要求:在现有的行政区划边界范围外,添加一层类似于云雾效果的图层。

参考:https://blog.csdn.net/laijieyao/article/details/41862473

思路:
由于openlayers支持添加canvas的作为图层,所以,利用canvas绘制一个云层的图层,然后叠加到map中

效果图:

实现:
ImageCanvas:https://openlayers.org/en/v4.6.5/apidoc/ol.source.ImageCanvas.html

//1.创建一个canvas对象
var canvasOption = new Object();
canvasOption.ratio = 1;//参考api,ratio设置为1时,canvas的画布整体大小为初始化时map所对应的窗口。
var isFirst = true;
var multipolygon;//这里是你的multipolygon对象,也就是要切割的行政区划边界
canvasOption.canvasFunction = function (extent, resolution, pixelRatio, size, projection) {if (isFirst)//这里必须要做一个判断,每次的范围变动都会引起重绘,从而触发该回调函数,不判断的话,将会导致canvas无法被绘制到地图上,出现闪现的情况{isFirst = false;var canvas = document.createElement('canvas');canvas.width = size[0];canvas.height = size[1];var context = canvas.getContext('2d');var image = new Image();image.src = 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";image.onload = function(){context.drawImage(image,0,0,canvas.width,canvas.height);context.fillStyle = "rgba(255,255,255,0.2)";context.fillRect(0,0,canvas.width,canvas.height);context.beginPath();var ll = multipolygon.getGeometry().getCoordinates();for (var l=0; l<ll.length; l++){  var c = ll[l];for (var i=0; i<c.length; i++){var pt = map.getPixelFromCoordinate(c[i][0]);//map.getPixelFromCoordinate(coordinate)的作用是将地图上的点,转换成canvas中的像素点context.moveTo (pt[0], pt[1]);for (var j=1; j<c[i].length; j++){pt = map.getPixelFromCoordinate(c[i][j]);context.lineTo (pt[0], pt[1]);}}}context.closePath();context.stroke();context.clip();//裁剪当前所绘的图形context.globalCompositeOperation='destination-out';//destination-out是取两个所绘图形的差集context.fillStyle = "black";context.fillRect(0, 0, size[0], size[1]);}return canvas;}
};
//为ImageCanvasLayer创建数据源
var imageCanvas = new ol.source.ImageCanvas(canvasOption);
//创建一个ImageCanvasLayer图层
var imageCanvasLayer = new ol.layer.Image({source: imageCanvas
});
map.addLayer(imageCanvasLayer);

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