194 lines
8.4 KiB
Python
194 lines
8.4 KiB
Python
import torch
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import numpy as np
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import requests
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import os
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import io
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import json
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from PIL import Image
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import folder_paths
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try:
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import cairosvg
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except ImportError:
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print("Warning: cairosvg is not installed. The 'scaled_png' output format will not work.")
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print("Please install it by running: pip install cairosvg")
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print("On Debian/Ubuntu, you may also need to run: sudo apt-get install libcairo2-dev pkg-config")
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cairosvg = None
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class VectorizerAINode:
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def __init__(self):
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self.output_dir = folder_paths.get_output_directory()
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self.config_id = None
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self.config_secret = None
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try:
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node_dir = os.path.dirname(__file__)
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config_path = os.path.join(node_dir, 'config.json')
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with open(config_path, 'r') as f:
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config = json.load(f)
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self.config_id = config.get('api_id')
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self.config_secret = config.get('api_secret')
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if self.config_id and self.config_secret:
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print("Vectorizer.ai API: Found and loaded credentials from config.json.")
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except FileNotFoundError:
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pass
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except Exception as e:
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print(f"Vectorizer.ai API: Error loading config.json: {e}")
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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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"api_id": ("STRING", {"default": "YOUR_API_ID_HERE"}),
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"api_secret": ("STRING", {"default": "YOUR_API_SECRET_HERE", "multiline": True}),
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"output_format": (["svg", "png", "scaled_png"], {"default": "svg"}),
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"mode": (["production", "test"], {"default": "production"}),
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"scale": ("FLOAT", {"default": 4.0, "min": 1.0, "max": 16.0, "step": 0.5}),
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},
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"optional": {
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"filename_prefix": ("STRING", {"default": "vectorized/vector"}),
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"max_colors": ("INT", {"default": 0, "min": 0, "max": 256, "step": 1, "display": "slider"}),
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"min_shape_area": ("FLOAT", {"default": 0.125, "min": 0.0, "max": 100.0, "step": 0.001}),
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"adobe_compatibility": ("BOOLEAN", {"default": False}),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "process_vectorization"
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CATEGORY = "Conversion"
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def process_vectorization(self, image, api_id, api_secret, output_format, mode, scale, filename_prefix="vectorized/vector", max_colors=0, min_shape_area=0.125, adobe_compatibility=False):
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final_api_id = api_id
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final_api_secret = api_secret
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is_id_empty = (final_api_id == "YOUR_API_ID_HERE" or not final_api_id.strip())
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is_secret_empty = (final_api_secret == "YOUR_API_SECRET_HERE" or not final_api_secret.strip())
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if is_id_empty and is_secret_empty:
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if self.config_id and self.config_secret:
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print("Using credentials from config.json.")
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final_api_id = self.config_id
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final_api_secret = self.config_secret
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if not final_api_id or final_api_id == "YOUR_API_ID_HERE":
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print("Vectorizer.AI Error: API ID is missing. Please provide it in the UI or in config.json.")
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return (image,)
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pil_image = Image.fromarray((255. * image[0].cpu().numpy()).astype(np.uint8))
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img_buffer = io.BytesIO()
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pil_image.save(img_buffer, format='PNG')
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img_buffer.seek(0)
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api_output_format = 'svg' if output_format == 'scaled_png' else output_format
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data = {"mode": mode, "output.file_format": api_output_format}
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if max_colors > 0: data["processing.max_colors"] = max_colors
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if min_shape_area > 0: data["processing.shapes.min_area_px"] = min_shape_area
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if api_output_format == 'svg' and adobe_compatibility: data["output.svg.adobe_compatibility_mode"] = "true"
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print("Sending image to Vectorizer.AI for processing...")
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try:
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response = requests.post(
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'https://vectorizer.ai/api/v1/vectorize',
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files={'image': img_buffer}, data=data,
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auth=(final_api_id, final_api_secret),
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timeout=60
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)
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response.raise_for_status()
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except requests.exceptions.RequestException as e:
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print(f"Vectorizer.AI API Error: {e}")
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return (image,)
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print("Processing complete.")
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full_output_folder, filename, counter, subfolder, _ = folder_paths.get_save_image_path(filename_prefix, self.output_dir)
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if output_format == 'scaled_png':
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if cairosvg is None: return (image,)
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svg_filename = f"{filename}_{counter:05}.svg"
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svg_path = os.path.join(full_output_folder, svg_filename)
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with open(svg_path, 'wb') as f: f.write(response.content)
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print(f"Saved vector file to: {svg_path}")
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try:
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print(f"Scaling SVG to {scale}x PNG...")
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png_bytes = cairosvg.svg2png(bytestring=response.content, scale=scale)
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png_filename = f"{filename}_{counter:05}_scaled_{scale}x.png"
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png_path = os.path.join(full_output_folder, png_filename)
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with open(png_path, 'wb') as f: f.write(png_bytes)
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print(f"Saved scaled PNG to: {png_path}")
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processed_pil_image = Image.open(io.BytesIO(png_bytes)).convert("RGB")
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processed_np_image = np.array(processed_pil_image).astype(np.float32) / 255.0
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return (torch.from_numpy(processed_np_image)[None,],)
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except Exception as e:
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print(f"ERROR during SVG to PNG conversion: {e}")
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return (image,)
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elif output_format == 'png':
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file = f"{filename}_{counter:05}.png"
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path = os.path.join(full_output_folder, file)
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with open(path, 'wb') as f: f.write(response.content)
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print(f"Saved API-generated PNG to: {path}")
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processed_pil_image = Image.open(io.BytesIO(response.content)).convert("RGB")
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processed_np_image = np.array(processed_pil_image).astype(np.float32) / 255.0
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return (torch.from_numpy(processed_np_image)[None,],)
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else:
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file = f"{filename}_{counter:05}.svg"
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path = os.path.join(full_output_folder, file)
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with open(path, 'wb') as f: f.write(response.content)
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print(f"Saved vector file to: {path}")
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return (image,)
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class BackgroundRemoverNode:
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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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"threshold": ("FLOAT", {"default": 0.1, "min": 0.0, "max": 1.0, "step": 0.01}),
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"invert_mask": ("BOOLEAN", {"default": False}),
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}
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}
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RETURN_TYPES = ("IMAGE", "MASK",)
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RETURN_NAMES = ("image_rgba", "mask",)
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FUNCTION = "remove_background"
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CATEGORY = "Conversion"
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def remove_background(self, image, threshold, invert_mask):
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img_np = np.clip(255. * image[0].cpu().numpy(), 0, 255).astype(np.uint8)
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top_border = img_np[0, :, :]
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bottom_border = img_np[-1, :, :]
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left_border = img_np[:, 0, :]
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right_border = img_np[:, -1, :]
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border_pixels = np.concatenate([top_border, bottom_border, left_border, right_border], axis=0)
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key_color = np.mean(border_pixels, axis=0)
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print(f"Automatically detected background color: R={int(key_color[0])}, G={int(key_color[1])}, B={int(key_color[2])}")
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distances = np.linalg.norm(img_np.astype(np.float32) - key_color.astype(np.float32), axis=2)
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threshold_value = threshold * 255.0
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mask_bool = distances > threshold_value
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if invert_mask:
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mask_bool = ~mask_bool
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mask_float = mask_bool.astype(np.float32)
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alpha_channel = (mask_float * 255).astype(np.uint8)
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rgba_np = np.dstack((img_np, alpha_channel))
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image_rgba_tensor = torch.from_numpy(rgba_np.astype(np.float32) / 255.0)[None,]
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mask_tensor = torch.from_numpy(mask_float)[None,]
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return (image_rgba_tensor, mask_tensor)
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NODE_CLASS_MAPPINGS = {
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"VectorizerAINode": VectorizerAINode,
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"BackgroundRemoverNode": BackgroundRemoverNode
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"VectorizerAINode": "Vectorizer.ai API",
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"BackgroundRemoverNode": "Background Remover (Color)"
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}
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