I'm not an expert in the topic, but my understanding is RGB is a poor color space for computing color difference. This could be why your mosaics end up so washed out. [1] suggests using a CIELAB color space [2].
Edit: Looking at the code more closely it looks like you were using Lab at one point but commented it out[3], so I'm guessing you're already aware of this.
Both! You can experiment with —-best-k and —randomness <1.0 and sort of get things in the middle.
Basically what this will do is assign each tile less than 100% of the time randomly and then for each that isn’t, you choose among the top best K matches on L2 distance with equal probability. Gives it a little bit of both.
The face montage building trains a linear classifier on top of the pretrained embedding network - it’s the portion that talks about creating a training folder of your face.
But yes the photomosiacs strictly don’t use ML, unless you count the internal fun stuff Faiss (the similarity search lib) does to construct fast indexes.
It's using the KMeans[1] library from sklearn.cluster. But this isn't really ML, is it? My ML knowledge is limited. Regardless, it's a cool project. OP might want to update the title to remove ML.
like others here, i really like the aligned face montage gif and it seems like it would be a great product for people to package up their selfies over a timeline
[+] [-] dfbrown|7 years ago|reply
Edit: Looking at the code more closely it looks like you were using Lab at one point but commented it out[3], so I'm guessing you're already aware of this.
1: https://stackoverflow.com/a/9019461/185171
2: https://en.wikipedia.org/wiki/CIELAB_color_space#CIELAB
3: https://github.com/worldveil/photomosaic/blob/bb720efda11383...
[+] [-] muzakthings|7 years ago|reply
But you’re correct, generally that’s the space you want to be in.
[+] [-] fireattack|7 years ago|reply
[+] [-] muzakthings|7 years ago|reply
Basically what this will do is assign each tile less than 100% of the time randomly and then for each that isn’t, you choose among the top best K matches on L2 distance with equal probability. Gives it a little bit of both.
[+] [-] PavlovsCat|7 years ago|reply
[+] [-] rmonroe|7 years ago|reply
[+] [-] androidgirl|7 years ago|reply
[+] [-] aaaaaaaaaab|7 years ago|reply
We’ve been creating these mosaics for decades...
[+] [-] muzakthings|7 years ago|reply
But yes the photomosiacs strictly don’t use ML, unless you count the internal fun stuff Faiss (the similarity search lib) does to construct fast indexes.
[+] [-] simple10|7 years ago|reply
[1] https://github.com/worldveil/photomosaic/blob/master/emosaic...
[+] [-] itronitron|7 years ago|reply
[+] [-] giladoved|7 years ago|reply