File size: 6,985 Bytes
e4545e2 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 |
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
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, will fall back to edge_pad")
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
def g_diffuser(img,mask):
return img, mask
def dummy_fill(img,mask):
return img,mask
functbl = {
"gaussian": gaussian_noise,
"perlin": perlin_noise,
"edge_pad": edge_pad,
"patchmatch": patch_match_func if patch_match_compiled else edge_pad,
"cv2_ns": cv2_ns,
"cv2_telea": cv2_telea,
"g_diffuser": g_diffuser,
"g_diffuser_lib": dummy_fill,
}
try:
from postprocess import PhotometricCorrection
correction_func = PhotometricCorrection()
except Exception as e:
print(e, "so PhotometricCorrection is disabled")
class DummyCorrection:
def __init__(self):
self.backend=""
pass
def run(self,a,b,**kwargs):
return b
correction_func=DummyCorrection()
if "taichi" in correction_func.backend:
import sys
import io
import base64
from PIL import Image
def base64_to_pil(base64_str):
data = base64.b64decode(str(base64_str))
pil = Image.open(io.BytesIO(data))
return pil
def pil_to_base64(out_pil):
out_buffer = io.BytesIO()
out_pil.save(out_buffer, format="PNG")
out_buffer.seek(0)
base64_bytes = base64.b64encode(out_buffer.read())
base64_str = base64_bytes.decode("ascii")
return base64_str
from subprocess import Popen, PIPE, STDOUT
class SubprocessCorrection:
def __init__(self):
self.backend=correction_func.backend
self.child= Popen(["python", "postprocess.py"], stdin=PIPE, stdout=PIPE, stderr=STDOUT)
def run(self,img_input,img_inpainted,mode):
if mode=="disabled":
return img_inpainted
base64_str_input = pil_to_base64(img_input)
base64_str_inpainted = pil_to_base64(img_inpainted)
try:
if self.child.poll():
self.child= Popen(["python", "postprocess.py"], stdin=PIPE, stdout=PIPE, stderr=STDOUT)
self.child.stdin.write(f"{base64_str_input},{base64_str_inpainted},{mode}\n".encode())
self.child.stdin.flush()
out = self.child.stdout.readline()
base64_str=out.decode().strip()
while base64_str and base64_str[0]=="[":
print(base64_str)
out = self.child.stdout.readline()
base64_str=out.decode().strip()
ret=base64_to_pil(base64_str)
except:
print("[PIE] not working, photometric correction is disabled")
ret=img_inpainted
return ret
correction_func = SubprocessCorrection()
|