From d7c945fa90de7fc715ef05e70cf46f3199785ae4 Mon Sep 17 00:00:00 2001
From: keit <keit0728.job@gmail.com>
Date: Thu, 27 Feb 2025 23:04:51 +0900
Subject: [PATCH] =?UTF-8?q?=E7=94=BB=E5=83=8F=E3=81=AE2=E5=80=A4=E5=8C=96?=
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---
 nodes.py | 57 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 57 insertions(+)

diff --git a/nodes.py b/nodes.py
index d8ca0e5..6ffba38 100644
--- a/nodes.py
+++ b/nodes.py
@@ -58,10 +58,67 @@ class BrightnessTransparency:
         return (result_tensor,)
 
 
+class BinarizeImage:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "images": ("IMAGE", {"tooltip": "Images to binarize."}),
+                "threshold": (
+                    "INT",
+                    {
+                        "default": 127,
+                        "min": 0,
+                        "max": 255,
+                        "step": 1,
+                        "tooltip": "Values above threshold become white (1.0), below become black (0.0).",
+                    },
+                ),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "run"
+    CATEGORY = "ComfyUI-Image-Toolkit"
+
+    def run(self, images, threshold):
+        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 = []
+        normalized_threshold = threshold / 255.0
+
+        for image in images_np:
+            processed_image = image.copy()
+            grayscale = np.mean(image[:, :, :3], axis=2)
+            binary_mask = np.where(grayscale >= normalized_threshold, 1.0, 0.0)
+
+            for i in range(3):
+                processed_image[:, :, i] = binary_mask
+
+            result_images.append(processed_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,
+    "BinarizeImage": BinarizeImage,
 }
 
 NODE_DISPLAY_NAME_MAPPINGS = {
     "BrightnessTransparency": "BrightnessTransparency",
+    "BinarizeImage": "BinarizeImage",
 }