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from PIL import Image
from PIL import ImageFilter
import cv2
import numpy as np
import scipy
import scipy.signal
from scipy.spatial import cKDTree
import os
from perlin2d import *
patch_match_compiled = True
from PyPatchMatch import patch_match
def edge_pad(img, mask, mode=1):
if mode == 0:
nmask = mask.copy()
nmask[nmask > 0] = 1
res0 = 1 - nmask
res1 = nmask
p0 = np.stack(res0.nonzero(), axis=0).transpose()
p1 = np.stack(res1.nonzero(), axis=0).transpose()
min_dists, min_dist_idx = cKDTree(p1).query(p0, 1)
loc = p1[min_dist_idx]
for (a, b), (c, d) in zip(p0, loc):
img[a, b] = img[c, d]
elif mode == 1:
record = {}
kernel = [[1] * 3 for _ in range(3)]
nmask = mask.copy()
nmask[nmask > 0] = 1
res = scipy.signal.convolve2d(
nmask, kernel, mode="same", boundary="fill", fillvalue=1
)
res[nmask < 1] = 0
res[res == 9] = 0
res[res > 0] = 1
ylst, xlst = res.nonzero()
queue = [(y, x) for y, x in zip(ylst, xlst)]
# bfs here
cnt = res.astype(np.float32)
acc = img.astype(np.float32)
step = 1
h = acc.shape[0]
w = acc.shape[1]
offset = [(1, 0), (-1, 0), (0, 1), (0, -1)]
while queue:
target = []
for y, x in queue:
val = acc[y][x]
for yo, xo in offset:
yn = y + yo
xn = x + xo
if 0 <= yn < h and 0 <= xn < w and nmask[yn][xn] < 1:
if record.get((yn, xn), step) == step:
acc[yn][xn] = acc[yn][xn] * cnt[yn][xn] + val
cnt[yn][xn] += 1
acc[yn][xn] /= cnt[yn][xn]
if (yn, xn) not in record:
record[(yn, xn)] = step
target.append((yn, xn))
step += 1
queue = target
img = acc.astype(np.uint8)
else:
nmask = mask.copy()
ylst, xlst = nmask.nonzero()
yt, xt = ylst.min(), xlst.min()
yb, xb = ylst.max(), xlst.max()
content = img[yt : yb + 1, xt : xb + 1]
img = np.pad(
content,
((yt, mask.shape[0] - yb - 1), (xt, mask.shape[1] - xb - 1), (0, 0)),
mode="edge",
)
return img, mask
def perlin_noise(img, mask):
lin = np.linspace(0, 5, mask.shape[0], endpoint=False)
x, y = np.meshgrid(lin, lin)
avg = img.mean(axis=0).mean(axis=0)
# noise=[((perlin(x, y)+1)*128+avg[i]).astype(np.uint8) for i in range(3)]
noise = [((perlin(x, y) + 1) * 0.5 * 255).astype(np.uint8) for i in range(3)]
noise = np.stack(noise, axis=-1)
# mask=skimage.measure.block_reduce(mask,(8,8),np.min)
# mask=mask.repeat(8, axis=0).repeat(8, axis=1)
# mask_image=Image.fromarray(mask)
# mask_image=mask_image.filter(ImageFilter.GaussianBlur(radius = 4))
# mask=np.array(mask_image)
nmask = mask.copy()
# nmask=nmask/255.0
nmask[mask > 0] = 1
img = nmask[:, :, np.newaxis] * img + (1 - nmask[:, :, np.newaxis]) * noise
# img=img.astype(np.uint8)
return img, mask
def gaussian_noise(img, mask):
noise = np.random.randn(mask.shape[0], mask.shape[1], 3)
noise = (noise + 1) / 2 * 255
noise = noise.astype(np.uint8)
nmask = mask.copy()
nmask[mask > 0] = 1
img = nmask[:, :, np.newaxis] * img + (1 - nmask[:, :, np.newaxis]) * noise
return img, mask
def cv2_telea(img, mask):
ret = cv2.inpaint(img, 255 - mask, 5, cv2.INPAINT_TELEA)
return ret, mask
def cv2_ns(img, mask):
ret = cv2.inpaint(img, 255 - mask, 5, cv2.INPAINT_NS)
return ret, mask
def patch_match_func(img, mask):
ret = patch_match.inpaint(img, mask=255 - mask, patch_size=3)
return ret, mask
def mean_fill(img, mask):
avg = img.mean(axis=0).mean(axis=0)
img[mask < 1] = avg
return img, mask
functbl = {
"gaussian": gaussian_noise,
"perlin": perlin_noise,
"edge_pad": edge_pad,
"patchmatch": patch_match_func if (os.name != "nt" and patch_match_compiled) else edge_pad,
"cv2_ns": cv2_ns,
"cv2_telea": cv2_telea,
"mean_fill": mean_fill,
}
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