from PIL import Image # 使用PIL打开图像 image_path = 'path/to/your/image.jpg' image = Image.open(image_path).convert('RGB') 2. 使用PyTorch的transforms功能来处理图像 PyTorch的torchvision.transforms模块提供了多种图像处理功能,包括将图像转换为Tensor。你可以创建一个转换操作,并应用于图像: python ...
imshow(np.transpose(img, (1, 2, 0))) # convert from Tensor image # obtain one batch of imges from train dataset dataiter = iter(train_loader) images, labels = dataiter.next() images = images.numpy() # convert images to numpy for display # plot the images in one batch with the ...
# 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) 2将PIL图片转化为Tensor # 输入PIL格式图片# 返回tensor变量def PIL_to_tensor(image):image = loader(image).unsqueeze(...
也就是把像素值正则化成 [0.0, 1.0]的范围。通过例⼦理解⼀下：import torchvision.transforms as transforms import cv2 as cv img = cv.imread('image/000001.jpg')transf = transforms.ToTensor()img_tensor = transf(img)print('opencv', img)print('torch', img_tensor)
img=Image.open(img_path).convert('RGB')#将图像读入并转换成RGB形式 img=img.resize(img_size,img_size)#调整读入图像像素大小 img=transforms.ToTensor()(img)#将图像转化为tensor img=img.unsqueeze(0)#在0维上增加一个维度returnimg defshow_img(img):#图像输出 ...
Converts a PIL Image or numpy.ndarray (H x W x C) in the range [0, 255] to a torch.FloatTensor of shape (C x H x W) in the range [0.0, 1.0]. """def__call__(self, pic):""" Args: pic (PIL Image or numpy.ndarray): Image to be converted to tensor. ...
input_var = Variable(torch.FloatTensor(input_tensor)) # get PyTorch ResNet18 model model_to_transfer = FullyConvolutionalResnet18(pretrained=pretrained_resnet) model_to_transfer.eval() # convert PyTorch model to Keras model = pytorch_to_keras( ...
img = Image.open(fn).convert('RGB') # 像素值 0~255,在transfrom.totensor会除以255,使像素值变成 0~1 if self.transform is not None: img = self.transform(img) # 在这里做transform,转为tensor等等 return img, label def __len__(self): ...
，源码如下：def to_tensor(pic): """Convert a ``PIL Image`` or ``numpy.ndarray`` to ...
batch_data = torch.unsqueeze(input_data, 0) return batch_data input = preprocess_image("turkish_coffee.jpg").cuda() 现在我们可以进行推理了。不要忘记将模型切换到评估模式并将其也复制到 GPU。结果,我们将得到对象属于哪个类的概率 tensor[1, 1000]。