From b8cbcd9fd30169fbd8cd5b6f031c0d49856f7082 Mon Sep 17 00:00:00 2001
From: keit <keit0728.job@gmail.com>
Date: Thu, 27 Feb 2025 22:58:09 +0900
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---
 __init__.py |  3 +++
 nodes.py    | 67 +++++++++++++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 70 insertions(+)
 create mode 100644 __init__.py
 create mode 100644 nodes.py

diff --git a/__init__.py b/__init__.py
new file mode 100644
index 0000000..39a8c6b
--- /dev/null
+++ b/__init__.py
@@ -0,0 +1,3 @@
+from .nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
+
+__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"]
diff --git a/nodes.py b/nodes.py
new file mode 100644
index 0000000..d8ca0e5
--- /dev/null
+++ b/nodes.py
@@ -0,0 +1,67 @@
+import numpy as np
+import torch
+
+
+class BrightnessTransparency:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "images": (
+                    "IMAGE",
+                    {
+                        "tooltip": "Input images to adjust transparency. Bright areas will become transparent."
+                    },
+                ),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "run"
+    CATEGORY = "ComfyUI-Image-Toolkit"
+
+    def run(self, images):
+        is_tensor = isinstance(images, torch.Tensor)
+
+        if is_tensor:
+            if images.device.type != "cpu":
+                images = images.cpu()
+            images_np = images.numpy()
+        else:
+            images_np = images
+
+        result_images = []
+
+        for image in images_np:
+            if image.shape[2] == 3:
+                rgba_image = np.zeros(
+                    (image.shape[0], image.shape[1], 4), dtype=np.float32
+                )
+                rgba_image[:, :, :3] = image
+                rgba_image[:, :, 3] = 1.0  # Range 0.0-1.0
+            else:
+                rgba_image = image.copy()
+
+            # Set alpha values inversely proportional to brightness (bright=transparent, dark=opaque)
+            brightness = np.mean(rgba_image[:, :, :3], axis=2)
+            alpha = 1.0 - brightness
+            rgba_image[:, :, 3] = alpha
+
+            result_images.append(rgba_image)
+
+        result_np = np.stack(result_images)
+        result_tensor = torch.from_numpy(result_np)
+
+        if is_tensor and images.device.type != "cpu":
+            result_tensor = result_tensor.to(images.device)
+
+        return (result_tensor,)
+
+
+NODE_CLASS_MAPPINGS = {
+    "BrightnessTransparency": BrightnessTransparency,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "BrightnessTransparency": "BrightnessTransparency",
+}