adjust_maximum implementation operates with real data maximum found in real file data in visible area. In this specific file some pixels in G2 channel are way above other channels (hotpixels?) that's why adjust_maximum fooled.
See this RawDigger screenshot: https://www.dropbox.com/s/o7d3324n43aurln/screenshot%202020-09-13%2009.1...
Notes:
- black subtraction turned off, that's why entire image is pink
- matte overlay above most image is selection
- both full area and selection stats (two upper arrows) shows G2 channel maximum equal to 16200 (black not subtracted), so problem not in edge pixels but in image area.
Possible solutions:
- implement own adjust_maximum that will ignore outliers (e.g. by calculating full histogram and ignore upper bins with 1-3-10 pixel in it)
- use imgdata.color.linear_max[] as real image maximums.
adjust_maximum implementation operates with real data maximum found in real file data in visible area. In this specific file some pixels in G2 channel are way above other channels (hotpixels?) that's why adjust_maximum fooled.
See this RawDigger screenshot: https://www.dropbox.com/s/o7d3324n43aurln/screenshot%202020-09-13%2009.1...
Notes:
- black subtraction turned off, that's why entire image is pink
- matte overlay above most image is selection
- both full area and selection stats (two upper arrows) shows G2 channel maximum equal to 16200 (black not subtracted), so problem not in edge pixels but in image area.
Possible solutions:
- implement own adjust_maximum that will ignore outliers (e.g. by calculating full histogram and ignore upper bins with 1-3-10 pixel in it)
- use imgdata.color.linear_max[] as real image maximums.