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View Code? Open in Web Editor NEWMiscellaneous tools for reverse engineering camera response curves
License: GNU General Public License v3.0
Miscellaneous tools for reverse engineering camera response curves
License: GNU General Public License v3.0
The OpenCV implementation is based on Robertson's 1999 paper, and lacks the enhancements from his 2003 paper
Also, it requires compiling and reinstalling all of OpenCV to make tweaks
Reimplement the algorithm using numpy operations, even if it may cost some speed
Allows:
Setting saturation/clamp limits based on darkest and lightest images. For example, S-Log3 maxes out at around 225, so the algorithm often has weird behaviors above this. Same with any algorithm that has a lower bound
Changing the scaling at each iteration to set the maximum to 1.0, instead of the middle to 0.5 - this is more consistent when trying to determine behaviors relative to sensor clip point
Calibrating R, G, and B as one unified curve (may be possible anyway by reshaping each image to a tiled representation of R, G and B in one greyscale...) since most cameras do per-channel transforms with a common curve
Enforcing monotonicity of the result after each iteration, since almost all real cameras are monotonic in response
There's a citation somewhere for this, I don't remember where
Consider replacing the colored gradients in fs_gradient_capture with ones that have been desaturated somewhat
Do we even need the colors for response estimation? A pure white gradient may be better
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