def anomaly(self,scores):
if isExtract:
self.score_result=np.empty_like(self.select.reshape(-1,1))
# scale the scores
self.score_result[self.ns_changePos]=DataProcess.scaleNormalize(scores,(0,500)).reshape(-1,)
self.score_result[self.ns_nonChangePos]=0
else:
self.score_result=DataProcess.scaleNormalize(scores,(0,500)).reshape(-1,)
# give labels
self.outlier_result=highRank.getOutliers(self.score_result,99)
# generate picture
GeoProcess.getSHP(img_path=self.root_dir,img_name=self.file_name,
save_path="C:\\Users\\DELL\\Projects\\VHR_CD\\repository\\code-v2",extend_name="VAE_noEXT_",result_array=self.outlier_result)
def RunPyodOutlier(classifiers, outlier_save_path, isExtract=True):
# Get data, n_bands=4
norm_img_path = "C:\\Users\\DELL\\Projects\\MLS_cluster\\image-v2-timeseries\\newest"
img = "4Band_Subtracted_20040514_20050427"
dataset = oi.open_tiff(norm_img_path, img)
H = dataset[1]
W = dataset[2]
n_bands = dataset[3]
org_data = art.tif2vec(dataset[0]) #NOTE: this step is really important
#NOTE: Normalize the scale of the orignialdata
org_data = org_data / org_data.max(axis=0)
#TODO: normalize the data?
if isExtract:
# extract out the changed area
select_path = "C:\\Users\\DELL\\Projects\\MLS_cluster\\image-v2-timeseries\\EXTRACT"
select_img = "SOMOCLU_20_20_HDBSCAN_cl_2_2004_2005_min_cluster_size_4_alg_best_"
simg = oi.open_tiff(select_path, select_img)
select = simg[0] #(2720000)
changePos = DataProcess.selectArea(select, n_bands, -1, isStack=True)
ns_changePos = DataProcess.selectArea(select,
n_bands,
-1,
isStack=False)
ns_nonChangePos = DataProcess.selectArea(select,
n_bands,
0,
isStack=False)
X_train = org_data[changePos].reshape(-1, n_bands)
print("shape of original data: ", org_data.shape)
print("shape of extracted data: ", X_train.shape)
# to save the final result
outlier_result = np.zeros_like(select.reshape(-1, 1))
score_result = np.empty_like(select.reshape(-1, 1))
else:
X_train = org_data.reshape(-1, n_bands)
print("shape of training data: ", X_train.shape)
for clf_name, clf in classifiers.items():
if not isExtract:
clf_name = "no_extract_" + clf_name
print("running " + clf_name + "...")
t0 = time.clock()
clf.fit(X_train)
usingTime = time.clock() - t0
# get the prediction labels and outlier scores of the training data
y_train_pred = clf.labels_ # binary labels (0: inliers, 1: outliers)
y_train_scores = clf.decision_scores_ # raw outlier scores
if isExtract:
# combine the extraction non-changed label&&scores and the algorithm result
outlier_result[ns_changePos] = y_train_pred
outlier_result[ns_nonChangePos] = 0
score_result[ns_changePos] = DataProcess.scaleNormalize(
y_train_scores, (0, 500)).reshape(-1, )
score_result[ns_nonChangePos] = 0
#save the outlier detection result as .tif and .shp file
else:
# combine the extraction non-changed label and the algorithm result
outlier_result = y_train_pred
score_result = DataProcess.scaleNormalize(y_train_scores,
(0, 500)).reshape(-1, )
print("the scale of the y_train_score is:", y_train_scores.min(),
y_train_scores.max())
print("the scale of the score_result is:", score_result.min(),
score_result.max())
DataProcess.int_to_csv(outlier_save_path, img, outlier_result,
clf_name + "_outliers")
GeoProcess.getSHP(norm_img_path, img, outlier_save_path,
clf_name + "_outliers", outlier_result)
#save the outlier scores as heatmap
DataProcess.saveHeatMap(score_result.reshape(H, W),
outlier_save_path + "\\" + clf_name)
print("save the information to txt file...")
with open(
outlier_save_path + '/' +
"Outlier Detection Algorithms Running Time.txt", 'a') as f:
f.write("detetion algorithm: " + clf_name +
"\ndetection using time: " + str(usingTime))
f.write("\n----------------------------------------------\n")