*Memos:
-
My post explains ElasticTransform() about
alphaandfillargument. -
My post explains ElasticTransform() about
alphaandsigmaargument. - My post explains OxfordIIITPet().
ElasticTransform() can do random morphological transformation for an image as shown below. *It's about sigma and fill argument:
from torchvision.datasets import OxfordIIITPet
from torchvision.transforms.v2 import ElasticTransform
from torchvision.transforms.functional import InterpolationMode
origin_data = OxfordIIITPet(
root="data",
transform=None
)
a5000s01_data = OxfordIIITPet( # `a` is alpha and `s` is sigma.
root="data",
transform=ElasticTransform(alpha=5000, sigma=0.1)
# transform=ElasticTransform(alpha=5000, sigma=[0.1, 0.1])
)
a5000s1_data = OxfordIIITPet(
root="data",
transform=ElasticTransform(alpha=5000, sigma=1)
)
a5000s5_data = OxfordIIITPet(
root="data",
transform=ElasticTransform(alpha=5000, sigma=5)
)
a5000s10_data = OxfordIIITPet(
root="data",
transform=ElasticTransform(alpha=5000, sigma=10)
)
a5000s20_data = OxfordIIITPet(
root="data",
transform=ElasticTransform(alpha=5000, sigma=20)
)
a5000s40_data = OxfordIIITPet(
root="data",
transform=ElasticTransform(alpha=5000, sigma=40)
)
a5000s40_01_data = OxfordIIITPet(
root="data",
transform=ElasticTransform(alpha=5000, sigma=[40, 0.1])
)
a5000s40_1_data = OxfordIIITPet(
root="data",
transform=ElasticTransform(alpha=5000, sigma=[40, 1])
)
a5000s40_5_data = OxfordIIITPet(
root="data",
transform=ElasticTransform(alpha=5000, sigma=[40, 5])
)
a5000s40_10_data = OxfordIIITPet(
root="data",
transform=ElasticTransform(alpha=5000, sigma=[40, 10])
)
a5000s40_20_data = OxfordIIITPet(
root="data",
transform=ElasticTransform(alpha=5000, sigma=[40, 20])
)
a5000s40_40_data = OxfordIIITPet(
root="data",
transform=ElasticTransform(alpha=5000, sigma=[40, 40])
)
a5000s01_40_data = OxfordIIITPet(
root="data",
transform=ElasticTransform(alpha=5000, sigma=[0.1, 40])
)
a5000s1_40_data = OxfordIIITPet(
root="data",
transform=ElasticTransform(alpha=5000, sigma=[1, 40])
)
a5000s5_40_data = OxfordIIITPet(
root="data",
transform=ElasticTransform(alpha=5000, sigma=[5, 40])
)
a5000s10_40_data = OxfordIIITPet(
root="data",
transform=ElasticTransform(alpha=5000, sigma=[10, 40])
)
a5000s20_40_data = OxfordIIITPet(
root="data",
transform=ElasticTransform(alpha=5000, sigma=[20, 40])
)
a5000s40_40_data = OxfordIIITPet(
root="data",
transform=ElasticTransform(alpha=5000, sigma=[40, 40])
)
a5000s5fgray_data = OxfordIIITPet(
root="data",
transform=ElasticTransform(alpha=5000, sigma=5, fill=150)
)
a5000s10fgray_data = OxfordIIITPet(
root="data",
transform=ElasticTransform(alpha=5000, sigma=10, fill=150)
)
a5000s5fpurple_data = OxfordIIITPet(
root="data",
transform=ElasticTransform(alpha=5000, sigma=5, fill=[160, 32, 240])
)
a5000s10fpurple_data = OxfordIIITPet(
root="data",
transform=ElasticTransform(alpha=5000, sigma=10, fill=[160, 32, 240])
)
import matplotlib.pyplot as plt
def show_images1(data, main_title=None):
plt.figure(figsize=[10, 5])
plt.suptitle(t=main_title, y=0.8, fontsize=14)
for i, (im, _) in zip(range(1, 6), data):
plt.subplot(1, 5, i)
plt.imshow(X=im)
plt.xticks(ticks=[])
plt.yticks(ticks=[])
plt.tight_layout()
plt.show()
show_images1(data=origin_data, main_title="origin_data")
print()
show_images1(data=a5000s01_data, main_title="a5000s01_data")
show_images1(data=a5000s1_data, main_title="a5000s1_data")
show_images1(data=a5000s5_data, main_title="a5000s5_data")
show_images1(data=a5000s10_data, main_title="a5000s10_data")
show_images1(data=a5000s20_data, main_title="a5000s20_data")
show_images1(data=a5000s40_data, main_title="a5000s40_data")
print()
show_images1(data=a5000s40_01_data, main_title="a5000s40_01_data")
show_images1(data=a5000s40_1_data, main_title="a5000s40_1_data")
show_images1(data=a5000s40_5_data, main_title="a5000s40_5_data")
show_images1(data=a5000s40_10_data, main_title="a5000s40_10_data")
show_images1(data=a5000s40_20_data, main_title="a5000s40_20_data")
show_images1(data=a5000s40_40_data, main_title="a5000s40_40_data")
print()
show_images1(data=a5000s01_40_data, main_title="a5000s01_40_data")
show_images1(data=a5000s1_40_data, main_title="a5000s1_40_data")
show_images1(data=a5000s5_40_data, main_title="a5000s5_40_data")
show_images1(data=a5000s10_40_data, main_title="a5000s10_40_data")
show_images1(data=a5000s20_40_data, main_title="a5000s20_40_data")
show_images1(data=a5000s40_40_data, main_title="a5000s40_40_data")
print()
show_images1(data=a5000fgray_data, main_title="a5000fgray_data")
show_images1(data=a10000fgray_data, main_title="a10000fgray_data")
show_images1(data=a5000fpurple_data, main_title="a5000fpurple_data")
show_images1(data=a10000fpurple_data, main_title="a10000fpurple_data")
# ↓ ↓ ↓ ↓ ↓ ↓ The code below is identical to the code above. ↓ ↓ ↓ ↓ ↓ ↓
def show_images2(data, main_title=None, a=None, s=5,
ip=InterpolationMode.BILINEAR, f=0):
plt.figure(figsize=[10, 5])
plt.suptitle(t=main_title, y=0.8, fontsize=14)
if a != None:
for i, (im, _) in zip(range(1, 6), data):
plt.subplot(1, 5, i)
et = ElasticTransform(alpha=a, sigma=s,
interpolation=ip, fill=f)
plt.imshow(X=et(im))
plt.xticks(ticks=[])
plt.yticks(ticks=[])
else:
for i, (im, _) in zip(range(1, 6), data):
plt.subplot(1, 5, i)
plt.imshow(X=im)
plt.xticks(ticks=[])
plt.yticks(ticks=[])
plt.tight_layout()
plt.show()
show_images2(data=origin_data, main_title="origin_data")
print()
show_images2(data=origin_data, main_title="a5000s01_data", a=5000, s=0.1)
show_images2(data=origin_data, main_title="a5000s1_data", a=5000, s=1)
show_images2(data=origin_data, main_title="a5000s5_data", a=5000, s=5)
show_images2(data=origin_data, main_title="a5000s10_data", a=5000, s=10)
show_images2(data=origin_data, main_title="a5000s20_data", a=5000, s=20)
show_images2(data=origin_data, main_title="a5000s40_data", a=5000, s=40)
print()
show_images2(data=origin_data, main_title="a5000s40_01_data", a=5000,
s=[40, 0.1])
show_images2(data=origin_data, main_title="a5000s40_1_data", a=5000,
s=[40, 1])
show_images2(data=origin_data, main_title="a5000s40_5_data", a=5000,
s=[40, 5])
show_images2(data=origin_data, main_title="a5000s40_10_data", a=5000,
s=[40, 10])
show_images2(data=origin_data, main_title="a5000s40_20_data", a=5000,
s=[40, 20])
show_images2(data=origin_data, main_title="a5000s40_40_data", a=5000,
s=[40, 40])
print()
show_images2(data=origin_data, main_title="a5000s01_40_data", a=5000,
s=[0.1, 40])
show_images2(data=origin_data, main_title="a5000s1_40_data", a=5000,
s=[1, 40])
show_images2(data=origin_data, main_title="a5000s5_40_data", a=5000,
s=[5, 40])
show_images2(data=origin_data, main_title="a5000s10_40_data", a=5000,
s=[10, 40])
show_images2(data=origin_data, main_title="a5000s20_40_data", a=5000,
s=[20, 40])
show_images2(data=origin_data, main_title="a5000s40_40_data", a=5000,
s=[40, 40])
print()
show_images2(data=origin_data, main_title="a5000fgray_data", a=5000, f=150)
show_images2(data=origin_data, main_title="a10000fgray_data", a=10000, f=150)
show_images2(data=origin_data, main_title="a5000fpurple_data", a=5000,
f=[160, 32, 240])
show_images2(data=origin_data, main_title="a10000fpurple_data", a=10000,
f=[160, 32, 240])
print()
show_images2(data=origin_data, main_title="a5000s5fgray_data", a=5000,
s=5, f=150)
show_images2(data=origin_data, main_title="a5000s10fgray_data", a=5000,
s=10, f=150)
print()
show_images2(data=origin_data, main_title="a5000s5fpurple_data", a=5000,
s=5, f=[160, 32, 240])
show_images2(data=origin_data, main_title="a5000s10fpurple_data", a=5000,
s=10, f=[160, 32, 240])
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