Netatmo technical test

The subject is available here: Test Algo

Question 1

As a preprocessing step of a second algorithm A2, we would like to combine all the regions corresponding to the bounding boxes into a new image FRegions of dimension D × D, where D given by A2 and D < min(M, N )

This is a packing problem, as described on Wikipedia. Since this is NP-hard, we know the "exact" solution might be unreachable but we could find a solution that is good enough for our needs.

I looked up some solutions online and found a great article by David Colson: "Exploring rectangle packing algorithms". It gives a lot a references and compares different algorithms.

I decided to implement his naive "row packer" to quickly have an implementation and try it out.

If I were to implement a more robust algorithm, I would choose the Skyline approach. It looks likes a good trade-off of quality and performance.

Question 2

A2 then takes as input F Regions and outputs new bounding boxes B′. We would like now to compute the location of each of these new bounding boxes in F reference

To map the new bounding boxes into the original frame F, we need to have a mapping between the bounding boxes of F and the ones we packed into FRegions. During the packing, I saved the bounding boxes position in FRegions, so the mapping was straightforward:

detect in which bounding box in FRegions the new bounding box is contained
apply the transformation from FRegions to F

Because we look into every bounding box to find the enclosing one, we have a complexity of N^2. Depending of the numbers of boxes, it could be problematic. We can make it faster by using a spatially sorted structure like a quad-tree, but it seems a bit overkill in this case.

If we didn't have knowledge of the bounding boxes position in FRegions, we could recompute them by looking at pixel similarity between F and FRegions but it would be costly, and a bit wasteful since we already computed the bounding boxes.

Question 3

We would like now to be able to provide to the algorithm A2 either the region-based image or the initial image without transformation, which modifications to your code architecture do you suggest in order to handle this?

To support either a packed image (the region-based one) or a sparse image (the initial image limited to the bounding boxes), the easiest solution is to add support for a binary mask. In the same way A2 is suppose to ignore zeros valud in the region-based frame, it could be modified to ignore areas in an image where the mask is set to zero.

Installation

GUI

In order to easily debug and better visualize the problem, I chose to implement a minimal GUI using raylib.

You can build it with ./build-gui.sh (you need to installed raylib required libraries).

You can add, move, and resize boxes. Processing steps are triggered with buttons.

CLI

A commandline sample is also available, in case the raylib library cannot be built, or if we need to benchmark performance.

You can build it with ./build-cli.sh (you need to installed raylib required libraries).

PNG support

I chose to support PNG files through the stb files: github.com/nothings/stb. I could have implemented basic image support with the PNM format but I think it is nicer to support common image formats with a simple library.

3rd party libraries

I use 2 external libraries for better visualization:

stb files (for PNG)
raylib (for GUI)

I don't rely on them for the algorithm implementation and the core of the exercise doesn't rely on external libraries.

To avoid name collision, I created my own namespace freling.

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