import numpy as np
import skimage
import skimage.transform
import skimage.feature
import skimage.io
import os
from os.path import join

# train_dir = "copy"

# items = sorted([x for x in os.listdir(train_dir) if x.endswith(".png")], key=lambda x: int(x.split(".")[0]))
# for name in items:
#     image = skimage.io.imread(join(train_dir, name), as_grey=True)
#     trans = np.where(skimage.filters.gaussian(image, 0.8) > 0.6, 255, 0)
#     skimage.io.imsave("copyprocessed/process" + name, trans)

# train_dir = "valid"
# items = sorted([x for x in os.listdir(train_dir) if x.endswith(".png")], key=lambda x: int(x.split(".")[0]))
# for name in items:
#     image = skimage.io.imread(join(train_dir, name), as_grey=True)
#     trans = np.where(skimage.filters.gaussian(image, 0.8) > 0.6, 255, 0)
#     skimage.io.imsave("validprocessed/process" + name, trans)

train_dir = "../3"
items = sorted([x for x in os.listdir(train_dir) if x.endswith(".jpg")], key=lambda x: int(x.split(".")[0]))
for name in items:
    image = skimage.io.imread(join(train_dir, name), as_grey=True)
    trans = np.where(skimage.filters.gaussian(image, 0.8) > 0.6, 255, 0)
    skimage.io.imsave("../3processed/" + name, trans)

3e5a059a416cf7e75231e35d182df514d11bf7de
<mask token>

<mask token>
for name in items:
    image = skimage.io.imread(join(train_dir, name), as_grey=True)
    trans = np.where(skimage.filters.gaussian(image, 0.8) > 0.6, 255, 0)
    skimage.io.imsave('../3processed/' + name, trans)

<mask token>
train_dir = '../3'
items = sorted([x for x in os.listdir(train_dir) if x.endswith('.jpg')],
    key=lambda x: int(x.split('.')[0]))
for name in items:
    image = skimage.io.imread(join(train_dir, name), as_grey=True)
    trans = np.where(skimage.filters.gaussian(image, 0.8) > 0.6, 255, 0)
    skimage.io.imsave('../3processed/' + name, trans)

import numpy as np
import skimage
import skimage.transform
import skimage.feature
import skimage.io
import os
from os.path import join
train_dir = '../3'
items = sorted([x for x in os.listdir(train_dir) if x.endswith('.jpg')],
    key=lambda x: int(x.split('.')[0]))
for name in items:
    image = skimage.io.imread(join(train_dir, name), as_grey=True)
    trans = np.where(skimage.filters.gaussian(image, 0.8) > 0.6, 255, 0)
    skimage.io.imsave('../3processed/' + name, trans)

import numpy as np
import skimage
import skimage.transform
import skimage.feature
import skimage.io
import os
from os.path import join

# train_dir = "copy"

# items = sorted([x for x in os.listdir(train_dir) if x.endswith(".png")], key=lambda x: int(x.split(".")[0]))
# for name in items:
#     image = skimage.io.imread(join(train_dir, name), as_grey=True)
#     trans = np.where(skimage.filters.gaussian(image, 0.8) > 0.6, 255, 0)
#     skimage.io.imsave("copyprocessed/process" + name, trans)

# train_dir = "valid"
# items = sorted([x for x in os.listdir(train_dir) if x.endswith(".png")], key=lambda x: int(x.split(".")[0]))
# for name in items:
#     image = skimage.io.imread(join(train_dir, name), as_grey=True)
#     trans = np.where(skimage.filters.gaussian(image, 0.8) > 0.6, 255, 0)
#     skimage.io.imsave("validprocessed/process" + name, trans)

train_dir = "../3"
items = sorted([x for x in os.listdir(train_dir) if x.endswith(".jpg")], key=lambda x: int(x.split(".")[0]))
for name in items:
    image = skimage.io.imread(join(train_dir, name), as_grey=True)
    trans = np.where(skimage.filters.gaussian(image, 0.8) > 0.6, 255, 0)
    skimage.io.imsave("../3processed/" + name, trans)
