*Memos:
- My post explains OxfordIIITPet().
CenterCrop() can crop an image, centering on it as shown below:
*Memos:
- The 1st argument for initialization is
size
(Required-Type:int
ortuple/list
(int
) or size()): *Memos:- It's
[height, width]
. - It must be
1 <= x
. - A tuple/list must be the 1D with 1 or 2 elements.
- A single value(
int
ortuple/list
(int
)) means[size, size]
.
- It's
- The 1st argument is
img
(Required-Type:PIL Image
ortensor
(int
)): *Memos:- A tensor must be 2D or 3D.
- Don't use
img=
.
-
v2
is recommended to use according to V1 or V2? Which one should I use?.
from torchvision.datasets import OxfordIIITPet
from torchvision.transforms.v2 import CenterCrop
centercrop = CenterCrop(size=100)
centercrop
# CenterCrop(size=(100, 100))
centercrop.size
# (100, 100)
origin_data = OxfordIIITPet(
root="data",
transform=None
)
s600_data = OxfordIIITPet( # `s` is size.
root="data",
transform=CenterCrop(size=600)
# transform=CenterCrop(size=[600])
# transform=CenterCrop(size=[600, 600])
)
s400_data = OxfordIIITPet(
root="data",
transform=CenterCrop(size=400)
)
s200_data = OxfordIIITPet(
root="data",
transform=CenterCrop(size=200)
)
s100_data = OxfordIIITPet(
root="data",
transform=CenterCrop(size=100)
)
s50_data = OxfordIIITPet(
root="data",
transform=CenterCrop(size=50)
)
s10_data = OxfordIIITPet(
root="data",
transform=CenterCrop(size=10)
)
s200_300_data = OxfordIIITPet(
root="data",
transform=CenterCrop(size=[200, 300])
)
s300_200_data = OxfordIIITPet(
root="data",
transform=CenterCrop(size=[300, 200])
)
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.tight_layout()
plt.show()
show_images1(data=origin_data, main_title="origin_data")
show_images1(data=s600_data, main_title="s600_data")
show_images1(data=s400_data, main_title="s400_data")
show_images1(data=s200_data, main_title="s200_data")
show_images1(data=s100_data, main_title="s100_data")
show_images1(data=s50_data, main_title="s50_data")
show_images1(data=s10_data, main_title="s10_data")
print()
show_images1(data=origin_data, main_title="origin_data")
show_images1(data=s200_300_data, main_title="s200_300_data")
show_images1(data=s300_200_data, main_title="s300_200_data")
# ↓ ↓ ↓ ↓ ↓ ↓ The code below is identical to the code above. ↓ ↓ ↓ ↓ ↓ ↓
def show_images2(data, main_title=None, s=None):
plt.figure(figsize=(10, 5))
plt.suptitle(t=main_title, y=0.8, fontsize=14)
temp_s = s
for i, (im, _) in zip(range(1, 6), data):
plt.subplot(1, 5, i)
if not temp_s:
s = [im.size[1], im.size[0]]
cc = CenterCrop(size=s) # Here
plt.imshow(X=cc(im)) # Here
plt.tight_layout()
plt.show()
show_images2(data=origin_data, main_title="origin_data")
show_images2(data=origin_data, main_title="s600_data", s=600)
show_images2(data=origin_data, main_title="s400_data", s=400)
show_images2(data=origin_data, main_title="s200_data", s=200)
show_images2(data=origin_data, main_title="s100_data", s=100)
show_images2(data=origin_data, main_title="s50_data", s=50)
show_images2(data=origin_data, main_title="s10_data", s=10)
print()
show_images2(data=origin_data, main_title="origin_data")
show_images2(data=origin_data, main_title="s200_300_data", s=[200, 300])
show_images2(data=origin_data, main_title="s300_200_data", s=[300, 200])
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