a tutorial about how to extract patches from a large image and to rebuild the original image from the extracted patches(其实主要是为了应对内存不足时的捉襟见肘。。。土豪请略过) toc: true badges: true comments: true sticky_rank: 3 author: Bowen categories: [pytorch, fastai2] tensor unfold ...
importnumpyasnpfromPILimportImagedefextract_patches(image,patch_size):"""从给定图像中提取 Patch"""width,height=image.size patches=[]# 遍历图像并提取 Patchforiinrange(0,width,patch_size):forjinrange(0,height,patch_size):box=(i,j,min(i+patch_size,width),min(j+patch_size,height))patch=i...
transforms.CenterCrop(image_size), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), ])) # Create the dataloader dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True, num_workers=workers) # Decide...
# Read the patch data and create a list of patches and a list of corresponding labelsdataset_path = global_save_dir / "kather100k-validation-sample"# Set the path to the datasetimage_ext = ".tif" # file extension of each image# Obtain the mapping between the label ID and the class ...
(label_path):#读取某张图片的标签信息 # 读取一张图片内的边界框:txt文件包含的边界框的坐标信息是归一化后的坐标 boxes = torch.from_numpy(np.loadtxt(label_path).reshape(-1, 5)) # Extract coordinates for unpadded + unscaled image # 将归一化后的坐标变为适应于原图片的坐标 x1 = w_factor *...
现在,我们可以定义一个函数,通过扰动原始输入来创建对抗性示例。fgsm_attack函数接受三个输入,image是原始干净图像(xx),epsilon是像素级扰动量(ϵϵ),data_grad是损失相对于输入图像的梯度(∇xJ(θ,x,y)∇xJ(θ,x,y))。然后,函数创建扰动图像如下: ...
104 ExtractImagePatches extract_patches 105 LogSoftmax reduce_max, log, reduce_sum, exp 106 Einsum einsum 107 ScatterUpdate scatter_update 108 Result Identity Output ↥ Back to top 4. Setup 4-1. [Environment construction pattern 1] Execution by Docker (strongly recommended) You do not...
defcrop_img(img, vertices, labels, length, index):'''crop img patches to obtain batch and augment Input: img : PIL Image vertices : vertices of text regions <numpy.ndarray, (n,8)> labels : 1->valid, 0->ignore, <numpy.ndarray, (n,)> ...
image_size: int. Image size. If you have rectangular images, make sure your image size is the maximum of the width and height patch_size: int. Number of patches.image_sizemust be divisible bypatch_size. The number of patches is:n = (image_size // patch_size) ** 2andnmust be grea...
super(Patches, self).__init__() self.patch_size = patch_size def call(self, images): batch_size = tf.shape(images)[0] patches = tf.image.extract_patches( images=images, sizes=[1, self.patch_size, self.patch_size, 1], strides=[1, self.patch_size, self.patch_size, 1], ...