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Super Kai (Kazuya Ito)
Super Kai (Kazuya Ito)

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RandomRotation in PyTorch

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*Memos:

RandomRotation() can rotate an image randomly as shown below:

*Memos:

  • The 1st argument for initialization is degrees(Required-Type:int, float or tuple/list(int or float)): *Memos:
    • It can do rotation.
    • It's the range of the degrees [min, max] so it must be min <= max.
    • A tuple/list must be the 1D with 2 elements.
    • A single value must be 0 <= x.
    • A single value means [-degrees, +degrees].
  • The 2nd argument for initialization is interpolation(Optional-Default:InterpolationMode.NEAREST-Type:InterpolationMode).
  • The 3rd argument for initialization is expand(Optional-Default:False-Type:bool).
  • The 4th argument for initialization is center(Optional-Default:None-Type:tuple/list(int or float)): *Memos:
    • It can change the center position of an image.
    • It must be the 1D with 2 elements.
  • The 5th argument for initialization is fill(Optional-Default:0-Type:int, float or tuple/list(int or float)): *Memos:
    • It can change the background of an image. *The background can be seen when rotating an image.
    • A tuple/list must be the 1D with 1 or 3 elements.
  • The 1st argument is img(Required-Type:PIL Image or tensor(int)): *Memos:
    • A tensor must be 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 RandomRotation
from torchvision.transforms.functional import InterpolationMode

randomrotation = RandomRotation(degrees=90)
randomrotation = RandomRotation(degrees=[-90, 90], 
                                interpolation=InterpolationMode.NEAREST,
                                expand=False,
                                center=None,
                                fill=0)
randomrotation
# RandomRotation(degrees=[-90.0, 90.0],
#                interpolation=InterpolationMode.NEAREST,
#                expand=False,
#                fill=0)

randomrotation.degrees
# [-90.0, 90.0]

randomrotation.interpolation
# <InterpolationMode.NEAREST: 'nearest'>

randomrotation.expand
# False

print(randomrotation.center)
# None

randomrotation.fill
# 0

origin_data = OxfordIIITPet(
    root="data",
    transform=None
    # transform=RandomRotation(degrees=[0, 0])
)

d90_data = OxfordIIITPet( # `d` is degrees.
    root="data",
    transform=RandomRotation(degrees=90)
    # transform=RandomRotation(degrees=[-90, 90])
)

d90_90_data = OxfordIIITPet(
    root="data",
    transform=RandomRotation(degrees=[90, 90])
)

dn90n90expand_data = OxfordIIITPet( # `n` is negative.
    root="data",
    transform=RandomRotation(degrees=[-90, -90], expand=True)
)

d180_180c270_200_data = OxfordIIITPet( # `c` is center.
    root="data",
    transform=RandomRotation(degrees=[180, 180], center=[270, 200])
)

dn45n45fgray_data = OxfordIIITPet( # `f` is fill.
    root="data",
    transform=RandomRotation(degrees=[-45, -45], fill=150)
)

d135_135fpurple_data = OxfordIIITPet(
    root="data",
    transform=RandomRotation(degrees=[135, 135], 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")
show_images1(data=d90_data , main_title="d90_data ")
show_images1(data=d90_90_data, main_title="d90_90_data")
show_images1(data=dn90n90expand_data, main_title="dn90n90expand_data")
show_images1(data=d180_180c270_200_data, main_title="d180_180c270_200_data")
show_images1(data=dn45n45fgray_data, main_title="dn45n45fgray_data")
show_images1(data=d135_135fpurple_data, main_title="d135_135fpurple_data")

# ↓ ↓ ↓ ↓ ↓ ↓ The code below is identical to the code above. ↓ ↓ ↓ ↓ ↓ ↓
def show_images2(data, main_title=None, d=0, e=False, c=None, f=0):
    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)
        rr = RandomRotation(degrees=d, expand=e, center=c, fill=f) # Here
        plt.imshow(X=rr(im)) # Here
        plt.xticks(ticks=[])
        plt.yticks(ticks=[])
    plt.tight_layout()
    plt.show()

show_images2(data=origin_data, main_title="origin_data")
show_images2(data=origin_data, main_title="d90_data", d=90)
show_images2(data=origin_data, main_title="d90_90_data", d=[90, 90])
show_images2(data=origin_data, main_title="dn90n90expand_data", d=[-90, -90], 
             e=True)
show_images2(data=origin_data, main_title="d180_180c270_200_data",
             d=[180, 180], c=[270, 200])
show_images2(data=origin_data, main_title="dn45n45fgray_data",
             d=[-45, -45], f=150)
show_images2(data=origin_data, main_title="d135_135fpurple_data",
             d=[135, 135], f=[160, 32, 240])
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