File size: 14,518 Bytes
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import sys
import copy
import torch
import numpy as np
from PIL import Image, ImageFilter
from torch.hub import download_url_to_file
import folder_paths
from segment_anything import sam_model_registry, SamPredictor
from groundingdino.util.slconfig import SLConfig
from groundingdino.models import build_model
from groundingdino.util.utils import clean_state_dict
from groundingdino.util import box_ops
from transformers import AutoProcessor, AutoModelForZeroShotObjectDetection
from AILab_ImageMaskTools import pil2tensor, tensor2pil
SAM_MODELS = {
"sam_vit_h (2.56GB)": {
"model_url": "https://huggingface.co/1038lab/sam/resolve/main/sam_vit_h.pth",
"model_type": "vit_h",
"filename": "sam_vit_h.pth"
},
"sam_vit_l (1.25GB)": {
"model_url": "https://huggingface.co/1038lab/sam/resolve/main/sam_vit_l.pth",
"model_type": "vit_l",
"filename": "sam_vit_l.pth"
},
"sam_vit_b (375MB)": {
"model_url": "https://huggingface.co/1038lab/sam/resolve/main/sam_vit_b.pth",
"model_type": "vit_b",
"filename": "sam_vit_b.pth"
},
"sam_hq_vit_h (2.57GB)": {
"model_url": "https://huggingface.co/1038lab/sam/resolve/main/sam_hq_vit_h.pth",
"model_type": "vit_h",
"filename": "sam_hq_vit_h.pth"
},
"sam_hq_vit_l (1.25GB)": {
"model_url": "https://huggingface.co/1038lab/sam/resolve/main/sam_hq_vit_l.pth",
"model_type": "vit_l",
"filename": "sam_hq_vit_l.pth"
},
"sam_hq_vit_b (379MB)": {
"model_url": "https://huggingface.co/1038lab/sam/resolve/main/sam_hq_vit_b.pth",
"model_type": "vit_b",
"filename": "sam_hq_vit_b.pth"
}
}
DINO_MODELS = {
"GroundingDINO_SwinT_OGC (694MB)": {
"config_url": "https://huggingface.co/1038lab/GroundingDINO/resolve/main/GroundingDINO_SwinT_OGC.cfg.py",
"model_url": "https://huggingface.co/1038lab/GroundingDINO/resolve/main/groundingdino_swint_ogc.pth",
"config_filename": "GroundingDINO_SwinT_OGC.cfg.py",
"model_filename": "groundingdino_swint_ogc.pth"
},
"GroundingDINO_SwinB (938MB)": {
"config_url": "https://huggingface.co/1038lab/GroundingDINO/resolve/main/GroundingDINO_SwinB.cfg.py",
"model_url": "https://huggingface.co/1038lab/GroundingDINO/resolve/main/groundingdino_swinb_cogcoor.pth",
"config_filename": "GroundingDINO_SwinB.cfg.py",
"model_filename": "groundingdino_swinb_cogcoor.pth"
}
}
def get_or_download_model_file(filename, url, dirname):
local_path = folder_paths.get_full_path(dirname, filename)
if local_path:
return local_path
folder = os.path.join(folder_paths.models_dir, dirname)
os.makedirs(folder, exist_ok=True)
local_path = os.path.join(folder, filename)
if not os.path.exists(local_path):
print(f"Downloading {filename} from {url} ...")
download_url_to_file(url, local_path)
return local_path
def process_mask(mask_image: Image.Image, invert_output: bool = False,
mask_blur: int = 0, mask_offset: int = 0) -> Image.Image:
if invert_output:
mask_np = np.array(mask_image)
mask_image = Image.fromarray(255 - mask_np)
if mask_blur > 0:
mask_image = mask_image.filter(ImageFilter.GaussianBlur(radius=mask_blur))
if mask_offset != 0:
filter_type = ImageFilter.MaxFilter if mask_offset > 0 else ImageFilter.MinFilter
size = abs(mask_offset) * 2 + 1
for _ in range(abs(mask_offset)):
mask_image = mask_image.filter(filter_type(size))
return mask_image
def apply_background_color(image: Image.Image, mask_image: Image.Image,
background: str = "Alpha",
background_color: str = "#222222") -> Image.Image:
rgba_image = image.copy().convert('RGBA')
rgba_image.putalpha(mask_image.convert('L'))
if background == "Color":
def hex_to_rgba(hex_color):
hex_color = hex_color.lstrip('#')
r, g, b = int(hex_color[0:2], 16), int(hex_color[2:4], 16), int(hex_color[4:6], 16)
return (r, g, b, 255)
rgba = hex_to_rgba(background_color)
bg_image = Image.new('RGBA', image.size, rgba)
composite_image = Image.alpha_composite(bg_image, rgba_image)
return composite_image.convert('RGB')
return rgba_image
def get_groundingdino_model(device):
processor = AutoProcessor.from_pretrained("IDEA-Research/grounding-dino-tiny")
model = AutoModelForZeroShotObjectDetection.from_pretrained("IDEA-Research/grounding-dino-tiny").to(device)
return processor, model
def get_boxes(processor, model, img_pil, prompt, threshold):
inputs = processor(images=img_pil, text=prompt, return_tensors="pt").to(model.device)
with torch.no_grad():
outputs = model(**inputs)
results = processor.post_process_grounded_object_detection(
outputs,
inputs.input_ids,
box_threshold=threshold,
text_threshold=threshold,
target_sizes=[img_pil.size[::-1]]
)
return results[0]["boxes"]
class SegmentV2:
@classmethod
def INPUT_TYPES(cls):
tooltips = {
"prompt": "Enter the object or scene you want to segment. Use tag-style or natural language for more detailed prompts.",
"threshold": "Adjust mask detection strength (higher = more strict)",
"mask_blur": "Apply Gaussian blur to mask edges (0 = disabled)",
"mask_offset": "Expand/Shrink mask boundary (positive = expand, negative = shrink)",
"invert_output": "Invert the mask output",
"background": (["Alpha", "Color"], {"default": "Alpha", "tooltip": "Choose background type"}),
"background_color": "Choose background color (Alpha = transparent)",
}
return {
"required": {
"image": ("IMAGE",),
"prompt": ("STRING", {"default": "", "multiline": True, "placeholder": "Object to segment", "tooltip": tooltips["prompt"]}),
"sam_model": (list(SAM_MODELS.keys()),),
"dino_model": (list(DINO_MODELS.keys()),),
},
"optional": {
"threshold": ("FLOAT", {"default": 0.35, "min": 0.05, "max": 0.95, "step": 0.01, "tooltip": tooltips["threshold"]}),
"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_v2"
CATEGORY = "🧪AILab/🧽RMBG"
def __init__(self):
self.dino_model_cache = {}
self.sam_model_cache = {}
def segment_v2(self, image, prompt, sam_model, dino_model, threshold=0.30,
mask_blur=0, mask_offset=0, background="Alpha",
background_color="#222222", invert_output=False):
device = "cuda" if torch.cuda.is_available() else "cpu"
batch_size = image.shape[0] if len(image.shape) == 4 else 1
if len(image.shape) == 3:
image = image.unsqueeze(0)
result_images = []
result_masks = []
result_mask_images = []
for b in range(batch_size):
img_pil = tensor2pil(image[b])
img_np = np.array(img_pil.convert("RGB"))
dino_info = DINO_MODELS[dino_model]
config_path = get_or_download_model_file(dino_info["config_filename"], dino_info["config_url"], "grounding-dino")
weights_path = get_or_download_model_file(dino_info["model_filename"], dino_info["model_url"], "grounding-dino")
dino_key = (config_path, weights_path, device)
if dino_key not in self.dino_model_cache:
args = SLConfig.fromfile(config_path)
model = build_model(args)
checkpoint = torch.load(weights_path, map_location="cpu")
model.load_state_dict(clean_state_dict(checkpoint["model"]), strict=False)
model.eval()
model.to(device)
self.dino_model_cache[dino_key] = model
dino = self.dino_model_cache[dino_key]
sam_info = SAM_MODELS[sam_model]
sam_ckpt_path = get_or_download_model_file(sam_info["filename"], sam_info["model_url"], "SAM")
sam_key = (sam_info["model_type"], sam_ckpt_path, device)
if sam_key not in self.sam_model_cache:
try:
sam = sam_model_registry[sam_info["model_type"]]()
state_dict = torch.load(sam_ckpt_path, map_location="cpu")
sam.load_state_dict(state_dict, strict=False)
sam.to(device)
self.sam_model_cache[sam_key] = SamPredictor(sam)
except RuntimeError as e:
if "Unexpected key(s) in state_dict" in str(e):
print("Warning: SAM model loading issue detected, please try using SegmentV1 node instead")
print(f"Error details: {str(e)}")
width, height = img_pil.size
empty_mask = torch.zeros((1, height, width), dtype=torch.float32, device="cpu")
empty_mask_rgb = empty_mask.reshape((-1, 1, height, width)).movedim(1, -1).expand(-1, -1, -1, 3)
result_image = apply_background_color(img_pil, Image.fromarray((empty_mask[0].numpy() * 255).astype(np.uint8)), background, background_color)
result_images.append(pil2tensor(result_image))
result_masks.append(empty_mask)
result_mask_images.append(empty_mask_rgb)
continue
else:
raise e
predictor = self.sam_model_cache[sam_key]
from groundingdino.datasets.transforms import Compose, RandomResize, ToTensor, Normalize
transform = Compose([
RandomResize([800], max_size=1333),
ToTensor(),
Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
])
image_tensor, _ = transform(img_pil.convert("RGB"), None)
image_tensor = image_tensor.unsqueeze(0).to(device)
text_prompt = prompt if prompt.endswith(".") else prompt + "."
with torch.no_grad():
outputs = dino(image_tensor, captions=[text_prompt])
logits = outputs["pred_logits"].sigmoid()[0]
boxes = outputs["pred_boxes"][0]
filt_mask = logits.max(dim=1)[0] > threshold
boxes_filt = boxes[filt_mask]
if boxes_filt.shape[0] == 0:
width, height = img_pil.size
empty_mask = torch.zeros((1, height, width), dtype=torch.float32, device="cpu")
empty_mask_rgb = empty_mask.reshape((-1, 1, height, width)).movedim(1, -1).expand(-1, -1, -1, 3)
result_image = apply_background_color(img_pil, Image.fromarray((empty_mask[0].numpy() * 255).astype(np.uint8)), background, background_color)
result_images.append(pil2tensor(result_image))
result_masks.append(empty_mask)
result_mask_images.append(empty_mask_rgb)
continue
H, W = img_pil.size[1], img_pil.size[0]
boxes_xyxy = box_ops.box_cxcywh_to_xyxy(boxes_filt)
boxes_xyxy = boxes_xyxy * torch.tensor([W, H, W, H], dtype=torch.float32, device=boxes_xyxy.device)
boxes_xyxy = boxes_xyxy.cpu().numpy()
predictor.set_image(img_np)
boxes_tensor = torch.tensor(boxes_xyxy, dtype=torch.float32, device=predictor.device)
transformed_boxes = predictor.transform.apply_boxes_torch(boxes_tensor, img_np.shape[:2])
masks, _, _ = predictor.predict_torch(
point_coords=None,
point_labels=None,
boxes=transformed_boxes,
multimask_output=False
)
combined_mask = torch.max(masks, dim=0)[0]
mask = combined_mask.float().cpu().numpy()
mask = mask.squeeze(0)
mask = (mask * 255).astype(np.uint8)
mask_pil = Image.fromarray(mask, mode="L")
mask_image = process_mask(mask_pil, invert_output, mask_blur, mask_offset)
result_image = apply_background_color(img_pil, mask_image, background, background_color)
if background == "Color":
result_image = result_image.convert("RGB")
else:
result_image = result_image.convert("RGBA")
mask_tensor = torch.from_numpy(np.array(mask_image).astype(np.float32) / 255.0).unsqueeze(0)
mask_image_vis = mask_tensor.reshape((-1, 1, mask_image.height, mask_image.width)).movedim(1, -1).expand(-1, -1, -1, 3)
result_images.append(pil2tensor(result_image))
result_masks.append(mask_tensor)
result_mask_images.append(mask_image_vis)
if len(result_images) == 0:
width, height = tensor2pil(image[0]).size
empty_mask = torch.zeros((batch_size, 1, height, width), dtype=torch.float32, device="cpu")
empty_mask_rgb = empty_mask.reshape((-1, 1, height, width)).movedim(1, -1).expand(-1, -1, -1, 3)
return (image, empty_mask, empty_mask_rgb)
return (torch.cat(result_images, dim=0),
torch.cat(result_masks, dim=0),
torch.cat(result_mask_images, dim=0))
NODE_CLASS_MAPPINGS = {
"SegmentV2": SegmentV2,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"SegmentV2": "Segmentation V2 (RMBG)",
}
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