Restructuring fixes

__init__.py

@@ -40,11 +40,14 @@ NODE_CONFIG = {
     #images
     "AddLabel": {"class": AddLabel, "name": "Add Label"},
     "ColorMatch": {"class": ColorMatch, "name": "Color Match"},
+    "CrossFadeImages": {"class": CrossFadeImages, "name": "Cross Fade Images"},
+    "GetImageRangeFromBatch": {"class": GetImageRangeFromBatch, "name": "Get Image Range From Batch"},
     "GetImageSizeAndCount": {"class": GetImageSizeAndCount, "name": "Get Image Size & Count"},
     "ImageAndMaskPreview": {"class": ImageAndMaskPreview},
     "ImageBatchMulti": {"class": ImageBatchMulti, "name": "Image Batch Multi"},
+    "ImageBatchRepeatInterleaving": {"class": ImageBatchRepeatInterleaving},
     "ImageBatchTestPattern": {"class": ImageBatchTestPattern, "name": "Image Batch Test Pattern"},
-    "ImageConcanate": {"class": ImageConcanate, "name": "Image Concanate"},
+    "ImageConcanate": {"class": ImageConcanate, "name": "Image Concatenate"},
     "ImageGrabPIL": {"class": ImageGrabPIL, "name": "Image Grab PIL"},
     "ImageGridComposite2x2": {"class": ImageGridComposite2x2, "name": "Image Grid Composite 2x2"},
     "ImageGridComposite3x3": {"class": ImageGridComposite3x3, "name": "Image Grid Composite 3x3"},
@@ -54,12 +57,11 @@ NODE_CONFIG = {
     "ImageUpscaleWithModelBatched": {"class": ImageUpscaleWithModelBatched, "name": "Image Upscale With Model Batched"},
     "InsertImagesToBatchIndexed": {"class": InsertImagesToBatchIndexed, "name": "Insert Images To Batch Indexed"},
     "MergeImageChannels": {"class": MergeImageChannels, "name": "Merge Image Channels"},
-    "ReverseImageBatch": {"class": ReverseImageBatch, "name": "Reverse Image Batch"},
     "RemapImageRange": {"class": RemapImageRange, "name": "Remap Image Range"},
+    "ReverseImageBatch": {"class": ReverseImageBatch, "name": "Reverse Image Batch"},
+    "ReplaceImagesInBatch": {"class": ReplaceImagesInBatch, "name": "Replace Images In Batch"},
     "SaveImageWithAlpha": {"class": SaveImageWithAlpha, "name": "Save Image With Alpha"},
     "SplitImageChannels": {"class": SplitImageChannels, "name": "Split Image Channels"},    
-    "CrossFadeImages": {"class": CrossFadeImages, "name": "Cross Fade Images"},
-    "GetImageRangeFromBatch": {"class": GetImageRangeFromBatch, "name": "Get Image Range From Batch"},
     #batch cropping
     "BatchCropFromMask": {"class": BatchCropFromMask, "name": "Batch Crop From Mask"},
     "BatchCropFromMaskAdvanced": {"class": BatchCropFromMaskAdvanced, "name": "Batch Crop From Mask Advanced"},

nodes/batchcrop_nodes.py

@@ -6,6 +6,23 @@ from torchvision.transforms import Resize, CenterCrop, InterpolationMode
 import math
 
 #based on nodes from mtb https://github.com/melMass/comfy_mtb
+
+def bbox_to_region(bbox, target_size=None):
+    bbox = bbox_check(bbox, target_size)
+    return (bbox[0], bbox[1], bbox[0] + bbox[2], bbox[1] + bbox[3])
+
+def bbox_check(bbox, target_size=None):
+    if not target_size:
+        return bbox
+
+    new_bbox = (
+        bbox[0],
+        bbox[1],
+        min(target_size[0] - bbox[0], bbox[2]),
+        min(target_size[1] - bbox[1], bbox[3]),
+    )
+    return new_bbox
+
 class BatchCropFromMask:
 
     @classmethod
@@ -136,23 +153,6 @@ class BatchCropFromMask:
         
         return (original_images, cropped_out, bounding_boxes, self.max_bbox_width, self.max_bbox_height, )
 
-
-def bbox_to_region(bbox, target_size=None):
-    bbox = bbox_check(bbox, target_size)
-    return (bbox[0], bbox[1], bbox[0] + bbox[2], bbox[1] + bbox[3])
-
-def bbox_check(bbox, target_size=None):
-    if not target_size:
-        return bbox
-
-    new_bbox = (
-        bbox[0],
-        bbox[1],
-        min(target_size[0] - bbox[0], bbox[2]),
-        min(target_size[1] - bbox[1], bbox[3]),
-    )
-    return new_bbox
-
 class BatchUncrop:
 
     @classmethod
@@ -532,22 +532,6 @@ Returns:
 
         return (images_after_insert, )
 
-def bbox_to_region(bbox, target_size=None):
-    bbox = bbox_check(bbox, target_size)
-    return (bbox[0], bbox[1], bbox[0] + bbox[2], bbox[1] + bbox[3])
-
-def bbox_check(bbox, target_size=None):
-    if not target_size:
-        return bbox
-
-    new_bbox = (
-        bbox[0],
-        bbox[1],
-        min(target_size[0] - bbox[0], bbox[2]),
-        min(target_size[1] - bbox[1], bbox[3]),
-    )
-    return new_bbox
-
 class BatchUncropAdvanced:
 
     @classmethod
@@ -675,3 +659,79 @@ Splits the specified bbox list at the given index into two lists.
         bboxes_b = bboxes[index:]  # Sub-list from the index to the end of bboxes
 
         return (bboxes_a, bboxes_b,)
+    
+class BboxToInt:
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "bboxes": ("BBOX",),
+                "index": ("INT", {"default": 0,"min": 0, "max": 99999999, "step": 1}),
+            },
+        }
+
+    RETURN_TYPES = ("INT","INT","INT","INT","INT","INT",)
+    RETURN_NAMES = ("x_min","y_min","width","height", "center_x","center_y",)
+    FUNCTION = "bboxtoint"
+    CATEGORY = "KJNodes/masking"
+    DESCRIPTION = """
+Returns selected index from bounding box list as integers.
+"""
+    def bboxtoint(self, bboxes, index):
+        x_min, y_min, width, height = bboxes[index]
+        center_x = int(x_min + width / 2)
+        center_y = int(y_min + height / 2)
+        
+        return (x_min, y_min, width, height, center_x, center_y,)
+
+class BboxVisualize:
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "images": ("IMAGE",),
+                "bboxes": ("BBOX",),
+                "line_width": ("INT", {"default": 1,"min": 1, "max": 10, "step": 1}),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("images",)
+    FUNCTION = "visualizebbox"
+    DESCRIPTION = """
+Visualizes the specified bbox on the image.
+"""
+
+    CATEGORY = "KJNodes/masking"
+
+    def visualizebbox(self, bboxes, images, line_width):
+        image_list = []
+        for image, bbox in zip(images, bboxes):
+            x_min, y_min, width, height = bbox
+            image = image.permute(2, 0, 1)
+
+            img_with_bbox = image.clone()
+            
+            # Define the color for the bbox, e.g., red
+            color = torch.tensor([1, 0, 0], dtype=torch.float32)
+            
+            # Draw lines for each side of the bbox with the specified line width
+            for lw in range(line_width):
+                # Top horizontal line
+                img_with_bbox[:, y_min + lw, x_min:x_min + width] = color[:, None]
+                
+                # Bottom horizontal line
+                img_with_bbox[:, y_min + height - lw, x_min:x_min + width] = color[:, None]
+                
+                # Left vertical line
+                img_with_bbox[:, y_min:y_min + height, x_min + lw] = color[:, None]
+                
+                # Right vertical line
+                img_with_bbox[:, y_min:y_min + height, x_min + width - lw] = color[:, None]
+        
+            img_with_bbox = img_with_bbox.permute(1, 2, 0).unsqueeze(0)
+            image_list.append(img_with_bbox)
+
+        return (torch.cat(image_list, dim=0),)

nodes/nodes.py

@@ -730,82 +730,6 @@ To see node id's, enable node id display from Manager badge menu.
             raise NameError(f"Node not found: {id}")
         return (', '.join(results).strip(', '), )
 
-class BboxToInt:
-
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "bboxes": ("BBOX",),
-                "index": ("INT", {"default": 0,"min": 0, "max": 99999999, "step": 1}),
-            },
-        }
-
-    RETURN_TYPES = ("INT","INT","INT","INT","INT","INT",)
-    RETURN_NAMES = ("x_min","y_min","width","height", "center_x","center_y",)
-    FUNCTION = "bboxtoint"
-    CATEGORY = "KJNodes/masking"
-    DESCRIPTION = """
-Returns selected index from bounding box list as integers.
-"""
-    def bboxtoint(self, bboxes, index):
-        x_min, y_min, width, height = bboxes[index]
-        center_x = int(x_min + width / 2)
-        center_y = int(y_min + height / 2)
-        
-        return (x_min, y_min, width, height, center_x, center_y,)
-
-class BboxVisualize:
-
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "images": ("IMAGE",),
-                "bboxes": ("BBOX",),
-                "line_width": ("INT", {"default": 1,"min": 1, "max": 10, "step": 1}),
-            },
-        }
-
-    RETURN_TYPES = ("IMAGE",)
-    RETURN_NAMES = ("images",)
-    FUNCTION = "visualizebbox"
-    DESCRIPTION = """
-Visualizes the specified bbox on the image.
-"""
-
-    CATEGORY = "KJNodes/masking"
-
-    def visualizebbox(self, bboxes, images, line_width):
-        image_list = []
-        for image, bbox in zip(images, bboxes):
-            x_min, y_min, width, height = bbox
-            image = image.permute(2, 0, 1)
-
-            img_with_bbox = image.clone()
-            
-            # Define the color for the bbox, e.g., red
-            color = torch.tensor([1, 0, 0], dtype=torch.float32)
-            
-            # Draw lines for each side of the bbox with the specified line width
-            for lw in range(line_width):
-                # Top horizontal line
-                img_with_bbox[:, y_min + lw, x_min:x_min + width] = color[:, None]
-                
-                # Bottom horizontal line
-                img_with_bbox[:, y_min + height - lw, x_min:x_min + width] = color[:, None]
-                
-                # Left vertical line
-                img_with_bbox[:, y_min:y_min + height, x_min + lw] = color[:, None]
-                
-                # Right vertical line
-                img_with_bbox[:, y_min:y_min + height, x_min + width - lw] = color[:, None]
-        
-            img_with_bbox = img_with_bbox.permute(1, 2, 0).unsqueeze(0)
-            image_list.append(img_with_bbox)
-
-        return (torch.cat(image_list, dim=0),)
-
 class DummyLatentOut:
 
     @classmethod