131 lines
5.1 KiB
Python
131 lines
5.1 KiB
Python
# layerstyle advance
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from .imagefunc import *
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from .segment_anything_func import *
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class LS_LoadSAMModels:
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def __init__(self):
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self.NODE_NAME = 'SegmentAnythingUltra V3'
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"sam_model": (list_sam_model(), ),
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"grounding_dino_model": (list_groundingdino_model(),),
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},
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"optional": {
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}
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}
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RETURN_TYPES = ("LS_SAM_MODELS",)
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RETURN_NAMES = ("sam_models", )
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FUNCTION = "load_sam_models"
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CATEGORY = '😺dzNodes/LayerMask'
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def load_sam_models(self, sam_model, grounding_dino_model):
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SAM_MODEL = load_sam_model(sam_model)
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DINO_MODEL = load_groundingdino_model(grounding_dino_model)
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return ({"SAM_MODEL":SAM_MODEL, "DINO_MODEL":DINO_MODEL},)
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class LS_SegmentAnythingUltraV3:
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def __init__(self):
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self.NODE_NAME = 'SegmentAnythingUltra V3'
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@classmethod
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def INPUT_TYPES(cls):
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method_list = ['VITMatte', 'VITMatte(local)', 'vitmatte-base-composition-1k', 'PyMatting', 'GuidedFilter', ]
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device_list = ['cuda','cpu']
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return {
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"required": {
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"image": ("IMAGE",),
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"sam_models": ("LS_SAM_MODELS", ),
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"threshold": ("FLOAT", {"default": 0.3, "min": 0, "max": 1.0, "step": 0.01}),
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"detail_method": (method_list,),
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"detail_erode": ("INT", {"default": 6, "min": 1, "max": 255, "step": 1}),
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"detail_dilate": ("INT", {"default": 6, "min": 1, "max": 255, "step": 1}),
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"black_point": ("FLOAT", {"default": 0.15, "min": 0.01, "max": 0.98, "step": 0.01, "display": "slider"}),
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"white_point": ("FLOAT", {"default": 0.99, "min": 0.02, "max": 0.99, "step": 0.01, "display": "slider"}),
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"process_detail": ("BOOLEAN", {"default": True}),
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"prompt": ("STRING", {"default": "subject"}),
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"device": (device_list,),
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"max_megapixels": ("FLOAT", {"default": 2.0, "min": 1, "max": 999, "step": 0.1}),
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},
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"optional": {
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}
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}
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RETURN_TYPES = ("IMAGE", "MASK",)
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RETURN_NAMES = ("image", "mask",)
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FUNCTION = "segment_anything_ultra_v3"
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CATEGORY = '😺dzNodes/LayerMask'
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def segment_anything_ultra_v3(self, image, sam_models, threshold,
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detail_method, detail_erode, detail_dilate,
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black_point, white_point, process_detail, prompt,
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device, max_megapixels,
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):
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if detail_method == 'VITMatte(local)':
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local_files_only = True
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else:
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local_files_only = False
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SAM_MODEL = sam_models["SAM_MODEL"]
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DINO_MODEL = sam_models["DINO_MODEL"]
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ret_images = []
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ret_masks = []
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for i in image:
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i = torch.unsqueeze(i, 0)
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i = pil2tensor(tensor2pil(i).convert('RGB'))
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_image = tensor2pil(i).convert('RGBA')
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boxes = groundingdino_predict(DINO_MODEL, _image, prompt, threshold)
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if boxes.shape[0] == 0:
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break
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(_, _mask) = sam_segment(SAM_MODEL, _image, boxes)
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_mask = _mask[0]
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detail_range = detail_erode + detail_dilate
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if process_detail:
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if detail_method == 'GuidedFilter':
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_mask = guided_filter_alpha(i, _mask, detail_range // 6 + 1)
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_mask = tensor2pil(histogram_remap(_mask, black_point, white_point))
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elif detail_method == 'PyMatting':
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_mask = tensor2pil(mask_edge_detail(i, _mask, detail_range // 8 + 1, black_point, white_point))
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else:
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_trimap = generate_VITMatte_trimap(_mask, detail_erode, detail_dilate)
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_mask = generate_VITMatte(_image, _trimap, local_files_only=local_files_only, device=device, max_megapixels=max_megapixels, method=detail_method)
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_mask = tensor2pil(histogram_remap(pil2tensor(_mask), black_point, white_point))
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else:
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_mask = mask2image(_mask)
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_image = RGB2RGBA(tensor2pil(i).convert('RGB'), _mask.convert('L'))
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ret_images.append(pil2tensor(_image))
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ret_masks.append(image2mask(_mask))
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if len(ret_masks) == 0:
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_, height, width, _ = image.size()
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empty_mask = torch.zeros((1, height, width), dtype=torch.uint8, device="cpu")
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return (empty_mask, empty_mask)
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log(f"{self.NODE_NAME} Processed {len(ret_masks)} image(s).", message_type='finish')
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return (torch.cat(ret_images, dim=0), torch.cat(ret_masks, dim=0),)
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NODE_CLASS_MAPPINGS = {
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"LayerMask: SegmentAnythingUltra V3": LS_SegmentAnythingUltraV3,
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"LayerMask: LoadSegmentAnythingModels": LS_LoadSAMModels,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"LayerMask: SegmentAnythingUltra V3": "LayerMask: SegmentAnythingUltra V3(Advance)",
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"LayerMask: LoadSegmentAnythingModels": "LayerMask: Load SegmentAnything Models(Advance)",
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}
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