Adds AsciiArt effect

makes image composed of ascii characters

README.md

@@ -19,6 +19,7 @@ Both images have the workflow attached, and are included with the repo. Feel fre
 <br/>
 
  - ArithmeticBlend: Blends two images using arithmetic operations like addition, subtraction, and difference.
+ - AsciiArt: Transforms an image into being composed of ASCII characters
  - Blend: Blends two images together with a variety of different modes
  - Blur: Applies a Gaussian blur to the input image, softening the details
  - CannyEdgeMask: Creates a mask using canny edge detection

post_processing/ascii_art.py

@@ -0,0 +1,74 @@
+from PIL import Image, ImageDraw, ImageFont
+import numpy as np
+import torch
+
+class AsciiArt:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "char_size": ("INT", {
+                    "default": 12,
+                    "min": 0,
+                    "max": 64,
+                    "step": 2,
+                }),
+                "font_size": ("INT", {
+                    "default": 12,
+                    "min": 0,
+                    "max": 64,
+                    "step": 2,
+                }),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "apply_ascii_art_effect"
+
+    CATEGORY = "postprocessing/Effects"
+
+    def apply_ascii_art_effect(self, image: torch.Tensor, char_size: int, font_size: int):
+        batch_size, height, width, channels = image.shape
+        result = torch.zeros_like(image)
+
+        for b in range(batch_size):
+            img_b = image[b] * 255.0
+            img_b = Image.fromarray(img_b.numpy().astype('uint8'), 'RGB')
+            result_b = ascii_art_effect(img_b, char_size, font_size)
+            result_b = torch.tensor(np.array(result_b)) / 255.0
+            result[b] = result_b
+
+        return (result,)
+
+
+def ascii_art_effect(image: torch.Tensor, char_size: int, font_size: int):
+    chars = " .'`^\",:;I1!i><-+_-?][}{1)(|\/tfjrxnuvczXYUCLQ0OZmwqpbdkhao*#MW&8%B@$"
+    small_image = image.resize((image.size[0] // char_size, image.size[1] // char_size), Image.Resampling.NEAREST)
+
+    def get_char(value):
+        return chars[value * len(chars) // 256]
+
+    ascii_image = Image.new('RGB', image.size, (0, 0, 0))
+    font = ImageFont.truetype("arial.ttf", font_size)
+    draw_image = ImageDraw.Draw(ascii_image)
+
+    for i in range(small_image.height):
+        for j in range(small_image.width):
+            r, g, b = small_image.getpixel((j, i))
+            k = (r + g + b) // 3
+            draw_image.text(
+                (j * char_size, i * char_size),
+                get_char(k),
+                font=font,
+                fill=(r, g, b)
+            )
+
+    return ascii_image
+
+NODE_CLASS_MAPPINGS = {
+    "AsciiArt": AsciiArt,
+}

post_processing_nodes.py

@@ -1,7 +1,8 @@
 import torch
+from PIL import Image, ImageDraw, ImageFont
+import numpy as np
 import torch.nn.functional as F
 import cv2
-import numpy as np
 from PIL import Image, ImageEnhance
 from PIL import Image
 
@@ -47,6 +48,73 @@ class ArithmeticBlend:
     def difference(self, img1, img2):
         return torch.abs(img1 - img2)
 
+class AsciiArt:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "char_size": ("INT", {
+                    "default": 12,
+                    "min": 0,
+                    "max": 64,
+                    "step": 2,
+                }),
+                "font_size": ("INT", {
+                    "default": 12,
+                    "min": 0,
+                    "max": 64,
+                    "step": 2,
+                }),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "apply_ascii_art_effect"
+
+    CATEGORY = "postprocessing/Effects"
+
+    def apply_ascii_art_effect(self, image: torch.Tensor, char_size: int, font_size: int):
+        batch_size, height, width, channels = image.shape
+        result = torch.zeros_like(image)
+
+        for b in range(batch_size):
+            img_b = image[b] * 255.0
+            img_b = Image.fromarray(img_b.numpy().astype('uint8'), 'RGB')
+            result_b = ascii_art_effect(img_b, char_size, font_size)
+            result_b = torch.tensor(np.array(result_b)) / 255.0
+            result[b] = result_b
+
+        return (result,)
+
+
+def ascii_art_effect(image: torch.Tensor, char_size: int, font_size: int):
+    chars = " .'`^\",:;I1!i><-+_-?][}{1)(|\/tfjrxnuvczXYUCLQ0OZmwqpbdkhao*#MW&8%B@$"
+    small_image = image.resize((image.size[0] // char_size, image.size[1] // char_size), Image.Resampling.NEAREST)
+
+    def get_char(value):
+        return chars[value * len(chars) // 256]
+
+    ascii_image = Image.new('RGB', image.size, (0, 0, 0))
+    font = ImageFont.truetype("arial.ttf", font_size)
+    draw_image = ImageDraw.Draw(ascii_image)
+
+    for i in range(small_image.height):
+        for j in range(small_image.width):
+            r, g, b = small_image.getpixel((j, i))
+            k = (r + g + b) // 3
+            draw_image.text(
+                (j * char_size, i * char_size),
+                get_char(k),
+                font=font,
+                fill=(r, g, b)
+            )
+
+    return ascii_image
+
 class Blend:
     def __init__(self):
         pass
@@ -1372,6 +1440,7 @@ def pixel_sort(img, mask, horizontal_sort=False, span_limit=None, sort_by='H', r
 
 NODE_CLASS_MAPPINGS = {
     "ArithmeticBlend": ArithmeticBlend,
+    "AsciiArt": AsciiArt,
     "Blend": Blend,
     "Blur": Blur,
     "CannyEdgeMask": CannyEdgeMask,