File size: 14,703 Bytes
6c8ffa0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 | import math
import torch
class SpriteHeadStabilizeX:
"""
Stabilize sprite animation wiggle (X only) using a Y-band (e.g. head region).
Align frames 1..N to frame 0 by estimating horizontal shift from alpha visibility
inside the selected Y-range.
Methods:
- bbox_center: leftmost/rightmost visible pixel columns -> center
- alpha_com: alpha-weighted center-of-mass (recommended)
- profile_corr: phase correlation on horizontal alpha profile (very robust)
- hybrid: profile_corr with a sanity check fallback to alpha_com
Inputs support:
- True RGBA IMAGE tensor (C>=4) => alpha taken from channel 4
- Or IMAGE (RGB) + MASK (ComfyUI LoadImage mask) => alpha derived from mask
"""
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"images": ("IMAGE", {}),
# Head band
"y_min": ("INT", {"default": 210, "min": -99999, "max": 99999, "step": 1}),
"y_max": ("INT", {"default": 332, "min": -99999, "max": 99999, "step": 1}),
# Alpha tolerance: visible if alpha > threshold_8bit / 255
"alpha_threshold_8bit": ("INT", {"default": 5, "min": 0, "max": 255, "step": 1}),
"method": (["bbox_center", "alpha_com", "profile_corr", "hybrid"], {"default": "alpha_com"}),
# ComfyUI LoadImage produces MASK from alpha and inverts it.
# If your mask is already alpha (0=transparent,1=opaque), set False.
"mask_is_inverted": ("BOOLEAN", {"default": True}),
# Optional safety clamps/smoothing
"max_abs_shift": ("INT", {"default": 0, "min": 0, "max": 99999, "step": 1}),
"temporal_median": ("INT", {"default": 1, "min": 1, "max": 99, "step": 1}),
# Hybrid sanity check: if corr shift differs from COM shift by more than this,
# use COM shift instead.
"hybrid_tolerance_px": ("INT", {"default": 8, "min": 0, "max": 99999, "step": 1}),
},
"optional": {
"mask": ("MASK", {}),
}
}
RETURN_TYPES = ("IMAGE", "MASK", "STRING")
RETURN_NAMES = ("images", "mask", "shifts_x")
FUNCTION = "stabilize"
CATEGORY = "image/sprite"
SEARCH_ALIASES = ["wiggle stabilize", "sprite stabilize", "head stabilize", "animation stabilize", "sprite jitter fix"]
# ---------- helpers ----------
def _get_alpha(self, images: torch.Tensor, mask: torch.Tensor | None, mask_is_inverted: bool) -> torch.Tensor:
"""
Returns alpha in [0..1], shape [B,H,W].
"""
if images.dim() != 4:
raise ValueError(f"images must have shape [B,H,W,C], got {tuple(images.shape)}")
B, H, W, C = images.shape
if C >= 4:
return images[..., 3]
if mask is None:
raise ValueError("Need RGBA images (C>=4) OR provide a MASK input.")
if mask.dim() == 2:
mask = mask.unsqueeze(0)
if mask.dim() != 3:
raise ValueError(f"mask must have shape [B,H,W] or [H,W], got {tuple(mask.shape)}")
if mask.shape[1] != H or mask.shape[2] != W:
raise ValueError(f"mask H/W must match images; mask={tuple(mask.shape)} images={tuple(images.shape)}")
if mask.shape[0] == 1 and B > 1:
mask = mask.repeat(B, 1, 1)
if mask.shape[0] != B:
raise ValueError(f"mask batch must match images batch; mask B={mask.shape[0]} images B={B}")
alpha = 1.0 - mask if mask_is_inverted else mask
return alpha
def _clamp_y(self, H: int, y_min: int, y_max: int) -> tuple[int, int]:
y0 = int(y_min)
y1 = int(y_max)
if y1 < y0:
y0, y1 = y1, y0
y0 = max(0, min(H - 1, y0))
y1 = max(0, min(H - 1, y1))
return y0, y1
def _bbox_center_x(self, alpha_hw: torch.Tensor, thr: float) -> float | None:
"""
alpha_hw: [H,W]
Returns center X using leftmost/rightmost visible columns, or None if empty.
"""
# visible: [H,W]
visible = alpha_hw > thr
cols = visible.any(dim=0) # [W]
if not bool(cols.any()):
return None
W = alpha_hw.shape[1]
left = int(torch.argmax(cols.float()).item())
right = int((W - 1) - torch.argmax(torch.flip(cols, dims=[0]).float()).item())
return (left + right) / 2.0
def _com_center_x(self, alpha_hw: torch.Tensor, thr: float) -> float | None:
"""
alpha_hw: [H,W]
Alpha-weighted center-of-mass of X within visible area, or None if empty.
"""
W = alpha_hw.shape[1]
weights = alpha_hw
if thr > 0:
weights = weights * (weights > thr)
profile = weights.sum(dim=0) # [W]
total = float(profile.sum().item())
if total <= 0.0:
return None
x = torch.arange(W, device=alpha_hw.device, dtype=profile.dtype)
center = float((profile * x).sum().item() / total)
return center
def _phase_corr_shift_x(self, alpha_hw: torch.Tensor, ref_profile: torch.Tensor, thr: float) -> int | None:
"""
Estimate integer shift to APPLY to current frame (X) so it matches reference.
Uses 1D phase correlation on horizontal alpha profile.
Returns shift_x (int), or None if empty.
"""
weights = alpha_hw
if thr > 0:
weights = weights * (weights > thr)
prof = weights.sum(dim=0).float()
if float(prof.sum().item()) <= 0.0:
return None
# Remove DC component
prof = prof - prof.mean()
ref = ref_profile
# Phase correlation
F = torch.fft.rfft(prof)
R = torch.fft.rfft(ref)
cps = F * torch.conj(R)
cps = cps / (torch.abs(cps) + 1e-9)
corr = torch.fft.irfft(cps, n=prof.numel())
peak = int(torch.argmax(corr).item())
W = prof.numel()
lag = peak if peak <= W // 2 else peak - W # lag = "current is shifted by lag relative to ref"
shift_x = -lag # apply negative to align to ref
return int(shift_x)
def _shift_frame_x(self, img_hwc: torch.Tensor, shift_x: int) -> torch.Tensor:
"""
img_hwc: [H,W,C]
shift_x: int (positive -> move right)
"""
H, W, C = img_hwc.shape
out = torch.zeros_like(img_hwc)
if shift_x == 0:
return img_hwc
if abs(shift_x) >= W:
return out
if shift_x > 0:
out[:, shift_x:, :] = img_hwc[:, : W - shift_x, :]
else:
sx = -shift_x
out[:, : W - sx, :] = img_hwc[:, sx:, :]
return out
def _shift_mask_x(self, m_hw: torch.Tensor, shift_x: int, fill_val: float) -> torch.Tensor:
"""
m_hw: [H,W]
"""
H, W = m_hw.shape
out = torch.full_like(m_hw, fill_val)
if shift_x == 0:
return m_hw
if abs(shift_x) >= W:
return out
if shift_x > 0:
out[:, shift_x:] = m_hw[:, : W - shift_x]
else:
sx = -shift_x
out[:, : W - sx] = m_hw[:, sx:]
return out
def _median_smooth(self, shifts: list[int], window: int) -> list[int]:
"""
Median filter over shifts with odd window size. Keeps shifts[0] unchanged.
"""
if window <= 1 or len(shifts) <= 2:
return shifts
w = int(window)
if w % 2 == 0:
w += 1
r = w // 2
out = shifts[:]
out[0] = shifts[0]
n = len(shifts)
for i in range(1, n):
lo = max(1, i - r)
hi = min(n, i + r + 1)
vals = sorted(shifts[lo:hi])
out[i] = vals[len(vals) // 2]
return out
# ---------- main ----------
def stabilize(
self,
images: torch.Tensor,
y_min: int = 210,
y_max: int = 332,
alpha_threshold_8bit: int = 5,
method: str = "alpha_com",
mask_is_inverted: bool = True,
max_abs_shift: int = 0,
temporal_median: int = 1,
hybrid_tolerance_px: int = 8,
mask: torch.Tensor | None = None,
):
if not torch.is_tensor(images):
raise TypeError("images must be a torch.Tensor")
if images.dim() != 4:
raise ValueError(f"images must have shape [B,H,W,C], got {tuple(images.shape)}")
B, H, W, C = images.shape
if B < 1:
raise ValueError("images batch is empty")
alpha = self._get_alpha(images, mask, mask_is_inverted) # [B,H,W]
y0, y1 = self._clamp_y(H, y_min, y_max)
thr = float(alpha_threshold_8bit) / 255.0
roi_alpha = alpha[:, y0:y1 + 1, :] # [B, Hr, W]
# Reference (frame 0)
ref_roi = roi_alpha[0] # [Hr,W]
# Prepare reference for methods
ref_center_bbox = None
ref_center_com = None
ref_profile = None
if method in ("bbox_center", "hybrid"):
ref_center_bbox = self._bbox_center_x(ref_roi, thr)
if method in ("alpha_com", "hybrid"):
ref_center_com = self._com_center_x(ref_roi, thr)
if method in ("profile_corr", "hybrid"):
# reference profile for phase correlation
w = ref_roi
if thr > 0:
w = w * (w > thr)
ref_profile = w.sum(dim=0).float()
ref_profile = ref_profile - ref_profile.mean()
# Fallback reference center if missing
if ref_center_bbox is None and ref_center_com is None and ref_profile is None:
# Nothing visible even in reference head region; do nothing.
out_mask = None
if mask is not None:
out_mask = mask if mask.dim() == 3 else mask.unsqueeze(0)
elif C >= 4:
a = images[..., 3]
out_mask = (1.0 - a) if mask_is_inverted else a
else:
fill_val = 1.0 if mask_is_inverted else 0.0
out_mask = torch.full((B, H, W), fill_val, device=images.device, dtype=images.dtype)
return (images, out_mask, "[0]" if B == 1 else str([0] * B))
# For center-based methods, pick a reference center
# Preference: COM, else BBOX, else image center
if ref_center_com is not None:
ref_center = ref_center_com
elif ref_center_bbox is not None:
ref_center = ref_center_bbox
else:
ref_center = W / 2.0
shifts = [0] * B
shifts[0] = 0 # frame 0 stays
for i in range(1, B):
a_hw = roi_alpha[i]
shift_i = 0
if method == "bbox_center":
c = self._bbox_center_x(a_hw, thr)
if c is None:
shift_i = 0
else:
shift_i = int(round(ref_center - c))
elif method == "alpha_com":
c = self._com_center_x(a_hw, thr)
if c is None:
shift_i = 0
else:
shift_i = int(round(ref_center - c))
elif method == "profile_corr":
s = self._phase_corr_shift_x(a_hw, ref_profile, thr) # already int shift to APPLY
shift_i = 0 if s is None else int(s)
elif method == "hybrid":
# corr shift
s_corr = self._phase_corr_shift_x(a_hw, ref_profile, thr) if ref_profile is not None else None
# com shift
c = self._com_center_x(a_hw, thr)
s_com = None if c is None else int(round(ref_center - c))
if s_corr is None and s_com is None:
shift_i = 0
elif s_corr is None:
shift_i = int(s_com)
elif s_com is None:
shift_i = int(s_corr)
else:
if abs(int(s_corr) - int(s_com)) > int(hybrid_tolerance_px):
shift_i = int(s_com)
else:
shift_i = int(s_corr)
else:
raise ValueError(f"Unknown method: {method}")
# Clamp extreme shifts if requested
if max_abs_shift and max_abs_shift > 0:
shift_i = int(max(-max_abs_shift, min(max_abs_shift, shift_i)))
shifts[i] = shift_i
# Optional temporal median smoothing (keeps shifts[0] anchored)
shifts = self._median_smooth(shifts, int(temporal_median))
# Apply per-frame shifts
out_images = torch.zeros_like(images)
# Output mask handling:
# - If input mask provided: shift it
# - Else if RGBA: derive from shifted alpha
# - Else: produce blank
out_mask = None
in_mask_bhw = None
if mask is not None:
in_mask_bhw = mask
if in_mask_bhw.dim() == 2:
in_mask_bhw = in_mask_bhw.unsqueeze(0)
if in_mask_bhw.shape[0] == 1 and B > 1:
in_mask_bhw = in_mask_bhw.repeat(B, 1, 1)
fill_val = 1.0 if mask_is_inverted else 0.0
out_mask = torch.full_like(in_mask_bhw, fill_val)
for i in range(B):
sx = int(shifts[i])
out_images[i] = self._shift_frame_x(images[i], sx)
if out_mask is not None and in_mask_bhw is not None:
fill_val = 1.0 if mask_is_inverted else 0.0
out_mask[i] = self._shift_mask_x(in_mask_bhw[i], sx, fill_val)
if out_mask is None:
if out_images.shape[-1] >= 4:
a = out_images[..., 3]
out_mask = (1.0 - a) if mask_is_inverted else a
else:
fill_val = 1.0 if mask_is_inverted else 0.0
out_mask = torch.full((B, H, W), fill_val, device=images.device, dtype=images.dtype)
shifts_str = str(shifts)
return (out_images, out_mask, shifts_str)
NODE_CLASS_MAPPINGS = {
"SpriteHeadStabilizeX": SpriteHeadStabilizeX,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"SpriteHeadStabilizeX": "Sprite Head Stabilize X (Batch)",
} |