new node : cut image from mask

README.md

@@ -1,4 +1,4 @@
-# 🔗 Comfyui : Bjornulf_custom_nodes v0.13 🔗
+# 🔗 Comfyui : Bjornulf_custom_nodes v0.14 🔗
 
 # Dependencies
 
@@ -22,6 +22,7 @@
 - **v0.11**: Add a new node : Text with random Seed - Generate a random seed, along with text.
 - **v0.12**: Combine images : Add option to move vertically and horizontally. (from -50% to 150%)
 - **v0.13**: Add a new node: Load image with transparency (alpha) - Load an image with transparency.
+- **v0.14**: Add a new node: Cut image from a mask
 
 # 📝 Nodes descriptions
 
@@ -233,4 +234,10 @@ FLUX : Here is an example of 4 images without Random Seed node on the left, and
 
 **Description:**  
 Load an image with transparency.  
-The default `Load Image` node will not load the transparency.  
+The default `Load Image` node will not load the transparency.  
+
+## 30 - 🖼✂ Cut image with a mask
+![Cut image](screenshots/image_mask_cut.png)
+
+**Description:**  
+Cut an image from a mask.  

__init__.py

@@ -34,6 +34,7 @@ from .random_line_from_input import RandomLineFromInput
 from .loop_lines import LoopAllLines
 from .random_seed_with_text import TextToStringAndSeed
 from .load_image_alpha import LoadImageWithTransparency
+from .image_mask_cutter import ImageMaskCutter
 # from .check_black_image import CheckBlackImage
 # from .clear_vram import ClearVRAM
 
@@ -42,6 +43,7 @@ from .load_image_alpha import LoadImageWithTransparency
 NODE_CLASS_MAPPINGS = {
     # "Bjornulf_CustomStringType": CustomStringType,
     "Bjornulf_ollamaLoader": ollamaLoader,
+    "Bjornulf_ImageMaskCutter": ImageMaskCutter,
     "Bjornulf_LoadImageWithTransparency": LoadImageWithTransparency,
     "Bjornulf_LoopAllLines": LoopAllLines,
     "Bjornulf_TextToStringAndSeed": TextToStringAndSeed,
@@ -84,6 +86,7 @@ NODE_CLASS_MAPPINGS = {
 NODE_DISPLAY_NAME_MAPPINGS = {
     # "Bjornulf_CustomStringType": "!!! CUSTOM STRING TYPE !!!",
     "Bjornulf_ollamaLoader": "🦙 Ollama (Description)",
+    "Bjornulf_ImageMaskCutter": "🖼✂ Cut Image with Mask",
     "Bjornulf_LoadImageWithTransparency": "🖼 Load Image with Transparency ▢",
     "Bjornulf_GreenScreenToTransparency": "🟩➜▢ Green Screen to Transparency",
     # "Bjornulf_CheckBlackImage": "🔲 Check Black Image (Empty mask)",

combine_background_overlay.py

@@ -8,7 +8,8 @@ class CombineBackgroundOverlay:
         return {
             "required": {
                 "background": ("IMAGE",),
-                "overlay_alpha": ("IMAGE",),
+                "overlay": ("IMAGE",),
+                "mask": ("MASK",),
                 "horizontal_position": ("FLOAT", {"default": 50, "min": -50, "max": 150, "step": 0.1}),
                 "vertical_position": ("FLOAT", {"default": 50, "min": -50, "max": 150, "step": 0.1}),
             },
@@ -18,7 +19,7 @@ class CombineBackgroundOverlay:
     FUNCTION = "combine_background_overlay"
     CATEGORY = "Bjornulf"
 
-    def combine_background_overlay(self, background, overlay_alpha, horizontal_position, vertical_position):
+    def combine_background_overlay(self, background, overlay, mask, horizontal_position, vertical_position):
         # Convert background from torch tensor to numpy array
         bg = background[0].numpy()
         bg = (bg * 255).astype(np.uint8)
@@ -26,17 +27,23 @@ class CombineBackgroundOverlay:
 
         results = []
 
-        for overlay in overlay_alpha:
+        for ov, m in zip(overlay, mask):
             # Convert overlay from torch tensor to numpy array
-            ov = overlay.numpy()
+            ov = ov.numpy()
             ov = (ov * 255).astype(np.uint8)
 
+            # Convert mask from torch tensor to numpy array
+            m = m.numpy()
+            m = (m * 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')
+            ov_img = Image.fromarray(ov, 'RGB')
+            
+            # Create alpha channel from mask
+            alpha = Image.fromarray(m, 'L')
+            
+            # Combine RGB overlay with alpha mask
+            ov_img.putalpha(alpha)
 
             # Calculate horizontal position
             x = int((horizontal_position / 100) * (bg_img.width - ov_img.width))
@@ -56,9 +63,8 @@ class CombineBackgroundOverlay:
             # 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]
+            # Convert RGBA to RGB
+            result_np = result_np[:,:,:3]
 
             result_tensor = torch.from_numpy(result_np).float() / 255.0
 

image_mask_cutter.py

@@ -0,0 +1,46 @@
+import torch
+
+class ImageMaskCutter:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "mask": ("MASK",),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "cut_image"
+
+    CATEGORY = "Bjornulf"
+
+    def cut_image(self, image, mask):
+        print(f"Image shape: {image.shape}")
+        print(f"Mask shape: {mask.shape}")
+
+        # Check if image channels are in the last dimension
+        if image.shape[-1] == 3 or image.shape[-1] == 4:
+            # Move channels to second dimension
+            image = image.permute(0, 3, 1, 2)
+
+        # Ensure image and mask have compatible dimensions
+        if image.shape[2:] != mask.shape[1:]:
+            raise ValueError(f"Image and mask must have compatible dimensions. Got image shape {image.shape} and mask shape {mask.shape}")
+
+        # Convert mask to float and ensure it's in the range [0, 1]
+        mask = mask.float()
+        mask = torch.clamp(mask, 0, 1)
+
+        # If image is RGB, convert to RGBA
+        if image.shape[1] == 3:
+            alpha = torch.ones((image.shape[0], 1, image.shape[2], image.shape[3]), device=image.device)
+            image = torch.cat([image, alpha], dim=1)
+
+        # Use the mask as the alpha channel
+        image[:, 3:4, :, :] = mask.unsqueeze(1)
+
+        # Move channels back to the last dimension
+        cut_image = image.permute(0, 2, 3, 1)
+
+        return (cut_image,)

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "bjornulf_custom_nodes"
 description = "Nodes: Ollama, Green Screen to Transparency, Save image for Bjornulf LobeChat, Text with random Seed, Random line from input, Combine images (Background+Overlay alpha), Image to grayscale (black & white), Remove image Transparency (alpha), Resize Image, ..."
-version = "0.13"
+version = "0.14"
 license = {file = "LICENSE"}
 
 [project.urls]