updated app

app.py

@@ -58,7 +58,6 @@ from captum.attr._utils.visualization import visualize_image_attr
 device = torch.device('cpu')
 opt = get_args(is_train=False)
 
-""" vocab / character number configuration """
 if opt.sensitive:
     opt.character = string.printable[:-6]  # same with ASTER setting (use 94 char).
 
@@ -125,7 +124,6 @@ if modelName=="vitstr":
     model = torch.nn.DataParallel(model_obj).to(device)
     modelCopy = copy.deepcopy(model)
 
-    """ evaluation """
     scoring_singlechar = STRScore(opt=opt, converter=converter, device=device, enableSingleCharAttrAve=True)
     super_pixel_model_singlechar = torch.nn.Sequential(
         # super_pixler,
@@ -193,7 +191,25 @@ if opt.blackbg:
 # x = st.slider('Select a value')
 # st.write(x, 'squared is', x * x)
 
-labels = st.text_input('You need to put the text of the image here...', 'BALLYS')
+### Acquire pixelwise attributions and replace them with ranked numbers averaged
+### across segmentation with the largest contribution having the largest number
+### and the smallest set to 1, which is the minimum number.
+### attr - original attribution
+### segm - image segmentations
+def rankedAttributionsBySegm(attr, segm):
+    aveSegmentations, sortedDict = averageSegmentsOut(attr[0,0], segm)
+    totalSegm = len(sortedDict.keys()) # total segmentations
+    sortedKeys = [k for k, v in sorted(sortedDict.items(), key=lambda item: item[1])]
+    sortedKeys = sortedKeys[::-1] ### A list that should contain largest to smallest score
+    currentRank = totalSegm
+    rankedSegmImg = torch.clone(attr)
+    for totalSegToHide in range(0, len(sortedKeys)):
+        currentSegmentToHide = sortedKeys[totalSegToHide]
+        rankedSegmImg[0,0][segm == currentSegmentToHide] = currentRank
+        currentRank -= 1
+    return rankedSegmImg
+
+labels = st.text_input('You need to put the text of the image here (e.g. BALLYS)')
 
 image = Image.open('demo_image/demo_ballys.jpg') #Brand logo image (optional)
 image2 = Image.open('demo_image/demo_ronaldo.jpg') #Brand logo image (optional)