nodes/machinepainting-ComfyUI-MachinePaintingNodes
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity
Files
dev
machinepainting-ComfyUI-Mac…/inpaint_mask_pro.py
T
MachinePainting 3a574494eb Add short DESCRIPTION text to all 41 nodes 
None of the nodes showed description text in ComfyUI because ComfyUI
reads obj_class.DESCRIPTION when it builds node_info - it does not read
the class docstring, which is what these nodes had.

Every registered node now carries a one-line DESCRIPTION, kept to
29-67 characters so it fits the limited space in ComfyUI Manager and
the node search results. Image Transform Pro's four-sentence
description is shortened to match.

Descriptions are derived from each node's existing docstring, or from
the README for the five classes that had no docstring
(ImageBlendPro, ColorMatchBlend and the three Boolean switch nodes).

README also updated for the recent changes: the New Nodes table now
lists the real "Rotate & Scale" category, the XY placement direction is
documented on both nodes (positive X right, positive Y up), and the
v2.2.0 changelog records the placement convention, the node outputs and
the MP_ImageTransform internal name.
2026-09-03 03:32:00 -07:00

221 lines
8.5 KiB
Python
Raw Permalink Blame History

import torch
import numpy as np
import cv2
from PIL import Image
import folder_paths
import os
class InpaintMaskPro:
"""
Combines an inpaint mask with an optional silhouette mask to produce a clean,
cropped inpaint region. Provides multi-color overlay preview with independent
color/opacity control per mask layer.
"""
DESCRIPTION = "Crop an inpaint mask to clean boundaries with a silhouette mask."
IN_MASK_COLORS = ["red", "light_blue", "black", "white"]
MASK_COLORS = ["lime_green", "purple", "black", "white"]
COLOR_MAP = {
"red": np.array([1.0, 0.2, 0.2], dtype=np.float32),
"light_blue": np.array([0.3, 0.7, 1.0], dtype=np.float32),
"black": np.array([0.0, 0.0, 0.0], dtype=np.float32),
"white": np.array([1.0, 1.0, 1.0], dtype=np.float32),
"lime_green": np.array([0.2, 1.0, 0.2], dtype=np.float32),
"purple": np.array([0.7, 0.2, 1.0], dtype=np.float32),
}
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"image": ("IMAGE",),
"inpaint_mask": ("MASK",),
},
"optional": {
# --- Inpaint mask settings ---
"invert_mask": ("BOOLEAN", {"default": False, "label_on": "inverted", "label_off": "normal"}),
"inpaint_expand": ("INT", {
"default": 0,
"min": -100,
"max": 100,
"step": 1,
"display": "slider"
}),
"inpaint_blur": ("FLOAT", {
"default": 0.0,
"min": 0.0,
"max": 50.0,
"step": 0.5,
"display": "slider"
}),
# --- Inpaint mask display ---
"show_in_mask": ("BOOLEAN", {"default": True, "label_on": "on", "label_off": "off"}),
"in_mask_color": (cls.IN_MASK_COLORS, {"default": "red"}),
"in_mask_opacity": ("FLOAT", {
"default": 0.5,
"min": 0.0,
"max": 1.0,
"step": 0.01,
"display": "slider"
}),
# --- Mask crop settings (advanced) ---
"use_mask": ("BOOLEAN", {"default": False, "label_on": "on", "label_off": "off", "advanced": True}),
"mask": ("MASK",),
"mask_blur": ("FLOAT", {
"default": 0.0,
"min": 0.0,
"max": 50.0,
"step": 0.5,
"display": "slider",
"advanced": True,
}),
"mask_expand": ("INT", {
"default": 0,
"min": -100,
"max": 100,
"step": 1,
"display": "slider",
"advanced": True,
}),
# --- Mask display (advanced) ---
"show_mask": ("BOOLEAN", {"default": False, "label_on": "on", "label_off": "off", "advanced": True}),
"mask_color": (cls.MASK_COLORS, {"default": "lime_green", "advanced": True}),
"mask_opacity": ("FLOAT", {
"default": 0.3,
"min": 0.0,
"max": 1.0,
"step": 0.01,
"display": "slider",
"advanced": True,
}),
},
"hidden": {
"unique_id": "UNIQUE_ID",
}
}
RETURN_TYPES = ("IMAGE", "MASK", "IMAGE", "IMAGE")
RETURN_NAMES = ("image_thru", "mask", "mask_bw", "preview_out")
OUTPUT_NODE = True
FUNCTION = "process"
CATEGORY = "MachinePaintingNodes/Mask"
def process(self, image, inpaint_mask,
invert_mask=False, inpaint_expand=0, inpaint_blur=0.0,
show_in_mask=True, in_mask_color="red", in_mask_opacity=0.5,
use_mask=False, mask=None, mask_blur=0.0, mask_expand=0,
show_mask=False, mask_color="lime_green", mask_opacity=0.3,
unique_id=None):
img = image[0].cpu().numpy().astype(np.float32)
h, w = img.shape[:2]
# --- Process inpaint mask ---
inpaint = self._to_numpy_mask(inpaint_mask, h, w)
if invert_mask:
inpaint = 1.0 - inpaint
# Expand/shrink inpaint mask
if inpaint_expand != 0:
kernel_size = abs(inpaint_expand) * 2 + 1
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (kernel_size, kernel_size))
inpaint_u8 = (inpaint * 255).astype(np.uint8)
if inpaint_expand > 0:
inpaint_u8 = cv2.dilate(inpaint_u8, kernel, iterations=1)
else:
inpaint_u8 = cv2.erode(inpaint_u8, kernel, iterations=1)
inpaint = inpaint_u8.astype(np.float32) / 255.0
if inpaint_blur > 0:
ksize = max(int(inpaint_blur * 2) | 1, 3)
inpaint = cv2.GaussianBlur(inpaint, (ksize, ksize), inpaint_blur)
# --- Process crop mask (from mask input) ---
crop_mask = None
if use_mask and mask is not None:
crop_mask = self._to_numpy_mask(mask, h, w)
if mask_expand != 0:
kernel_size = abs(mask_expand) * 2 + 1
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (kernel_size, kernel_size))
mask_u8 = (crop_mask * 255).astype(np.uint8)
if mask_expand > 0:
mask_u8 = cv2.dilate(mask_u8, kernel, iterations=1)
else:
mask_u8 = cv2.erode(mask_u8, kernel, iterations=1)
crop_mask = mask_u8.astype(np.float32) / 255.0
if mask_blur > 0:
ksize = max(int(mask_blur * 2) | 1, 3)
crop_mask = cv2.GaussianBlur(crop_mask, (ksize, ksize), mask_blur)
# --- Combine: intersect inpaint mask with crop mask ---
result_mask = inpaint.copy()
if crop_mask is not None:
result_mask = result_mask * crop_mask
result_mask = np.clip(result_mask, 0.0, 1.0)
# --- Create preview overlay ---
preview_np = img.copy()
# Layer 1: Mask input (silhouette)
if show_mask and crop_mask is not None and mask_opacity > 0:
preview_np = self._overlay_color(preview_np, crop_mask, mask_color, mask_opacity)
# Layer 2: Inpaint mask (final result) — drawn last (top layer)
if show_in_mask and in_mask_opacity > 0:
preview_np = self._overlay_color(preview_np, result_mask, in_mask_color, in_mask_opacity)
preview_np = np.clip(preview_np, 0.0, 1.0)
# --- Build outputs ---
mask_tensor = torch.from_numpy(result_mask).unsqueeze(0)
mask_bw = np.stack([result_mask, result_mask, result_mask], axis=-1)
mask_bw_tensor = torch.from_numpy(mask_bw).unsqueeze(0)
preview_tensor = torch.from_numpy(preview_np).unsqueeze(0)
# Save in-node preview
preview_u8 = (preview_np * 255).astype(np.uint8)
preview_results = self._save_preview(preview_u8, unique_id)
return {
"ui": {"images": preview_results},
"result": (image, mask_tensor, mask_bw_tensor, preview_tensor)
}
# -------------------------------------------------------------------------
# Helpers
# -------------------------------------------------------------------------
def _to_numpy_mask(self, mask_tensor, h, w):
if len(mask_tensor.shape) == 3:
m = mask_tensor[0].cpu().numpy().astype(np.float32)
else:
m = mask_tensor.cpu().numpy().astype(np.float32)
if m.shape[:2] != (h, w):
m = cv2.resize(m, (w, h), interpolation=cv2.INTER_LINEAR)
return m
def _overlay_color(self, img, mask, color_name, opacity):
color = self.COLOR_MAP.get(color_name, self.COLOR_MAP["red"])
mask_3ch = np.expand_dims(mask, axis=-1)
color_layer = np.ones_like(img) * color
return img * (1.0 - mask_3ch * opacity) + color_layer * (mask_3ch * opacity)
def _save_preview(self, img_np, unique_id):
temp_dir = folder_paths.get_temp_directory()
filename = f"inpaint_mask_{unique_id}.png"
filepath = os.path.join(temp_dir, filename)
img_pil = Image.fromarray(img_np, mode='RGB')
img_pil.save(filepath)
return [{"filename": filename, "subfolder": "", "type": "temp"}]
Reference in New Issue View Git Blame Copy Permalink