rui40000andClaude Opus 4.6
adb1de6329
接入 FeyNobg/Lucida/BiRefNet 等抠图节点输出的 mask， 执行 max(unmult_α, subject_mask) 合并， 防止主体中与背景色相近的区域被误判为透明。 不连接时行为不变，向后兼容。 Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
179 lines
7.2 KiB
Python
179 lines
7.2 KiB
Python
# -*- coding: utf-8 -*-
|
||
"""
|
||
半透明抠图节点（Unmult）—— Ruinode
|
||
==================================
|
||
纯数学去底算法，等效 AE Unmult 效果。
|
||
适用于渲染在纯色底上的光效、火焰、烟雾、粒子、UI 特效等半透明素材。
|
||
|
||
原理：纯色背景合成图满足 C = αF + (1-α)B，
|
||
其中 B 为已知背景色。通过各通道与背景色的差异反推 α 和前景色 F。
|
||
支持任意背景色（黑/白/绿幕/自定义），纯数学变换，无模型推理。
|
||
|
||
AI 主体保护（可选）：接入 BiRefNet / FeyNobg 等抠图节点输出的 subject_mask，
|
||
用 max(unmult_α, subject_mask) 合并，防止主体中与背景色相近的区域被误判为透明。
|
||
典型场景：黑底光效中人物穿黑衣 → 纯 Unmult 会让黑衣半透明，
|
||
接入主体 mask 后黑衣区域强制保留为不透明。
|
||
|
||
支持批量输入（序列帧/视频帧），逐帧处理后堆叠输出。
|
||
"""
|
||
|
||
import numpy as np
|
||
import torch
|
||
|
||
|
||
def _hex_to_rgb01(hex_str: str) -> tuple:
|
||
"""将 #RRGGBB 格式的颜色字符串转为 (r, g, b) 浮点元组，值域 [0,1]。"""
|
||
h = hex_str.strip().lstrip("#")
|
||
if len(h) == 3:
|
||
h = h[0] * 2 + h[1] * 2 + h[2] * 2
|
||
if len(h) != 6:
|
||
raise ValueError(f"无效的颜色格式：{hex_str}，需要 #RRGGBB")
|
||
r = int(h[0:2], 16) / 255.0
|
||
g = int(h[2:4], 16) / 255.0
|
||
b = int(h[4:6], 16) / 255.0
|
||
return (r, g, b)
|
||
|
||
|
||
def _unmult_frame(rgb: np.ndarray, bg_color: tuple,
|
||
alpha_low: float, alpha_high: float,
|
||
subject_mask: np.ndarray = None,
|
||
epsilon: float = 1e-6) -> tuple:
|
||
"""对单帧图像执行 Unmult 去底，可选主体保护。
|
||
|
||
参数:
|
||
rgb: float32 [H,W,3] 值域 [0,1]
|
||
bg_color: (R,G,B) 值域 [0,1]
|
||
alpha_low: 黑点（低于此值的 alpha 映射为 0）
|
||
alpha_high: 白点（高于此值的 alpha 映射为 1）
|
||
subject_mask: float32 [H,W] 值域 [0,1]，主体区域为 1
|
||
|
||
返回:
|
||
(foreground [H,W,3], alpha [H,W]) 均 float32
|
||
"""
|
||
bg = np.array(bg_color, dtype=np.float32).reshape(1, 1, 3)
|
||
diff = rgb.astype(np.float32) - bg
|
||
|
||
scale = np.array([max(bg_color[c], 1.0 - bg_color[c], epsilon)
|
||
for c in range(3)], dtype=np.float32).reshape(1, 1, 3)
|
||
norm_diff = np.abs(diff) / scale
|
||
alpha = np.max(norm_diff, axis=-1)
|
||
alpha = np.clip(alpha, 0.0, 1.0)
|
||
|
||
if alpha_low > 0.0 or alpha_high < 1.0:
|
||
span = max(alpha_high - alpha_low, epsilon)
|
||
alpha = np.clip((alpha - alpha_low) / span, 0.0, 1.0)
|
||
|
||
if subject_mask is not None:
|
||
alpha = np.maximum(alpha, subject_mask)
|
||
|
||
alpha_safe = np.maximum(alpha, epsilon)
|
||
foreground = bg + diff / alpha_safe[..., np.newaxis]
|
||
foreground = np.clip(foreground, 0.0, 1.0)
|
||
|
||
transparent = alpha < epsilon
|
||
foreground[transparent] = 0.0
|
||
|
||
return foreground.astype(np.float32), alpha.astype(np.float32)
|
||
|
||
|
||
class RuiUnmult:
|
||
"""半透明抠图（Unmult）：指定背景色，纯数学去底，输出 RGBA 图像 + Alpha 蒙版。
|
||
可选接入 AI 主体保护 mask，防止主体中近似背景色的区域被误判为透明。"""
|
||
|
||
@classmethod
|
||
def INPUT_TYPES(cls):
|
||
return {
|
||
"required": {
|
||
"image": ("IMAGE", {
|
||
"tooltip": "待去底的图像，支持批量（序列帧/视频帧逐帧处理）。"
|
||
}),
|
||
"bg_color": ("STRING", {
|
||
"default": "#000000",
|
||
"tooltip": "要去除的背景色，#RRGGBB 格式。\n"
|
||
"常用值：#000000（黑底）、#FFFFFF（白底）、"
|
||
"#00FF00（绿幕）、#FF00FF（品红）。"
|
||
}),
|
||
"黑点": ("FLOAT", {
|
||
"default": 0.0, "min": 0.0, "max": 0.5, "step": 0.01,
|
||
"display": "slider",
|
||
"tooltip": "黑点（主体保护）：低于此值的 alpha 强制归零。\n"
|
||
"调高可清除背景残留噪点，但过高会丢失边缘细节。"
|
||
}),
|
||
"白点": ("FLOAT", {
|
||
"default": 1.0, "min": 0.3, "max": 1.0, "step": 0.01,
|
||
"display": "slider",
|
||
"tooltip": "白点（主体保护）：高于此值的 alpha 强制归一。\n"
|
||
"调低可让主体更实、减少半透明损失，但过低会让边缘硬化。"
|
||
}),
|
||
},
|
||
"optional": {
|
||
"subject_mask": ("MASK", {
|
||
"tooltip": "AI 主体保护遮罩（可选）。\n"
|
||
"接入 FeyNobg / Lucida / BiRefNet 等抠图节点输出的 alpha，\n"
|
||
"节点会执行 max(unmult_α, subject_mask) 合并：\n"
|
||
"主体内部强制不透明，光效边缘保留半透明。\n\n"
|
||
"典型场景：黑底光效中人物穿黑衣 →\n"
|
||
"纯 Unmult 会让黑衣半透明，接入主体 mask 后黑衣保留。\n\n"
|
||
"不连接时等同于纯 Unmult，无 AI 介入。"
|
||
}),
|
||
},
|
||
}
|
||
|
||
RETURN_TYPES = ("IMAGE", "MASK")
|
||
RETURN_NAMES = ("rgba_image", "alpha")
|
||
FUNCTION = "unmult"
|
||
CATEGORY = "Rui-Node🐶/抠图✂️"
|
||
|
||
def unmult(self, **kwargs):
|
||
image = kwargs.get("image")
|
||
bg_color = kwargs.get("bg_color", "#000000")
|
||
alpha_low = kwargs.get("黑点", kwargs.get("alpha_low", 0.0))
|
||
alpha_high = kwargs.get("白点", kwargs.get("alpha_high", 1.0))
|
||
subject_mask_tensor = kwargs.get("subject_mask", None)
|
||
|
||
bg_rgb = _hex_to_rgb01(bg_color)
|
||
|
||
B, H, W, C = image.shape
|
||
if C == 4:
|
||
image = image[..., :3]
|
||
elif C == 1:
|
||
image = image.repeat(1, 1, 1, 3)
|
||
|
||
fg_list = []
|
||
alpha_list = []
|
||
|
||
for i in range(B):
|
||
frame = image[i].cpu().numpy().astype(np.float32)
|
||
|
||
smask = None
|
||
if subject_mask_tensor is not None:
|
||
if subject_mask_tensor.dim() == 2:
|
||
raw = subject_mask_tensor.cpu().numpy().astype(np.float32)
|
||
else:
|
||
idx = min(i, subject_mask_tensor.shape[0] - 1)
|
||
raw = subject_mask_tensor[idx].cpu().numpy().astype(np.float32)
|
||
if raw.shape[0] != H or raw.shape[1] != W:
|
||
import cv2
|
||
raw = cv2.resize(raw, (W, H), interpolation=cv2.INTER_LINEAR)
|
||
smask = np.clip(raw, 0.0, 1.0)
|
||
|
||
fg, a = _unmult_frame(frame, bg_rgb,
|
||
float(alpha_low), float(alpha_high),
|
||
subject_mask=smask)
|
||
rgba = np.concatenate([fg, a[..., np.newaxis]], axis=-1)
|
||
fg_list.append(torch.from_numpy(rgba))
|
||
alpha_list.append(torch.from_numpy(a))
|
||
|
||
rgba_out = torch.stack(fg_list) # [B, H, W, 4]
|
||
alpha_out = torch.stack(alpha_list) # [B, H, W]
|
||
|
||
return (rgba_out, alpha_out)
|
||
|
||
|
||
NODE_CLASS_MAPPINGS = {
|
||
"RuiUnmult": RuiUnmult,
|
||
}
|
||
NODE_DISPLAY_NAME_MAPPINGS = {
|
||
"RuiUnmult": "半透明抠图 / Unmult Matting",
|
||
}
|