Update app.py

app.py

@@ -107,44 +107,40 @@ def server(input, output, session: Session):
             id = "image_" + str(i)
             opacity = ui.input_slider(id, "Opacity", 0, 1.0, 0.5)
             
-            # def create_plot_function(name, opacity):
-            #     @render.plot
-            #     def plot_predicitons():
-            #         fig, ax = plt.subplots()
-        
-            #         ax = plt.Axes(fig, [0., 0., 1., 1.])
-            #         ax.set_axis_off()
-            #         fig.add_axes(ax)
-                    
-            #         v = Visualizer(r["image"][:, :, ::-1],
-            #                        scale=1, instance_mode=ColorMode.SEGMENTATION, font_size_scale=1)
-        
-            #         colours = []
-            #         for cls in r["instances"].pred_classes:
-            #             if cls == 0:
-            #                 colours.append([1,0,0])
-            #             elif cls == 1:
-            #                 colours.append([1,1,0])
-            #             elif cls == 2:
-            #                 colours.append([0,0,0])
-        
-            #         out = v.overlay_instances(masks = r["instances"].pred_masks.to("cpu"),
-            #                                   assigned_colors = colours, 
-            #                                   alpha = opacity)
-            #         ax.imshow(cv2.cvtColor(out.get_image()[:, :, ::-1], cv2.COLOR_BGR2RGB))
-                    
-            #         return fig
+            @render.plot
+            def plot_predicitons():
+                fig, ax = plt.subplots()
+    
+                ax = plt.Axes(fig, [0., 0., 1., 1.])
+                ax.set_axis_off()
+                fig.add_axes(ax)
                 
-            #     plot_prediction.__name__ = name
-            #     return plot_prediction
-            
-            # plot_function = create_plot_function("plot_" + str(i), input[id]())
+                v = Visualizer(r["image"][:, :, ::-1],
+                               scale=1, instance_mode=ColorMode.SEGMENTATION, font_size_scale=1)
+    
+                colours = []
+                for cls in r["instances"].pred_classes:
+                    if cls == 0:
+                        colours.append([1,0,0])
+                    elif cls == 1:
+                        colours.append([1,1,0])
+                    elif cls == 2:
+                        colours.append([0,0,0])
+    
+                out = v.overlay_instances(masks = r["instances"].pred_masks.to("cpu"),
+                                          assigned_colors = colours, 
+                                          alpha = opacity)
+                ax.imshow(cv2.cvtColor(out.get_image()[:, :, ::-1], cv2.COLOR_BGR2RGB))
+
+                return fig
+                
+            exec("plot_" + str(i) + " = plot_prediction")
                 
             output.append(
                 ui.div(
                     ui.row(
                         ui.column(4, ui.img(src=f"data:image/png;base64,{r['image_base64']}")),
-                        # ui.column(4, ui.output_plot(plot_function.__name__)),
+                        ui.column(4, ui.output_plot("plot_" + str(i))),
                     ),
                     opacity,
                     ui.h5(r['filename'], style="margin-top: 15px;"),