378 lines
14 KiB
Python
378 lines
14 KiB
Python
# nodes.py
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import json
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import random
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import numpy as np
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import torch
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import torch.nn.functional as F
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from PIL import Image, ImageDraw, ImageFont
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from scipy.cluster.vq import vq
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from sklearn.cluster import KMeans
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class ColorshiftColorNode:
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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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"images": ("IMAGE",),
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"color_count": ("INT", {"default": 8, "min": 2, "max": 64}),
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},
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"optional": {
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"lock_masks": ("MASK", {"default": None}),
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"palette_override": ("PALETTE", {"default": None}),
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"font_size": ("INT", {"default": 20, "min": 10, "max": 50}),
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"sampling_rate": ("FLOAT", {"default": 0.25, "min": 0.01, "max": 0.5}),
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"n_init": ("INT", {"default": 3, "min": 1, "max": 10}),
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},
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}
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RETURN_TYPES = ("IMAGE", "PALETTE", "MASK", "IMAGE")
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RETURN_NAMES = ("images", "palette", "index_maps", "palette_preview")
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FUNCTION = "process"
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CATEGORY = "Image/Color"
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def process(
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self,
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images,
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color_count,
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lock_masks=None,
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palette_override=None,
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font_size=20,
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sampling_rate=0.25,
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n_init=3,
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):
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# デバイスを取得
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device = images.device
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# バッチ処理のために画像をスタック
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resized_images = F.interpolate(
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images.permute(0, 3, 1, 2),
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scale_factor=0.5,
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mode="bilinear",
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align_corners=False,
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).permute(0, 2, 3, 1)
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batch_size, height, width, channels = (
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resized_images.shape
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) # resized_imagesの形状を使用
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pixels = resized_images.reshape(
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batch_size, -1, 3
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) # resized_imagesの形状を元にpixelsを整形
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if palette_override is not None:
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palette = palette_override.to(device)
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# バッチ全体に対して一回の距離計算でラベルを取得
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labels = self._match_palette_batch(pixels, palette)
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else:
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# 複数の画像に対して、それぞれでクラスタリングをすると、計算量も増えるし、パレットがずれる。
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# 解決策として、複数の画像を混ぜてクラスタリングを行う
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# 混ぜてクラスタリング
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pixels_for_clustering = pixels.cpu().numpy().reshape(-1, 3)
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random_indices = np.random.choice(
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pixels_for_clustering.shape[0],
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size=int(pixels_for_clustering.shape[0] * sampling_rate),
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replace=False,
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)
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pixels_for_clustering = pixels_for_clustering[random_indices]
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kmeans = KMeans(n_clusters=color_count, random_state=42, n_init=n_init)
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kmeans.fit(pixels_for_clustering)
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# バッチ内の各画像に対してラベルを取得 (scipy.cluster.vq.vq を使用)
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labels, dist = vq(
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pixels.cpu().numpy().reshape(-1, 3), kmeans.cluster_centers_
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)
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labels = torch.from_numpy(labels.reshape(batch_size, -1)).long().to(device)
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palette = torch.from_numpy(kmeans.cluster_centers_).float().to(device)
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# Generate palette preview (最初の画像のみ, CPUで処理)
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preview_img = self.generate_palette_preview(palette.cpu().numpy(), font_size)
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# バッチ全体のインデックスマップを取得
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index_maps = labels.reshape(batch_size, height, width)
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# GPUで一括処理
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if lock_masks is not None:
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lock_masks_batch = []
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for i in range(batch_size):
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lock_mask = lock_masks[i] if i < lock_masks.shape[0] else lock_masks[-1]
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lock_masks_batch.append(lock_mask)
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lock_masks = torch.stack(lock_masks_batch).to(device)
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pixel_masks = torch.zeros_like(index_maps, dtype=torch.bool)
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for i in range(batch_size):
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for j, is_locked in enumerate(lock_masks[i]):
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if is_locked:
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pixel_masks[i][index_maps[i] == j] = True
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original_pixels = pixels.clone() # ディープコピー
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new_pixels = palette[labels]
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pixels[~pixel_masks] = new_pixels[~pixel_masks]
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pixels[pixel_masks] = original_pixels[pixel_masks]
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else:
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pixels = palette[labels]
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processed_images_tensor = pixels.reshape(batch_size, height, width, 3)
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return (processed_images_tensor, palette, index_maps, preview_img)
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def generate_palette_preview(self, palette, font_size):
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if isinstance(palette, torch.Tensor):
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colors = palette.numpy()
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else:
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colors = palette
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patch_size = 100
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cols = 8
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rows = (len(colors) + cols - 1) // cols
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img = Image.new("RGB", (cols * patch_size, rows * patch_size), (40, 40, 40))
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draw = ImageDraw.Draw(img)
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try:
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font = ImageFont.truetype("arial.ttf", font_size)
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except OSError:
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font = ImageFont.load_default()
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for i, color in enumerate(colors):
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x = (i % cols) * patch_size
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y = (i // cols) * patch_size
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draw.rectangle(
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[x, y, x + patch_size, y + patch_size],
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fill=tuple((color * 255).astype(int)),
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)
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text = str(i)
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bbox = draw.textbbox((x, y), text, font=font)
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text_w = bbox[2] - bbox[0]
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text_h = bbox[3] - bbox[1]
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draw.text(
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(x + (patch_size - text_w) // 2, y + (patch_size - text_h) // 2),
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text,
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fill="white",
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font=font,
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)
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img_tensor = torch.from_numpy(
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np.array(img).astype(np.float32) / 255.0
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).unsqueeze(0)
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return img_tensor
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def _match_palette_batch(self, pixels, palette):
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distances = torch.cdist(pixels, palette)
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return torch.argmin(distances, dim=2)
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class PaletteEditorNode:
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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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"images": ("IMAGE",),
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"palette": ("PALETTE",),
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"index_maps": ("MASK",),
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},
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"optional": {
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"seed": ("INT", {"default": 0, "min": 0, "max": 0xFFFFFFFFFFFFFFFF}),
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"hue_random_enable": ("BOOLEAN", {"default": False}),
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"hue_shift": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0}),
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"saturation_random_enable": ("BOOLEAN", {"default": False}),
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"saturation_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 5.0}),
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"value_random_enable": ("BOOLEAN", {"default": False}),
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"value_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 5.0}),
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"lock_color_num": (
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"STRING",
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{"default": "0,", "placeholder": "例: 0,1,2,3,4"},
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),
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"mask_enable": ("BOOLEAN", {"default": False}),
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"invert_mask": ("BOOLEAN", {"default": False}),
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"operations": ("STRING", {"default": "[]", "multiline": True}),
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},
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}
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RETURN_TYPES = ("IMAGE", "PALETTE", "MASK")
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RETURN_NAMES = ("images", "palette", "mask")
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FUNCTION = "process_palette"
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CATEGORY = "Image/Color"
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def rgb_to_hsv(self, rgb):
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r, g, b = rgb.unbind(-1)
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max_rgb, argmax_rgb = rgb.max(-1)
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min_rgb, _ = rgb.min(-1)
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delta = max_rgb - min_rgb
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h = torch.zeros_like(r)
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s = torch.zeros_like(r)
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v = max_rgb
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non_zero_delta = delta != 0
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h[non_zero_delta] = torch.where(
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argmax_rgb[non_zero_delta] == 0,
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(g[non_zero_delta] - b[non_zero_delta]) / delta[non_zero_delta],
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torch.where(
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argmax_rgb[non_zero_delta] == 1,
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2 + (b[non_zero_delta] - r[non_zero_delta]) / delta[non_zero_delta],
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4 + (r[non_zero_delta] - g[non_zero_delta]) / delta[non_zero_delta],
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),
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)
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h = (h / 6.0) % 1.0
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s[non_zero_delta] = delta[non_zero_delta] / max_rgb[non_zero_delta]
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return torch.stack((h, s, v), dim=-1)
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def hsv_to_rgb(self, hsv):
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h, s, v = hsv.unbind(-1)
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c = v * s
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x = c * (1 - torch.abs((h * 6) % 2 - 1))
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m = v - c
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rgb_prime = torch.zeros_like(hsv)
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h_category = (h * 6).long() % 6
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rgb_prime[..., 0] = torch.where(
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(h_category == 0) | (h_category == 5),
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c,
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torch.where((h_category == 1) | (h_category == 4), x, 0),
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)
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rgb_prime[..., 1] = torch.where(
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(h_category == 1) | (h_category == 2),
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c,
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torch.where((h_category == 0) | (h_category == 3), x, 0),
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)
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rgb_prime[..., 2] = torch.where(
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(h_category == 3) | (h_category == 4),
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c,
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torch.where((h_category == 2) | (h_category == 5), x, 0),
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)
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rgb = rgb_prime + m[..., None]
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return rgb
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def process_palette(
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self,
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images,
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palette,
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index_maps,
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seed,
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hue_random_enable,
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hue_shift,
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saturation_random_enable,
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saturation_scale,
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value_random_enable,
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value_scale,
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lock_color_num="0,",
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mask_enable=False,
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invert_mask=False,
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operations="[]",
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):
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device = images.device
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palette = palette.to(device)
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index_maps = index_maps.to(device)
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batch_size = images.shape[0]
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height = images.shape[1]
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width = images.shape[2]
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if not mask_enable:
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color_mask = torch.zeros(len(palette), dtype=torch.bool, device=device)
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else:
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try:
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indices = [
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int(idx.strip()) for idx in lock_color_num.split(",") if idx.strip()
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]
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color_mask = torch.zeros(len(palette), dtype=torch.bool, device=device)
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for idx in indices:
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if 0 <= idx < len(palette):
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color_mask[idx] = True
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if invert_mask:
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color_mask = ~color_mask
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except ValueError:
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color_mask = torch.zeros(len(palette), dtype=torch.bool, device=device)
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current_hue = random.uniform(-180, 180) if hue_random_enable else hue_shift
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current_saturation = (
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random.uniform(0, 5) if saturation_random_enable else saturation_scale
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)
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current_value = random.uniform(0, 5) if value_random_enable else value_scale
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modified_palette = self.edit_palette(
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palette,
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operations,
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color_mask,
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current_hue,
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current_saturation,
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current_value,
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)
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# バッチ処理
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pixels = images.reshape(batch_size, -1, 3)
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labels = index_maps.reshape(batch_size, -1)
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new_pixels = modified_palette[labels.long()]
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original_pixels = pixels.clone()
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# ピクセルマスクの生成（2D形式）
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pixel_mask = torch.zeros(
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(batch_size, height, width), dtype=torch.float32, device=device
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)
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for i, is_masked in enumerate(color_mask):
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pixel_mask[index_maps == i] = 1.0 if is_masked else 0.0
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# 画像の処理
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pixels_mask = pixel_mask.reshape(batch_size, -1, 1).expand(-1, -1, 3)
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processed_pixels = torch.where(pixels_mask > 0.5, original_pixels, new_pixels)
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processed_images = processed_pixels.reshape(images.shape)
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random.seed(None)
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return (processed_images, modified_palette, pixel_mask)
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def edit_palette(
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self, palette, operations, mask, hue_shift, saturation_scale, value_scale
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):
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hsv_palette = self.rgb_to_hsv(palette)
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hsv_palette[~mask, 0] = (hsv_palette[~mask, 0] + hue_shift / 360.0) % 1.0
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hsv_palette[~mask, 1] = torch.clamp(
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hsv_palette[~mask, 1] * saturation_scale, 0, 1
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)
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hsv_palette[~mask, 2] = torch.clamp(hsv_palette[~mask, 2] * value_scale, 0, 1)
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try:
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ops = json.loads(operations)
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if isinstance(ops, list):
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for op in ops:
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if isinstance(op, dict) and "index" in op:
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idx = op["index"]
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if 0 <= idx < len(palette):
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if "color" in op and len(op["color"]) == 3:
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new_color = torch.tensor(
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op["color"],
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dtype=torch.float32,
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device=palette.device,
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)
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palette[idx] = new_color
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hsv_palette[idx] = self.rgb_to_hsv(
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new_color.reshape(1, 3)
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)[0]
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if "hsv" in op and len(op["hsv"]) == 3:
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hsv_palette[idx] = torch.clamp(
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torch.tensor(op["hsv"], device=palette.device), 0, 1
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)
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except Exception as e:
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print(f"Operation Error: {str(e)}")
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modified_palette = self.hsv_to_rgb(hsv_palette)
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return modified_palette
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NODE_CLASS_MAPPINGS = {
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"ColorshiftColor": ColorshiftColorNode,
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"CsCPaletteEditor": PaletteEditorNode,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"ColorshiftColor": "ColorshiftColor",
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"CsCPaletteEditor": "CsCPaletteEditor",
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}
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