1 Hint:

Create a variable containing your Betas IP for easy access.

export BETA=192.168.1.101

2 Preparations

Install on your AXIOM Beta:

pacman -S python-numpy

Install the following packages on your PC:

dcraw octave

For Ubuntu this would look like:

sudo apt-get install dcraw octave

2.1 Step 1: Check range of the input signal

Set gain with:

ssh root@$BETA "./set_gain.sh 1"

Take an overexposed image and check the levels:

ssh root@$BETA "./cmv_snap3 -2 -b -r -e 100ms" > snap.raw12
./raw2dng snap.raw12 --totally-raw
octave
   octave:1> a = read_raw('snap.DNG')
   octave:2> prctile(a(:), [0.1 1 50 99 99.9])

You can get the read_raw function from https://github.com/apertus-open-source-cinema/misc-tools-utilities/blob/master/darkframes/read_raw.m

Lower numbers should be around 50...300 (certainly not zero). Higher numbers should be around 4000, but not 4095.

Repeat for gains 2, 3, 4.

Put this in startup script (ie: kick_manual.sh) :

./set_gain.sh 1

2.2 Step 2: RCN calibration

Clear the old RCN values:

ssh root@$BETA "./rcn_clear.py"

Take 64 dark frames at 10ms, gain x1.

for i in `seq 1 64`; do
  ssh root@$BETA "./cmv_snap3 -2 -b -r -e 10ms" > dark-x1-10ms-$i.raw12 
done

Compute a temporary dark frame for RCN calibration:

raw2dng --no-blackcol --calc-darkframe dark-x1-10ms-*.raw12

Upload it to the beta:

mv darkframe-x1.pgm darkframe-rcn.pgm
scp darkframe-rcn.pgm root@$BETA:/root/

Set the RCN values:

ssh root@$BETA "./rcn_darkframe.py darkframe-rcn.pgm"

Put this in startup script (ie : kick_manual.sh) :

./rcn_darkframe.py darkframe-rcn.pgm

If you get an error report like this:

Traceback (most recent call last):
  File "rcn_darkframe.py", line 17, in <module>
    import png
ImportError: No module named 'png'

Make sure the Beta is connected to the Internet via Ethernet and run:

pip install pypng

2.2.1 Validation

Method 1:

Put the lens cap on the camera, and check the image on the HDMI.

Set the matrix gains to x10 or x20. The fixed components of the row and column noise should be gone. You will still see a lot of dynamic row noise though.

Method 2:

ssh root@$BETA "./cmv_snap3 -2 -b -r -e 10ms" > dark-check-1.raw12 
ssh root@$BETA "./cmv_snap3 -2 -b -r -e 10ms" > dark-check-2.raw12 
raw2dng --no-darkframe --check-darkframe dark-check-1.raw12

The column noise should disappear, and the only row noise left should be dynamic (not static). Visual inspection: the dark frame should have only horizontal lines, not vertical ones.

Sample output:

Average     : 127.36               # about 128, OK
Pixel noise : 5.44                 # this one is a bit high because we only corrected row and column offsets (it's OK)
Row noise   : 2.30 (42.2%)         # this one should be only dynamic, we have captured two frames in order to check that (see below)
Col noise   : 0.20 (3.8%)          # this one is very small, that's what we need to check here

To check whether the entire row noise is dynamic, load the two raw images in octave and check the autocorrelation between the two row noise samples:

pkg load signal
a = read_raw('dark-check-1.DNG');
b = read_raw('dark-check-2.DNG');
ra = mean(a'); ra = ra - mean(ra);
rb = mean(b'); rb = rb - mean(rb);
xcov(ra, rb, 0, 'coeff')

Result should be very small (about 0.1 or lower). When running this check on two uncalibrated dark frames, you will get around 0.8 - 0.9.

2.3 Step 3: Dark frame calibration

Take dark frames at various exposure times and gains.

for i in 1 2 3 4; do
  for e in `seq 1 100`; do
    for g in 1 2 3 4; do
      ssh root@$BETA "./set_gain.sh $g"
      ssh root@$BETA "./cmv_snap3 -2 -b -r -e ${e}ms" > dark-x${g}-${e}ms-$i.raw12
    done
  done
done

Compute dark frames for each gain:

raw2dng --swap-lines --calc-dcnuframe dark-x1-*.raw12
raw2dng --swap-lines --calc-dcnuframe dark-x2-*.raw12
raw2dng --swap-lines --calc-dcnuframe dark-x3-*.raw12
raw2dng --swap-lines --calc-dcnuframe dark-x4-*.raw12

Save the following files (N=1..4):

darkframe-xN.pgm
dcnuframe-xN.pgm

These files should be used in postprocessing. Place them in the directory where you capture raw12 files, so raw2dng will use them.

2.3.1 Validation

On the same dark frames, or - even better - on a new set of dark frames, run:

raw2dng --swap-lines --check-darkframe dark*.raw12 > dark-check.log

Upload the log.

2.4 Step 4: Color profiling

Set gain x1.

ssh root@$BETA "./set_gain.sh 1"

Take a picture of the IT8 chart, correctly exposed.

Edit the coordinates and the raw file name in calib_argyll.sh.

ssh root@$BETA "./cmv_snap3 -2 -b -r -e 10ms" > it8chart.raw12
./calib_argyll.sh IT8

Save the following files:

ICC profile (*.icc)
OCIO configuration (copy/paste from terminal) + LUT file (*.spi1d)

2.4.1 Validation

Render the IT8 chart in Blender, using the OCIO configuration.

Same with the ICC profile (Adobe? RawTherapee? What apps support ICC?)

(todo: detailed steps)

2.5 Step 5: HDMI dark frames

Record a 1-minute clip with lens cap on.

Average odd and even frames.

(todo: polish and upload the averaging script)

(todo: check if the HDMI dark frames can be computed from regular dark frames)

Results: darkframe-hdmi-A.ppm and darkframe-hdmi-B.ppm.

2.6 Step 6: HDMI filters for raw recovery

This calibration is for to the recorder, not for the camera. It's for recovering the original raw data from the HDMI, so it has nothing to do with sensor profiling and such.

Record some scene with high detail AND rich colors.

Take a raw12 snapshot in the middle of recording. The HDMI stream will pause for a few seconds.

Upload two frames from the paused clip, together with the raw12 file. This calibration will be hardcoded in hdmi4k.

The two frames must be in the native format of your video recorder (not DNG). You should be able to cut the video with ffmpeg -vcodec copy.

2.7 TODO

batch script to copy all the utilities for the workflow
implement this: https://wiki.apertus.org/index.php/Calibration_files
automate HDMI calibration
remind Herbert to fix the line swapping bug

Factory Calibration

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