113 lines
4.4 KiB
Python
113 lines
4.4 KiB
Python
'''
|
|
推理部分代码来自https://github.com/hustvl/EVF-SAM
|
|
'''
|
|
import sys
|
|
|
|
from .imagefunc import *
|
|
sys.path.append(os.path.join(os.path.dirname(__file__), 'evf-sam'))
|
|
from inference import evf_sam_main
|
|
class EVF_SAM_Ultra:
|
|
|
|
def __init__(self):
|
|
self.NODE_NAME = 'EVF_SAM Ultra'
|
|
pass
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
model_list = ["evf-sam2","evf-sam"]
|
|
precision_list = ["fp16", "bf16", "fp32"]
|
|
load_in_bit_list = [16, 8, 4]
|
|
method_list = ['VITMatte', 'VITMatte(local)', 'PyMatting', 'GuidedFilter', ]
|
|
device_list = ['cuda', 'cpu']
|
|
return {"required":
|
|
{
|
|
"image": ("IMAGE",),
|
|
"model": (model_list,),
|
|
"precision": (precision_list,),
|
|
"load_in_bit": (load_in_bit_list,),
|
|
"prompt": ("STRING", {"default": "subject"}),
|
|
"detail_method": (method_list,),
|
|
"detail_erode": ("INT", {"default": 6, "min": 1, "max": 255, "step": 1}),
|
|
"detail_dilate": ("INT", {"default": 4, "min": 1, "max": 255, "step": 1}),
|
|
"black_point": ("FLOAT", {"default": 0.01, "min": 0.01, "max": 0.98, "step": 0.01, "display": "slider"}),
|
|
"white_point": ("FLOAT", {"default": 0.99, "min": 0.02, "max": 0.99, "step": 0.01, "display": "slider"}),
|
|
"process_detail": ("BOOLEAN", {"default": True}),
|
|
"device": (device_list,),
|
|
"max_megapixels": ("FLOAT", {"default": 2.0, "min": 1, "max": 999, "step": 0.1}),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("IMAGE", "MASK",)
|
|
RETURN_NAMES = ("image", "mask",)
|
|
FUNCTION = "evf_sam_ultra"
|
|
CATEGORY = '😺dzNodes/LayerMask'
|
|
|
|
def evf_sam_ultra(self, image, model, precision, load_in_bit, prompt,
|
|
detail_method, detail_erode, detail_dilate, black_point, white_point,
|
|
process_detail, device, max_megapixels,
|
|
):
|
|
|
|
ret_images = []
|
|
ret_masks = []
|
|
|
|
if detail_method == 'VITMatte(local)':
|
|
local_files_only = True
|
|
else:
|
|
local_files_only = False
|
|
|
|
if model == 'evf-sam2':
|
|
model_type = 'sam2'
|
|
elif model == 'evf-sam':
|
|
model_type = 'ori'
|
|
else:
|
|
model_type = 'effi'
|
|
|
|
model_path = ""
|
|
model_folder_name = 'EVF-SAM'
|
|
try:
|
|
model_path = os.path.join(
|
|
os.path.normpath(folder_paths.folder_names_and_paths[model_folder_name][0][0]), model)
|
|
except:
|
|
pass
|
|
if not os.path.exists(model_path):
|
|
model_path = os.path.join(folder_paths.models_dir, model_folder_name, model)
|
|
|
|
for i in image:
|
|
i = torch.unsqueeze(i, 0)
|
|
orig_image = tensor2pil(i).convert('RGB')
|
|
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
|
|
|
|
mask_image = evf_sam_main(model_path, model_type, precision, load_in_bit, orig_image, prompt)
|
|
_mask = pil2tensor(mask_image)
|
|
|
|
detail_range = detail_erode + detail_dilate
|
|
if process_detail:
|
|
if detail_method == 'GuidedFilter':
|
|
_mask = guided_filter_alpha(i, _mask, detail_range // 6 + 1)
|
|
_mask = tensor2pil(histogram_remap(_mask, black_point, white_point))
|
|
elif detail_method == 'PyMatting':
|
|
_mask = tensor2pil(mask_edge_detail(i, _mask, detail_range // 8 + 1, black_point, white_point))
|
|
else:
|
|
_trimap = generate_VITMatte_trimap(_mask, detail_erode, detail_dilate)
|
|
_mask = generate_VITMatte(orig_image, _trimap, local_files_only=local_files_only, device=device,
|
|
max_megapixels=max_megapixels)
|
|
_mask = tensor2pil(histogram_remap(pil2tensor(_mask), black_point, white_point))
|
|
else:
|
|
_mask = mask2image(_mask)
|
|
|
|
ret_image = RGB2RGBA(orig_image, _mask.convert('L'))
|
|
ret_images.append(pil2tensor(ret_image))
|
|
ret_masks.append(image2mask(_mask))
|
|
|
|
log(f"{self.NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
|
|
return (torch.cat(ret_images, dim=0), torch.cat(ret_masks, dim=0),)
|
|
|
|
NODE_CLASS_MAPPINGS = {
|
|
"LayerMask: EVFSAMUltra": EVF_SAM_Ultra
|
|
}
|
|
|
|
NODE_DISPLAY_NAME_MAPPINGS = {
|
|
"LayerMask: EVFSAMUltra": "LayerMask: EVF-SAM Ultra"
|
|
}
|
|
|