• 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 torchfrom PIL import Imageimport 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 cv2import torchimport matplotlib.pyplot as pltdef 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256def tensor_to_np(tensor): img = tensor.mul(255).byte() img = img.cpu().numpy().squeeze(0).transpose((1, 2, 0)) return imgdef 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:

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。