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<a class="post-title-link" href="/2019/07/09/在jupyter-notebook里添加R-kernel/" itemprop="url">在jupyter notebook里添加R kernel</a></h1>
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<h2 id="1、Installing-via-CRAN"><a href="#1、Installing-via-CRAN" class="headerlink" title="1、Installing via CRAN"></a>1、Installing via CRAN</h2><p>系统环境：windows 7</p>
<p><code>install.packages('IRkernel')</code></p>
<h2 id="2、-Making-the-kernel-available-to-Jupyter"><a href="#2、-Making-the-kernel-available-to-Jupyter" class="headerlink" title="2、 Making the kernel available to Jupyter"></a>2、 Making the kernel available to Jupyter</h2><p><code>IRkernel::installspec()</code></p>
<p><code>IRkernel::installspec(user = FALSE)</code></p>
<p>参考文献：</p>
<p><<a href="https://irkernel.github.io/installation/" target="_blank" rel="noopener">https://irkernel.github.io/installation/</a>></p>
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<a class="post-title-link" href="/2019/07/02/jupyter-notebook修改默认启动目录/" itemprop="url">jupyter notebook修改默认启动目录</a></h1>
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<p> 在windows系统中第一次使用Jupyter Notebook时，它默认打开windows的C盘目录，并以此作为它的默认工作目录。为避免后期开发Jupyter Notebook生成的相关文件消耗C盘存储空间，我们需要修改它的默认工作目录。</p>
<h1 id="Jupyter-Notebook-默认工作界面"><a href="#Jupyter-Notebook-默认工作界面" class="headerlink" title="Jupyter Notebook 默认工作界面"></a>Jupyter Notebook 默认工作界面</h1><p><img src="/images/jupyterNotebookDefault.png" alt></p>
<h1 id="生成配置文件"><a href="#生成配置文件" class="headerlink" title="生成配置文件"></a>生成配置文件</h1><p>打开Anaconda Prompt，输入如下命令：</p>
<p><code>jupyter notebook --generate-config</code></p>
<p><img src="/images/jupyterNotebookConfig.png" alt></p>
<h1 id="修改配置文件"><a href="#修改配置文件" class="headerlink" title="修改配置文件"></a>修改配置文件</h1><p>打开jupyter_notebook_config.py文件，全局搜索notebook_dir，并修改其内容，得到下图</p>
<p><img src="/images/jupyter_notebook修改默认启动目录3.png" alt></p>
<h2 id="修改JupyterNotebook快捷方式的目标属性"><a href="#修改JupyterNotebook快捷方式的目标属性" class="headerlink" title="修改JupyterNotebook快捷方式的目标属性"></a>修改JupyterNotebook快捷方式的目标属性</h2><p>右击JupyterNotebook快捷方式，选择【属性】，删除【目标】属性中的【%USERPROFILE%】，点击【应用】–【确定】。</p>
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<h1 id="cv-goodFeaturesToTrack"><a href="#cv-goodFeaturesToTrack" class="headerlink" title="cv.goodFeaturesToTrack()"></a>cv.goodFeaturesToTrack()</h1><p>与Harris Corner Detector 相比显示出更好的结果</p>
<p><code>corners = cv.goodFeaturesToTrack( image, maxCorners, qualityLevel, minDistance[, corners[, mask[, blockSize[, useHarrisDetector[, k]]]]] )</code></p>
<h2 id="参数"><a href="#参数" class="headerlink" title="参数"></a>参数</h2><ul>
<li><p>image：输入8-bit或者浮点32位单通道图像</p>
</li>
<li><p>corners：检测到的角落的输出向量</p>
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<li><p>maxCorners：要返回的最大角数。如果角落多于找到的角落，则返回最大的角落。maxCorners<=0意味着不设置最大值限制并返回所有检测到的角点</p>
</li>
<li>qualityLevel：表征图像角落的最小可接受质量。参数值乘以最佳拐角质量度量，即最小特征值或Harris函数响应。质量测量小于产品的角落被拒绝。例如，如果最佳角点的质量度量为2000，质量等级为0.01，则质量度量小于15的所有角落都被拒绝。</li>
<li>minDistance：返回角落的最小可能欧几里德距离</li>
<li>mask：可选的感兴趣区域。如果图像不为空（它需要类型为CV_8UC1冰盒输入图像一样的大小），则它指定检测的角区域</li>
<li>BLOCKSIZE：用于计算每个像素领域上的derivative covariation matrix 的平均块大小</li>
<li>useHarrisDetector：指示是否使用Harris检测器</li>
<li>k：Harris检测器的自由参数</li>
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<a class="post-title-link" href="/2019/05/14/cv-cornerHarris-函数/" itemprop="url">cv.cornerHarris()函数</a></h1>
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<h2 id="哈里斯边角侦测"><a href="#哈里斯边角侦测" class="headerlink" title="哈里斯边角侦测"></a>哈里斯边角侦测</h2><p>哈里斯边角侦测（Harris Corner Detector）是被广泛运用在电脑死机的演算法，主要是用于从影像中找出代表边角的特征点。</p>
<h2 id="概要介绍"><a href="#概要介绍" class="headerlink" title="概要介绍"></a>概要介绍</h2><p> 角落的概念是它相邻的区域有两条截然不同方向的边，换句话说，角落也是两条边的连接点，而这条边的附近有剧烈的亮度变化。</p>
<p> 为了找出影响中的边角，科学家们提出了不同种的边角测试器包含Kanade-Lucas-Tomasi（KLT）算子，哈里斯算子是其中最简单，有效，及可信赖的方法。这两种受欢迎的方法均是以局部结构矩阵来当作基础，想较于Kanade-Lucas-Tomasi（KLT）边角侦测，就算是影响经过旋转或者是亮度的调整，哈里斯边角侦测具有良好的结果重现性。</p>
<h2 id="函数"><a href="#函数" class="headerlink" title="函数"></a>函数</h2><p><code>dst=cv.cornerHarris(src, blockSize, ksize, k[, dst[, borderType]])</code></p>
<h3 id="参数"><a href="#参数" class="headerlink" title="参数"></a>参数</h3><ul>
<li>src：输入图像（应该是灰度和float32类型）</li>
<li>dst：存储Harris探测器响应的图像，类型为CV_32FC1，图像大小与src相同</li>
<li>blockSize：领域大小</li>
<li>ksize：Sobel算子的孔径参数</li>
<li>k：哈里斯探测器自由参数</li>
<li>borderType：像素外推法</li>
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<figure class="highlight plain"><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><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br></pre></td><td class="code"><pre><span class="line">import cv2</span><br><span class="line">import numpy as np</span><br><span class="line"></span><br><span class="line">filename = 'chessboard.png'</span><br><span class="line">img = cv2.imread(filename)</span><br><span class="line"></span><br><span class="line">gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)</span><br><span class="line"></span><br><span class="line">gray = np.float32(gray)</span><br><span class="line">dst = cv2.cornerHarris(gray,2,3,0.04)</span><br><span class="line"></span><br><span class="line"></span><br><span class="line"># result is dilated for marking the corners, not important</span><br><span class="line">dst = cv2.dilate(dst,None)</span><br><span class="line"></span><br><span class="line"># Threshold for an optimal value, it may vary depending on the image</span><br><span class="line">img[dst>0.01*dst.max()]=[0,0,255]</span><br><span class="line"></span><br><span class="line">cv2.imshow('dst',img)</span><br><span class="line">if cv2.waitKey(0) & Oxff == 27:</span><br><span class="line"> cv2.destroyAllWindows()</span><br></pre></td></tr></table></figure>
<p>输入图片：</p>
<p><img src="/images/chessboard.png" alt></p>
<p><img src="/images/chess1.jpg" alt></p>
<p>结果图（需要方大才看得清楚）：</p>
<p><img src="/images/chessboard_result.jpg" alt></p>
<p><img src="/images/chessboard_result1.jpg" alt></p>
<p>有时可能需要以最高精度找到角落。opencv附带了一个函数cv2.cornerSubPix()，它进一步以亚精度检测到的角点。该函数迭代以找到角点或径向鞍点的亚像素精度位置</p>
<figure class="highlight plain"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">corners = cv.cornerSubPix( image, corners, winSize, zeroZone, criteria )</span><br></pre></td></tr></table></figure>
<ul>