Building an Instagram clone - Part 2

In part 1 we took a look at some of the UI layout implementation details of the InstaFuzz app. You can get the source code for the app from here if you wish to run it locally. In this installment we'll take a look at some of the other bits such as how drag/drop, File API, Canvas and Web Workers are used.


One of the things that InstaFuzz supports is the ability to drag and drop image files directly on to the big blackish/blue box. Support for this is enabled by handling the "drop" event on the CANVAS element. When a file is dropped onto an HTML element the browser fires the "drop" event on that element and passes in a dataTransfer object which contains a files property that contains a reference to the list of files that were dropped. Here's how this is handled in the app ("picture" is the ID of the CANVAS element on the page):

var pic = $("#picture");
pic.bind("drop", function (e) {
    var files = e.originalEvent.dataTransfer.files;
    // more code here to open the file
pic.bind("dragover", suppressEvent).bind("dragenter", suppressEvent);
function suppressEvent(e) {

The files property is a collection of File objects that can then subsequently be used with the File API to access the file contents (covered in the next section). We also handle the dragover and dragenter events and basically prevent those events from propagating to the browser thereby preventing the browser from handling the file drop. IE for instance might unload the current page and attempt to open the file directly otherwise.

File API

Once the file has been dropped, the app attempts to open the image and render it in the canvas. It does this by using the File API. The File API is a W3C specification that allows web apps to programmatically access files from the local file system in a secure fashion. In InstaFuzz we use the FileReader object to read the file contents as a data URL string like so using the readAsDataURL method:

var reader = new FileReader();
reader.onloadend = function (e2) {

Here, files is the collection of File objects retrieved from the function handling the "drop" event on the CANVAS element. Since we are interested only in a single file we simply pick the first file from the collection and ignore the rest if there are any. The actual file contents are loaded asynchronously and once the load completes, the onloadend event is fired where we get the file contents as a data URL which we then subsequently draw on to the canvas.

Rendering the filters

Now the core functionality here is of course the application of the filters. In order to be able to apply the filter to the image we need a way to access the individual pixels from the image. And before we can access the pixels we need to have actually rendered the image on to our canvas. So let's first take a look at the code that renders the image that the user picked on to the canvas element.

Rendering images on to the canvas

The canvas element supports the rendering of Image objects via the drawImage method. To load up the image file in an Image instance, InstaFuzz uses the following utility routine:

App.Namespace.define("InstaFuzz.Utils", {
    loadImage: function (url, complete) {
        var img = new Image();
        img.src = url;
        img.onload = function () {

This allows the app to load up image objects from a URL using code such as the following:

function drawImageToCanvas(url) {
    InstaFuzz.Utils.loadImage(url, function (img) {
        // save reference to source image
        sourceImage = img;


        // load image filter previews

Here, mainRenderer is an instance created from the FilterRenderer constructor function defined in filter-renderer.js. The app uses FilterRenderer objects to manage canvas elements - both in the preview pane as well as the main canvas element on the right. The renderImage method on the FilterRenderer has been defined like so:

FilterRenderer.prototype.renderImage = function (img) {
    var imageWidth = img.width;
    var imageHeight = img.height;
    var canvasWidth = this.size.width;
    var canvasHeight = this.size.height;
    var width, height;

    if ((imageWidth / imageHeight) >= (canvasWidth / canvasHeight)) {
        width = canvasWidth;
        height = (imageHeight * canvasWidth / imageWidth);
    } else {
        width = (imageWidth * canvasHeight / imageHeight);
        height = canvasHeight;

    var x = (canvasWidth - width) / 2;
    var y = (canvasHeight - height) / 2;
    this.context.drawImage(img, x, y, width, height);

That might seem like a lot of code but all it does ultimately is to figure out the best way to render the image in the available screen area considering the aspect ratio of the image. The key piece of code that actually renders the image on the canvas occurs on the last line of the method. The context member refers to the 2D context acquired from the canvas object by calling its getContext method.

Fetching pixels from the canvas

Now that the image has been rendered we will need access to the individual pixels in order to apply all the different filters that are available. This is easily acquired by calling getImageData on the canvas's context object. Here's how InstaFuzz calls this from instafuzz.js.

var imageData = renderer.context.getImageData(
    0, 0,

The object returned by getImageData provides access to the individual pixels via its data property which in turn is an array like object that contains a collection of byte values where each value represents the color rendered for a single channel of a single pixel. Each pixel is represented using 4 bytes that specify values for the red, green, blue and alpha channels. It also has a length property that returns the length of the buffer. If you have a 2D co-ordinate you can easily transform that into an index into this array using code such as the following. The color intensity values of each channel ranges from 0 through 255. Here's the utility function from filters.js that accepts as input an image data object along with 2D coordinates for the pixel the caller is interested in and returns an object containing the color values:

function getPixel(imageData, x, y) {
    var data =, index = 0;

    // normalize x and y and compute index
    x = (x < 0) ? (imageData.width + x) : x;
    y = (y < 0) ? (imageData.height + y) : y;
    index = (x + y * imageData.width) * 4;

    return {
        r: data[index],
        g: data[index + 1],
        b: data[index + 2]

Applying the filters

Now that we have access to the individual pixels, applying the filter is fairly straightforward. Here, for instance is the function that applies a weighted grayscale filter on the image. It simply picks intensities from the red, green and blue channels and sums them up after applying a multiplication factor on each channel and then assigns the result for all 3 channels.

// "Weighted Grayscale" filter
    name: "Weighted Grayscale",
    apply: function (imageData) {
        var w = imageData.width, h = imageData.height;
        var data =;
        var index;
        for (var y = 0; y < h; ++y) {
            for (var x = 0; x < w; ++x) {
                index = (x + y * imageData.width) * 4;
                var luminance = parseInt((data[index + 0] * 0.3) +
                                         (data[index + 1] * 0.59) +
                                         (data[index + 2] * 0.11));
                        data[index + 0] = data[index + 1] =
                    data[index + 2] = luminance;

            Filters.notifyProgress(imageData, x, y, this);

        Filters.notifyProgress(imageData, w, h, this);

Once the filter has been applied we can have that reflected on the canvas by calling the putImageData method passing in the modified image data object. While the weighted grayscale filter is fairly simple most of the other filters use an image processing technique known as convolution. The code for all the filters is available in filters.js and the convolution filters were ported from the C code available here.

Web Workers

As you might imagine doing all this number crunching to apply the filters can potentially take a long time to complete. The motion blur filter for instance uses a 9x9 filter matrix for computing the new value for every single pixel and is in fact the most CPU intensive filter among them all. If we were to do all this computation on the UI thread of the browser then the app would essentially freeze every time a filter was being applied. To provide a responsive user experience the app delegates the core image processing tasks to a background script using the support for W3C Web Workers.aspx) in modern browsers.

Web workers allow web applications to have scripts run in a background task that executes in parallel along with the UI thread. Communication between the worker and the UI thread is accomplished by passing messages using the postMessage API. On both ends (i.e. the UI thread and the worker) this manifests as an event notification that you can handle. You can only pass "data" between workers and the UI thread, i.e., you cannot pass anything that has to do with the user interface - you cannot for instance, pass DOM elements to the worker from the UI thread.

In InstaFuzz the worker is implemented in the file filter-worker.js. All it does in the worker is handle the onmessage event and apply a filter and then pass the results back via postMessage. As it turns out, even though we cannot pass DOM elements (which means we cannot just hand a CANVAS element to the worker to have the filter applied) we can in fact pass the image data object as returned by the getImageData method that we discussed earlier. Here's the filter processing code from filter-worker.js:

importScripts("ns.js", "filters.js");

var tag = null;
onmessage = function (e) {
    var opt =;
    var imageData = opt.imageData;
    var filter;

    tag = opt.tag;
    filter = InstaFuzz.Filters.getFilter(opt.filterKey);

    var start =;
    var end =;

        type: "image",
        imageData: imageData,
        tag: tag,
        timeTaken: end - start

The first line pulls in some script files that the worker depends on by calling importScripts. This is similar to including a JavaScript file in a HTML document using the SCRIPT tag. Then we set up a handler for the onmessage event in response to which we simply apply the filter in question and pass the result back to the UI thread by calling postMessage. Simple enough!

The code that initializes the worker is in instafuzz.js and looks like this:

var worker = new Worker("js/filter-worker.js");

Not much is it? When a message is sent by the worker to the UI thread we handle it by specifying a handler for the onmessage event on the worker object. Here's how this is done in InstaFuzz:

worker.onmessage = function (e) {
    var isPreview =;
    switch ( {
        case "image":
            if (isPreview) {
              , 0, 0);
            } else {
          , 0, 0);

        // more code here

The code should be fairly self-explanatory. It simply picks the image data object sent by the worker and applies it to the relevant canvas's context object causing the modified image to be rendered on screen. Scheduling a filter for conversion with the worker is equally simple. Here's the routine that performs this function in InstaFuzz:

function scheduleFilter(filterId,
                        img, isPreview,
                        resetRender) {
    if (resetRender) {

    var imageData = renderer.context.getImageData(
        0, 0,

        imageData: imageData,
        width: imageData.width,
        height: imageData.height,
        filterKey: filterId,
        tag: isPreview

In conclusion

We saw that fairly intricate user experiences are possible today with HTML5 technologies such as Canvas, Drag/Drop, File API and Web Workers. Support for all of these technologies is quite good in pretty much all modern browsers. One thing that we did not address here is the question of making the app compatible with older browsers. That, truth be told, is a non-trivial but necessary task that I will hopefully be able to talk about in a future article.

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