HeatMap attention 高亮显示

原创已于 2023-12-24 14:55:25 修改 · 2k 阅读

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#计算机视觉

于 2018-04-04 22:04:29 首次发布

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本文介绍如何使用Matlab进行注意力矩阵的可视化处理，包括文本注意力热图的绘制及图像注意力区域的高亮显示。通过具体代码实例展示了如何加载注意力矩阵、调整注意力权重并将其应用于图像上，最终实现对特定文本词汇的关注度可视化。

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%load attention matrix,   h_img,img_h,p_h,h_p   9824*49*49
clear;
load('attention.mat');
%parameters
example_index = 2734;% matlab array index start from 1
h_word_index = 3;%the index of the key word in hypothesis
end_index =11;%the length of premise sentence

text_attention = h_p(example_index,h_word_index,1:end_index);
text_attention = reshape(text_attention,1,end_index);
text_attention = mapminmax(text_attention, -0.4, 1);
h = HeatMap(text_attention,'Colormap',gray);


image_name = '4455622662.jpg';

image_example = imread(image_name);
imshow(image_example);

image_size = size(image_example);

width = image_size(1,1);
height = image_size(1,2);

% attention resize
% image_attention = h_img(example_index,h_word_index,:);% keyword attention attention 1*1*49
% image_attention = reshape(image_attention,7,7)';      %7*7
% image_attention_norm_ori = mapminmax(image_attention, 0, 1);


load('img_norm_light.mat');

image_attention_norm = imresize(image_attention_norm_ori, [width height]);


% FlattenedData = image_attention(:)'; % 展开矩阵为一列，然后转置为一行。
% MappedFlattened = mapminmax(FlattenedData, 0, 1); % 归一化。
% image_attention_norm_ori = reshape(MappedFlattened, size(image_attention));
% image_attention_norm = imresize(image_attention_norm_ori, [width height]);
%heatmap
%imshow(image_attention_norm);
hmap = HeatMap(image_attention_norm);

im_cam_tri = zeros(width, height, 3);

im_cam_tri(:, :, 1) = image_attention_norm * 255;
im_cam_tri(:, :, 2) = image_attention_norm * 255;
im_cam_tri(:, :, 3) = image_attention_norm * 255;

% for k=1:3
% im_cam_tri(:,:,k)=flipud(im_cam_tri(:,:,k));%上下翻转
% end


new_image = double(0.05*image_example) + 0.95*(im_cam_tri);

figure;
imshow(new_image/255);

% new_image = double(image).* (im_cam_tri > 200);
% imshow(new_image/255);