21–30 of 38 posts
I would love to see this integrated with something like GitHub's image diff presenter. Example: https://github.com/cameronmcefee/Image-Diff-View-Modes/commi... Information: https://github.com/blog/817-behold-image-view-modes
I would love to see this integrated with something like GitHub's image diff presenter. Example: https://github.com/cameronmcefee/Image-Diff-View-Modes/commi... Information: https://github.com/blog/817-behold-image-view-modes
I don't see the value of this. What sort of use case does this have?
Disclosure: I'm a developer of this tool & work for BBC News. We use this for catching things that we'd never spot, a good example of how it has helped us is after a giant Sass refactor, we ran this tool against the old CSS site and the new CSS site, this showed us where things were off by a pixel or two (as well as big formatting changes). We've found this tool rather valuable, we're not saying everyone will, or tha…
See also https://github.com/bslatkin/dpxdt , a more featureful/complex implementation of the same idea. It also has some discussion of why you'd want to do this.
Any recommendations? I already know of OpenCV.
I am interested in limited image recognition and pattern matching for an iPad app of mine. For example, I'd like to recognize when there are the red, yello, and green buttons to the left of a point in an image and the edge of an active window just above, so I can provide instant feedback of some kind (probably based on a filtered+recolored version of the top of the window which is being moved) for a VNC app. Eventual…
What I do is think of ways I can cheat. Sometimes an algorithm that looks really complex in mathematical notation boils down to a for loop for X, and a for loop for Y, iterating over every pixel in the image looking for some feature.
If you know where your buttons will appear, roughly, you can scan that rectangle in your double for loop until you hit a long enough sequence of red, then continue scanning for a sequence of yellow, then green. If you're looking for the edge of a window, scan for a solid line of the window border color.
Basically, think of the simplest possible way to express what you want to know. Your algorithm doesn't need to have a clue what a button is, or have any real intelligence. It feels like cheating, really, but it works quite well for simple tasks.
Is there any reason why you couldn't do this non-visually? That is, using PhantomJS to determine that a DOM element is within certain bounds for a position using if statements?
See also https://github.com/bslatkin/dpxdt , a more featureful/complex implementation of the same idea. It also has some discussion of why you'd want to do this.
Maybe it's time to revive it.
I am interested in limited image recognition and pattern matching for an iPad app of mine. For example, I'd like to recognize when there are the red, yello, and green buttons to the left of a point in an image and the edge of an active window just above, so I can provide instant feedback of some kind (probably based on a filtered+recolored version of the top of the window which is being moved) for a VNC app. Eventual…
I'm by no means a computer vision expert, nor did any of my education cover the subject, but I have been dabbling in it long enough that I started a company based on limited CV. What I do is think of ways I can cheat. Sometimes an algorithm that looks really complex in mathematical notation boils down to a for loop for X, and a for loop for Y, iterating over every pixel in the image looking for some feature. If you k…
Is there any reason why you couldn't do this non-visually? That is, using PhantomJS to determine that a DOM element is within certain bounds for a position using if statements?
There's a few reasons: Simplicity. This automatically tests the whole page "for free". Robustness of feedback for time spent. A small visual change could cascade into lots of tests cases that must be updated (rendering is hard, look how long it took IE to get it right). You don't need to update any tests with this tool, you just give the OK to a visual diff with expected changes. Dynamic content. The BBC homepage is…
Also, the difference between a wanted visual change and a break is dependent on the change you want. That makes this quite a flexible tool (it's not just "any difference is wrong") that can be used simply and is unlikely to have false negatives (while making it easy to ignore false positives).