The code checks for the camera-stage mapper matrix by looking at its truth value.
However, this is ambiguous because the matrix has many elements. That means that
if it's missing, we get the expected result, but if it's present
we get an error.
I have avoided dict.get() and instead look it up with [] notation.
This will raise a KeyError if it's missing, which I
handle helpfully.
If the key is present but the value is not castable to an array, we'll
get an exception anyway.
adjust_shutter_and_gain_from_raw now uses a simpler algorithm.
The code is longer overall but hides some details in fuctions.
I've also split the loop into one for shutter speed and one for gain.
This isn't the algorithm that I originally wrote, but it seems to
work and is more readable.
I don't know why these didn't fail before - possibly because of
incomplete type information from old numpy...
I removed a few annotations because they were failing (e.g.
np.sum can return a scalar or an array), but I don't think
this should be a problem - the function inputs and return values
are still typed.
numpy 1.20 deprecates np.float, which was only ever an alias for
``float``. I've replaced all occurrences of np.float with float, as
recommended. There should be no change in functionality.
The picamera object isn't thread safe; I have now wrapped all access
in context managers that acquire the BaseCamera.lock
I also added an endpoint to reset the LST without auto-gain.
When testing the UI I noticed that the different recalibrate routines
don't use locks properly, so they try to run
concurrently.
This commit isn't tested yet...
The previous "auto calibrate" button for the camera relied on a
combination of the built-in autoexposure/AWB and
some JPEG-based tweaks to get the exposure settings right.
This often led to confusing results, e.g. oscillating between green and
pink images.
I have rewritten the auto-exposure code to adjust gain and shutter
speed based on the raw image. This seems to be
very reliable, at the expense of being quite slow. That's a price I'm
happy to pay.
I have replaced both the camera's auto white balance and my
JPEG-based AWB hack with a single-shot AWB method that
looks at the raw image.
The scan API view now uses a schema with validation
constraints to enforce a minimum size of 1 in any dimension.
This requires better error handling in the app, which I've added.
It also needs a fix in LabThings so the error propagates correctly
