##安装MJPG-streamer

安装依赖 sudo apt-get updatesudo sudo apt-get install subversion sudo apt-get install libjpeg8-dev sudo apt-get install imagemagick sudo apt-get install libv4l-dev sudo apt-get install cmake sudo apt-get install git
下载项目

sudo git clone github.com/jacksonliam/mjpg-streamer.git cd mjpg-streamer/mjpg-streamer-experimentalsudo make all sudo make install

指令启动普通USB摄像头指令： ./mjpg_streamer -i "./input_uvc.so" -o "./output_http.so -w ./www"
启动树莓派专用摄像头RaspiCamera的指令： ./mjpg_streamer -i "./input_raspicam.so" -o "./output_http.so -w ./www" https://blog.csdn.net/LIEVE_Z/article/details/79551628 https://blog.csdn.net/m0_38106923/article/details/86562451

https://blog.csdn.net/qq_41204464/article/details/83098603 有一个现成的轮子，pistreaming： sudo apt-get install libav-tools git python3-picamera python3-ws4py git clone https://github.com/waveform80/pistreaming.git $ cd pistreaming $ python3 server.py

https://github.com/yueritian/RaspberryPi_SmartCarV1/blob/master/templates/index.html 控制

资料 https://www.cnblogs.com/darkcircle/articles/9327537.html https://blog.csdn.net/u011426247/article/details/80551749 c++s https://blog.csdn.net/u012736685/article/details/77131633 c++s https://blog.csdn.net/Mr_Curry/article/details/52347797 <<<<<<< HEAD

使用spark 运行ros 运行模拟

测试数据集 www.cvlibs.net/datasets/kitti/

knowlege of MASK-RCNN traffic segment reconignzs

http://blog.csdn.net/AdamShan/article/details/78248421?locationNum=7&fps=1

http://blog.csdn.net/AdamShan/article/details/78265754?locationNum=2&fps=1

https://github.com/udacity/CarND-Extended-Kalman-Filter-Project/tree/master/src

http://blog.csdn.net/lybaihu/article/details/54943545?locationNum=8&fps=1

http://blog.csdn.net/weixin_37239947/article/details/74939650处理

卡尔曼公式 http://bilgin.esme.org/BitsAndBytes/KalmanFilterforDummies https://blog.csdn.net/heyijia0327/article/details/44033751 python + opencv: kalman 跟踪 http://www.cnblogs.com/gaoxiang12/p/5175118.html rgb-d salm

https://blog.csdn.net/heyijia0327/article/details/17487467

https://blog.csdn.net/liaoshenglan/article/details/78484851

https://mp.weixin.qq.com/s?__biz=MzIzNjc1NzUzMw==&mid=2247486559&idx=5&sn=c3170e4254db6072199687c4709fa6aa&chksm=e8d3bb2ddfa4323b3b9e7f292a5ea315c5bea9b3ebae9959cbbe2deae6ee4dcb9b37b80e4cce&mpshare=1&scene=1&srcid=0627otxvbfmlbxw8jN2ko3R2#rd]

https://mp.weixin.qq.com/s?__biz=MjM5ODU3OTIyOA==&mid=2650670095&idx=3&sn=34e8d76f9e8fec2b46209c06ecc7351d&chksm=bec2397c89b5b06a5b4433cb8309b6a04a76a8dd9bee5a5797fac8d73fe8747ce66d45d795e4&mpshare=1&scene=1&srcid=0313lP98KcXrSZQzBxmK7grL#rd]

https://github.com/jeasinema/VoxelNet-tensorflow

https://mp.weixin.qq.com/s?__biz=MzA3MzI4MjgzMw==&mid=2650729880&idx=4&sn=d1f30057a9235b54a953989438d3576b&chksm=871b29e6b06ca0f01758f366aed1c0ec24b2f8c5704c1282ecfdefcc824fb614a5dfd65768e8&mpshare=1&scene=1&srcid=0816R7TVCZgDoe6FcO2M49hz#rd]

https://mp.weixin.qq.com/s?__biz=MzIzNjc1NzUzMw==&mid=2247487991&idx=1&sn=e75d56296b16b5349b5055044849d416&chksm=e8d3a685dfa42f939b4f1ab385a1c73733e2c4f48515a35b518f320c538fb2622f1144bdf3a6&mpshare=1&scene=1&srcid=0816al7xMC4UO6yxsMhF9Myi#rd]

https://mp.weixin.qq.com/s?__biz=MzA3MzI4MjgzMw==&mid=2650740213&idx=2&sn=43e07e08d3f6ac5cbd8ef79218df7667&chksm=871ad18bb06d589dfef295be1a3ac7a6d1423f1df281a2fb153a5dbbd73abf7ba0dc7f2294e5&mpshare=1&scene=1&srcid=0403vwSmNPpgYku2MR5Epbnd#rd]

https://mp.weixin.qq.com/s?__biz=MjM5ODU3OTIyOA==&mid=2650669314&idx=2&sn=bd4e825f3cda99f6b7641fbc61115038&chksm=bec23c7189b5b567ef78552dbdac26e2368b93bae8721f8fab6b50bc1a6b0ec20f4a3b432f8f&mpshare=1&scene=1&srcid=0122eFcMymMOKDz4NP6SiEmx#rd]

https://github.com/koryako/selfcarRaspberryPi https://github.com/priya-dwivedi

Laplace 算子 http://www.opencv.org.cn/opencvdoc/2.3.2/html/doc/tutorials/imgproc/imgtrans/laplace_operator/laplace_operator.html?highlight=laplace

sobel算子 http://www.opencv.org.cn/opencvdoc/2.3.2/html/doc/tutorials/imgproc/imgtrans/sobel_derivatives/sobel_derivatives.html?highlight=sobel

Canny 原理 http://www.pclcn.org/study/shownews.php?lang=cn&id=111 http://www.opencv.org.cn/opencvdoc/2.3.2/html/doc/tutorials/imgproc/imgtrans/canny_detector/canny_detector.html?highlight=canny#canny

http://selfdrivingcars.mit.edu/resources

三角剖分的算法比较成熟。目前有很多的库（包括命令行的和GUI的可以用）。

常用的算法叫Delaunay Triangulation，具体算法原理见 http://www.cnblogs.com/soroman/archive/2007/05/17/750430.html

这里收集一些开元的做可以测试三角剖分的库

Shewchuk的http://www.cs.cmu.edu/~quake/triangle.html，据说效率非常高！
MeshLab http://www.cs.cmu.edu/~quake/triangle.html，非常易于上手，只要新建工程，读入三维坐标点，用工具里面的Delaunay Trianglulation来可视化就好了。而且它是开源的！具体教程去网站上找吧。
Qhull http://www.qhull.org/
PCL库，http://pointclouds.org/documentation/tutorials/greedy_projection.php

无序点云快速三角化

http://www.pclcn.org/study/shownews.php?lang=cn&id=111

opencv 基本操作 https://segmentfault.com/a/1190000003742422

cv 到cv2的不同 http://www.aiuxian.com/article/p-395730.html

已经fork https://github.com/mbeyeler/opencv-machine-learning

前言

https://github.com/mbeyeler/opencv-machine-learning/blob/master/notebooks/00.00-Preface.ipynb

https://github.com/mbeyeler/opencv-machine-learning/blob/master/notebooks/00.01-Foreword-by-Ariel-Rokem.ipynb

机器学习的味道

https://github.com/mbeyeler/opencv-machine-learning/blob/master/notebooks/01.00-A-Taste-of-Machine-Learning.ipynb

在OpenCV中使用数据

https://github.com/mbeyeler/opencv-machine-learning/blob/master/notebooks/02.00-Working-with-Data-in-OpenCV.ipynb

使用Python的NumPy软件包处理数据 https://github.com/mbeyeler/opencv-machine-learning/blob/master/notebooks/02.01-Dealing-with-Data-Using-Python-NumPy.ipynb 在Python中加载外部数据集 https://github.com/mbeyeler/opencv-machine-learning/blob/master/notebooks/02.02-Loading-External-Datasets-in-Python.ipynb 使用Matplotlib可视化数据 https://github.com/mbeyeler/opencv-machine-learning/blob/master/notebooks/02.03-Visualizing-Data-Using-Matplotlib.ipynb 使用OpenCV的TrainData容器处理数据 https://github.com/mbeyeler/opencv-machine-learning/blob/master/notebooks/02.05-Dealing-with-Data-Using-the-OpenCV-TrainData-Container-in-C%2B%2B.ipynb 监督学习的第一步

https://github.com/mbeyeler/opencv-machine-learning/blob/master/notebooks/03.00-First-Steps-in-Supervised-Learning.ipynb

用评分功能测量模型性能 https://github.com/mbeyeler/opencv-machine-learning/blob/master/notebooks/03.01-Measuring-Model-Performance-with-Scoring-Functions.ipynb 了解k-NN算法 https://github.com/mbeyeler/opencv-machine-learning/blob/master/notebooks/03.02-Understanding-the-k-NN-Algorithm.ipynb 使用回归模型预测持续成果 https://github.com/mbeyeler/opencv-machine-learning/blob/master/notebooks/03.03-Using-Regression-Models-to-Predict-Continuous-Outcomes.ipynb 应用拉索和岭回归 https://github.com/mbeyeler/opencv-machine-learning/blob/master/notebooks/03.04-Applying-Lasso-and-Ridge-Regression.ipynb 使用Logistic回归分类虹膜物种 https://github.com/mbeyeler/opencv-machine-learning/blob/master/notebooks/03.05-Classifying-Iris-Species-Using-Logistic-Regression.ipynb 代表数据和工程特性

https://github.com/mbeyeler/opencv-machine-learning/blob/master/notebooks/04.00-Representing-Data-and-Engineering-Features.ipynb

预处理数据 https://github.com/mbeyeler/opencv-machine-learning/blob/master/notebooks/04.01-Preprocessing-Data.ipynb 减少数据的维度 https://github.com/mbeyeler/opencv-machine-learning/blob/master/notebooks/04.02-Reducing-the-Dimensionality-of-the-Data.ipynb 代表分类变量 https://github.com/mbeyeler/opencv-machine-learning/blob/master/notebooks/04.03-Representing-Categorical-Variables.ipynb 表示文本特征 https://github.com/mbeyeler/opencv-machine-learning/blob/master/notebooks/04.04-Represening-Text-Features.ipynb 代表图像 https://github.com/mbeyeler/opencv-machine-learning/blob/master/notebooks/04.05-Representing-Images.ipynb 使用决策树进行医学诊断

https://github.com/mbeyeler/opencv-machine-learning/blob/master/notebooks/05.00-Using-Decision-Trees-to-Make-a-Medical-Diagnosis.ipynb

建立你的第一决策树 https://github.com/mbeyeler/opencv-machine-learning/blob/master/notebooks/05.01-Building-Your-First-Decision-Tree.ipynb 使用决策树诊断乳腺癌 https://github.com/mbeyeler/opencv-machine-learning/blob/master/notebooks/05.02-Using-Decision-Trees-to-Diagnose-Breast-Cancer.ipynb 使用决策树回归 https://github.com/mbeyeler/opencv-machine-learning/blob/master/notebooks/05.03-Using-Decision-Trees-for-Regression.ipynb 用支持向量机检测行人

https://github.com/mbeyeler/opencv-machine-learning/blob/master/notebooks/06.00-Detecting-Pedestrians-with-Support-Vector-Machines.ipynb

实施您的第一支持向量机 https://github.com/mbeyeler/opencv-machine-learning/blob/master/notebooks/06.01-Implementing-Your-First-Support-Vector-Machine.ipynb 检测野外行人 https://github.com/mbeyeler/opencv-machine-learning/blob/master/notebooks/06.02-Detecting-Pedestrians-in-the-Wild.ipynb 附加SVM练习 https://github.com/mbeyeler/opencv-machine-learning/blob/master/notebooks/06.03-Additional-SVM-Exercises.ipynb 用贝叶斯学习实现垃圾邮件过滤器

https://github.com/mbeyeler/opencv-machine-learning/blob/master/notebooks/07.00-Implementing-a-Spam-Filter-with-Bayesian-Learning.ipynb

实现我们的第一个贝叶斯分类器 https://github.com/mbeyeler/opencv-machine-learning/blob/master/notebooks/07.01-Implementing-Our-First-Bayesian-Classifier.ipynb 分类电子邮件使用朴素贝叶斯 https://github.com/mbeyeler/opencv-machine-learning/blob/master/notebooks/07.02-Classifying-Emails-Using-Naive-Bayes.ipynb 用无监督学习发现隐藏的结构

https://github.com/mbeyeler/opencv-machine-learning/blob/master/notebooks/08.00-Discovering-Hidden-Structures-with-Unsupervised-Learning.ipynb

了解k均值聚类 https://github.com/mbeyeler/opencv-machine-learning/blob/master/notebooks/08.01-Understanding-k-Means-Clustering.ipynb 使用k-Means压缩彩色图像 https://github.com/mbeyeler/opencv-machine-learning/blob/master/notebooks/08.02-Compressing-Color-Images-Using-k-Means.ipynb 使用k-Means分类手写数字 https://github.com/mbeyeler/opencv-machine-learning/blob/master/notebooks/08.03-Classifying-Handwritten-Digits-Using-k-Means.ipynb 实施聚集层次聚类 https://github.com/mbeyeler/opencv-machine-learning/blob/master/notebooks/08.04-Implementing-Agglomerative-Hierarchical-Clustering.ipynb 使用深度学习分类手写数字

https://github.com/mbeyeler/opencv-machine-learning/blob/master/notebooks/09.00-Using-Deep-Learning-to-Classify-Handwritten-Digits.ipynb

了解感知器 https://github.com/mbeyeler/opencv-machine-learning/blob/master/notebooks/09.01-Understanding-Perceptrons.ipynb 在OpenCV中实现多层感知器 https://github.com/mbeyeler/opencv-machine-learning/blob/master/notebooks/09.02-Implementing-a-Multi-Layer-Perceptron-in-OpenCV.ipynb 认识深度学习 https://github.com/mbeyeler/opencv-machine-learning/blob/master/notebooks/09.03-Getting-Acquainted-with-Deep-Learning.ipynb 在OpenCV中培训MLP以分类手写数字 https://github.com/mbeyeler/opencv-machine-learning/blob/master/notebooks/09.04-Training-an-MLP-in-OpenCV-to-Classify-Handwritten-Digits.ipynb 训练深层神经网络使用Keras分类手写数字 https://github.com/mbeyeler/opencv-machine-learning/blob/master/notebooks/09.05-Training-a-Deep-Neural-Net-to-Classify-Handwritten-Digits-Using-Keras.ipynb 将不同的算法合并成一个合奏

https://github.com/mbeyeler/opencv-machine-learning/blob/master/notebooks/10.00-Combining-Different-Algorithms-Into-an-Ensemble.ipynb

了解组合方法 https://github.com/mbeyeler/opencv-machine-learning/blob/master/notebooks/10.01-Understanding-Ensemble-Methods.ipynb 将决策树组合成随机森林 https://github.com/mbeyeler/opencv-machine-learning/blob/master/notebooks/10.02-Combining-Decision-Trees-Into-a-Random-Forest.ipynb 使用随机森林进行人脸识别 https://github.com/mbeyeler/opencv-machine-learning/blob/master/notebooks/10.03-Using-Random-Forests-for-Face-Recognition.ipynb 实施AdaBoost https://github.com/mbeyeler/opencv-machine-learning/blob/master/notebooks/10.04-Implementing-AdaBoost.ipynb 将不同的模型组合成投票分类器 https://github.com/mbeyeler/opencv-machine-learning/blob/master/notebooks/10.05-Combining-Different-Models-Into-a-Voting-Classifier.ipynb 使用超参数调整选择正确的模型

https://github.com/mbeyeler/opencv-machine-learning/blob/master/notebooks/11.00-Selecting-the-Right-Model-with-Hyper-Parameter-Tuning.ipynb

评估模型 https://github.com/mbeyeler/opencv-machine-learning/blob/master/notebooks/11.01-Evaluating-a-Model.ipynb 了解交叉验证，Bootstrapping和McNemar的测试 https://github.com/mbeyeler/opencv-machine-learning/blob/master/notebooks/11.02-Understanding-Cross-Validation-Bootstrapping-and-McNemar's-Test.ipynb 使用网格搜索调整超参数 https://github.com/mbeyeler/opencv-machine-learning/blob/master/notebooks/11.03-Tuning-Hyperparameters-with-Grid-Search.ipynb 链接算法一起形成管道 https://github.com/mbeyeler/opencv-machine-learning/blob/master/notebooks/11.04-Chaining-Algorithms-Together-to-Form-a-Pipeline.ipynb 结束语

https://github.com/mbeyeler/opencv-machine-learning/blob/master/notebooks/12.00-Wrapping-Up.ipynb

https://github.com/tensorflow/models/tree/master/object_detection mobilenet

https://research.googleblog.com/2017/06/mobilenets-open-source-models-for.html

带有MobileNets的SSD(Single Shot Multibox Detector)

带有Inception V2的SSD

带有Resnet 101的R-FCN（Region-based Fully Convolutional Networks）

带有Resnet 101的 Faster RCNN

带有Inception Resnet v2的Faster RCNN

https://cloud.google.com/blog/big-data/2017/06/training-an-object-detector-using-cloud-machine-learning-engine

https://github.com/tensorflow/tensorflow/commit/055500bbcea60513c0160d213a10a7055f079312

mobil net https://github.com/tensorflow/models/tree/master/inception 准备数据 https://github.com/zehaos/MobileNet

https://github.com/balancap/SSD-Tensorflow

2017.9

https://github.com/udacity/CarND-Term1-Starter-Kit 环境配置

http://blog.csdn.net/xukai871105/article/details/39255089 树莓派mqtt

P1.ipynb

https://github.com/priya-dwivedi

Laplace 算子 http://www.opencv.org.cn/opencvdoc/2.3.2/html/doc/tutorials/imgproc/imgtrans/laplace_operator/laplace_operator.html?highlight=laplace

sobel算子 http://www.opencv.org.cn/opencvdoc/2.3.2/html/doc/tutorials/imgproc/imgtrans/sobel_derivatives/sobel_derivatives.html?highlight=sobel

http://selfdrivingcars.mit.edu/resources

三角剖分的算法比较成熟。目前有很多的库（包括命令行的和GUI的可以用）。

常用的算法叫Delaunay Triangulation，具体算法原理见 http://www.cnblogs.com/soroman/archive/2007/05/17/750430.html

这里收集一些开元的做可以测试三角剖分的库

Shewchuk的http://www.cs.cmu.edu/~quake/triangle.html，据说效率非常高！
MeshLab http://www.cs.cmu.edu/~quake/triangle.html，非常易于上手，只要新建工程，读入三维坐标点，用工具里面的Delaunay Trianglulation来可视化就好了。而且它是开源的！具体教程去网站上找吧。
Qhull http://www.qhull.org/
PCL库，http://pointclouds.org/documentation/tutorials/greedy_projection.php

无序点云快速三角化

http://www.pclcn.org/study/shownews.php?lang=cn&id=111

opencv 基本操作 https://segmentfault.com/a/1190000003742422

cv 到cv2的不同 http://www.aiuxian.com/article/p-395730.html

已经fork https://github.com/mbeyeler/opencv-machine-learning

前言

https://github.com/mbeyeler/opencv-machine-learning/blob/master/notebooks/00.00-Preface.ipynb

https://github.com/mbeyeler/opencv-machine-learning/blob/master/notebooks/00.01-Foreword-by-Ariel-Rokem.ipynb

机器学习的味道

https://github.com/mbeyeler/opencv-machine-learning/blob/master/notebooks/01.00-A-Taste-of-Machine-Learning.ipynb

在OpenCV中使用数据

https://github.com/mbeyeler/opencv-machine-learning/blob/master/notebooks/02.00-Working-with-Data-in-OpenCV.ipynb

https://github.com/mbeyeler/opencv-machine-learning/blob/master/notebooks/03.00-First-Steps-in-Supervised-Learning.ipynb

https://github.com/mbeyeler/opencv-machine-learning/blob/master/notebooks/04.00-Representing-Data-and-Engineering-Features.ipynb

https://github.com/mbeyeler/opencv-machine-learning/blob/master/notebooks/05.00-Using-Decision-Trees-to-Make-a-Medical-Diagnosis.ipynb

https://github.com/mbeyeler/opencv-machine-learning/blob/master/notebooks/06.00-Detecting-Pedestrians-with-Support-Vector-Machines.ipynb

https://github.com/mbeyeler/opencv-machine-learning/blob/master/notebooks/07.00-Implementing-a-Spam-Filter-with-Bayesian-Learning.ipynb

https://github.com/mbeyeler/opencv-machine-learning/blob/master/notebooks/08.00-Discovering-Hidden-Structures-with-Unsupervised-Learning.ipynb

https://github.com/mbeyeler/opencv-machine-learning/blob/master/notebooks/09.00-Using-Deep-Learning-to-Classify-Handwritten-Digits.ipynb

https://github.com/mbeyeler/opencv-machine-learning/blob/master/notebooks/10.00-Combining-Different-Algorithms-Into-an-Ensemble.ipynb

https://github.com/mbeyeler/opencv-machine-learning/blob/master/notebooks/11.00-Selecting-the-Right-Model-with-Hyper-Parameter-Tuning.ipynb

https://github.com/mbeyeler/opencv-machine-learning/blob/master/notebooks/12.00-Wrapping-Up.ipynb

https://github.com/tensorflow/models/tree/master/object_detection mobilenet

https://research.googleblog.com/2017/06/mobilenets-open-source-models-for.html

带有MobileNets的SSD(Single Shot Multibox Detector)

带有Inception V2的SSD

带有Resnet 101的R-FCN（Region-based Fully Convolutional Networks）

带有Resnet 101的 Faster RCNN

带有Inception Resnet v2的Faster RCNN

https://cloud.google.com/blog/big-data/2017/06/training-an-object-detector-using-cloud-machine-learning-engine

https://github.com/tensorflow/tensorflow/commit/055500bbcea60513c0160d213a10a7055f079312

mobil net https://github.com/tensorflow/models/tree/master/inception 准备数据 https://github.com/zehaos/MobileNet

https://github.com/balancap/SSD-Tensorflow

2017.9

https://github.com/udacity/CarND-Term1-Starter-Kit 环境配置

http://blog.csdn.net/xukai871105/article/details/39255089 树莓派mqtt

https://github.com/udacity/CarND-LaneLines-P1/blob/master/P1.ipynb

交通识别问题,行人识别，目标追踪

目标检测

传统方法 a.2001 paul viola 和Micahel jones 鲁棒实时目标检测的 viola-jones 框架

b.梯度直方图 Hog

2.深度学习 a. overFeat 利用卷积多尺度窗口滑动 b. r-cnn 选择性搜索 selective Search 提取可能目标；使用cnn 在该区域上提取特征；向量机分类 c. fast-rcnn 选择性搜索，cnn 提取特征，区域兴趣池化 Region of interest ,ROI; 反向传播做分类和边框回归 d. yolo e. faster-rcnn cnn 提取特征;regio Proosal network 根据物体的分数来输出可能的目标；区域兴趣池化 Region of interest ,ROI pooling; 反向传播做分类和边框回归 f. SSd 在yolo 上改进，使用了多尺度特征图 g。 R-fcn 使用了 Faster-Rcnn的架构 https://tryolabs.com/blog/ 3.数据集 imageNet coco Pascal VOC Oxford-IIIT Pet kitti Vision

http://www.dev-c.com/nativedb/

github.com/osrf/car_demo

https://github.com/openai/roboschool

gym.openai.com

https://mujoco.org

https://github.com/DartEnv/ddart-env

https://github.com/openai/baselines

目标跟踪

http://www.cs.cityu.edu.hk/~yibisong/iccv17/index.html convolutional Residual learning for visual tracking

pytorch caffe2 cntk 之间模型转换用onnx格式 github.com/onnx/onnx

github.com/nottombrown/rl-teacher https://github.com/nottombrown/rl-teacher.git

https://github.com/aleju/self-driving-truck