added slider for confidence

app.py

@@ -7,6 +7,8 @@ Using as reference:
 
 https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/DETR/DETR_panoptic_segmentation_minimal_example_(with_DetrFeatureExtractor).ipynb
 
+https://arxiv.org/abs/2005.12872
+
 Additions
 - add shown labels as strings
 - show only animal masks (ask an nlp model?)
@@ -178,7 +180,7 @@ def ade_palette():
                                            
 
 def predict_animal_mask(im,
-                        flag_high_confidence):
+                        gr_slider_confidence):
     image = Image.fromarray(im) # im: numpy array 3d: 480, 640, 3: to PIL Image
     image = image.resize((200,200)) #  PIL image # could I upsample output instead? better?
 
@@ -191,12 +193,9 @@ def predict_animal_mask(im,
 
     # keep only the masks with high confidence?--------------------------------
     # compute the prob per mask (i.e., class), excluding the "no-object" class (the last one)
-    if flag_high_confidence:
-        prob_per_query = outputs.logits.softmax(-1)[..., :-1].max(-1)[0] # why logits last dim 251?
-        # threshold the confidence
-        keep = prob_per_query > 0.85
-    else:
-        keep = torch.ones(outputs.logits.shape[0:2], dtype=torch.bool)
+    prob_per_query = outputs.logits.softmax(-1)[..., :-1].max(-1)[0] # why logits last dim 251?
+    # threshold the confidence
+    keep = prob_per_query > gr_slider_confidence/100.0
 
     # postprocess the mask (numpy arrays)
     label_per_pixel = torch.argmax(masks[keep].squeeze(),dim=0).detach().numpy() # from the masks per class, select the highest per pixel
@@ -217,18 +216,18 @@ model = DetrForSegmentation.from_pretrained('facebook/detr-resnet-50-panoptic')
 
 # gradio components -inputs
 gr_image_input = gr.inputs.Image()
-gr_checkbox_high_confidence = gr.inputs.Checkbox(False,
-                                                 label='disply high confidence only?')
+gr_slider_confidence = gr.inputs.Slider(0,100,5,85,
+                                        label='Set confidence threshold for masks')
 # gradio outputs
 gr_image_output = gr.outputs.Image() 
 
 ####################################################
 # Create user interface and launch
 gr.Interface(predict_animal_mask, 
-    inputs = [gr_image_input,gr_checkbox_high_confidence],
-    outputs = gr_image_output,
-    title = 'Animals* segmentation in images',
-    description = "An animal* segmentation image webapp using DETR (End-to-End Object Detection) model with ResNet-50 backbone").launch()
+                inputs = [gr_image_input,gr_slider_confidence],
+                outputs = gr_image_output,
+                title = 'Image segmentation with varying confidence',
+                description = "An image segmentation webapp using DETR (End-to-End Object Detection) model with ResNet-50 backbone").launch()
 
 
 ####################################