JPEG in PyTorch

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*Memos:

• My post explains OxfordIIITPet().

JPEG() can randomly apply JPEG compression to an image as shown below:

*Memos:

• The 1st argument for initialization is quality(Required-Type:int): *Memos:
  • It's the range of the quality [min, max] so it must be min <= max.
  • It must be 0 <= x <= 100.
  • A tuple/list must be the 1D with 2 elements.
  • A single value means [quality, quality].
• There is the 1st argument(Required-Type:PIL Image or tensor(int(uint8))). *A tensor must be 3D.
• 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 JPEG

jpeg = JPEG(quality=100)

jpeg
# JPEG(quality=[100, 100])

jpeg.quality
# [100, 100]

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

q100origin_data = OxfordIIITPet( # `q` is quality
    root="data",
    transform=JPEG(quality=100)
    # transform=JPEG(quality=[100, 100])
)

q1_100_data = OxfordIIITPet(
    root="data",
    transform=JPEG(quality=[1, 100])
)

q1_50_data = OxfordIIITPet(
    root="data",
    transform=JPEG(quality=[1, 50])
)

q50_100_data = OxfordIIITPet(
    root="data",
    transform=JPEG(quality=[50, 100])
)

q50_data = OxfordIIITPet(
    root="data",
    transform=JPEG(quality=50)
)

q10_data = OxfordIIITPet(
    root="data",
    transform=JPEG(quality=10)
)

q5_data = OxfordIIITPet(
    root="data",
    transform=JPEG(quality=5)
)

q1_data = OxfordIIITPet(
    root="data",
    transform=JPEG(quality=1)
)

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=q100origin_data, main_title="q100origin_data")
show_images1(data=q1_100_data, main_title="q1_100_data")
show_images1(data=q1_50_data, main_title="q1_50_data")
show_images1(data=q50_100_data, main_title="q50_100_data")
print()
show_images1(data=q100origin_data, main_title="q100origin_data")
show_images1(data=q50_data, main_title="q50_data")
show_images1(data=q10_data, main_title="q10_data")
show_images1(data=q5_data, main_title="q5_data")
show_images1(data=q1_data, main_title="q1_data")

# ↓ ↓ ↓ ↓ ↓ ↓ The code below is identical to the code above. ↓ ↓ ↓ ↓ ↓ ↓
def show_images2(data, main_title=None, q=100):
    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)
        jpeg = JPEG(quality=q)
        plt.imshow(X=jpeg(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="q100origin_data", q=100)
show_images2(data=origin_data, main_title="q1_100_data", q=[1, 100])
show_images2(data=origin_data, main_title="q1_50_data", q=[1, 50])
show_images2(data=origin_data, main_title="q50_100_data", q=[50, 100])
print()
show_images2(data=origin_data, main_title="q100origin_data", q=100)
show_images2(data=origin_data, main_title="q50_data", q=50)
show_images2(data=origin_data, main_title="q10_data", q=10)
show_images2(data=origin_data, main_title="q5_data", q=5)
show_images2(data=origin_data, main_title="q1_data", q=1)

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