Update app.py

app.py

@@ -31,14 +31,15 @@ cfg.MODEL.WEIGHTS = "model_weights/chatswood_buildings_poc.pth"
 cfg.MODEL.ROI_HEADS.NUM_CLASSES = 8
 predictor = DefaultPredictor(cfg)
 
-def segment_buildings(inp):
+def segment_buildings(im):
 
-    im = cv2.imread(inp)
+    im = np.array(im)
     outputs = predictor(im)
     v = Visualizer(im[:, :, ::-1],
                    scale=0.5,
                    instance_mode=ColorMode.IMAGE_BW
     )
+    print(len(outputs["instances"])," buildings detected.")
     out = v.draw_instance_predictions(outputs["instances"].to("cpu"))
     
     return Image.fromarray(out.get_image()[:, :, ::-1])
@@ -49,16 +50,14 @@ gr_slider_confidence = gr.inputs.Slider(0,1,.1,.7,
                                         label='Set confidence threshold % for masks')
 """
 # gradio outputs
-# inputs = gr.inputs.Image(type="pil", label="Input Image")
-# outputs = gr.outputs.Image(type="pil", label="Output Image")
+inputs = gr.inputs.Image(type="pil", label="Input Image")
+outputs = gr.outputs.Image(type="pil", label="Output Image")
 
 title = "Building Segmentation"
 description = "An instance segmentation demo for identifying boundaries of buildings in aerial images using DETR (End-to-End Object Detection) model with MaskRCNN-101 backbone"
 
 # Create user interface and launch
 gr.Interface(segment_buildings, 
-                inputs = "image",
-                outputs = "image",
-                title = title,
-                enable_queue = True,
-                description = description).launch()
+                inputs = inputs,
+                outputs = outputs,
+                description = description).launch(debug=True)