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收藏 本章给大家介绍html5如何利用canvas实现图片转素描效果。有一定的参考价值，有需要的朋友可以参考一下，希望对你们有所帮助。
素描滤镜原理：
最基础的算法就是：
1、去色；（去色公式：gray = 0.3 red + 0.59 green + 0.11 * blue）
2、复制去色图层，并且反色；
3、对反色图像进行高斯模糊；
4、模糊后的图像叠加模式选择颜色减淡效果。
减淡公式：C =MIN( A +（A×B）/（255-B）,255)，其中C为混合结果，A为去色后的像素点，B为高斯模糊后的像素点。
先看看效果对比图：
sigma可以调节效果。
代码实例：
<!DOCTYPE html> <html> <head> <meta charset="UTF-8"> <title></title> </head> <body> <div id="controls"> <input type="file" name="" id="imgs" value=""/> <br /> <!--<input type="range" name="" id="range_radius" value="10" oninput="changeRadius()"/> radius：<span id="value_radius">1</span> <br />--> <input type="range" name="" id="range_sigma" value="40" oninput="changeSigma()"/> sigma：<span id="value_sigma">0.8</span> <br /> <a href="" download="canvas_love.png" id="save_href">下载</a> </div> <canvas id="canvas1" width="" height=""></canvas> <br> <canvas id="canvas2" width="" height=""></canvas> <script type="text/javascript"> var eleImg = document.getElementById("imgs"); var eleRadius = document.getElementById("range_radius"); var eleSigma = document.getElementById("range_sigma"); var valueRadius = document.getElementById("value_radius"); var valueSigma = document.getElementById("value_sigma"); var svaeHref = document.getElementById("save_href"); var imgSrc = "img/2.jpg"; var radius = 1; var sigma = 0.8; eleImg.addEventListener("input",function (e) { var fileObj = e.currentTarget.files[0] if (window.FileReader) { var reader = new FileReader(); reader.readAsDataURL(fileObj); //监听文件读取结束后事件 reader.onloadend = function (e) { imgSrc = e.target.result; //e.target.result就是最后的路径地址 sketch() }; } }); var butSave = document.getElementById("save"); function changeRadius() { valueRadius.innerText = eleRadius.value/10; radius = eleRadius.value/10; sketch() } function changeSigma() { valueSigma.innerText = eleSigma.value/50; sigma = eleSigma.value/50; sketch() } var canvas1 = document.querySelector("#canvas1"); var cxt1 = canvas1.getContext("2d"); var canvas = document.querySelector("#canvas2"); var cxt = canvas.getContext("2d"); function sketch() { cxt1.clearRect(0,0,canvas1.width,canvas1.height); cxt.clearRect(0,0,canvas.width,canvas.height); var img = new Image(); img.src = imgSrc; img.onload = function () { canvas1.width = 600; canvas1.height = (img.height/img.width)*600; cxt1.drawImage(img, 0, 0, canvas1.width, canvas1.height); canvas.width = 600; canvas.height = (img.height/img.width)*600; cxt.drawImage(img, 0, 0, canvas.width, canvas.height); var imageData = cxt.getImageData(0, 0, canvas.width, canvas.height); //对于 ImageData 对象中的每个像素，都存在着四方面的信息，即 RGBA 值 var imageData_length = imageData.data.length/4; // var originData = JSON.parse(JSON.stringify(imageData)) // 解析之后进行算法运算 var originData = []; for (var i = 0; i < imageData_length; i++) { var red = imageData.data[i*4]; var green = imageData.data[i*4 + 1]; var blue = imageData.data[i*4 + 2]; var gray = 0.3 * red + 0.59 * green + 0.11 * blue;//去色 originData.push(gray) originData.push(gray) originData.push(gray) originData.push(imageData.data[i * 4 + 3]) var anti_data = 255 - gray;//取反 imageData.data[i * 4] = anti_data; imageData.data[i * 4 + 1] = anti_data; imageData.data[i * 4 + 2] = anti_data; } imageData = gaussBlur(imageData, radius, sigma)//高斯模糊 for (var i = 0; i < imageData_length; i++) { var dodge_data = Math.min((originData[i*4] + (originData[i*4]*imageData.data[i * 4])/(255-imageData.data[i * 4])), 255)//减淡 imageData.data[i * 4] = dodge_data; imageData.data[i * 4 + 1] = dodge_data; imageData.data[i * 4 + 2] = dodge_data; } console.log(imageData) cxt.putImageData(imageData, 0, 0); var tempSrc = canvas.toDataURL("image/png"); svaeHref.href=tempSrc; } } sketch() function gaussBlur(imgData, radius, sigma) { var pixes = imgData.data, width = imgData.width, height = imgData.height; radius = radius || 5; sigma = sigma || radius / 3; var gaussEdge = radius * 2 + 1; // 高斯矩阵的边长 var gaussMatrix = [], gaussSum = 0, a = 1 / (2 * sigma * sigma * Math.PI), b = -a * Math.PI; for (var i=-radius; i<=radius; i++) { for (var j=-radius; j<=radius; j++) { var gxy = a * Math.exp((i * i + j * j) * b); gaussMatrix.push(gxy); gaussSum += gxy; // 得到高斯矩阵的和，用来归一化 } } var gaussNum = (radius + 1) * (radius + 1); for (var i=0; i<gaussNum; i++) { gaussMatrix[i] = gaussMatrix[i] / gaussSum; // 除gaussSum是归一化 } //console.log(gaussMatrix); // 循环计算整个图像每个像素高斯处理之后的值 for (var x=0; x<width;x++) { for (var y=0; y<height; y++) { var r = 0, g = 0, b = 0; //console.log(1); // 计算每个点的高斯处理之后的值 for (var i=-radius; i<=radius; i++) { // 处理边缘 var m = handleEdge(i, x, width); for (var j=-radius; j<=radius; j++) { // 处理边缘 var mm = handleEdge(j, y, height); var currentPixId = (mm * width + m) * 4; var jj = j + radius; var ii = i + radius; r += pixes[currentPixId] * gaussMatrix[jj * gaussEdge + ii]; g += pixes[currentPixId + 1] * gaussMatrix[jj * gaussEdge + ii]; b += pixes[currentPixId + 2] * gaussMatrix[jj * gaussEdge + ii]; } } var pixId = (y * width + x) * 4; pixes[pixId] = ~~r; pixes[pixId + 1] = ~~g; pixes[pixId + 2] = ~~b; } } imgData.data = pixes; return imgData; } function handleEdge(i, x, w) { var m = x + i; if (m < 0) { m = -m; } else if (m >= w) { m = w + i - x; } return m; } </script> </body> </html>
上面就是canvas实现图片转素描效果的全部代码，大家可以自己动手编译调试。
