fix pingpong, new node combine image background + overlay

combine_background_overlay.py

@@ -0,0 +1,66 @@
+import torch
+import numpy as np
+from PIL import Image
+
+class CombineBackgroundOverlay:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "background": ("IMAGE",),
+                "overlay_alpha": ("IMAGE",),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "combine_background_overlay"
+    CATEGORY = "Bjornulf"
+
+    def combine_background_overlay(self, background, overlay_alpha):
+        # Convert background from torch tensor to numpy array
+        bg = background[0].numpy()
+        bg = (bg * 255).astype(np.uint8)
+        bg_img = Image.fromarray(bg, 'RGB')
+
+        results = []
+
+        for overlay in overlay_alpha:
+            # Convert overlay from torch tensor to numpy array
+            ov = overlay.numpy()
+            ov = (ov * 255).astype(np.uint8)
+
+            # Create PIL Image for overlay
+            if ov.shape[2] == 4:
+                ov_img = Image.fromarray(ov, 'RGBA')
+            else:
+                ov_img = Image.fromarray(ov, 'RGB')
+                ov_img = ov_img.convert('RGBA')
+
+            # Calculate position to center the overlay
+            x = (bg_img.width - ov_img.width) // 2
+            y = (bg_img.height - ov_img.height) // 2
+
+            # Create a new image for this overlay
+            result = Image.new('RGBA', bg_img.size, (0, 0, 0, 0))
+
+            # Paste the background
+            result.paste(bg_img, (0, 0))
+
+            # Paste the overlay in the center
+            result.paste(ov_img, (x, y), ov_img)
+
+            # Convert back to numpy array and then to torch tensor
+            result_np = np.array(result)
+            
+            # If the result is RGBA, convert to RGB
+            if result_np.shape[2] == 4:
+                result_np = result_np[:,:,:3]
+
+            result_tensor = torch.from_numpy(result_np).float() / 255.0
+
+            results.append(result_tensor)
+
+        # Stack all results into a single tensor
+        final_result = torch.stack(results)
+
+        return (final_result,)