Update app.py

app.py

@@ -92,15 +92,6 @@ class Detectron2Monitor():
         with open("monitors_dict.pkl", 'rb') as f:
             monitors_dict = load(f)
         return monitors_dict
-        # monitors_dict = {}
-        # for class_name in self.label_list:
-        #     if class_name == "train" or class_name == "OOD":
-        #         continue
-        #     monitor_path = f"monitors/{class_name}/monitor_for_clustering_parameter" + "_tau_" + str(tau) + ".pkl"
-        #     with open(monitor_path, 'rb') as f:
-        #         monitor = load(f)
-        #     monitors_dict[class_name] = monitor
-        # return monitors_dict
     
     def _fasterrcnn_reshape_transform(self, x):
         target_size = x['p6'].size()[-2 : ]
@@ -144,14 +135,13 @@ class Detectron2Monitor():
                         reshape_transform=self._fasterrcnn_reshape_transform)
         grayscale_cam = cam(input_image_dict, targets)
         cam = self._postprocess_cam(grayscale_cam, input_image_dict[0]["width"], input_image_dict[0]["height"])
-        # plt.rcParams["figure.figsize"] = (30,10)
-        # plt.imshow(img_detection[..., ::-1], interpolation='none')
-        # plt.imshow(cam, cmap='jet', alpha=0.5)
-        # plt.axis("off")
-        # img_buff = io.BytesIO()
-        # plt.savefig(img_buff, format='png', bbox_inches='tight', pad_inches=0)
-        # img_cam = Image.open(img_buff)
-        img_cam = image
+        plt.rcParams["figure.figsize"] = (30,10)
+        plt.imshow(img_detection[..., ::-1], interpolation='none')
+        plt.imshow(cam, cmap='jet', alpha=0.5)
+        plt.axis("off")
+        img_buff = io.BytesIO()
+        plt.savefig(img_buff, format='png', bbox_inches='tight', pad_inches=0)
+        img_cam = Image.open(img_buff)
         image_dict = {}
         image_dict["image"] = image
         image_dict["cam"] = img_cam