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hakoniwa
2025-02-01 03:21:46 +09:00
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# nodes.py
import json
import random
import numpy as np
import torch
import torch.nn.functional as F
from PIL import Image, ImageDraw, ImageFont
from scipy.cluster.vq import vq
from sklearn.cluster import KMeans
class ColorshiftColorNode:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"images": ("IMAGE",),
"color_count": ("INT", {"default": 8, "min": 2, "max": 64}),
},
"optional": {
"lock_masks": ("MASK", {"default": None}),
"palette_override": ("PALETTE", {"default": None}),
"font_size": ("INT", {"default": 20, "min": 10, "max": 50}),
"sampling_rate": ("FLOAT", {"default": 0.25, "min": 0.01, "max": 0.5}),
"n_init": ("INT", {"default": 3, "min": 1, "max": 10}),
},
}
RETURN_TYPES = ("IMAGE", "PALETTE", "MASK", "IMAGE")
RETURN_NAMES = ("images", "palette", "index_maps", "palette_preview")
FUNCTION = "process"
CATEGORY = "Image/Color"
def process(
self,
images,
color_count,
lock_masks=None,
palette_override=None,
font_size=20,
sampling_rate=0.25,
n_init=3,
):
# デバイスを取得
device = images.device
# バッチ処理のために画像をスタック
resized_images = F.interpolate(
images.permute(0, 3, 1, 2),
scale_factor=0.5,
mode="bilinear",
align_corners=False,
).permute(0, 2, 3, 1)
batch_size, height, width, channels = (
resized_images.shape
) # resized_imagesの形状を使用
pixels = resized_images.reshape(
batch_size, -1, 3
) # resized_imagesの形状を元にpixelsを整形
if palette_override is not None:
palette = palette_override.to(device)
# バッチ全体に対して一回の距離計算でラベルを取得
labels = self._match_palette_batch(pixels, palette)
else:
# 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。
# 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
# 混ぜてクラスタリング
pixels_for_clustering = pixels.cpu().numpy().reshape(-1, 3)
random_indices = np.random.choice(
pixels_for_clustering.shape[0],
size=int(pixels_for_clustering.shape[0] * sampling_rate),
replace=False,
)
pixels_for_clustering = pixels_for_clustering[random_indices]
kmeans = KMeans(n_clusters=color_count, random_state=42, n_init=n_init)
kmeans.fit(pixels_for_clustering)
# 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)
labels, dist = vq(
pixels.cpu().numpy().reshape(-1, 3), kmeans.cluster_centers_
)
labels = torch.from_numpy(labels.reshape(batch_size, -1)).long().to(device)
palette = torch.from_numpy(kmeans.cluster_centers_).float().to(device)
# Generate palette preview (最初の画像のみ, CPUで処理)
preview_img = self.generate_palette_preview(palette.cpu().numpy(), font_size)
# バッチ全体のインデックスマップを取得
index_maps = labels.reshape(batch_size, height, width)
# GPUで一括処理
if lock_masks is not None:
lock_masks_batch = []
for i in range(batch_size):
lock_mask = lock_masks[i] if i < lock_masks.shape[0] else lock_masks[-1]
lock_masks_batch.append(lock_mask)
lock_masks = torch.stack(lock_masks_batch).to(device)
pixel_masks = torch.zeros_like(index_maps, dtype=torch.bool)
for i in range(batch_size):
for j, is_locked in enumerate(lock_masks[i]):
if is_locked:
pixel_masks[i][index_maps[i] == j] = True
original_pixels = pixels.clone() # ディープコピー
new_pixels = palette[labels]
pixels[~pixel_masks] = new_pixels[~pixel_masks]
pixels[pixel_masks] = original_pixels[pixel_masks]
else:
pixels = palette[labels]
processed_images_tensor = pixels.reshape(batch_size, height, width, 3)
return (processed_images_tensor, palette, index_maps, preview_img)
def generate_palette_preview(self, palette, font_size):
if isinstance(palette, torch.Tensor):
colors = palette.numpy()
else:
colors = palette
patch_size = 100
cols = 8
rows = (len(colors) + cols - 1) // cols
img = Image.new("RGB", (cols * patch_size, rows * patch_size), (40, 40, 40))
draw = ImageDraw.Draw(img)
try:
font = ImageFont.truetype("arial.ttf", font_size)
except OSError:
font = ImageFont.load_default()
for i, color in enumerate(colors):
x = (i % cols) * patch_size
y = (i // cols) * patch_size
draw.rectangle(
[x, y, x + patch_size, y + patch_size],
fill=tuple((color * 255).astype(int)),
)
text = str(i)
bbox = draw.textbbox((x, y), text, font=font)
text_w = bbox[2] - bbox[0]
text_h = bbox[3] - bbox[1]
draw.text(
(x + (patch_size - text_w) // 2, y + (patch_size - text_h) // 2),
text,
fill="white",
font=font,
)
img_tensor = torch.from_numpy(
np.array(img).astype(np.float32) / 255.0
).unsqueeze(0)
return img_tensor
def _match_palette_batch(self, pixels, palette):
distances = torch.cdist(pixels, palette)
return torch.argmin(distances, dim=2)
class PaletteEditorNode:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"images": ("IMAGE",),
"palette": ("PALETTE",),
"index_maps": ("MASK",),
},
"optional": {
"seed": ("INT", {"default": 0, "min": 0, "max": 0xFFFFFFFFFFFFFFFF}),
"hue_random_enable": ("BOOLEAN", {"default": False}),
"hue_shift": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0}),
"saturation_random_enable": ("BOOLEAN", {"default": False}),
"saturation_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 5.0}),
"value_random_enable": ("BOOLEAN", {"default": False}),
"value_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 5.0}),
"lock_color_num": (
"STRING",
{"default": "0,", "placeholder": "例: 0,1,2,3,4"},
),
"mask_enable": ("BOOLEAN", {"default": False}),
"invert_mask": ("BOOLEAN", {"default": False}),
"operations": ("STRING", {"default": "[]", "multiline": True}),
},
}
RETURN_TYPES = ("IMAGE", "PALETTE", "MASK")
RETURN_NAMES = ("images", "palette", "mask")
FUNCTION = "process_palette"
CATEGORY = "Image/Color"
def rgb_to_hsv(self, rgb):
r, g, b = rgb.unbind(-1)
max_rgb, argmax_rgb = rgb.max(-1)
min_rgb, _ = rgb.min(-1)
delta = max_rgb - min_rgb
h = torch.zeros_like(r)
s = torch.zeros_like(r)
v = max_rgb
non_zero_delta = delta != 0
h[non_zero_delta] = torch.where(
argmax_rgb[non_zero_delta] == 0,
(g[non_zero_delta] - b[non_zero_delta]) / delta[non_zero_delta],
torch.where(
argmax_rgb[non_zero_delta] == 1,
2 + (b[non_zero_delta] - r[non_zero_delta]) / delta[non_zero_delta],
4 + (r[non_zero_delta] - g[non_zero_delta]) / delta[non_zero_delta],
),
)
h = (h / 6.0) % 1.0
s[non_zero_delta] = delta[non_zero_delta] / max_rgb[non_zero_delta]
return torch.stack((h, s, v), dim=-1)
def hsv_to_rgb(self, hsv):
h, s, v = hsv.unbind(-1)
c = v * s
x = c * (1 - torch.abs((h * 6) % 2 - 1))
m = v - c
rgb_prime = torch.zeros_like(hsv)
h_category = (h * 6).long() % 6
rgb_prime[..., 0] = torch.where(
(h_category == 0) | (h_category == 5),
c,
torch.where((h_category == 1) | (h_category == 4), x, 0),
)
rgb_prime[..., 1] = torch.where(
(h_category == 1) | (h_category == 2),
c,
torch.where((h_category == 0) | (h_category == 3), x, 0),
)
rgb_prime[..., 2] = torch.where(
(h_category == 3) | (h_category == 4),
c,
torch.where((h_category == 2) | (h_category == 5), x, 0),
)
rgb = rgb_prime + m[..., None]
return rgb
def process_palette(
self,
images,
palette,
index_maps,
seed,
hue_random_enable,
hue_shift,
saturation_random_enable,
saturation_scale,
value_random_enable,
value_scale,
lock_color_num="0,",
mask_enable=False,
invert_mask=False,
operations="[]",
):
device = images.device
palette = palette.to(device)
index_maps = index_maps.to(device)
batch_size = images.shape[0]
height = images.shape[1]
width = images.shape[2]
if not mask_enable:
color_mask = torch.zeros(len(palette), dtype=torch.bool, device=device)
else:
try:
indices = [
int(idx.strip()) for idx in lock_color_num.split(",") if idx.strip()
]
color_mask = torch.zeros(len(palette), dtype=torch.bool, device=device)
for idx in indices:
if 0 <= idx < len(palette):
color_mask[idx] = True
if invert_mask:
color_mask = ~color_mask
except ValueError:
color_mask = torch.zeros(len(palette), dtype=torch.bool, device=device)
current_hue = random.uniform(-180, 180) if hue_random_enable else hue_shift
current_saturation = (
random.uniform(0, 5) if saturation_random_enable else saturation_scale
)
current_value = random.uniform(0, 5) if value_random_enable else value_scale
modified_palette = self.edit_palette(
palette,
operations,
color_mask,
current_hue,
current_saturation,
current_value,
)
# バッチ処理
pixels = images.reshape(batch_size, -1, 3)
labels = index_maps.reshape(batch_size, -1)
new_pixels = modified_palette[labels.long()]
original_pixels = pixels.clone()
# ピクセルマスクの生成（2D形式）
pixel_mask = torch.zeros(
(batch_size, height, width), dtype=torch.float32, device=device
)
for i, is_masked in enumerate(color_mask):
pixel_mask[index_maps == i] = 1.0 if is_masked else 0.0
# 画像の処理
pixels_mask = pixel_mask.reshape(batch_size, -1, 1).expand(-1, -1, 3)
processed_pixels = torch.where(pixels_mask > 0.5, original_pixels, new_pixels)
processed_images = processed_pixels.reshape(images.shape)
random.seed(None)
return (processed_images, modified_palette, pixel_mask)
def edit_palette(
self, palette, operations, mask, hue_shift, saturation_scale, value_scale
):
hsv_palette = self.rgb_to_hsv(palette)
hsv_palette[~mask, 0] = (hsv_palette[~mask, 0] + hue_shift / 360.0) % 1.0
hsv_palette[~mask, 1] = torch.clamp(
hsv_palette[~mask, 1] * saturation_scale, 0, 1
)
hsv_palette[~mask, 2] = torch.clamp(hsv_palette[~mask, 2] * value_scale, 0, 1)
try:
ops = json.loads(operations)
if isinstance(ops, list):
for op in ops:
if isinstance(op, dict) and "index" in op:
idx = op["index"]
if 0 <= idx < len(palette):
if "color" in op and len(op["color"]) == 3:
new_color = torch.tensor(
op["color"],
dtype=torch.float32,
device=palette.device,
)
palette[idx] = new_color
hsv_palette[idx] = self.rgb_to_hsv(
new_color.reshape(1, 3)
)[0]
if "hsv" in op and len(op["hsv"]) == 3:
hsv_palette[idx] = torch.clamp(
torch.tensor(op["hsv"], device=palette.device), 0, 1
)
except Exception as e:
print(f"Operation Error: {str(e)}")
modified_palette = self.hsv_to_rgb(hsv_palette)
return modified_palette
NODE_CLASS_MAPPINGS = {
"ColorshiftColor": ColorshiftColorNode,
"CsCPaletteEditor": PaletteEditorNode,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"ColorshiftColor": "ColorshiftColor",
"CsCPaletteEditor": "CsCPaletteEditor",
}