94 lines
3.5 KiB
Python
94 lines
3.5 KiB
Python
# layerstyle advance
|
|
|
|
from .imagefunc import *
|
|
from .segment_anything_func import *
|
|
|
|
NODE_NAME = 'SegmentAnythingUltra'
|
|
|
|
class SegmentAnythingUltra:
|
|
def __init__(self):
|
|
self.SAM_MODEL = None
|
|
self.DINO_MODEL = None
|
|
self.previous_sam_model = ""
|
|
self.previous_dino_model = ""
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
|
|
return {
|
|
"required": {
|
|
"image": ("IMAGE",),
|
|
"sam_model": (list_sam_model(), ),
|
|
"grounding_dino_model": (list_groundingdino_model(),),
|
|
"threshold": ("FLOAT", {"default": 0.3, "min": 0, "max": 1.0, "step": 0.01}),
|
|
"detail_range": ("INT", {"default": 16, "min": 1, "max": 256, "step": 1}),
|
|
"black_point": ("FLOAT", {"default": 0.15, "min": 0.01, "max": 0.98, "step": 0.01}),
|
|
"white_point": ("FLOAT", {"default": 0.99, "min": 0.02, "max": 0.99, "step": 0.01}),
|
|
"process_detail": ("BOOLEAN", {"default": True}),
|
|
"prompt": ("STRING", {"default": "subject"}),
|
|
"cache_model": ("BOOLEAN", {"default": False}),
|
|
},
|
|
"optional": {
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("IMAGE", "MASK", )
|
|
RETURN_NAMES = ("image", "mask", )
|
|
FUNCTION = "segment_anything_ultra"
|
|
CATEGORY = '😺dzNodes/LayerMask'
|
|
|
|
def segment_anything_ultra(self, image, sam_model, grounding_dino_model, threshold,
|
|
detail_range, black_point, white_point, process_detail,
|
|
prompt, cache_model):
|
|
|
|
if self.previous_sam_model != sam_model or self.SAM_MODEL is None:
|
|
self.SAM_MODEL = load_sam_model(sam_model)
|
|
self.previous_sam_model = sam_model
|
|
if self.previous_dino_model != grounding_dino_model or self.DINO_MODEL is None:
|
|
self.DINO_MODEL = load_groundingdino_model(grounding_dino_model)
|
|
self.previous_dino_model = grounding_dino_model
|
|
|
|
|
|
ret_images = []
|
|
ret_masks = []
|
|
|
|
for i in image:
|
|
i = torch.unsqueeze(i, 0)
|
|
i = pil2tensor(tensor2pil(i).convert('RGB'))
|
|
item = tensor2pil(i).convert('RGBA')
|
|
boxes = groundingdino_predict(self.DINO_MODEL, item, prompt, threshold)
|
|
if boxes.shape[0] == 0:
|
|
break
|
|
(_, _mask) = sam_segment(self.SAM_MODEL, item, boxes)
|
|
_mask = _mask[0]
|
|
if process_detail:
|
|
_mask = tensor2pil(mask_edge_detail(i, _mask, detail_range, black_point, white_point))
|
|
else:
|
|
_mask = mask2image(_mask)
|
|
_image = RGB2RGBA(tensor2pil(i).convert('RGB'), _mask.convert('L'))
|
|
|
|
ret_images.append(pil2tensor(_image))
|
|
ret_masks.append(image2mask(_mask))
|
|
if len(ret_masks) == 0:
|
|
_, height, width, _ = image.size()
|
|
empty_mask = torch.zeros((1, height, width), dtype=torch.uint8, device="cpu")
|
|
return (empty_mask, empty_mask)
|
|
|
|
if not cache_model:
|
|
self.SAM_MODEL = None
|
|
self.DINO_MODEL = None
|
|
self.previous_sam_model = ""
|
|
self.previous_dino_model = ""
|
|
clear_memory()
|
|
|
|
log(f"{NODE_NAME} Processed {len(ret_masks)} image(s).", message_type='finish')
|
|
return (torch.cat(ret_images, dim=0), torch.cat(ret_masks, dim=0),)
|
|
|
|
NODE_CLASS_MAPPINGS = {
|
|
"LayerMask: SegmentAnythingUltra": SegmentAnythingUltra,
|
|
}
|
|
|
|
NODE_DISPLAY_NAME_MAPPINGS = {
|
|
"LayerMask: SegmentAnythingUltra": "LayerMask: SegmentAnythingUltra(Advance)",
|
|
}
|