242 lines
8.0 KiB
Python
242 lines
8.0 KiB
Python
import vtracer
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import os
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import time
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import folder_paths
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import numpy as np
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from PIL import Image
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from typing import List, Tuple
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import torch
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from io import BytesIO
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import fitz
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import random
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import folder_paths
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from PIL import Image
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from nodes import SaveImage
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# Tensor to PIL
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def tensor2pil(image):
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return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
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# PIL to Tensor
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def pil2tensor(image):
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return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
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class ConvertRasterToVectorColor:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"image": ("IMAGE",),
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"hierarchical": (["stacked", "cutout"], {"default": "stacked"}),
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"mode": (["spline", "polygon", "none"], {"default": "spline"}),
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"filter_speckle": ("INT", {"default": 4, "min": 0, "max": 100, "step": 1}),
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"color_precision": ("INT", {"default": 6, "min": 0, "max": 10, "step": 1}),
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"layer_difference": ("INT", {"default": 16, "min": 0, "max": 256, "step": 1}),
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"corner_threshold": ("INT", {"default": 60, "min": 0, "max": 180, "step": 1}),
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"length_threshold": ("FLOAT", {"default": 4.0, "min": 0.0, "max": 10.0, "step": 0.1}),
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"max_iterations": ("INT", {"default": 10, "min": 1, "max": 70, "step": 1}),
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"splice_threshold": ("INT", {"default": 45, "min": 0, "max": 180, "step": 1}),
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"path_precision": ("INT", {"default": 3, "min": 0, "max": 10, "step": 1}),
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}
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}
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RETURN_TYPES = ("STRING",)
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OUTPUT_IS_LIST = (True,)
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FUNCTION = "convert_to_svg"
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CATEGORY = "💎TOSVG"
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def convert_to_svg(self, image, hierarchical, mode, filter_speckle, color_precision, layer_difference, corner_threshold,
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length_threshold, max_iterations, splice_threshold, path_precision):
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svg_strings = []
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for i in image:
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i = torch.unsqueeze(i, 0)
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_image = tensor2pil(i)
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if _image.mode != 'RGBA':
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alpha = Image.new('L', _image.size, 255)
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_image.putalpha(alpha)
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pixels = list(_image.getdata())
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size = _image.size
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svg_str = vtracer.convert_pixels_to_svg(
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pixels,
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size=size,
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colormode="color",
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hierarchical=hierarchical,
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mode=mode,
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filter_speckle=filter_speckle,
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color_precision=color_precision,
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layer_difference=layer_difference,
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corner_threshold=corner_threshold,
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length_threshold=length_threshold,
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max_iterations=max_iterations,
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splice_threshold=splice_threshold,
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path_precision=path_precision,
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)
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svg_strings.append(svg_str)
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return (svg_strings,)
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class ConvertRasterToVectorBW:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"image": ("IMAGE",),
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"mode": (["spline", "polygon", "none"], {"default": "spline"}),
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"filter_speckle": ("INT", {"default": 4, "min": 0, "max": 100, "step": 1}),
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"corner_threshold": ("INT", {"default": 60, "min": 0, "max": 180, "step": 1}),
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"length_threshold": ("FLOAT", {"default": 4.0, "min": 0.0, "max": 10.0, "step": 0.1}),
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"splice_threshold": ("INT", {"default": 45, "min": 0, "max": 180, "step": 1}),
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}
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}
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RETURN_TYPES = ("STRING",)
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OUTPUT_IS_LIST = (True,)
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FUNCTION = "convert_to_svg"
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CATEGORY = "💎TOSVG"
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def convert_to_svg(self, image, mode, filter_speckle, corner_threshold, length_threshold, splice_threshold):
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svg_strings = []
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for i in image:
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i = torch.unsqueeze(i, 0)
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_image = tensor2pil(i)
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if _image.mode != 'RGBA':
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alpha = Image.new('L', _image.size, 255)
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_image.putalpha(alpha)
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pixels = list(_image.getdata())
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size = _image.size
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svg_str = vtracer.convert_pixels_to_svg(
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pixels,
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size=size,
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colormode="binary",
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mode=mode,
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filter_speckle=filter_speckle,
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corner_threshold=corner_threshold,
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length_threshold=length_threshold,
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splice_threshold=splice_threshold,
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)
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svg_strings.append(svg_str)
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return (svg_strings,)
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class ConvertVectorToRaster:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"svg_strings": ("STRING", {"forceInput": True})
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}
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "convert_svg_to_image"
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CATEGORY = "💎TOSVG"
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def convert_svg_to_image(self, svg_strings):
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doc = fitz.open(stream=svg_strings.encode('utf-8'), filetype="svg")
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page = doc.load_page(0)
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pix = page.get_pixmap()
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image_data = pix.tobytes("png")
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pil_image = Image.open(BytesIO(image_data)).convert("RGB")
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return (pil2tensor(pil_image),)
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class SaveSVG:
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def __init__(self):
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self.output_dir = folder_paths.get_output_directory()
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"svg_strings": ("STRING", {"forceInput": True}),
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"filename_prefix": ("STRING", {"default": "ComfyUI_SVG"}),
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},
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"optional": {
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"append_timestamp": ("BOOLEAN", {"default": True}),
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"custom_output_path": ("STRING", {"default": "", "multiline": False}),
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}
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}
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CATEGORY = "💎TOSVG"
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DESCRIPTION = "Save SVG data to a file."
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RETURN_TYPES = ()
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OUTPUT_NODE = True
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FUNCTION = "save_svg_file"
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def generate_unique_filename(self, prefix, timestamp=False):
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if timestamp:
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timestamp_str = time.strftime("%Y%m%d%H%M%S")
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return f"{prefix}_{timestamp_str}.svg"
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else:
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return f"{prefix}.svg"
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def save_svg_file(self, svg_strings, filename_prefix="ComfyUI_SVG", append_timestamp=True, custom_output_path=""):
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output_path = custom_output_path if custom_output_path else self.output_dir
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os.makedirs(output_path, exist_ok=True)
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unique_filename = self.generate_unique_filename(f"{filename_prefix}", append_timestamp)
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final_filepath = os.path.join(output_path, unique_filename)
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with open(final_filepath, "w") as svg_file:
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svg_file.write(svg_strings)
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ui_info = {"ui": {"saved_svg": unique_filename, "path": final_filepath}}
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return ui_info
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class SVGPreview(SaveImage):
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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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"svg_strings": ("STRING", {"forceInput": True})
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}
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}
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FUNCTION = "svg_preview"
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CATEGORY = "💎TOSVG"
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OUTPUT_NODE = True
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def __init__(self):
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self.output_dir = folder_paths.get_temp_directory()
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self.type = "temp"
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self.prefix_append = "_temp_" + ''.join(random.choice("abcdefghijklmnopqrstupvxyz1234567890") for x in range(5))
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self.compress_level = 4
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def svg_preview(self, svg_strings):
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doc = fitz.open(stream=svg_strings.encode('utf-8'), filetype="svg")
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page = doc.load_page(0)
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pix = page.get_pixmap()
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image_data = pix.tobytes("png")
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pil_image = Image.open(BytesIO(image_data)).convert("RGB")
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preview = pil2tensor(pil_image)
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return self.save_images(preview, "PointPreview")
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