img_PIL = transforms.ToPILImage([mode])(img_tensor) img_tensor = transforms.PILToTenso()(img_PIL)# 不进行 scaleimg_tensor = transforms.ToTensor()(img_PIL)# 默认进行 scale mode的解释 PIL -- opencv img_PIL = Image.open("../datasets/MVTec/bottle/train/good/000.png") img_cv2 = cv2...
img = cv.imread('image/000001.jpg') transf = transforms.ToTensor() img_tensor = transf(img) 个人觉得使用opencv转tensor还是比较方便的,但是tensor转opencv图像,可能会遇到一些问题。 tensor->opencv tensor转opencv首先将tensor转为numpy数组,再将numpy转为opencv的mat数据。
1、numpy和PIL互转 fromPILimportImageimportnumpy as npimportcv2 img= cv2.imread('image.jpg') np.size(img,0)#0,1,2print(type(img))#numpy to PILpil_img=Image.fromarray(img)print(type(pil_img))#PIL to numpynp_img=np.array(pil_img)print(type(np_img)) 2、tensor(pytorch)和numpy互转 ...
2、tensor(pytorch)和numpy互转 importtorchimportnumpy as np#tensor to numpytorch_img = torch.ones(5)print(torch_img) torch_img+=1np_img=torch_img.numpy()print(torch_img, np_img)#numpy to tensornp_img = np.ones(9)print(np_img) torch_img=torch.from_numpy(np_img) np_img+=1print(...
print("maskimg.shape:",maskimg.shape) # plt.imshow(maskimg) # plt.show() # maskimg = Image.fromarray(maskimg,mode='P')# 转成索引图 事实证明这样是不行的. maskimg = Image.fromarray(maskimg).convert('P') # 转成索引图 colors=[ ...
plt.subplot(221),plt.imshow(img[:,:,::-1]),plt.title('Original') plt.xticks([]), plt.yticks([]) plt.subplot(222),plt.imshow(dst[:,:,::-1]),plt.title('Averaging') plt.xticks([]), plt.yticks([]) plt.subplot(223),plt.im...
img=Image.open("image-2022.webp")img.load()img.save("image-2022.png") 输出结果: 人生苦短,快学Python!今天我们分享了将一些奇怪的图片格式(webp格式、jfif格式、svg格式)转换为常见的jpg、png图片格式。实际上图片格式不止这么多,但其他图片格式进行转换基本也是类似的方法,如果今后大家需要这类问题可以试试...
重点:this transform will normalize each channel of the inputtorch.*Tensori.e.,output[channel] = (input[channel] - mean[channel]) / std[channel] 这里我们也可以通过numpy来实现操作。 这里利用到了广播机制,可以参考这里:https://www.cnblogs.com/jiaxin359/p/9021726.html ...
将PIL对象转换为Tensor 借助torchvision.transforms 即可很方便地实现这个转换,之后再调整一下对应的维度即可 from torchvision import transforms from PIL import Image img = Image.open('data/cars/img1/0021.jpg') loader = transforms.Compose([ transforms.ToTensor() ]) img_tensor = loader(img) img_tensor...
classSaveFeatures():def__init__(self, module):self.hook= module.register_forward_hook(self.hook_fn) def hook_fn(self, module, input, output): self.features = torch.tensor(output,requires_grad=True).cuda() def close(self): self.hook.remove() 当执行 hook 时,调用方法 hook_fn。hook_fn...