Fill
0bcd5c3c7c
added the ability to schedule some values in the FX nodes, as well as optimized some code to make them a little faster. Iv also added a % complete print in the console so you can see how far along the processing is =)
104 lines
3.7 KiB
Python
104 lines
3.7 KiB
Python
from PIL import Image, ImageDraw, ImageFont
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import numpy as np
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import torch
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import sys
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class FL_Ascii:
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def __init__(self):
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self.spacing_index = 0
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self.font_size_index = 0
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"image": ("IMAGE",),
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"spacing": ("INT", {
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"default": 20,
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"min": 4,
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"max": 100,
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"step": 2,
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}),
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"font_size": ("INT", {
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"default": 20,
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"min": 4,
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"max": 100,
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"step": 2,
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}),
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"characters": ("STRING", {
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"default": "\._♥♦♣MachineDelusions♣♦♥_./",
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"description": "characters to use"
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}),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "apply_ascii_art_effect"
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CATEGORY = "🏵️Fill Nodes"
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def apply_ascii_art_effect(self, image: torch.Tensor, spacing: int, font_size: int, characters):
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batch_size, height, width, channels = image.shape
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result = torch.zeros_like(image)
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for b in range(batch_size):
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img_b = image[b] * 255.0
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img_b = Image.fromarray(img_b.numpy().astype('uint8'), 'RGB')
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# Check if spacing is a list and get the current value
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if isinstance(spacing, list):
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if self.spacing_index >= len(spacing):
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print("Warning: Spacing list index out of range. Using the last value.")
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self.spacing_index = len(spacing) - 1
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current_spacing = spacing[self.spacing_index]
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self.spacing_index = (self.spacing_index + 1) % len(spacing)
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else:
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current_spacing = spacing
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# Check if font_size is a list and get the current value
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if isinstance(font_size, list):
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if self.font_size_index >= len(font_size):
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print("Warning: Font size list index out of range. Using the last value.")
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self.font_size_index = len(font_size) - 1
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current_font_size = font_size[self.font_size_index]
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self.font_size_index = (self.font_size_index + 1) % len(font_size)
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else:
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current_font_size = font_size
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result_b = ascii_art_effect(img_b, current_spacing, current_font_size, characters)
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result_b = torch.tensor(np.array(result_b)) / 255.0
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result[b] = result_b
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# Update the print log
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progress = (b + 1) / batch_size * 100
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sys.stdout.write(f"\rProcessing images: {progress:.2f}%")
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sys.stdout.flush()
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# Print a new line after the progress log
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print()
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return (result,)
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def ascii_art_effect(image: torch.Tensor, spacing: int, font_size: int, characters):
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chars = characters
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small_image = image.resize((image.size[0] // spacing, image.size[1] // spacing), Image.Resampling.NEAREST)
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def get_char(value):
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return chars[value * len(chars) // 256]
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ascii_image = Image.new('RGB', image.size, (0, 0, 0))
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font = ImageFont.truetype("arial.ttf", font_size)
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draw_image = ImageDraw.Draw(ascii_image)
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for i in range(small_image.height):
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for j in range(small_image.width):
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r, g, b = small_image.getpixel((j, i))
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k = (r + g + b) // 3
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draw_image.text(
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(j * spacing, i * spacing),
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get_char(k),
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font=font,
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fill=(r, g, b)
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)
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return ascii_image |