初遇canvas之三

###canvas中的变换
    translate(x, y)
        我们先介绍 translate 方法,它用来移动 canvas的原点到一个不同的位置。
        translate 方法接受两个参数。x 是左右偏移量,y 是上下偏移量,
        
        注意:在canvas中translate是累加的,而CSS中不会,会覆盖
            translate会改变rotate的中心点

    rotate(angle)
        这个方法只接受一个参数:旋转的角度(angle),它是顺时针方向的,以弧度为单位的值。
        旋转的中心点始终是 canvas 的原点,如果要改变它,我们需要用到 translate 方法
        
        在canvas中rotate是累加的
        
        
    scale(x, y)
        scale 方法接受两个参数。x,y 分别是横轴和纵轴的缩放因子,它们都必须是正值。
        值比 1.0 小表示缩小,比 1.0 大则表示放大,值为 1.0 时什么效果都没有。
        缩放一般我们用它来增减图形在 canvas 中的像素数目,对形状,位图进行缩小或者放大。
        
        在canvas中scale是累乘的
    
    注意:
        translate、rotate、scale只能在图形绘制之前才能生效,写在图形绘制之后则不能生效,这一点和CSS十分不同。

<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.0 Transitional//EN"> <HTML> <HEAD> <TITLE></TITLE> <META NAME="Generator" CONTENT="EditPlus"> <META NAME="Author" CONTENT=""> <META NAME="Keywords" CONTENT=""> <META NAME="Description" CONTENT=""> <style> html, body { height: 100%; padding: 0; margin: 0; background: #000; } canvas { position: absolute; width: 100%; height: 100%; } .child { position: fixed; top: 50%; left: 50%; margin-top: -75px; margin-left: -100px; } h4 { font-size: 30px; font-family:'Verdana, Geneva, Tahoma, sans-serif'; color: #ffffff; position: relative; left: 10px; } </style> </HEAD> <BODY> <div class="child"> <h4>I LOVE YOU 王奕蕾</h4> </div> <canvas id="pinkboard"></canvas> <script> /* * Settings */ var settings = { particles: { length: 500, // maximum amount of particles duration: 2, // particle duration in sec velocity: 100, // particle velocity in pixels/sec effect: -0.75, // play with this for a nice effect size: 30, // particle size in pixels }, }; /* * RequestAnimationFrame polyfill by Erik Möller */ (function () { var b = 0; var c = ["ms", "moz", "webkit", "o"]; for (var a = 0; a < c.length && !window.requestAnimationFrame; ++a) { window.requestAnimationFrame = window[c[a] + "RequestAnimationFrame"]; window.cancelAnimationFrame = window[c[a] + "CancelAnimationFrame"] || window[c[a] + "CancelRequestAnimationFrame"] } if (!window.requestAnimationFrame) { window.requestAnimationFrame = function (h, e) { var d = new Date().getTime(); var f = Math.max(0, 16 - (d - b)); var g = window.setTimeout(function () { h(d + f) }, f); b = d + f; return g } } if (!window.cancelAnimationFrame) { window.cancelAnimationFrame = function (d) { clearTimeout(d) } } }()); /* * Point class */ var Point = (function () { function Point(x, y) { this.x = (typeof x !== 'undefined') ? x : 0; this.y = (typeof y !== 'undefined') ? y : 0; } Point.prototype.clone = function () { return new Point(this.x, this.y); }; Point.prototype.length = function (length) { if (typeof length == 'undefined') return Math.sqrt(this.x * this.x + this.y * this.y); this.normalize(); this.x *= length; this.y *= length; return this; }; Point.prototype.normalize = function () { var length = this.length(); this.x /= length; this.y /= length; return this; }; return Point; })(); /* * Particle class */ var Particle = (function () { function Particle() { this.position = new Point(); this.velocity = new Point(); this.acceleration = new Point(); this.age = 0; } Particle.prototype.initialize = function (x, y, dx, dy) { this.position.x = x; this.position.y = y; this.velocity.x = dx; this.velocity.y = dy; this.acceleration.x = dx * settings.particles.effect; this.acceleration.y = dy * settings.particles.effect; this.age = 0; }; Particle.prototype.update = function (deltaTime) { this.position.x += this.velocity.x * deltaTime; this.position.y += this.velocity.y * deltaTime; this.velocity.x += this.acceleration.x * deltaTime; this.velocity.y += this.acceleration.y * deltaTime; this.age += deltaTime; }; Particle.prototype.draw = function (context, image) { function ease(t) { return (--t) * t * t + 1; } var size = image.width * ease(this.age / settings.particles.duration); context.globalAlpha = 1 - this.age / settings.particles.duration; context.drawImage(image, this.position.x - size / 2, this.position.y - size / 2, size, size); }; return Particle; })(); /* * ParticlePool class */ var ParticlePool = (function () { var particles, firstActive = 0, firstFree = 0, duration = settings.particles.duration; function ParticlePool(length) { // create and populate particle pool particles = new Array(length); for (var i = 0; i < particles.length; i++) particles[i] = new Particle(); } ParticlePool.prototype.add = function (x, y, dx, dy) { particles[firstFree].initialize(x, y, dx, dy); // handle circular queue firstFree++; if (firstFree == particles.length) firstFree = 0; if (firstActive == firstFree) firstActive++; if (firstActive == particles.length) firstActive = 0; }; ParticlePool.prototype.update = function (deltaTime) { var i; // update active particles if (firstActive < firstFree) { for (i = firstActive; i < firstFree; i++) particles[i].update(deltaTime); } if (firstFree < firstActive) { for (i = firstActive; i < particles.length; i++) particles[i].update(deltaTime); for (i = 0; i < firstFree; i++) particles[i].update(deltaTime); } // remove inactive particles while (particles[firstActive].age >= duration && firstActive != firstFree) { firstActive++; if (firstActive == particles.length) firstActive = 0; } }; ParticlePool.prototype.draw = function (context, image) { // draw active particles if (firstActive < firstFree) { for (i = firstActive; i < firstFree; i++) particles[i].draw(context, image); } if (firstFree < firstActive) { for (i = firstActive; i < particles.length; i++) particles[i].draw(context, image); for (i = 0; i < firstFree; i++) particles[i].draw(context, image); } }; return ParticlePool; })(); /* * Putting it all together */ (function (canvas) { var context = canvas.getContext('2d'), particles = new ParticlePool(settings.particles.length), particleRate = settings.particles.length / settings.particles.duration, // particles/sec time; // get point on heart with -PI <= t <= PI function pointOnHeart(t) { return new Point( 160 * Math.pow(Math.sin(t), 3), 130 * Math.cos(t) - 50 * Math.cos(2 * t) - 20 * Math.cos(3 * t) - 10 * Math.cos(4 * t) + 25 ); } // creating the particle image using a dummy canvas var image = (function () { var canvas = document.createElement('canvas'), context = canvas.getContext('2d'); canvas.width = settings.particles.size; canvas.height = settings.particles.size; // helper function to create the path function to(t) { var point = pointOnHeart(t); point.x = settings.particles.size / 2 + point.x * settings.particles.size / 350; point.y = settings.particles.size / 2 - point.y * settings.particles.size / 350; return point; } // create the path context.beginPath(); var t = -Math.PI; var point = to(t); context.moveTo(point.x, point.y); while (t < Math.PI) { t += 0.01; // baby steps! point = to(t); context.lineTo(point.x, point.y); } context.closePath(); // create the fill context.fillStyle = '#ea80b0'; context.fill(); // create the image var image = new Image(); image.src = canvas.toDataURL(); return image; })(); // render that thing! function render() { // next animation frame requestAnimationFrame(render); // update time var newTime = new Date().getTime() / 1000, deltaTime = newTime - (time || newTime); time = newTime; // clear canvas context.clearRect(0, 0, canvas.width, canvas.height); // create new particles var amount = particleRate * deltaTime; for (var i = 0; i < amount; i++) { var pos = pointOnHeart(Math.PI - 2 * Math.PI * Math.random()); var dir = pos.clone().length(settings.particles.velocity); particles.add(canvas.width / 2 + pos.x, canvas.height / 2 - pos.y, dir.x, -dir.y); } // update and draw particles particles.update(deltaTime); particles.draw(context, image); } // handle (re-)sizing of the canvas function onResize() { canvas.width = canvas.clientWidth; canvas.height = canvas.clientHeight; } window.onresize = onResize; // delay rendering bootstrap setTimeout(function () { onResize(); render(); }, 10); })(document.getElementById('pinkboard')); </script> </BODY> </HTML>
08-15
内容概要:本文系统介绍了算术优化算法(AOA)的基本原理、核心思想及Python实现方法,并通过图像分割的实际案例展示了其应用价值。AOA是一种基于种群的元启发式算法,其核心思想来源于四则运算,利用乘除运算进行全局勘探,加减运算进行局部开发,通过数学优化器加速函数(MOA)和数学优化概率(MOP)动态控制搜索过程,在全局探索与局部开发之间实现平衡。文章详细解析了算法的初始化、勘探与开发阶段的更新策略,并提供了完整的Python代码实现,结合Rastrigin函数进行测试验证。进一步地,以Flask框架搭建前后端分离系统,将AOA应用于图像分割任务,展示了其在实际工程中的可行性与高效性。最后,通过收敛速度、寻优精度等指标评估算法性能,并提出自适应参数调整、模型优化和并行计算等改进策略。; 适合人群:具备一定Python编程基础和优化算法基础知识的高校学生、科研人员及工程技术人员,尤其适合从事人工智能、图像处理、智能优化等领域的从业者;; 使用场景及目标:①理解元启发式算法的设计思想与实现机制;②掌握AOA在函数优化、图像分割等实际问题中的建模与求解方法;③学习如何将优化算法集成到Web系统中实现工程化应用;④为算法性能评估与改进提供实践参考; 阅读建议:建议读者结合代码逐行调试,深入理解算法流程中MOA与MOP的作用机制,尝试在不同测试函数上运行算法以观察性能差异,并可进一步扩展图像分割模块,引入更复杂的预处理或后处理技术以提升分割效果。
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