前言

torchvision.transforms.CenterCrop()函数是中心裁剪函数，可以将图片按照指定的size进行裁剪，并保留中心点

官方Api介绍

_Crops the given image at the center.
If the image is torch Tensor, it is expected
to have […, H, W] shape, where … means an arbitrary number of leading dimensions.
If image size is smaller than output size along any edge, image is padded with 0 and then center cropped.

Args:
size (sequence or int): Desired output size of the crop. If size is an
int instead of sequence like (h, w), a square crop (size, size) is
made. If provided a sequence of length 1, it will be interpreted as (size[0], size[0])._

需准备的东西

随便的一张图片

演示代码

导入库

# 导入库
import torchvision.transforms as transforms
import torchvision as tv
import matplotlib.pyplot as plt
import matplotlib
# 设置字体 这两行需要手动设置
matplotlib.rcParams['font.sans-serif']=['SimHei']
matplotlib.rcParams['axes.unicode_minus']=False

将图片按照中心1000像素点进行裁剪

# 1. 中心裁剪
transform = transforms.Compose([
    transforms.CenterCrop(1000)
])
# 读取图片
picTensor = tv.io.read_image('testpic.png')
# 转换图片
picTransformed = transform(picTensor)
# 显示
print('图像处理之前的图片大小：', picTensor.shape)
print('图像之后的图片大小：', picTransformed.shape)
picNumpy = picTensor.permute(1, 2, 0).numpy()
plt.imshow(picNumpy)
plt.title('图像处理之前的图片')
plt.show()
picNumpy = picTransformed.permute(1, 2, 0).numpy()
plt.imshow(picNumpy)
plt.title('图像处理之后的图片')
plt.show()

裁剪之前的图片大小： torch.Size([3, 2100, 3174]) 裁剪之后的图片大小： torch.Size([3, 1000, 1000])

将图片按照指定size=(1200, 900)进行裁剪

# 1. 中心裁剪
transform = transforms.Compose([
    transforms.CenterCrop(size=(1200, 900))
])
# 读取图片
picTensor = tv.io.read_image('testpic.png')
# 转换图片
picTransformed = transform(picTensor)
# 显示
print('裁剪之前的图片大小：', picTensor.shape)
print('裁剪之后的图片大小：', picTransformed.shape)
picNumpy = picTensor.permute(1, 2, 0).numpy()
plt.imshow(picNumpy)
plt.show()
picNumpy = picTransformed.permute(1, 2, 0).numpy()
plt.imshow(picNumpy)
plt.show()

裁剪之前的图片大小： torch.Size([3, 2100, 3174]) 裁剪之后的图片大小： torch.Size([3, 1200, 900])