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Planetary.js 旋转地球插件

(function() { var canvas = document.getElementById('quakeCanvas'); // Create our Planetary.js planet and set some initial values; // we use several custom plugins, defined at the bottom of the file var planet = planetaryjs.planet(); planet.loadPlugin(autocenter({extraHeight: -120})); planet.loadPlugin(autoscale({extraHeight: -120})); planet.loadPlugin(planetaryjs.plugins.earth({ topojson: { file: 'https://101.43.39.125/HexoFiles/js/planetaryjs/world-110m.json' }, oceans: { fill: '#001320' }, land: { fill: '#06304e' }, borders: { stroke: '#001320' } })); planet.loadPlugin(planetaryjs.plugins.pings()); planet.loadPlugin(planetaryjs.plugins.zoom({ scaleExtent: [50, 5000] })); planet.loadPlugin(planetaryjs.plugins.drag({ onDragStart: function() { this.plugins.autorotate.pause(); }, onDragEnd: function() { this.plugins.autorotate.resume(); } })); planet.loadPlugin(autorotate(5)); planet.projection.rotate([100, -10, 0]); planet.draw(canvas); // Plugin to resize the canvas to fill the window and to // automatically center the planet when the window size changes function autocenter(options) { options = options || {}; var needsCentering = false; var globe = null; var resize = function() { var width = window.outerWidth /2 + (options.extraWidth || 0); var height = window.outerHeight/2 + (options.extraHeight || 0); globe.canvas.width = width; globe.canvas.height = height; globe.projection.translate([width / 2, height / 2]); }; return function(planet) { globe = planet; planet.onInit(function() { needsCentering = true; d3.select(window).on('resize', function() { needsCentering = true; }); }); planet.onDraw(function() { if (needsCentering) { resize(); needsCentering = false; } }); }; }; // Plugin to automatically scale the planet's projection based // on the window size when the planet is initia

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大盘点|三维视觉与自动驾驶数据集(40个)

简介:KITTI数据集由德国卡尔斯鲁厄理工学院和丰田美国技术研究院联合创办,是目前国际上最大的自动驾驶场景下的算法评测数据集。该数据集用于评测立体图像(stereo),光流(optical flow),视觉测距(visual odometry),3D物体检测(object detection)和3D跟踪(tracking)等计算机视觉技术在车载环境下的性能。KITTI包含市区、乡村和高速公路等场景采集的真实图像数据,每张图像中最多达15辆车和30个行人,还有各种程度的遮挡与截断。整个数据集由389对立体图像和光流图,39.2 km视觉测距序列以及超过200k 3D标注物体的图像组成,以10Hz的频率采样及同步。对于3D物体检测,label细分为car, van, truck, pedestrian, pedestrian(sitting), cyclist, tram以及misc。

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