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Expose each datamodel node's description through the pybind binding (DataModelWrapper.h, alongside name/path/node_type) and emit it as a class, enum-class and property docstring in the generated Python wrappers, so the Python API reference gains the field-level documentation the C++ and C# references already have. The datamodel wrappers are regenerated in-tree against SDK 2.18 and verified: _zivid exposes the descriptions, the wrappers compile, and Sphinx autodoc renders them (Settings, Acquisition, Aperture, ...). Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Building zivid-python in a pixi / conda-forge environment failed with the compiler reporting missing Zivid headers. The cause is an interplay between CMake and the compiler. CMake maintains a list of "implicit include directories" gathered by probing the compiler -- directories the compiler always searches. For a normal GCC installation this includes /usr/include. CMake omits these from the compiler invocation, since they are redundant and since naming them explicitly would disturb the order in which the compiler searches its own directories. When a compiler has different implicit include directories, say <prefix>/sysroot/usr/include, CMake treats them as a sysroot alias of /usr/include and still omits /usr/include from the invocation. But such a compiler does not implicitly read /usr/include, so the Zivid headers installed there are never found. This has been CMake's behavior since 3.14 and is unchanged as of 4.4, so it is not a regression to wait out or pin around. This detects the situation and re-adds /usr/include with -idirafter. That flag appends the directory after the compiler's own directories, so the sysroot's libc and libstdc++ headers keep priority; plain -I would reintroduce the very header shadowing CMake was guarding against. Note that an environment which replaces the system compiler toolchain arguably should not discover a Zivid CMake package installed outside the environment in the first place. Either CMAKE_FIND_ROOT_PATH together with CMAKE_FIND_ROOT_PATH_MODE_PACKAGE=ONLY, or CMAKE_IGNORE_PREFIX_PATH="/usr;/", makes the system-installed package correctly invisible from inside such an environment. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
device_colors() accepted only the four 8-bit color formats, while the equivalent accessors on Frame2D.image_device_array and organized PointCloud.device_image already accepted RGBAF. The Zivid SDK has always supported it; only the binding and the allow-list entry were missing. This matters to consumers feeding unorganized point clouds into GPU frameworks that want linear float32 colors from 0 to 1, which otherwise had to convert with astype(float32) / 255.0 on every capture. RGBAF is 4-channel, so callers that want a packed 3-channel float32 buffer still have to drop the alpha channel themselves. test_unorganized_device_colors_rejects_rgbaf asserted the old restriction, so it is replaced by a positive test. RGB takes over as the rejected-format case, since the accessor still has to reject something. A third test compares RGBAF against RGBA / 255.0 on the GPU to confirm the two formats agree; it needs CUDA and torch, so it skips elsewhere. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
DeviceArray implemented __cuda_array_interface__ but not DLPack, so a consumer that wants a capsule directly had to build a DLManagedTensor by hand with ctypes, the way the CaptureAndConvertToDlpackTensorOnCuda sample does. That was never a deliberate choice; nobody had implemented it. __dlpack__ and __dlpack_device__ are added in the pybind layer, where the capsule holds a copy of the reference-counted DeviceArray and drops it from the capsule deleter once the consumer is done. Both methods reach every device-array type through the two existing addCommon*DeviceArrayMethods helpers. The consumer's stream argument is accepted and ignored: the buffer was already ordered against the stream or queue passed at acquisition. The device ordinal comes from cuPointerGetAttribute, resolved from the CUDA driver already loaded into the process, because neither DeviceArray nor ComputeDevice exposes it. The resolved pointer is typed __stdcall on Windows to match the CUDA driver API's CUDAAPI convention, since calling it as cdecl would corrupt the stack on 32-bit Windows. One deviation from offensive programming to note for review: the capsule destructor returns without freeing when the capsule is no longer named "dltensor". That is not a fallback but the DLPack hand-off protocol -- a consumer that takes ownership renames the capsule to "used_dltensor", and the producer must then not free the tensor. Freeing unconditionally would double-free every imported tensor. test_device_array_dlpack_protocol_without_a_consumer covers the protocol without PyTorch, so the ordinal lookup, capsule creation and the capsule deleter are exercised in CI, where the PyTorch CUDA tests are skipped. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
jorgen
approved these changes
Sep 22, 2026
vawale
approved these changes
Sep 22, 2026
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