DCV is @nogc nothrow now.

[Ferhat Kurtulmuş]

I have been working on DCV for some time to make it GC-free. GC 
is good. However, people have a prejudice against GC, especially 
if computer vision is in question. You may probably ask if any 
performance improvement has been gained. I don't know at the 
moment; with some limited tests (for instance, convolution), 
there is no difference, I would say. I tested most of the things 
in video loops to detect memory leaks, and I believe we don't 
have any. For the rest, I will copy and paste some of the below 
information from the GitHub page https://github.com/libmir/dcv.


- In the new API, all functions accept slice shells as Slice!(T*, 
N) as input. On the other hand, API functions return RC-allocated 
Slice!(RCI!T, N). In this way, ref-counted slices can be passed 
to API functions like input.lightScope. It is recommended to 
avoid using lightScope in loop bodies; instead, create a 
lightScope outside of the loop body before using it. It has a 
cost.
- nogc capabilities of mir libraries and dplug:core are utilized 
when needed.
- ThreadPool of dplug:core is used in the entire library for 
parallelism.
- class Image and class Figure use manual memory management. A 
call of function destroyFigures deallocates all allocated figures 
automatically. If an Image instance is initialized with non-null 
ubyte[] data, this time, the Image instance behaves like a slice 
shell, and it does not attempt to deallocate the borrowed data 
slice.
- All examples are up-to-date. Please refer to the examples 
instead of the docs at the moment.

- There may be bugs in the library. We need more people to test 
for bugs. The docs and unittests are outdated.

Please give it a try and destroy me :)