Update utilities.py

utilities.py

@@ -3,6 +3,7 @@
 utilities.py - Helper functions and utilities for Video Background Replacement
 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
 """
 
 import os
@@ -253,8 +254,11 @@ def create_green_screen_background(frame):
     green_screen = np.full((h, w, 3), (0, 177, 64), dtype=np.uint8)
     return green_screen
 
+# ============================================================================ #
+# CRITICAL FIX: REPLACE THIS ENTIRE FUNCTION TO FIX TRANSPARENCY ISSUE
+# ============================================================================ #
 def replace_background_hq(frame, mask, background):
-    """High-quality background replacement with advanced compositing"""
+    """High-quality background replacement with FIXED binary masking - NO TRANSPARENCY"""
     try:
         # Resize background to match frame
         background = cv2.resize(background, (frame.shape[1], frame.shape[0]), interpolation=cv2.INTER_LANCZOS4)
@@ -263,38 +267,45 @@ def replace_background_hq(frame, mask, background):
         if len(mask.shape) == 3:
             mask = cv2.cvtColor(mask, cv2.COLOR_BGR2GRAY)
         
-        # Normalize mask to 0-1 range
-        mask_float = mask.astype(np.float32) / 255.0
+        # ===== 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
+        _, mask_binary = cv2.threshold(mask, threshold, 255, cv2.THRESH_BINARY)
         
-        # Edge feathering for smooth transitions
-        feather_radius = 3
+        # 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
         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
+        mask_feathered = np.where(mask_feathered > 0.5, 
+                                  np.minimum(mask_feathered * 1.2, 1.0),  # Boost high values (but cap at 1.0)
+                                  mask_feathered * 0.8)  # Reduce low values
+        
         # Create 3-channel mask
         mask_3channel = np.stack([mask_feathered] * 3, axis=2)
         
-        # Linear gamma correction for proper compositing
-        frame_linear = np.power(frame.astype(np.float32) / 255.0, 2.2)
-        background_linear = np.power(background.astype(np.float32) / 255.0, 2.2)
-        
-        # Composite in linear space
-        result_linear = frame_linear * mask_3channel + background_linear * (1 - mask_3channel)
-        
-        # Convert back to gamma space
-        result = np.power(result_linear, 1/2.2) * 255.0
+        # Simple compositing (skip gamma correction for now to ensure solidity)
+        result = frame * mask_3channel + background * (1 - mask_3channel)
         result = np.clip(result, 0, 255).astype(np.uint8)
         
         return result
         
     except Exception as e:
         logger.error(f"Background replacement error: {e}")
-        # Fallback to simple replacement
+        # Fallback to simple replacement - ALSO FIXED WITH BINARY MASK
         try:
             if len(mask.shape) == 3:
                 mask = cv2.cvtColor(mask, cv2.COLOR_BGR2GRAY)
             background = cv2.resize(background, (frame.shape[1], frame.shape[0]))
-            mask_normalized = mask.astype(np.float32) / 255.0
+            # Also fix the fallback with binary threshold
+            _, mask_binary = cv2.threshold(mask, 128, 255, cv2.THRESH_BINARY)
+            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)
             return result.astype(np.uint8)