debugging: xai output distortion

app.py

@@ -141,7 +141,7 @@ def classify_image(input_image, model_name):
         from inference_resnet import inference_resnet_finer
         model,n_classes= get_model(model_name)
         result = inference_resnet_finer(input_image,model,size=600,n_classes=n_classes)
-        return result 
+        return result
     elif 'Mummified 170' ==model_name:
         from inference_resnet import inference_resnet_finer
         model, n_classes= get_model(model_name)
@@ -169,7 +169,7 @@ def get_embeddings(input_image,model_name):
         from inference_resnet import inference_resnet_embedding
         model,n_classes= get_model(model_name)
         result = inference_resnet_embedding(input_image,model,size=600,n_classes=n_classes)
-        return result 
+        return result
     elif 'Mummified 170' ==model_name:
         from inference_resnet import inference_resnet_embedding
         model, n_classes= get_model(model_name)

explanations.py

@@ -29,13 +29,8 @@ def preprocess_image(image, output_size=(300, 300)):
     return image_resized
 
 def show(img, output_size,p=False, **kwargs):
-    img = np.array(img, dtype=np.float32)
-    h, w = img.shape[:2]
-    print(h,w)
 
-    img = preprocess_image(img, output_size=(output_size,output_size))
-    h, w = img.shape[:2]
-    print(h,w)
+    #img = preprocess_image(img, output_size=(output_size,output_size))
 
     # check if channel first
     if img.shape[0] == 1:
@@ -53,6 +48,7 @@ def show(img, output_size,p=False, **kwargs):
     # check if clip percentile
     if p is not False:
         img = np.clip(img, np.percentile(img, p), np.percentile(img, 100-p))
+    img = preprocess_image(img, output_size=(output_size,output_size))
     plt.imshow(img, **kwargs)
     plt.axis('off')