Hello,
is there a way to use AETTR and record the deviation of a preset exposure in XMP?
If I f.e. set exposure to f/8, 1/100, 100ISO and AETTR changes exposure to f/8, 1/200, 100ISO for one image and f/8, 1/100, 100ISO for the next could ML then write a XMP that causes the images to look equally exposed in ACR / Lightroom, even if image content is totally different? I know the deflicker module can do it, if the image content doesn't change much (exposure normalization), but that's not the point.
And what the point is?
I just can't imagine.
If how image looks depends only on settings - there is no need in AETTR, post deflicker and so on.
There are several tools that can already do this:
http://davidmilligan.github.io/BridgeRamp
https://github.com/davidmilligan/LrDeflick
http://www.magiclantern.fm/forum/index.php?topic=7022
Many thanks! Do I understand correctly these tools do exposure normalization in post? This would depend on more or less similar content, wouldn't it? I just want to shoot different images of a given scene, loose no highlights with best SNR (hence AETTR) and have uniform exposure for common details (just as a starting point for further editing).
You just keep asking but never read answers (http://www.magiclantern.fm/forum/index.php?topic=16158.msg157146#msg157146)
Quote from: Erik Krause on November 20, 2015, 10:36:53 PM
...and have uniform exposure for common details ...
Unfortunately, the tools mentioned above don't do this. They don't make the common details look exposed in the same way - instead, they make the overall image look exposed pretty much in the same way (even if the image content differs a lot). Same for the deflicker module. All these tools are based on the same idea - by keeping some statistical attribute constant (e.g. median), the images would look pretty similar in exposure.
What you need can be done in post, based on exif data, and should be pretty easy to implement (exercise for the reader).
Ok, I see. Should have found this idea myself. If I have a number of images I can use (f.e.) their average or median exposure and for each image add the difference. ML is so marvelous, one thinks it could solve all problems ;-) Sorry for the hassle...
Many thanks!