This is the data that's sent with the request. This indicates that this is a multipart request.įinally, observe the request payload. We'll need this later.Īlso observe the Content-Type header. This is the exact URL that the image was posted to. Make a note of the request URL (highlighted in the screenshot). Most of these will be GET requests, but there should be one POST request – this is the image upload that we're looking for.Ĭlick on this request to reveal the request inspector. Before you click "Submit", make sure you have the "Preserve log" option activated (see screenshot).Īs the file is uploading and the browser window is updating, you'll see a bunch of activity flying by in the Network tab. Now select an image using the file selector popup and choose any filter. Open the Web Inspector (View -> Developer -> Developer Tools), and switch to the "Network" tab. Otherwise, how would the web app be able to process the pictures for us? But we don't need to stinkin' API documentation when we have our trusty Chrome Developer Tools.įirst of all, it's obvious that there must be a public API. You might be thinking "great idea, Chris, I thought about this too, but when I went to the site there was no mention of a public API!" Also, Dreamscope does not require an email address. Mainly because it's much faster than than Dream Deeply, even though (or maybe because) the latter is much more popular. If you know what npm is, you've seen a callback function before, and you can tell a HTTP GET request from a POST request, you should be able to follow along.įor the purposes of this tutorial, we'll use Dreamscope. NOTE: This tutorial is aimed at beginner to intermediate Node.js developer.
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This article will show you how to do just that. However, if you're anything like me, you soon start getting annoyed with this sort of point-and-click interface, and wish there was an easy way to batch-process a bunch of files.
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Even though the original source code claims rather cheerily that it is "designed to have as few dependencies as possible", it requires not only a working Python installation, but a number of additional modules, including the Caffe neural network library, which doesn't exactly provide a 1-click installer.Ī couple of programmers immediately sensed an opportunity and started building web-based front-ends to Google's code that make producing Deep Dream images accessible to the average Facebook user. If you've at all been near a computer during the last few weeks, there is no doubt you've seen the trippy, psychedelic pictures that are the result of Google's Deep Dream algorithm, which was recently released as Open Source.