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OpenCV图像处理篇之采样金字塔.html
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<h1>OpenCV图像处理篇之采样金字塔</h1>
<h4>2014-09-20 / xiahouzuoxin</h4>
<h4>Tags: OpenCV</h4>
转载请注明出处: <a href="http://xiahouzuoxin.github.io/notes/">http://xiahouzuoxin.github.io/notes/</a>
<div id="TOC">
<ul>
<li><a href="#图像金字塔">图像金字塔</a></li>
<li><a href="#程序分析">程序分析</a></li>
<li><a href="#结果显示">结果显示</a></li>
</ul>
</div>
<!---title:OpenCV图像处理篇之采样金字塔-->
<!---keywords:OpenCV-->
<!---date:2014-09-20-->
<h2 id="图像金字塔">图像金字塔</h2>
<p>图像金字塔是通过将原始图像经过平滑、下采样所生成一系列具有不同分辨率的图像的集合。金字塔结构(Pyramid)适于多分辨率处理的一种图像存储数据结构。</p>
<p>最常用的生成图像金字塔的方法是采用高斯函数平滑图像，每次将分辨率降低为原来的一半，由此得到一个图像序列{ML，ML-1，……，M0}，图像金字塔的存储量为<code>N^2*(1+1/4+1/16+...)=(4*N^2)/3</code>。</p>
<div class="figure">
<img src="../images/OpenCV图像处理篇之采样金字塔/example.png" alt="example" /><p class="caption">example</p>
</div>
<p>如上图：最右边为原始图像，从右到左依次为使用高斯平滑1次、2次和3次后的图像，这些图像共同组成了图像金字塔。</p>
<p>图像金字塔这种结构在图像处理中有广泛的用途。最著名的特征匹配算子SIFT就是通过构造图像金字塔来完成的。有关图像金字塔在SIFT特征提取算法中的应用可参见Rachel Zhang的博文<a href="http://blog.csdn.net/abcjennifer/article/details/7639681">"SIFT特征提取分析"</a>。</p>
<h2 id="程序分析">程序分析</h2>
<pre class="sourceCode c"><code class="sourceCode c"><span class="co">/*</span>
<span class="co"> * FileName : pyramids.cpp</span>
<span class="co"> * Author : xiahouzuoxin @163.com</span>
<span class="co"> * Version : v1.0</span>
<span class="co"> * Date : Sat 20 Sep 2014 07:04:29 PM CST</span>
<span class="co"> * Brief : </span>
<span class="co"> * </span>
<span class="co"> * Copyright (C) MICL,USTB</span>
<span class="co"> */</span>
<span class="ot">#include <iostream></span>
<span class="ot">#include "cv.h" </span>
<span class="ot">#include "highgui.h"</span>
<span class="ot">#include "opencv2/imgproc/imgproc.hpp"</span>
using namespace std;
using namespace cv;
<span class="dt">const</span> <span class="dt">char</span> *wn = <span class="st">"Pyramids Demo"</span>;
<span class="dt">int</span> main(<span class="dt">int</span> argc, <span class="dt">char</span> *argv[])
{
<span class="kw">if</span> (argc < <span class="dv">2</span>) {
cout<<<span class="st">"Usage: ./pyramids [file name]"</span><<endl;
<span class="kw">return</span> -<span class="dv">1</span>;
}
Mat src = imread(argv[<span class="dv">1</span>]);
<span class="kw">if</span> (!src.data) {
cout<<<span class="st">"Error: read image error."</span><<endl;
<span class="kw">return</span> -<span class="dv">1</span>;
}
<span class="co">/* Size of input image must be 2^n */</span>
<span class="kw">if</span> ( src.cols & (src.cols<span class="dv">-1</span>) ) { <span class="co">// src.cols > 0 first</span>
cout<<<span class="st">"Error: input image's column must be 2^n"</span><<endl;
<span class="kw">return</span> -<span class="dv">1</span>;
}
<span class="kw">if</span> ( src.rows & (src.rows<span class="dv">-1</span>) ) { <span class="co">// src.cols > 0 first</span>
cout<<<span class="st">"Error: input image's row must be 2^n"</span><<endl;
<span class="kw">return</span> -<span class="dv">1</span>;
}
cout<<<span class="st">"User Guide:"</span><<endl;
cout<<<span class="st">"---------------------"</span><<endl;
cout<<<span class="st">"u -> Zoom out"</span><<endl;
cout<<<span class="st">"d -> Zoom in"</span><<endl;
cout<<<span class="st">"ESC -> Exit program"</span><<endl;
namedWindow(wn, WINDOW_AUTOSIZE);
imshow(wn, src);
Mat cur = src;
Mat dst = cur;
<span class="dt">int</span> end_while = <span class="dv">0</span>;
<span class="kw">while</span>(!end_while) {
<span class="dt">char</span> c;
c = waitKey(<span class="dv">10</span>);
<span class="kw">switch</span> (c) {
<span class="kw">case</span> <span class="dv">27</span>: <span class="co">/* ESC */</span>
end_while = <span class="dv">1</span>;
<span class="kw">break</span>;
<span class="kw">case</span> 'u':
pyrUp(cur, dst, Size(cur.cols*<span class="dv">2</span>, cur.rows*<span class="dv">2</span>));
imshow(wn, dst);
cur = dst;
<span class="kw">break</span>;
<span class="kw">case</span> 'd':
pyrDown(cur, dst, Size(cur.cols/<span class="dv">2</span>, cur.rows/<span class="dv">2</span>));
imshow(wn, dst);
cur = dst;
<span class="kw">break</span>;
<span class="kw">default</span>:
<span class="kw">break</span>;
}
}
}</code></pre>
<ol style="list-style-type: decimal">
<li><p>使用高斯图像金字塔进行降采样和插值的函数分别是<code>pyrDown</code>和<code>pyrUp</code>，参数依次为原图像、采样结果图像、采样后的图像尺寸。</p></li>
<li><p>上述程序中的降采样操作因为都是按2为倍数进行的，因此要求输入图像的长宽都必须是2<sup>n。<code>if ( src.cols & (src.cols-1) )</code>是用来判断原图像的列是否为2</sup>n的语句。请仔细体会这种判断某个数是否是2^n的方法——x*(x-1)返回0表示x是2^n，否则不是。</p></li>
</ol>
<h2 id="结果显示">结果显示</h2>
<p>下面一系列图片展现的是先将原图像通过图像金字塔降采样（会存在数据丢失），再通过金字塔插值恢复图像过程中图像的变化过程。由于降采样过程中存在数据丢失，所以可以看到恢复到原图像大小后的图像比原图像模糊。</p>
<div class="figure">
<img src="../images/OpenCV图像处理篇之采样金字塔/origin.png" alt="origin" /><p class="caption">origin</p>
</div>
<p><strong>图注</strong> 原图像</p>
<div class="figure">
<img src="../images/OpenCV图像处理篇之采样金字塔/div2.png" alt="div2" /><p class="caption">div2</p>
</div>
<p><strong>图注</strong> 1次降采样后的图像</p>
<div class="figure">
<img src="../images/OpenCV图像处理篇之采样金字塔/div4.png" alt="div4" /><p class="caption">div4</p>
</div>
<p><strong>图注</strong> 2次降采样后的图像</p>
<div class="figure">
<img src="../images/OpenCV图像处理篇之采样金字塔/MUL2.png" alt="MUL2" /><p class="caption">MUL2</p>
</div>
<p><strong>图注</strong> 2次降采样后，再经过1次图像金字塔插值操作后的图像，大小与1次降采样后图像相同，但变得模糊</p>
<div class="figure">
<img src="../images/OpenCV图像处理篇之采样金字塔/MUL4.png" alt="MUL4" /><p class="caption">MUL4</p>
</div>
<p><strong>图注</strong> 2次降采样后，再经过2次图像金字塔插值操作后的图像，大小与原图相同，但变得模糊</p>
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