如何设置自己的图标标记的人聚类在谷歌地图 [英] How to set my own icon for markers in clusterer in Google Maps

问题描述

我使用的GoogleMaps集群。我有4个不同类型的标记，每个从一个抽象类扩展，各有其OWM图标，它MarkerOptions字段中定义。我用ClusterManager这个抽象类。 当我只是用

I'm using GoogleMaps clustering. I have 4 different types of markers, each extends from one abstract class, each has its owm icon and it defined in MarkerOptions field. I use ClusterManager for this abstract class. When I just use

//marker is instance of one of extended classes and has abstract class type
clusterManager.addItem(marker);

集群后，它的图标设置为默认值。我该如何解决这个问题？我试图用这样的：

After clustering it sets icon to default. How I can fix this? I was trying to use something like this:

//marker is abstract marker, getMarker returns the MarkerOptions of this marker
clusterManager.getMarkerCollection().addMarker(marker.getMarker());

，但它不工作太，标记印刷有在地图上的所需要的图标，但没有聚集

but it doesn't work too, marker is printed with needed icon on map, but without clustering.

我应该建立在我的抽象类中创建了一些方法还是有一些方法来做到这一点从DefaultClusterRenderer延伸？我还没有找到它使用谷歌或学习谷歌地图的lib的一些信息。

Should I create create some method in my abstract class or there are some way to do this extending from DefaultClusterRenderer? I haven't found some information about it using Google or learning google maps lib.

感谢您的帮助！

推荐答案

所以，这是我的愚蠢。此外，它表明，我应该更多地关注，研究图书馆。如果有人有兴趣的答案，那就是： 我是正确的假设，我需要重写DefaultClusterRenderer一些方法。所以，充分的方式： 创建自己的类和DefaultClusterRenderer扩展它：

So, it was my foolish. Again, it shows, that I should pay more attention, studying library. If somebody is interested in answer, here it is: I was right supposing, that I need to override some method in DefaultClusterRenderer. So, the full way: Create own class and extend it from DefaultClusterRenderer:

public class OwnIconRendered extends DefaultClusterRenderer<AbstractMarker>

然后重写方法 onBeforeClusterItemRendered

@Override
protected void onBeforeClusterItemRendered(AbstractMarker item,
        MarkerOptions markerOptions) {
    markerOptions.icon(item.getMarker().getIcon());
}

该方法是相当简单的，但在我看来，那集群开始工作较慢。因此，这就足够了。

The way is rather simple, but it seems to me, that clustering started to work slower. So, that's enough.

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