https://simplestory.github.io/posts/Recent content in 文章 on SimplestoryHugozh-cnSimplestorySat, 17 Aug 2024 00:00:00 +0000https://simplestory.github.io/posts/20240817_d9d0d187039bec56d5fc1024c51a882c/Sat, 17 Aug 2024 00:00:00 +0000https://simplestory.github.io/posts/20240817_d9d0d187039bec56d5fc1024c51a882c/Denoising Diffusion Implicit Modelshttps://simplestory.github.io/posts/20240817_60a1868ea6b6f7b05126264f9d212a9c/Sat, 17 Aug 2024 00:00:00 +0000https://simplestory.github.io/posts/20240817_60a1868ea6b6f7b05126264f9d212a9c/Denoising Diffusion Probabilistic Modelshttps://simplestory.github.io/posts/20240817_86b1d339bba4e0c154df43f21e99fe50/Sat, 17 Aug 2024 00:00:00 +0000https://simplestory.github.io/posts/20240817_86b1d339bba4e0c154df43f21e99fe50/Latent Diffusion Modelshttps://simplestory.github.io/posts/20240811_787700a547309b9912534d7c3540b79b/Sun, 11 Aug 2024 00:00:00 +0000https://simplestory.github.io/posts/20240811_787700a547309b9912534d7c3540b79b/Generative Adversarial Networkhttps://simplestory.github.io/posts/20240811_a9423d60164f001a6715546c56c77154/Sun, 11 Aug 2024 00:00:00 +0000https://simplestory.github.io/posts/20240811_a9423d60164f001a6715546c56c77154/Varitional Autoencoderhttps://simplestory.github.io/posts/20240811_f09978b03d9ad93516b6003551892ddc/Sun, 11 Aug 2024 00:00:00 +0000https://simplestory.github.io/posts/20240811_f09978b03d9ad93516b6003551892ddc/Vector Quantized Generative Adversarial Networkhttps://simplestory.github.io/posts/20240811_520db3fb8aba368d31c1f3e5e9fbd8ba/Sun, 11 Aug 2024 00:00:00 +0000https://simplestory.github.io/posts/20240811_520db3fb8aba368d31c1f3e5e9fbd8ba/Vector Quantised Variational AutoEncoderhttps://simplestory.github.io/posts/20230624_593a3023b826e242be5651751b69d09e/Sat, 24 Jun 2023 00:00:00 +0000https://simplestory.github.io/posts/20230624_593a3023b826e242be5651751b69d09e/Lift, Splat, Shoothttps://simplestory.github.io/posts/20230623_f25271ca7acc8b883a6ebeba1a0b263d/Fri, 23 Jun 2023 00:00:00 +0000https://simplestory.github.io/posts/20230623_f25271ca7acc8b883a6ebeba1a0b263d/<p>单目3D检测在自动驾驶领域是一个重要且充满挑战的任务，目前的难点是如何准确对目标深度进行预测。MonoRCNN利用几何关系将深度估计问题解耦为目标实际高度和在图像上的投影高度问题。</p>https://simplestory.github.io/posts/20230623_e5106aece831780fe8decad391998e6a/Fri, 23 Jun 2023 00:00:00 +0000https://simplestory.github.io/posts/20230623_e5106aece831780fe8decad391998e6a/<p>在2D实时目标检测框架yolo上引入一个轻量级立体匹配模块来实现高效的3D物体检测。大致结构如下图：</p> <p><img loading="lazy" src="https://simplestory-blog-img.oss-cn-guangzhou.aliyuncs.com/in_posts/20230623/yolostereo3d_architecture.png" title="yolostereo3d_architecture"></p> <h2 id="anchors">Anchors</h2> <p>对于3D目标检测，原本yolo针对2D物体的anchors需要重新设计，作者采用了12个参数来表示一个anchor：</p>https://simplestory.github.io/posts/20230305_f73b955eec390396d75e03ababcc4909/Sun, 05 Mar 2023 00:00:00 +0000https://simplestory.github.io/posts/20230305_f73b955eec390396d75e03ababcc4909/<p>点云有着分布上稀疏、结构上不规则、几何上无序的特性，所以直接在点云上使用卷积操作可能会导致生成的特征极度地偏离它原本的表达。</p> <h1 id="voxelnet">VoxelNet</h1> <p>该模型是对点云进行体素化，然后利用3D卷积来获取特征。具体是：</p>https://simplestory.github.io/posts/20230127_2a9bce23520b48c68e00a87aa00aa611/Fri, 27 Jan 2023 00:00:00 +0000https://simplestory.github.io/posts/20230127_2a9bce23520b48c68e00a87aa00aa611/<blockquote> <p>该网络通过双目视觉来实现3D目标检测，属于3D detection中image based的result-lifting方法，这类方法就是将3D检测分解为2D object detection和depth estimation这两部分。</p> </blockquote>https://simplestory.github.io/posts/20221006_1087bec7ef12911010976d203f36fd7f/Thu, 06 Oct 2022 00:00:00 +0000https://simplestory.github.io/posts/20221006_1087bec7ef12911010976d203f36fd7f/目标检测中的标签分配问题https://simplestory.github.io/posts/20210807_1c4ff5cd7d69cb58295eab75872ba2fd/Sat, 07 Aug 2021 00:00:00 +0000https://simplestory.github.io/posts/20210807_1c4ff5cd7d69cb58295eab75872ba2fd/<blockquote> <p>PointNet、PointNet++和Frustum PointNet。常用的3D点云目标检测。</p> </blockquote> <h2 id="pointnet">PointNet</h2> <p>PointNet将深度学习直接应用于点云数据。点云数据是欧式空间下的点的一个子集，有无序性、点与点的空间关系以及不变性这三个性质。</p>https://simplestory.github.io/posts/20210806_8e5f2ccbcaba22801139e42b929ed820/Fri, 06 Aug 2021 00:00:00 +0000https://simplestory.github.io/posts/20210806_8e5f2ccbcaba22801139e42b929ed820/MV3Dhttps://simplestory.github.io/posts/20210805_59094eb979620e0e11f92ffaa71cf228/Thu, 05 Aug 2021 00:00:00 +0000https://simplestory.github.io/posts/20210805_59094eb979620e0e11f92ffaa71cf228/<blockquote> <p>当前工业界常用的多目标跟踪框架。</p> </blockquote> <h2 id="sort">SORT</h2> <p>该算法基于目标检测网络（Faster Rcnn），并利用卡尔曼滤波和匈牙利算法（或KM算法），极大地提高了多目标跟踪的速度。大致流程如下：</p>https://simplestory.github.io/posts/20210804_697dd2d087a31ba4aab4ff575bde80be/Wed, 04 Aug 2021 00:00:00 +0000https://simplestory.github.io/posts/20210804_697dd2d087a31ba4aab4ff575bde80be/动态网络头https://simplestory.github.io/posts/20210803_2d8c6e5a2f995c3808422afc8967c85e/Tue, 03 Aug 2021 00:00:00 +0000https://simplestory.github.io/posts/20210803_2d8c6e5a2f995c3808422afc8967c85e/<blockquote> <p>让它动起来。</p> </blockquote> <h2 id="dynamic-convolution">Dynamic Convolution</h2> <p>一般的静态卷积用同一个卷积核对所有输入图像做相同的操作，而动态卷积会对不同的图像做调整，用更适合的卷积参数进行处理，这样可以在不增加网络深度或宽度的情况下增加模型的表达能力。</p>https://simplestory.github.io/posts/20210516_5df2f4dc23f490c5d68321fa51cabc59/Sun, 16 May 2021 00:00:00 +0000https://simplestory.github.io/posts/20210516_5df2f4dc23f490c5d68321fa51cabc59/条件随机场https://simplestory.github.io/posts/20210430_b30ab79aff271e301dde9ca18c2e8085/Fri, 30 Apr 2021 00:00:00 +0000https://simplestory.github.io/posts/20210430_b30ab79aff271e301dde9ca18c2e8085/<blockquote> <p>图像分割是对图像进行像素级的分类，主要有语义分割(Semantic Segmentation)和实例分割(Instance Segmentation)。语义分割只要求做到像素层面的分割，而实例分割要求在语义分割的基础上实现对不同个体的分割。这篇文章主要简单介绍几种基础的语义分割网络。</p> </blockquote>https://simplestory.github.io/posts/20201120_0595a0de31914bca4c526fb50d30b2dd/Fri, 20 Nov 2020 00:00:00 +0000https://simplestory.github.io/posts/20201120_0595a0de31914bca4c526fb50d30b2dd/Object Detection in 20 Yearshttps://simplestory.github.io/posts/20201025_93f382c06f3476ad1f59351e0d99f842/Sun, 25 Oct 2020 00:00:00 +0000https://simplestory.github.io/posts/20201025_93f382c06f3476ad1f59351e0d99f842/Objects as Pointshttps://simplestory.github.io/posts/20201018_25c3043509dadf35f8ecc40adf5a369c/Sun, 18 Oct 2020 00:00:00 +0000https://simplestory.github.io/posts/20201018_25c3043509dadf35f8ecc40adf5a369c/some loss functionhttps://simplestory.github.io/posts/20201003_fc3691951ecd38cc130a442bcc0566ed/Sat, 03 Oct 2020 00:00:00 +0000https://simplestory.github.io/posts/20201003_fc3691951ecd38cc130a442bcc0566ed/EfficientX basic familyhttps://simplestory.github.io/posts/20200926_ffb198e96e96a613e471e2f2eb1ae86c/Sat, 26 Sep 2020 00:00:00 +0000https://simplestory.github.io/posts/20200926_ffb198e96e96a613e471e2f2eb1ae86c/<blockquote> <p>IoU是目标检测里的一个重要指标，它是通过计算预测框与真实框的交集和并集的比值来衡量预测框的优劣。但通常的预测框调整函数一般采用的是L2范数，即以MSE的方式来计算损失，而目前来看，一些研究表明这并非最优的优化函数，所以就有了IOU损失函数。</p> </blockquote>https://simplestory.github.io/posts/20200426_02a6a4457d33491491495f7fe2b383d1/Sun, 26 Apr 2020 00:00:00 +0000https://simplestory.github.io/posts/20200426_02a6a4457d33491491495f7fe2b383d1/Inverted Residuals and Linear Bottleneckshttps://simplestory.github.io/posts/20200210_ba14ebd4efe67efd08cdc6410e758633/Mon, 10 Feb 2020 00:00:00 +0000https://simplestory.github.io/posts/20200210_ba14ebd4efe67efd08cdc6410e758633/without bells and whistleshttps://simplestory.github.io/posts/20200115_1789a504337212c61f0b46c458695b7c/Fri, 17 Jan 2020 00:00:00 +0000https://simplestory.github.io/posts/20200115_1789a504337212c61f0b46c458695b7c/An Incremental Improvementhttps://simplestory.github.io/posts/20200110_aa4020065e766e48007d2b3ae229757d/Fri, 10 Jan 2020 00:00:00 +0000https://simplestory.github.io/posts/20200110_aa4020065e766e48007d2b3ae229757d/Better, Faster, Strongerhttps://simplestory.github.io/posts/20200101_83ba412c3f5a7a54968bf73cc917cd13/Wed, 01 Jan 2020 00:00:00 +0000https://simplestory.github.io/posts/20200101_83ba412c3f5a7a54968bf73cc917cd13/You Only Look Oncehttps://simplestory.github.io/posts/20191003_34df20bab5e85dc75bfc94ef569cced9/Thu, 03 Oct 2019 00:00:00 +0000https://simplestory.github.io/posts/20191003_34df20bab5e85dc75bfc94ef569cced9/Single Shot MultiBox Detectorhttps://simplestory.github.io/posts/20190922_5e8040095d43e97ccedd09771f9eab38/Sun, 22 Sep 2019 00:00:00 +0000https://simplestory.github.io/posts/20190922_5e8040095d43e97ccedd09771f9eab38/目标检测识别算法https://simplestory.github.io/posts/20190921_817fe11366b1149fdc44a5a4c92685c4/Sat, 21 Sep 2019 00:00:00 +0000https://simplestory.github.io/posts/20190921_817fe11366b1149fdc44a5a4c92685c4/经典目标检测识别算法https://simplestory.github.io/posts/20190919_13243638926ee79bc7810c80a460408c/Thu, 19 Sep 2019 00:00:00 +0000https://simplestory.github.io/posts/20190919_13243638926ee79bc7810c80a460408c/经典目标检测识别算法https://simplestory.github.io/posts/20190910_cefb8aa8290a0b4cfddf3f88bf5a2a3c/Tue, 10 Sep 2019 00:00:00 +0000https://simplestory.github.io/posts/20190910_cefb8aa8290a0b4cfddf3f88bf5a2a3c/<blockquote> <p>首先祝所有教师节日快乐，感谢你们赠人玫瑰手留余香的奉献精神。然后时隔将近一个月，我又滚回来更新博客了，之前一直在忙着入职这件事，希望能尽快融入公司开发业务中而”忙碌“了一个月。接下来继续经典图像识别网络模型的介绍。上文我们已经介绍了AlexNet和VGG网络，这两个网络都将当时的神经网络的深度发挥到了极致，后续的神经网络模型性能基本上好不了多少。这是由于随着网络的加深，在误差后向传播时，靠前的网络层权重基本得不到更新，一直维持在某一个值附近。在2015年，何凯明大神提出了一种快捷连接方式将误差传播到了前面的网络层，很大程度上提高了网络的性能。下面简单解析一下ResNet网络。</p> </blockquote>https://simplestory.github.io/posts/20190811_58bd041ca5bf7b009cdc8d07392dc061/Sun, 11 Aug 2019 00:00:00 +0000https://simplestory.github.io/posts/20190811_58bd041ca5bf7b009cdc8d07392dc061/<blockquote> <p>神经网络已经有几个归一化算法了，它们长得都很相似，基本上都有统一的形式。本文参考了知乎专栏SIGAI关于归一化算法的文章，具体链接在文章底部。</p> </blockquote>https://simplestory.github.io/posts/20190730_1036fd1af9bc59d1a811b4e2ab23d60c/Tue, 30 Jul 2019 00:00:00 +0000https://simplestory.github.io/posts/20190730_1036fd1af9bc59d1a811b4e2ab23d60c/经典的卷积神经网络https://simplestory.github.io/posts/20190722_cdebdc3cc4ecd47a04cff48d03a688d3/Mon, 22 Jul 2019 00:00:00 +0000https://simplestory.github.io/posts/20190722_cdebdc3cc4ecd47a04cff48d03a688d3/经典的卷积神经网络https://simplestory.github.io/posts/20190324_161af8745042398050ad585874c3eec3/Sun, 24 Mar 2019 00:00:00 +0000https://simplestory.github.io/posts/20190324_161af8745042398050ad585874c3eec3/A Highly Efficient Gradient Boosting Decision Treehttps://simplestory.github.io/posts/20190317_8fc39e31f30337e40756914df1f8b607/Sun, 17 Mar 2019 00:00:00 +0000https://simplestory.github.io/posts/20190317_8fc39e31f30337e40756914df1f8b607/A Scalabe Tree Boosting Systeamhttps://simplestory.github.io/posts/20181103_7b202c9a380844d1b8ae2c4f5ee3461f/Sat, 03 Nov 2018 00:00:00 +0000https://simplestory.github.io/posts/20181103_7b202c9a380844d1b8ae2c4f5ee3461f/<blockquote> <p>最优化是一个十分困难的问题，通常要精心设计一个目标函数及相关约束，同时还要确保所要优化的问题为凸函数，但现实中的问题大多为非凸函数，针对非凸优化问题，这里有一些基本的优化方法：SGD、Momentum、AdaGrad、RMSProp及Adam等</p> </blockquote>https://simplestory.github.io/posts/20181028_fa81994273e28bed9a6b443a7af7f69e/Sun, 28 Oct 2018 00:00:00 +0000https://simplestory.github.io/posts/20181028_fa81994273e28bed9a6b443a7af7f69e/<blockquote> <p>CTR(点击率)预测在内容推荐投放中有着重要地位，而FFM模型擅长于处理这方面的数据，FFM模型是FM模型的变种，或者说FM模型是FFM模型的特例</p> </blockquote>https://simplestory.github.io/posts/20180829_3cb42cd7fd688b586f483bd682fc8209/Wed, 29 Aug 2018 00:00:00 +0000https://simplestory.github.io/posts/20180829_3cb42cd7fd688b586f483bd682fc8209/<blockquote> <p>决策树，一种常见的机器学习方法，有种类似于流程图那样的。就是对一个样本按其某个特征的取值将其划分到一个集合中，并递归下去</p> </blockquote> <h1 id="基本算法">基本算法</h1> <p>Input: 训练集$D=\\{(\mathbf{x_1},y_1),(\mathbf{x_2},y_2),...,(\mathbf{x_m},y_m)\\}$; 属性集$A=\\{a_1,a_2,...,a_d\\}$</p>https://simplestory.github.io/posts/20180826_8cb44c1627565d8ede47d43e49e9c8ae/Sun, 26 Aug 2018 00:00:00 +0000https://simplestory.github.io/posts/20180826_8cb44c1627565d8ede47d43e49e9c8ae/<blockquote> <p>这次整理一下常用的scikit-learn包，作为备忘</p> </blockquote> <p>K近邻算法：</p> <p><a href="http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html">from sklearn.neighbors import KNeighborsClassifier</a></p> <p>线性回归：</p> <p><a href="http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html">from sklearn.linear_model import LinearRegression</a></p> <p>逻辑回归：</p> <p><a href="http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html">from sklearn.linear_model import LogisticRgression</a></p> <p>决策树：</p> <p><a href="http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html">from sklearn.tree import DecisionTreeClassifier</a></p> <p>朴素贝叶斯：</p> <ol> <li>高斯分布：<a href="http://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html">from sklearn.neive_bayes import GaussianNB</a></li> <li>多项式分布：<a href="http://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.MultinomialNB.html#sklearn.naive_bayes.MultinomialNB">from sklearn.neive_baybes import MultinomnalNB</a></li> <li>伯努利分布：<a href="http://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.BernoulliNB.html#sklearn.naive_bayes.BernoulliNB">from sklearn.neive_baybes import BernoulliNB</a></li> </ol> <p>支持向量机：</p>https://simplestory.github.io/posts/20180819_72c962ee57eaf5ebe2b4645dd6827ce3/Sun, 19 Aug 2018 00:00:00 +0000https://simplestory.github.io/posts/20180819_72c962ee57eaf5ebe2b4645dd6827ce3/<blockquote> <p>贝叶斯决策论考虑的是如何基于相关的概率和误判损失来选择最优的类别标记</p> </blockquote> <p>假设有N种可能的类别标记，即$\mathbf{y}=\\{c_1,c_2,...,c_N\\}$，$\lambda_{ij}$是将一个真实标记为$c_j$的样本误分类为$c_i$所产生的损失。基于后验概率$P(c_i\vert\mathbf{x})$可获得将样本$\mathbf{x}$分类为$c_i$所产生的期望损失，即在样本$\mathbf{x}$上的“条件风险”：</p>https://simplestory.github.io/posts/20180816_7ed73172fd758adf99c76ebc108ea063/Thu, 16 Aug 2018 00:00:00 +0000https://simplestory.github.io/posts/20180816_7ed73172fd758adf99c76ebc108ea063/<blockquote> <p>SVM是机器学习里面一个非常重要的模型，它是由统计学习理论之父弗拉基米尔·瓦普尼克提出的</p> </blockquote> <p><strong>本文以SVM二分类为例</strong></p> <h2 id="推导过程">推导过程</h2> <p>给定训练数据集$D=\\{(\mathbf{x_1},y_1),(\mathbf{x_2},y_2),...,(\mathbf{x_m},y_m)\\}, y_i \in \\{-1, +1\\}$，分类器最基本的想法就是基于训练集D在样本空间中找到一个划分空间，将不同类别的样本分开。划分超平面可用如下的线性方程描述：</p>https://simplestory.github.io/posts/20180813_135c495c1e86bceb067f1bc3135c278b/Mon, 13 Aug 2018 00:00:00 +0000https://simplestory.github.io/posts/20180813_135c495c1e86bceb067f1bc3135c278b/<blockquote> <p>好久没更博客了，停了大半年，也看了大半年的机器学习和深度学习方面的书籍，现在是时候输出一下了</p> </blockquote> <h1 id="线性回归">线性回归</h1> <p>给定数据集$D=\\{(\mathbf{x_1},y_1),(\mathbf{x_2},y_2),...,(\mathbf{x_m},y_m)\\}$，其中$\mathbf{x_i}=(x_{i1};x_{i2};...;x_{id}), \ y_{i} \in R$. 线性回归试图学得一个线性模型</p>https://simplestory.github.io/posts/20180302_5ed219da904f7b6718ebebb4c8b47d3c/Fri, 02 Mar 2018 00:00:00 +0000https://simplestory.github.io/posts/20180302_5ed219da904f7b6718ebebb4c8b47d3c/<blockquote> <p>G++是Unix/Linux下C++的编译器，gdb则是Unix/Linux下有力的调试工具。大多数时候，我们编写的C/C++代码并不是运行在Windows上，而是在Linux系统上跑。这里我记录了一些有关于g++和gdb的一些使用方法以备不时之需。</p> </blockquote>https://simplestory.github.io/posts/20180219_c851e812699d1207837d404c0af593ce/Mon, 19 Feb 2018 00:00:00 +0000https://simplestory.github.io/posts/20180219_c851e812699d1207837d404c0af593ce/<blockquote> <p>2018新春之际，鬼知道我经历了些什么，在大年初四推博文。Django其实是在新年前配置好的，当时刚好学到了python做web开发方面的知识就顺手配置了。</p> </blockquote>https://simplestory.github.io/posts/20180204_68b9e972b5a534bc31d12d9be53b57ec/Sun, 04 Feb 2018 00:00:00 +0000https://simplestory.github.io/posts/20180204_68b9e972b5a534bc31d12d9be53b57ec/<blockquote> <p>套用<a href="http://www.tensorfly.cn/">TensorFlow中文社区</a>的一句话：TensorFlow，一个用于人工智能的开源神器。</p> </blockquote> <p><strong>配置使用环境</strong></p> <ul> <li>系统：Ubuntu16.04(LTS)</li> <li>python版本：python3.6（使用Anaconda建立虚拟环境）</li> <li>Nvidia卡：Geforce 920M（如果没有Nvidia卡，则只能安装CPU版本）</li> </ul> <h1 id="tensorflow安装">Tensorflow安装</h1> <p>tensorflow分为两个CPU/GPU两个版本，我建立了两个python虚拟环境，分别安装了这两个版本。在此记录一下安装的痛苦过程</p>https://simplestory.github.io/posts/20180120_3e2c28e497c306d34f88081093e32c89/Sat, 20 Jan 2018 00:00:00 +0000https://simplestory.github.io/posts/20180120_3e2c28e497c306d34f88081093e32c89/<blockquote> <p>以下是Linux机器的渗透测试备忘录，我无意间在网上看到的，便搬运到这边。</p> </blockquote> <h2 id="linux机器渗透测试">Linux机器渗透测试</h2> <table> <thead> <tr> <th style="text-align:left">命令</th> <th style="text-align:left">描述</th> </tr> </thead> <tbody> <tr> <td style="text-align:left">netstat -tulpn</td> <td style="text-align:left">在Linux中显示对应于ID（PID）的网络端口</td> </tr> <tr> <td style="text-align:left">watch ss -stplu</td> <td style="text-align:left">通过套接字实时观察TCP，UDP端口</td> </tr> <tr> <td style="text-align:left">lsof -i</td> <td style="text-align:left">显示确认了的连接</td> </tr> <tr> <td style="text-align:left">macchanger -m MACADDR INTR</td> <td style="text-align:left">在KALI Linux上修改MAC地址</td> </tr> <tr> <td style="text-align:left">ifconfig eth0 192.168.2.1/24</td> <td style="text-align:left">在Linux中设置ID地址</td> </tr> <tr> <td style="text-align:left">ifconfig eth0:192.168.2.3/24</td> <td style="text-align:left">在Linux中向现有的网络接口添加IP地址</td> </tr> <tr> <td style="text-align:left">ifconfig eth0 hw ether MAC ADDR</td> <td style="text-align:left">使用ifconfig修改Linux中的地址</td> </tr> <tr> <td style="text-align:left">ifconfig eth0 mtu 1500</td> <td style="text-align:left">使用ifconfig修改MTU的大小，将1500改为想要的MTU</td> </tr> <tr> <td style="text-align:left">dig -x 192.168.1.1</td> <td style="text-align:left">对IP地址进行反向查找</td> </tr> <tr> <td style="text-align:left">host 192.168.1.1</td> <td style="text-align:left">在一个IP地址上进行反向查找，适用于没有安装dig的情况</td> </tr> <tr> <td style="text-align:left">dig @192.168.2.2 domain.com -t AXFR</td> <td style="text-align:left">使用dig执行一次DNS区域传输</td> </tr> <tr> <td style="text-align:left">host -l domain.com nameserver</td> <td style="text-align:left">使用host执行一次DNS区域传输</td> </tr> <tr> <td style="text-align:left">nbtstat -A x.x.x.x</td> <td style="text-align:left">获取IP地址对应的域名</td> </tr> <tr> <td style="text-align:left">ip addr add 192.168.2.22/24 dev eth0</td> <td style="text-align:left">添加一个隐藏的IP地址，执行ifconfig时不会显示这个IP地址</td> </tr> <tr> <td style="text-align:left">tcpkill -9 host goole.com</td> <td style="text-align:left">阻止从主机访问goole.com</td> </tr> <tr> <td style="text-align:left">echo &quot;1&quot;&gt;/proc/sys/net/ipv4/ip_forward</td> <td style="text-align:left">启用IP转发，将Linux盒子变成一个路由器——这样就方便通过这个盒子进行路由流量的控制</td> </tr> <tr> <td style="text-align:left">echo &quot;8.8.8.8&quot;&gt;/etc/resolv.conf</td> <td style="text-align:left">使用Goole的DNS</td> </tr> </tbody> </table> <h2 id="系统信息命令">系统信息命令</h2> <p>对于本地的枚举检查有很大的帮助</p>https://simplestory.github.io/posts/20171201_7eec743fae4833726b0d923cf6db1da6/Fri, 01 Dec 2017 00:00:00 +0000https://simplestory.github.io/posts/20171201_7eec743fae4833726b0d923cf6db1da6/<p><strong>Server version: 5.7.20-0ubuntu0.16.04.1 (Ubuntu)</strong></p> <p>进入Mysql后,可用如下命令查询相关文件的储存路径:</p> <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-sql" data-lang="sql"><span class="line"><span class="cl"><span class="k">show</span><span class="w"> </span><span class="n">variables</span><span class="w"> </span><span class="k">like</span><span class="w"> </span><span class="s1">&#39;%dir%&#39;</span><span class="p">;</span><span class="w"> </span></span></span></code></pre></div><p>输出如下图:</p> <p><img loading="lazy" src="https://simplestory-blog-img.oss-cn-guangzhou.aliyuncs.com/in_posts/20171201/mysql.png" title="mysql"></p> <p>通常有: 数据库文件默认位置为: <code>/usr/share/mysql/</code> 配置文件: <code>/etc/mysql/my.cnf</code></p> <p>数据库目录: <code>/var/lib/mysql</code> 配置文件: <code>/etc/share/mysql</code>(mysql.server命令及配置文件) mysql相关命令: <code>/usr/bin</code> 启动脚本: <code>/etc/init.d/</code></p>https://simplestory.github.io/posts/20171107_619e2d5ba49593e40e16667bafdcf2c1/Tue, 07 Nov 2017 00:00:00 +0000https://simplestory.github.io/posts/20171107_619e2d5ba49593e40e16667bafdcf2c1/一些鲜为人知的Linux命令https://simplestory.github.io/posts/20171104_bb6e9cb8623b3bf044c62bbbca472d53/Sun, 05 Nov 2017 00:00:00 +0000https://simplestory.github.io/posts/20171104_bb6e9cb8623b3bf044c62bbbca472d53/<blockquote> <p>Opencv包含了超过2500个函数，几乎任何一个能想到的成熟算法都可以通过调用Opencv的函数来实现，超级方便。这篇博文结合了Python与Opencv来实现在监控视频中识别汽车并检测其是否违规（压双黄线）。</p> </blockquote>https://simplestory.github.io/posts/20171020_df3860e05b3ca04bdc2f774c5c1914fd/Fri, 20 Oct 2017 00:00:00 +0000https://simplestory.github.io/posts/20171020_df3860e05b3ca04bdc2f774c5c1914fd/<blockquote> <p>近期由于项目需要，要用到计算机视觉识别，所以折腾起了OpenCV，虽然安装过程曲折漫长，但是配合Python使用起来体验还是相当不错的。在此记录OpenCV安装编译过程。</p> </blockquote>https://simplestory.github.io/posts/20171001_f9fd02f8e181115a55cec2936857675c/Sun, 01 Oct 2017 00:00:00 +0000https://simplestory.github.io/posts/20171001_f9fd02f8e181115a55cec2936857675c/<blockquote> <p>Visual Studio Code是微软推出的一款轻量型编辑器,通过安装各种插件可以转变成一款强大的多语言,跨平台的编译器.近日,由于实践需要,需要在调试一些C/C++代码,于是折腾起Vscode,以下是记录折腾过程中困扰着我的问题.</p> </blockquote>https://simplestory.github.io/posts/20170820_7cb802827b60ef8c770d824ec28a65a0/Sun, 20 Aug 2017 00:00:00 +0000https://simplestory.github.io/posts/20170820_7cb802827b60ef8c770d824ec28a65a0/人生苦短，我用Pythonhttps://simplestory.github.io/posts/20170810_3193456a2ce85ef1e01f5a219b37cebd/Thu, 10 Aug 2017 00:00:00 +0000https://simplestory.github.io/posts/20170810_3193456a2ce85ef1e01f5a219b37cebd/人生苦短，我用Pythonhttps://simplestory.github.io/posts/20170704_497c40137cedefb55b1afe3bd1e6b13f/Tue, 04 Jul 2017 00:00:00 +0000https://simplestory.github.io/posts/20170704_497c40137cedefb55b1afe3bd1e6b13f/Git的一些基本操作