dodge and burn, dissolve, and arithmetic blend

2023-04-02 19:10:51 -04:00
parent d38160eb73
commit 8da09a5812
6 changed files with 327 additions and 83 deletions
@@ -0,0 +1,56 @@
+import torch
+import torch.nn.functional as F
+
+class Blur:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "blur_radius": ("INT", {
+                    "default": 1,
+                    "min": 1,
+                    "max": 15,
+                    "step": 1
+                }),
+                "sigma": ("FLOAT", {
+                    "default": 1.0,
+                    "min": 0.1,
+                    "max": 10.0,
+                    "step": 0.1
+                }),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "blur"
+
+    CATEGORY = "postprocessing"
+
+    def gaussian_kernel(self, kernel_size: int, sigma: float):
+        x, y = torch.meshgrid(torch.linspace(-1, 1, kernel_size), torch.linspace(-1, 1, kernel_size))
+        d = torch.sqrt(x * x + y * y)
+        g = torch.exp(-(d * d) / (2.0 * sigma * sigma))
+        return g / g.sum()
+
+    def blur(self, image: torch.Tensor, blur_radius: int, sigma: float):
+        if blur_radius == 0:
+            return (image,)
+
+        batch_size, height, width, channels = image.shape
+
+        kernel_size = blur_radius * 2 + 1
+        kernel = self.gaussian_kernel(kernel_size, sigma).repeat(channels, 1, 1).unsqueeze(1)
+
+        image = image.permute(0, 3, 1, 2) # Torch wants (B, C, H, W) we use (B, H, W, C)
+        blurred = F.conv2d(image, kernel, padding=kernel_size // 2, groups=channels)
+        blurred = blurred.permute(0, 2, 3, 1)
+
+        return (blurred,)
+
+NODE_CLASS_MAPPINGS = {
+    "Blur": Blur
+}