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Javascript图像转换? [缩放和旋转]

[英]Javascript Image Transform? [scale and rotate]

I'm looking to build an image transform tool with Javascript. 我正在寻找使用Javascript构建图像转换工具。 Something that utilizes handles around the image similar to Photoshop and allows the user to scale and rotate. 利用类似于Photoshop的图像周围的手柄进行处理的操作,并允许用户缩放和旋转。 I'm looking to make this work in IE 6 and up and Firefox 3+ and Safari 3+. 我希望在IE 6和更高版本以及Firefox 3+和Safari 3+中实现此功能。

Does anyone know of a library or tool that could help with this? 有谁知道可以帮助您解决此问题的库或工具? I've seen a lot of tools that utilize the Canvas element but that leaves out IT. 我已经看到了许多利用Canvas元素的工具,但这些工具都没有使用IT。 I've also seen the Raphael library which might work. 我还看到了可能有效的Raphael库。 Any other options out there? 还有其他选择吗?

Have a look at this rotorzoom. 看看这个转子变焦。 It rotates it zooms it's fast and i can do with a few more hits. 它旋转旋转,变焦很快,我可以再打几下。

http://codepen.io/hex2bin/pen/tHwhF http://codepen.io/hex2bin/pen/tHwhF

 var requestId = 0; var animationStartTime = 0; var img = new Image(); initimg(img); dst = document.getElementById("dst").getContext("2d"); dst.drawImage(img, 0, 0, 256, 256); // read the width and height of the canvas i = 0; var imageDataDst = dst.getImageData(0, 0, 1024, 512); var bufDst = new ArrayBuffer(imageDataDst.data.length); var buf8Dst = new Uint8ClampedArray(bufDst); var data32Dst = new Uint32Array(bufDst); var data32Src = new Uint32Array(256*256); var scan1=0; var scan4=0 // fill the source array with the image for (var y = 0; y < 256; ++y) { scan4=y*1024*4; for (var x = 0; x < 256; ++x) { data32Src[scan1++] = (255 << 24) + // alpha (imageDataDst.data[scan4+2] << 16) + // blue (imageDataDst.data[scan4+1] << 8) + // green imageDataDst.data[scan4]; // red scan4=scan4+4; } } animationStartTime = window.performance.now(); requestId = window.requestAnimationFrame(animate); var j=0; function animate(time) { var height=512; var width=1024; j=j+1; var timestamp = j / 100; var pos; var startY = 128; var startX = 128; var i=0; var scaledHeight = 512 + Math.sin(timestamp*1.4) * 500; var scaledWidth = 512 + Math.sin(timestamp*1.4) * 500 var angleRadians = timestamp; var deltaX1 = Math.cos(angleRadians) * scaledHeight / 256; var deltaY1 = Math.sin(angleRadians) * scaledHeight / 256; var deltaY1x256=deltaY1*256; var deltaX2 =Math.sin(angleRadians ) * scaledWidth / 256; var deltaY2 =- Math.cos(angleRadians ) * scaledWidth / 256; var h = height; while (h--) { var x =262144+ startX+deltaX1*-512+deltaX2*-256; var y =262144+ startY+deltaY1*-512+deltaY2*-256; var y256=y*256; var w = width; while (w--) { //Optimised inner loop. Can it be done better? pos =(y256>>0&0xff00)+(x>>0&0xff); data32Dst[i++] =data32Src[pos]; x += deltaX1; y256 += deltaY1x256; //end of inner loop } startX += deltaX2 startY += deltaY2; } imageDataDst.data.set(buf8Dst); dst.putImageData(imageDataDst, 0, 0); requestId = window.requestAnimationFrame(animate); } function initimg(image1) { image1.src = 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'; 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 <body> <canvas id="dst" width="1024", height="512"> Random Canvas </canvas> </body> 

在Github上查看Raphael库: https : //github.com/DmitryBaranovskiy/raphael

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