gradcam working on cv image

app.py

@@ -197,7 +197,7 @@ def explain(img, model):
 
             state.write(f"Populating Gradcam++ for lesion #{i}...")
             st.subheader("Gradcam++")
-            fig = plot_gradcam(model=MODEL, file=FILE, instance=i, fig_h=1600, fig_w=1200, fig_dpi=200, th=TH, layer="backbone.bottom_up.res5.2.conv3")
+            fig = plot_gradcam(model=MODEL, img=img, instance=i, fig_h=1600, fig_w=1200, fig_dpi=200, th=TH, layer="backbone.bottom_up.res5.2.conv3")
             st.pyplot(fig)
 
     state.write("All done...")
@@ -207,7 +207,7 @@ MODEL = "./models/model.pth"
 PCA_MODEL = "./models/pca.pkl"
 FEATURES_DATABASE = "./assets/features/features.json"
 
-st.header("Explainable oral lesion detection")
+st.header("Explainable Oral Lesion Detection")
 st.markdown("""Demo for the paper [Explainable diagnosis of oral cancer via deep learning and case-based reasoning](https://mlpi.ing.unipi.it/doctoralai/)
 
 Upload an image using the form below and click on "Process"

plots/gradcam/detectron2_gradcam.py

@@ -86,7 +86,7 @@ class Detectron2GradCAM():
       checkpointer = DetectionCheckpointer(model)
       checkpointer.load(self.cfg.MODEL.WEIGHTS)
 
-      image = read_image(img, format="BGR")
+      image = img
       input_image_dict = self._get_input_dict(image)
 
       if grad_cam_type == "GradCAM":

plots/plot_gradcam.py

@@ -31,7 +31,7 @@ def plot_gradcam(**kwargs):
 
 
     cam_extractor = Detectron2GradCAM(config_file, cfg_list)
-    image_dict, cam_orig = cam_extractor.get_cam(img=kwargs.file, target_instance=kwargs.instance, layer_name=kwargs.layer, grad_cam_type="GradCAM++")
+    image_dict, cam_orig = cam_extractor.get_cam(img=kwargs.img, target_instance=kwargs.instance, layer_name=kwargs.layer, grad_cam_type="GradCAM++")
 
     with torch.no_grad():
         fig = plt.figure(figsize=(kwargs.fig_h/kwargs.fig_dpi, kwargs.fig_w/kwargs.fig_dpi), dpi=kwargs.fig_dpi)