Update utilities.py

utilities.py

@@ -4,6 +4,7 @@
 Contains all the utility functions, background creation functions
 UPDATED: Models passed as parameters instead of globals
 CRITICAL FIX: Fixed transparency issue in replace_background_hq function
+EDGE IMPROVEMENT: Added morphological operations and adjusted threshold
 """
 
 import os
@@ -255,10 +256,10 @@ def create_green_screen_background(frame):
     return green_screen
 
 # ============================================================================ #
-# CRITICAL FIX: REPLACE THIS ENTIRE FUNCTION TO FIX TRANSPARENCY ISSUE
+# IMPROVED EDGE HANDLING: Enhanced background replacement function
 # ============================================================================ #
 def replace_background_hq(frame, mask, background):
-    """High-quality background replacement with FIXED binary masking - NO TRANSPARENCY"""
+    """High-quality background replacement with improved edge handling"""
     try:
         # Resize background to match frame
         background = cv2.resize(background, (frame.shape[1], frame.shape[0]), interpolation=cv2.INTER_LANCZOS4)
@@ -267,30 +268,36 @@ def replace_background_hq(frame, mask, background):
         if len(mask.shape) == 3:
             mask = cv2.cvtColor(mask, cv2.COLOR_BGR2GRAY)
         
-        # ===== CRITICAL FIX: BINARIZE THE MASK TO PREVENT TRANSPARENCY =====
-        # This is the key change - force mask to be either 0 or 1, no in-between values
-        threshold = 128  # You can adjust this value between 100-200 if needed
+        # IMPROVED EDGE HANDLING: Adjusted threshold and morphological operations
+        threshold = 140  # Increased from 128 for tighter mask
         _, mask_binary = cv2.threshold(mask, threshold, 255, cv2.THRESH_BINARY)
         
+        # Clean up edges with morphological operations
+        kernel = np.ones((3,3), np.uint8)
+        mask_binary = cv2.morphologyEx(mask_binary, cv2.MORPH_CLOSE, kernel)  # Fill small holes
+        mask_binary = cv2.morphologyEx(mask_binary, cv2.MORPH_OPEN, kernel)   # Remove small noise
+        
+        # Optional: Apply erosion then dilation for cleaner edges
+        # mask_binary = cv2.erode(mask_binary, kernel, iterations=1)
+        # mask_binary = cv2.dilate(mask_binary, kernel, iterations=1)
+        
         # Convert to float and normalize
         mask_float = mask_binary.astype(np.float32) / 255.0
         
-        # Optional: Apply MINIMAL edge feathering AFTER binarization
-        # This gives smooth edges without making the whole person transparent
-        feather_radius = 2  # Reduced from 3 to minimize transparency
+        # Minimal edge feathering for smooth but sharp edges
+        feather_radius = 1  # Reduced from 2 for sharper edges
         kernel_size = feather_radius * 2 + 1
         mask_feathered = cv2.GaussianBlur(mask_float, (kernel_size, kernel_size), feather_radius/3)
         
-        # Ensure strong opacity in the center (prevent ghosting)
-        # Boost high values and reduce low values to maintain solid appearance
+        # Ensure strong opacity in the center
         mask_feathered = np.where(mask_feathered > 0.5, 
-                                  np.minimum(mask_feathered * 1.2, 1.0),  # Boost high values (but cap at 1.0)
+                                  np.minimum(mask_feathered * 1.2, 1.0),  # Boost high values
                                   mask_feathered * 0.8)  # Reduce low values
         
         # Create 3-channel mask
         mask_3channel = np.stack([mask_feathered] * 3, axis=2)
         
-        # Simple compositing (skip gamma correction for now to ensure solidity)
+        # Simple compositing
         result = frame * mask_3channel + background * (1 - mask_3channel)
         result = np.clip(result, 0, 255).astype(np.uint8)
         
@@ -298,13 +305,15 @@ def replace_background_hq(frame, mask, background):
         
     except Exception as e:
         logger.error(f"Background replacement error: {e}")
-        # Fallback to simple replacement - ALSO FIXED WITH BINARY MASK
+        # Fallback with same improvements
         try:
             if len(mask.shape) == 3:
                 mask = cv2.cvtColor(mask, cv2.COLOR_BGR2GRAY)
             background = cv2.resize(background, (frame.shape[1], frame.shape[0]))
-            # Also fix the fallback with binary threshold
-            _, mask_binary = cv2.threshold(mask, 128, 255, cv2.THRESH_BINARY)
+            _, mask_binary = cv2.threshold(mask, 140, 255, cv2.THRESH_BINARY)
+            kernel = np.ones((3,3), np.uint8)
+            mask_binary = cv2.morphologyEx(mask_binary, cv2.MORPH_CLOSE, kernel)
+            mask_binary = cv2.morphologyEx(mask_binary, cv2.MORPH_OPEN, kernel)
             mask_normalized = mask_binary.astype(np.float32) / 255.0
             mask_3channel = np.stack([mask_normalized] * 3, axis=2)
             result = frame * mask_3channel + background * (1 - mask_3channel)