") and f.startswith("Segments_1D_20")),
os.listdir(path_results))))
nbr_images = len(image_name_segm_list)
print(image_name_segm_list)
for i in range(nbr_images):
image_name_segm = image_name_segm_list[i]
date = (re.search("_([0-9]*).TIF", image_name_segm)).group(1)
print(date)
date_list.append(date)
image_array_seg, H, W, geo, proj, bands_nb = open_tiff(
path_results,
os.path.splitext(image_name_segm)[0])
segm_array_list.append(image_array_seg)
nbr_images = np.max(bb_final_list[:, 0]) + 1
# we get synopses
if type == "mean":
segments = bb_final_list[:, 8]
else:
segments = bb_final_list[:, 7]
feat_nb = len(segments[0][0])
# We zero-pad all the sequences, so they have the same length over the dataset (equal to dataset length). See the article
segments_padding = np.zeros((len(bb_final_list), nbr_images, feat_nb))
for s in range(len(segments)):
segments_padding[s][:len(segments[s])] = segments[s]
print(segments_padding.shape)
# We prepare the training dataset
image = ImageDataset(segments_padding, args.patch_size, 0,
np.arange(len(segments)),
feat_nb) # we create a dataset with tensor patches
loader_pretrain = dsloader(image, gpu, args.batch_size, shuffle=True)
loader_enc = dsloader(image, gpu, batch_size=1000, shuffle=False)
# We initialize the model
encoder = Encoder(feat_nb, args.nb_features,
args.nb_features_final) # On CPU
decoder = Decoder(feat_nb, args.nb_features,
args.nb_features_final) # On CPU
if gpu:
encoder = encoder.cuda() # On GPU
decoder = decoder.cuda() # On GPU
print_stats(stats_file, str(encoder), print_to_console=False)
# We pretrain the model
pretrain_lstm(args.epoch_nb, encoder, decoder, loader_pretrain, args)
# pretrain_lstm(0, encoder, decoder, loader_pretrain, args)
end_time = time.clock()
total_time_pretraining = end_time - start_time
total_time_pretraining = str(
datetime.timedelta(seconds=total_time_pretraining))
print_stats(
args.stats_file,
"Total time pretraining =" + str(total_time_pretraining) + "\n")
# We start encoding and clustering
start_time = time.time()
bb_final_list_flipped = np.flip(np.copy(bb_final_list), axis=0)
print_stats(stats_file, 'Initializing clusters...')
cl_nb = list(range(5, 51, 5))
labels_list, labels_h_list, hidden_array = encode_lstm(
encoder, W, loader_enc, cl_nb)
for c in range(len(cl_nb)):
feat_cl = cl_nb[c]
print(feat_cl)
labels, labels_h = labels_list[c], labels_h_list[c]
labels, labels_h = np.flip(labels, axis=0), np.flip(labels_h, axis=0)
new_labels = np.zeros((H * W))
new_labels_h = np.zeros((H * W))
# We optionally write clustering results to the BB list
for l in range(len(labels)):
if feat_cl == 15:
bb_final_list_flipped[l, 9] = labels_h[l] + 1
if feat_cl == 20:
bb_final_list_flipped[l, 10] = labels_h[l] + 1
if feat_cl == 25:
bb_final_list_flipped[l, 11] = labels_h[l] + 1
if feat_cl == 30:
bb_final_list_flipped[l, 12] = labels_h[l] + 1
if feat_cl == 35:
bb_final_list_flipped[l, 13] = labels_h[l] + 1
if feat_cl == 40:
bb_final_list_flipped[l, 14] = labels_h[l] + 1
if feat_cl == 45:
bb_final_list_flipped[l, 15] = labels_h[l] + 1
if feat_cl == 50:
bb_final_list_flipped[l, 16] = labels_h[l] + 1
img, ind = bb_final_list_flipped[l, 0:2]
coverage_ind = np.where(segm_array_list[img].flatten() == ind)[0]
new_labels[coverage_ind] = labels[l] + 1
new_labels_h[coverage_ind] = labels_h[l] + 1
ds = create_tiff(
1, args.path_results + "Kmeans_initial_clusters_" +
str(feat_cl) + ".TIF", W, H, gdal.GDT_Int16,
np.reshape(new_labels, (H, W)), geo, proj)
ds.GetRasterBand(1).SetNoDataValue(0)
vectorize_tiff(path_results, "Kmeans_initial_clusters_" + str(feat_cl),
ds)
ds = None
ds = create_tiff(
1, args.path_results + "Hierarchical_initial_clusters_" +
str(feat_cl) + ".TIF", W, H, gdal.GDT_Int16,
np.reshape(new_labels_h, (H, W)), geo, proj)
ds.GetRasterBand(1).SetNoDataValue(0)
vectorize_tiff(path_results,
"Hierarchical_initial_clusters_" + str(feat_cl), ds)
ds = None
np.save(
args.path_results + "Graph_list_synopsys_clusters_alpha_" +
str(alpha) + "_t1_" + str(t1) + "_t2_" + str(t2) + "_t3_" + str(t3),
np.flip(np.copy(bb_final_list_flipped), axis=0))
end_time = time.time()
total_time_pretraining = end_time - start_time
total_time_pretraining = str(
datetime.timedelta(seconds=total_time_pretraining))
print_stats(stats_file,
"Total time encoding =" + str(total_time_pretraining) + "\n")
def otsu(image_array_loss1,
image_array_loss2,
H,
W,
geo,
proj,
path_results,
images_date,
threshold=0.995,
changes=None,
mask=None):
image_array_loss = np.divide((image_array_loss1 + image_array_loss2), 2)
max_ = np.max(image_array_loss)
coef = max_ / 256
image_array_loss = image_array_loss / coef
image_array_loss = np.asarray(image_array_loss, dtype=int)
if mask is not None:
val = filters.threshold_otsu(
np.sort(
image_array_loss.flatten()[mask])[0:int(len(mask) *
threshold)])
else:
val = filters.threshold_otsu(
np.sort(image_array_loss.flatten())[0:int(H * W * threshold)])
image_array_outliers = np.zeros(H * W)
image_array_outliers[image_array_loss.flatten() > val] = 1
if mask is not None:
defected_mask = np.setdiff1d(np.arange(H * W), mask)
image_array_outliers[defected_mask] = 0
outliers_image_mean = "Outliers_average_" + images_date + "_" + str(
threshold)
dst_ds = create_tiff(1, path_results + "/" + outliers_image_mean + ".TIF",
W, H, gdal.GDT_Byte,
np.reshape(image_array_outliers, (H, W)), geo, proj)
gdal.SieveFilter(dst_ds.GetRasterBand(1), None, dst_ds.GetRasterBand(1), 5,
4)
dst_ds.FlushCache()
vectorize_tiff(path_results, "/" + outliers_image_mean, dst_ds)
dst_ds = None
if changes is not None:
if changes in ["changes_2004_2005", "changes_2006_2008"]:
path_cm = 'C:/Users/Ekaterina_the_Great/Dropbox/IJCNN/images/' + changes
path_cm = '/home/user/Dropbox/IJCNN/images/' + changes
path_cm = "/media/user/DATA/Results/RESULTS_CHANGE_DETECTION/GT_Montpellier/" + changes
cm_truth_name = "mask_changes_small1"
print(image_array_outliers.shape)
if changes == "changes_2004_2005":
cm_predicted = (np.reshape(image_array_outliers,
(H, W))[0:600, 600:1400]).flatten()
if changes == "changes_2006_2008":
cm_predicted = (np.reshape(image_array_outliers,
(H, W))[100:370,
1000:1320]).flatten()
else:
if changes in [
"changes_Rostov_20150830_20150919",
"changes_Rostov_20170918_20180111"
]:
print("hello")
path_cm = "/media/user/DATA/Results/RESULTS_CHANGE_DETECTION/GT_Rostov/"
cm_truth_name = changes + "_1"
if changes == "changes_Rostov_20150830_20150919":
print(image_array_outliers.shape)
print(np.reshape(image_array_outliers, (H, W)).shape)
cm_predicted = (np.reshape(image_array_outliers,
(H, W))[0:700,
0:900]).flatten()
# cm_predicted = np.asarray(np.reshape(image_array_outliers, (H, W))[0:700, 0:900]).flatten()
if changes == "changes_Rostov_20170918_20180111":
cm_predicted = (np.reshape(image_array_outliers,
(H, W))[2100:2400,
900:1400]).flatten()
cm_predicted[cm_predicted == 0] = 0
cm_predicted[cm_predicted == 1] = 1
print(cm_predicted.shape)
cm_truth, _, _, _, _, _ = open_tiff(path_cm, cm_truth_name)
cm_truth = cm_truth.flatten()
cm_truth[cm_truth == 0] = 0
cm_truth[cm_truth == 1] = 1
print(cm_truth.shape)
cm_truth[cm_truth == 255] = 0
print(
classification_report(cm_truth,
cm_predicted,
target_names=["no changes", "changes"]))
print(accuracy_score(cm_truth, cm_predicted))
print(cohen_kappa_score(cm_truth, cm_predicted))
conf = confusion_matrix(cm_truth, cm_predicted)
print(confusion_matrix(cm_truth, cm_predicted))
omission = conf[1][0] / sum(conf[1])
print(omission)
def otsu(image_array_loss1,
image_array_loss2,
H,
W,
geo,
proj,
path_results,
images_date,
changes=None):
# We calculate the average reconstruction error image
image_array_loss = np.divide((image_array_loss1 + image_array_loss2), 2)
# We rescale the image values to 8 bits so it works with the functions from skimage
max_ = np.max(image_array_loss)
coef = max_ / 256
image_array_loss = image_array_loss / coef
image_array_loss = np.asarray(image_array_loss, dtype=int)
# THIS IS VERY IMPORTANT VALUE
# Otsu threshold is automatic, however before applying it, we exclude 0.5% of the highest reconstruction error values as they ae considered to be outliers
# This parameter can be modified if needed
threshold = 0.995
val = filters.threshold_otsu(
np.sort(image_array_loss.flatten())
[0:int(H * W * threshold)]) # Obtained threshold value
# We get binary change map (1 - changes, 0 - no changes) using the threshold and write it to tiff and shp
image_array_outliers = np.zeros(H * W)
image_array_outliers[image_array_loss.flatten() > val] = 1
outliers_image_mean = "Outliers_average_" + images_date + "_" + str(
threshold)
dst_ds = create_tiff(1, path_results + "/" + outliers_image_mean + ".TIF",
W, H, gdal.GDT_Int16,
np.reshape(image_array_outliers, (H, W)), geo, proj)
vectorize_tiff(path_results, "/" + outliers_image_mean, dst_ds)
dst_ds = None
# We calculate the stats if the ground truth is available for this couple of images
if changes is not None:
# path of ground truth image, I have only 2 GT
path_cm = '/home/user/Dropbox/IJCNN/images/' + changes
cm_truth_name = "mask_changes_small1"
if changes == "changes_2004_2005":
cm_predicted = (np.reshape(image_array_outliers,
(H, W))[0:600, 600:1400]).flatten()
if changes == "changes_2006_2008":
cm_predicted = (np.reshape(image_array_outliers,
(H, W))[100:370, 1000:1320]).flatten()
cm_truth, _, _, _, _, _ = open_tiff(path_cm, cm_truth_name)
cm_truth = cm_truth.flatten()
cm_truth[cm_truth == 255] = 0
#Different stats taken from scikit
print(
classification_report(cm_truth,
cm_predicted,
target_names=["no changes", "changes"]))
print(accuracy_score(cm_truth, cm_predicted))
print(cohen_kappa_score(cm_truth, cm_predicted))
conf = confusion_matrix(cm_truth, cm_predicted)
print(confusion_matrix(cm_truth, cm_predicted))
omission = conf[1][0] / sum(conf[1])
print(omission)
def otsu_independent(image_array_loss1,
image_array_loss2,
H,
W,
geo,
proj,
path_results,
images_date,
changes=None):
# We calculate the change map for the 1st reconstruction error image. Same principle as in otsu() function
max_ = np.max(image_array_loss1)
coef = max_ / 256
image_array_loss1 = image_array_loss1 / coef
image_array_loss1 = np.asarray(image_array_loss1, dtype=int)
threshold = 0.995
val = filters.threshold_otsu(
np.sort(image_array_loss1.flatten())[0:int(H * W * threshold)])
image_array_outliers = np.zeros(H * W)
image_array_outliers[image_array_loss1.flatten() > val] = 1
# We calculate the change map for the 2nd reconstruction error image. Same principle as in otsu() function
max_ = np.max(image_array_loss2)
coef = max_ / 256
image_array_loss2 = image_array_loss2 / coef
image_array_loss2 = np.asarray(image_array_loss2, dtype=int)
threshold = 0.995
val = filters.threshold_otsu(
np.sort(image_array_loss2.flatten())[0:int(H * W * threshold)])
image_array_outliers[image_array_loss2.flatten(
) > val] = 1 # we add the change pixels to the results obtained from the 1st image
# We write tiff and shp
outliers_image_mean = "Outliers_average_" + images_date + "_independent_" + str(
threshold)
dst_ds = create_tiff(1, path_results + "/" + outliers_image_mean + ".TIF",
W, H, gdal.GDT_Int16,
np.reshape(image_array_outliers, (H, W)), geo, proj)
vectorize_tiff(path_results, "/" + outliers_image_mean, dst_ds)
dst_ds = None
# We calculate the classification stats if the ground truth if available
if changes is not None:
path_cm = 'C:/Users/Ekaterina_the_Great/Dropbox/IJCNN/images/' + changes
path_cm = '/home/user/Dropbox/IJCNN/images/' + changes
cm_truth_name = "mask_changes_small1"
print(image_array_outliers.shape)
if changes == "changes_2004_2005":
cm_predicted = (np.reshape(image_array_outliers,
(H, W))[0:600, 600:1400]).flatten()
if changes == "changes_2006_2008":
cm_predicted = (np.reshape(image_array_outliers,
(H, W))[100:370, 1000:1320]).flatten()
cm_truth, _, _, _, _, _ = open_tiff(path_cm, cm_truth_name)
cm_truth = cm_truth.flatten()
cm_truth[cm_truth == 255] = 0
#Different stats taken from scikit
print(
classification_report(cm_truth,
cm_predicted,
target_names=["no changes", "changes"]))
print(accuracy_score(cm_truth, cm_predicted))
print(cohen_kappa_score(cm_truth, cm_predicted))
conf = confusion_matrix(cm_truth, cm_predicted)
print(confusion_matrix(cm_truth, cm_predicted))
omission = conf[1][0] / sum(conf[1])
print(omission)
print("modified segment")
else:
new_segm[ind_seg_i, ind_seg_j] = new_seg_id
new_seg_id += 1
else:
new_segm[ind_seg_i, ind_seg_j] = new_seg_id
new_seg_id += 1
else:
new_segm[ind_seg_i, ind_seg_j] = new_seg_id
new_seg_id += 1
ds = None
ds_mask = None
else:
new_segm[ind_seg_i, ind_seg_j] = new_seg_id
new_seg_id += 1
# processing.runalg("otb:segmentationmeanshift", input, 0, spatial_r, range_r, conv_tr, min_nb_iter, min_reg_size, 0, 0,
# mask, True, True, min_obj_size, 0.1, "layer", "DN", 1700, 1, "", output)
print("total modified " + str(i))
ds = create_tiff(
1, path_encoded + segmentation_name_enc + "/" +
"Finetuned_segmentation_l_enc.TIF", W, H, gdal.GDT_Int16, new_segm, geo,
proj)
vectorize_tiff(path_encoded + segmentation_name_enc + "/",
"Finetuned_segmentation_l_enc", ds)
ds.GetRasterBand(1).SetNoDataValue(-9999)
ds.FlushCache()
ds = None
create_tiff(
1, path_encoded + segmentation_name_enc + "/" + "Linear_segments_enc.TIF",
W, H, gdal.GDT_Byte, linear_segm, geo, proj)
def calculate_stats(folder_enc, segmentation_name, clustering_final_name, apply_mask_outliers=True, S2=False):
print("S2", S2)
stats_file = path_main + folder_enc + 'stats.txt'
path_cm = os.path.expanduser('~/Desktop/Datasets/occupation_des_sols/')
# We open Corina Land Cover GT maps, they have 3 levels of precision
# We combinate different classes to create a desired GT map
cm_truth_name = "clc_2008_lvl1"
cm_truth_name2 = "clc_2008_lvl2"
cm_truth_name3 = "clc_2008_lvl3"
if S2:
cm_truth_name = "clc_2017_lvl1"
cm_truth_name2 = "clc_2017_lvl2"
cm_truth_name3 = "clc_2017_lvl3"
cm_truth, H, W, geo, proj, _ = open_tiff(path_cm, cm_truth_name)
cm_truth2, _, _, _, _, _ = open_tiff(path_cm, cm_truth_name2)
cm_truth3, _, _, _, _, _ = open_tiff(path_cm, cm_truth_name3)
cm_truth = cm_truth.flatten()
cm_truth2 = cm_truth2.flatten()
cm_truth3 = cm_truth3.flatten()
cm_truth[cm_truth == 1] = 1 # city
cm_truth[cm_truth == 2] = 1 # industrial area
cm_truth[cm_truth == 3] = 1 # extractions des materiaux
cm_truth[cm_truth == 4] = 6 #espaces vertes
cm_truth[cm_truth3 == 511] = 6 #Jardins familiaux
cm_truth[cm_truth3 == 512] = 6 #Espaces libres urbains
cm_truth[cm_truth3 == 513] = 513 #Cultures annuelles
cm_truth[cm_truth3 == 514] = 514 # Prairies
cm_truth[cm_truth3 == 521] = 521 # vignes
cm_truth[cm_truth3 == 522] = 522 # vergers
cm_truth[cm_truth3 == 523] = 523 # oliveraies
cm_truth[cm_truth == 6] = 6 #espaces boisés
cm_truth[cm_truth == 7] = 7 #espaces non-boisés
cm_truth[cm_truth == 8] = 8 #sea
cm_truth[cm_truth3 == 240] = 0 #aeroport
_, cm_truth_mod = np.unique(cm_truth, return_inverse=True)
print(np.unique(cm_truth))
ds = create_tiff(1, path_cm + cm_truth_name + "_custom", W, H,
gdal.GDT_Int16,
np.reshape(cm_truth_mod+1, (H,W)), geo, proj)
vectorize_tiff(path_cm, cm_truth_name + "_custom", ds)
ds.FlushCache()
ds = None
outliers_total, _, _, _, _, _ = open_tiff(path_main, "Outliers_total")
mask = np.where(outliers_total.flatten() == 1)[0]
for mean_or_median in ["mean", "median"]:
print("Descriptor type " + mean_or_median)
nmi_list = []
ari_list = []
print_stats(stats_file, "\n " + str("New classes"), print_to_console=True)
print_stats(stats_file, "\n " + str(segmentation_name) + "_" + str(clustering_final_name), print_to_console=True)
for cl in range(8, 16):
print("Clusters="+str(cl))
image_name_clust = clustering_final_name + "_" + mean_or_median + "_" + str(cl)
image_array_cl, H, W, geo, proj, _ = open_tiff(path_main + folder_enc + segmentation_name + "/" + clustering_final_name + "/", image_name_clust)
cm_predicted = image_array_cl.flatten()
cm_truth = cm_truth_mod
ind = np.where(cm_predicted<0)[0]
if len(ind)==1:
cm_predicted[-1] = cm_predicted[-2]
if apply_mask_outliers == True:
ind = np.intersect1d(mask, np.where(cm_truth>0)[0])
else:
ind = np.where(cm_truth > 0)[0]
cm_truth = cm_truth[ind]
cm_predicted = cm_predicted[ind]
nmi = normalized_mutual_info_score(cm_truth, cm_predicted)
ari = adjusted_rand_score(cm_truth, cm_predicted)
print(nmi)
print(ari)
nmi_list.append(np.round(nmi,2))
ari_list.append(np.round(ari,2))
if apply_mask_outliers:
print_stats(stats_file, mean_or_median + " WITH MASK", print_to_console=True)
else:
print_stats(stats_file, mean_or_median + " WITHOUT MASK", print_to_console=True)
print_stats(stats_file, "NMI", print_to_console=True)
print_stats(stats_file, str(nmi_list), print_to_console=True)
print_stats(stats_file, "ARI", print_to_console=True)
print_stats(stats_file, str(ari_list), print_to_console=True)
np.save(path_results_final + "Graph_list_alpha_"+str(alpha)+"_t1_"+str(t1)+"_t2_"+str(t2) + "_t3_" + str(t3), bb_final_list)
# We write the grids to rasters
for i in range(nbr_images):
grid = covered_grids_flatten[i]
grid = np.reshape(grid, ((H, W)))
ds = create_tiff(1, path_results_final + "BB_"+date_list[i]+"_alpha_"+str(alpha)+"_t1_"+str(t1)+"_t2_"+str(t2) + "_t3_" + str(t3) +".TIF", W, H,
gdal.GDT_Int32, grid, geo,
proj)
ds.GetRasterBand(1).SetNoDataValue(-1)
ds.FlushCache()
# gdal.SieveFilter(ds.GetRasterBand(1), ds_outliers.GetRasterBand(1), ds.GetRasterBand(1), 4, 4)
vectorize_tiff(path_results_final, "BB_"+date_list[i]+"_alpha_"+str(alpha)+"_t1_"+str(t1)+"_t2_"+str(t2) + "_t3_" + str(t3), ds)
grid_filled = covered_grids_flatten_to_be_filled[i]
grid_filled = np.reshape(grid_filled, ((H, W)))
unique, count = np.unique(grid_filled, return_counts=True)
# We compute the overall graph coverage
if 0 in unique:
perc =int(count[1]/(count[1]+count[2])*100)
else:
perc=0
print("Image " + str(i) + " has " + str(perc) + "% uncovered pixels")
ds = create_tiff(1, path_results_final + "Filled_grid_"+date_list[i]+"_alpha_"+str(alpha)+"_t1_"+str(t1)+"_t2_"+str(t2) + "_t3_" + str(t3) +"_perc_unc_"+str(perc)+".TIF", W, H,
gdal.GDT_Int32, grid_filled, geo,
proj)
vectorize_tiff(path_results_final, "Filled_grid_"+date_list[i]+"_alpha_"+str(alpha)+"_t1_"+str(t1)+"_t2_"+str(t2) + "_t3_" + str(t3)+"_perc_unc_"+str(perc), ds)
