我正在努力理解toMat在akka流中的作用。例如:
val sink1:Sink[Int, Future[Int]]=Sink.fold[Int,Int](0)(_ + _)
val flow=Flow[Int].fold[Int](0){(x,y)=> x+y}
val runnable = Source (1 to 10).viaMat(flow)(Keep.right).toMat(sink1)(Keep.both)
viaMat和via的用途是什么?
toMat在viaMat到toMat之间做什么?
keep.both的用途是什么,这是否意味着我可以从以前的和当
如下所示:
import org.bytedeco.javacpp.opencv_core.Mat
import org.bytedeco.javacpp.{opencv_core, opencv_imgcodecs}
val jpegRawBytes: Array[Byte] = ??? // this is given, not a problem
val matRaw = new Mat(1, jpegRawBytes.length, opencv_core.CV_8UC1)
??? // mat.put(jpegRawBytes)
val matImg = opencv_imgc
我看了很多关于这方面的例子和帖子。我让它在某种程度上发挥了作用,但我还没有完全明白这个想法,当我试图将一个文件读入记录对象流(每一行一个)时,我仍然会被Future[IOResult]绊倒,我想要的是Future[List[LineRecordCaseClass]]。
val source = FileIO.fromPath(Paths.get("/tmp/junk_data.csv"))
val flow = makeFlow() // Framing.delimiter->split(",")->map to LineRecordCaseClas
我正在尝试查找图像中的图像匹配项。我已经编写了以下代码来使用OpenCVSharp3获取单个匹配:
Mat src = OpenCvSharp.Extensions.BitmapConverter.ToMat(Resources.all);
Mat template = OpenCvSharp.Extensions.BitmapConverter.ToMat(Resources.img);
Mat result = src.MatchTemplate(template, TemplateMatchModes.CCoeffNormed);
double minVal, maxVal;
Open
我试图将图像(16位灰度)转换为位图(16位灰度)。
这个例子不起作用(例外: ArgumentException)
private Mat ToMat16bGrayscale(Image im)
{
Bitmap bmp2 = new Bitmap(im);
}
但是这个例子很有效:
private Mat ToMat16bGrayscale(Image im)
{
//Cast obligatoire la création ou la copie d'une bitmap ne fonctionne pas ici..
我开始使用scala,并来到scala中这个特定的web套接字聊天室。
它们使用MessageHub.source()和BroadcastHub.sink()作为其信源和信宿,将消息发送到所有连接的客户端。
这个示例在交换消息时工作得很好。
private val (chatSink, chatSource) = {
// Don't log MergeHub$ProducerFailed as error if the client disconnects.
// recoverWithRetries -1 is essentially "recoverWith
我正在学习akka流,并在代码中遇到Keep.left和Keep.right:
implicit val system = ActorSystem("KafkaProducer")
implicit val materializer = ActorMaterializer()
val source = Source(List("a", "b", "c"))
val sink = Sink.fold[String, String]("")(_ + _)
val runnable: RunnableGraph[Fu
为了理解Keep.left或Keep.right的工作,我尝试找出不同的例子
我有以下代码:
val numSource = Source(1 to 10)
val numSource = Source(1 to 10)
val incrementFlow = Flow[Int].map(x => x +1)
val doubleFlow = Flow[Int].map(x => x * 10)
val someFuture: NotUsed = numSource.via(incrementFlow).via(doubleFlow).to(S
在c++中,我可以对矩阵执行行算法:
Mat pPrime = Mat::ones(Size(3, 3), CV_64F);
pPrime.row(0) = pPrime.row(0) / pPrime.row(2);
用OpenCVSharp可以执行行算法吗?试图复制c++代码会导致错误The left-hand side of an assignment must be a variable, property or indexer
Mat pPrime = Mat.Ones(new Size(3, 3), MatType.CV_64F).ToMat();
pPrime.Row(0) =
public void searchIMG(Bitmap screen_img, Bitmap find_img)
{
//스크린 이미지 선언
//using (Mat ScreenMat = OpenCvSharp.Extensions.BitmapConverter.ToMat(screen_img))
Mat ScreenMat = OpenCvSharp.Extensions.BitmapConverter.ToMat(screen_img);
//찾을 이미지 선언
Mat FindMat =
Mat a = new Mat(3, 1, MatType.CV_8UC1);
Mat b = new Mat(1,3, MatType.CV_8UC1);
for( int i = 0; i < 3; i++ )
{
a.Set<byte>(i, 0, (byte)i);
b.Set<byte>(0, i, (byte)(i + 3));
}
var c = a * b;
mat = c.ToMat
我正在对Akka流进行实验,以便确切地了解如何使用TCP Server从客户端接收到的内容(服务器不需要响应客户端)。
下面是一个标准的TCP服务器实现(在应用了我从@heiko的简明解释中了解到的内容之后):
def runServer(system: ActorSystem, address: String, port: Int, collectingSink: Sink[ByteString,NotUsed]): Unit = {
implicit val sys = system
import system.dispatcher
implicit val mat
参考中提到的以下实现:
val poolClientFlow = Http().cachedHostConnectionPool[Promise[HttpResponse]]("akka.io")
val queue =
Source.queue[(HttpRequest, Promise[HttpResponse])](QueueSize, OverflowStrategy.dropNew)
.via(poolClientFlow)
.toMat(Sink.foreach({
case ((Success(resp), p)) => p