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import cv2
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from ..utils import models_dir, np2tensor
# TODO: check if I can make a torch script device independant
# for now I forced it to use cuda.
class MTB_LoadVitMatteModel:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"kind": (("Composition-1K", "Distinctions-646"),),
"autodownload": ("BOOLEAN", {"default": True}),
},
}
RETURN_TYPES = ("VITMATTE_MODEL",)
RETURN_NAMES = ("torch_script",)
CATEGORY = "mtb/vitmatte"
FUNCTION = "execute"
def execute(self, *, kind: str, autodownload: bool):
dest = models_dir / "vitmatte"
dest.mkdir(exist_ok=True)
name = "dist" if kind == "Distinctions-646" else "com"
file = hf_hub_download(
repo_id="melmass/pytorch-scripts",
filename=f"vitmatte_b_{name}.pt",
local_dir=dest.as_posix(),
local_files_only=not autodownload,
)
model = torch.jit.load(file).to("cuda")
return (model,)
class MTB_GenerateTrimap:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
# "image": ("IMAGE",),
"mask": ("MASK",),
"erode": ("INT", {"default": 10}),
"dilate": ("INT", {"default": 10}),
},
}
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("trimap",)
CATEGORY = "mtb/vitmatte"
FUNCTION = "execute"
def execute(
self,
# image:torch.Tensor,
mask: torch.Tensor,
erode: int = 10,
dilate: int = 10,
):
# TODO: not sure what's the most practical between IMAGE or MASK
# image = image.to("cuda").half()
mask = mask.to("cuda").half()
trimaps = []
for m in mask:
mask_arr = m.squeeze(0).to(torch.uint8).cpu().numpy() * 255
erode_kernel = np.ones((erode, erode), np.uint8)
dilate_kernel = np.ones((dilate, dilate), np.uint8)
eroded = cv2.erode(mask_arr, erode_kernel, iterations=5)
dilated = cv2.dilate(mask_arr, dilate_kernel, iterations=5)
trimap = np.zeros_like(mask_arr)
trimap[dilated == 255] = 128
trimap[eroded == 255] = 255
trimaps.append(trimap)
return (np2tensor(trimaps),)
class MTB_ApplyVitMatte:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"model": ("VITMATTE_MODEL",),
"image": ("IMAGE",),
"trimap": ("IMAGE",),
"returns": (("RGB", "RGBA"),),
},
}
RETURN_TYPES = ("IMAGE", "MASK")
RETURN_NAMES = ("image (rgba)", "mask")
CATEGORY = "mtb/utils"
FUNCTION = "execute"
def execute(
self, model, image: torch.Tensor, trimap: torch.Tensor, returns: str
):
im_count = image.shape[0]
tm_count = trimap.shape[0]
if im_count != tm_count:
raise ValueError("image and trimap must have the same batch size")
outputs_m: list[torch.Tensor] = []
outputs_i: list[torch.Tensor] = []
for i, im in enumerate(image):
tm = trimap[i].half().unsqueeze(2).permute(2, 0, 1).to("cuda")
im = im.half().permute(2, 0, 1).to("cuda")
inputs = {"image": im.unsqueeze(0), "trimap": tm.unsqueeze(0)}
fine_mask = model(inputs)
foreground = im * fine_mask + (1 - fine_mask)
if returns == "RGBA":
rgba_image = torch.cat(
(foreground, fine_mask.unsqueeze(0)), dim=0
)
outputs_i.append(rgba_image.unsqueeze(0))
else:
outputs_i.append(foreground.unsqueeze(0))
outputs_m.append(fine_mask.unsqueeze(0))
result_m = torch.cat(outputs_m, dim=0)
result_i = torch.cat(outputs_i, dim=0)
return (result_i.permute(0, 2, 3, 1), result_m)
__nodes__ = [MTB_LoadVitMatteModel, MTB_GenerateTrimap, MTB_ApplyVitMatte]
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