From 3b47c2bbc048146803dc68470225913ae9cf5f87 Mon Sep 17 00:00:00 2001 From: AI Lab <129358391+1038lab@users.noreply.github.com> Date: Thu, 1 Jan 2026 19:27:47 -0800 Subject: [PATCH] Delete AILab_SegmentV2.py --- AILab_SegmentV2.py | 285 --------------------------------------------- 1 file changed, 285 deletions(-) delete mode 100644 AILab_SegmentV2.py diff --git a/AILab_SegmentV2.py b/AILab_SegmentV2.py deleted file mode 100644 index 1d4ea58..0000000 --- a/AILab_SegmentV2.py +++ /dev/null @@ -1,285 +0,0 @@ -import os -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)", -}