Add files via upload

Mango_random_nodes.py

@@ -5,6 +5,7 @@ from PIL import Image, ImageOps
 import numpy as np
 import cv2
 import time
+import re
 
 
 class RandomFilePathNode:
@@ -94,7 +95,24 @@ class RandomImagePathNode:
 
         # 根据排序模式选择文件
         elif sort_mode == "顺序循环":
-            files.sort(key=lambda x: os.path.basename(x).lower())
+            def natural_sort_key(path):
+                # 提取文件名（不含路径和扩展名）
+                filename = os.path.splitext(os.path.basename(path))[0]
+                
+                # 将文件名分解为数字和非数字部分
+                def convert(text):
+                    # 如果是纯数字，转换为整数
+                    if text.isdigit():
+                        return int(text)
+                    # 移除常见的分隔符
+                    text = text.replace('_', '').replace('-', '').replace(' ', '')
+                    return text.lower()
+                
+                # 使用正则表达式分割，保留所有部分（包括非数字部分）
+                parts = re.split('([0-9]+)', filename)
+                return [convert(part) for part in parts if part]
+            
+            files.sort(key=natural_sort_key)
             path = files[self.current_index]
             self.current_index = (self.current_index + 1) % len(files)
         else:  # 完全随机模式
@@ -258,14 +276,69 @@ class FrameGenerator:
         return iter(self.frames)
 
 
+class RandomImageNode:
+    def __init__(self):
+        self.current_index = 0
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "sort_mode": (["完全随机", "顺序循环"],),
+                "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+            },
+            "optional": {
+                "image1": ("IMAGE",),
+                "image2": ("IMAGE",),
+                "image3": ("IMAGE",),
+                "image4": ("IMAGE",),
+                "image5": ("IMAGE",),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "random_image"
+    CATEGORY = "🥭 芒果节点/图片"
+
+    @classmethod
+    def IS_CHANGED(cls, **kwargs):
+        return float("NaN")
+
+    def random_image(self, sort_mode, seed, **kwargs):
+        # 收集所有输入的图片
+        images = []
+        for key, value in kwargs.items():
+            if value is not None:  # 只处理已连接的图片输入
+                if isinstance(value, torch.Tensor):
+                    images.extend([value[i:i+1] for i in range(value.shape[0])])
+        
+        if not images:
+            raise ValueError("至少需要连接一个图片输入")
+            
+        if len(images) == 1:
+            return (images[0],)
+            
+        if sort_mode == "顺序循环":
+            selected_index = self.current_index % len(images)
+            self.current_index = (self.current_index + 1) % len(images)
+            selected_image = images[selected_index]
+        else:  # 完全随机模式
+            random.seed(seed)
+            selected_image = random.choice(images)
+        
+        return (selected_image,)
+
+
 NODE_CLASS_MAPPINGS = {
     "Random Video Path": RandomVideoPathNode,
     "Random Image Path": RandomImagePathNode,
     "Random File Path": RandomFilePathNode,
+    "Random Image": RandomImageNode,
 }
 
 NODE_DISPLAY_NAME_MAPPINGS = {
     "Random Video Path": "随机视频 🥭",
-    "Random Image Path": "随机图片 🥭",
+    "Random Image Path": "随机图片_路径 🥭",
     "Random File Path": "随机文件 🥭",
+    "Random Image": "随机图片 🥭",
 }