Update app.py

app.py

@@ -237,10 +237,17 @@ def filter_mask_by_size(masks,minimum_pixels):
     renumbered_masks = np.zeros_like(filtered_masks)
     for new_id, old_id in enumerate(unique_ids, start=1):
         renumbered_masks[filtered_masks == old_id] = new_id
+
     
-    return renumbered_masks, removed_count
+    return renumbered_masks, removed_count, mean_cell_size
     
-        
+
+def rec_min_size(masks):
+    num_cells = len(np.unique(masks)) - 1
+    if num_cells <= 0:
+        return 0
+    mean_cell_size = np.count_nonzero(masks) / num_cells
+    return int(round(mean_cell_size))
 
 @spaces.GPU
 def run_segmentation_editor(editor_data, model_choice, min_cell_size):
@@ -277,6 +284,11 @@ def run_segmentation_editor(editor_data, model_choice, min_cell_size):
         # Run Cellpose segmentation
         masks, flows, styles = model.eval(processed_image_np, diameter=None, channels=[0, 0])
 
+
+        # Minimum size filtering
+
+        recommend_min = recommend_min_pixels(masks) 
+        
         if min_cell_size > 0:
             masks, removed_count = filter_mask_by_size(masks, min_cell_size)
             filter_msg = f"Removed {removed_count} small objects (< {min_cell_size} pixels).\n"
@@ -313,6 +325,7 @@ def run_segmentation_editor(editor_data, model_choice, min_cell_size):
             pack_array(masks),
             pack_array(processed_image_np),
             confluency,
+            gr.update(value = recommend_min)
         )
 
     except Exception as e:
@@ -371,6 +384,8 @@ with gr.Blocks(
     masks_state = gr.State(value=None)
     image_state = gr.State(value=None)
 
+
+    
     with gr.Tab("Image Editor (Draw Selection)"):
         gr.Markdown("### Draw selection and run segmentation")
 
@@ -434,7 +449,7 @@ with gr.Blocks(
         segment_btn1.click(
             fn=run_segmentation_editor,
             inputs=[image_editor, model_dropdown1, min_size_slider1],
-            outputs=[cell_count_output1, overlay_output1, info_output1, viability_section1, masks_state, image_state, confluency_output1]
+            outputs=[cell_count_output1, overlay_output1, info_output1, viability_section1, masks_state, image_state, confluency_output1, min_size_slider1]
         ).then(  # Chain the initial viability assessment after segmentation
             fn=update_viability_realtime,
             inputs=[blue_threshold1, masks_state, image_state],  # Pass stored state as inputs