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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. No uncovered type parameters P1.=Pn may appear in T0.Ti (excluding Ti).At least one of the types T0.=Tn must be a local type.Given impl Trait for T0, an impl is valid only if at least one of the following is true: 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. 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. 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. 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).

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