first commit

__init__.py

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+from .image_kmeans_filter import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
+
+__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']

image_kmeans_filter.py

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+import numpy as np
+from PIL import Image
+import torch
+import cv2
+
+# Tensor to PIL
+
+
+def tensor2pil(image):
+    return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
+
+# PIL to Tensor
+
+
+def pil2tensor(image):
+    return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
+
+
+class ImageKmeansFilter:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": ("IMAGE", ),
+                "k": ("INT", {"default": 18}),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("image",)
+    FUNCTION = "apply_kmeans_filter"
+    CATEGORY = "Tools"
+
+    def reduce_colors_kmeans(image, n_clusters=8):
+        """
+        使用 K-means 聚类算法减少图像颜色。
+
+        Args:
+        image: PIL Image 对象.
+        n_clusters: 聚类数量，即最终图像的颜色数量.
+
+        Returns:
+        颜色减少后的 PIL Image 对象.
+        """
+
+        # 将 PIL Image 转换为 NumPy 数组
+        img = np.array(image)
+
+        # OpenCV 期望颜色通道顺序为 BGR，PIL 使用 RGB
+        img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
+
+        # 将图像转换为二维数组，每行代表一个像素
+        pixels = img.reshape((-1, 3))
+
+        # 将像素数据类型转换为 float32
+        pixels = np.float32(pixels)
+
+        # 定义 K-means 聚类参数
+        criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)
+
+        # 执行 K-means 聚类
+        _, labels, centers = cv2.kmeans(pixels, n_clusters, None, criteria, 10, cv2.KMEANS_PP_CENTERS)
+
+        # 将聚类中心转换为 uint8 类型
+        centers = np.uint8(centers)
+
+        # 使用聚类标签重建图像
+        reduced_image = centers[labels.flatten()]
+
+        # 将图像 reshape 回原始尺寸
+        reduced_image = reduced_image.reshape(img.shape)
+
+        # 将图像转换回 RGB 色彩空间
+        reduced_image = cv2.cvtColor(reduced_image, cv2.COLOR_BGR2RGB)
+
+        # 将 NumPy 数组转换回 PIL Image
+        reduced_image = Image.fromarray(reduced_image)
+
+        return reduced_image
+
+    def apply_kmeans_filter(self, image, k):
+        tensors = []
+        if len(image) > 1:
+            for img in image:
+
+                pil_image = None
+                # PIL Image
+                pil_image = tensor2pil(img)
+
+                # Apply Median Fliter
+                new_img = self.reduce_colors_kmeans(pil_image, k)
+ 
+                # Output image
+                out_image = (pil2tensor(new_img) if pil_image else img)
+
+                tensors.append(out_image)
+
+            tensors = torch.cat(tensors, dim=0)
+
+        else:
+            pil_image = None
+            img = image
+            # PIL Image
+
+            pil_image = tensor2pil(img)
+            # Apply Median Fliter
+            new_img = self.reduce_colors_kmeans(pil_image, k)
+
+            # Output image
+            out_image = (pil2tensor(new_img) if pil_image else img)
+
+            tensors = out_image
+
+        return (tensors, )
+
+
+# Set the web directory, any .js file in that directory will be loaded by the frontend as a frontend extension
+# WEB_DIRECTORY = "./somejs"
+
+
+# A dictionary that contains all nodes you want to export with their names
+# NOTE: names should be globally unique
+NODE_CLASS_MAPPINGS = {
+    "ImageKmeansFilter": ImageKmeansFilter
+}
+
+# A dictionary that contains the friendly/humanly readable titles for the nodes
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "ImageKmeansFilter": "Apply Kmeans Filter"
+}