remove the YOLO-World engine and add a container build to verify it #17
@@ -34,24 +34,19 @@ Camera and detection behavior is configured entirely through environment variabl
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- **`src/zed/camera.cc`** — Implementation. SDK enum parsers live in an anonymous namespace.
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- **`src/engines/engine.h`** — `engines::DetectedObject` struct and `engines::Engine` abstract base class (`enable`, `detect`, `objects`, `label_names`).
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- **`src/engines/zed_native.h/cc`** — `engines::ZedNative`: wraps ZED SDK's TensorRT-optimized object detection module.
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- **`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.
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- **`src/engines/yolo_helpers.h`** — Header-only shared helpers used by the ONNX engine: `RawDetection`, `nms()`, `letterbox()`, `fuse_bbox()`, `backproject()`, `split_labels()`. Lives in `engines::detail` namespace.
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- **`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`.
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- **`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)`.
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- **`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).
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- **`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.
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- **`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 `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. Handles reconnection on grab failure.
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- **`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.
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- **`dataflow.yml`** — Example dora dataflow definition using the `git:` source format.
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- **`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/`.
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- **`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`.
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- **`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/`.
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- **`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.
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- **`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`.
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## Key APIs
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- **dora C++ API** (`<dora-node-api.h>`): `init_dora_node()`, `dora_node.events->next()`, `event_type()`, `send_output_with_metadata()`, `new_metadata()`.
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- **ZED SDK** (`<sl/Camera.hpp>`): `sl::Camera`, `sl::InitParameters`, `sl::RuntimeParameters`, `sl::Mat`, `sl::ObjectDetectionParameters`, `sl::Objects`.
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- **ONNX Runtime** (`<onnxruntime_cxx_api.h>`): `Ort::Env`, `Ort::Session`, `Ort::SessionOptions`, `Ort::Value`. Used by the `open_vocab` engine.
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- **ONNX Runtime** (`<onnxruntime_cxx_api.h>`): `Ort::Env`, `Ort::Session`, `Ort::SessionOptions`, `Ort::Value`. Used by the `yolo_onnx` engine.
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- Output data is sent as raw byte slices (`rust::Slice<const uint8_t>`) with typed metadata (shape, dtype, encoding).
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## Tools
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@@ -25,7 +25,6 @@ add_executable(dora_zed
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src/zed/camera.cc
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src/engines/zed_native.cc
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src/engines/yolo_onnx.cc
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src/engines/yoloworld_onnx.cc
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${node_bridge}
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)
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add_dependencies(dora_zed Dora_cxx)
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@@ -65,21 +64,3 @@ target_link_libraries(test_yolo_onnx PRIVATE
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${ONNXRUNTIME_LIBRARIES}
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)
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install(TARGETS test_yolo_onnx DESTINATION ${CMAKE_CURRENT_SOURCE_DIR}/bin)
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add_executable(test_yoloworld_onnx
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src/test_yoloworld_onnx.cc
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src/engines/yoloworld_onnx.cc
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)
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target_include_directories(test_yoloworld_onnx PRIVATE
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${CMAKE_CURRENT_SOURCE_DIR}/src
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${ZED_INCLUDE_DIRS}
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${OpenCV_INCLUDE_DIRS}
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${CUDAToolkit_INCLUDE_DIRS}
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${ONNXRUNTIME_INCLUDE_DIRS}
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)
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target_link_libraries(test_yoloworld_onnx PRIVATE
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${ZED_LIBRARIES}
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${OpenCV_LIBS}
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${ONNXRUNTIME_LIBRARIES}
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)
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install(TARGETS test_yoloworld_onnx DESTINATION ${CMAKE_CURRENT_SOURCE_DIR}/bin)
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@@ -32,7 +32,7 @@ See [`dataflow.yml`](dataflow.yml) for a complete runnable example with a camera
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| [ZED SDK](https://www.stereolabs.com/developers/release) | 5.x | Bundles OpenCV; default path `/usr/local/zed` |
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| [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads) | — | Required by ZED SDK |
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| [Rust toolchain](https://rustup.rs) | stable | Builds dora-rs C++ bindings |
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| [ONNX Runtime](https://github.com/microsoft/onnxruntime/releases) | 1.20+ | Required only for `open_vocab` engine |
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| [ONNX Runtime](https://github.com/microsoft/onnxruntime/releases) | 1.20+ | Required only for the `yolo_onnx` engine |
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### Installation scripts
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@@ -42,7 +42,7 @@ See [`dataflow.yml`](dataflow.yml) for a complete runnable example with a camera
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| NVIDIA Jetson (ARM64) | `sudo scripts/install_linux_arm.sh` |
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| Windows x64 (PowerShell admin) | `scripts/install_windows.ps1` |
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> **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).
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> **Jetson:** No official ONNX Runtime GPU binary for ARM64. The `native` engine works without it. The `yolo_onnx` engine requires [building from source](https://onnxruntime.ai/docs/build/eps.html#tensorrt).
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>
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> **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`
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@@ -82,7 +82,6 @@ Set `ZED_ENGINE` to select the detection backend:
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|---|---|---|
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| `native` (default) | No — ZED SDK built-in TensorRT | Fixed: 7 categories |
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| `yolo_onnx` (alias: `onnx`) | Yes — any single-input YOLO ONNX (v5/v8/v11) | Fixed labels from file or env var |
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| `yoloworld_onnx` (alias: `open_vocab`) | Yes — YOLO-World two-input ONNX | Defined at runtime via CLIP |
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#### `native` engine
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@@ -131,49 +130,6 @@ dora start dataflow.yml
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| `ZED_ONNX_INPUT_SIZE` | `640` | Model input size (px) |
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| `ZED_ONNX_OUTPUT_FORMAT` | `auto` | Output layout: `auto`, `nc_first`/`yolov8`/`yolov11`, `nc_last`, `nc_last_obj`/`yolov5`. `auto` detects from tensor shape. |
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#### `yoloworld_onnx` engine (open-vocabulary)
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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.
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Export models once:
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```bash
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uv run scripts/export_yoloworld.py --model s --num-classes 10 --output-dir models/
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uv run scripts/export_clip_text_encoder.py --output-dir models/
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```
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Then run:
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```bash
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ZED_ENGINE=yoloworld_onnx \
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ZED_ONNX_MODEL_PATH=models/yolov8s-worldv2_n10_i640.onnx \
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ZED_ONNX_TEXT_ENCODER_PATH=models/clip_text_encoder.onnx \
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ZED_ONNX_CLIP_VOCAB=models/clip_vocab.txt \
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ZED_ONNX_CLIP_MERGES=models/clip_merges.txt \
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ZED_ONNX_LABELS="person,car,chair" \
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dora start dataflow.yml
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```
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**Required:**
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| Variable | Description |
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|---|---|
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| `ZED_ONNX_MODEL_PATH` | Path to YOLO-World ONNX model |
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| `ZED_ONNX_TEXT_ENCODER_PATH` | Path to `clip_text_encoder.onnx` |
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| `ZED_ONNX_CLIP_VOCAB` | Path to `clip_vocab.txt` |
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| `ZED_ONNX_CLIP_MERGES` | Path to `clip_merges.txt` |
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| `ZED_ONNX_LABELS` | Comma-separated labels, e.g. `person,car,chair` |
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**Optional:**
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| Variable | Default | Description |
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|---|---|---|
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| `ZED_ONNX_CONFIDENCE` | `0.25` | Confidence threshold (0.0–1.0) |
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| `ZED_ONNX_NMS_THRESHOLD` | `0.45` | NMS IoU threshold |
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| `ZED_ONNX_INPUT_SIZE` | `640` | Model input size (px) |
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Labels can be updated at runtime without restarting — send a new comma-separated string to the `update_vocabulary` input.
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### Point cloud
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| `ZED_PC_MODE` | Shape | Description |
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@@ -187,7 +143,6 @@ Labels can be updated at runtime without restarting — send a new comma-separat
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| Input | Description |
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|---|---|
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| `tick` | Triggers a camera grab and detection. Connect to `dora/timer/millis/N`. |
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| `update_vocabulary` | (`open_vocab` only) UTF-8 comma-separated label string. Re-encodes CLIP embeddings immediately. |
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## Outputs
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@@ -1,105 +0,0 @@
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# /// script
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# requires-python = ">=3.10"
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# dependencies = ["transformers>=4.30", "torch", "onnx"]
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# ///
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"""Export the CLIP ViT-B/32 text encoder to ONNX and save vocab files.
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Compatible with all YOLOv8-worldv2 sizes (s/m/l/x) — run once and reuse.
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The C++ node uses these three files to compute text embeddings at runtime:
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clip_text_encoder.onnx input: input_ids (1, 77) int64
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output: text_embeds (1, 512) float32 (L2-normalize in C++)
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clip_vocab.txt token → id mapping (tab-separated, one per line)
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clip_merges.txt BPE merge rules (space-separated pairs, one per line)
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Usage:
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uv run scripts/export_clip_text_encoder.py
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uv run scripts/export_clip_text_encoder.py --output-dir models/
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"""
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import argparse
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import sys
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from pathlib import Path
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CLIP_MODEL = "openai/clip-vit-base-patch32"
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def parse_args():
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p = argparse.ArgumentParser(description="Export CLIP ViT-B/32 text encoder to ONNX")
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p.add_argument(
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"--output-dir", "-o",
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default="models",
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help="Directory for output files. Default: models/",
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)
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return p.parse_args()
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def main():
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args = parse_args()
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try:
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import torch
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from transformers import CLIPTextModelWithProjection, CLIPTokenizer
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except ImportError as e:
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print(f"Error: {e}\nRun: uv run scripts/export_clip_text_encoder.py", file=sys.stderr)
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sys.exit(1)
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out = Path(args.output_dir)
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out.mkdir(parents=True, exist_ok=True)
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print(f"Loading {CLIP_MODEL} ...")
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model = CLIPTextModelWithProjection.from_pretrained(CLIP_MODEL)
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tokenizer = CLIPTokenizer.from_pretrained(CLIP_MODEL)
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model.eval()
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# --- Export ONNX text encoder ---
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# Input: input_ids (1, 77) int64 — BPE token IDs, padded/truncated to 77
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# Output: text_embeds (1, 512) float32 — projected features (NOT normalized; C++ normalizes)
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onnx_path = out / "clip_text_encoder.onnx"
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print(f"Exporting ONNX → {onnx_path} ...")
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dummy = torch.zeros(1, 77, dtype=torch.long)
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torch.onnx.export(
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model,
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dummy,
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str(onnx_path),
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input_names=["input_ids"],
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output_names=["text_embeds"],
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dynamic_axes={
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"input_ids": {0: "batch_size"},
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"text_embeds": {0: "batch_size"},
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},
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opset_version=14,
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)
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# --- Save vocab as tab-separated text (token\tid) ---
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# Avoids JSON parsing in C++. Tokens with special chars are escaped.
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vocab_path = out / "clip_vocab.txt"
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print(f"Saving vocab → {vocab_path} ...")
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vocab = tokenizer.get_vocab()
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with open(vocab_path, "w", encoding="utf-8") as f:
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for token, idx in sorted(vocab.items(), key=lambda x: x[1]):
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token_esc = (token
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.replace("\\", "\\\\")
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.replace("\t", "\\t")
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.replace("\n", "\\n"))
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f.write(f"{token_esc}\t{idx}\n")
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# --- Save BPE merge rules ---
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merges_path = out / "clip_merges.txt"
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print(f"Saving merges → {merges_path} ...")
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with open(merges_path, "w", encoding="utf-8") as f:
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for (a, b) in tokenizer.bpe_ranks.keys():
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f.write(f"{a} {b}\n")
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print(f"\nDone. Files written to {out.resolve()}/")
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print(f" clip_text_encoder.onnx — ONNX model")
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print(f" clip_vocab.txt — {len(vocab)} tokens")
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print(f" clip_merges.txt — {len(tokenizer.bpe_ranks)} merge rules")
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print(f"\nSet env vars:")
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print(f" ZED_ONNX_TEXT_ENCODER_PATH={out.resolve()}/clip_text_encoder.onnx")
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print(f" ZED_ONNX_CLIP_VOCAB={out.resolve()}/clip_vocab.txt")
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print(f" ZED_ONNX_CLIP_MERGES={out.resolve()}/clip_merges.txt")
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if __name__ == "__main__":
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main()
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@@ -1,124 +0,0 @@
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# /// script
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# requires-python = ">=3.10"
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# dependencies = ["ultralytics>=8.0"]
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# ///
|
||||
"""Export a YOLOv8-worldv2 model to ONNX with N open-vocabulary class slots.
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|
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The exported model has two inputs:
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images (1, 3, imgsz, imgsz) float32
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text_feats (1, N, 512) float32 ← CLIP embeddings, set at runtime
|
||||
|
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N is fixed per export. Use export_clip_text_encoder.py to generate the text
|
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encoder and vocab files used to compute embeddings at runtime in the C++ node.
|
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|
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Usage:
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uv run scripts/export_yoloworld.py
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uv run scripts/export_yoloworld.py --model m --num-classes 20
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uv run scripts/export_yoloworld.py --model l --num-classes 5 --output-dir models/
|
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"""
|
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|
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import argparse
|
||||
import shutil
|
||||
import sys
|
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from pathlib import Path
|
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|
||||
MODELS = {
|
||||
"s": "yolov8s-worldv2",
|
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"m": "yolov8m-worldv2",
|
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"l": "yolov8l-worldv2",
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"x": "yolov8x-worldv2",
|
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}
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|
||||
|
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def parse_args():
|
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p = argparse.ArgumentParser(
|
||||
description="Export YOLOv8-worldv2 to ONNX with open-vocabulary text inputs"
|
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)
|
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p.add_argument(
|
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"--model", "-m",
|
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choices=list(MODELS),
|
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default="s",
|
||||
help="Model size: s (~14 MB), m (~30 MB), l (~50 MB), x (~100 MB). Default: s",
|
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)
|
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p.add_argument(
|
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"--num-classes", "-n",
|
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type=int,
|
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default=10,
|
||||
help="Number of concurrent class slots (must match number of labels at runtime). Default: 10",
|
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)
|
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p.add_argument(
|
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"--imgsz",
|
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type=int,
|
||||
default=640,
|
||||
help="Input image size in pixels (square). Default: 640",
|
||||
)
|
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p.add_argument(
|
||||
"--output-dir", "-o",
|
||||
default=".",
|
||||
help="Output directory for the .onnx file. Default: current directory",
|
||||
)
|
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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)
|
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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()
|
||||
@@ -12,7 +12,7 @@
|
||||
#
|
||||
# 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:
|
||||
# To use the yolo_onnx engine, build ONNX Runtime from source:
|
||||
# https://onnxruntime.ai/docs/build/eps.html#tensorrt
|
||||
set -euo pipefail
|
||||
|
||||
@@ -67,7 +67,7 @@ fi
|
||||
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 " To use the yolo_onnx engine, build from source:"
|
||||
echo " https://onnxruntime.ai/docs/build/eps.html#tensorrt"
|
||||
echo ""
|
||||
|
||||
|
||||
@@ -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;
|
||||
};
|
||||
|
||||
@@ -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_;
|
||||
}
|
||||
|
||||
@@ -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;
|
||||
|
||||
|
||||
@@ -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
|
||||
@@ -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
|
||||
-18
@@ -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();
|
||||
|
||||
@@ -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;
|
||||
}
|
||||
@@ -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
|
||||
Reference in New Issue
Block a user