20090216-20090219 (about Red5 and Flex)

MXML is an XML-based user interface markup language first introduced by Macromedia in March 2004. Adobe Systems (which acquired Macromedia in December 2005) gives no official meaning for the acronym, but some developers suggest it should stand for "Magic eXtensible Markup Language" (which is a backronym).
MXML其实就类似于xhtml,xml用于定义控件及其属性,actionscript用来定义一些复杂的操作,类似的MXML也是事件驱动的,由event触发actionscript。
但是MXML比起xhtml的优势又在于哪里呢?
跨浏览器,更丰富的图形操作媒体播放能力。


关于ShareObject的开发,关键是先创建NetConnection,在其连接成功后用其创建一个share object(SharedObject.getRemote)。监听share object的SyncEvent.SYNC事件可以获取更新,修改share object可以对其进行更新。


对于如何录制视频,关键在于使用NetStream,将NetConnection,Camera和Microphone设置到NetStream上,然后调用他的publish()方法。


播放使用VideoDisplay控件,设置它的source属性然后调用它的play()方法,或者创建一个NetStream连接到媒体流上,然后调用video display的attachNetStream()和net stream的play()方法。
播放自己摄像头的图像使用VideoDisplay的attachCamera方法。


Application类有几个重要事件如下:
Application.onAppStart 当这个应用程序被服务器装载时调用。
Application.onAppStop 当这个应用程序被服务器卸载时调用。
Application.onConnect 当一个客户机连接到这个应用程序时调用。
Application.onDisconnect 当一个客户机从这个应用程序断开连接时调用。
Application类有几个重要方法如下:
Application.acceptConnection() 接受一个来自客户机的至一个应用程序的连接。
Application.broadcastMsg() 向所有连接的客户机广播一条消息。
Application.disconnect() 从服务器断开一个客户机的连接。
Application.rejectConnection() 拒绝至一个应用程序的连接。


NetConnection用于客户端(主动发起连接的一方,所以也可能是服务器连接另一台服务器)
NetConnection.client上的方法可以被连接的另一方调用(所以flash上经常有netConnection.client = this的方法)
NetConnection.call()用于调用对方的方法。


Client用于服务器端(被动连接的一方)
Client上的方法可以被连接的另一方调用。
Client.call用于调用对方的方法。


做视频聊天是需要综合利用上面提到各个类,除此之外还需要维护一个参加聊天的用户列表,在用户加入/离开时更新所有客户端。


Red5的ApplicationAdapter默认已经实现了各种功能,比如媒体录制、播放、共享对象等等,所以只有真的有需要才重载它的方法。
ApplicationAdapter的子类上所有的(public?)方法都可以被客户端call,要call客户端的方法就要把IConnection转成IServiceCapableConnection。

这个是库的 文档说明 react-native-vision Library for accessing VisionKit and visual applications of CoreML from React Native. iOS Only Incredibly super-alpha, and endeavors to provide a relatively thin wrapper between the underlying vision functionality and RN. Higher-level abstractions are @TODO and will be in a separate library. Installation yarn add react-native-vision react-native-swift react-native link Note react-native-swift is a peer dependency of react-native-vision. If you are running on a stock RN deployment (e.g. from react-native init) you will need to make sure your app is targeting IOS 11 or higher: yarn add react-native-fix-ios-version react-native link Since this module uses the camera, it will work much better on a device, and setting up permissions and codesigning in advance will help: yarn add -D react-native-camera-ios-enable yarn add -D react-native-setdevteam react-native link react-native setdevteam Then you are ready to run! react-native run-ios --device Command line - adding a Machine Learning Model with add-mlmodel react-native-vision makes it easier to bundle a pre-built machine learning model into your app. After installing, you will find the following command available: react-native add-mlmodel /path/to/mymodel.mlmodel You may also refere to the model from a URL, which is handy when getting something off the interwebs. For example, to apply the pre-built mobileNet model from apple, you can: react-native add-mlmodel https://docs-assets.developer.apple.com/coreml/models/MobileNet.mlmodel Note that the name of your model in the code will be the same as the filename minus the "mlmodel". In the above case, the model in code can be referenced as "MobileNet" Easy Start 1 : Full Frame Object Detection One of the most common easy use cases is just detecting what is in front of you. For this we use the VisionCamera component that lets you apply a model and get the classification via render props. Setup react-native init imagedetector; cd imagedetector yarn add react-native-swift react-native-vision yarn add react-native-fix-ios-version react-native-camera-ios-enable react-native-setdevteam react-native link react-native setdevteam Load your model with MobileNet A free download from Apple! react-native add-mlmodel https://docs-assets.developer.apple.com/coreml/models/MobileNet.mlmodel Add Some App Code import React from "react"; import { Text } from "react-native"; import { VisionCamera } from "react-native-vision"; export default () => ( <VisionCamera style={{ flex: 1 }} classifier="MobileNet"> {({ label, confidence }) => ( <Text style={{ width: "75%", fontSize: 50, position: "absolute", right: 50, bottom: 100 }} > {label + " :" + (confidence * 100).toFixed(0) + "%"} </Text> )} </VisionCamera> ); Easy Start 2: GeneratorView - for Style Transfer Most machine learning application are classifiers. But generators can be useful and a lot of fun. The GeneratorView lets you look at style transfer models that show how you can use deep learning techniques for creating whole new experiences. Setup react-native init styletest; cd styletest yarn add react-native-swift react-native-vision yarn add react-native-fix-ios-version react-native-camera-ios-enable react-native-setdevteam react-native link react-native setdevteam Load your model with add-mlmodel Apple has not published a style transfer model, but there are a few locations on the web where you can download them. Here is one: https://github.com/mdramos/fast-style-transfer-coreml So go to his github, navigate to his google drive, and then download the la_muse model to your personal Downloads directory. react-native add-mlmodel ~/Downloads/la_muse.mlmodel App Code This is the insanely short part. Note that the camera view is not necessary for viewing the style-transferred view: its just for reference. import React from "react"; import { GeneratorView, RNVCameraView } from "react-native-vision"; export default () => ( <GeneratorView generator="FNS-The-Scream" style={{ flex: 1 }}> <RNVCameraView style={{ position: "absolute", height: 200, width: 100, top: 0, right: 0 }} resizeMode="center" /> </GeneratorView> ); Easy Start 3: Face Camera Detect what faces are where in your camera view! Taking a page (and the model!) from (https://github.com/gantman/nicornot)[Gant Laborde's NicOrNot app], here is the entirety of an app that discerns whether the target is nicolas cage. Setup react-native init nictest; cd nictest yarn add react-native-swift react-native-vision yarn add react-native-fix-ios-version react-native-camera-ios-enable react-native-setdevteam react-native link react-native setdevteam Load your model with add-mlmodel react-native add-mlmodel https://s3.amazonaws.com/despiteallmyrage/MegaNic50_linear_5.mlmodel App Code import React from "react"; import { Text, View } from "react-native"; import { FaceCamera } from "react-native-vision"; import { Identifier } from "react-native-identifier"; export default () => ( <FaceCamera style={{ flex: 1 }} classifier="MegaNic50_linear_5"> {({ face, faceConfidence, style }) => face && (face == "nic" ? ( <Identifier style={{ ...style }} accuracy={faceConfidence} /> ) : ( <View style={{ ...style, justifyContent: "center", alignItems: "center" }} > <Text style={{ fontSize: 50, color: "red", opacity: faceConfidence }}> X </Text> </View> )) } </FaceCamera> ); Face Detection Component Reference FacesProvider Context Provider that extends <RNVisionProvider /> to detect, track, and identify faces. Props Inherits from <RNVisionProvider />, plus: interval: How frequently (in ms) to run the face detection re-check. (Basically lower values here keeps the face tracking more accurate) Default: 500 classifier: File URL to compiled MLModel (e.g. mlmodelc) that will be applied to detected faces updateInterval: How frequently (in ms) to update the detected faces - position, classified face, etc. Smaller values will mean smoother animation, but at the price of processor intensity. Default: 100 Example <FacesProvider isStarted={true} isCameraFront={true} classifier={this.state.classifier} > {/* my code for handling detected faces */} </FacesProvider> FacesConsumer Consumer of <FacesProvider /> context. As such, takes no props and returns a render prop function. Render Prop Members faces: Keyed object of information about the detected face. Elements of each object include: region: The key associated with this object (e.g. faces[k].region === k) x, y, height, width: Position and size of the bounding box for the detected face. faces: Array of top-5 results from face classifier, with keys label and confidence face: Label of top-scoring result from classifier (e.g. the face this is most likely to be) faceConfidence: Confidence score of top-scoring result above. Note that when there is no classifier specified, faces, face and faceConfidence are undefined Face Render prop generator to provision information about a single detected face. Can be instantiated by spread-propping the output of a single face value from <FacesConsumer> or by appling a faceID that maps to the key of a face. Returns null if no match. Props faceID: ID of the face (corresponding to the key of the faces object in FacesConsumer) Render Prop Members region: The key associated with this object (e.g. faces[k].region === k) x, y, height, width: Position and size of the bounding box for the detected face. Note These are adjusted for the visible camera view when you are rendering from that context. faces: Array of top-5 results from face classifier, with keys label and confidence face: Label of top-scoring result from classifier (e.g. the face this is most likely to be) faceConfidence: Confidence score of top-scoring result above. Note These arguments are the sam Faces A render-prop generator to provision information about all detected faces. Will map all detected faces into <Face> components and apply the children prop to each, so you have one function to generate all your faces. Designed to be similar to FlatMap implentation. Required Provider Context This component must be a descendant of a <FacesProvider> Props None Render Prop Members Same as <Face> above, but output will be mapped across all detected faces. Example of use is in the primary Face Recognizer demo code above. Props faceID: ID of the face applied. isCameraView: Whether the region frame information to generate should be camera-aware (e.g. is it adjusted for a preview window or not) Render Props This largely passes throught the members of the element that you could get from the faces collection from FaceConsumer, with the additional consideration that when isCameraView is set, style: A spreadable set of styling members to position the rectangle, in the same style as a RNVCameraRegion If faceID is provided but does not map to a member of the faces collection, the function will return null. Core Component References The package exports a number of components to facilitate the vision process. Note that the <RNVisionProvider /> needs to be ancestors to any others in the tree. So a simple single-classifier using dominant image would look something like: <RNVisionProvider isStarted={true}> <RNVDefaultRegion classifiers={[{url: this.state.FileUrlOfClassifier, max: 5}]}> {({classifications})=>{ return ( <Text> {classifications[this.state.FileUrlOfClassifier][0].label} </Text> }} </RNVDefaultRegion> </RNVisionProvider> RNVisionProvider Context provider for information captured from the camera. Allows the use of regional detection methods to initialize identification of objects in the frame. Props isStarted: Whether the camera should be activated for vision capture. Boolean isCameraFront: Facing of the camera. False for the back camera, true to use the front. Note only one camera facing can be used at a time. As of now, this is a hardware limitation. regions: Specified regions on the camera capture frame articulated as {x,y,width,height} that should always be returned by the consumer trackedObjects: Specified regions that should be tracked as objects, so that the regions returned match these object IDs and show current position. onRegionsChanged: Fires when the list of regions has been altered onDetectedFaces: Fires when the number of detected faces has changed Class imperative member detectFaces: Triggers one call to detect faces based on current active frame. Directly returns locations. RNVisionConsumer Consumer partner of RNVisionProvider. Must be its descendant in the node tree. Render Prop Members imageDimensions: Object representing size of the camera frame in {width, height} isCameraFront: Relaying whether camera is currently in selfie mode. This is important if you plan on displaying camera output, because in selfie mode a preview will be mirrored. regions: The list of detected rectangles in the most recently captured frame, where detection is driven by the RNVisionProvider props RNVRegion Props region: ID of the region (Note the default region, which is the whole frame, has an id of "" - blank.) classifiers: CoreML classifiers passed as file URLs to the classifier mlmodelc itself. Array generators: CoreML image generators passed as file URLs to the classifier mlmodelc itself. Array generators: CoreML models that generate a collection of output values passed as file URLs to the classifier mlmodelc itself. bottlenecks: A collection of CoreML models that take other CoreML model outputs as their inputs. Keys are the file URLs of the original models (that take an image as their input) and values are arrays of mdoels that generate the output passed via render props. onFrameCaptured: Callback to fire when a new image of the current frame in this region has been captured. Making non-null activates frame capture, setting to null turns it off. The callback passes a URL of the saved frame image file. Render Prop members key: ID of the region x, y, width, height: the elements of the frame containing the region. All values expressed as percentages of the overall frame size, so a 50x100 frame at origin 5,10 in a 500x500 frame would come across as {x: 0.01, y: 0.02, width: .1, height: .2}. Changes in these values are often what drives the re-render of the component (and therefore re-run of the render prop) confidence: If set, the confidence that the object identified as key is actually at this location. Used by tracked objects API of iOS Vision. Sometimes null. classifications: Collection, keyed by the file URL of the classifier passed in props, of collections of labels and probabilities. (e.g. {"file:///path/to/myclassifier.mlmodelc": {"label1": 0.84, "label2": 0.84}}) genericResults: Collection of generic results returned from generic models passed in via props to the region RNVDefaultRegion Convenience region that references the full frame. Same props as RNVRegion, except region is always set to "" - the full frame. Useful for simple style transfers or "dominant image" classifiers. Props Same as RNVRegion, with the exception that region is forced to "" Render Prop Members Same as RNVRegion, with the note that key will always be "" RNVCameraView Preview of the camera captured by the RNVisionProvider. Note that the preview is flipped in selfie mode (e.g. when isCameraFront is true) Props The properties of a View plus: gravity: how to scale the captured camera frame in the view. String. Valid values: fill: Fills the rectangle much like the "cover" in an Image resize: Leaves transparent (or style:{backgroundColor}) the parts of the rectangle that are left over from a resized version of the image. RNVCameraConsumer Render prop consumer for delivering additional context that regions will find helpful, mostly for rendering rectangles that map to the regions identified. Render Prop Members viewPortDimensions: A collection of {width, height} of the view rectangle. viewPortGravity: A pass-through of the gravity prop to help decide how to manage the math converting coordinates. RNVCameraRegion A compound consumer that blends the render prop members of RNVRegion and RNVCameraConsumer and adds a style prop that can position the region on a specified camera preview Props Same as RNVRegion Render Prop Members Includes members from RNVRegion and RNVCameraConsumer and adds: style: A pre-built colleciton of style prop members {position, width, height, left, top} that are designed to act in the context of the RNVCameraView rectangle. Spread-prop with your other style preferences (border? backgroundColor?) for easy on-screen representation. RNVImageView View for displaying output of image generators. Link it to , and the resulting image will display in this view. Useful for style transfer models. More performant because there is no round trip to JavaScript notifying of each frame update. Props id: the ID of an image generator model attached to a region. Usually is the file:/// URL of the .mlmodelc. Otherwise conforms to Image and View API. 请叫我如何做
11-06
基于数据驱动的 Koopman 算子的递归神经网络模型线性化,用于纳米定位系统的预测控制研究(Matlab代码实现)内容概要:本文围绕“基于数据驱动的Koopman算子的递归神经网络模型线性化”展开,旨在研究纳米定位系统的预测控制方法。通过结合数据驱动技术与Koopman算子理论,将非线性系统动态近似为高维线性系统,进而利用递归神经网络(RNN)建模并实现系统行为的精确预测。文中详细阐述了模型构建流程、线性化策略及在预测控制中的集成应用,并提供了完整的Matlab代码实现,便于科研人员复现实验、优化算法并拓展至其他精密控制系统。该方法有效提升了纳米级定位系统的控制精度与动态响应性能。; 适合人群:具备自动控制、机器学习或信号处理背景,熟悉Matlab编程,从事精密仪器控制、智能制造或先进控制算法研究的研究生、科研人员及工程技术人员。; 使用场景及目标:①实现非线性动态系统的数据驱动线性化建模;②提升纳米定位平台的轨迹跟踪与预测控制性能;③为高精度控制系统提供可复现的Koopman-RNN融合解决方案; 阅读建议:建议结合Matlab代码逐段理解算法实现细节,重点关注Koopman观测矩阵构造、RNN训练流程与模型预测控制器(MPC)的集成方式,鼓励在实际硬件平台上验证并调整参数以适应具体应用场景。
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