No source package is shared between node repositories, so the manifest and its
type URNs are the only contract a consumer has. Declaring them makes a wiring
mistake fail at validate time with the producer and consumer URNs, instead of
at runtime.
Every payload this node sends is a raw buffer whose shape and dtype live in the
metadata rather than an Arrow struct, so the URNs name the element type:
UInt8 for image_bgr, Float32 for depth, camera_info and point_cloud, String for
camera_state. tick is UInt64, matching how dora types timer inputs.
dataflow.yml now builds the node from the checkout with path: + build:. A
git:-sourced node does not pick up an adjacent manifest, so validating the
previous form checked nothing; with path: the CLI reports "injected 6 contract
type(s)" and a deliberately mismatched annotation exits non-zero under
strict_types. The README keeps the tag: form for consumers.
The workflow needs no camera and no ZED SDK, since validation never runs the
node. Also ignores the git/ and out/ working directories dora build creates,
and drops the ignore rules for the deleted test assets.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Y7g2cBJ5e3r15ZbJ9WZn8c
Camera::open() retried forever inside a fixed sleep, so a camera that did not
come back left the node stuck there: it stopped reading dora events and no
longer honoured Stop or AllInputsClosed.
Recovery is now a state machine over ticks. open() makes one attempt and
returns; the loop decides when to try again from a 1/2/5/10 s backoff deadline,
and nothing sleeps, so events keep being read while the camera is down. The
first attempt closes the handle; past that the device is wedged and the node
escalates to sl::Camera::reboot(), guarded to the ZED 2 and newer since it does
nothing on the ZED and ZED Mini. Reopening pins the serial so recovery gets the
same physical camera back.
Set async_grab_camera_recovery so a link hiccup returns CAMERA_REBOOTING
immediately instead of blocking the grab for ~17 s, and enable_image_validity_
check so the SDK flags frames a stalled camera hands back looking valid. Those
transient errors no longer drop the handle, since the SDK is already
reconnecting it.
An all-black frame now counts as a failed grab rather than being published, and
a camera delivering nothing usable for 5 s (30 s while the SDK reports it is
reconnecting) triggers recovery. A new camera_state output publishes
opening/streaming/recovering so a downstream node can tell "no frame yet" from
"camera lost".
ZED_RETRY_INTERVAL_SECONDS is replaced by the backoff.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Y7g2cBJ5e3r15ZbJ9WZn8c
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.
