Gaussianblur در Pytorch (1) – Community Dev

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

gaussianblur = GaussianBlur(kernel_size=1)
gaussianblur = GaussianBlur(kernel_size=1, sigma=(0.1, 2.0))

gaussianblur
# GaussianBlur(kernel_size=(1, 1), sigma=[0.1, 2.0])

gaussianblur.kernel_size 
# (1, 1)

gaussianblur.sigma
# [0.1, 2.0]

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

ks1_data = OxfordIIITPet( # `ks` is kernel_size.
    root="data",
    transform=GaussianBlur(kernel_size=1)
    # transform=GaussianBlur(kernel_size=[1])
    # transform=GaussianBlur(kernel_size=[1, 1])
)

ks3_data = OxfordIIITPet(
    root="data",
    transform=GaussianBlur(kernel_size=3)
)

ks5_data = OxfordIIITPet(
    root="data",
    transform=GaussianBlur(kernel_size=5)
)

ks7_data = OxfordIIITPet(
    root="data",
    transform=GaussianBlur(kernel_size=7)
)

ks9_data = OxfordIIITPet(
    root="data",
    transform=GaussianBlur(kernel_size=9)
)

ks11_data = OxfordIIITPet(
    root="data",
    transform=GaussianBlur(kernel_size=11)
)

ks51_data = OxfordIIITPet(
    root="data",
    transform=GaussianBlur(kernel_size=51)
)

ks101_data = OxfordIIITPet(
    root="data",
    transform=GaussianBlur(kernel_size=101)
)

ks9_51_data = OxfordIIITPet(
    root="data",
    transform=GaussianBlur(kernel_size=[9, 51])
)

ks51_9_data = OxfordIIITPet(
    root="data",
    transform=GaussianBlur(kernel_size=[51, 9])
)

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=ks1_data, main_title="ks1_data")
show_images1(data=ks3_data, main_title="ks3_data")
show_images1(data=ks5_data, main_title="ks5_data")
show_images1(data=ks7_data, main_title="ks7_data")
show_images1(data=ks9_data, main_title="ks9_data")
show_images1(data=ks11_data, main_title="ks11_data")
show_images1(data=ks51_data, main_title="ks51_data")
show_images1(data=ks101_data, main_title="ks101_data")
print()
show_images1(data=origin_data, main_title="origin_data")
show_images1(data=ks9_51_data, main_title="ks9_51_data")
show_images1(data=ks51_9_data, main_title="ks51_9_data")

# ↓ ↓ ↓ ↓ ↓ ↓ The code below is identical to the code above. ↓ ↓ ↓ ↓ ↓ ↓
def show_images2(data, main_title=None, ks=None, s=(0.1, 2.0)):
    plt.figure(figsize=[10, 5])
    plt.suptitle(t=main_title, y=0.8, fontsize=14)
    if ks:
        for i, (im, _) in zip(range(1, 6), data):
            plt.subplot(1, 5, i)
            gb = GaussianBlur(kernel_size=ks, sigma=s)
            plt.imshow(X=gb(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")
show_images2(data=origin_data, main_title="ks1_data", ks=1)
show_images2(data=origin_data, main_title="ks3_data", ks=3)
show_images2(data=origin_data, main_title="ks5_data", ks=5)
show_images2(data=origin_data, main_title="ks7_data", ks=7)
show_images2(data=origin_data, main_title="ks9_data", ks=9)
show_images2(data=origin_data, main_title="ks11_data", ks=11)
show_images2(data=origin_data, main_title="ks51_data", ks=51)
show_images2(data=origin_data, main_title="ks101_data", ks=101)
print()
show_images2(data=origin_data, main_title="origin_data")
show_images2(data=origin_data, main_title="ks9_51data", ks=[9, 51])
show_images2(data=origin_data, main_title="ks51_9_data", ks=[51, 9])