File size: 10,936 Bytes
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# This custom node for ComfyUI provides functionality for background removal using various models,
# including RMBG-2.0, INSPYRENET, and BEN. It leverages deep learning techniques
# to process images and generate masks for background removal.
#
# This integration script follows GPL-3.0 License.
# When using or modifying this code, please respect both the original model licenses
# and this integration's license terms.
#
# Source: https://github.com/AILab-AI/ComfyUI-RMBG
import os
import torch
import torch.nn as nn
import numpy as np
from typing import Tuple, Union
from PIL import Image, ImageFilter
import onnxruntime
import folder_paths
from huggingface_hub import hf_hub_download
import shutil
from torchvision import transforms
def pil2tensor(image: Image.Image) -> torch.Tensor:
return torch.from_numpy(np.array(image).astype(np.float32) / 255.0)[None,]
def tensor2pil(image: torch.Tensor) -> Image.Image:
return Image.fromarray(np.clip(255. * image.cpu().numpy(), 0, 255).astype(np.uint8))
def image2mask(image: Image.Image) -> torch.Tensor:
if isinstance(image, Image.Image):
image = pil2tensor(image)
return image.squeeze()[..., 0]
def mask2image(mask: torch.Tensor) -> Image.Image:
if len(mask.shape) == 2:
mask = mask.unsqueeze(0)
return tensor2pil(mask)
def RGB2RGBA(image: Image.Image, mask: Union[Image.Image, torch.Tensor]) -> Image.Image:
if isinstance(mask, torch.Tensor):
mask = mask2image(mask)
if mask.size != image.size:
mask = mask.resize(image.size, Image.Resampling.LANCZOS)
return Image.merge('RGBA', (*image.convert('RGB').split(), mask.convert('L')))
device = "cuda" if torch.cuda.is_available() else "cpu"
folder_paths.add_model_folder_path("rmbg", os.path.join(folder_paths.models_dir, "RMBG"))
class BodySegment:
def __init__(self):
self.model = None
self.cache_dir = os.path.join(folder_paths.models_dir, "RMBG", "body_segment")
self.model_file = "deeplabv3p-resnet50-human.onnx"
@classmethod
def INPUT_TYPES(cls):
available_classes = [
"Hair", "Glasses", "Top-clothes", "Bottom-clothes",
"Torso-skin", "Face", "Left-arm", "Right-arm",
"Left-leg", "Right-leg", "Left-foot", "Right-foot"
]
tooltips = {
"process_res": "Processing resolution (fixed at 512x512)",
"mask_blur": "Blur amount for mask edges",
"mask_offset": "Expand/Shrink mask boundary",
"invert_output": "Invert both image and mask output",
"background": "Choose background type: Alpha (transparent) or Color (custom background color).",
"background_color": "Choose background color (Alpha = transparent)"
}
return {
"required": {
"images": ("IMAGE",),
},
"optional": {
**{cls_name: ("BOOLEAN", {"default": False})
for cls_name in available_classes},
"mask_blur": ("INT", {"default": 0, "min": 0, "max": 64, "step": 1, "tooltip": tooltips["mask_blur"]}),
"mask_offset": ("INT", {"default": 0, "min": -64, "max": 64, "step": 1, "tooltip": tooltips["mask_offset"]}),
"invert_output": ("BOOLEAN", {"default": False, "tooltip": tooltips["invert_output"]}),
"background": (["Alpha", "Color"], {"default": "Alpha", "tooltip": tooltips["background"]}),
"background_color": ("COLORCODE", {"default": "#222222", "tooltip": tooltips["background_color"]}),
},
}
RETURN_TYPES = ("IMAGE", "MASK", "IMAGE")
RETURN_NAMES = ("IMAGE", "MASK", "MASK_IMAGE")
FUNCTION = "segment_body"
CATEGORY = "🧪AILab/🧽RMBG"
def check_model_cache(self):
model_path = os.path.join(self.cache_dir, self.model_file)
if not os.path.exists(model_path):
return False, "Model file not found"
return True, "Model cache verified"
def clear_model(self):
if self.model is not None:
del self.model
self.model = None
def download_model_files(self):
model_id = "Metal3d/deeplabv3p-resnet50-human"
os.makedirs(self.cache_dir, exist_ok=True)
print("Downloading body segmentation model...")
try:
downloaded_path = hf_hub_download(
repo_id=model_id,
filename=self.model_file,
local_dir=self.cache_dir,
local_dir_use_symlinks=False
)
if os.path.dirname(downloaded_path) != self.cache_dir:
target_path = os.path.join(self.cache_dir, self.model_file)
shutil.move(downloaded_path, target_path)
return True, "Model file downloaded successfully"
except Exception as e:
return False, f"Error downloading model file: {str(e)}"
def segment_body(self, images, mask_blur=0, mask_offset=0, background="Alpha", background_color="#222222", invert_output=False, **class_selections):
try:
# Check and download model if needed
cache_status, message = self.check_model_cache()
if not cache_status:
print(f"Cache check: {message}")
download_status, download_message = self.download_model_files()
if not download_status:
raise RuntimeError(download_message)
# Load model if needed
if self.model is None:
self.model = onnxruntime.InferenceSession(
os.path.join(self.cache_dir, self.model_file)
)
# Class mapping
class_map = {
"Hair": 2, "Glasses": 4, "Top-clothes": 5,
"Bottom-clothes": 9, "Torso-skin": 10, "Face": 13,
"Left-arm": 14, "Right-arm": 15, "Left-leg": 16,
"Right-leg": 17, "Left-foot": 18, "Right-foot": 19
}
# Get selected classes
selected_classes = [name for name, selected in class_selections.items() if selected]
if not selected_classes:
selected_classes = ["Face", "Hair", "Top-clothes", "Bottom-clothes"]
batch_tensor = []
batch_masks = []
for image in images:
orig_image = tensor2pil(image)
w, h = orig_image.size
# Resize to 512x512 (model requirement)
input_image = orig_image.resize((512, 512))
input_array = np.array(input_image).astype(np.float32) / 127.5 - 1
# Add batch dimension
input_array = np.expand_dims(input_array, axis=0)
# Run inference
input_name = self.model.get_inputs()[0].name
output_name = self.model.get_outputs()[0].name
result = self.model.run([output_name], {input_name: input_array})
# Process results
result = np.array(result[0])
pred_seg = result.argmax(axis=3).squeeze(0)
# Combine selected class masks
combined_mask = np.zeros_like(pred_seg, dtype=np.float32)
for class_name in selected_classes:
mask = (pred_seg == class_map[class_name]).astype(np.float32)
combined_mask = np.clip(combined_mask + mask, 0, 1)
# Convert to PIL and resize back to original size
mask_image = Image.fromarray((combined_mask * 255).astype(np.uint8))
mask_image = mask_image.resize((w, h), Image.Resampling.LANCZOS)
if mask_blur > 0:
mask_image = mask_image.filter(ImageFilter.GaussianBlur(radius=mask_blur))
if mask_offset != 0:
if mask_offset > 0:
mask_image = mask_image.filter(ImageFilter.MaxFilter(size=mask_offset * 2 + 1))
else:
mask_image = mask_image.filter(ImageFilter.MinFilter(size=-mask_offset * 2 + 1))
if invert_output:
mask_image = Image.fromarray(255 - np.array(mask_image))
# Handle background color
if background == "Alpha":
rgba_image = RGB2RGBA(orig_image, mask_image)
result_image = pil2tensor(rgba_image)
else:
def hex_to_rgba(hex_color):
hex_color = hex_color.lstrip('#')
if len(hex_color) == 6:
r, g, b = int(hex_color[0:2], 16), int(hex_color[2:4], 16), int(hex_color[4:6], 16)
a = 255
elif len(hex_color) == 8:
r, g, b, a = int(hex_color[0:2], 16), int(hex_color[2:4], 16), int(hex_color[4:6], 16), int(hex_color[6:8], 16)
else:
raise ValueError("Invalid color format")
return (r, g, b, a)
rgba_image = RGB2RGBA(orig_image, mask_image)
rgba = hex_to_rgba(background_color)
bg_image = Image.new('RGBA', orig_image.size, rgba)
composite_image = Image.alpha_composite(bg_image, rgba_image)
result_image = pil2tensor(composite_image.convert('RGB'))
batch_tensor.append(result_image)
batch_masks.append(pil2tensor(mask_image))
# Create mask image for visualization
mask_images = []
for mask_tensor in batch_masks:
# Convert mask to RGB image format for visualization
mask_image = mask_tensor.reshape((-1, 1, mask_tensor.shape[-2], mask_tensor.shape[-1])).movedim(1, -1).expand(-1, -1, -1, 3)
mask_images.append(mask_image)
mask_image_output = torch.cat(mask_images, dim=0)
# Prepare final output
batch_tensor = torch.cat(batch_tensor, dim=0)
batch_masks = torch.cat(batch_masks, dim=0)
return (batch_tensor, batch_masks, mask_image_output)
except Exception as e:
self.clear_model()
raise RuntimeError(f"Error in Body Segmentation processing: {str(e)}")
finally:
self.clear_model()
NODE_CLASS_MAPPINGS = {
"BodySegment": BodySegment
}
NODE_DISPLAY_NAME_MAPPINGS = {
"BodySegment": "Body Segment (RMBG)"
} |