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remove the YOLO-World engine and add a container build to verify it #17

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cristhian merged 7 commits from remove-yoloworld-engine into main 2026-09-07 20:07:26 +00:00
Conversation 5 Commits 7 Files Changed 19
+171 -1173
19 changed files with 171 additions and 1173 deletions
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.dockerignore
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@@ -0,0 +1,5 @@
.git/
build/
bin/
tmp/
models/
AGENTS.md
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@@ -18,6 +18,8 @@ The binary is installed to `bin/dora_zed` relative to the repo root.
The first build clones dora-rs from GitHub and compiles `dora-node-api-cxx` via Cargo. This takes a while. Subsequent builds are incremental. If `DORA_ROOT_DIR` is set to a local dora checkout, it builds against that instead.
For a build without installing the SDK stack on the host, see the `Dockerfile` (Ubuntu 24.04 — the newest release with a ZED SDK; there is no Stereolabs build for 26.04). Run the compile with `--gpus all`.
## Run
```bash
@@ -34,24 +36,20 @@ Camera and detection behavior is configured entirely through environment variabl
- **`src/zed/camera.cc`** — Implementation. SDK enum parsers live in an anonymous namespace.
- **`src/engines/engine.h`** — `engines::DetectedObject` struct and `engines::Engine` abstract base class (`enable`, `detect`, `objects`, `label_names`).
- **`src/engines/zed_native.h/cc`** — `engines::ZedNative`: wraps ZED SDK's TensorRT-optimized object detection module.
- **`src/engines/yolo_helpers.h`** — Header-only shared helpers used by both ONNX engines: `RawDetection`, `nms()`, `letterbox()`, `fuse_bbox()`, `backproject()`, `split_labels()`. Lives in `engines::detail` namespace.
- **`src/engines/yolo_helpers.h`** — Header-only shared helpers used by the ONNX engine: `RawDetection`, `nms()`, `letterbox()`, `fuse_bbox()`, `backproject()`. Lives in `engines::detail` namespace.
- **`src/engines/yolo_onnx.h/cc`** — `engines::YoloOnnx` / `YoloOnnxConfig`: closed-vocabulary detection for any single-input YOLO ONNX model (YOLOv8, YOLOv11, YOLOv5, etc.). Auto-detects output layout (`NC_FIRST` / `NC_LAST` / `NC_LAST_OBJ`) from tensor shape; override with `ZED_ONNX_OUTPUT_FORMAT`. Labels loaded from `ZED_ONNX_LABELS` or `ZED_ONNX_LABELS_FILE`.
- **`src/engines/yoloworld_onnx.h/cc`** — `engines::YoloWorldOnnx` / `YoloWorldOnnxConfig`: open-vocabulary detection via YOLO-World ONNX (two inputs: `images` + `text_feats`). Reads `ZED_ONNX_LABELS` on every tick; when labels change, re-tokenizes and re-encodes via the CLIP text encoder ONNX session, passing `(1, N, 512)` L2-normalized embeddings to the visual model. Output is always NC_FIRST `(1, 4+N, anchors)`.
- **`src/util/clip_tokenizer.h`** — Header-only C++ CLIP BPE tokenizer (no external deps). Implements the GPT-2 `bytes_to_unicode()` byte encoder + iterative BPE merge algorithm. Loads `clip_vocab.txt` and `clip_merges.txt` generated by `scripts/export_clip_text_encoder.py`. Returns 77 `int64` token IDs (BOS=49406, EOS=49407, padded with 0).
- **`src/main.cc`** — Dora event loop. Selects engine via `ZED_ENGINE` env var (`yolo_onnx`/`onnx`, `yoloworld_onnx`/`open_vocab`, or `native`), holds a `std::unique_ptr<engines::Engine>`. Parses `ZED_PC_MODE` (`FULL`/`HALF`/`ROI`) once before the loop. On `tick` input: calls `camera.grab()` and `detector->detect()`, then sends `image_bgr`, `point_cloud`, `camera_info`, and `objects` outputs. Point cloud output varies by mode: `FULL` sends the raw `sl::Mat` pointer (no copy); `HALF` downsamples by 2 in each dimension; `ROI` emits only valid points within detected bounding boxes as a flat `[N, 4]` array. On `update_vocabulary` input: decodes payload as a UTF-8 label string and calls `detector->set_labels()` for immediate re-encoding. Handles reconnection on grab failure.
- **`src/main.cc`** — Dora event loop. Selects engine via `ZED_ENGINE` env var (`yolo_onnx`/`onnx` or `native`), holds a `std::unique_ptr<engines::Engine>`. Parses `ZED_PC_MODE` (`FULL`/`HALF`/`ROI`) once before the loop. On any input event (intended to be driven by a tick timer; the id is not inspected): calls `camera.grab()` and `detector->detect()`, then sends `image_bgr`, `point_cloud`, `camera_info`, and `objects` outputs. Point cloud output varies by mode: `FULL` sends the raw `sl::Mat` pointer (no copy); `HALF` downsamples by 2 in each dimension; `ROI` emits only valid points within detected bounding boxes as a flat `[N, 4]` array. Handles reconnection on grab failure.
- **`DoraTargets.cmake`** — Handles building or fetching dora-rs and generating the CXX bridge files. Two paths: local (`DORA_ROOT_DIR`) or git clone. Produces `dora_node_api_cxx` library and the `node_bridge.cc` glue.
- **`dataflow.yml`** — Example dora dataflow definition using the `git:` source format.
- **`Dockerfile`** — Ubuntu 24.04 x86-64 build environment (CUDA 12.8, ZED SDK 5.2, ONNX Runtime, Rust). No ARM64/Jetson image; build natively there. No camera needed; run the compile with `--gpus all`. Sets `NVIDIA_DRIVER_CAPABILITIES=compute,utility,video` — the `video` capability is what makes the container runtime mount NVDEC/NVENC, which `libsl_zed.so` links against. ONNX Runtime's CMake package files are relocated to `/usr/local/lib/cmake` because Ubuntu's multiarch CMake does not search `lib64`. Rust lives at `/usr/local/{rustup,cargo}` rather than `/root` so the build can run as the invoking user. `docker build -t dora-zed-build . && docker run --rm --gpus all --user "$(id -u):$(id -g)" -v "$PWD:/src" -v dora-zed-cargo:/usr/local/cargo/registry dora-zed-build`.
- **`scripts/export_yolo.py`** — PEP 723 uv script. Exports a standard YOLOv11 model to ONNX for closed-vocabulary detection. Run: `uv run scripts/export_yolo.py --model n --output-dir models/`.
- **`scripts/export_yoloworld.py`** — PEP 723 uv script. Exports a YOLOv8-worldv2 model to ONNX with N class slots (labels arbitrary at runtime). Run: `uv run scripts/export_yoloworld.py --model s --num-classes 10`.
- **`scripts/export_clip_text_encoder.py`** — PEP 723 uv script. Exports CLIP ViT-B/32 text encoder to ONNX and saves `clip_vocab.txt` / `clip_merges.txt`. Run once; reused across all YOLO-World model sizes. Run: `uv run scripts/export_clip_text_encoder.py --output-dir models/`.
- **`src/test_yolo_onnx.cc`** — Standalone C++ test binary (`bin/test_yolo_onnx`). Exercises `YoloOnnx` with an image (default `test/assets/yolo.png`). No dora and no live camera required. Uses the same env vars as the main binary. Saves an annotated result image alongside the input.
- **`src/test_yoloworld_onnx.cc`** — Standalone C++ test binary (`bin/test_yoloworld_onnx`). Exercises `YoloWorldOnnx` with an image (default `test/assets/yoloworld.png`). Same interface as `test_yolo_onnx`.
## Key APIs
- **dora C++ API** (`<dora-node-api.h>`): `init_dora_node()`, `dora_node.events->next()`, `event_type()`, `send_output_with_metadata()`, `new_metadata()`.
- **ZED SDK** (`<sl/Camera.hpp>`): `sl::Camera`, `sl::InitParameters`, `sl::RuntimeParameters`, `sl::Mat`, `sl::ObjectDetectionParameters`, `sl::Objects`.
- **ONNX Runtime** (`<onnxruntime_cxx_api.h>`): `Ort::Env`, `Ort::Session`, `Ort::SessionOptions`, `Ort::Value`. Used by the `open_vocab` engine.
- **ONNX Runtime** (`<onnxruntime_cxx_api.h>`): `Ort::Env`, `Ort::Session`, `Ort::SessionOptions`, `Ort::Value`. Used by the `yolo_onnx` engine.
- Output data is sent as raw byte slices (`rust::Slice<const uint8_t>`) with typed metadata (shape, dtype, encoding).
## Tools
CMakeLists.txt
-19
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@@ -25,7 +25,6 @@ add_executable(dora_zed
src/zed/camera.cc
src/engines/zed_native.cc
src/engines/yolo_onnx.cc
src/engines/yoloworld_onnx.cc
${node_bridge}
)
add_dependencies(dora_zed Dora_cxx)
@@ -65,21 +64,3 @@ target_link_libraries(test_yolo_onnx PRIVATE
${ONNXRUNTIME_LIBRARIES}
)
install(TARGETS test_yolo_onnx DESTINATION ${CMAKE_CURRENT_SOURCE_DIR}/bin)
add_executable(test_yoloworld_onnx
src/test_yoloworld_onnx.cc
src/engines/yoloworld_onnx.cc
)
target_include_directories(test_yoloworld_onnx PRIVATE
${CMAKE_CURRENT_SOURCE_DIR}/src
${ZED_INCLUDE_DIRS}
${OpenCV_INCLUDE_DIRS}
${CUDAToolkit_INCLUDE_DIRS}
${ONNXRUNTIME_INCLUDE_DIRS}
)
target_link_libraries(test_yoloworld_onnx PRIVATE
${ZED_LIBRARIES}
${OpenCV_LIBS}
${ONNXRUNTIME_LIBRARIES}
)
install(TARGETS test_yoloworld_onnx DESTINATION ${CMAKE_CURRENT_SOURCE_DIR}/bin)
Dockerfile
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@@ -0,0 +1,75 @@
# Reproducible build environment for dora-zed.
#
# Compiles only — no camera is needed. Requires an NVIDIA driver and the NVIDIA
# Container Toolkit on the host, as the ZED SDK does anyway.
#
# NVIDIA_DRIVER_CAPABILITIES below includes `video` because libsl_zed.so links
# against NVDEC (libnvcuvid) and NVENC (libnvidia-encode); the container runtime
# mounts those from the host only when `video` is in the list.
#
# Ubuntu 24.04 is the newest release Stereolabs ships a ZED SDK for.
#
# docker build -t dora-zed-build .
# docker run --rm --gpus all --user "$(id -u):$(id -g)" \
# -v "$PWD:/src" -v dora-zed-cargo:/usr/local/cargo/registry dora-zed-build
#
# --user keeps build/ and bin/ owned by you instead of root.
#
# The first run clones dora-rs and compiles it with Cargo, which takes a while;
# the named volume keeps the registry cache across runs.
FROM nvidia/cuda:12.8.1-devel-ubuntu24.04
ARG ZED_VERSION=5.2
ARG ORT_VERSION=1.24.4
ENV DEBIAN_FRONTEND=noninteractive
RUN apt-get update && apt-get install -y --no-install-recommends \
build-essential cmake git curl ca-certificates pkg-config \
libopencv-dev zstd udev sudo \
&& rm -rf /var/lib/apt/lists/*
# The toolchain lives outside /root so the container can run as the invoking
# user (--user) and still write the cargo caches. registry/ is created here so
# that a named volume mounted over it inherits these permissions rather than
# being created root-owned.
ENV RUSTUP_HOME=/usr/local/rustup \
CARGO_HOME=/usr/local/cargo \
PATH=/usr/local/cargo/bin:${PATH}
RUN curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs \
| sh -s -- -y --profile minimal --default-toolchain stable \
&& mkdir -p ${CARGO_HOME}/registry \
&& chmod -R a+w ${RUSTUP_HOME} ${CARGO_HOME}
# The installer leaves /usr/local/zed as drwxrwx--- root:zed, which an arbitrary
# --user uid cannot read, so relax it to make find_package(zed) work for one.
RUN curl -fSL -o /tmp/ZED_SDK.run \
"https://download.stereolabs.com/zedsdk/${ZED_VERSION}/cu12/ubuntu24" \
&& chmod +x /tmp/ZED_SDK.run \
&& /tmp/ZED_SDK.run -- silent skip_python skip_tools skip_drivers skip_cuda \
&& chmod -R a+rX /usr/local/zed \
&& rm -f /tmp/ZED_SDK.run
# The tarball puts its CMake package files under lib/, but onnxruntimeTargets.cmake
# resolves the library as <prefix>/lib64. Ubuntu is multiarch, so CMake does not
# search lib64 for packages and ld.so does not search /usr/local/lib64 at all:
# keep the libraries in lib64, move only the package files to lib/cmake, and add
# lib64 to the linker path.
RUN curl -fSL -o /tmp/ort.tgz \
"https://github.com/microsoft/onnxruntime/releases/download/v${ORT_VERSION}/onnxruntime-linux-x64-gpu-${ORT_VERSION}.tgz" \
&& tar -xzf /tmp/ort.tgz -C /tmp \
&& mkdir -p /usr/local/include/onnxruntime /usr/local/lib64 \
&& cp -r /tmp/onnxruntime-linux-x64-gpu-${ORT_VERSION}/include/* /usr/local/include/onnxruntime/ \
&& cp -r /tmp/onnxruntime-linux-x64-gpu-${ORT_VERSION}/lib/* /usr/local/lib64/ \
&& mkdir -p /usr/local/lib/cmake \
&& mv /usr/local/lib64/cmake/onnxruntime /usr/local/lib/cmake/ \
&& rmdir /usr/local/lib64/cmake \
&& echo /usr/local/lib64 > /etc/ld.so.conf.d/onnxruntime.conf \
&& ldconfig \
&& rm -rf /tmp/ort.tgz /tmp/onnxruntime-linux-x64-gpu-${ORT_VERSION}
ENV NVIDIA_DRIVER_CAPABILITIES=compute,utility,video
WORKDIR /src
CMD ["bash", "-lc", "mkdir -p build && cd build && cmake .. && make -j$(nproc) install"]
DoraTargets.cmake
+45 -21
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@@ -5,6 +5,16 @@ set(dora_c_include_dir "${CMAKE_CURRENT_BINARY_DIR}/include/c")
set(dora_cxx_include_dir "${CMAKE_CURRENT_BINARY_DIR}/include/cxx")
set(node_bridge "${CMAKE_CURRENT_BINARY_DIR}/node_bridge.cc")
# Match the cargo profile to the CMake build type, so a release build does not
# link unoptimized dora bindings.
if(CMAKE_BUILD_TYPE MATCHES "^(Release|RelWithDebInfo|MinSizeRel)$")
set(_cargo_profile_flag --release)
set(_cargo_target_subdir release)
else()
set(_cargo_profile_flag "")
set(_cargo_target_subdir debug)
endif()
if(DORA_ROOT_DIR AND EXISTS "${DORA_ROOT_DIR}/Cargo.toml")
include(ExternalProject)
ExternalProject_Add(Dora
@@ -12,10 +22,10 @@ if(DORA_ROOT_DIR AND EXISTS "${DORA_ROOT_DIR}/Cargo.toml")
BUILD_IN_SOURCE True
CONFIGURE_COMMAND ""
BUILD_COMMAND
cargo build
cargo build ${_cargo_profile_flag}
--package dora-node-api-c
&&
cargo build
cargo build ${_cargo_profile_flag}
--package dora-node-api-cxx
INSTALL_COMMAND ""
)
@@ -37,39 +47,53 @@ if(DORA_ROOT_DIR AND EXISTS "${DORA_ROOT_DIR}/Cargo.toml")
add_custom_target(Dora_c DEPENDS ${dora_c_include_dir})
add_custom_target(Dora_cxx DEPENDS ${node_bridge} ${dora_cxx_include_dir})
set(dora_link_dirs ${DORA_ROOT_DIR}/target/debug)
set(dora_link_dirs ${DORA_ROOT_DIR}/target/${_cargo_target_subdir})
else()
include(ExternalProject)
set(DORA_GIT_TAG "main")
find_program(DORA_CLI dora)
if(DORA_CLI)
execute_process(
COMMAND ${DORA_CLI} --version
RESULT_VARIABLE _dora_version_result
OUTPUT_VARIABLE _dora_version_out
ERROR_VARIABLE _dora_version_err
OUTPUT_STRIP_TRAILING_WHITESPACE
)
if(_dora_version_result EQUAL 0)
string(REGEX MATCH "([0-9]+\\.[0-9]+\\.[0-9]+)" _dora_version_match "${_dora_version_out}")
if(_dora_version_match)
set(DORA_GIT_TAG "v${CMAKE_MATCH_1}")
# This node targets dora 1.0+. Set DORA_GIT_TAG to pin a tag explicitly;
# left empty it follows the installed dora CLI so the bindings match the
# runtime, and falls back to the default below when no CLI is present.
set(DORA_GIT_TAG "" CACHE STRING "dora-rs git tag to build against (empty: follow the dora CLI)")
set(_dora_tag "${DORA_GIT_TAG}")
if(NOT _dora_tag)
set(_dora_tag "v1.0.1")
find_program(DORA_CLI dora)
if(DORA_CLI)
execute_process(
COMMAND ${DORA_CLI} --version
RESULT_VARIABLE _dora_version_result
OUTPUT_VARIABLE _dora_version_out
ERROR_VARIABLE _dora_version_err
OUTPUT_STRIP_TRAILING_WHITESPACE
)
if(_dora_version_result EQUAL 0)
string(REGEX MATCH "([0-9]+\\.[0-9]+\\.[0-9]+)" _dora_version_match "${_dora_version_out}")
if(_dora_version_match)
set(_dora_tag "v${CMAKE_MATCH_1}")
if(CMAKE_MATCH_1 VERSION_LESS 1.0)
message(WARNING "dora CLI ${CMAKE_MATCH_1} is older than the 1.0 this node targets")
endif()
endif()
endif()
endif()
endif()
message(STATUS "dora-rs: building C++ bindings from ${_dora_tag}")
ExternalProject_Add(Dora
PREFIX ${CMAKE_CURRENT_BINARY_DIR}/dora
GIT_REPOSITORY https://github.com/dora-rs/dora.git
GIT_TAG ${DORA_GIT_TAG}
GIT_TAG ${_dora_tag}
BUILD_IN_SOURCE True
CONFIGURE_COMMAND ""
BUILD_COMMAND
cargo build
cargo build ${_cargo_profile_flag}
--package dora-node-api-c
--target-dir ${CMAKE_CURRENT_BINARY_DIR}/dora/src/Dora/target
&&
cargo build
cargo build ${_cargo_profile_flag}
--package dora-node-api-cxx
--target-dir ${CMAKE_CURRENT_BINARY_DIR}/dora/src/Dora/target
INSTALL_COMMAND ""
@@ -90,7 +114,7 @@ else()
cp ../apis/c/node ${CMAKE_CURRENT_BINARY_DIR}/include/c -r
)
set(dora_link_dirs ${CMAKE_CURRENT_BINARY_DIR}/dora/src/Dora/target/debug)
set(dora_link_dirs ${CMAKE_CURRENT_BINARY_DIR}/dora/src/Dora/target/${_cargo_target_subdir})
add_custom_target(Dora_c DEPENDS ${dora_c_include_dir})
add_custom_target(Dora_cxx DEPENDS ${node_bridge} ${dora_cxx_include_dir})
README.md
+25 -47
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@@ -32,7 +32,7 @@ See [`dataflow.yml`](dataflow.yml) for a complete runnable example with a camera
| [ZED SDK](https://www.stereolabs.com/developers/release) | 5.x | Bundles OpenCV; default path `/usr/local/zed` |
| [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads) | — | Required by ZED SDK |
| [Rust toolchain](https://rustup.rs) | stable | Builds dora-rs C++ bindings |
| [ONNX Runtime](https://github.com/microsoft/onnxruntime/releases) | 1.20+ | Required only for `open_vocab` engine |
| [ONNX Runtime](https://github.com/microsoft/onnxruntime/releases) | 1.20+ | Required at configure time; used at runtime only by `yolo_onnx` |
### Installation scripts
@@ -42,7 +42,7 @@ See [`dataflow.yml`](dataflow.yml) for a complete runnable example with a camera
| NVIDIA Jetson (ARM64) | `sudo scripts/install_linux_arm.sh` |
| Windows x64 (PowerShell admin) | `scripts/install_windows.ps1` |
> **Jetson:** No official ONNX Runtime GPU binary for ARM64. The `native` engine works without it. The `open_vocab` engine requires [building from source](https://onnxruntime.ai/docs/build/eps.html#tensorrt).
> **Jetson:** No official ONNX Runtime GPU binary for ARM64. It is still needed to configure the build; only the `yolo_onnx` engine uses it at runtime. Requires [building from source](https://onnxruntime.ai/docs/build/eps.html#tensorrt).
>
> **Windows:** Install ZED SDK manually from [stereolabs.com](https://www.stereolabs.com/developers/release), then pass the ONNX Runtime path: `cmake .. -DCMAKE_PREFIX_PATH=C:\onnxruntime`
@@ -56,6 +56,29 @@ mkdir -p build && cd build && cmake .. && make install
Binary installed to `bin/dora_zed`.
### Containerized build
[`Dockerfile`](Dockerfile) provides the full toolchain — CUDA, ZED SDK, ONNX Runtime and Rust — on Ubuntu 24.04, the newest release Stereolabs ships a ZED SDK for. No camera is needed.
```bash
docker build -t dora-zed-build .
docker run --rm --gpus all --user "$(id -u):$(id -g)" \
-v "$PWD:/src" -v dora-zed-cargo:/usr/local/cargo/registry dora-zed-build
```
The first run clones dora-rs and compiles it with Cargo; the named volume keeps that cache across runs.
`--user` keeps `build/` and `bin/` owned by you rather than root.
x86-64 only. On Jetson, run [`scripts/install_linux_arm.sh`](scripts/install_linux_arm.sh) on the
device and build natively — there is no ARM64 image here.
Needs an NVIDIA driver and the
[NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html)
on the host, as the ZED SDK does anyway. The image sets
`NVIDIA_DRIVER_CAPABILITIES=compute,utility,video`: `libsl_zed.so` links against NVDEC
(`libnvcuvid`) and NVENC (`libnvidia-encode`), which the container runtime mounts from the host
only when `video` is in the capability list.
## Configuration
All behavior is controlled via environment variables.
@@ -82,7 +105,6 @@ Set `ZED_ENGINE` to select the detection backend:
|---|---|---|
| `native` (default) | No — ZED SDK built-in TensorRT | Fixed: 7 categories |
| `yolo_onnx` (alias: `onnx`) | Yes — any single-input YOLO ONNX (v5/v8/v11) | Fixed labels from file or env var |
| `yoloworld_onnx` (alias: `open_vocab`) | Yes — YOLO-World two-input ONNX | Defined at runtime via CLIP |
#### `native` engine
@@ -131,49 +153,6 @@ dora start dataflow.yml
| `ZED_ONNX_INPUT_SIZE` | `640` | Model input size (px) |
| `ZED_ONNX_OUTPUT_FORMAT` | `auto` | Output layout: `auto`, `nc_first`/`yolov8`/`yolov11`, `nc_last`, `nc_last_obj`/`yolov5`. `auto` detects from tensor shape. |
#### `yoloworld_onnx` engine (open-vocabulary)
For YOLO-World two-input ONNX models with a CLIP text encoder. Labels are defined at runtime — no recompilation required. Use `ZED_ENGINE=open_vocab` as a legacy alias.
Export models once:
```bash
uv run scripts/export_yoloworld.py --model s --num-classes 10 --output-dir models/
uv run scripts/export_clip_text_encoder.py --output-dir models/
```
Then run:
```bash
ZED_ENGINE=yoloworld_onnx \
ZED_ONNX_MODEL_PATH=models/yolov8s-worldv2_n10_i640.onnx \
ZED_ONNX_TEXT_ENCODER_PATH=models/clip_text_encoder.onnx \
ZED_ONNX_CLIP_VOCAB=models/clip_vocab.txt \
ZED_ONNX_CLIP_MERGES=models/clip_merges.txt \
ZED_ONNX_LABELS="person,car,chair" \
dora start dataflow.yml
```
**Required:**
| Variable | Description |
|---|---|
| `ZED_ONNX_MODEL_PATH` | Path to YOLO-World ONNX model |
| `ZED_ONNX_TEXT_ENCODER_PATH` | Path to `clip_text_encoder.onnx` |
| `ZED_ONNX_CLIP_VOCAB` | Path to `clip_vocab.txt` |
| `ZED_ONNX_CLIP_MERGES` | Path to `clip_merges.txt` |
| `ZED_ONNX_LABELS` | Comma-separated labels, e.g. `person,car,chair` |
**Optional:**
| Variable | Default | Description |
|---|---|---|
| `ZED_ONNX_CONFIDENCE` | `0.25` | Confidence threshold (0.0–1.0) |
| `ZED_ONNX_NMS_THRESHOLD` | `0.45` | NMS IoU threshold |
| `ZED_ONNX_INPUT_SIZE` | `640` | Model input size (px) |
Labels can be updated at runtime without restarting — send a new comma-separated string to the `update_vocabulary` input.
### Point cloud
| `ZED_PC_MODE` | Shape | Description |
@@ -187,7 +166,6 @@ Labels can be updated at runtime without restarting — send a new comma-separat
| Input | Description |
|---|---|
| `tick` | Triggers a camera grab and detection. Connect to `dora/timer/millis/N`. |
| `update_vocabulary` | (`open_vocab` only) UTF-8 comma-separated label string. Re-encodes CLIP embeddings immediately. |
## Outputs
scripts/export_clip_text_encoder.py
-105
View File
@@ -1,105 +0,0 @@
# /// script
# requires-python = ">=3.10"
# dependencies = ["transformers>=4.30", "torch", "onnx"]
# ///
"""Export the CLIP ViT-B/32 text encoder to ONNX and save vocab files.
Compatible with all YOLOv8-worldv2 sizes (s/m/l/x) — run once and reuse.
The C++ node uses these three files to compute text embeddings at runtime:
clip_text_encoder.onnx input: input_ids (1, 77) int64
output: text_embeds (1, 512) float32 (L2-normalize in C++)
clip_vocab.txt token → id mapping (tab-separated, one per line)
clip_merges.txt BPE merge rules (space-separated pairs, one per line)
Usage:
uv run scripts/export_clip_text_encoder.py
uv run scripts/export_clip_text_encoder.py --output-dir models/
"""
import argparse
import sys
from pathlib import Path
CLIP_MODEL = "openai/clip-vit-base-patch32"
def parse_args():
p = argparse.ArgumentParser(description="Export CLIP ViT-B/32 text encoder to ONNX")
p.add_argument(
"--output-dir", "-o",
default="models",
help="Directory for output files. Default: models/",
)
return p.parse_args()
def main():
args = parse_args()
try:
import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer
except ImportError as e:
print(f"Error: {e}\nRun: uv run scripts/export_clip_text_encoder.py", file=sys.stderr)
sys.exit(1)
out = Path(args.output_dir)
out.mkdir(parents=True, exist_ok=True)
print(f"Loading {CLIP_MODEL} ...")
model = CLIPTextModelWithProjection.from_pretrained(CLIP_MODEL)
tokenizer = CLIPTokenizer.from_pretrained(CLIP_MODEL)
model.eval()
# --- Export ONNX text encoder ---
# Input: input_ids (1, 77) int64 — BPE token IDs, padded/truncated to 77
# Output: text_embeds (1, 512) float32 — projected features (NOT normalized; C++ normalizes)
onnx_path = out / "clip_text_encoder.onnx"
print(f"Exporting ONNX → {onnx_path} ...")
dummy = torch.zeros(1, 77, dtype=torch.long)
torch.onnx.export(
model,
dummy,
str(onnx_path),
input_names=["input_ids"],
output_names=["text_embeds"],
dynamic_axes={
"input_ids": {0: "batch_size"},
"text_embeds": {0: "batch_size"},
},
opset_version=14,
)
# --- Save vocab as tab-separated text (token\tid) ---
# Avoids JSON parsing in C++. Tokens with special chars are escaped.
vocab_path = out / "clip_vocab.txt"
print(f"Saving vocab → {vocab_path} ...")
vocab = tokenizer.get_vocab()
with open(vocab_path, "w", encoding="utf-8") as f:
for token, idx in sorted(vocab.items(), key=lambda x: x[1]):
token_esc = (token
.replace("\\", "\\\\")
.replace("\t", "\\t")
.replace("\n", "\\n"))
f.write(f"{token_esc}\t{idx}\n")
# --- Save BPE merge rules ---
merges_path = out / "clip_merges.txt"
print(f"Saving merges → {merges_path} ...")
with open(merges_path, "w", encoding="utf-8") as f:
for (a, b) in tokenizer.bpe_ranks.keys():
f.write(f"{a} {b}\n")
print(f"\nDone. Files written to {out.resolve()}/")
print(f" clip_text_encoder.onnx — ONNX model")
print(f" clip_vocab.txt — {len(vocab)} tokens")
print(f" clip_merges.txt — {len(tokenizer.bpe_ranks)} merge rules")
print(f"\nSet env vars:")
print(f" ZED_ONNX_TEXT_ENCODER_PATH={out.resolve()}/clip_text_encoder.onnx")
print(f" ZED_ONNX_CLIP_VOCAB={out.resolve()}/clip_vocab.txt")
print(f" ZED_ONNX_CLIP_MERGES={out.resolve()}/clip_merges.txt")
if __name__ == "__main__":
main()
scripts/export_yoloworld.py
-124
View File
@@ -1,124 +0,0 @@
# /// script
# requires-python = ">=3.10"
# dependencies = ["ultralytics>=8.0"]
# ///
"""Export a YOLOv8-worldv2 model to ONNX with N open-vocabulary class slots.
The exported model has two inputs:
images (1, 3, imgsz, imgsz) float32
text_feats (1, N, 512) float32 ← CLIP embeddings, set at runtime
N is fixed per export. Use export_clip_text_encoder.py to generate the text
encoder and vocab files used to compute embeddings at runtime in the C++ node.
Usage:
uv run scripts/export_yoloworld.py
uv run scripts/export_yoloworld.py --model m --num-classes 20
uv run scripts/export_yoloworld.py --model l --num-classes 5 --output-dir models/
"""
import argparse
import shutil
import sys
from pathlib import Path
MODELS = {
"s": "yolov8s-worldv2",
"m": "yolov8m-worldv2",
"l": "yolov8l-worldv2",
"x": "yolov8x-worldv2",
}
def parse_args():
p = argparse.ArgumentParser(
description="Export YOLOv8-worldv2 to ONNX with open-vocabulary text inputs"
)
p.add_argument(
"--model", "-m",
choices=list(MODELS),
default="s",
help="Model size: s (~14 MB), m (~30 MB), l (~50 MB), x (~100 MB). Default: s",
)
p.add_argument(
"--num-classes", "-n",
type=int,
default=10,
help="Number of concurrent class slots (must match number of labels at runtime). Default: 10",
)
p.add_argument(
"--imgsz",
type=int,
default=640,
help="Input image size in pixels (square). Default: 640",
)
p.add_argument(
"--output-dir", "-o",
default=".",
help="Output directory for the .onnx file. Default: current directory",
)
return p.parse_args()
def verify_inputs(onnx_path: Path, expected_inputs: int):
try:
import onnx
model = onnx.load(str(onnx_path))
actual = len(model.graph.input)
if actual != expected_inputs:
print(f"\nWarning: expected {expected_inputs} inputs, got {actual}.")
print("The visual model may have baked text embeddings (ultralytics version dependent).")
print("In that case, use a fixed vocabulary instead of the text encoder.")
else:
inputs = [(i.name, [d.dim_value for d in i.type.tensor_type.shape.dim])
for i in model.graph.input]
print(f" Inputs: {inputs}")
except ImportError:
print(" (install 'onnx' to verify model inputs)")
def main():
args = parse_args()
try:
from ultralytics import YOLO
except ImportError:
print("Error: ultralytics not installed. Run: uv run scripts/export_yoloworld.py", file=sys.stderr)
sys.exit(1)
model_name = MODELS[args.model]
out_dir = Path(args.output_dir)
out_dir.mkdir(parents=True, exist_ok=True)
out_name = f"{model_name}_n{args.num_classes}_i{args.imgsz}.onnx"
dst = out_dir / out_name
print(f"Model: {model_name}.pt")
print(f"Class slots: {args.num_classes}")
print(f"Input size: {args.imgsz}")
print(f"Output: {dst}")
print()
model = YOLO(f"{model_name}.pt")
# Set N dummy class slots — actual label meanings are provided at runtime via text_feats
model.set_classes(["object"] * args.num_classes)
exported = Path(model.export(format="onnx", imgsz=args.imgsz))
if exported.resolve() != dst.resolve():
shutil.move(str(exported), str(dst))
print(f"\nVerifying exported model...")
verify_inputs(dst, expected_inputs=2)
print(f"\nExported: {dst.resolve()}")
print(f"\nNext step — export the CLIP text encoder (once, reused for all model sizes):")
print(f" uv run scripts/export_clip_text_encoder.py --output-dir models/")
print(f"\nRun with:")
print(f" ZED_ENGINE=open_vocab \\")
print(f" ZED_ONNX_MODEL_PATH={dst.resolve()} \\")
print(f" ZED_ONNX_TEXT_ENCODER_PATH=models/clip_text_encoder.onnx \\")
print(f' ZED_ONNX_LABELS="person,car,bag,... (exactly {args.num_classes} labels)" \\')
print(f" dora start dataflow.yml")
if __name__ == "__main__":
main()
scripts/install_linux_arm.sh
+4 -4
View File
@@ -11,8 +11,8 @@
# JetPack 7.1 (L4T 38.4)
#
# ONNX Runtime: Microsoft does not publish official GPU binaries for ARM64.
# The native engine (ZED SDK built-in TensorRT) works without ONNX Runtime.
# To use the open_vocab engine, build ONNX Runtime from source:
# ONNX Runtime is still needed to configure the build; only the yolo_onnx
# engine uses it at runtime. Build it from source:
# https://onnxruntime.ai/docs/build/eps.html#tensorrt
set -euo pipefail
@@ -66,8 +66,8 @@ fi
# --- ONNX Runtime ---
echo ""
echo ">>> ONNX Runtime (ARM64 GPU): no official pre-built binary available."
echo " The native ZED engine works without ONNX Runtime."
echo " To use the open_vocab engine, build from source:"
echo " Needed to configure the build; only yolo_onnx uses it at runtime."
echo " Build from source:"
echo " https://onnxruntime.ai/docs/build/eps.html#tensorrt"
echo ""
scripts/install_linux_x64.sh
+10 -1
View File
@@ -35,7 +35,7 @@ else
fi
# --- ONNX Runtime (C++ GPU, installs to /usr/local) ---
if [ -f /usr/local/lib64/libonnxruntime.so ] && [ -d /usr/local/include/onnxruntime ]; then
if [ -f /usr/local/lib64/libonnxruntime.so ] && [ -d /usr/local/lib/cmake/onnxruntime ]; then
echo ">>> ONNX Runtime already installed — skipping."
else
echo ">>> Downloading ONNX Runtime ${ORT_VERSION} GPU..."
@@ -49,6 +49,15 @@ else
sudo cp -r "${TMP_DIR}/onnxruntime-linux-x64-gpu-${ORT_VERSION}/include/"* /usr/local/include/onnxruntime/
sudo mkdir -p /usr/local/lib64
sudo cp -r "${TMP_DIR}/onnxruntime-linux-x64-gpu-${ORT_VERSION}/lib/"* /usr/local/lib64/
# onnxruntimeTargets.cmake resolves the library as <prefix>/lib64, but Ubuntu
# is multiarch: CMake does not search lib64 for packages and ld.so does not
# search /usr/local/lib64 at all. Move only the package files to lib/cmake
# and put lib64 on the linker path.
sudo mkdir -p /usr/local/lib/cmake
sudo rm -rf /usr/local/lib/cmake/onnxruntime
sudo mv /usr/local/lib64/cmake/onnxruntime /usr/local/lib/cmake/
sudo rmdir /usr/local/lib64/cmake
echo /usr/local/lib64 | sudo tee /etc/ld.so.conf.d/onnxruntime.conf > /dev/null
sudo ldconfig
fi
src/engines/engine.h
-1
View File
@@ -20,7 +20,6 @@ public:
virtual void enable(sl::Camera& camera) = 0;
virtual void detect(sl::Camera& camera) = 0;
virtual void set_labels(const std::string& /*labels*/) {}
virtual const std::vector<DetectedObject>& objects() const = 0;
virtual std::string label_names() const = 0;
};
src/engines/yolo_helpers.h
-17
View File
@@ -5,8 +5,6 @@
#include <algorithm>
#include <cmath>
#include <sstream>
#include <string>
#include <vector>
namespace engines::detail {
@@ -135,19 +133,4 @@ inline void backproject(float u, float v, float z,
Y = (v - cy) * z / fy;
}
// --- Label parsing ---
inline std::vector<std::string> split_labels(const std::string& s) {
std::vector<std::string> out;
std::stringstream ss(s);
std::string tok;
while (std::getline(ss, tok, ',')) {
size_t l = tok.find_first_not_of(" \t");
size_t r = tok.find_last_not_of(" \t");
if (l != std::string::npos)
out.push_back(tok.substr(l, r - l + 1));
}
return out;
}
} // namespace engines::detail
src/engines/yolo_onnx.cc
+1 -5
View File
@@ -216,7 +216,7 @@ void YoloOnnx::enable(sl::Camera& /* camera */) {
size_t num_inputs = impl_->session->GetInputCount();
if (num_inputs >= 2) {
std::cerr << "YoloOnnx: model has " << num_inputs << " inputs — "
"this looks like a YOLO-World model. Use ZED_ENGINE=yoloworld_onnx instead."
"this engine only supports single-input models."
<< std::endl;
}
@@ -358,10 +358,6 @@ void YoloOnnx::detect(const cv::Mat& image_bgr) {
}
}
void YoloOnnx::set_labels(const std::string& labels) {
config_.labels = labels;
}
const std::vector<DetectedObject>& YoloOnnx::objects() const {
return detected_;
}
src/engines/yolo_onnx.h
-1
View File
@@ -35,7 +35,6 @@ public:
void enable(sl::Camera& camera) override;
void detect(sl::Camera& camera) override;
void detect(const cv::Mat& image_bgr); // image-only; no 3D fusion
void set_labels(const std::string& labels) override;
const std::vector<DetectedObject>& objects() const override;
std::string label_names() const override;
src/engines/yoloworld_onnx.cc
-415
View File
@@ -1,415 +0,0 @@
#include "engines/yoloworld_onnx.h"
#include "engines/yolo_helpers.h"
#include "util/clip_tokenizer.h"
#include "util/env.h"
#include <onnxruntime_cxx_api.h>
#include <opencv2/opencv.hpp>
#include <sl/Camera.hpp>
#include <algorithm>
#include <cmath>
#include <iostream>
#include <string>
#include <vector>
namespace engines {
using namespace detail;
// --- Impl ---
struct YoloWorldOnnx::Impl {
// Visual model
Ort::Env env{ORT_LOGGING_LEVEL_WARNING, "yoloworld_onnx"};
Ort::SessionOptions session_options;
std::unique_ptr<Ort::Session> session;
Ort::AllocatorWithDefaultOptions allocator;
std::string input_name; // image input
std::string text_input_name; // text_feats input
std::string output_name;
int num_classes = 0;
// Text encoder
Ort::SessionOptions text_session_options;
std::unique_ptr<Ort::Session> text_session;
std::string text_enc_input_name; // "input_ids"
std::string text_enc_output_name; // "text_embeds"
// Tokenizer + embedding cache
util::ClipTokenizer tokenizer;
std::vector<float> cached_text_feats; // (num_classes * 512) flat, row-major
std::string cached_labels;
std::vector<RawDetection> run_inference(const YoloWorldOnnxConfig& config,
const cv::Mat& image_bgr);
};
// --- Impl::run_inference ---
std::vector<RawDetection> YoloWorldOnnx::Impl::run_inference(
const YoloWorldOnnxConfig& config, const cv::Mat& image_bgr)
{
if (!session) return {};
if (cached_text_feats.empty()) return {};
int orig_w = image_bgr.cols;
int orig_h = image_bgr.rows;
cv::Mat rgb;
cv::cvtColor(image_bgr, rgb, cv::COLOR_BGR2RGB);
float scale, pad_x, pad_y;
cv::Mat lb = letterbox(rgb, config.input_size, scale, pad_x, pad_y);
cv::Mat lb_float;
lb.convertTo(lb_float, CV_32F, 1.0f / 255.0f);
int sz = config.input_size;
std::vector<float> input_data(3 * sz * sz);
for (int c = 0; c < 3; c++) {
for (int h = 0; h < sz; h++) {
for (int w = 0; w < sz; w++) {
input_data[c * sz * sz + h * sz + w] =
lb_float.at<cv::Vec3f>(h, w)[c];
}
}
}
auto mem_info = Ort::MemoryInfo::CreateCpu(
OrtAllocatorType::OrtArenaAllocator, OrtMemTypeDefault);
std::vector<int64_t> image_shape{1, 3, sz, sz};
Ort::Value image_tensor = Ort::Value::CreateTensor<float>(
mem_info, input_data.data(), input_data.size(),
image_shape.data(), image_shape.size());
std::vector<int64_t> text_shape{1, num_classes, 512};
Ort::Value text_tensor = Ort::Value::CreateTensor<float>(
mem_info,
cached_text_feats.data(),
cached_text_feats.size(),
text_shape.data(), text_shape.size());
const char* input_names[2] = {input_name.c_str(), text_input_name.c_str()};
Ort::Value input_tensors[2] = {std::move(image_tensor), std::move(text_tensor)};
const char* output_name_cstr = output_name.c_str();
std::vector<Ort::Value> outputs;
try {
outputs = session->Run(
Ort::RunOptions{nullptr},
input_names, input_tensors, 2,
&output_name_cstr, 1);
} catch (const Ort::Exception& e) {
std::cerr << "YoloWorldOnnx: inference failed: " << e.what() << std::endl;
return {};
}
// Output: (1, 4+num_classes, num_anchors) — NC_FIRST only
auto out_shape = outputs[0].GetTensorTypeAndShapeInfo().GetShape();
if (out_shape.size() < 3) return {};
int num_anchors = static_cast<int>(out_shape[2]);
int nc = num_classes;
if (nc <= 0) return {};
const float* out = outputs[0].GetTensorData<float>();
std::vector<RawDetection> candidates;
for (int a = 0; a < num_anchors; a++) {
float cx_lb = out[0 * num_anchors + a];
float cy_lb = out[1 * num_anchors + a];
float w_lb = out[2 * num_anchors + a];
float h_lb = out[3 * num_anchors + a];
float best_score = 0.0f;
int best_class = 0;
for (int c = 0; c < nc; c++) {
float s = out[(4 + c) * num_anchors + a];
if (s > best_score) { best_score = s; best_class = c; }
}
if (best_score < config.confidence_threshold) continue;
float x1_lb = cx_lb - w_lb / 2.0f;
float y1_lb = cy_lb - h_lb / 2.0f;
float x2_lb = cx_lb + w_lb / 2.0f;
float y2_lb = cy_lb + h_lb / 2.0f;
float x1 = std::max(0.0f, std::min((x1_lb - pad_x) / scale, static_cast<float>(orig_w - 1)));
float y1 = std::max(0.0f, std::min((y1_lb - pad_y) / scale, static_cast<float>(orig_h - 1)));
float x2 = std::max(0.0f, std::min((x2_lb - pad_x) / scale, static_cast<float>(orig_w - 1)));
float y2 = std::max(0.0f, std::min((y2_lb - pad_y) / scale, static_cast<float>(orig_h - 1)));
candidates.push_back({x1, y1, x2, y2, best_score, best_class});
}
return nms(std::move(candidates), config.nms_threshold);
}
// --- Config ---
YoloWorldOnnxConfig YoloWorldOnnxConfig::from_env() {
YoloWorldOnnxConfig c;
c.model_path = util::get_env_str("ZED_ONNX_MODEL_PATH", c.model_path);
c.confidence_threshold = util::get_env_float("ZED_ONNX_CONFIDENCE", c.confidence_threshold);
c.nms_threshold = util::get_env_float("ZED_ONNX_NMS_THRESHOLD", c.nms_threshold);
c.input_size = util::get_env_int("ZED_ONNX_INPUT_SIZE", c.input_size);
c.labels = util::get_env_str("ZED_ONNX_LABELS", c.labels);
c.text_encoder_path = util::get_env_str("ZED_ONNX_TEXT_ENCODER_PATH", c.text_encoder_path);
c.clip_vocab_path = util::get_env_str("ZED_ONNX_CLIP_VOCAB", c.clip_vocab_path);
c.clip_merges_path = util::get_env_str("ZED_ONNX_CLIP_MERGES", c.clip_merges_path);
return c;
}
// --- YoloWorldOnnx ---
YoloWorldOnnx::YoloWorldOnnx(const YoloWorldOnnxConfig& config)
: config_(config), impl_(std::make_unique<Impl>()) {}
YoloWorldOnnx::~YoloWorldOnnx() = default;
void YoloWorldOnnx::enable(sl::Camera& /* camera */) {
if (config_.model_path.empty()) {
std::cerr << "YoloWorldOnnx: ZED_ONNX_MODEL_PATH is not set." << std::endl;
return;
}
// --- Load visual model ---
OrtCUDAProviderOptions cuda_opts{};
cuda_opts.device_id = 0;
try {
impl_->session_options.AppendExecutionProvider_CUDA(cuda_opts);
} catch (const Ort::Exception& e) {
std::cerr << "YoloWorldOnnx: CUDA unavailable (" << e.what() << "), using CPU." << std::endl;
}
impl_->session_options.SetGraphOptimizationLevel(GraphOptimizationLevel::ORT_ENABLE_ALL);
std::cout << "YoloWorldOnnx: loading visual model (" << config_.model_path << ")..." << std::endl;
try {
impl_->session = std::make_unique<Ort::Session>(
impl_->env, config_.model_path.c_str(), impl_->session_options);
} catch (const Ort::Exception& e) {
std::cerr << "YoloWorldOnnx: failed to load visual model: " << e.what() << std::endl;
return;
}
size_t num_inputs = impl_->session->GetInputCount();
if (num_inputs < 2) {
std::cerr << "YoloWorldOnnx: model has only " << num_inputs << " input(s) — "
"expected a two-input YOLO-World model. Use ZED_ENGINE=yolo_onnx for single-input models."
<< std::endl;
return;
}
impl_->input_name = impl_->session->GetInputNameAllocated(0, impl_->allocator).get();
impl_->text_input_name = impl_->session->GetInputNameAllocated(1, impl_->allocator).get();
impl_->output_name = impl_->session->GetOutputNameAllocated(0, impl_->allocator).get();
auto out_info = impl_->session->GetOutputTypeInfo(0).GetTensorTypeAndShapeInfo();
auto shape = out_info.GetShape();
if (shape.size() == 3 && shape[1] > 4) {
impl_->num_classes = static_cast<int>(shape[1]) - 4;
}
std::cout << "YoloWorldOnnx: visual model loaded."
<< " Classes=" << impl_->num_classes
<< std::endl;
// --- Load text encoder + tokenizer ---
if (config_.text_encoder_path.empty()) {
std::cerr << "YoloWorldOnnx: ZED_ONNX_TEXT_ENCODER_PATH is not set." << std::endl;
return;
}
impl_->text_session_options.SetGraphOptimizationLevel(GraphOptimizationLevel::ORT_ENABLE_ALL);
std::cout << "YoloWorldOnnx: loading text encoder (" << config_.text_encoder_path << ")..." << std::endl;
try {
impl_->text_session = std::make_unique<Ort::Session>(
impl_->env, config_.text_encoder_path.c_str(), impl_->text_session_options);
} catch (const Ort::Exception& e) {
std::cerr << "YoloWorldOnnx: failed to load text encoder: " << e.what() << std::endl;
return;
}
impl_->text_enc_input_name =
impl_->text_session->GetInputNameAllocated(0, impl_->allocator).get();
impl_->text_enc_output_name =
impl_->text_session->GetOutputNameAllocated(0, impl_->allocator).get();
if (!config_.clip_vocab_path.empty() && !config_.clip_merges_path.empty()) {
try {
impl_->tokenizer.load(config_.clip_vocab_path, config_.clip_merges_path);
std::cout << "YoloWorldOnnx: tokenizer loaded." << std::endl;
} catch (const std::exception& e) {
std::cerr << "YoloWorldOnnx: failed to load tokenizer: " << e.what() << std::endl;
return;
}
} else {
std::cerr << "YoloWorldOnnx: ZED_ONNX_CLIP_VOCAB / ZED_ONNX_CLIP_MERGES not set." << std::endl;
return;
}
if (!config_.labels.empty()) {
encode_labels(config_.labels);
}
}
void YoloWorldOnnx::encode_labels(const std::string& labels_str) {
if (!impl_->text_session || !impl_->tokenizer.is_loaded()) return;
auto label_vec = split_labels(labels_str);
int N = impl_->num_classes;
impl_->cached_text_feats.assign(static_cast<size_t>(N) * 512, 0.0f);
auto mem_info = Ort::MemoryInfo::CreateCpu(
OrtAllocatorType::OrtArenaAllocator, OrtMemTypeDefault);
int encode_n = std::min(static_cast<int>(label_vec.size()), N);
for (int i = 0; i < encode_n; i++) {
auto tokens = impl_->tokenizer.tokenize(label_vec[i]);
std::vector<int64_t> input_shape{1, util::ClipTokenizer::SEQ_LEN};
Ort::Value token_tensor = Ort::Value::CreateTensor<int64_t>(
mem_info, tokens.data(), tokens.size(),
input_shape.data(), input_shape.size());
const char* in = impl_->text_enc_input_name.c_str();
const char* out = impl_->text_enc_output_name.c_str();
std::vector<Ort::Value> outputs;
try {
outputs = impl_->text_session->Run(
Ort::RunOptions{nullptr}, &in, &token_tensor, 1, &out, 1);
} catch (const Ort::Exception& e) {
std::cerr << "YoloWorldOnnx: text encoder failed for \"" << label_vec[i]
<< "\": " << e.what() << std::endl;
continue;
}
const float* emb = outputs[0].GetTensorData<float>();
float norm = 0.0f;
for (int j = 0; j < 512; j++) norm += emb[j] * emb[j];
norm = std::sqrt(norm);
float inv_norm = (norm > 1e-8f) ? 1.0f / norm : 0.0f;
for (int j = 0; j < 512; j++)
impl_->cached_text_feats[i * 512 + j] = emb[j] * inv_norm;
}
impl_->cached_labels = labels_str;
std::cout << "YoloWorldOnnx: encoded " << encode_n << " label(s): " << labels_str << std::endl;
}
void YoloWorldOnnx::detect(sl::Camera& camera) {
detected_.clear();
if (!impl_->session) return;
// Re-encode if labels changed (on-the-fly vocabulary switching)
const char* env_labels = std::getenv("ZED_ONNX_LABELS");
std::string current_labels = env_labels ? env_labels : config_.labels;
if (current_labels != impl_->cached_labels) {
encode_labels(current_labels);
}
if (impl_->cached_text_feats.empty()) return;
sl::Mat img_mat;
camera.retrieveImage(img_mat, sl::VIEW::LEFT);
sl::Mat pc_mat;
camera.retrieveMeasure(pc_mat, sl::MEASURE::XYZRGBA);
sl::CameraInformation info = camera.getCameraInformation();
auto calib = info.camera_configuration.calibration_parameters.left_cam;
float fx = calib.fx, fy = calib.fy, cx = calib.cx, cy = calib.cy;
int orig_w = static_cast<int>(img_mat.getWidth());
int orig_h = static_cast<int>(img_mat.getHeight());
cv::Mat rgba(orig_h, orig_w, CV_8UC4, img_mat.getPtr<sl::uchar1>());
cv::Mat bgr;
cv::cvtColor(rgba, bgr, cv::COLOR_RGBA2BGR);
auto kept = impl_->run_inference(config_, bgr);
for (const auto& det : kept) {
FusionResult fusion = fuse_bbox(pc_mat,
static_cast<int>(det.x1), static_cast<int>(det.y1),
static_cast<int>(det.x2), static_cast<int>(det.y2));
DetectedObject obj{};
obj.label_id = det.class_id;
obj.confidence = det.score * 100.0f;
if (fusion.valid) {
obj.position[0] = fusion.cx;
obj.position[1] = fusion.cy;
obj.position[2] = fusion.cz;
}
obj.bounding_box_2d[0] = det.x1; obj.bounding_box_2d[1] = det.y1;
obj.bounding_box_2d[2] = det.x2; obj.bounding_box_2d[3] = det.y1;
obj.bounding_box_2d[4] = det.x2; obj.bounding_box_2d[5] = det.y2;
obj.bounding_box_2d[6] = det.x1; obj.bounding_box_2d[7] = det.y2;
if (fusion.valid) {
float corners_u[4] = {det.x1, det.x2, det.x2, det.x1};
float corners_v[4] = {det.y1, det.y1, det.y2, det.y2};
float depths[2] = {fusion.depth_near, fusion.depth_far};
for (int face = 0; face < 2; face++) {
for (int c = 0; c < 4; c++) {
float X, Y, Z;
backproject(corners_u[c], corners_v[c], depths[face],
fx, fy, cx, cy, X, Y, Z);
int idx = (face * 4 + c) * 3;
obj.bounding_box_3d[idx + 0] = X;
obj.bounding_box_3d[idx + 1] = Y;
obj.bounding_box_3d[idx + 2] = Z;
}
}
}
detected_.push_back(obj);
}
}
void YoloWorldOnnx::detect(const cv::Mat& image_bgr) {
detected_.clear();
if (!impl_->session) return;
const char* env_labels = std::getenv("ZED_ONNX_LABELS");
std::string current_labels = env_labels ? env_labels : config_.labels;
if (current_labels != impl_->cached_labels) {
encode_labels(current_labels);
}
if (impl_->cached_text_feats.empty()) return;
auto kept = impl_->run_inference(config_, image_bgr);
for (const auto& det : kept) {
DetectedObject obj{};
obj.label_id = det.class_id;
obj.confidence = det.score * 100.0f;
obj.bounding_box_2d[0] = det.x1; obj.bounding_box_2d[1] = det.y1;
obj.bounding_box_2d[2] = det.x2; obj.bounding_box_2d[3] = det.y1;
obj.bounding_box_2d[4] = det.x2; obj.bounding_box_2d[5] = det.y2;
obj.bounding_box_2d[6] = det.x1; obj.bounding_box_2d[7] = det.y2;
detected_.push_back(obj);
}
}
void YoloWorldOnnx::set_labels(const std::string& labels) {
config_.labels = labels;
encode_labels(labels);
}
const std::vector<DetectedObject>& YoloWorldOnnx::objects() const {
return detected_;
}
std::string YoloWorldOnnx::label_names() const {
return impl_->cached_labels.empty() ? config_.labels : impl_->cached_labels;
}
} // namespace engines
src/engines/yoloworld_onnx.h
-47
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@@ -1,47 +0,0 @@
#pragma once
#include "engines/engine.h"
#include <opencv2/core.hpp>
#include <memory>
#include <string>
namespace engines {
struct YoloWorldOnnxConfig {
std::string model_path; // ZED_ONNX_MODEL_PATH (required)
float confidence_threshold = 0.25f; // ZED_ONNX_CONFIDENCE
float nms_threshold = 0.45f; // ZED_ONNX_NMS_THRESHOLD
int input_size = 640; // ZED_ONNX_INPUT_SIZE
std::string labels; // ZED_ONNX_LABELS (comma-separated)
// CLIP text encoder (required)
std::string text_encoder_path; // ZED_ONNX_TEXT_ENCODER_PATH
std::string clip_vocab_path; // ZED_ONNX_CLIP_VOCAB
std::string clip_merges_path; // ZED_ONNX_CLIP_MERGES
static YoloWorldOnnxConfig from_env();
};
class YoloWorldOnnx : public Engine {
public:
explicit YoloWorldOnnx(const YoloWorldOnnxConfig& config);
~YoloWorldOnnx() override;
void enable(sl::Camera& camera) override;
void detect(sl::Camera& camera) override;
void detect(const cv::Mat& image_bgr); // image-only; no 3D fusion
void set_labels(const std::string& labels) override;
const std::vector<DetectedObject>& objects() const override;
std::string label_names() const override;
private:
YoloWorldOnnxConfig config_;
struct Impl;
std::unique_ptr<Impl> impl_;
std::vector<DetectedObject> detected_;
void encode_labels(const std::string& labels_str);
};
} // namespace engines
src/main.cc
-18
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@@ -1,6 +1,5 @@
#include "engines/engine.h"
#include "engines/yolo_onnx.h"
#include "engines/yoloworld_onnx.h"
#include "engines/zed_native.h"
#include "util/env.h"
#include "zed/camera.h"
@@ -78,9 +77,6 @@ int main() {
if (engine_name == "yolo_onnx" || engine_name == "onnx") {
auto cfg = engines::YoloOnnxConfig::from_env();
detector = std::make_unique<engines::YoloOnnx>(cfg);
} else if (engine_name == "yoloworld_onnx" || engine_name == "open_vocab") {
auto cfg = engines::YoloWorldOnnxConfig::from_env();
detector = std::make_unique<engines::YoloWorldOnnx>(cfg);
} else {
auto cfg = engines::DetectionConfig::from_env();
detector = std::make_unique<engines::ZedNative>(cfg);
@@ -105,20 +101,6 @@ int main() {
continue;
}
auto input = event_as_input(std::move(event));
std::string input_id = std::string(input.id);
if (input_id == "update_vocabulary") {
if (!input.data.empty()) {
std::string labels(
reinterpret_cast<const char*>(input.data.data()),
input.data.size());
detector->set_labels(labels);
std::cout << "Vocabulary updated: " << labels << std::endl;
}
continue;
}
if (!camera.grab()) {
std::cerr << "Grab failed; attempting to reconnect..." << std::endl;
camera.close();
src/test_yoloworld_onnx.cc
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@@ -1,114 +0,0 @@
// Standalone test for the YoloWorldOnnx (open-vocabulary) detection engine.
// No dora framework, no live ZED camera — loads an image from disk.
//
// Usage:
// bin/test_yoloworld_onnx [image_path]
// image_path default: test/assets/yoloworld.png
//
// Required env vars:
// ZED_ONNX_MODEL_PATH path to YOLO-World ONNX model (two-input)
// ZED_ONNX_TEXT_ENCODER_PATH path to clip_text_encoder.onnx
// ZED_ONNX_CLIP_VOCAB path to clip_vocab.txt
// ZED_ONNX_CLIP_MERGES path to clip_merges.txt
// ZED_ONNX_LABELS comma-separated class labels
//
// Optional env vars:
// ZED_ONNX_CONFIDENCE detection threshold (default 0.25)
// ZED_ONNX_NMS_THRESHOLD NMS IoU threshold (default 0.45)
// ZED_ONNX_INPUT_SIZE model input size (default 640)
#include "engines/yoloworld_onnx.h"
#include "util/env.h"
#include <opencv2/opencv.hpp>
#include <sl/Camera.hpp>
#include <filesystem>
#include <iostream>
#include <sstream>
#include <string>
#include <vector>
namespace fs = std::filesystem;
namespace {
std::vector<std::string> parse_csv(const std::string& s) {
std::vector<std::string> out;
std::istringstream ss(s);
std::string tok;
while (std::getline(ss, tok, ',')) {
size_t l = tok.find_first_not_of(" \t");
size_t r = tok.find_last_not_of(" \t");
if (l != std::string::npos)
out.push_back(tok.substr(l, r - l + 1));
}
return out;
}
void draw_detections(cv::Mat& img,
const std::vector<engines::DetectedObject>& objects,
const std::vector<std::string>& labels) {
for (const auto& obj : objects) {
int x1 = static_cast<int>(obj.bounding_box_2d[0]);
int y1 = static_cast<int>(obj.bounding_box_2d[1]);
int x2 = static_cast<int>(obj.bounding_box_2d[4]);
int y2 = static_cast<int>(obj.bounding_box_2d[5]);
std::string name = (obj.label_id >= 0 && obj.label_id < static_cast<int>(labels.size()))
? labels[obj.label_id] : std::to_string(obj.label_id);
char buf[64];
std::snprintf(buf, sizeof(buf), " %.2f", obj.confidence / 100.0f);
cv::rectangle(img, {x1, y1}, {x2, y2}, {0, 200, 0}, 2);
cv::putText(img, name + buf, {x1, std::max(y1 - 6, 14)},
cv::FONT_HERSHEY_SIMPLEX, 0.55, {0, 200, 0}, 2, cv::LINE_AA);
}
}
} // namespace
int main(int argc, char* argv[]) {
std::string image_path = (argc > 1) ? argv[1] : "test/assets/yoloworld.png";
auto config = engines::YoloWorldOnnxConfig::from_env();
sl::Camera dummy_cam;
engines::YoloWorldOnnx engine(config);
engine.enable(dummy_cam);
cv::Mat img = cv::imread(image_path);
if (img.empty()) {
std::cerr << "Error: cannot read image: " << image_path << std::endl;
return 1;
}
engine.detect(img);
const auto& objects = engine.objects();
auto labels = parse_csv(engine.label_names());
std::cout << "[" << image_path << "] " << objects.size() << " detection(s):" << std::endl;
for (const auto& obj : objects) {
std::string name = (obj.label_id >= 0 && obj.label_id < static_cast<int>(labels.size()))
? labels[obj.label_id] : std::to_string(obj.label_id);
std::cout << " " << name
<< " conf=" << obj.confidence / 100.0f
<< " bbox=["
<< obj.bounding_box_2d[0] << "," << obj.bounding_box_2d[1]
<< " -> "
<< obj.bounding_box_2d[4] << "," << obj.bounding_box_2d[5]
<< "]" << std::endl;
}
cv::Mat annotated = img.clone();
draw_detections(annotated, objects, labels);
fs::path stem = fs::path(image_path).stem();
fs::path parent = fs::path(image_path).parent_path();
std::string result_path = (parent / (stem.string() + "_result.png")).string();
cv::imwrite(result_path, annotated);
std::cout << "Saved: " << result_path << std::endl;
return 0;
}
src/util/clip_tokenizer.h
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@@ -1,226 +0,0 @@
#pragma once
// Minimal CLIP ViT-B/32 BPE tokenizer — header-only, C++17, no external deps.
//
// Loads clip_vocab.txt (token\tid, tab-separated) and clip_merges.txt
// (a b, space-separated BPE pairs) produced by export_clip_text_encoder.py.
// Returns 77 int64 token IDs compatible with clip_text_encoder.onnx.
#include <algorithm>
#include <climits>
#include <fstream>
#include <sstream>
#include <stdexcept>
#include <string>
#include <unordered_map>
#include <vector>
namespace util {
class ClipTokenizer {
public:
static constexpr int SEQ_LEN = 77;
static constexpr int64_t BOS = 49406;
static constexpr int64_t EOS = 49407;
static constexpr int64_t PAD = 0;
void load(const std::string& vocab_path, const std::string& merges_path) {
load_vocab(vocab_path);
load_merges(merges_path);
init_byte_encoder();
}
bool is_loaded() const { return !vocab_.empty(); }
// Tokenize a label string. Returns exactly SEQ_LEN int64 token IDs.
std::vector<int64_t> tokenize(const std::string& text) const {
std::vector<int64_t> ids;
ids.reserve(SEQ_LEN);
ids.push_back(BOS);
std::string lower = text;
std::transform(lower.begin(), lower.end(), lower.begin(),
[](unsigned char c) { return static_cast<char>(std::tolower(c)); });
// Split on whitespace — sufficient for typical detection label names
std::istringstream ss(lower);
std::string word;
while (ss >> word) {
if (static_cast<int>(ids.size()) >= SEQ_LEN - 1) break;
// Byte-encode the word via the GPT-2 byte encoder
std::string encoded;
encoded.reserve(word.size() * 3);
for (unsigned char c : word) {
auto it = byte_encoder_.find(c);
if (it != byte_encoder_.end())
encoded += utf8_of(it->second);
}
if (encoded.empty()) continue;
// Apply BPE
auto tokens = bpe_encode(encoded);
for (const auto& tok : tokens) {
if (static_cast<int>(ids.size()) >= SEQ_LEN - 1) break;
auto vit = vocab_.find(tok);
if (vit != vocab_.end())
ids.push_back(static_cast<int64_t>(vit->second));
}
}
ids.push_back(EOS);
ids.resize(SEQ_LEN, PAD);
return ids;
}
private:
std::unordered_map<std::string, int> vocab_; // token string → id
std::unordered_map<std::string, int> merge_ranks_; // "a b" → rank
std::unordered_map<unsigned char, uint32_t> byte_encoder_; // byte → unicode codepoint
// -----------------------------------------------------------------
// Byte encoder — matches Python's bytes_to_unicode() in OpenAI CLIP
// -----------------------------------------------------------------
void init_byte_encoder() {
// Printable ASCII + Latin-1 supplement ranges that map to themselves
std::vector<int> bs;
for (int i = '!'; i <= '~'; i++) bs.push_back(i);
for (int i = 0xA1; i <= 0xAC; i++) bs.push_back(i);
for (int i = 0xAE; i <= 0xFF; i++) bs.push_back(i);
std::vector<uint32_t> cs;
cs.reserve(bs.size());
for (int b : bs) cs.push_back(static_cast<uint32_t>(b));
// Remaining bytes map to codepoints starting at U+0100
int n = 0;
for (int b = 0; b < 256; b++) {
if (std::find(bs.begin(), bs.end(), b) == bs.end()) {
bs.push_back(b);
cs.push_back(256u + static_cast<uint32_t>(n));
++n;
}
}
byte_encoder_.clear();
for (size_t i = 0; i < bs.size(); i++)
byte_encoder_[static_cast<unsigned char>(bs[i])] = cs[i];
}
// Encode a unicode codepoint as a UTF-8 std::string
static std::string utf8_of(uint32_t cp) {
std::string s;
if (cp < 0x80u) {
s += static_cast<char>(cp);
} else if (cp < 0x800u) {
s += static_cast<char>(0xC0u | (cp >> 6));
s += static_cast<char>(0x80u | (cp & 0x3Fu));
} else if (cp < 0x10000u) {
s += static_cast<char>(0xE0u | (cp >> 12));
s += static_cast<char>(0x80u | ((cp >> 6) & 0x3Fu));
s += static_cast<char>(0x80u | (cp & 0x3Fu));
} else {
s += static_cast<char>(0xF0u | (cp >> 18));
s += static_cast<char>(0x80u | ((cp >> 12) & 0x3Fu));
s += static_cast<char>(0x80u | ((cp >> 6) & 0x3Fu));
s += static_cast<char>(0x80u | (cp & 0x3Fu));
}
return s;
}
// -----------------------------------------------------------------
// BPE
// -----------------------------------------------------------------
// Split a UTF-8 string into individual Unicode character substrings
static std::vector<std::string> split_utf8_chars(const std::string& s) {
std::vector<std::string> chars;
for (size_t i = 0; i < s.size(); ) {
unsigned char c = static_cast<unsigned char>(s[i]);
int len = (c < 0x80) ? 1 : (c < 0xE0) ? 2 : (c < 0xF0) ? 3 : 4;
chars.push_back(s.substr(i, len));
i += len;
}
return chars;
}
// BPE-encode a single byte-encoded word, returning the token strings
std::vector<std::string> bpe_encode(const std::string& word_str) const {
std::vector<std::string> word = split_utf8_chars(word_str);
if (word.size() <= 1) return word;
while (true) {
// Find the pair with the lowest merge rank
int best_rank = INT_MAX;
int best_i = -1;
for (int i = 0; i + 1 < static_cast<int>(word.size()); i++) {
auto it = merge_ranks_.find(word[i] + " " + word[i + 1]);
if (it != merge_ranks_.end() && it->second < best_rank) {
best_rank = it->second;
best_i = i;
}
}
if (best_i < 0) break;
// Apply merge at best_i
std::string merged = word[best_i] + word[best_i + 1];
word.erase(word.begin() + best_i, word.begin() + best_i + 2);
word.insert(word.begin() + best_i, std::move(merged));
}
return word;
}
// -----------------------------------------------------------------
// File loading
// -----------------------------------------------------------------
void load_vocab(const std::string& path) {
std::ifstream f(path);
if (!f.is_open())
throw std::runtime_error("ClipTokenizer: cannot open vocab file: " + path);
std::string line;
while (std::getline(f, line)) {
auto tab = line.rfind('\t');
if (tab == std::string::npos) continue;
std::string token = unescape(line.substr(0, tab));
int id = std::stoi(line.substr(tab + 1));
vocab_[token] = id;
}
}
void load_merges(const std::string& path) {
std::ifstream f(path);
if (!f.is_open())
throw std::runtime_error("ClipTokenizer: cannot open merges file: " + path);
std::string line;
int rank = 0;
while (std::getline(f, line)) {
if (line.empty() || line[0] == '#') continue;
auto sp = line.find(' ');
if (sp == std::string::npos) continue;
merge_ranks_[line] = rank++; // key is "a b" as written in the file
}
}
static std::string unescape(const std::string& s) {
std::string r;
r.reserve(s.size());
for (size_t i = 0; i < s.size(); i++) {
if (s[i] == '\\' && i + 1 < s.size()) {
switch (s[++i]) {
case '\\': r += '\\'; break;
case 'n': r += '\n'; break;
case 't': r += '\t'; break;
default: r += '\\'; r += s[i]; break;
}
} else {
r += s[i];
}
}
return r;
}
};
} // namespace util