ColorJitter in PyTorch (4)

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*Memos:

• My post explains ColorJitter() about brightness argument.
• My post explains ColorJitter() about contrast argument.
• My post explains ColorJitter() about saturation argument.
• My post explains OxfordIIITPet().

ColorJitter() can randomly change the brightness, contrast, saturation and hue of an image as shown below. *It's about hue argument:

from torchvision.datasets import OxfordIIITPet
from torchvision.transforms.v2 import ColorJitter

origin_data = OxfordIIITPet(
    root="data",
    transform=None
)

huen05_05_data = OxfordIIITPet(
    root="data",
    transform=ColorJitter(hue=[-0.5, 0.5])
    # transform=ColorJitter(hue=0.5)
)

huen05_0_data = OxfordIIITPet(
    root="data",
    transform=ColorJitter(hue=[-0.5, 0])
)

hue0_05_data = OxfordIIITPet(
    root="data",
    transform=ColorJitter(hue=[0, 0.5])
)

hue01_01_data = OxfordIIITPet(
    root="data",
    transform=ColorJitter(hue=[0.1, 0.1])
)

hue02_02_data = OxfordIIITPet(
    root="data",
    transform=ColorJitter(hue=[0.2, 0.2])
)

hue03_03_data = OxfordIIITPet(
    root="data",
    transform=ColorJitter(hue=[0.3, 0.3])
)

hue04_04_data = OxfordIIITPet(
    root="data",
    transform=ColorJitter(hue=[0.4, 0.4])
)

hue05_05_data = OxfordIIITPet(
    root="data",
    transform=ColorJitter(hue=[0.5, 0.5])
)

huen01n01_data = OxfordIIITPet(
    root="data",
    transform=ColorJitter(hue=[-0.1, -0.1])
)

huen02n02_data = OxfordIIITPet(
    root="data",
    transform=ColorJitter(hue=[-0.2, -0.2])
)

huen03n03_data = OxfordIIITPet(
    root="data",
    transform=ColorJitter(hue=[-0.3, -0.3])
)

huen04n04_data = OxfordIIITPet(
    root="data",
    transform=ColorJitter(hue=[-0.4, -0.4])
)

huen05n05_data = OxfordIIITPet(
    root="data",
    transform=ColorJitter(hue=[-0.5, -0.5])
)

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=hue0_0origin_data, main_title="hue0_0origin_data")
show_images1(data=huen05_05_data, main_title="huen05_05_data")
show_images1(data=huen05_0_data, main_title="huen05_0_data")
show_images1(data=hue0_05_data, main_title="hue0_05_data")
print()
show_images1(data=hue0_0origin_data, main_title="hue0_0origin_data")
show_images1(data=hue01_01_data, main_title="hue01_01_data")
show_images1(data=hue02_02_data, main_title="hue02_02_data")
show_images1(data=hue03_03_data, main_title="hue03_03_data")
show_images1(data=hue04_04_data, main_title="hue04_04_data")
show_images1(data=hue05_05_data, main_title="hue05_05_data")
print()
show_images1(data=hue0_0origin_data, main_title="hue0_0origin_data")
show_images1(data=huen01n01_data, main_title="huen01n01_data")
show_images1(data=huen02n02_data, main_title="huen02n02_data")
show_images1(data=huen03n03_data, main_title="huen03n03_data")
show_images1(data=huen04n04_data, main_title="huen04n04_data")
show_images1(data=huen05n05_data, main_title="huen05n05_data")

# ↓ ↓ ↓ ↓ ↓ ↓ The code below is identical to the code above. ↓ ↓ ↓ ↓ ↓ ↓
def show_images2(data, main_title=None, b=0, c=0, s=0, h=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)
        cj = ColorJitter(brightness=b, contrast=c,
                         saturation=s, hue=h)
        plt.imshow(X=cj(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="hue0_0origin_data", h=[0, 0])
show_images2(data=origin_data, main_title="huen05_05_data", h=[-0.5, 0.5])
# ↑ show_images2(data=origin_data, main_title="huen05_data", h=0.5)
show_images2(data=origin_data, main_title="huen05_0_data", h=[-0.5, 0])
show_images2(data=origin_data, main_title="hue0_05_data", h=[0, 0.5])
print()
show_images2(data=origin_data, main_title="hue0_0origin_data", h=[0, 0])
show_images2(data=origin_data, main_title="hue01_01_data", h=[0.1, 0.1])
show_images2(data=origin_data, main_title="hue02_02_data", h=[0.2, 0.2])
show_images2(data=origin_data, main_title="hue03_03_data", h=[0.3, 0.3])
show_images2(data=origin_data, main_title="hue04_04_data", h=[0.4, 0.4])
show_images2(data=origin_data, main_title="hue05_05_data", h=[0.5, 0.5])
print()
show_images2(data=origin_data, main_title="hue0_0origin_data", h=[0, 0])
show_images2(data=origin_data, main_title="huen01n01_data", h=[-0.1, -0.1])
show_images2(data=origin_data, main_title="huen02n02_data", h=[-0.2, -0.2])
show_images2(data=origin_data, main_title="huen03n03_data", h=[-0.3, -0.3])
show_images2(data=origin_data, main_title="huen04n04_data", h=[-0.4, -0.4])
show_images2(data=origin_data, main_title="huen05n05_data", h=[-0.5, -0.5])

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