Update app.py

app.py

@@ -11,6 +11,7 @@ from PIL import Image
 import numpy as np
 from detectron2.data import MetadataCatalog, DatasetCatalog
 from detectron2.utils.visualizer import Visualizer
+from collections import Counter
 
 # Define configuration parameters
 output_directories = {
@@ -42,6 +43,9 @@ def run_instance_segmentation(im, predictor, num_classes):
         # Calculate the total area of Inorganic Material masks
         total_inorganic_area = np.sum(inorganic_material_masks)
 
+        # Contar la cantidad de máscaras por categoría
+        mask_counts = Counter(classes)
+
     elif num_classes == 4:
             
         # Create masks for Nest and Inorganic Material
@@ -60,19 +64,27 @@ def run_instance_segmentation(im, predictor, num_classes):
         rope_cloth_area = np.sum(rope_cloth_masks)
         total_inorganic_area = np.sum([plastic_area, fishing_net_area, rope_cloth_area])
 
-    return total_nest_area, total_inorganic_area
+        # Contar la cantidad de máscaras por categoría
+        mask_counts = Counter(classes)
+
+    return total_nest_area, total_inorganic_area, mask_counts
 
 def click_instance_segmentation(image, model_selection, predictor):
     im = np.array(image)[:, :, ::-1]
 
     if st.button("Run Instance Segmentation"):
         num_classes = 2 if model_selection == "2 Classes" else 4
-        total_nest_area, total_inorganic_area = run_instance_segmentation(im, predictor, num_classes)
+        total_nest_area, total_inorganic_area, mask_counts = run_instance_segmentation(im, predictor, num_classes)
 
         # Calculate the percentage of inorganic material within the Nest
         percentage_inorganic_in_nest = (total_inorganic_area / total_nest_area) * 100
 
         st.write(f"Percentage of Inorganic Material in Nest: {percentage_inorganic_in_nest:.2f}%")
+
+        # Mostrar la cantidad de máscaras por categoría
+        st.write("Mask Counts by Category:")
+        for category, count in mask_counts.items():
+            st.write(f"Category {category}: {count} masks")
     
 
 def app():