The T in Box is not considered covered, and Box is considered local.Ī trait which was defined in the current crate. Note that for the purposes of coherence, fundamental types are special. Only the appearance of uncovered type parameters is restricted.
Translation-only working example of padded affine transformation, which follows largely this repo, explained in this answer: I have tried to calculate what should be the correct offset (see this question's answers again), but I can't get it working in all scenarios. However, I am getting thoroughly lost combining the two. I can get translations only working (an example is shown below) and I can get rotations only working (largely hacking around the below and taking inspiration from the use of the reshape argument in ). The transformations from src to dst can have translations and rotation.
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The latter question did give some insight into the wonderful world of scipy's affine transformation, but I have as yet been unable to crack my particular needs. Much too late, after repeatedly hitting a brick wall trying to translate the above question's answer to scipy, I came across this issue and subsequently followed to this question. I unfortunately need this for scipy's implementation.
This question is almost a duplicate of this one - and the excellent answer and repository there provides this functionality for OpenCV transformations.
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What I need is the full extent of both images, placed on the same pixel coordinate system. The problem is that, when the images are not fuly overlapping, the resultant image is cropped to only the common footprint of the two images. I am already able to calculate the Affine Transformation rotation and offset matrix, which I feed to _transform to recover the dst-aligned src image. If you find a bunch of people who are willing to try out something new or revive something old, this one is a very safe bet.I have source ( src) image(s) I wish to align to a destination ( dst) image using an Affine Transformation whilst retaining the full extent of both images during alignment (even the non-overlapping areas). Worth trying out and experimenting withĮven though the age of Counter-Strike 1.6 is almost gone, games like AssaultCube Reloaded can still be loads of fun. And here we come back to the open-source bit. Another little setback are the weapons, or more accurately, the lack of them.
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By default, the game looks really good when compared to other free games, it even gives Counter-Strike a run for its money. And again, since it’s open-source, you can change that. On the downside, AssaultCube Reloaded is a bit old, and the graphics that are used might not seem all that appealing. Along with that factor in the existence of game modes such as Deathmatch, One Shot One Kill, Last Swiss Standing, Pistol Frenzy, Capture the Flag and others, and you can see how little you can get bored with it. Dynamic and fun gameplayĪssaultCube Reloaded features fast-paced combat that’s similar to Quake. With a game that’s delivered like this, if you have the skills, there’s little you can’t do. You can create your own models and use them in the game, devise new weapons, build new maps or enhance existing ones.
Moreover, AssaultCube Reloaded provides support for both singleplayer and multiplayer, and since it’s open-source, it’s easy to see just how much freedom you have with it.
The latter is an open-source product and as you can figure it out, AssaultCube Reloaded is also free to use, modify and enhance as you wish, given you respect the license. Simple, practical and open sourceĪssaultCube Reloaded is built to improve on AssaultCube, which uses the Cube engine. On top of that, you get several game modes and an editor that allows you to do just about anything you want in the game. It has the fast-paced gameplay of the first and a visual style that resembles the second. AssaultCube Reloaded is a first-person shooter that places itself somewhere between Quake and Counter-Strike.
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