Image split, getchannel nodes

comfy_extras/nodes_post_processing.py

@@ -320,6 +320,82 @@ class Rotate:
 
         return (result,)
 
+class GetChannel:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "channel": ([
+                    "Red",
+                    "Green",
+                    "Blue",
+                ],),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "getchannel"
+
+    CATEGORY = "image/postprocessing"
+
+    def getchannel(self, image: torch.Tensor, channel: str):
+        batch_size, height, width, _ = image.shape
+        result = torch.zeros_like(image)
+
+        for b in range(batch_size):
+            tensor_image = image[b]
+            img = (tensor_image * 255).to(torch.uint8).numpy()
+            pil_image = Image.fromarray(img, mode='RGB')
+
+            output_image = pil_image.getchannel(channel[0])
+
+            output_array = torch.tensor(np.array(output_image.convert("RGB"))).float() / 255
+            result[b] = output_array
+
+        return (result,)
+
+class Split:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE", "IMAGE", "IMAGE")
+    FUNCTION = "split"
+
+    CATEGORY = "image/postprocessing"
+
+    def split(self, image: torch.Tensor):
+        batch_size, height, width, _ = image.shape
+        result_r = torch.zeros_like(image)
+        result_g = torch.zeros_like(image)
+        result_b = torch.zeros_like(image)
+
+        for b in range(batch_size):
+            tensor_image = image[b]
+            img = (tensor_image * 255).to(torch.uint8).numpy()
+            pil_image = Image.fromarray(img, mode='RGB')
+
+            output_r, output_g, output_b = pil_image.split()
+
+            output_array_r = torch.tensor(np.array(output_r.convert("RGB"))).float() / 255
+            output_array_g = torch.tensor(np.array(output_g.convert("RGB"))).float() / 255
+            output_array_b = torch.tensor(np.array(output_b.convert("RGB"))).float() / 255
+            result_r[b], result_g[b], result_b[b] = output_array_r, output_array_g, output_array_b
+
+        return (result_r, result_g, result_b)
+
+
 NODE_CLASS_MAPPINGS = {
     "ImageBlend": Blend,
     "ImageBlur": Blur,
@@ -327,4 +403,6 @@ NODE_CLASS_MAPPINGS = {
     "ImageSharpen": Sharpen,
     "ImageTranspose": Transpose,
     "ImageRotate": Rotate,
+    "ImageGetChannel": GetChannel,
+    "ImageSplit": Split,
 }