ONNX Runtime is a configure-time dependency for every build, not only
for the yolo_onnx engine. The event loop no longer inspects the input
id, so it grabs on any input rather than specifically on tick.
Deletes the yoloworld_onnx engine, the CLIP BPE tokenizer, the standalone
test binary and both export scripts. Drops the update_vocabulary input and
the yoloworld_onnx/open_vocab dispatch from main.cc, along with the now
unused Engine::set_labels. Documentation follows.
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.
Three modes: FULL (full HxW grid, zero-copy), HALF (stride-2
downsample, ~4x reduction), ROI (valid points within detected
bounding boxes, flat [N,4] array). Parsed once before the event loop.
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 OpenVocab engine: YOLO-World ONNX detection with runtime
vocabulary via CLIP text embeddings (two-input model support)
- Add header-only C++ CLIP BPE tokenizer (no external deps)
- Add detect(cv::Mat) overload to OpenVocab for camera-free testing
- Add standalone test binary (test_open_vocab) using test/assets images
- Add export scripts for YOLO-World and CLIP text encoder (uv/PEP 723)
- Add installation scripts for Linux x64, ARM (Jetson), and Windows
- Update CMakeLists to use find_package(onnxruntime) instead of FetchContent
- Update README and AGENTS with env vars, setup workflow, and test usage
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.
cristhian
cristhian aguilera