torch 中各种图像格式转换的实现方法

 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-600' .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)