Pytorch的中心牛（中心.Pytorch...）

wufei1232025-02-15python11

给我买咖啡☕

*备忘录：

我的帖子解释了牛津iiitpet（）。

centercrop（）可以裁剪图像，以此为中心：

*备忘录：

单个值（int或tuple/list（int））是指[size，size]。

张量必须为2d或3d。

不使用img =。

建议根据v1或v2使用v2？我应该使用哪一个？

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)
)

s1_data = OxfordIIITPet(
    root="data",
    transform=CenterCrop(size=1)
)

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")
show_images1(data=s1_data, main_title="s1_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)
show_images2(data=origin_data, main_title="s1_data", s=1)
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])

image description