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.gitattributes

@@ -37,3 +37,4 @@ ComfyUI/output/ComfyUI_00001_.png filter=lfs diff=lfs merge=lfs -text
 ComfyUI/output/ComfyUI_00002_.png filter=lfs diff=lfs merge=lfs -text
 ComfyUI/temp/ComfyUI_temp_zprxs_00001_.png filter=lfs diff=lfs merge=lfs -text
 ComfyUI/temp/ComfyUI_temp_zprxs_00002_.png filter=lfs diff=lfs merge=lfs -text
+ComfyUI/custom_nodes/merge_cross_images/result3.png filter=lfs diff=lfs merge=lfs -text

ComfyUI/custom_nodes/merge_cross_images/__init__.py

@@ -0,0 +1,67 @@
+import cv2
+import numpy as np
+import os
+import torch
+from PIL import Image
+
+class MergeImagesToTemplate:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+            "image1": ("IMAGE",), 
+            "image2": ("IMAGE",),
+            "image3": ("IMAGE",),
+            "image4": ("IMAGE",)
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "merge_images"
+    CATEGORY = "image"
+    OUTPUT_NODE = True
+
+    def merge_images(self, image1, image2, image3, image4):
+        current_directory = os.path.dirname(os.path.abspath(__file__))
+        template = cv2.imread(current_directory+"/template_cross2.png", cv2.IMREAD_UNCHANGED)
+        scale_top = 0.85
+        scale_bottom = 1.434
+        top_1 = (271,9)
+        top_2 = (831,9)
+        bottom_1 = (15,474)
+        bottom_2 = (786,474)
+            
+        def prepare_image(img):
+            img = np.clip(255.0 * img.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)
+            return cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
+        
+        image1 = prepare_image(image1)
+        image2 = prepare_image(image2)
+        image3 = prepare_image(image3)
+        image4 = prepare_image(image4)
+        
+        img1_resized = cv2.resize(image1, (int(512 * scale_top), int(512 * scale_top)))
+        img2_resized = cv2.resize(image2, (int(512 * scale_top), int(512 * scale_top)))
+        img3_resized = cv2.resize(image3, (int(512 * scale_bottom), int(512 * scale_bottom)))
+        img4_resized = cv2.resize(image4, (int(512 * scale_bottom), int(512 * scale_bottom)))
+        
+        background = np.zeros((template.shape[0], template.shape[1], 3), dtype=np.uint8)
+        background[:] = (255, 255, 255)
+        
+        background[top_1[1]:top_1[1]+img1_resized.shape[0], top_1[0]:top_1[0]+img1_resized.shape[1]] = img1_resized
+        background[top_2[1]:top_2[1]+img2_resized.shape[0], top_2[0]:top_2[0]+img2_resized.shape[1]] = img2_resized
+        background[bottom_1[1]:bottom_1[1]+img3_resized.shape[0], bottom_1[0]:bottom_1[0]+img3_resized.shape[1]] = img3_resized
+        background[bottom_2[1]:bottom_2[1]+img4_resized.shape[0], bottom_2[0]:bottom_2[0]+img4_resized.shape[1]] = img4_resized
+                
+        alpha_channel = template[:, :, 3] / 255.0
+        for c in range(0, 3):
+            background[:, :, c] = background[:, :, c] * (1 - alpha_channel) + template[:, :, c] * alpha_channel    
+        
+        cv2.imwrite(current_directory+'/result3.png', background)
+        background_rgba = cv2.cvtColor(background, cv2.COLOR_BGR2RGBA)
+        
+        result = torch.from_numpy(background_rgba.astype(np.float32) / 255.0).unsqueeze(0) 
+        return (result,)
+
+
+NODE_CLASS_MAPPINGS = {"MergeImagesToTemplate": MergeImagesToTemplate}