Publish sl::MEASURE::DEPTH as float32 [H, W] millimetres on every frame that
carries image_bgr, with the intrinsics, frame_id and timestamps the other
outputs carry.
The map is copied out rather than published in place: sl::Mat rows can be
padded, so the buffer is not necessarily H * W floats. inf and nan are cleared
to 0 in the same pass, matching the reference driver — a hole is meaningful and
a consumer cannot back-project a non-finite value.
Consumers that need 3D can now back-project depth with the published
intrinsics instead of receiving a point cloud.
Also drops the stale objects entry from the dataflow outputs, missed when
detection was removed.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Y7g2cBJ5e3r15ZbJ9WZn8c
dora 1.0 runs a node's build command directly rather than through a shell, so
the mkdir/cd/&& sequence in dataflow.yml could not work. Move it to
scripts/build.sh and invoke that instead.
Default CMAKE_BUILD_TYPE to Release so DoraTargets.cmake picks the release
cargo profile and the binary does not link debug dora bindings.
Pin the dataflow source with tag: — 1.0 replaced git_checkout with the
mutually exclusive tag/rev/branch.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Y7g2cBJ5e3r15ZbJ9WZn8c
Split the monolithic open_vocab engine into two focused engines:
yolo_onnx (closed-vocabulary, any single-input YOLO ONNX) and
yoloworld_onnx (open-vocabulary YOLO-World with CLIP encoder).
Shared NMS/letterbox/fuse helpers extracted to yolo_helpers.h.
Add export_yolo.py script and separate test binaries for each engine.
The open_vocab engine now accepts an update_vocabulary input event
carrying a UTF-8 comma-separated label string. Labels are re-encoded
immediately via the CLIP text encoder; the next tick uses the new
vocabulary. ZedNative ignores the input via a no-op base class method.
Add engines::ZedNative class wrapping ZED SDK's TensorRT-optimized
object detection. On every frame grab, detected objects (label,
confidence, 3D position, 2D/3D bounding boxes) are serialized as a
flat float32 array and sent as a new "objects" dora output with
frame_id correlation to image_bgr.
Extract shared env-var helpers into src/util/env.h and expose
sl::Camera& via Camera::handle() for engine access.
Closes#3
Implements a dora-rs node that captures frames from ZED cameras
and publishes BGR images, XYZRGBA point clouds, and camera
intrinsics. Includes CMake build system with automatic dora-rs
C++ bindings, dataflow definition, and documentation.
