451 lines
15 KiB
Python
451 lines
15 KiB
Python
"""
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ComfyUI custom nodes for embedding images into PSD smart object mockups.
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The main node takes a PSD mockup file together with an input image, finds
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smart-object layers, and replaces their content by warping the input image
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to the smart-object's perspective box.
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"""
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from __future__ import annotations
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import json
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import math
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import os
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from dataclasses import dataclass
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from datetime import datetime
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from pathlib import Path
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from typing import Iterable, List, Optional, Sequence, Tuple
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import numpy as np
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from PIL import Image, ImageDraw
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from psd_tools import PSDImage
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from psd_tools.api.layers import Layer, SmartObjectLayer
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try:
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import torch
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except Exception: # pragma: no cover - torch is always available inside ComfyUI
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torch = None
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try:
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import folder_paths
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except Exception: # pragma: no cover - allows running outside ComfyUI
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folder_paths = None
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ALLOWED_PSD_EXTENSIONS = (".psd", ".psb")
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QUAD_INDICES = ((0, 1), (2, 3), (4, 5), (6, 7))
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PNG_SUFFIX = ".png"
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@dataclass
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class SmartLayerInfo:
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"""Small helper that keeps metadata for a smart-object layer."""
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layer_id: int
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name: str
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visible: bool
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bbox: Tuple[int, int, int, int]
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transform_box: Optional[Tuple[float, ...]]
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def to_dict(self) -> dict:
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return {
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"layer_id": self.layer_id,
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"name": self.name,
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"visible": self.visible,
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"bbox": self.bbox,
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"transform_box": self.transform_box,
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}
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def _resolve_input_path(filename: str, allowed_exts: Sequence[str]) -> str:
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if not filename:
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raise ValueError("A file name must be provided.")
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if os.path.isabs(filename):
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candidate = filename
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if not os.path.exists(candidate):
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raise FileNotFoundError(f"File not found: {candidate}")
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else:
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search_roots: List[str] = []
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if folder_paths:
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getter = getattr(folder_paths, "get_input_directory", None)
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if callable(getter):
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search_roots.append(getter())
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get_extra = getattr(folder_paths, "get_input_directories", None)
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if callable(get_extra):
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search_roots.extend(get_extra())
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search_roots.append(os.getcwd())
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candidate = ""
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for root in search_roots:
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path = os.path.join(root, filename)
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if os.path.exists(path):
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candidate = path
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break
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if not candidate:
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raise FileNotFoundError(
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f"Unable to resolve '{filename}'. Place the file under ComfyUI/input or provide an absolute path."
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)
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_, ext = os.path.splitext(candidate)
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ext = ext.lower()
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if allowed_exts and ext not in allowed_exts:
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raise ValueError(f"Unsupported file extension '{ext}'. Expected one of {allowed_exts}.")
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return candidate
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def _list_input_files(allowed_exts: Sequence[str]) -> List[str]:
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if not folder_paths or not hasattr(folder_paths, "get_filename_list"):
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return []
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try:
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files = folder_paths.get_filename_list("input")
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except Exception:
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return []
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if not allowed_exts:
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return files
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allowed = tuple(ext.lower() for ext in allowed_exts)
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filtered = [name for name in files if name.lower().endswith(allowed)]
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return filtered or files
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def _collect_smart_layers(group: Iterable[Layer]) -> List[SmartLayerInfo]:
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layers: List[SmartLayerInfo] = []
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def walk(sub_layers: Iterable[Layer]):
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for layer in sub_layers:
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if isinstance(layer, SmartObjectLayer):
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layers.append(
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SmartLayerInfo(
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layer_id=layer.layer_id,
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name=layer.name,
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visible=layer.is_visible(),
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bbox=tuple(map(int, layer.bbox)),
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transform_box=layer.smart_object.transform_box,
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)
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)
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if layer.is_group():
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walk(layer)
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walk(group)
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return layers
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def _tensor_to_pil(image_tensor) -> Image.Image:
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if image_tensor is None:
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return None
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if torch is None:
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raise RuntimeError("torch is required to convert IMAGE values. Are you running inside ComfyUI?")
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tensor = image_tensor
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if isinstance(tensor, list):
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tensor = tensor[0]
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if hasattr(tensor, "detach"):
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tensor = tensor.detach().cpu()
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array = np.clip(tensor.numpy(), 0.0, 1.0)
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if array.ndim == 4:
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array = array[0]
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array = (array * 255).astype(np.uint8)
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if array.shape[-1] == 1:
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array = np.repeat(array, 3, axis=-1)
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return Image.fromarray(array)
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def _pil_to_tensor(image: Image.Image):
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if torch is None:
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raise RuntimeError("torch is required to convert PIL images back to ComfyUI tensors.")
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rgb = image.convert("RGB")
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array = np.array(rgb).astype(np.float32) / 255.0
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tensor = torch.from_numpy(array)[None, ...]
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return tensor
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def _load_image_from_path(path: str) -> Image.Image:
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if not os.path.exists(path):
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raise FileNotFoundError(f"Image not found: {path}")
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image = Image.open(path)
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return image.convert("RGBA")
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def _normalize_name(name: str) -> str:
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return name.strip().lower()
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def _select_layers(layers: List[SmartLayerInfo], names: Sequence[str]) -> List[SmartLayerInfo]:
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if not names or len(names) == 0:
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return layers[:1] if layers else []
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lookup = {_normalize_name(name) for name in names if name.strip()}
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if not lookup:
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return layers[:1] if layers else []
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result = [layer for layer in layers if _normalize_name(layer.name) in lookup]
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if not result:
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return layers[:1] if layers else []
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return result
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def _split_names(raw_names: str) -> List[str]:
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if not raw_names:
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return []
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return [part.strip() for part in raw_names.split(",") if part.strip()]
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def _warp_image_to_canvas(image: Image.Image, quad: Sequence[float], canvas_size: Tuple[int, int]) -> Image.Image:
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try:
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import cv2
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except Exception as exc: # pragma: no cover - cv2 is installed via requirements
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raise RuntimeError("opencv-python-headless is required for perspective warping.") from exc
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if quad is None or len(quad) != 8:
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raise ValueError("transform_box must contain eight values.")
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width, height = canvas_size
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if width <= 0 or height <= 0:
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raise ValueError("Canvas size must be positive.")
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image_rgba = image.convert("RGBA")
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src_w, src_h = image_rgba.size
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src_pts = np.float32([[0, 0], [src_w, 0], [src_w, src_h], [0, src_h]])
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dst_pts = np.float32([[quad[i], quad[i + 1]] for i, _ in QUAD_INDICES])
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matrix = cv2.getPerspectiveTransform(src_pts, dst_pts)
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canvas_w, canvas_h = int(math.ceil(width)), int(math.ceil(height))
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rgba_array = np.array(image_rgba)
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warped = cv2.warpPerspective(
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rgba_array,
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matrix,
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(canvas_w, canvas_h),
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flags=cv2.INTER_LINEAR,
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borderMode=cv2.BORDER_CONSTANT,
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borderValue=(0, 0, 0, 0),
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)
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return Image.fromarray(warped, mode="RGBA")
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def _compose_without_layers(psd: PSDImage, skip_ids: Sequence[int]) -> Image.Image:
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skip_set = set(skip_ids)
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def layer_filter(layer: Layer) -> bool:
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if hasattr(layer, "layer_id") and layer.layer_id in skip_set:
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return False
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return layer.is_visible()
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return psd.composite(layer_filter=layer_filter)
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def _polygon_mask(quad: Sequence[float], canvas_size: Tuple[int, int]) -> Image.Image:
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mask = Image.new("L", tuple(map(int, canvas_size)), 0)
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draw = ImageDraw.Draw(mask)
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draw.polygon([(quad[i], quad[i + 1]) for i, _ in QUAD_INDICES], fill=255)
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return mask
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def _build_layer_lookup(psd: PSDImage) -> dict:
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lookup = {}
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for layer in psd.descendants():
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layer_id = getattr(layer, "layer_id", None)
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if layer_id is not None:
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lookup[layer_id] = layer
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return lookup
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def _collect_clipping_stack(layer: Layer) -> List[Layer]:
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stack = [layer]
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parent = layer.parent
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if parent is None:
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return stack
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siblings = list(parent)
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try:
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idx = siblings.index(layer)
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except ValueError:
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return stack
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for sib in siblings[idx + 1 :]:
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if not getattr(sib, "clipping", False):
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break
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stack.append(sib)
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return stack
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def _composite_layers(psd: PSDImage, layer_ids: Sequence[int]) -> Image.Image:
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include = set(layer_ids)
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def filter_fn(layer: Layer) -> bool:
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if hasattr(layer, "layer_id") and layer.layer_id in include:
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return True
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return False
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return psd.composite(layer_filter=filter_fn)
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def _alpha_mask_from_image(image: Image.Image) -> Optional[Image.Image]:
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if image is None:
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return None
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if image.mode != "RGBA":
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image = image.convert("RGBA")
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alpha = image.split()[-1]
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if not alpha.getbbox():
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return None
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return alpha
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def _output_directory() -> str:
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if folder_paths and hasattr(folder_paths, "get_output_directory"):
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return folder_paths.get_output_directory()
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default_dir = os.path.join(os.getcwd(), "output")
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os.makedirs(default_dir, exist_ok=True)
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return default_dir
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def _save_png(image: Image.Image, psd_path: str) -> str:
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base_name = Path(psd_path).stem or "psd_mockup"
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timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
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filename = f"{base_name}_{timestamp}{PNG_SUFFIX}"
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out_dir = _output_directory()
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out_path = os.path.join(out_dir, filename)
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image.save(out_path, "PNG")
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return out_path
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class PSDSmartObjectInspector:
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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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"psd_file": ("STRING", {"default": "", "multiline": False}),
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"include_hidden": ("BOOLEAN", {"default": False}),
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}
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}
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CATEGORY = "psd/mockup"
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RETURN_TYPES = ("STRING",)
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FUNCTION = "inspect"
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def inspect(self, psd_file: str, include_hidden: bool):
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psd_path = _resolve_input_path(psd_file, ALLOWED_PSD_EXTENSIONS)
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psd = PSDImage.open(psd_path, strict=False)
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layers = _collect_smart_layers(psd)
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payload = []
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for layer in layers:
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if not include_hidden and not layer.visible:
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continue
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payload.append(layer.to_dict())
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return (json.dumps({"psd": os.path.basename(psd_path), "layers": payload}, indent=2),)
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class PSDMockupEmbedder:
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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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"psd_file": (
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"STRING",
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{
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"default": "",
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"multiline": False,
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"placeholder": "Upload or paste PSD/PSB path",
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"tooltip": "Use the built-in upload button or paste a path manually",
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},
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),
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"mockup_image": ("IMAGE",),
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},
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"optional": {
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"smart_object_names": ("STRING", {"default": "", "multiline": False}),
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"output_width": (
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"INT",
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{
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"default": 800,
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"min": 0,
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"max": 8192,
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"step": 1,
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"tooltip": "Resize final image to this width (0 keeps original size)",
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},
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),
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},
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}
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CATEGORY = "psd/mockup"
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RETURN_TYPES = ("IMAGE", "STRING", "STRING")
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RETURN_NAMES = ("mockup_image", "debug_info", "png_path")
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FUNCTION = "apply"
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def apply(
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self,
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psd_file: str,
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mockup_image,
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smart_object_names: str = "",
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output_width: int = 800,
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):
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resolved_psd = _resolve_input_path(psd_file, ALLOWED_PSD_EXTENSIONS)
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psd = PSDImage.open(resolved_psd, strict=False)
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source_image = _tensor_to_pil(mockup_image)
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if source_image is None:
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raise ValueError("mockup_image input is required. Use MockupImageUpload or any IMAGE source.")
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layers = _collect_smart_layers(psd)
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requested_names = _split_names(smart_object_names)
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selected = _select_layers(layers, requested_names)
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if not selected:
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raise ValueError("No smart-object layers available inside the PSD.")
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fallback_used = False
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if requested_names:
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requested_norm = {_normalize_name(name) for name in requested_names}
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selected_norm = {_normalize_name(layer.name) for layer in selected}
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fallback_used = not bool(requested_norm & selected_norm)
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layer_lookup = _build_layer_lookup(psd)
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canvas_size = psd.size
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base = _compose_without_layers(psd, [info.layer_id for info in selected])
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base_rgba = base.convert("RGBA")
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metadata = []
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for info in selected:
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layer = layer_lookup.get(info.layer_id)
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if layer is None:
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continue
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quad = info.transform_box
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if quad is None:
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left, top, right, bottom = info.bbox
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quad = (left, top, right, top, right, bottom, left, bottom)
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stack_layers = _collect_clipping_stack(layer)
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overlay_layers = stack_layers[1:] if len(stack_layers) > 1 else []
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overlay_ids = [l.layer_id for l in overlay_layers if hasattr(l, "layer_id")]
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overlay_image = _composite_layers(psd, overlay_ids) if overlay_ids else None
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base_image = _composite_layers(psd, [layer.layer_id])
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alpha_mask = _alpha_mask_from_image(base_image) or _polygon_mask(quad, canvas_size)
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warped = _warp_image_to_canvas(source_image, quad, canvas_size)
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base_rgba = Image.composite(warped, base_rgba, alpha_mask)
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if overlay_image is not None:
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base_rgba = Image.alpha_composite(base_rgba, overlay_image.convert("RGBA"))
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metadata.append(
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{
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"layer_id": info.layer_id,
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"name": info.name,
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"quad": quad,
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}
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)
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if isinstance(output_width, (int, float)) and output_width and output_width > 0:
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target_w = int(output_width)
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if target_w > 0 and target_w != base_rgba.width:
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ratio = target_w / base_rgba.width
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target_h = max(1, int(round(base_rgba.height * ratio)))
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base_rgba = base_rgba.resize((target_w, target_h), Image.LANCZOS)
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result_tensor = _pil_to_tensor(base_rgba)
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saved_path = _save_png(base_rgba, resolved_psd)
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debug_payload = {
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"psd": os.path.basename(resolved_psd),
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"layer_count": len(selected),
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"layers": metadata,
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"output_size": {"width": base_rgba.width, "height": base_rgba.height},
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}
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if fallback_used:
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debug_payload["note"] = "Requested smart object names were not found; falling back to the first smart object layer."
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return (result_tensor, json.dumps(debug_payload, indent=2), saved_path)
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NODE_CLASS_MAPPINGS = {
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"PSDSmartObjectInspector": PSDSmartObjectInspector,
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"PSDMockupEmbedder": PSDMockupEmbedder,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"PSDSmartObjectInspector": "PSD → Smart Object Info",
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"PSDMockupEmbedder": "PSD Mockup Embedder",
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}
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