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Signed-off-by: Tomi Valkeinen <[email protected]>
libcamera uses its own names which do not always match pixutils' names. And trying to match based on fourccs is challenging. So let's add a new field, 'libcamera_name', so that we can explicitly map between libcamera and pixutils formats. Signed-off-by: Tomi Valkeinen <[email protected]>
The 'packed' field was a bit too ambiguous. Signed-off-by: Tomi Valkeinen <[email protected]>
Add a helper to get the bitspp for RAW formats. Signed-off-by: Tomi Valkeinen <[email protected]>
Signed-off-by: Tomi Valkeinen <[email protected]>
Signed-off-by: Tomi Valkeinen <[email protected]>
Disable GC for the run, do a bit more warmup, use perf_counter. Signed-off-by: Tomi Valkeinen <[email protected]>
Signed-off-by: Tomi Valkeinen <[email protected]>
Signed-off-by: Tomi Valkeinen <[email protected]>
Signed-off-by: Tomi Valkeinen <[email protected]>
Start fixing the multi-plane stride issues by fixing the public API. Signed-off-by: Tomi Valkeinen <[email protected]>
If the caller gives an integer as the stride, and the format is a multi-plane format, use similar stride extrapolation as libcamera does. Signed-off-by: Tomi Valkeinen <[email protected]>
Signed-off-by: Tomi Valkeinen <[email protected]>
Signed-off-by: Tomi Valkeinen <[email protected]>
Signed-off-by: Tomi Valkeinen <[email protected]>
Signed-off-by: Tomi Valkeinen <[email protected]>
Signed-off-by: Tomi Valkeinen <[email protected]>
Signed-off-by: Tomi Valkeinen <[email protected]>
Signed-off-by: Tomi Valkeinen <[email protected]>
Signed-off-by: Tomi Valkeinen <[email protected]>
Signed-off-by: Tomi Valkeinen <[email protected]>
Many of them were quite wrong... Signed-off-by: Tomi Valkeinen <[email protected]>
Signed-off-by: Tomi Valkeinen <[email protected]>
Convert a random buffer to BGR888 with each available backend (opencv, numba, numpy) and compare the outputs pairwise. Catches bugs such as channel swaps, wrong plane offsets, or off-by-one unpacking that locked per-backend SHAs in the existing test cannot detect. Comparison uses max-per-channel mean, 99th percentile, and max of the per-pixel absolute difference. Per-channel (not aggregate) mean is the key: a channel swap leaves one channel near zero while the other two diverge by ~85 on random data, which stands out even when the aggregate mean overlaps with legitimate algorithmic differences (e.g. different demosaic methods). Tolerances are split by color class (RGB exact, YUV a few LSB, RAW loose). Test discovery probes each format at 32x32 with a shared oversized buffer and emits test methods only for formats where >=2 backends accept the conversion, keeping the reported skip count at zero.
Signed-off-by: Tomi Valkeinen <[email protected]>
Allocate the output once and do the channel swap into it directly. The alpha-drop paths use a slice-copy instead of np.delete, which is simpler and avoids the strided fancy-indexing. Results are now naturally C-contiguous, and gives a nice increase in perf. Signed-off-by: Tomi Valkeinen <[email protected]>
Shift the intermediate 16-bit array in place and then cast, instead of allocating a shifted copy and then casting. Saves one full-size buffer allocation in the RAW conversion path.
np.clip() allocates a new array by default; passing out= makes it modify the dot-product result in place. Avoids one intermediate float-array allocation per frame.
All three planes are overwritten immediately after allocation, so np.zeros() needlessly zero-fills the buffer. np.empty() skips the fill.
Signed-off-by: Tomi Valkeinen <[email protected]>
Signed-off-by: Tomi Valkeinen <[email protected]>
Signed-off-by: Tomi Valkeinen <[email protected]>
Signed-off-by: Tomi Valkeinen <[email protected]>
Signed-off-by: Tomi Valkeinen <[email protected]>
Signed-off-by: Tomi Valkeinen <[email protected]>
Camshark calls internal conversion functions with an integer stride, which fails now. Add a hack to convert the stride-int to a stride-tuple. Remove later. Signed-off-by: Tomi Valkeinen <[email protected]>
In most cases this should be no-op, but it makes sure we will handle a non-contig and shaped arrays correctly too. Signed-off-by: Tomi Valkeinen <[email protected]>
Signed-off-by: Tomi Valkeinen <[email protected]>
Make numpy/numba converters return None for unsupported formats so that the top-level conv.py can manage unsupported formats in one place. Signed-off-by: Tomi Valkeinen <[email protected]>
Signed-off-by: Tomi Valkeinen <[email protected]>
Catching any exception hides actual issues. Signed-off-by: Tomi Valkeinen <[email protected]>
The earlier check did the job, but it wasn't quite correct either. Signed-off-by: Tomi Valkeinen <[email protected]>
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@tomba I skimmed through the changes and that looks all sane. I think we could take the risk of just merging it :-) |
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Author
Risk on! |
stefanklug
reviewed
Apr 27, 2026
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