宜配屋

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）

PIL与Tensor相互转换

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

#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']))

numpy 与 tensor相互转换

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)

N张图片一起转换.

# 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)

参考:/post/177291.htm

以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持【听图阁-专注于Python设计】。