lnyan's picture
Add files
5704551
raw
history blame
4.79 kB
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
if os.name != "nt":
try:
from PyPatchMatch import patch_match
except Exception as e:
try:
import patch_match
except Exception as e:
patch_match_compiled = False
try:
patch_match
except NameError:
print("patch_match compiling failed")
patch_match_compiled = False
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,
}