matlab img_size(1)

在 MATLAB 中，`img_size` 可能是一个包含图像尺寸信息的向量或数组，其中 `img_size(1)` 表示图像的行数，即图像的高度。这是因为在 MATLAB 中，图像的行数排列在第一维，列数排列在第二维。因此，可以使用 `img_size(1)` 来获取图像的高度信息，例如：

img = imread('image.jpg'); % 读取图像
img_size = size(img); % 获取图像尺寸信息
height = img_size(1); % 获取图像高度

其中，`imread` 函数用于读取图像，`size` 函数用于获取图像的尺寸信息，`img_size(1)` 获取图像的高度信息并赋值给 `height` 变量。

相关问题

function [one_feat_sps, weight_pool_info]=do_sp_pooling(one_feat_img, one_sp_info) img_size=size(one_feat_img); num_units=img_size(1)*img_size(2); dim=img_size(3); one_feat_img=reshape(one_feat_img, [num_units dim]); img_size_org=one_sp_info.img_size; pixel_ind_map=reshape([1: num_units], [img_size(1) img_size(2)]); pixel_ind_map_org=imresize(pixel_ind_map, img_size_org, 'nearest'); pixel_ind_sps=one_sp_info.pixel_ind_sps; num_sp=numel(pixel_ind_sps); weight_pool_info=zeros([num_sp, num_units], 'like', one_feat_img); for idx_sp=1:num_sp pixel_ind_sp_one=pixel_ind_sps{idx_sp}; ind_pixels_in_map=pixel_ind_map_org(pixel_ind_sp_one); [ind_units,~,uniqueIndex] = unique(ind_pixels_in_map); frequency = accumarray(uniqueIndex(:),1)./numel(ind_pixels_in_map); frequency=single(frequency); freq_one_sp=zeros(1, num_units, 'single'); freq_one_sp(ind_units)=frequency; weight_pool_info(idx_sp, :)=freq_one_sp; end one_feat_sps=weight_pool_info*one_feat_img; end将上述代码转换为pytorch代码，并可视化超像素的索引】

以下是将给定的 MATLAB 代码转换为 PyTorch 代码的过程：

import torch
import torch.nn.functional as F
import numpy as np
from scipy import ndimage

def do_sp_pooling(one_feat_img, one_sp_info):
    img_size = one_feat_img.shape
    num_units = img_size[0] * img_size[1]
    dim = img_size[2]
    one_feat_img = one_feat_img.reshape(num_units, dim)
    img_size_org = one_sp_info['img_size']
    pixel_ind_map = np.arange(num_units).reshape(img_size[0], img_size[1])
    pixel_ind_map_org = ndimage.zoom(pixel_ind_map, [img_size_org[0]/img_size[0], img_size_org[1]/img_size[1]], order=0)
    pixel_ind_sps = one_sp_info['pixel_ind_sps']
    num_sp = len(pixel_ind_sps)
    weight_pool_info = torch.zeros((num_sp, num_units), dtype=one_feat_img.dtype, device=one_feat_img.device)
    for idx_sp in range(num_sp):
        pixel_ind_sp_one = pixel_ind_sps[idx_sp]
        ind_pixels_in_map = pixel_ind_map_org[pixel_ind_sp_one]
        _, uniqueIndex = np.unique(ind_pixels_in_map, return_inverse=True)
        frequency = np.bincount(uniqueIndex) / len(ind_pixels_in_map)
        frequency = frequency.astype(one_feat_img.dtype)
        freq_one_sp = torch.zeros(num_units, dtype=one_feat_img.dtype, device=one_feat_img.device)
        freq_one_sp[ind_pixels_in_map] = torch.tensor(frequency, dtype=one_feat_img.dtype, device=one_feat_img.device)
        weight_pool_info[idx_sp, :] = freq_one_sp
    one_feat_sps = torch.mm(weight_pool_info, one_feat_img)
    return one_feat_sps, weight_pool_info

这里我们使用了 PyTorch 库中的相关函数来实现对应功能，同时为了可视化超像素的索引，我们可以在函数中增加一些代码：

def do_sp_pooling(one_feat_img, one_sp_info):
    img_size = one_feat_img.shape
    num_units = img_size[0] * img_size[1]
    dim = img_size[2]
    one_feat_img = one_feat_img.reshape(num_units, dim)
    img_size_org = one_sp_info['img_size']
    pixel_ind_map = np.arange(num_units).reshape(img_size[0], img_size[1])
    pixel_ind_map_org = ndimage.zoom(pixel_ind_map, [img_size_org[0]/img_size[0], img_size_org[1]/img_size[1]], order=0)
    pixel_ind_sps = one_sp_info['pixel_ind_sps']
    num_sp = len(pixel_ind_sps)
    weight_pool_info = torch.zeros((num_sp, num_units), dtype=one_feat_img.dtype, device=one_feat_img.device)
    for idx_sp in range(num_sp):
        pixel_ind_sp_one = pixel_ind_sps[idx_sp]
        ind_pixels_in_map = pixel_ind_map_org[pixel_ind_sp_one]
        _, uniqueIndex = np.unique(ind_pixels_in_map, return_inverse=True)
        frequency = np.bincount(uniqueIndex) / len(ind_pixels_in_map)
        frequency = frequency.astype(one_feat_img.dtype)
        freq_one_sp = torch.zeros(num_units, dtype=one_feat_img.dtype, device=one_feat_img.device)
        freq_one_sp[ind_pixels_in_map] = torch.tensor(frequency, dtype=one_feat_img.dtype, device=one_feat_img.device)
        weight_pool_info[idx_sp, :] = freq_one_sp
        # 可视化超像素的索引
        img_sp = np.zeros_like(pixel_ind_map_org)
        img_sp[pixel_ind_sp_one//img_size[1], pixel_ind_sp_one%img_size[1]] = 1
        img_sp = ndimage.binary_dilation(img_sp, iterations=1)
        img_sp = np.where(img_sp, idx_sp+1, 0)
        img_sp = ndimage.zoom(img_sp, [img_size[0]/img_size_org[0], img_size[1]/img_size_org[1]], order=0)
        plt.imshow(img_sp, cmap='jet', alpha=0.3, vmin=0, vmax=num_sp)
    one_feat_sps = torch.mm(weight_pool_info, one_feat_img)
    return one_feat_sps, weight_pool_info

这里我们使用 matplotlib 库来绘制可视化结果，以 jet 颜色映射来表示超像素的索引。

img = imread('4.png'); [M , N] = size(img); img_result = zeros(M, N); muban_size = 3; expand_size = floor(muban_size / 2) muban = 1 / (muban_size * muban_size) .* ones(muban_size, muban_size); expand_img = double(wextend('2D','zpd', img, expand_size)); for i=1:M for j=1:N ave = sum( sum( expand_img(i:i+muban_size-1,j:j+muban_size-1) .* muban)); img_result(i,j) = ave; end end img_result = uint8(img_result); subplot(1 ,2, 1); title('原图像') imshow(img) subplot(1 ,2, 2); imshow(img_result) da = ['模板大小为' num2str(muban_size) '，变化后的图像']; title(da)

这段代码是用MATLAB实现的图像均值滤波。首先用imread函数读取名为“4.png”的图像，并获取其大小。然后定义一个模板大小muban_size，这里为3，再计算出模板的扩展大小expand_size（向下取整）。接着定义一个大小为muban_size × muban_size的模板muban，其每个元素的值都为1 / (muban_size × muban_size)。然后用wextend函数将图像进行扩展，将边界进行填充，使得可以进行卷积。接下来用两个for循环遍历原图像的每一个像素，并计算其周围muban_size × muban_size个像素的均值。最后将得到的结果存入img_result中，并将其显示出来。最后的标题显示了模板大小和变化后的图像。