300 lines
13 KiB
Python
300 lines
13 KiB
Python
# nodes.py
|
|
import torch
|
|
import numpy as np
|
|
from sklearn.cluster import KMeans # GPUでクラスタリングが難しいので、CPUで実行する
|
|
import colorsys
|
|
import json
|
|
import random
|
|
from PIL import Image, ImageDraw, ImageFont
|
|
import io
|
|
import time
|
|
|
|
class ColorshiftColorNode:
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"images": ("IMAGE",),
|
|
"color_count": ("INT", {"default": 8, "min": 2, "max": 32}),
|
|
},
|
|
"optional": {
|
|
"lock_masks": ("MASK", {"default": None}),
|
|
"palette_override": ("PALETTE", {"default": None}),
|
|
"font_size": ("INT", {"default": 20, "min": 10, "max": 50}),
|
|
}
|
|
}
|
|
|
|
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):
|
|
# 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
|
|
batch_size, height, width, channels = images.shape
|
|
pixels = images.reshape(batch_size, -1, 3)
|
|
|
|
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().copy().reshape(-1,3)
|
|
random_indices = np.random.choice(pixels_for_clustering.shape[0], size=int(pixels_for_clustering.shape[0] * 0.5), replace=False)
|
|
pixels_for_clustering = pixels_for_clustering[random_indices]
|
|
kmeans = KMeans(n_clusters=color_count, random_state=42, n_init=10)
|
|
kmeans.fit(pixels_for_clustering)
|
|
|
|
# バッチ内の各画像に対してラベルを取得
|
|
labels = torch.from_numpy(kmeans.predict(pixels.cpu().numpy().reshape(-1,3)).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):
|
|
# 既存のコード (変更なし)
|
|
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:
|
|
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):
|
|
# バッチ処理のために距離計算を最適化（PyTorchで書き換え）
|
|
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):
|
|
# PyTorchで書き換え
|
|
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):
|
|
# PyTorchで書き換え
|
|
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, mask_type="lock", operations="[]"):
|
|
|
|
# デバイスを取得
|
|
device = images.device
|
|
palette = palette.to(device)
|
|
index_maps = index_maps.to(device)
|
|
|
|
batch_size = images.shape[0]
|
|
|
|
# If mask is not enabled, apply changes to the entire palette
|
|
if not mask_enable:
|
|
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()]
|
|
mask = torch.zeros(len(palette), dtype=torch.bool, device=device)
|
|
for idx in indices:
|
|
if 0 <= idx < len(palette):
|
|
mask[idx] = True
|
|
if invert_mask:
|
|
mask = ~mask
|
|
except:
|
|
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, 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()
|
|
|
|
if mask_type == "change":
|
|
# Create pixel_mask based on color_mask
|
|
pixel_mask = torch.zeros_like(labels, dtype=torch.bool)
|
|
for i, is_masked in enumerate(mask):
|
|
if is_masked:
|
|
pixel_mask[labels == i] = True
|
|
# Apply new_pixels to masked areas
|
|
pixels = torch.where(pixel_mask.unsqueeze(-1), new_pixels, original_pixels)
|
|
else:
|
|
# Create pixel_mask based on color_mask
|
|
pixel_mask = torch.zeros_like(labels, dtype=torch.bool)
|
|
for i, is_masked in enumerate(mask):
|
|
if is_masked:
|
|
pixel_mask[labels == i] = True
|
|
# Apply new_pixels to non-masked areas
|
|
pixels = torch.where(pixel_mask.unsqueeze(-1), original_pixels, new_pixels)
|
|
|
|
processed_images = pixels.reshape(images.shape)
|
|
|
|
random.seed(None)
|
|
return (processed_images, modified_palette, mask)
|
|
|
|
def edit_palette(self, palette, operations, mask,
|
|
hue_shift, saturation_scale, value_scale):
|
|
hsv_palette = self.rgb_to_hsv(palette)
|
|
|
|
# Apply HSV adjustments to unmasked colors
|
|
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",
|
|
} |