![]() Use three nitros to help you win if it’s a hard text, and sometimes even on easy texts if you find that you are struggling to keep ahead of The Wampus. People usually get really nervous when The Wampus shows up, so take a few deep breaths and focus on winning the race, and not on the other racers. ![]() If you beat it, you earn an extra $50,000 at the end of the race! Unlike the other NT bots, The Wampus is an elusive rodent that appears on the track when you least expect it. Including The Wampus, there are 17 Nitro Type bots. Without them, races could take a very long time to load – even hours for uber-fast typers. They are there for a good reason: to provide you competition when there are no real racers racing in your speed range. Because they are computerized, you’ll find that their accuracy reads N/A, that they never use nitros, and that they all have the title “Nitro Type Bot”. Nitro Type bots are basically robotic opponents, programmed to race at about your speed. For example, on the “Achievements” page in the “King of the Race” section, you will find many cars which can be achieved by doing a large amount of races. ![]() The more races you complete, the more achievements you’ll earn. Cash is used to buy cars, nitros, teams, or even to give to friends or team mates, while exp. The better your place at the end of a race, the more cash and exp. Racing gains you lots of NT cash quickly, along with experience points. This will also include explanations on “Nitro Type Bots”, session and accuracy bonuses, and more. This will include tips on doing race sessions, tips on racing with friends, and basically everything you want to know about racing on Nitro Type. 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).Below you will learn about the many aspects of racing on Nitro Type. 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. 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. What I need is the full extent of both images, placed on the same pixel coordinate system. This question is almost a duplicate of this one - and the excellent answer and repository there provides this functionality for OpenCV transformations. I unfortunately need this for scipy's implementation. 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. 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. ![]() The transformations from src to dst can have translations and rotation. 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 ).
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