torch 中各种图像格式转化

• PIL：使用python自带图像处理库读取出来的图片格式
• numpy：使用python-opencv库读取出来的图片格式
• tensor：pytorch中训练时所采取的向量格式（当然也可以说图片）

 

a = torch.randn(3,496,740)
c = a.numpy()
print('c',c.shape)
d  =c.transpose(1,2,0)
print('d',d.shape)
e  = Image.fromarray(np.uint8(d))
print('e',e.size)
b = transforms.ToPILImage()(torch.squeeze(a.data.cpu(), 0))

print('b',b.size)#(740,496)
print(b)

c (3, 496, 740)
d (496, 740, 3)
e (740, 496)
b (740, 496)
<PIL.Image.Image image mode=RGB size=740x496 at 0x7F2A3D868208>

torchvision.transforms.ToPILImage
对于一个Tensor的转化过程是：

将张量的每个元素乘上255
将张量的数据类型有FloatTensor转化成Uint8
将张量转化成numpy的ndarray类型
对ndarray对象做permute (1, 2, 0)的操作
利用Image下的fromarray函数，将ndarray对象转化成PILImage形式
输出PILImage

 

 

import torch
from PIL import Image
import matplotlib.pyplot as plt

# loader使用torchvision中自带的transforms函数
loader = transforms.Compose([
    transforms.ToTensor()])  

unloader = transforms.ToPILImage()

# 输入图片地址
# 返回tensor变量
def image_loader(image_name):
    image = Image.open(image_name).convert('RGB')
    image = loader(image).unsqueeze(0)#用来满足网络的输入维度的假batch维度，即不足之处补0
    return image.to(device, torch.float)

# 输入PIL格式图片
# 返回tensor变量
def PIL_to_tensor(image):
    image = loader(image).unsqueeze(0)
    return image.to(device, torch.float)

# 输入tensor变量
# 输出PIL格式图片
def tensor_to_PIL(tensor):
    image = tensor.cpu().clone()
    image = image.squeeze(0)#移除假batch维度，即删掉上面添加的0
    image = unloader(image)
    return image

#直接展示tensor格式图片
def imshow(tensor, title=None):
    image = tensor.cpu().clone()  # we clone the tensor to not do changes on it
    image = image.squeeze(0)  # remove the fake batch dimension
    image = unloader(image)
    plt.imshow(image)
    if title is not None:
        plt.title(title)
    plt.pause(0.001)  # pause a bit so that plots are updated

#直接保存tensor格式图片
def save_image(tensor, **para):
    dir = 'results'
    image = tensor.cpu().clone()  # we clone the tensor to not do changes on it
    image = image.squeeze(0)  # remove the fake batch dimension
    image = unloader(image)
    if not osp.exists(dir):
        os.makedirs(dir)
    image.save('results_{}/s{}-c{}-l{}-e{}-sl{:4f}-cl{:4f}.jpg'
               .format(num, para['style_weight'], para['content_weight'], para['lr'], para['epoch'],
                       para['style_loss'], para['content_loss']))