但是这样就多了一个数据转换,所以不想这么干,简介的步骤就是将opencv的numpy格式的数据直接转为tensor,然后进行resize。 基于以上需求,在这个过程中,主要问题花在数据对其上,就是需要对比以下转换的数据是否一致 import cv2 import torch from PIL import Image from torchvision import transforms #pil加载的图像默认是...
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) # ...
单看函数名,ToTorch只需要把数据类型换一下,没必要做形状变化。就因为ToTorch这一步,后续还要用transpose把形状转回来,那用ToTorch转换形状岂不是多此一举? 答: pytorch选择设计成chw而不是hwc(毕竟传统的读图片的函数opencv的cv2.imread或者sklearn的imread都是读成hwc的格式的)这点确实比较令初学者困惑。个人感...
fromtorchvision.modelsimportresnet34importtorch.nn.functional as Fimporttorch.nn as nnimporttorchimportcv2#读取一张图片,并转换成[1,3,224,224]的float张量并归一化image = cv2.imread("flower.jpg") image= cv2.resize(image,(224,224)) input_tensor= torch.tensor(image).permute(2,0,1).unsqueeze(...
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 de...
tensor_image.unsqueeze_(0); tensor_image = tensor_image.toType(c10::kFloat).sub(127.5).mul(0.0078125); tensor_image.to(c10::DeviceType::CPU); } ### Input comparison :andhere are the tensor values both in PythonandC++Pytorchinput(`img[:, :, :10, :10]`): ...
image = PIL.Image.fromarray(torch.clamp(tensor*255, min=0, max=255).byte.permute(1,2,0).cpu.numpy) image = torchvision.transforms.functional.to_pil_image(tensor)# Equivalently way # PIL.Image -> torch.Tensor path = r'./figure.jpg' ...
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) label = self.dataDf.iloc[idx, 2] boxes = self._convertXyxy(self.dataDf.iloc[idx, 1]) if self.transform: img = self.transform(img) target = {} target['boxes'] = torch.tensor([boxes], dtype=torch.float32) ...
2.1 `Image` 、`Tensor` 与 `ndarray` 之间的相互转化 2.1.1 ToTensor() 2.1.2 PILToTensor() 2.1.3 ToPILImage() 2.2 常见的图像操作 2.2.1 TF.adjust_brightness() 2.2.2 TF.adjust_contrast() 2.2.3 TF.adjust_saturation() 2.2.4 TF.adjust_sharpness() ...
将torch.Tensor转换为PIL图像格式: 代码语言:txt 复制 pil_image = Image.fromarray(image_tensor.numpy()) 显示图像: 代码语言:txt 复制 pil_image.show() 上述代码中的path_to_image.jpg需要替换为实际的图像文件路径。这样就能够从torch.Tensor以PIL格式显示图像了。 推荐的腾讯云相关产品:腾讯云人工智能计算平台...