first commit

README.md

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+个人使用小工具
+1.图片覆盖
+2.int转string,float转string,float转int四舍五入

__init__.py

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+
+from PIL import Image
+import numpy as np
+import torch
+class ImageOverlap:
+
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(s):
+
+        return {
+            "required": {
+                "base_image": ("IMAGE",),
+                "additional_image": ("IMAGE",),
+                "x": ("INT", {
+                    "default": 0,
+                    "min": 0,
+                    "max": 4096,
+                    "step": 1,
+                    "display": "number"
+                }),
+                "y": ("INT", {
+                    "default": 0,
+                    "min": 0,
+                    "max": 4096,
+                    "step": 1,
+                    "display": "number"
+                }),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    # RETURN_NAMES = ("image_output_name",)
+
+    FUNCTION = "overlap"
+
+    # OUTPUT_NODE = False
+
+    CATEGORY = "badger"
+
+    def tensorToImg(self, imageTensor):
+        imaget = imageTensor[0]
+        i = 255. * imaget.cpu().numpy()
+        img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
+        return img
+
+    def imgToTensor(self,img):
+        image = np.array(img).astype(np.float32) / 255.0
+        imaget = torch.from_numpy(image)[None,]
+        return imaget
+
+    def overlap(self, base_image, additional_image, x, y):
+        b_image = self.tensorToImg(base_image)
+        a_image = self.tensorToImg(additional_image)
+
+        b_image.paste(a_image, (x, y))
+        o_image = self.imgToTensor(b_image)
+        return (o_image,)
+
+
+class FloatToInt:
+
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(s):
+
+        return {
+            "required": {
+                  "float": ("FLOAT", {
+                    "default": 1.0,
+                    "min": 0.0,
+                    "max": 4096.0,
+                    "step": 0.01,
+                    "round": 0.01,
+                    "display": "number"})
+            },
+        }
+
+    RETURN_TYPES = ("INT",)
+    # RETURN_NAMES = ("image_output_name",)
+
+    FUNCTION = "floatToInt"
+
+    # OUTPUT_NODE = False
+
+    CATEGORY = "badger"
+
+    def floatToInt(self, float):
+        return (round(float),)
+
+
+class IntToString:
+
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(s):
+
+        return {
+            "required": {
+                  "int": ("INT", {
+                    "default": 0,
+                    "min": 0,
+                    "max": 4096,
+                    "step": 1,
+                    "display": "number"
+                })
+            },
+        }
+
+    RETURN_TYPES = ("STRING",)
+    # RETURN_NAMES = ("image_output_name",)
+
+    FUNCTION = "intToString"
+
+    # OUTPUT_NODE = False
+
+    CATEGORY = "badger"
+
+    def intToString(self, int):
+        return (str(int),)
+
+class FloatToString:
+
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(s):
+
+        return {
+            "required": {
+                    "float": ("FLOAT", {
+                    "default": 1.0,
+                    "min": 0.0,
+                    "max": 10.0,
+                    "step": 0.00001,
+                    "round": False,
+                    "display": "number"})
+
+            },
+        }
+
+    RETURN_TYPES = ("STRING",)
+    # RETURN_NAMES = ("image_output_name",)
+
+    FUNCTION = "floatToString"
+
+    # OUTPUT_NODE = False
+
+    CATEGORY = "badger"
+
+    def floatToString(self, float):
+        return (str(float),)
+
+
+NODE_CLASS_MAPPINGS = {
+    "ImageOverlap-badger": ImageOverlap,
+    "FloatToInt-badger": FloatToInt,
+    "IntToString-badger": IntToString,
+    "FloatToString-badger": FloatToString
+
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "ImageOverlap": "Example test"
+}