Initial version of voronoi mask

works but WIP

nodes.py

@@ -4,6 +4,8 @@ import torch.nn.functional as F
 from torchvision.transforms import Resize, CenterCrop, InterpolationMode
 from torchvision.transforms import functional as TF
 import scipy.ndimage
+from scipy.spatial import Voronoi, voronoi_plot_2d
+import matplotlib.pyplot as plt
 import numpy as np
 from PIL import ImageFilter, Image, ImageDraw, ImageFont
 from PIL.PngImagePlugin import PngInfo
@@ -2115,6 +2117,70 @@ class CreateShapeMask:
 
         return (torch.cat(out, dim=0), 1.0 - torch.cat(out, dim=0),)
     
+class CreateVoronoiMask:
+    
+    RETURN_TYPES = ("MASK", "MASK",)
+    RETURN_NAMES = ("mask", "mask_inverted",)
+    FUNCTION = "createvoronoi"
+    CATEGORY = "KJNodes/masking/generate"
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                 "frames": ("INT", {"default": 1,"min": 1, "max": 4096, "step": 1}),
+                 "num_points": ("INT", {"default": 15,"min": 1, "max": 4096, "step": 1}),
+                 "line_width": ("INT", {"default": 4,"min": 1, "max": 4096, "step": 1}),
+                 "speed": ("FLOAT", {"default": 0.5,"min": 0.0, "max": 1.0, "step": 0.01}),
+                 "frame_width": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
+                 "frame_height": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
+        },
+    } 
+
+    def createvoronoi(self, frames, num_points, line_width, speed, frame_width, frame_height):
+        # Define the number of images in the batch
+        batch_size = frames
+        out = []
+          
+         # Create start and end points for each point
+        start_points = np.random.rand(num_points, 2)
+        end_points = np.random.rand(num_points, 2)
+
+        for i in range(batch_size):
+            # Interpolate the points' positions based on the current frame
+            t = (i * speed) / (batch_size - 1)  # normalize to [0, 1] over the frames
+            t = np.clip(t, 0, 1)  # ensure t is in [0, 1]
+            points = (1 - t) * start_points + t * end_points  # lerp
+
+            vor = Voronoi(points)
+
+            # Create a blank image with a white background
+            fig, ax = plt.subplots()
+            plt.subplots_adjust(left=0, right=1, bottom=0, top=1)
+            ax.set_xlim([0, 1]); ax.set_ylim([0, 1])
+            ax.axis('off')
+            ax.margins(0, 0)
+            fig.set_size_inches(frame_width/100, frame_height/100)
+            ax.fill_between([0, 1], [0, 1], color='white')
+
+            # Plot each Voronoi ridge
+            for simplex in vor.ridge_vertices:
+                simplex = np.asarray(simplex)
+                if np.all(simplex >= 0):
+                    plt.plot(vor.vertices[simplex, 0], vor.vertices[simplex, 1], 'k-', linewidth=line_width)
+
+            fig.canvas.draw()
+            img = np.array(fig.canvas.renderer._renderer)
+
+            plt.close(fig)
+
+            pil_img = Image.fromarray(img).convert("L")
+            mask = torch.tensor(np.array(pil_img)) / 255.0
+
+            out.append(mask)
+
+        return (torch.stack(out, dim=0), 1.0 - torch.stack(out, dim=0),)
+    
 NODE_CLASS_MAPPINGS = {
     "INTConstant": INTConstant,
     "FloatConstant": FloatConstant,
@@ -2153,6 +2219,7 @@ NODE_CLASS_MAPPINGS = {
     "OffsetMask": OffsetMask,
     "WidgetToString": WidgetToString,
     "CreateShapeMask": CreateShapeMask,
+    "CreateVoronoiMask": CreateVoronoiMask,
 }
 NODE_DISPLAY_NAME_MAPPINGS = {
     "INTConstant": "INT Constant",
@@ -2191,4 +2258,5 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "OffsetMask": "OffsetMask",
     "WidgetToString": "WidgetToString",
     "CreateShapeMask": "CreateShapeMask",
+    "CreateVoronoiMask": "CreateVoronoiMask",
 }