I1=imreadbw([img1_dir img1_file]) ; I1_rgb =imresize(I1_rgb, [240 320]); I1=I1-min(I1(:)) ; I1=I1/max(I1(:)) ; 什么意思
时间: 2024-05-24 18:11:29 浏览: 10
这是一段MATLAB代码,作用是读取一张灰度图像并将其转换为RGB格式,然后将其大小调整为240x320像素。接着对图像进行了归一化处理,将其像素值缩放到0~1之间。具体解释如下:
- imreadbw:读取灰度图像。
- img1_dir和img1_file:指定图像文件的路径和名称,需要根据具体情况进行修改。
- imresize:调整图像大小。
- I1_rgb:将灰度图像转换为RGB格式。
- I1-min(I1(:))和I1/max(I1(:)):对图像进行归一化处理,将像素值缩放到0~1之间。其中min(I1(:))表示图像中最小的像素值,max(I1(:))表示图像中最大的像素值。
相关问题
I1=imreadbw([img1_dir img1_file]) ; I1_rgb = imread([img1_dir img1_file]) ; I1=imresize(I1, [240 320]);什么意思
这段代码的作用是从一个指定路径读取一张图片文件,然后对其进行处理,具体如下:
1. imreadbw([img1_dir img1_file]) 读取一张黑白图片,并将其存储在变量I1中。
2. imread([img1_dir img1_file]) 读取一张彩色图片,并将其存储在变量I1_rgb中。
3. imresize(I1, [240 320]) 将变量I1的大小调整为240x320,这里是将其缩小了。
总的来说,这段代码的作用是将一张图片读取并调整大小,以便后续处理。
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。
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