v1.1.7

load_base64_transparency.py

@@ -0,0 +1,67 @@
+import base64
+import cv2
+import numpy as np
+import torch
+
+class loadImageBase64Transparency:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "base64_data": ("STRING", {"default": ""}),
+            },
+            "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
+        }
+
+    RETURN_TYPES = ("IMAGE", "MASK")
+    OUTPUT_NODE = True
+    FUNCTION = "load_image"
+    CATEGORY = "EasyUse/Image/LoadImage"
+
+    def convert_color(self, image):
+        """Convert image color space while preserving alpha channel"""
+        if len(image.shape) > 2 and image.shape[2] >= 4:
+            # Handle BGRA to RGBA conversion
+            return cv2.cvtColor(image, cv2.COLOR_BGRA2RGBA)
+        elif len(image.shape) > 2 and image.shape[2] == 3:
+            # Handle BGR to RGB conversion
+            return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+        else:
+            # Grayscale image
+            return image
+
+    def load_image(self, base64_data, prompt=None, extra_pnginfo=None):
+        # Decode base64 data to image
+        nparr = np.frombuffer(base64.b64decode(base64_data), np.uint8)
+        result = cv2.imdecode(nparr, cv2.IMREAD_UNCHANGED)
+        
+        # Split channels to check for alpha
+        channels = cv2.split(result)
+        has_alpha = len(channels) > 3
+        
+        if has_alpha:
+            # Extract alpha channel as mask
+            mask = channels[3].astype(np.float32) / 255.0
+            mask = torch.from_numpy(mask)
+        else:
+            # Create full opacity mask if no alpha channel
+            mask = torch.ones(channels[0].shape, dtype=torch.float32, device="cpu")
+
+        # Convert color space while preserving channels
+        result = self.convert_color(result)
+        
+        # Normalize to 0-1 range
+        result = result.astype(np.float32) / 255.0
+        
+        # Convert to tensor and add batch dimension
+        if has_alpha:
+            # Keep all 4 channels (RGBA)
+            new_images = torch.from_numpy(result)[None,]
+        else:
+            # RGB only
+            new_images = torch.from_numpy(result)[None,]
+
+        # Ensure mask has batch dimension
+        mask = mask.unsqueeze(0)
+
+        return (new_images, mask)