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import os
import sys
sys.path.append(
os.path.dirname(os.path.abspath(__file__))
)
import copy
import torch
import numpy as np
from PIL import Image
import logging
from torch.hub import download_url_to_file
from urllib.parse import urlparse
import folder_paths
import comfy.model_management
from sam_hq.predictor import SamPredictorHQ
from sam_hq.build_sam_hq import sam_model_registry
import glob
import folder_paths
logger = logging.getLogger('comfyui_segment_anything')
sam_model_dir_name = "sams"
sam_model_list = {
"sam_vit_h (2.56GB)": {
"model_url": "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth"
},
"sam_vit_l (1.25GB)": {
"model_url": "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_l_0b3195.pth"
},
"sam_vit_b (375MB)": {
"model_url": "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth"
},
"sam_hq_vit_h (2.57GB)": {
"model_url": "https://huggingface.co/lkeab/hq-sam/resolve/main/sam_hq_vit_h.pth"
},
"sam_hq_vit_l (1.25GB)": {
"model_url": "https://huggingface.co/lkeab/hq-sam/resolve/main/sam_hq_vit_l.pth"
},
"sam_hq_vit_b (379MB)": {
"model_url": "https://huggingface.co/lkeab/hq-sam/resolve/main/sam_hq_vit_b.pth"
},
"mobile_sam(39MB)": {
"model_url": "https://github.com/ChaoningZhang/MobileSAM/blob/master/weights/mobile_sam.pt"
}
}
groundingdino_model_dir_name = "grounding-dino"
groundingdino_model_list = {
"GroundingDINO_SwinT_OGC (694MB)": {
"config_url": "https://huggingface.co/ShilongLiu/GroundingDINO/resolve/main/GroundingDINO_SwinT_OGC.cfg.py",
"model_url": "https://huggingface.co/ShilongLiu/GroundingDINO/resolve/main/groundingdino_swint_ogc.pth",
},
"GroundingDINO_SwinB (938MB)": {
"config_url": "https://huggingface.co/ShilongLiu/GroundingDINO/resolve/main/GroundingDINO_SwinB.cfg.py",
"model_url": "https://huggingface.co/ShilongLiu/GroundingDINO/resolve/main/groundingdino_swinb_cogcoor.pth"
},
}
def get_bert_base_uncased_model_path():
comfy_bert_model_base = os.path.join(folder_paths.models_dir, 'bert-base-uncased')
if glob.glob(os.path.join(comfy_bert_model_base, '**/model.safetensors'), recursive=True):
print('grounding-dino is using models/bert-base-uncased')
return comfy_bert_model_base
return 'bert-base-uncased'
def list_sam_model():
return list(sam_model_list.keys())
def load_sam_model(model_name):
sam_checkpoint_path = get_local_filepath(
sam_model_list[model_name]["model_url"], sam_model_dir_name)
model_file_name = os.path.basename(sam_checkpoint_path)
model_type = model_file_name.split('.')[0]
if 'hq' not in model_type and 'mobile' not in model_type:
model_type = '_'.join(model_type.split('_')[:-1])
sam = sam_model_registry[model_type](checkpoint=sam_checkpoint_path)
sam_device = comfy.model_management.get_torch_device()
sam.to(device=sam_device)
sam.eval()
sam.model_name = model_file_name
return sam
def get_local_filepath(url, dirname, local_file_name=None):
if not local_file_name:
parsed_url = urlparse(url)
local_file_name = os.path.basename(parsed_url.path)
destination = folder_paths.get_full_path(dirname, local_file_name)
if destination:
logger.warn(f'using extra model: {destination}')
return destination
folder = os.path.join(folder_paths.models_dir, dirname)
if not os.path.exists(folder):
os.makedirs(folder)
destination = os.path.join(folder, local_file_name)
if not os.path.exists(destination):
logger.warn(f'downloading {url} to {destination}')
download_url_to_file(url, destination)
return destination
def load_groundingdino_model(model_name):
from local_groundingdino.util.utils import clean_state_dict as local_groundingdino_clean_state_dict
from local_groundingdino.util.slconfig import SLConfig as local_groundingdino_SLConfig
from local_groundingdino.models import build_model as local_groundingdino_build_model
dino_model_args = local_groundingdino_SLConfig.fromfile(
get_local_filepath(
groundingdino_model_list[model_name]["config_url"],
groundingdino_model_dir_name
),
)
if dino_model_args.text_encoder_type == 'bert-base-uncased':
dino_model_args.text_encoder_type = get_bert_base_uncased_model_path()
dino = local_groundingdino_build_model(dino_model_args)
checkpoint = torch.load(
get_local_filepath(
groundingdino_model_list[model_name]["model_url"],
groundingdino_model_dir_name,
),
)
dino.load_state_dict(local_groundingdino_clean_state_dict(
checkpoint['model']), strict=False)
device = comfy.model_management.get_torch_device()
dino.to(device=device)
dino.eval()
return dino
def list_groundingdino_model():
return list(groundingdino_model_list.keys())
def groundingdino_predict(
dino_model,
image,
prompt,
threshold
):
from local_groundingdino.datasets import transforms as T
def load_dino_image(image_pil):
transform = T.Compose(
[
T.RandomResize([800], max_size=1333),
T.ToTensor(),
T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
]
)
image, _ = transform(image_pil, None) # 3, h, w
return image
def get_grounding_output(model, image, caption, box_threshold):
caption = caption.lower()
caption = caption.strip()
if not caption.endswith("."):
caption = caption + "."
device = comfy.model_management.get_torch_device()
image = image.to(device)
with torch.no_grad():
outputs = model(image[None], captions=[caption])
logits = outputs["pred_logits"].sigmoid()[0] # (nq, 256)
boxes = outputs["pred_boxes"][0] # (nq, 4)
# filter output
logits_filt = logits.clone()
boxes_filt = boxes.clone()
filt_mask = logits_filt.max(dim=1)[0] > box_threshold
logits_filt = logits_filt[filt_mask] # num_filt, 256
boxes_filt = boxes_filt[filt_mask] # num_filt, 4
return boxes_filt.cpu()
dino_image = load_dino_image(image.convert("RGB"))
boxes_filt = get_grounding_output(
dino_model, dino_image, prompt, threshold
)
H, W = image.size[1], image.size[0]
for i in range(boxes_filt.size(0)):
boxes_filt[i] = boxes_filt[i] * torch.Tensor([W, H, W, H])
boxes_filt[i][:2] -= boxes_filt[i][2:] / 2
boxes_filt[i][2:] += boxes_filt[i][:2]
return boxes_filt
def create_tensor_output(image_np, masks, boxes_filt):
output_masks, output_images = [], []
boxes_filt = boxes_filt.numpy().astype(int) if boxes_filt is not None else None
for mask in masks:
image_np_copy = copy.deepcopy(image_np)
image_np_copy[~np.any(mask, axis=0)] = np.array([0, 0, 0, 0])
output_image, output_mask = split_image_mask(
Image.fromarray(image_np_copy))
output_masks.append(output_mask)
output_images.append(output_image)
return (output_images, output_masks)
def split_image_mask(image):
image_rgb = image.convert("RGB")
image_rgb = np.array(image_rgb).astype(np.float32) / 255.0
image_rgb = torch.from_numpy(image_rgb)[None,]
if 'A' in image.getbands():
mask = np.array(image.getchannel('A')).astype(np.float32) / 255.0
mask = torch.from_numpy(mask)[None,]
else:
mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu")
return (image_rgb, mask)
def sam_segment(
sam_model,
image,
boxes
):
if boxes.shape[0] == 0:
return None
sam_is_hq = False
# TODO: more elegant
if hasattr(sam_model, 'model_name') and 'hq' in sam_model.model_name:
sam_is_hq = True
predictor = SamPredictorHQ(sam_model, sam_is_hq)
image_np = np.array(image)
image_np_rgb = image_np[..., :3]
predictor.set_image(image_np_rgb)
transformed_boxes = predictor.transform.apply_boxes_torch(
boxes, image_np.shape[:2])
sam_device = comfy.model_management.get_torch_device()
masks, _, _ = predictor.predict_torch(
point_coords=None,
point_labels=None,
boxes=transformed_boxes.to(sam_device),
multimask_output=False)
masks = masks.permute(1, 0, 2, 3).cpu().numpy()
return create_tensor_output(image_np, masks, boxes)
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