fix imports

Auto_Encoder_detector.py

@@ -1,18 +1,18 @@
-from pycocotools.coco import COCO
-import cv2
 import json
-import numpy as np
 import os
+
+import cv2
+import numpy as np
 import tensorflow as tf
+from pycocotools.coco import COCO
 from tqdm import tqdm
 
 tf.keras.backend.clear_session()
 from tensorflow.keras.layers import Conv2D, Conv2DTranspose, \
     Dense, Reshape, InputLayer, Flatten
 
 from alibi_detect.od import OutlierAE
-from alibi_detect.utils.saving import save_detector, load_detector
-from crop_utils import batch_crop_images, view_annotation
+from crop_utils import batch_crop_images
 
 from anomaly_detector import create_destination
 
@@ -122,11 +122,6 @@ def detect_anomalies_auto_encoder(annotation_path, images_path, intermediate_rlt
             od.fit(X_train,
                    epochs=30)
 
-            # save the trained outlier detector
-            save_detector(od, model_path)
-
-        od = load_detector(model_path)
-
         resized_test = np.reshape(X_train_ls, (len(X_train_ls), 32, 32, 3))
         res = od.predict(resized_test)
 

Manually_based_detector.py

@@ -11,8 +11,8 @@
 from sklearn.neighbors import LocalOutlierFactor
 from sklearn.preprocessing import StandardScaler
 
-#from anomaly_analysis.anomaly_feature_extraction import get_roughness, get_histograms, \
-    #get_obj_colors, get_proportion
+from anomaly_analysis.anomaly_feature_extraction import get_roughness, get_histograms, \
+    get_obj_colors, get_proportion
 
 
 def combine_feature_dataset(annotation_file, img_folder, intermediate_rlt_path, cat_name=[]):

hog_based_detector.py

@@ -8,7 +8,7 @@
 from sklearn.ensemble import IsolationForest
 from sklearn.neighbors import LocalOutlierFactor
 
-#from anomaly_analysis.anomaly_feature_extraction import get_HOG
+from anomaly_analysis.anomaly_feature_extraction import get_HOG
 from anomaly_detector import create_destination
 from crop_utils import batch_crop_images