This means the solution is to prevent the canvas from being tainted in the first place. I did not find a way to allow the reading a tainted canvas. This means that a cross-origin image can only be displayed: drawing it to a canvas makes the canvas is tainted (= unsafe), and then any attempt to read back the canvas data are blocked for security reasons. Uncaught DOMException: Failed to execute 'getImageData' on 'CanvasRenderingContext2D': The canvas has been tainted by cross-origin data. However the browser prevents the image data from being accessed: using the CanvasRenderingContext2D.getImageData() API fails, and the browser throws an exception: It can even be displayed in the canvas2D with the CanvasRenderingContext2D.drawImage() API. In this case, I discovered the browser allows even a cross-origin image to be loaded if I use directly an Image object in javascript. If not allowed, queries from B to A are blocked for instance, any unauthorized cross-origin XMLHttpRequest fails. The domain A declares the allowed domains in an allow-list provided in the Access-Control-Allow-Origin response header. The base idea is that cross-origin resources hosted on a domain A can only be read from a domain B if B declared by A as an allowed domain. As described here, cross-origin requests have a few quirks. Most of the time, the URL is from another website, so the image is cross-origin. Intially, I wanted my to give the user the possibility to load an image from any URL. CORS issuesįor this project I encountered a Cross-origin resource sharing (CORS) issue I did not expect. Here is an example of curve showing how the difference varies with displacement:ĭepth map rebuilt by analyzing the Magic Eye image. To determine the period of the background plane, I compute the global difference for varying displacements, and choose the best one based on its value and its neighbours. Moving the slider manually can still give an idea of the scene. For stereograms that don't, for instance a ripple that takes the whole image, this technique is not very effective. Of course, this only works for stereograms that have a background plane. This is why I restricted my self to building only a binary version of the depth map: I determine what is in the background (black) and the rest of the scene is only shown as a colored silhouette. My guess is that I would need to apply a kind of blur to the source image beforehand, in order to remove artifacts. In theory, I could use this technique to automatically rebuild a full depth map of the image, however I did not achieve great result with it. This can very easily be visualized by slowly moving the slider that controls the size of the lateral shift.īy varying the displacement, one can clearly see the 3D at different depths. This way, when I hit the right period, the difference is very small and the pixels appear black. In this project, I simply compute the difference between the image and a shifted version of itself. This means that to reconstruct a depth map from a stereogram, we can first try to determine the frequency, and then we rebuild the depth map from it. It works for most of autosterograms, especially if they have a simple plane as background.Īs described in my Magic Eye generator project, a stereogram is all about horizontal repetition of a certain pattern, and the repetition frequency depends on the local depth of the scene. This website is a tool to reveal the 3D scene hidden in a stereogram by revealing its silhouette. In each image, there is a 3D object that can only be viewed by looking at the image a certain way, as if the screen was transparent and you looked at the wall behind it.Ĭorrectly viewing a Magic Eye definitely takes some practice, and some people are completely unable to see them. An autostereogram (also known as Magic Eye) is a 2D image designed to create the illusion of 3D.
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