FL Ascii

Documentation

• Class name: FL_Ascii
• Category: 🏵️Fill Nodes
• Output node: False

The FL_Ascii node transforms images into ASCII art by resizing the input image based on specified spacing and then mapping pixel brightness values to characters. This process creates a text-based visual representation of the image, utilizing a specified font size, font name, and character set.

Input types

Required

• image
  • The input image to be transformed into ASCII art. It serves as the canvas for the ASCII art effect.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor
• spacing
  • Determines the spacing between characters in the ASCII art, effectively controlling the resolution of the output.
  • Comfy dtype: INT
  • Python dtype: int
• font_size
  • Specifies the font size used for the characters in the ASCII art, impacting the detail and readability of the output.
  • Comfy dtype: INT
  • Python dtype: int
• font_name
  • The name of the font used to render characters in the ASCII art, influencing the style and appearance of the output.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• characters
  • A string of characters used to represent different brightness levels in the ASCII art, where each character's position in the string corresponds to a specific range of brightness.
  • Comfy dtype: STRING
  • Python dtype: str

Output types

• image
  • Comfy dtype: IMAGE
  • The resulting ASCII art as an image, where the original image is represented by a matrix of characters.
  • Python dtype: torch.Tensor

Usage tips

• Infra type: CPU
• Common nodes: unknown

Source code

class FL_Ascii:
    def __init__(self):
        pass


    @classmethod
    def INPUT_TYPES(s):
        font_list = [x.split('/')[-1] for x in matplotlib.font_manager.findSystemFonts(fontpaths=None, fontext="ttf" )]
        return {
            "required": {
                "image": ("IMAGE",),
                "spacing": (
                    "INT",
                    {
                        "default": 20,
                        "min": 4,
                        "max": 100,
                        "step": 2,
                    },
                ),
                "font_size": (
                    "INT",
                    {
                        "default": 20,
                        "min": 4,
                        "max": 100,
                        "step": 2,
                    },
                ),
                "font_name": (font_list,),
                "characters": (
                    "STRING",
                    {
                        "default": "",
                        "description": "characters to use",
                    },
                ),
            },
        }

    RETURN_TYPES = ("IMAGE",)
    FUNCTION = "apply_ascii_art_effect"
    CATEGORY = "🏵️Fill Nodes"

    def apply_ascii_art_effect(
        self,
        image: torch.Tensor,
        spacing: int,
        font_size: int,
        font_name: str,
        characters,
    ):
        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, spacing, font_size, font_name, characters)
            result_b = torch.tensor(np.array(result_b)) / 255.0
            result[b] = result_b

            # Update the print log
            progress = (b + 1) / batch_size * 100
            sys.stdout.write(f"\rProcessing images: {progress:.2f}%")
            sys.stdout.flush()

        # Print a new line after the progress log
        print()

        return (result,)