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<p><strong><em>1.tensorflow的存在价值</em></strong></p>
<p>1.tensorflow是目前深度学习框架使用最广泛，开源社区维护最多的一个框架，并且广泛应用工业。与tensorflow并驾的是pytorch框架，其主要用于学术研究</p>
<p>2.不可否认，深度学习主要任务就是通过大量样本进行学习特征。运用特征进行分类，分割或检测</p>
<p>3.学习特征的过程依赖于学习目标，即通过不断学习降低损失函数值从而提高分数。</p>
<p><strong><em>2.剖析tensorflow内核动机</em></strong></p>
<p>1.自tensorflow2.0发布，tensorflow1.0与tensorflow2.0几乎是两个深度学习框架，许多函数被废弃</p>
<p>2.个人复现paper时候，出现大量不同版本的tensorflow，很难去理解清楚</p>
<p>3.tensorflow主要用python完成，由于语言陌生问题，不易使用</p>
<p><strong><em>3.tensorflow源码目录</em></strong></p>
<p><img src="../images/tensorflow/source_code.jpg"></p>
<p>源目录当中3个文件夹：tensorflow，third_party，tools其中tensorflow存放源代码，至于third_party,tools与部署环境有关的文件</p>
<p>进入tensorflow源码文件夹</p>
<p><img src="../images/tensorflow/tf_source.jpg"></p>
<p>一共两类文件夹：</p>
<ul>
<li><p>以语言命名c，cc，go，java，js，python都是在不同语言的编译源码</p>
</li>
<li><p>功能文件，compiler编译器，core核心代码，example实例程序</p>
</li>
</ul>
<hr>
<p><strong><em>4.tensorflow core核心代码</em></strong></p>
<p>tensorflow中几乎所有的代码全部封装在core文件目录，core也是tensorflow最重要的部分，目录如下：</p>
<p><img src="../images/tensorflow/tf_core.jpg"></p>
<ul>
<li>common_runtime包含tensorflow的session通用逻辑流程,包括对内存，驱动的控制</li>
<li>distributed_runtime 包含tensorflow 与分布式相关的执行逻辑</li>
<li>framework对tensorflow进行计算过程中的通用组件进行定义和实现</li>
<li>graph tensorflow，定义图相关的逻辑</li>
<li>kernels是tensorflow中各个单步操作的具体实现</li>
</ul>
<p><u>在上述core文件外，python文件目录是最重要的文件目录，里边封装几乎所有的且可以应用的神经网络以及各种网络搭建和训练</u></p>
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<p>tensorflow2.0的数据集共有8个</p>
<p><img src="../images/datasets.jpg"></p>
<p>1.波斯顿房价数据boston_housing.py</p>
<p><img src="../images/datasets/boston_housing.jpg"></p>
<p>2.图像分类cifar10数据集</p>
<p><img src="../images/datasets/cifar10.jpg"></p>
<p>调用关键函数get_file()来自keras.util中的data_utils</p>
<p><img src="../images/datasets/get_file.jpg"></p>
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<p>在opengl渲染过程中，通常使用外部模型，这里介绍的插件assimp，该插件可以加载.obj,.3ds等各个模型。</p>
<p><img src="../images/assimp_structure.jpg"></p>
<p>上图对应scene的数据结构。</p>
<p>assimp中定义的scene为aiScene,如下图所示</p>
<p><img src="../images/scene_code.jpg"></p>
<p>对于一个基本的物体显示而言我们只关注mMeshes,mMaterials,mRootNode即可。</p>
<p>**加载模型过程:**（即他人模型转化成自己的数据)</p>
<ol>
<li>使用assimp插件读取模型转化成scene对象</li>
<li>从scene对象的根节点开始，遍历所有节点的网格数据</li>
<li>在网格数据中提取顶点，法向量，索引信息，材质信息</li>
<li>将所有数据保存为自己的数据结构中</li>
<li>对数据进行操作，然后又转换成模型进行显示</li>
</ol>
<p><strong>分析</strong></p>
<ul>
<li>加载模型：会得到一个aiScene</li>
<li>遍历所有的node</li>
<li>对每一个node进行绘制即可，我们可以将node看成一个小的场景</li>
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<a href="/2020/09/24/feature-detect/" class="post-title-link" itemprop="url">图像处理--油画,漫画滤镜</a>
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<p><strong>概述：</strong></p>
<p>油画：<img src="../images/oil.jpg"></p>
<hr>
<p><strong>油画算法：</strong></p>
<ol>
<li>确定核大小，并获得图像的灰度图</li>
<li>统计灰度图卷积核范围框内的像素等级</li>
<li>将等级最多的像素的像素值获取平均值</li>
<li>当前像素值设置为平均值</li>
</ol>
<p><strong>关键代码：</strong></p>
<p>[^附加第三方库：opencv，使用其中cvtColor,canny,以及访问像素的方法Mat.at]: </p>
<p>灰度图：</p>
<figure class="highlight cpp"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">cvtColor(srcImgae,gray,CV_BGR2GRAY);</span><br></pre></td></tr></table></figure>
<p>统计等级：</p>
<figure class="highlight cpp"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">for</span>(i=<span class="number">0</span>;i<core_size;i++){</span><br><span class="line"> <span class="keyword">for</span>(<span class="keyword">int</span> j=<span class="number">0</span>;j<core_size;j++){</span><br><span class="line"> <span class="keyword">int</span> level = <span class="keyword">int</span>(gray.at<uchar>(y+j-core_size/<span class="number">2</span>,x+i-core_size/<span class="number">2</span>)/level_len);</span><br><span class="line"> }</span><br><span class="line">}</span><br></pre></td></tr></table></figure>
<p><strong>漫画算法</strong></p>
<ol>
<li>获得原图像的油画模板图</li>
<li>获得原图像的边缘图并求反，因为通常边缘图边缘表现白色为255，而我们需要用黑色表示边缘线</li>
<li>将边缘图归一化为0-1，以至于能够作为运算的权值</li>
<li>将1和2的图像进行点乘运算，得到目标图像</li>
</ol>
<p>效果：</p>
<p><img src="../images/cartoon.jpg"></p>
<p><strong>关键代码：</strong></p>
<p>边缘图</p>
<figure class="highlight cpp"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br></pre></td><td class="code"><pre><span class="line">cvtColor(src,gray,CV_BGR2GRAY);</span><br><span class="line">canny(gray,gray,<span class="number">100</span>,<span class="number">255</span>,<span class="number">3</span>,<span class="literal">false</span>);</span><br><span class="line">gray = <span class="number">255</span> - gray;</span><br><span class="line">gray.convertTo(gray,CV_32FC1);</span><br><span class="line">gray = gray / <span class="number">255</span>;</span><br></pre></td></tr></table></figure>
<p> 卡通图</p>
<figure class="highlight cpp"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">dst.at<Vec3b>(y,x)[c] = oil.at<Vec3b>(y,x)[c] * gray.at<<span class="keyword">float</span>>(y,x);</span><br></pre></td></tr></table></figure>
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