Pytorch中Tensor与各种图像格式的相互转化详解

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)
  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)
  image = unloader(image)
  return image

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

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']))

import cv2
import torch
import matplotlib.pyplot as plt

def toTensor(img):
  assert type(img) == np.ndarray,'the img type is {}, but ndarry expected'.format(type(img))
  img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
  img = torch.from_numpy(img.transpose((2, 0, 1)))
  return img.float().div(255).unsqueeze(0) # 255也可以改为256

def tensor_to_np(tensor):
  img = tensor.mul(255).byte()
  img = img.cpu().numpy().squeeze(0).transpose((1, 2, 0))
  return img

def show_from_cv(img, title=None):
  img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
  plt.figure()
  plt.imshow(img)
  if title is not None:
    plt.title(title)
  plt.pause(0.001)

def show_from_tensor(tensor, title=None):
  img = tensor.clone()
  img = tensor_to_np(img)
  plt.figure()
  plt.imshow(img)
  if title is not None:
    plt.title(title)
  plt.pause(0.001)

# 将 N x H x W X C 的numpy格式图片转化为相应的tensor格式
def toTensor(img):
  img = torch.from_numpy(img.transpose((0, 3, 1, 2)))
  return img.float().div(255).unsqueeze(0)