enhanced depth map constraint

app.py

@@ -123,13 +123,18 @@ def normalize_depth_map(depth_map):
 #     return result
 
 def apply_depth_based_blur(image, depth_map, max_blur=25):
-    """Apply variable Gaussian blur based on depth"""
+    """Apply variable Gaussian blur based on depth with enhanced effect"""
     # Create output image
     result = np.zeros_like(image)
     
     # Normalize depth map
     normalized_depth = normalize_depth_map(depth_map)
     
+    # Enhance depth contrast to make the effect more noticeable
+    # Apply gamma correction to increase contrast between foreground and background
+    gamma = 0.5  # Values less than 1 will enhance contrast
+    normalized_depth = np.power(normalized_depth, gamma)
+    
     # Apply blur with intensity proportional to depth
     for blur_size in range(1, max_blur + 1, 2):  # Odd numbers for kernel size
         # Create a mask for pixels that should receive this blur level
@@ -161,6 +166,7 @@ def apply_depth_based_blur(image, depth_map, max_blur=25):
     
     return result
 
+
 # Process function for Gradio
 # def process_image(input_image, blur_effect_type, blur_strength, target_class):
 #     # Load models if not already loaded
@@ -207,7 +213,7 @@ def apply_depth_based_blur(image, depth_map, max_blur=25):
 #     else:
 #         return image_np  # Return original if no effect selected
 
-def process_image(input_image, blur_effect_type, blur_strength, target_class):
+def process_image(input_image, blur_effect_type, blur_strength, target_class, show_depth_map=False):
     try:
         # Load models if not already loaded
         if not hasattr(process_image, "models_loaded"):
@@ -234,10 +240,21 @@ def process_image(input_image, blur_effect_type, blur_strength, target_class):
             # Resize for depth estimation
             depth_input = cv2.resize(image_np, (512, 512))
             
+            # Convert to PIL image for the depth estimator
+            depth_input_pil = Image.fromarray(depth_input)
+            
             # Get depth map
-            depth_result = process_image.depth_estimator(depth_input)
+            depth_result = process_image.depth_estimator(depth_input_pil)
             depth_map = np.array(depth_result["depth"])
             
+            # If show_depth_map is True, return a visualization of the depth map
+            if show_depth_map:
+                # Normalize depth map for visualization
+                depth_vis = normalize_depth_map(depth_map)
+                # Convert to colormap for better visualization
+                depth_colormap = cv2.applyColorMap((depth_vis * 255).astype(np.uint8), cv2.COLORMAP_PLASMA)
+                return depth_colormap
+            
             # Apply depth-based blur
             result = apply_depth_based_blur(depth_input, depth_map, max_blur=blur_strength)