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svgnode.py
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svgnode.py
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import vtracer
import os
import time
import folder_paths
import numpy as np
from PIL import Image
from typing import List, Tuple
import torch
from base64 import b64encode
def RGB2RGBA(image:Image, mask:Image) -> Image:
(R, G, B) = image.convert('RGB').split()
return Image.merge('RGBA', (R, G, B, mask.convert('L')))
def pil2tensor(image:Image) -> torch.Tensor:
return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
def tensor2pil(t_image: torch.Tensor) -> Image:
return Image.fromarray(np.clip(255.0 * t_image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
class ConvertRasterToVector:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"image": ("IMAGE",),
"colormode": (["color", "binary"], {"default": "color"}),
"mode": (["spline", "polygon"], {"default": "spline"}),
"filter_speckle": ("INT", {"default": 4, "min": 0, "max": 20}),
"color_precision": ("INT", {"default": 8, "min": 1, "max": 16}),
"corner_threshold": ("INT", {"default": 80, "min": 0, "max": 180}),
"length_threshold": ("FLOAT", {"default": 2.0, "min": 0.5, "max": 10.0}),
"max_iterations": ("INT", {"default": 15, "min": 1, "max": 50}),
"splice_threshold": ("INT", {"default": 45, "min": 0, "max": 180}),
"path_precision": ("INT", {"default": 5, "min": 1, "max": 10}),
}
}
RETURN_TYPES = ("LIST",)
FUNCTION = "convert_to_svg"
CATEGORY = "AI API"
def convert_to_svg(self, image, colormode, mode, filter_speckle, color_precision, corner_threshold, length_threshold, max_iterations, splice_threshold, path_precision):
svg_strings = []
for i in image:
i = torch.unsqueeze(i, 0)
_image = tensor2pil(i)
if _image.mode != 'RGBA':
alpha = Image.new('L', _image.size, 255)
_image.putalpha(alpha)
pixels = list(_image.getdata())
size = _image.size
svg_str = vtracer.convert_pixels_to_svg(
pixels,
size=size,
colormode=colormode,
hierarchical="stacked", # Fixed to "stacked" for better results
mode=mode,
filter_speckle=filter_speckle,
color_precision=color_precision,
corner_threshold=corner_threshold,
length_threshold=length_threshold,
max_iterations=max_iterations,
splice_threshold=splice_threshold,
path_precision=path_precision
)
svg_strings.append(svg_str)
return (svg_strings,)
class SaveSVG:
def __init__(self):
self.output_dir = folder_paths.get_output_directory()
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"svg_strings": ("LIST", {"forceInput": True}),
"filename_prefix": ("STRING", {"default": "ComfyUI_SVG"}),
},
"optional": {
"append_timestamp": ("BOOLEAN", {"default": True}),
"custom_output_path": ("STRING", {"default": "", "multiline": False}),
}
}
CATEGORY = "AI API"
DESCRIPTION = "Save SVG data to a file."
RETURN_TYPES = ("IMAGE",)
OUTPUT_NODE = True
FUNCTION = "save_svg_file"
def generate_unique_filename(self, prefix, timestamp=False):
if timestamp:
timestamp_str = time.strftime("%Y%m%d%H%M%S")
return f"{prefix}_{timestamp_str}.svg"
else:
return f"{prefix}.svg"
def save_svg_file(self, svg_strings, filename_prefix="ComfyUI_SVG", append_timestamp=True, custom_output_path=""):
output_path = custom_output_path if custom_output_path else self.output_dir
os.makedirs(output_path, exist_ok=True)
ui_info_list = []
for index, svg_string in enumerate(svg_strings):
unique_filename = self.generate_unique_filename(f"{filename_prefix}_{index}", append_timestamp)
final_filepath = os.path.join(output_path, unique_filename)
with open(final_filepath, "w") as svg_file:
svg_file.write(svg_string)
# Encode SVG for preview
ui_info = {
"ui": {
"saved_svg": unique_filename,
"path": final_filepath,
}
}
ui_info_list.append(ui_info)
return ui_info_list