Adds SineWave Effect

Author: EllangoK

README.md

@@ -37,6 +37,7 @@ Both images have the workflow attached, and are included with the repo. Feel fre
  - Pixelize: Applies a pixelization effect, simulating the reducing of resolution
  - $\color{#00A7B5}\textbf{Quantize:}$ Set and dither the amount of colors in an image from 0-256, reducing color information
  - Sharpen: Enhances the details in an image by applying a sharpening filter
+ - SineWave: Runs a sine wave through the image, making it appear squiggly
  - $\color{#00A7B5}\textbf{Solarize:}$ Inverts image colors based on a threshold for a striking, high-contrast effect
  - Vignette: Applies a vignette effect, putting the corners of the image in shadow
 

post_processing/kuwahara_blur.py

@@ -1,6 +1,5 @@
 import cv2
 import numpy as np
-import multiprocessing as mp
 import torch
 
 class KuwaharaBlur:

post_processing/sine_wave.py

@@ -0,0 +1,56 @@
+import torch
+import numpy as np
+
+class SineWave:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "amplitude": ("FLOAT", {
+                    "default": 50,
+                    "min": 0,
+                    "max": 150,
+                    "step": 5
+                }),
+                "frequency": ("FLOAT", {
+                    "default": 5,
+                    "min": 0,
+                    "max": 20,
+                    "step": 1
+                }),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "apply_sine_wave"
+
+    CATEGORY = "postprocessing/Effects"
+
+    def apply_sine_wave(self, image: torch.Tensor, amplitude: float, frequency: float):
+        batch_size, height, width, channels = image.shape
+        result = torch.zeros_like(image)
+
+        for b in range(batch_size):
+            tensor_image = image[b]
+            result[b] = self.sine_wave_effect(tensor_image, amplitude, frequency)
+
+        return (result,)
+
+    def sine_wave_effect(self, image: torch.Tensor, amplitude: float, frequency: float):
+        height, width, _ = image.shape
+        shifted_image = torch.zeros_like(image)
+
+        for channel in range(3):
+            for i in range(height):
+                offset = int(amplitude * np.sin(2 * torch.pi * i * frequency / height))
+                shifted_image[i, :, channel] = torch.roll(image[i, :, channel], offset)
+
+        return shifted_image
+
+NODE_CLASS_MAPPINGS = {
+    "SineWave": SineWave,
+}

post_processing_nodes.py

@@ -3,7 +3,6 @@
 import cv2
 import numpy as np
 from PIL import Image, ImageEnhance
-import multiprocessing as mp
 from PIL import Image
 
 
@@ -1149,6 +1148,56 @@ def sharpen(self, image: torch.Tensor, blur_radius: int, alpha: float):
 
         return (result,)
 
+class SineWave:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "amplitude": ("FLOAT", {
+                    "default": 50,
+                    "min": 0,
+                    "max": 150,
+                    "step": 5
+                }),
+                "frequency": ("FLOAT", {
+                    "default": 5,
+                    "min": 0,
+                    "max": 20,
+                    "step": 1
+                }),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "apply_sine_wave"
+
+    CATEGORY = "postprocessing/Effects"
+
+    def apply_sine_wave(self, image: torch.Tensor, amplitude: float, frequency: float):
+        batch_size, height, width, channels = image.shape
+        result = torch.zeros_like(image)
+
+        for b in range(batch_size):
+            tensor_image = image[b]
+            result[b] = self.sine_wave_effect(tensor_image, amplitude, frequency)
+
+        return (result,)
+
+    def sine_wave_effect(self, image: torch.Tensor, amplitude: float, frequency: float):
+        height, width, _ = image.shape
+        shifted_image = torch.zeros_like(image)
+
+        for channel in range(3):
+            for i in range(height):
+                offset = int(amplitude * np.sin(2 * torch.pi * i * frequency / height))
+                shifted_image[i, :, channel] = torch.roll(image[i, :, channel], offset)
+
+        return shifted_image
+
 class Solarize:
     def __init__(self):
         pass
@@ -1335,6 +1384,7 @@ def pixel_sort(img, mask, horizontal_sort=False, span_limit=None, sort_by='H', r
     "Pixelize": Pixelize,
     "Quantize": Quantize,
     "Sharpen": Sharpen,
+    "SineWave": SineWave,
     "Solarize": Solarize,
     "Vignette": Vignette,
 }