#apertus IRC Channel Logs

off to bed now ... have a good one everyone!

morning!

guide me*

I wish to write a proposal for GSoc 2019 for this task as I am interested in this topic.

so, I can't speak for OpenCine, BAndiT1983 is the one you want to contact there, but what are your ideas for the AXIOM Beta side?

AXIOM Beta users can have the option to select an automatic white balance algorithm so that the scene's illuminant color can be predicted and the color cast can be removed by doing some linear color transformation(von kries hyposthesis). Since AXIOM Beta records RAW files, color temperature for footage can be easily modified in post, but for preview(in monitors) we need to specify a color space along with a white point

okay, so how do you plan to do the white balance on the preview?

https://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Cheng_Effective_Learning-Based_Illuminant_2015_CVPR_paper.pdf

0.3 secs on what hardware?

Yes, the runtime is calculated on a Intel Xeon 3.5 GHz processor. Certainly this won't be case for AXIOM Beta's processor.

But wouldn't this implementation of Automatic Balance should work fine for desktop OpenCine user with faster processor?

As far as I know the paper link which I provided is the fastest learning based algorithm.

okay, then please do so, also add some explanations why

Some of the classical AWB which might work in the case of Beta are "Gray World" and "White Patch".

Bertl: changes the task (T1142) with explanation.

well mat4 just sets per color factors

Thanks,are there any directions how we should go about doing actual white balancing

removing unrealistic color casts?

I have gone through this only regarding white balancing "https://www.cambridgeincolour.com/tutorials/white-balance.htm"

any work done on the qualification task?

great!

BAndiT1983: Hi, can you pl help me with the implementation for T1142

Hi BandiT1983

classical algorithms for automatic white balancing (Gray World, White Patch..etc)

To implement these algorithms I can build the neural architecture in some framework (Tensoflow, PyTorch) and train the model if the pretrained weights for the model are not available. After training I will get the trained weights for each layer and the network configuration file.

I have read about how to use trained models for inference in OpenCV dnn module.

don't know how i can help you there

Please refer this https://www.learnopencv.com/cpu-performance-comparison-of-opencv-and-other-deep-learning-frameworks/

this is something you have to tell me, as i have not much knowledge about this stuff and need more info, than just some abbreviations

everything, jow would you bind it to OC, at which stage of pipeline should it happen, what is involved?

I am still reading the OC code base and yet to understand it completely

yes, would be great, if someone would have time to sit down and create one

I will make one once I go through it completely.

https://www.google.com/search?q=lightroom+white+balance&rlz=1C1CHZL_enIN748IN748&source=lnms&tbm=isch&sa=X&ved=0ahUKEwj0ioWevaXhAhVdi3AKHVPvAzMQ_AUIDygC&biw=1920&bih=980#imgrc=45U2YGiTntQisM:

along with some presets (Daylight, Cloudy, Shade..etc)

off to bed now ... have a good one everyone!