feat: 新增八方向序列拆分节点（8方向行走动画管线）
把每帧排布着 8 个朝向的雪碧图序列，一次拆成 8 条独立动画序列， 完成方向编号与分组。整条管线只有这一环缺失，其余复用现成实现： 视频转帧用 VHS 的 force_rate，透明 webm 用 VHS 的 format=video/webm + pix_fmt=yuva420p，序列帧用 SaveImage （4 通道输入 PIL 会自动存成 RGBA）。 三个关键设计： - 用固定网格而非连通区域拆分：连通区域按包围盒排序，角色走动时 位置浮动，跨过排序行界方向就错乱，上百帧错一帧整条动画就废； 且各自 bbox 裁剪导致尺寸不一无法合成视频 - 白底转透明用边缘连通性判断：角色常穿白衣服，按亮度一刀切会把 白衬衫掏空，故只把与画面边缘相连的白色判为背景 - 裁剪框取全序列并集：逐帧各自裁剪会尺寸不一且角色帧间跳动 另加碎片清理（按连通块面积比）：网格切分会把相邻格探进来的手杖尖 切入本格，既难看又撑大裁剪范围。实测 E 方向 172x370 -> 150x335， 跨帧重心极差 8.0 -> 0.5 px。 实测 97 帧 834x1112 素材：拆分约 4 秒，导出 webm 回读透明像素占比 0.635 与素材一致。注意验证 webm 透明需显式 -c:v libvpx-vp9 解码， 内置 vp9 解码器不处理 alpha 边带，否则会误判为透明丢失。 Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
This commit is contained in:
rui40000
co-authored by
Claude Opus 4.8
Claude Opus 4.8
parent
b517c5714b
commit
28fd14aaad
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# -*- coding: utf-8 -*-
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"""
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八方向雪碧图序列拆分节点（Ruinode）
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===================================
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用于「8 方向行走动画」制作管线：把每帧排布着 8 个朝向的雪碧图序列，
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拆成 8 条各自独立、可直接成片的动画序列。
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典型管线：
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角色图 → (GPTimage2) 八方向静态图 → (Seedance 首尾帧) 循环行走视频
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→ VHS「Load Video」转序列帧（可选帧率）
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→ 【本节点】拆分 + 方向编号 + 分组
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→ 8×SaveImage 出序列帧，8×VHS「Video Combine」出透明 webm
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为什么用固定网格而不是连通区域拆分（本仓库的 RuiSpriteSplitterRGBA）：
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- 连通区域按包围盒排序，角色走动时位置会浮动，一旦跨过排序的行界，
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方向对应关系就错乱 —— 几十上百帧里错一帧，整条动画就废了；
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- 连通区域按各自 bbox 裁剪，每个 sprite 尺寸不同，无法直接合成视频。
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固定网格没有这两个问题：格子位置恒定，方向对应天然稳定，尺寸也一致。
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（连通区域拆分依然更适合单张静态合图，两者各有用武之地。）
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白底转透明的关键点：角色身上常有白色衣物，按亮度阈值一刀切会把白衬衫
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一起掏空。这里改为**从画面边缘做连通性判断**：只有与边缘相连的白色才算背景，
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被角色包围的白色（衣服、高光）一律保留。
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"""
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import numpy as np
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import torch
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try:
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import scipy.ndimage as ndi
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_HAS_SCIPY = True
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except Exception: # 理论上 ComfyUI 环境都有
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_HAS_SCIPY = False
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MAX_DIRS = 8
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_BG_MODES = {
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"白底转透明（推荐）": "white",
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"已带透明通道": "keep",
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"不处理（输出不透明）": "none",
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}
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# 与 3×3 中间留空的常见排布对应：行 1 面向观众、行 3 背对观众
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_DEFAULT_NAMES = "SW,S,SE,W,E,NW,N,NE"
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def _white_to_alpha(rgb, threshold, softness):
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"""
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白底 → alpha。rgb: (H,W,3) float[0,1]，返回 (H,W) float[0,1]。
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先按「离白色多远」算出软 alpha 保住边缘抗锯齿，再用连通性把
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与画面边缘相连的白色判为背景 —— 只有这一部分才真正抹成全透明。
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这样角色内部的白衬衫、白高光不会被误伤。
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"""
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dist = 1.0 - rgb.min(axis=2) # 纯白=0，越大越不白
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cut = max(1e-4, 1.0 - float(threshold))
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soft = np.clip(dist / (cut * max(1e-3, softness)), 0.0, 1.0)
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near_white = dist <= cut
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if _HAS_SCIPY and near_white.any():
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lab, n = ndi.label(near_white)
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if n > 0:
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border = np.concatenate([lab[0, :], lab[-1, :], lab[:, 0], lab[:, -1]])
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ids = np.unique(border)
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ids = ids[ids != 0]
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if ids.size:
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bg = np.isin(lab, ids)
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soft = np.where(bg, 0.0, soft)
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else:
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soft = np.where(near_white, 0.0, soft)
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return soft.astype(np.float32)
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def _drop_fragments(alpha, ratio):
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"""
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清掉远小于主体的连通碎片。
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网格切分难免会把相邻格子探过来的部件（手杖尖、飘起的衣角）切进本格，
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这些碎片不仅难看，还会把 auto_crop 的包围盒撑大。
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按「面积不足主体 ratio 倍」判定为碎片，这样与身体相连的道具不会被误删。
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"""
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if ratio <= 0 or not _HAS_SCIPY:
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return alpha
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solid = alpha > 0.1
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if not solid.any():
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return alpha
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lab, n = ndi.label(solid)
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if n <= 1:
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return alpha
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areas = np.bincount(lab.ravel())
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areas[0] = 0
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keep = areas >= areas.max() * float(ratio)
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keep[0] = False
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return np.where(keep[lab], alpha, 0.0).astype(np.float32)
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def _bbox(alpha, thr=0.02):
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"""内容包围盒 (y0,y1,x0,x1)，无内容时返回 None。"""
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m = alpha > thr
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if not m.any():
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return None
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ys = np.where(m.any(axis=1))[0]
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xs = np.where(m.any(axis=0))[0]
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return int(ys[0]), int(ys[-1]) + 1, int(xs[0]), int(xs[-1]) + 1
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def _parse_cells(text, total):
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out = set()
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for tok in str(text or "").replace("，", ",").split(","):
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tok = tok.strip()
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if not tok:
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continue
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try:
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v = int(tok)
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except ValueError:
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continue
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if 0 <= v < total:
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out.add(v)
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return out
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class RuiEightDirSplit:
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"""八方向雪碧图序列 → 8 条独立动画序列。"""
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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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"tooltip": "视频转出的序列帧，每帧是一张排布着多个朝向的雪碧图。"
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}),
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"grid_cols": ("INT", {
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"default": 3, "min": 1, "max": 8, "step": 1,
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"tooltip": "雪碧图的列数。"
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}),
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"grid_rows": ("INT", {
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"default": 3, "min": 1, "max": 8, "step": 1,
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"tooltip": "雪碧图的行数。"
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}),
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"empty_cells": ("STRING", {
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"default": "4", "multiline": False,
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"tooltip": "空格子的序号（行优先、从 0 开始，逗号分隔）。\n"
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"3×3 布局中间留空即填 4。留空表示没有空格。"
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}),
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"direction_names": ("STRING", {
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"default": _DEFAULT_NAMES, "multiline": False,
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"tooltip": "按「跳过空格后的先后顺序」给每个方向命名，逗号分隔。\n"
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"默认对应 3×3 中间留空、行 1 面向观众的排布：\n"
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" SW S SE\n"
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" W E\n"
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" NW N NE\n"
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"名字只用于 info 与你自己辨认，不影响画面内容。"
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}),
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"bg_mode": (list(_BG_MODES.keys()), {
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"default": "白底转透明（推荐）",
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"tooltip": "webm 要保留透明就必须先把白底转成 alpha。\n"
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"转换只把与画面边缘相连的白色判为背景，\n"
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"角色身上的白衣服、白高光会被保留。"
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}),
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"bg_threshold": ("FLOAT", {
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"default": 0.92, "min": 0.5, "max": 1.0, "step": 0.005,
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"tooltip": "白底判定阈值：像素三通道最小值高于它才算「接近白」。\n"
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"背景没扣干净就调低，角色边缘被啃掉就调高。"
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}),
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"edge_softness": ("FLOAT", {
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"default": 1.0, "min": 0.1, "max": 4.0, "step": 0.05,
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"tooltip": "边缘过渡宽度。原图边缘带抗锯齿，过渡太硬会有锯齿白边；\n"
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"调大更柔和，调小更锐利。"
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}),
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"fragment_threshold": ("FLOAT", {
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"default": 0.05, "min": 0.0, "max": 0.5, "step": 0.01,
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"tooltip": "清掉面积不足主体这一比例的连通碎片。\n"
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"网格切分会把相邻格子探过来的部件（手杖尖、飘起的衣角）\n"
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"切进本格，既难看又会撑大自动裁剪的范围。\n"
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"0 = 不清理；与身体相连的道具不会被误删。"
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}),
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"auto_crop": ("BOOLEAN", {
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"default": True,
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"tooltip": "按内容裁掉多余空白。\n"
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"裁剪框取「该方向所有帧的并集」，因此整条序列尺寸一致，\n"
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"既能合成视频，角色也不会在帧间跳动。"
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}),
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"crop_padding": ("INT", {
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"default": 8, "min": 0, "max": 200, "step": 1,
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"tooltip": "裁剪时在内容外保留的边距（像素）。"
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}),
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},
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"optional": {
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"masks": ("MASK", {
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"tooltip": "可选。已有的透明通道（如上游抠图结果），\n"
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"配合 bg_mode=已带透明通道 使用。"
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}),
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},
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}
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RETURN_TYPES = ("IMAGE",) * MAX_DIRS + ("STRING",)
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RETURN_NAMES = tuple(f"dir_{i + 1}" for i in range(MAX_DIRS)) + ("info",)
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FUNCTION = "split"
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CATEGORY = "Rui-Node🐶/图像调节🎨"
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@classmethod
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def VALIDATE_INPUTS(cls, **kwargs):
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return True
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def split(self, images, grid_cols, grid_rows, empty_cells,
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direction_names, bg_mode, bg_threshold, edge_softness,
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fragment_threshold, auto_crop, crop_padding, masks=None):
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mode = _BG_MODES.get(bg_mode, "white")
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B, H, W, C = images.shape
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cols, rows = int(grid_cols), int(grid_rows)
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total = cols * rows
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empties = _parse_cells(empty_cells, total)
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cell_ids = [i for i in range(total) if i not in empties]
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n_dir = len(cell_ids)
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names = [s.strip() for s in str(direction_names).replace("，", ",").split(",")
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if s.strip()]
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while len(names) < n_dir:
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names.append(f"dir{len(names) + 1}")
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arr = images.detach().cpu().float().numpy()
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if C == 4:
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rgb_all, a_in = arr[..., :3], arr[..., 3]
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else:
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rgb_all, a_in = arr[..., :3], None
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if masks is not None:
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m = masks.detach().cpu().float().numpy()
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if m.ndim == 2:
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m = m[None]
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if m.shape[0] != B:
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m = np.repeat(m[:1], B, axis=0)
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a_in = m
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# 格子边界按浮点等分再取整，避免整除不尽时累计误差（如 1112/3）
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ybnd = [int(round(r * H / rows)) for r in range(rows + 1)]
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xbnd = [int(round(c * W / cols)) for c in range(cols + 1)]
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# ---- 第一遍：切格 + 生成 alpha，同时累计每个方向的内容包围盒 ----
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per_dir = [[] for _ in range(n_dir)]
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boxes = [None] * n_dir
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for b in range(B):
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for di, cid in enumerate(cell_ids):
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r, c = divmod(cid, cols)
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y0, y1, x0, x1 = ybnd[r], ybnd[r + 1], xbnd[c], xbnd[c + 1]
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rgb = rgb_all[b, y0:y1, x0:x1]
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if mode == "white":
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alpha = _white_to_alpha(rgb, bg_threshold, edge_softness)
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elif mode == "keep" and a_in is not None:
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alpha = a_in[b, y0:y1, x0:x1]
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else:
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alpha = np.ones(rgb.shape[:2], dtype=np.float32)
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if mode != "none":
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alpha = _drop_fragments(alpha, fragment_threshold)
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per_dir[di].append((rgb, alpha))
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if auto_crop:
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bb = _bbox(alpha)
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if bb is not None:
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boxes[di] = bb if boxes[di] is None else (
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min(boxes[di][0], bb[0]), max(boxes[di][1], bb[1]),
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min(boxes[di][2], bb[2]), max(boxes[di][3], bb[3]))
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# ---- 第二遍：按并集包围盒统一裁剪并打包 ----
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outs, notes = [], []
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pad = int(crop_padding)
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for di in range(n_dir):
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frames = per_dir[di]
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ch, cw = frames[0][0].shape[:2]
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if auto_crop and boxes[di] is not None:
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y0, y1, x0, x1 = boxes[di]
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y0 = max(0, y0 - pad); x0 = max(0, x0 - pad)
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y1 = min(ch, y1 + pad); x1 = min(cw, x1 + pad)
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else:
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y0, y1, x0, x1 = 0, ch, 0, cw
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stack = np.empty((len(frames), y1 - y0, x1 - x0, 4), dtype=np.float32)
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for fi, (rgb, alpha) in enumerate(frames):
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stack[fi, ..., :3] = rgb[y0:y1, x0:x1]
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stack[fi, ..., 3] = alpha[y0:y1, x0:x1]
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outs.append(torch.from_numpy(stack))
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notes.append(f"{di + 1}.{names[di]} 格{cell_ids[di]} "
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f"{x1 - x0}×{y1 - y0}")
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info = (f"输入 {B} 帧 {W}×{H} → {cols}×{rows} 网格，"
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f"空格 {sorted(empties) if empties else '无'}，"
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f"得到 {n_dir} 个方向 × {B} 帧\n" + " | ".join(notes))
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if n_dir > MAX_DIRS:
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info += f"\n⚠ 方向数 {n_dir} 超过输出口数量 {MAX_DIRS}，只输出前 {MAX_DIRS} 个"
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elif n_dir < MAX_DIRS:
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info += (f"\n⚠ 方向数 {n_dir} 少于输出口数量 {MAX_DIRS}，"
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f"dir_{n_dir + 1}~dir_{MAX_DIRS} 为占位空图，请勿使用")
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print(f"[Ruinode-8Dir] {info}")
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# 输出口数量固定，方向不足时补占位图，避免下游拿到 None 直接报错
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blank = torch.zeros((1, 8, 8, 4), dtype=torch.float32)
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result = [outs[i] if i < len(outs) else blank for i in range(MAX_DIRS)]
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return tuple(result) + (info,)
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NODE_CLASS_MAPPINGS = {
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"RuiEightDirSplit": RuiEightDirSplit,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"RuiEightDirSplit": "八方向序列拆分 / 8-Direction Sprite Split",
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}
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Block a user