The addition of these two nodes, RH_Extract_Image_From_List and RH_Batch_Images_From_List, facilitates further processing of images returned by ExecuteNode, especially when dealing with multiple images.

RH_Utils.py

@@ -1,3 +1,13 @@
+import os
+import numpy as np
+import json
+import folder_paths
+import zipfile
+import shutil
+import numpy as np
+import torch
+from PIL import Image, ImageOps
+
 class AllTrue(str):
     def __init__(self, representation=None) -> None:
         self.repr = representation
@@ -55,3 +65,73 @@ class AnyToStringNode:
         else:
             # For non-string types, directly convert to string
             return (str(anything),)
+        
+class RH_Extract_Image_From_List():
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "images": ("IMAGE", {"tooltip": "The images list"}),
+                "image_index": ("INT", {"default": 0 }),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("image",)
+
+    FUNCTION = "rh_extract_image"
+
+    OUTPUT_NODE = False
+
+    CATEGORY = "RunningHub"
+
+    def rh_extract_image(self, images, image_index):
+        out = images[int(image_index)].unsqueeze(0)
+        return (out,)
+    
+class RH_Batch_Images_From_List():
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "images": ("IMAGE", {"tooltip": "The images list"}),
+                "image_indices": ("STRING", {"default":"0-3,4,5-7","tooltip": "Some like 0-2, 3, 4-5. Leaving it empty means selecting all."}),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("image",)
+
+    FUNCTION = "rh_batch_images"
+
+    OUTPUT_NODE = False
+
+    CATEGORY = "RunningHub"
+
+    def rh_batch_images(self, images, image_indices):
+        image_indices = image_indices.replace(" ", "")
+        out = []
+        if image_indices == "":
+            out = images
+        image_indices = image_indices.split(',')
+        for index in image_indices:
+            if '-' in index:
+                sindex = index.split('-')
+                out.extend(images[int(sindex[0]):int(sindex[1])+1])
+            else:
+                out.append(images[int(index)])
+        batchsize = len(out)
+        max_height = max(image.shape[0] for image in out)
+        max_width = max(image.shape[1] for image in out)
+        max_channels = max(image.shape[2] for image in out)
+        batch_images = torch.zeros([batchsize, max_height, max_width, max_channels])
+        for (batch_number, image) in enumerate(out):
+            h, w, c = image.shape
+            batch_images[batch_number, 0:h, 0:w, 0:c] = image
+        return (batch_images,)

__init__.py

@@ -2,7 +2,7 @@ from .RH_SettingsNode import SettingsNode
 from .RH_NodeInfoListNode import NodeInfoListNode
 from .RH_ExecuteNode import ExecuteNode
 from .RH_ImageUploaderNode import ImageUploaderNode
-from .RH_Utils import AnyToStringNode
+from .RH_Utils import *
 
 
 
@@ -12,6 +12,8 @@ NODE_CLASS_MAPPINGS = {
     "RH_ExecuteNode": ExecuteNode,
     "RH_ImageUploaderNode": ImageUploaderNode,
     "RH_Utils": AnyToStringNode,
+    "RH_ExtractImage": RH_Extract_Image_From_List,
+    "RH_BatchImages": RH_Batch_Images_From_List,
 
 }
 
@@ -21,7 +23,9 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "RH_ExecuteNode": "RH Execute",
     "RH_ImageUploaderNode": "RH Image Uploader",
     "RH_Utils": "RH Anything to String",
-
+    "RH_ExtractImage": "RH Extract Image From ImageList",
+    "RH_BatchImages": "RH Batch Images From ImageList",
 }
 
+
 __all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS",]