Update app.py

app.py

@@ -119,16 +119,14 @@ def crop_image_with_coords(image_np, coords):
     x_min, y_min, x_max, y_max = coords
     return image_np[y_min:y_max+1, x_min:x_max+1]
 
-def classify_cells_by_color(image_np, masks, hue_min, hue_max, sat_min, val_min, val_max):
+def classify_cells_by_blueness(image_np, masks, blue_threshold):
     """
-    Classify cells as dead (blue) or alive based on trypan blue color threshold
+    Classify cells as dead (blue) or alive based on single blueness metric
     
     Args:
         image_np: RGB image array
         masks: Cellpose segmentation masks
-        hue_min, hue_max: Hue range for blue color (0-179 in OpenCV)
-        sat_min: Minimum saturation (0-255)
-        val_min, val_max: Value (brightness) range (0-255)
+        blue_threshold: Single threshold value (0-100) for blueness detection
     
     Returns:
         dead_count, alive_count, colored_overlay
@@ -136,14 +134,20 @@ def classify_cells_by_color(image_np, masks, hue_min, hue_max, sat_min, val_min,
     # Convert RGB to HSV
     hsv = cv2.cvtColor(image_np, cv2.COLOR_RGB2HSV)
     
-    # Create color mask for trypan blue
-    if hue_min <= hue_max:
-        color_mask = cv2.inRange(hsv, (hue_min, sat_min, val_min), (hue_max, 255, val_max))
-    else:
-        # Handle hue wrap-around (e.g., 170-10 for red-blue boundary)
-        mask1 = cv2.inRange(hsv, (hue_min, sat_min, val_min), (179, 255, val_max))
-        mask2 = cv2.inRange(hsv, (0, sat_min, val_min), (hue_max, 255, val_max))
-        color_mask = cv2.bitwise_or(mask1, mask2)
+    # Calculate blueness index for each pixel
+    hue = hsv[:, :, 0].astype(np.float32)
+    saturation = hsv[:, :, 1].astype(np.float32)
+    
+    # Hue score: peaks around 115 (blue in HSV), drops off towards edges
+    # Handle hue wrap-around for blue detection (100-130 range)
+    hue_distance = np.minimum(np.abs(hue - 115), 180 - np.abs(hue - 115))
+    hue_score = np.maximum(0, 1 - hue_distance / 65)  # 65 gives good blue range
+    
+    # Combine hue proximity with saturation intensity
+    blueness = hue_score * (saturation / 255.0)
+    
+    # Convert threshold from 0-100 to 0-1 scale
+    threshold = blue_threshold / 100.0
     
     # Get unique cell IDs (excluding background)
     cell_ids = np.unique(masks)
@@ -157,12 +161,11 @@ def classify_cells_by_color(image_np, masks, hue_min, hue_max, sat_min, val_min,
         # Get mask for this specific cell
         cell_mask = (masks == cell_id)
         
-        # Calculate overlap with color mask
-        overlap = np.sum(color_mask[cell_mask] > 0)
-        total_pixels = np.sum(cell_mask)
+        # Calculate average blueness for this cell
+        cell_blueness = np.mean(blueness[cell_mask])
         
-        # If >30% of cell pixels are blue, classify as dead
-        if overlap / total_pixels > 0.3:
+        # Classify based on threshold
+        if cell_blueness > threshold:
             dead_cells.append(cell_id)
         else:
             alive_cells.append(cell_id)
@@ -187,9 +190,9 @@ def classify_cells_by_color(image_np, masks, hue_min, hue_max, sat_min, val_min,
     return len(dead_cells), len(alive_cells), overlay
 
 @spaces.GPU
-def segment_and_count_viability(editor_data, model_choice, hue_min, hue_max, sat_min, val_min, val_max, crop_coords=None):
+def segment_and_count_viability(editor_data, model_choice, blue_threshold, crop_coords=None):
     """
-    Segment cells and analyze viability using trypan blue color threshold
+    Segment cells and analyze viability using single blueness threshold
     """
     try:
         # Debug info
@@ -239,9 +242,9 @@ def segment_and_count_viability(editor_data, model_choice, hue_min, hue_max, sat
         # Run Cellpose on the selected region
         masks, flows, styles = model.eval(region_np, diameter=None, channels=[0, 0])
         
-        # Classify cells by color
-        dead_count, alive_count, viability_overlay = classify_cells_by_color(
-            region_np, masks, hue_min, hue_max, sat_min, val_min, val_max
+        # Classify cells by blueness
+        dead_count, alive_count, viability_overlay = classify_cells_by_blueness(
+            region_np, masks, blue_threshold
         )
         
         total_count = dead_count + alive_count
@@ -251,7 +254,7 @@ def segment_and_count_viability(editor_data, model_choice, hue_min, hue_max, sat
         overlay_image = Image.fromarray(viability_overlay)
         
         # Create info message
-        info_msg = f"Total cells: {total_count}\nAlive cells: {alive_count}\nDead cells: {dead_count}\nViability: {viability_percent:.1f}%"
+        info_msg = f"Total cells: {total_count}\nAlive cells: {alive_count}\nDead cells: {dead_count}\nViability: {viability_percent:.1f}%\nBlue threshold: {blue_threshold}%"
         if region_coords:
             info_msg += f"\nProcessed region: {region_coords[0]},{region_coords[1]} to {region_coords[2]},{region_coords[3]}"
         
@@ -261,7 +264,7 @@ def segment_and_count_viability(editor_data, model_choice, hue_min, hue_max, sat
         return 0, 0, 0, 0.0, None, f"Error occurred: {str(e)}\nDebug info:\n{debug_info if 'debug_info' in locals() else 'No debug info'}"
 
 @spaces.GPU
-def segment_with_coords_viability(image, model_choice, hue_min, hue_max, sat_min, val_min, val_max, crop_coords):
+def segment_with_coords_viability(image, model_choice, blue_threshold, crop_coords):
     """Segment using regular image input with manual coordinate specification and viability analysis"""
     if image is None:
         return 0, 0, 0, 0.0, None, "No image provided"
@@ -306,9 +309,9 @@ def segment_with_coords_viability(image, model_choice, hue_min, hue_max, sat_min
         # Run Cellpose
         masks, flows, styles = model.eval(image_np, diameter=None, channels=[0, 0])
         
-        # Classify cells by color
-        dead_count, alive_count, viability_overlay = classify_cells_by_color(
-            image_np, masks, hue_min, hue_max, sat_min, val_min, val_max
+        # Classify cells by blueness
+        dead_count, alive_count, viability_overlay = classify_cells_by_blueness(
+            image_np, masks, blue_threshold
         )
         
         total_count = dead_count + alive_count
@@ -316,7 +319,7 @@ def segment_with_coords_viability(image, model_choice, hue_min, hue_max, sat_min
         
         overlay_image = Image.fromarray(viability_overlay)
         
-        info_msg = f"Total cells: {total_count}\nAlive cells: {alive_count}\nDead cells: {dead_count}\nViability: {viability_percent:.1f}%"
+        info_msg = f"Total cells: {total_count}\nAlive cells: {alive_count}\nDead cells: {dead_count}\nViability: {viability_percent:.1f}%\nBlue threshold: {blue_threshold}%"
         if crop_coords:
             info_msg += f"\nProcessed with coordinates: {crop_coords}"
         
@@ -348,16 +351,13 @@ with gr.Blocks(title="Cell Viability Counter with Trypan Blue Analysis") as demo
                     value="Hemocytometer Model"
                 )
                 
-                # Color threshold controls
-                gr.Markdown("### Trypan Blue Color Threshold")
-                with gr.Row():
-                    hue_min1 = gr.Slider(0, 179, value=100, step=1, label="Hue Min (Blue)")
-                    hue_max1 = gr.Slider(0, 179, value=130, step=1, label="Hue Max (Blue)")
-                
-                with gr.Row():
-                    sat_min1 = gr.Slider(0, 255, value=50, step=1, label="Saturation Min")
-                    val_min1 = gr.Slider(0, 255, value=50, step=1, label="Value Min")
-                    val_max1 = gr.Slider(0, 255, value=255, step=1, label="Value Max")
+                # Single blue threshold control
+                gr.Markdown("### Trypan Blue Threshold")
+                blue_threshold1 = gr.Slider(
+                    0, 100, value=25, step=1, 
+                    label="Blue Threshold (%)",
+                    info="Higher values = more selective for blue cells"
+                )
                 
                 segment_btn1 = gr.Button("Analyze Viability", variant="primary")
                 
@@ -373,7 +373,7 @@ with gr.Blocks(title="Cell Viability Counter with Trypan Blue Analysis") as demo
         
         segment_btn1.click(
             fn=segment_and_count_viability,
-            inputs=[image_editor, model_dropdown1, hue_min1, hue_max1, sat_min1, val_min1, val_max1],
+            inputs=[image_editor, model_dropdown1, blue_threshold1],
             outputs=[total_count_output1, alive_count_output1, dead_count_output1, viability_output1, overlay_output1, info_output1]
         )
     
@@ -396,16 +396,13 @@ with gr.Blocks(title="Cell Viability Counter with Trypan Blue Analysis") as demo
                     info="Format: x_min,y_min,x_max,y_max"
                 )
                 
-                # Color threshold controls
-                gr.Markdown("### Trypan Blue Color Threshold")
-                with gr.Row():
-                    hue_min2 = gr.Slider(0, 179, value=100, step=1, label="Hue Min (Blue)")
-                    hue_max2 = gr.Slider(0, 179, value=130, step=1, label="Hue Max (Blue)")
-                
-                with gr.Row():
-                    sat_min2 = gr.Slider(0, 255, value=50, step=1, label="Saturation Min")
-                    val_min2 = gr.Slider(0, 255, value=50, step=1, label="Value Min")
-                    val_max2 = gr.Slider(0, 255, value=255, step=1, label="Value Max")
+                # Single blue threshold control
+                gr.Markdown("### Trypan Blue Threshold")
+                blue_threshold2 = gr.Slider(
+                    0, 100, value=25, step=1, 
+                    label="Blue Threshold (%)",
+                    info="Higher values = more selective for blue cells"
+                )
                 
                 segment_btn2 = gr.Button("Analyze Viability", variant="primary")
                 
@@ -421,7 +418,7 @@ with gr.Blocks(title="Cell Viability Counter with Trypan Blue Analysis") as demo
         
         segment_btn2.click(
             fn=segment_with_coords_viability,
-            inputs=[image_input, model_dropdown2, hue_min2, hue_max2, sat_min2, val_min2, val_max2, coord_input],
+            inputs=[image_input, model_dropdown2, blue_threshold2, coord_input],
             outputs=[total_count_output2, alive_count_output2, dead_count_output2, viability_output2, overlay_output2, info_output2]
         )
     
@@ -434,14 +431,17 @@ with gr.Blocks(title="Cell Viability Counter with Trypan Blue Analysis") as demo
         - Upload a trypan blue stained microscopy image
         - Dead cells should appear blue, alive cells should be unstained (clear)
         
-        ### 2. Color Threshold Adjustment
-        - **Hue Min/Max**: Adjust to capture the blue color range of trypan blue (typically 100-130)
-        - **Saturation Min**: Minimum color saturation to distinguish from gray areas (typically 50+)
-        - **Value Min/Max**: Brightness range for blue detection (typically 50-255)
-        
-        ### 3. Default Settings
-        - The default settings work well for most trypan blue images
-        - Fine-tune based on your specific staining conditions and lighting
+        ### 2. Blue Threshold Adjustment
+        - **Single Slider (0-100%)**: Adjusts sensitivity to blue coloration
+        - **Lower values (10-20%)**: More sensitive, detects lightly stained cells
+        - **Higher values (30-50%)**: More selective, only detects strongly blue cells
+        - **Default (25%)**: Works well for most standard trypan blue staining
+        
+        ### 3. How the Blue Detection Works
+        - Analyzes hue (color) and saturation (intensity) in each cell
+        - Focuses on blue range around 115° in HSV color space
+        - Combines color proximity with saturation strength
+        - Calculates average "blueness" for each detected cell
         
         ### 4. Output
         - **Green overlay**: Live cells