apply_segment_3.py

import os
import hashlib
from typing import List, Tuple

import numpy as np
import torch
import torch.nn.functional as F
from PIL import Image, ImageOps


# ============================================================
# Standalone assets helpers (no external utils required)
# Expects: <this_file_dir>/assets/images/*.png
# ============================================================

_ASSETS_DIR = os.path.join(os.path.dirname(os.path.realpath(__file__)), "assets", "images")


def list_pngs() -> List[str]:
    if not os.path.isdir(_ASSETS_DIR):
        return []
    files = []
    for f in os.listdir(_ASSETS_DIR):
        if f.lower().endswith(".png") and os.path.isfile(os.path.join(_ASSETS_DIR, f)):
            files.append(f)
    return sorted(files)


def safe_path(filename: str) -> str:
    # Prevent path traversal, force within _ASSETS_DIR
    candidate = os.path.join(_ASSETS_DIR, filename)
    real_assets = os.path.realpath(_ASSETS_DIR)
    real_candidate = os.path.realpath(candidate)
    if not real_candidate.startswith(real_assets + os.sep) and real_candidate != real_assets:
        raise ValueError("Unsafe path (path traversal detected).")
    return real_candidate


def file_hash(filename: str) -> str:
    path = safe_path(filename)
    h = hashlib.sha256()
    with open(path, "rb") as f:
        for chunk in iter(lambda: f.read(1024 * 1024), b""):
            h.update(chunk)
    return h.hexdigest()


def load_image_from_assets(filename: str) -> Tuple[torch.Tensor, torch.Tensor]:
    """
    Loads a PNG from assets/images and returns:
      - image: IMAGE tensor [1,H,W,3] float32 in [0,1]
      - mask:  MASK tensor  [1,H,W]   float32 in [0,1]

    IMPORTANT: mask follows ComfyUI LoadImage convention:
      if alpha exists: mask = 1 - alpha
      else:            mask = 1 - luminance
    """
    path = safe_path(filename)
    i = Image.open(path)
    i = ImageOps.exif_transpose(i)

    # Match Comfy style handling of mode 'I'
    if i.mode == "I":
        i = i.point(lambda px: px * (1 / 255))

    # IMAGE output (RGB)
    rgb = i.convert("RGB")
    rgb_np = np.array(rgb).astype(np.float32) / 255.0
    image = torch.from_numpy(rgb_np)[None, ...]  # [1,H,W,3]

    # MASK output
    bands = i.getbands()
    if "A" in bands:
        a = np.array(i.getchannel("A")).astype(np.float32) / 255.0
        alpha = torch.from_numpy(a)  # [H,W]
    else:
        # fallback: use luminance as alpha-like signal
        l = np.array(i.convert("L")).astype(np.float32) / 255.0
        alpha = torch.from_numpy(l)

    mask = 1.0 - alpha  # ComfyUI mask convention
    mask = mask.clamp(0.0, 1.0).unsqueeze(0)  # [1,H,W]
    return image, mask


# ============================================================
# Helpers (IMAGE / MASK validation + alpha paste)
# ============================================================

def _as_image(img: torch.Tensor) -> torch.Tensor:
    if not isinstance(img, torch.Tensor):
        raise TypeError("IMAGE must be a torch.Tensor")
    if img.dim() != 4:
        raise ValueError(f"Expected IMAGE shape [B,H,W,C], got {tuple(img.shape)}")
    if img.shape[-1] not in (3, 4):
        raise ValueError(f"Expected IMAGE channels 3 (RGB) or 4 (RGBA), got C={img.shape[-1]}")
    return img


def _as_mask(mask: torch.Tensor) -> torch.Tensor:
    if not isinstance(mask, torch.Tensor):
        raise TypeError("MASK must be a torch.Tensor")
    if mask.dim() == 2:
        mask = mask.unsqueeze(0)  # [1,H,W]
    if mask.dim() != 3:
        raise ValueError(f"Expected MASK shape [B,H,W] or [H,W], got {tuple(mask.shape)}")
    return mask


def _ensure_rgba(img: torch.Tensor) -> torch.Tensor:
    img = _as_image(img)
    if img.shape[-1] == 4:
        return img
    B, H, W, _ = img.shape
    alpha = torch.ones((B, H, W, 1), device=img.device, dtype=img.dtype)
    return torch.cat([img, alpha], dim=-1)


def _alpha_over_region(overlay: torch.Tensor, canvas: torch.Tensor, x: int, y: int) -> torch.Tensor:
    """
    Alpha-over paste overlay on top of canvas at (x,y) using overlay alpha.
    Supports RGB/RGBA for both. Returns same channel count as canvas.
    """
    overlay = _as_image(overlay)
    canvas = _as_image(canvas)

    # Batch handling: allow 1->N expansion
    if overlay.shape[0] != canvas.shape[0]:
        if overlay.shape[0] == 1 and canvas.shape[0] > 1:
            overlay = overlay.expand(canvas.shape[0], *overlay.shape[1:])
        elif canvas.shape[0] == 1 and overlay.shape[0] > 1:
            canvas = canvas.expand(overlay.shape[0], *canvas.shape[1:])
        else:
            raise ValueError(f"Batch mismatch: overlay {overlay.shape[0]} vs canvas {canvas.shape[0]}")

    B, Hc, Wc, Cc = canvas.shape
    _, Ho, Wo, _ = overlay.shape

    x = int(x)
    y = int(y)

    out = canvas.clone()

    # intersection on canvas
    x0c = max(0, x)
    y0c = max(0, y)
    x1c = min(Wc, x + Wo)
    y1c = min(Hc, y + Ho)

    if x1c <= x0c or y1c <= y0c:
        return out

    # corresponding region on overlay
    x0o = x0c - x
    y0o = y0c - y
    x1o = x0o + (x1c - x0c)
    y1o = y0o + (y1c - y0c)

    canvas_region = out[:, y0c:y1c, x0c:x1c, :]
    overlay_region = overlay[:, y0o:y1o, x0o:x1o, :]

    canvas_rgba = _ensure_rgba(canvas_region)
    overlay_rgba = _ensure_rgba(overlay_region)

    over_rgb = overlay_rgba[..., :3].clamp(0.0, 1.0)
    over_a = overlay_rgba[..., 3:4].clamp(0.0, 1.0)

    under_rgb = canvas_rgba[..., :3].clamp(0.0, 1.0)
    under_a = canvas_rgba[..., 3:4].clamp(0.0, 1.0)

    # premultiplied alpha composite
    over_pm = over_rgb * over_a
    under_pm = under_rgb * under_a

    out_a = over_a + under_a * (1.0 - over_a)
    out_pm = over_pm + under_pm * (1.0 - over_a)

    eps = 1e-6
    out_rgb = torch.where(out_a > eps, out_pm / (out_a + eps), torch.zeros_like(out_pm))
    out_rgb = out_rgb.clamp(0.0, 1.0)
    out_a = out_a.clamp(0.0, 1.0)

    if Cc == 3:
        out[:, y0c:y1c, x0c:x1c, :] = out_rgb
    else:
        out[:, y0c:y1c, x0c:x1c, :] = torch.cat([out_rgb, out_a], dim=-1)

    return out


# ============================================================
# RMBG EXACT MASK COMBINE LOGIC (same as your prior node)
# torch.maximum + PIL resize (LANCZOS)
# ============================================================

class _AILab_MaskCombiner_Exact:
    def combine_masks(self, mask_1, mode="combine", mask_2=None, mask_3=None, mask_4=None):
        masks = [m for m in [mask_1, mask_2, mask_3, mask_4] if m is not None]
        if len(masks) <= 1:
            return (masks[0] if masks else torch.zeros((1, 64, 64), dtype=torch.float32),)

        ref_shape = masks[0].shape
        masks = [self._resize_if_needed(m, ref_shape) for m in masks]

        if mode == "combine":
            result = torch.maximum(masks[0], masks[1])
            for mask in masks[2:]:
                result = torch.maximum(result, mask)
        elif mode == "intersection":
            result = torch.minimum(masks[0], masks[1])
        else:
            result = torch.abs(masks[0] - masks[1])

        return (torch.clamp(result, 0, 1),)

    def _resize_if_needed(self, mask, target_shape):
        if mask.shape == target_shape:
            return mask

        if len(mask.shape) == 2:
            mask = mask.unsqueeze(0)
        elif len(mask.shape) == 4:
            mask = mask.squeeze(1)

        target_height = target_shape[-2] if len(target_shape) >= 2 else target_shape[0]
        target_width = target_shape[-1] if len(target_shape) >= 2 else target_shape[1]

        resized_masks = []
        for i in range(mask.shape[0]):
            mask_np = mask[i].cpu().numpy()
            img = Image.fromarray((mask_np * 255).astype(np.uint8))
            img_resized = img.resize((target_width, target_height), Image.LANCZOS)
            mask_resized = np.array(img_resized).astype(np.float32) / 255.0
            resized_masks.append(torch.from_numpy(mask_resized))

        return torch.stack(resized_masks)


# ============================================================
# ComfyUI core "Join Image with Alpha" logic (EXACT)
# (from JoinImageWithAlpha implementation)
# ============================================================

def _resize_mask_comfy(alpha_mask: torch.Tensor, image_shape_hwc: Tuple[int, int, int]) -> torch.Tensor:
    # image_shape_hwc is image.shape[1:] => (H,W,C)
    H = int(image_shape_hwc[0])
    W = int(image_shape_hwc[1])
    return F.interpolate(
        alpha_mask.reshape((-1, 1, alpha_mask.shape[-2], alpha_mask.shape[-1])),
        size=(H, W),
        mode="bilinear",
    ).squeeze(1)


def _join_image_with_alpha_comfy(image: torch.Tensor, alpha: torch.Tensor) -> torch.Tensor:
    """
    EXACT logic:
      batch_size = min(len(image), len(alpha))
      alpha = 1.0 - resize_mask(alpha, image.shape[1:])
      out = cat(image[i][:,:,:3], alpha[i].unsqueeze(2))
    """
    image = _as_image(image)
    alpha = _as_mask(alpha)

    # Ensure same device/dtype for cat (core node assumes they already match)
    alpha = alpha.to(device=image.device, dtype=image.dtype)

    batch_size = min(len(image), len(alpha))
    out_images = []

    alpha_resized = 1.0 - _resize_mask_comfy(alpha, image.shape[1:])

    for i in range(batch_size):
        out_images.append(torch.cat((image[i][:, :, :3], alpha_resized[i].unsqueeze(2)), dim=2))

    return torch.stack(out_images)


# ============================================================
# NODE: apply_segment_3
# ============================================================

class apply_segment_3:
    CATEGORY = "image/salia"

    @classmethod
    def INPUT_TYPES(cls):
        choices = list_pngs() or ["<no pngs found>"]
        return {
            "required": {
                "mask": ("MASK",),
                "image": (choices, {}),          # dropdown asset (used for loaded mask)
                "img": ("IMAGE",),               # input image for Join Image with Alpha
                "canvas": ("IMAGE",),            # destination canvas
                "x": ("INT", {"default": 0, "min": -100000, "max": 100000, "step": 1}),
                "y": ("INT", {"default": 0, "min": -100000, "max": 100000, "step": 1}),
            }
        }

    RETURN_TYPES = ("MASK", "MASK", "IMAGE", "IMAGE")
    RETURN_NAMES = ("Inversed_Mask", "Alpha_Mask", "Alpha_Image", "Final_Image")
    FUNCTION = "run"

    def run(self, mask, image, img, canvas, x, y):
        if image == "<no pngs found>":
            raise FileNotFoundError("No PNGs found in assets/images next to apply_segment_3.py")

        # --- Step A: invert input mask (exactly your workflow)
        mask_in = _as_mask(mask)
        inversed_mask = 1.0 - mask_in  # [B,H,W]

        # --- Step B: combine_masks_with_loaded(inversed_mask) -> alpha_mask
        # combine_masks_with_loaded does: max(mask, 1 - loaded_mask)
        # loaded_mask comes from loader (Comfy LoadImage-style mask = 1 - alpha)
        # so (1 - loaded_mask) is alpha channel (or "mask" stored as alpha)
        _asset_img, loaded_mask = load_image_from_assets(image)

        combiner = _AILab_MaskCombiner_Exact()

        inv_cpu = inversed_mask.detach().cpu()
        loaded_cpu = _as_mask(loaded_mask).detach().cpu()

        alpha_mask, = combiner.combine_masks(inv_cpu, mode="combine", mask_2=(1.0 - loaded_cpu))
        alpha_mask = torch.clamp(alpha_mask, 0.0, 1.0)  # [B,H,W] on CPU

        # --- Step C: Join Image with Alpha (EXACT comfy core logic)
        alpha_image = _join_image_with_alpha_comfy(img, alpha_mask)

        # --- Step D: Paste_rect_to_img equivalent (alpha-over)
        canvas = _as_image(canvas)
        alpha_image = alpha_image.to(device=canvas.device, dtype=canvas.dtype)
        final = _alpha_over_region(alpha_image, canvas, x, y)

        return (inversed_mask, alpha_mask, alpha_image, final)

    @classmethod
    def IS_CHANGED(cls, mask, image, img, canvas, x, y):
        if image == "<no pngs found>":
            return image
        return file_hash(image)

    @classmethod
    def VALIDATE_INPUTS(cls, mask, image, img, canvas, x, y):
        if image == "<no pngs found>":
            return "No PNGs found in assets/images next to apply_segment_3.py"
        try:
            path = safe_path(image)
        except Exception as e:
            return str(e)
        if not os.path.isfile(path):
            return f"File not found in assets/images: {image}"
        return True


# ============================================================
# Node mappings (ONLY this node)
# ============================================================

NODE_CLASS_MAPPINGS = {
    "apply_segment_3": apply_segment_3,
}

NODE_DISPLAY_NAME_MAPPINGS = {
    "apply_segment_3": "apply_segment_3",
}