一，Pytorch

1. 直接保存Tensor

#!/usr/bin/env python
# _*_ coding:utf-8 _*_
import torch
from torchvision import utils as vutils
 
 
def save_image_tensor(input_tensor: torch.Tensor, filename):
    """
    将tensor保存为图片
    :param input_tensor: 要保存的tensor
    :param filename: 保存的文件名
    """
    assert (len(input_tensor.shape) == 4 and input_tensor.shape[0] == 1)
    # 复制一份
    input_tensor = input_tensor.clone().detach()
    # 到cpu
    input_tensor = input_tensor.to(torch.device('cpu'))
    # 反归一化
    # input_tensor = unnormalize(input_tensor)
    vutils.save_image(input_tensor, filename)

2.Tensor 转CV2 保存

二、python

1. opencv

import cv2 as cv
# load `在这里插入代码片`
img = cv.imread(imagepath)
# shape=(height, width, channel)
h,w,c = img.shape
# show
cv.imshow('window_title', img)
# save
cv.imwrite(savepath, img)

2.matplotlib:

import matplotlib.pyplot as plt # plt 用于显示图片
import matplotlib.image as mpimg # mpimg 用于读取图片
import numpy as np

# load
img = mpimg.imread('cat.jpg') 
# 此时 img 就已经是一个 np.array 了，可以对它进行任意处理
# height, width, channel=(360, 480, 3)
h,w,c = img.shape 

# show
plt.imshow(img) # 显示图片
plt.axis('off') # 不显示坐标轴
plt.show()

# save
# 适用于保存任何 matplotlib 画出的图像，相当于一个 screencapture
plt.savefig('fig_cat.png')

3. PIL

from PIL import Image
import matplotlib.pyplot as plt
img=Image.open('/home/wanghao/Pictures/001.jpg')
plt.figure("head")
plt.imshow(img)
plt.show()