u014734471的博客

2018功能安全（ISO26262）中英双语版； ISO 26262； iso26262 学习资料及模板，141篇doc；

遵从AUTOSAR Release 4.2 rev 2 包含以下模块： 微控制器驱动程序：MCU、WDG、GPT I/O驱动程序：DIO、PORT、PWM、ICU、ADC 存储器驱动程序：FLS、FEE 通信驱动程序：CAN、LIN、SPI、FR(仅部分软件包支持FR) 用户和集成手册，模块配置，模块生成 面向所有内含MCAL模块的源代码 开发流程遵从Automotive SPICE Level 3，QM 通过多款第三方编译器的测试(在各软件包的版本说明中列明) 包含第三方AUTOSAR配置工具(EB tresos Studio)

一款增强型自我校准的电池模型设计python3工具包。

调通的RobustPCA和论文 1. 1 为什么使用RPCA? 求解被高幅度尖锐噪声而不是高斯分布噪声污染的信号分离问 题。 1.2 主要问题 给定C = A*+B*, 其中A*是稀疏的尖锐噪声矩阵，B* 是低 秩矩阵, 目的是从C中恢复B*. B*= UΣV’, 其中U∈R n*k ,Σ∈R k*k ,V∈R n*k 3. 与PCA的区别 PCA和RPCA 的目的都是矩阵分解, 然而, 对于PCA, M = L0+N0,

这个是群里灰常厉害的人共享的资源，赞赞赞！！！！238！！！！

ch1 多项式的计算 ch10 常微分方程组的求解 ch11 数据处理 ch12 极值问题的求解 ch13 数学变换与滤波 ch14 特殊函数的计算 ch15 排序 ch16 查找 ch2 复数运算 ch3 随机数的产生 ch4 矩阵运算 ch5 矩阵特征值余特征向量的计算 ch6 线性方程组的求解 ch7 非线性方程与方程组的求解 ch8 插值与逼近 ch9 数值积分 Visual C++常用数值算法集

搞P2P，文件传输，文件共享，需要的一些包====java的，以后上传源代码。。。。 junit4.7-4.11.zip org.jdesktop.swingx.jar org.jsoup-1.10 org-jdesktop-swingx.jar.zip SWT_Designer.zip 收集不易，共享上传给需要的朋友童鞋。。。。

搞P2P需要的一些包====java的 com.borland.zip com.google.common.all.zip de.javawi.jstun-0.6.1.zip javax.media-com.sun.-net.sf.fmj.jar.zip 收集不易，共享上传给需要的朋友童鞋。。。。

2017年全国大学生英语竞赛A类决赛听力.mp3 全国大学生英语竞赛A类模拟试题一听力.mp3

由于实在是在网盘找不到，只能买书下载，共享给需要的朋友！

pbm，pgm，ppm格式可以用matlab看或者其他工具，有作者论文说明，文件列表如下： COPYING dt.cpp dt-final.pdf imconv.h input.pbm misc.h pnmfile.h dt dt.h image.h imutil.h Makefile output.pgm README

能够在win下编译输出，并且运行三种目标识别；拥有的资源有——HOG_cvpr2005.pdf Cascade Object Detection with Deformable Part Models-cascade.pdf Object Detection Grammars.-TR-2010-02.pdf Object Detection with Discriminatively Trained Part Based Models.pdf release4-notes.pdf Slides from a recent talk.pdf 科学网—[转载]Windows下运行DPM(voc-release4.pdf 在Windows下运行Felzenszwalb的Deformable Part Models(voc-release4.pdf 值得参考，都与voc-release相关论文、介绍、博客……

一种用于人体姿态估计的强大算法,可以检测并且定位人体的四肢及躯干和头部的位置，用于更高层次的分析。 (Pose estimation) 文件列表： parse_matlab parse_matlab\parseHorse.m parse_matlab\condenseLRResp.m parse_matlab\README parse_matlab\expected_genmodel_FHedgesN.m parse_matlab\util parse_matlab\util\imvq16.m parse_matlab\util\printSegs.m parse_matlab\util\oeFilterc.m parse_matlab\util\mydetGMc.m parse_matlab\util\assert.m parse_matlab\util\printSkel.m parse_matlab\util\sampleWithR.m parse_matlab\util\isum.m parse_matlab\util\buildHistExps.m parse_matlab\util\local_sum_zero.m parse_matlab\util\nonmax.m parse_matlab\horse001.jpg parse_matlab\partshiftZ0.m parse_matlab\GPL parse_matlab\horseModel.mat parse_matlab\expected_genmodel_FHedgecolsW.m parse_matlab\im0229.jpg parse_matlab\getSegmentsEdge.m parse_matlab\peopleModel.mat parse_matlab\sample_genmodel_FHedgecolsW.m parse_matlab\avgSegmentsEdge.m parse_matlab\HorseConvRun.mat parse_matlab\im0229.dat parse_matlab\parseim.m parse_matlab\parsePerson.m

利用骨骼进行的人体行为识别， 基于LOP 的行为识别，可以运行 (action recognition) 文件列表： actionletEnsemble-master actionletEnsemble-master\.gitignore actionletEnsemble-master\LICENSE actionletEnsemble-master\MSRAction3D actionletEnsemble-master\MSRAction3D\MSRAction3D_skeleton_features.mat actionletEnsemble-master\MSRAction3D\evaluate_on_MSR_action_3D.m actionletEnsemble-master\MSRDailyActivity3D actionletEnsemble-master\MSRDailyActivity3D\configDailyAcitity.m actionletEnsemble-master\MSRDailyActivity3D\evaluate_on_MSR_DailyAcitivity3D.m actionletEnsemble-master\MSRDailyActivity3D\extractAllLopFeatures.m actionletEnsemble-master\MSRDailyActivity3D\extractAllSkeletonFeatures.m actionletEnsemble-master\MSRDailyActivity3D\processOneSkeleton.m actionletEnsemble-master\MSRDailyActivity3D\trainClassifier.m actionletEnsemble-master\README.md actionletEnsemble-master\feature actionletEnsemble-master\feature\computeMotionField.m actionletEnsemble-master\feature\computePairwiseJointPositions.m actionletEnsemble-master\feature\computeSOPFeaturesSkeleton.m actionletEnsemble-master\feature\compute_motion_descriptors.m actionletEnsemble-master\feature\compute_motion_maps.m actionletEnsemble-master\feature\fftPyramid.m actionletEnsemble-master\feature\getSopFeature.m actionletEnsemble-master\feature\lopFeature.m actionletEnsemble-master\feature\lopFeatureSkeleton.m actionletEnsemble-master\feature\sopFeatureSkeleton.m actionletEnsemble-master\setup_path.m actionletEnsemble-master\util actionletEnsemble-master\util\ComputeMotion.mexw64 actionletEnsemble-master\util\ReadDepthBin.mexa64 actionletEnsemble-master\util\ReadDepthBin.mexw64 actionletEnsemble-master\util\iSaveX.m actionletEnsemble-master\util\normalizeFeature.m actionletEnsemble-master\util\predict.mexa64 actionletEnsemble-master\util\readDepthBin.m actionletEnsemble-master\util\readSkeleton.m actionletEnsemble-master\util\train.mexa64

嵌入式系统设计师历年考试题+考纲+笔记pdf ，希望能够帮助软考的同学…………………………………………………………

基于Kinect的头部姿态估计+两篇文档说明 比较难 3D Head Pose Estimation Using the Kinect.pdf Random forests for real time 3D face analysis.pdf

不错的文档，值得参考—— 文档 AudioCaptureRaw_Walkthrough.pdf ProgrammingGuide_KinectSDK.pdf SkeletalViewer_Walkthrough.pdf kinect核心技术简介.txt kinect机械学习机摘要.txt kinect软实力.doc kinect专利技术曝光.doc

基于Kinet+openni的人体骨架提取及姿态识别，可以参考使用。。。。。。。。。。。。。。。。。。。。。。。。。。

视频人脸识别，取代jmf。。。 Introduction JavaCV uses wrappers from the JavaCPP Presets of commonly used libraries by researchers in the field of computer vision (OpenCV, FFmpeg, libdc1394, PGR FlyCapture, OpenKinect, librealsense, CL PS3 Eye Driver, videoInput, ARToolKitPlus, and flandmark), and provides utility classes to make their functionality easier to use on the Java platform, including Android. JavaCV also comes with hardware accelerated full-screen image display (CanvasFrame and GLCanvasFrame), easy-to-use methods to execute code in parallel on multiple cores (Parallel), user-friendly geometric and color calibration of cameras and projectors (GeometricCalibrator, ProCamGeometricCalibrator, ProCamColorCalibrator), detection and matching of feature points (ObjectFinder), a set of classes that implement direct image alignment of projector-camera systems (mainly GNImageAligner, ProjectiveTransformer, ProjectiveColorTransformer, ProCamTransformer, and ReflectanceInitializer), a blob analysis package (Blobs), as well as miscellaneous functionality in the JavaCV class. Some of these classes also have an OpenCL and OpenGL counterpart, their names ending with CL or starting with GL, i.e.: JavaCVCL, GLCanvasFrame, etc. To learn how to use the API, since documentation currently lacks, please refer to the Sample Usage section below as well as the sample programs, including two for Android (FacePreview.java and RecordActivity.java), also found in the samples directory. You may also find it useful to refer to the source code of ProCamCalib and ProCamTracker as well as examples ported from OpenCV2 Cookbook and the associated wiki pages. Please keep me informed of any updates or fixes you make to the code so that I may integrate them into the next release. Thank you! And feel free to ask questions on the mailing list if you encounter any problems with the software! I am sure it is far from perfect... Downloads To install manually the JAR files, obtain the following archives and follow the instructions in the Manual Installation section below. JavaCV 1.3.3 binary archive javacv-platform-1.3.3-bin.zip (212 MB) JavaCV 1.3.3 source archive javacv-platform-1.3.3-src.zip (456 KB) The binary archive contains builds for Android, Linux, Mac OS X, and Windows. The JAR files for specific child modules or platforms can also be obtained individually from the Maven Central Repository. We can also have everything downloaded and installed automatically with: Maven (inside the pom.xml file) <dependency> <groupId>org.bytedeco</groupId> <artifactId>javacv-platform</artifactId> <version>1.3.3</version> </dependency> Gradle (inside the build.gradle file) dependencies { compile group: 'org.bytedeco', name: 'javacv-platform', version: '1.3.3' } sbt (inside the build.sbt file) libraryDependencies += "org.bytedeco" % "javacv-platform" % "1.3.3" This downloads binaries for all platforms, but to get binaries for only one platform we can set the javacpp.platform system property (via the -D command line option) to something like android-arm, linux-x86_64, macosx-x86_64, windows-x86_64, etc. Please refer to the README.md file of the JavaCPP Presets for details. Another option available for Scala users is sbt-javacv. Required Software To use JavaCV, you will first need to download and install the following software: An implementation of Java SE 7 or newer: OpenJDK http://openjdk.java.net/install/ or Sun JDK http://www.oracle.com/technetwork/java/javase/downloads/ or IBM JDK http://www.ibm.com/developerworks/java/jdk/ Further, although not always required, some functionality of JavaCV also relies on: CL Eye Platform SDK (Windows only) http://codelaboratories.com/downloads/ Android SDK API 14 or newer http://developer.android.com/sdk/ JOCL and JOGL from JogAmp http://jogamp.org/ Finally, please make sure everything has the same bitness: 32-bit and 64-bit modules do not mix under any circumstances. Manual Installation Simply put all the desired JAR files (opencv*.jar, ffmpeg*.jar, etc.), in addition to javacpp.jar and javacv.jar, somewhere in your class path. Here are some more specific instructions for common cases: NetBeans (Java SE 7 or newer): In the Projects window, right-click the Libraries node of your project, and select "Add JAR/Folder...". Locate the JAR files, select them, and click OK. Eclipse (Java SE 7 or newer): Navigate to Project > Properties > Java Build Path > Libraries and click "Add External JARs...". Locate the JAR files, select them, and click OK. IntelliJ IDEA (Android 4.0 or newer): Follow the instructions on this page: http://developer.android.com/training/basics/firstapp/ Copy all the JAR files into the app/libs subdirectory. Navigate to File > Project Structure > app > Dependencies, click +, and select "2 File dependency". Select all the JAR files from the libs subdirectory. After that, the wrapper classes for OpenCV and FFmpeg, for example, can automatically access all of their C/C++ APIs: OpenCV documentation FFmpeg documentation Sample Usage The class definitions are basically ports to Java of the original header files in C/C++, and I deliberately decided to keep as much of the original syntax as possible. For example, here is a method that tries to load an image file, smooth it, and save it back to disk: import static org.bytedeco.javacpp.opencv_core.*; import static org.bytedeco.javacpp.opencv_imgproc.*; import static org.bytedeco.javacpp.opencv_imgcodecs.*; public class Smoother { public static void smooth(String filename) { IplImage image = cvLoadImage(filename); if (image != null) { cvSmooth(image, image); cvSaveImage(filename, image); cvReleaseImage(image); } } } JavaCV also comes with helper classes and methods on top of OpenCV and FFmpeg to facilitate their integration to the Java platform. Here is a small demo program demonstrating the most frequently useful parts: import java.io.File; import java.net.URL; import org.bytedeco.javacv.*; import org.bytedeco.javacpp.*; import org.bytedeco.javacpp.indexer.*; import static org.bytedeco.javacpp.opencv_core.*; import static org.bytedeco.javacpp.opencv_imgproc.*; import static org.bytedeco.javacpp.opencv_calib3d.*; import static org.bytedeco.javacpp.opencv_objdetect.*; public class Demo { public static void main(String[] args) throws Exception { String classifierName = null; if (args.length > 0) { classifierName = args[0]; } else { URL url = new URL("https://raw.github.com/Itseez/opencv/2.4.0/data/haarcascades/haarcascade_frontalface_alt.xml"); File file = Loader.extractResource(url, null, "classifier", ".xml"); file.deleteOnExit(); classifierName = file.getAbsolutePath(); } // Preload the opencv_objdetect module to work around a known bug. Loader.load(opencv_objdetect.class); // We can "cast" Pointer objects by instantiating a new object of the desired class. CvHaarClassifierCascade classifier = new CvHaarClassifierCascade(cvLoad(classifierName)); if (classifier.isNull()) { System.err.println("Error loading classifier file \"" + classifierName + "\"."); System.exit(1); } // The available FrameGrabber classes include OpenCVFrameGrabber (opencv_videoio), // DC1394FrameGrabber, FlyCaptureFrameGrabber, OpenKinectFrameGrabber, OpenKinect2FrameGrabber, // RealSenseFrameGrabber, PS3EyeFrameGrabber, VideoInputFrameGrabber, and FFmpegFrameGrabber. FrameGrabber grabber = FrameGrabber.createDefault(0); grabber.start(); // CanvasFrame, FrameGrabber, and FrameRecorder use Frame objects to communicate image data. // We need a FrameConverter to interface with other APIs (Android, Java 2D, or OpenCV). OpenCVFrameConverter.ToIplImage converter = new OpenCVFrameConverter.ToIplImage(); // FAQ about IplImage and Mat objects from OpenCV: // - For custom raw processing of data, createBuffer() returns an NIO direct // buffer wrapped around the memory pointed by imageData, and under Android we can // also use that Buffer with Bitmap.copyPixelsFromBuffer() and copyPixelsToBuffer(). // - To get a BufferedImage from an IplImage, or vice versa, we can chain calls to // Java2DFrameConverter and OpenCVFrameConverter, one after the other. // - Java2DFrameConverter also has static copy() methods that we can use to transfer // data more directly between BufferedImage and IplImage or Mat via Frame objects. IplImage grabbedImage = converter.convert(grabber.grab()); int width = grabbedImage.width(); int height = grabbedImage.height(); IplImage grayImage = IplImage.create(width, height, IPL_DEPTH_8U, 1); IplImage rotatedImage = grabbedImage.clone(); // Objects allocated with a create*() or clone() factory method are automatically released // by the garbage collector, but may still be explicitly released by calling release(). // You shall NOT call cvReleaseImage(), cvReleaseMemStorage(), etc. on objects allocated this way. CvMemStorage storage = CvMemStorage.create(); // The OpenCVFrameRecorder class simply uses the CvVideoWriter of opencv_videoio, // but FFmpegFrameRecorder also exists as a more versatile alternative. FrameRecorder recorder = FrameRecorder.createDefault("output.avi", width, height); recorder.start(); // CanvasFrame is a JFrame containing a Canvas component, which is hardware accelerated. // It can also switch into full-screen mode when called with a screenNumber. // We should also specify the relative monitor/camera response for proper gamma correction. CanvasFrame frame = new CanvasFrame("Some Title", CanvasFrame.getDefaultGamma()/grabber.getGamma()); // Let's create some random 3D rotation... CvMat randomR = CvMat.create(3, 3), randomAxis = CvMat.create(3, 1); // We can easily and efficiently access the elements of matrices and images // through an Indexer object with the set of get() and put() methods. DoubleIndexer Ridx = randomR.createIndexer(), axisIdx = randomAxis.createIndexer(); axisIdx.put(0, (Math.random()-0.5)/4, (Math.random()-0.5)/4, (Math.random()-0.5)/4); cvRodrigues2(randomAxis, randomR, null); double f = (width + height)/2.0; Ridx.put(0, 2, Ridx.get(0, 2)*f); Ridx.put(1, 2, Ridx.get(1, 2)*f); Ridx.put(2, 0, Ridx.get(2, 0)/f); Ridx.put(2, 1, Ridx.get(2, 1)/f); System.out.println(Ridx); // We can allocate native arrays using constructors taking an integer as argument. CvPoint hatPoints = new CvPoint(3); while (frame.isVisible() && (grabbedImage = converter.convert(grabber.grab())) != null) { cvClearMemStorage(storage); // Let's try to detect some faces! but we need a grayscale image... cvCvtColor(grabbedImage, grayImage, CV_BGR2GRAY); CvSeq faces = cvHaarDetectObjects(grayImage, classifier, storage, 1.1, 3, CV_HAAR_FIND_BIGGEST_OBJECT | CV_HAAR_DO_ROUGH_SEARCH); int total = faces.total(); for (int i = 0; i < total; i++) { CvRect r = new CvRect(cvGetSeqElem(faces, i)); int x = r.x(), y = r.y(), w = r.width(), h = r.height(); cvRectangle(grabbedImage, cvPoint(x, y), cvPoint(x+w, y+h), CvScalar.RED, 1, CV_AA, 0); // To access or pass as argument the elements of a native array, call position() before. hatPoints.position(0).x(x-w/10) .y(y-h/10); hatPoints.position(1).x(x+w*11/10).y(y-h/10); hatPoints.position(2).x(x+w/2) .y(y-h/2); cvFillConvexPoly(grabbedImage, hatPoints.position(0), 3, CvScalar.GREEN, CV_AA, 0); } // Let's find some contours! but first some thresholding... cvThreshold(grayImage, grayImage, 64, 255, CV_THRESH_BINARY); // To check if an output argument is null we may call either isNull() or equals(null). CvSeq contour = new CvSeq(null); cvFindContours(grayImage, storage, contour, Loader.sizeof(CvContour.class), CV_RETR_LIST, CV_CHAIN_APPROX_SIMPLE); while (contour != null && !contour.isNull()) { if (contour.elem_size() > 0) { CvSeq points = cvApproxPoly(contour, Loader.sizeof(CvContour.class), storage, CV_POLY_APPROX_DP, cvContourPerimeter(contour)*0.02, 0); cvDrawContours(grabbedImage, points, CvScalar.BLUE, CvScalar.BLUE, -1, 1, CV_AA); } contour = contour.h_next(); } cvWarpPerspective(grabbedImage, rotatedImage, randomR); Frame rotatedFrame = converter.convert(rotatedImage); frame.showImage(rotatedFrame); recorder.record(rotatedFrame); } frame.dispose(); recorder.stop(); grabber.stop(); } } Furthermore, after creating a pom.xml file with the following content: <project> <modelVersion>4.0.0</modelVersion> <groupId>org.bytedeco.javacv</groupId> <artifactId>demo</artifactId> <version>1.3.3</version> <dependencies> <dependency> <groupId>org.bytedeco</groupId> <artifactId>javacv-platform</artifactId> <version>1.3.3</version> </dependency> </dependencies> </project> And by placing the source code above in src/main/java/Demo.java, we can use the following command to have everything first installed automatically and then executed by Maven: $ mvn compile exec:java -Dexec.mainClass=Demo Build Instructions If the binary files available above are not enough for your needs, you might need to rebuild them from the source code. To this end, the project files were created for: Maven 3.x http://maven.apache.org/download.html JavaCPP 1.3 https://github.com/bytedeco/javacpp JavaCPP Presets 1.3 https://github.com/bytedeco/javacpp-presets Once installed, simply call the usual mvn install command for JavaCPP, its Presets, and JavaCV. By default, no other dependencies than a C++ compiler for JavaCPP are required. Please refer to the comments inside the pom.xml files for further details. Project lead: Samuel Audet [samuel.audet at gmail.com](mailto:samuel.audet at gmail.com) Developer site: https://github.com/bytedeco/javacv Discussion group: http://groups.google.com/group/javacv