update

app.py

@@ -200,7 +200,7 @@ def colorize_mask(mask: np.ndarray, num_colors: int = 512) -> np.ndarray:
     return palette_arr[color_idx]
 
 # @spaces.GPU
-def segment_with_choice(use_box_choice, annot_value):
+def segment_with_choice(use_box_choice, annot_value, overlay_alpha):
     """Segmentation handler - supports bounding box, returns colorized overlay and original mask path"""
     if annot_value is None or len(annot_value) < 1:
         print("❌ No annotation input")
@@ -257,7 +257,7 @@ def segment_with_choice(use_box_choice, annot_value):
         return Image.new("RGB", mask.shape[::-1], (255, 0, 0)), None
 
     overlay = img_np.copy()
-    alpha = 0.5
+    alpha = float(np.clip(overlay_alpha, 0.0, 1.0))
 
     for inst_id in np.unique(inst_mask):
         if inst_id == 0:
@@ -279,7 +279,7 @@ def segment_with_choice(use_box_choice, annot_value):
 
 
 # @spaces.GPU
-def count_cells_handler(use_box_choice, annot_value):
+def count_cells_handler(use_box_choice, annot_value, overlay_alpha):
     """Counting handler - supports bounding box, returns only density map"""
     if annot_value is None or len(annot_value) < 1:
         return None, "⚠️ Please provide an image."
@@ -339,7 +339,7 @@ def count_cells_handler(use_box_choice, annot_value):
             density_normalized = (density_normalized - density_normalized.min()) / (density_normalized.max() - density_normalized.min())
         
         cmap = cm.get_cmap("jet")
-        alpha = 0.3
+        alpha = float(np.clip(overlay_alpha, 0.0, 1.0))
         density_colored = cmap(density_normalized)[:, :, :3]  # RGB only, ignore alpha
         
         overlay = img_np.copy()
@@ -357,7 +357,7 @@ def count_cells_handler(use_box_choice, annot_value):
 
 
         result_text = f"✅ Detected {round(count)} objects"
-        if use_box_choice == "Yes" and box:
+        if use_box_choice == "Yes" and box_array:
             result_text += f"\n📦 Using bounding box: {box_array}"
         
 
@@ -496,7 +496,7 @@ def extract_first_frame(tif_dir):
         return valid_tif_files[0]
     return None
 
-def create_tracking_visualization(tif_dir, output_dir, valid_tif_files):
+def create_tracking_visualization(tif_dir, output_dir, valid_tif_files, overlay_alpha=0.3):
     """
     Create an animated GIF/video showing tracked objects with consistent colors
     
@@ -534,7 +534,7 @@ def create_tracking_visualization(tif_dir, output_dir, valid_tif_files):
 
     
     frames = []
-    alpha = 0.3  # Transparency for overlay
+    alpha = float(np.clip(overlay_alpha, 0.0, 1.0))  # Transparency for overlay
     
     # Process each frame
     num_frames = min(len(valid_tif_files), len(mask_files))
@@ -664,7 +664,7 @@ def create_tracking_visualization(tif_dir, output_dir, valid_tif_files):
             return valid_tif_files[0]
 
 # @spaces.GPU
-def track_video_handler(use_box_choice, first_frame_annot, zip_file_obj):
+def track_video_handler(use_box_choice, first_frame_annot, zip_file_obj, overlay_alpha):
     """
     Tracking handler - processes a ZIP of TIF frames, supports bounding box, returns visualization and results ZIP
     
@@ -765,7 +765,8 @@ def track_video_handler(use_box_choice, first_frame_annot, zip_file_obj):
             tracking_video = create_tracking_visualization(
                 tif_dir, 
                 output_temp_dir, 
-                valid_tif_files
+                valid_tif_files,
+                overlay_alpha=overlay_alpha
             )
         except Exception as e:
             print(f"⚠️  Failed to create visualization: {e}")
@@ -799,7 +800,7 @@ def track_video_handler(use_box_choice, first_frame_annot, zip_file_obj):
             - README.txt (Results description)
             """
 
-        if use_box_choice == "Yes" and box:
+        if use_box_choice == "Yes" and box_array:
             result_text += f"\n📦 Using bounding box: {box_array}"
 
         print(f"\n✅ Tracking completed")
@@ -930,6 +931,13 @@ with gr.Blocks(
                             value="No",
                             label="🔲 Specify Bounding Box?"
                         )
+                    seg_alpha_slider = gr.Slider(
+                        minimum=0.0,
+                        maximum=1.0,
+                        step=0.05,
+                        value=0.5,
+                        label="🎚️ Overlay Opacity"
+                    )
                     with gr.Row():
                         run_seg_btn = gr.Button("▶️ Run Segmentation", variant="primary", size="lg")
                         clear_btn = gr.Button("🔄 Clear Selection", variant="secondary")
@@ -983,7 +991,7 @@ with gr.Blocks(
             # click event for segmentation
             run_seg_btn.click(
                 fn=segment_with_choice,
-                inputs=[use_box_radio, annotator],
+                inputs=[use_box_radio, annotator, seg_alpha_slider],
                 outputs=[seg_output, download_mask_btn]
             )
 
@@ -1105,7 +1113,14 @@ with gr.Blocks(
                             value="No",
                             label="🔲 Specify Bounding Box?"
                         )
-                    
+                    count_alpha_slider = gr.Slider(
+                        minimum=0.0,
+                        maximum=1.0,
+                        step=0.05,
+                        value=0.3,
+                        label="🎚️ Heatmap Opacity"
+                    )
+
                     with gr.Row():
                         count_btn = gr.Button("▶️ Run Counting", variant="primary", size="lg")
                         clear_btn = gr.Button("🔄 Clear Selection", variant="secondary")
@@ -1204,7 +1219,7 @@ with gr.Blocks(
             # Run counting
             count_btn.click(
                 fn=count_cells_handler,
-                inputs=[count_use_box_radio, count_annotator],
+                inputs=[count_use_box_radio, count_annotator, count_alpha_slider],
                 outputs=[count_output, download_density_btn, count_status]
             )
 
@@ -1292,7 +1307,14 @@ with gr.Blocks(
                             value="No",
                             label="🔲 Specify Bounding Box?"
                         )
-                    
+                    track_alpha_slider = gr.Slider(
+                        minimum=0.0,
+                        maximum=1.0,
+                        step=0.05,
+                        value=0.3,
+                        label="🎚️ Overlay Opacity"
+                    )
+
                     with gr.Row():
                         track_btn = gr.Button("▶️ Run Tracking", variant="primary", size="lg")
                         clear_btn = gr.Button("🔄 Clear Selection", variant="secondary")
@@ -1526,7 +1548,7 @@ with gr.Blocks(
             # Run tracking
             track_btn.click(
                 fn=track_video_handler,
-                inputs=[track_use_box_radio, track_first_frame_annotator, track_zip_upload],
+                inputs=[track_use_box_radio, track_first_frame_annotator, track_zip_upload, track_alpha_slider],
                 outputs=[track_download, track_output, track_download, track_first_frame_preview]
             )
 
@@ -1571,14 +1593,16 @@ with gr.Blocks(
     gr.Markdown(
         """
         ---
-        ### 💡 Technical Details
-
-        **MicroscopyMatching** - A general-purpose microscopy image analysis toolkit based on Stable Diffusion
-        
         ### 📒 Note:
 
         This project is currently available with usage limits for research trial use and feedback collection. We plan to release a free public version in the future. We are actively improving the toolkit and greatly appreciate your feedback!
 
+
+
+        ### 💡 Technical Details
+
+        **MicroscopyMatching** - A general-purpose microscopy image analysis toolkit based on Stable Diffusion
+        
         """
     )