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Add fill holes option to GrowMaskWithBlur

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Kijai 2024-04-05 14:45:53 +03:00
parent 6f88198cc5
commit 20bc04d287
1 changed files with 9 additions and 2 deletions
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11
nodes.py
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@ -9,7 +9,7 @@ import scipy.ndimage
from scipy.spatial import Voronoi from scipy.spatial import Voronoi
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
import numpy as np import numpy as np
from PIL import ImageFilter, Image, ImageDraw, ImageFont from PIL import ImageFilter, Image, ImageDraw, ImageFont, ImageOps
from PIL.PngImagePlugin import PngInfo from PIL.PngImagePlugin import PngInfo
import json import json
import re import re
@ -773,6 +773,9 @@ class GrowMaskWithBlur:
"lerp_alpha": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), "lerp_alpha": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
"decay_factor": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), "decay_factor": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
}, },
"optional": {
"fill_holes": ("BOOLEAN", {"default": False}),
},
} }
CATEGORY = "KJNodes/masking" CATEGORY = "KJNodes/masking"
@ -781,7 +784,7 @@ class GrowMaskWithBlur:
RETURN_NAMES = ("mask", "mask_inverted",) RETURN_NAMES = ("mask", "mask_inverted",)
FUNCTION = "expand_mask" FUNCTION = "expand_mask"
def expand_mask(self, mask, expand, tapered_corners, flip_input, blur_radius, incremental_expandrate, lerp_alpha, decay_factor): def expand_mask(self, mask, expand, tapered_corners, flip_input, blur_radius, incremental_expandrate, lerp_alpha, decay_factor, fill_holes=False):
alpha = lerp_alpha alpha = lerp_alpha
decay = decay_factor decay = decay_factor
if flip_input: if flip_input:
@ -805,6 +808,10 @@ class GrowMaskWithBlur:
current_expand -= abs(incremental_expandrate) current_expand -= abs(incremental_expandrate)
else: else:
current_expand += abs(incremental_expandrate) current_expand += abs(incremental_expandrate)
if fill_holes:
binary_mask = output > 0
output = scipy.ndimage.binary_fill_holes(binary_mask)
output = output.astype(np.uint8) * 255
output = torch.from_numpy(output) output = torch.from_numpy(output)
if alpha < 1.0 and previous_output is not None: if alpha < 1.0 and previous_output is not None:
# Interpolate between the previous and current frame # Interpolate between the previous and current frame