add open-vocab YOLO-World engine with CLIP text encoder

- Add OpenVocab engine: YOLO-World ONNX detection with runtime
  vocabulary via CLIP text embeddings (two-input model support)
- Add header-only C++ CLIP BPE tokenizer (no external deps)
- Add detect(cv::Mat) overload to OpenVocab for camera-free testing
- Add standalone test binary (test_open_vocab) using test/assets images
- Add export scripts for YOLO-World and CLIP text encoder (uv/PEP 723)
- Add installation scripts for Linux x64, ARM (Jetson), and Windows
- Update CMakeLists to use find_package(onnxruntime) instead of FetchContent
- Update README and AGENTS with env vars, setup workflow, and test usage

src/engines/engine.h

@@ -0,0 +1,27 @@
+#pragma once
+
+#include <sl/Camera.hpp>
+#include <string>
+#include <vector>
+
+namespace engines {
+
+struct DetectedObject {
+    int label_id;
+    float confidence;
+    float position[3];         // center x, y, z (mm)
+    float bounding_box_2d[8];  // 4 corners × 2 coords (pixels)
+    float bounding_box_3d[24]; // 8 corners × 3 coords (mm)
+};
+
+class Engine {
+public:
+    virtual ~Engine() = default;
+
+    virtual void enable(sl::Camera& camera) = 0;
+    virtual void detect(sl::Camera& camera) = 0;
+    virtual const std::vector<DetectedObject>& objects() const = 0;
+    virtual std::string label_names() const = 0;
+};
+
+} // namespace engines

src/engines/open_vocab.cc

@@ -0,0 +1,587 @@
+#include "engines/open_vocab.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 <numeric>
+#include <sstream>
+#include <string>
+#include <vector>
+
+namespace {
+
+// --- NMS helpers ---
+
+struct RawDetection {
+    float x1, y1, x2, y2;
+    float score;
+    int class_id;
+};
+
+float iou(const RawDetection& a, const RawDetection& b) {
+    float inter_x1 = std::max(a.x1, b.x1);
+    float inter_y1 = std::max(a.y1, b.y1);
+    float inter_x2 = std::min(a.x2, b.x2);
+    float inter_y2 = std::min(a.y2, b.y2);
+    float inter_w = std::max(0.0f, inter_x2 - inter_x1);
+    float inter_h = std::max(0.0f, inter_y2 - inter_y1);
+    float inter_area = inter_w * inter_h;
+    float area_a = (a.x2 - a.x1) * (a.y2 - a.y1);
+    float area_b = (b.x2 - b.x1) * (b.y2 - b.y1);
+    float union_area = area_a + area_b - inter_area;
+    return union_area > 0.0f ? inter_area / union_area : 0.0f;
+}
+
+std::vector<RawDetection> nms(std::vector<RawDetection> dets, float threshold) {
+    std::sort(dets.begin(), dets.end(),
+              [](const RawDetection& a, const RawDetection& b) {
+                  return a.score > b.score;
+              });
+    std::vector<bool> suppressed(dets.size(), false);
+    std::vector<RawDetection> result;
+    for (size_t i = 0; i < dets.size(); i++) {
+        if (suppressed[i]) continue;
+        result.push_back(dets[i]);
+        for (size_t j = i + 1; j < dets.size(); j++) {
+            if (!suppressed[j] && iou(dets[i], dets[j]) > threshold) {
+                suppressed[j] = true;
+            }
+        }
+    }
+    return result;
+}
+
+// --- Letterbox resize ---
+cv::Mat letterbox(const cv::Mat& src, int size, float& scale, float& pad_x, float& pad_y) {
+    float scale_w = static_cast<float>(size) / src.cols;
+    float scale_h = static_cast<float>(size) / src.rows;
+    scale = std::min(scale_w, scale_h);
+    int new_w = static_cast<int>(std::round(src.cols * scale));
+    int new_h = static_cast<int>(std::round(src.rows * scale));
+    pad_x = (size - new_w) / 2.0f;
+    pad_y = (size - new_h) / 2.0f;
+
+    cv::Mat resized;
+    cv::resize(src, resized, {new_w, new_h});
+
+    cv::Mat canvas(size, size, CV_8UC3, cv::Scalar(114, 114, 114));
+    resized.copyTo(canvas(cv::Rect(
+        static_cast<int>(pad_x), static_cast<int>(pad_y), new_w, new_h)));
+    return canvas;
+}
+
+// --- Point cloud fusion ---
+struct FusionResult {
+    float cx, cy, cz;
+    float depth_near, depth_far;
+    bool valid = false;
+};
+
+FusionResult fuse_bbox(const sl::Mat& pc, int x1, int y1, int x2, int y2) {
+    FusionResult result{};
+    int w = static_cast<int>(pc.getWidth());
+    int h = static_cast<int>(pc.getHeight());
+    x1 = std::max(x1, 0);
+    y1 = std::max(y1, 0);
+    x2 = std::min(x2, w - 1);
+    y2 = std::min(y2, h - 1);
+
+    std::vector<float> xs, ys, zs;
+    xs.reserve((x2 - x1) * (y2 - y1));
+    ys.reserve(xs.capacity());
+    zs.reserve(xs.capacity());
+
+    for (int y = y1; y <= y2; y++) {
+        for (int x = x1; x <= x2; x++) {
+            sl::float4 pt;
+            pc.getValue(x, y, &pt);
+            if (!std::isnan(pt.x) && !std::isnan(pt.y) && !std::isnan(pt.z) &&
+                !std::isinf(pt.x) && !std::isinf(pt.y) && !std::isinf(pt.z)) {
+                xs.push_back(pt.x);
+                ys.push_back(pt.y);
+                zs.push_back(pt.z);
+            }
+        }
+    }
+
+    if (xs.empty()) return result;
+
+    std::sort(xs.begin(), xs.end());
+    std::sort(ys.begin(), ys.end());
+    std::sort(zs.begin(), zs.end());
+
+    size_t n   = xs.size();
+    size_t mid = n / 2;
+    size_t p25 = n / 4;
+    size_t p75 = 3 * n / 4;
+
+    result.cx         = xs[mid];
+    result.cy         = ys[mid];
+    result.cz         = zs[mid];
+    result.depth_near = zs[p25];
+    result.depth_far  = zs[p75];
+    result.valid      = true;
+    return result;
+}
+
+void backproject(float u, float v, float z, float fx, float fy, float cx, float cy,
+                 float& X, float& Y, float& Z) {
+    Z = z;
+    X = (u - cx) * z / fx;
+    Y = (v - cy) * z / fy;
+}
+
+// Split a comma-separated string into a vector of trimmed tokens
+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
+
+namespace engines {
+
+// --- Impl ---
+
+struct OpenVocab::Impl {
+    // Visual model
+    Ort::Env env{ORT_LOGGING_LEVEL_WARNING, "open_vocab"};
+    Ort::SessionOptions session_options;
+    std::unique_ptr<Ort::Session> session;
+    Ort::AllocatorWithDefaultOptions allocator;
+
+    std::string input_name;       // visual model: image input
+    std::string output_name;      // visual model: detection output
+    std::string text_input_name;  // visual model: text_feats input (empty if single-input model)
+    int num_classes = 0;
+    bool has_text_input = false;
+
+    // Text encoder (optional; used when has_text_input == true)
+    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;
+
+    // Preprocessing + ONNX inference + NMS.
+    // Takes a BGR image (cv::imread format); returns 2D detections in original image coords.
+    std::vector<RawDetection> run_inference(const OpenVocabConfig& config,
+                                             const cv::Mat& image_bgr);
+};
+
+// --- Impl::run_inference ---
+
+std::vector<RawDetection> OpenVocab::Impl::run_inference(
+    const OpenVocabConfig& config, const cv::Mat& image_bgr)
+{
+    if (!session) return {};
+    if (has_text_input && cached_text_feats.empty()) return {};
+
+    int orig_w = image_bgr.cols;
+    int orig_h = image_bgr.rows;
+
+    // BGR → RGB (YOLO expects RGB)
+    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());
+
+    const char* output_name_cstr = output_name.c_str();
+    std::vector<Ort::Value> outputs;
+
+    try {
+        if (has_text_input) {
+            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)
+            };
+            outputs = session->Run(
+                Ort::RunOptions{nullptr},
+                input_names, input_tensors, 2,
+                &output_name_cstr, 1);
+        } else {
+            const char* input_name_cstr = input_name.c_str();
+            outputs = session->Run(
+                Ort::RunOptions{nullptr},
+                &input_name_cstr, &image_tensor, 1,
+                &output_name_cstr, 1);
+        }
+    } catch (const Ort::Exception& e) {
+        std::cerr << "OpenVocab: inference failed: " << e.what() << std::endl;
+        return {};
+    }
+
+    // Post-process: output shape (1, 4+num_classes, num_anchors)
+    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 ---
+
+OpenVocabConfig OpenVocabConfig::from_env() {
+    OpenVocabConfig 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;
+}
+
+// --- OpenVocab ---
+
+OpenVocab::OpenVocab(const OpenVocabConfig& config)
+    : config_(config), impl_(std::make_unique<Impl>()) {}
+
+OpenVocab::~OpenVocab() = default;
+
+void OpenVocab::enable(sl::Camera& /* camera */) {
+    if (config_.model_path.empty()) {
+        std::cerr << "OpenVocab: 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 << "OpenVocab: CUDA unavailable (" << e.what() << "), using CPU." << std::endl;
+    }
+    impl_->session_options.SetGraphOptimizationLevel(GraphOptimizationLevel::ORT_ENABLE_ALL);
+
+    std::cout << "OpenVocab: 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 << "OpenVocab: failed to load visual model: " << e.what() << std::endl;
+        return;
+    }
+
+    impl_->input_name  = impl_->session->GetInputNameAllocated(0, impl_->allocator).get();
+    impl_->output_name = impl_->session->GetOutputNameAllocated(0, impl_->allocator).get();
+
+    size_t num_inputs = impl_->session->GetInputCount();
+    impl_->has_text_input = (num_inputs >= 2);
+    if (impl_->has_text_input) {
+        impl_->text_input_name = impl_->session->GetInputNameAllocated(1, 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 << "OpenVocab: visual model loaded."
+              << " Inputs=" << num_inputs
+              << " Classes=" << impl_->num_classes
+              << (impl_->has_text_input ? " (open-vocab)" : " (fixed-vocab)")
+              << std::endl;
+
+    // --- Load text encoder + tokenizer (only needed for open-vocab model) ---
+    if (impl_->has_text_input) {
+        if (config_.text_encoder_path.empty()) {
+            std::cerr << "OpenVocab: ZED_ONNX_TEXT_ENCODER_PATH is not set "
+                         "(required for open-vocab model)." << std::endl;
+            return;
+        }
+
+        impl_->text_session_options.SetGraphOptimizationLevel(
+            GraphOptimizationLevel::ORT_ENABLE_ALL);
+
+        std::cout << "OpenVocab: 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 << "OpenVocab: 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 << "OpenVocab: tokenizer loaded." << std::endl;
+            } catch (const std::exception& e) {
+                std::cerr << "OpenVocab: failed to load tokenizer: " << e.what() << std::endl;
+                return;
+            }
+        } else {
+            std::cerr << "OpenVocab: ZED_ONNX_CLIP_VOCAB / ZED_ONNX_CLIP_MERGES not set." << std::endl;
+            return;
+        }
+
+        if (!config_.labels.empty()) {
+            encode_labels(config_.labels);
+        }
+    }
+}
+
+// Encode a comma-separated label string into cached_text_feats.
+// Always fills exactly num_classes * 512 floats (pads with zeros if fewer labels).
+void OpenVocab::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 << "OpenVocab: text encoder failed for \"" << label_vec[i]
+                      << "\": " << e.what() << std::endl;
+            continue;
+        }
+
+        const float* emb = outputs[0].GetTensorData<float>();
+
+        // L2 normalize
+        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 << "OpenVocab: encoded " << encode_n << " label(s): " << labels_str << std::endl;
+}
+
+// --- detect(sl::Camera&) — production path with 3D fusion ---
+
+void OpenVocab::detect(sl::Camera& camera) {
+    detected_.clear();
+    if (!impl_->session) return;
+
+    // Re-encode if labels changed (on-the-fly vocabulary switching)
+    if (impl_->has_text_input) {
+        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;
+    }
+
+    // Retrieve image and point cloud from camera
+    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());
+
+    // RGBA → BGR for run_inference
+    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);
+
+    // 3D fusion
+    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);
+    }
+}
+
+// --- detect(const cv::Mat&) — image-only path, no 3D fusion ---
+
+void OpenVocab::detect(const cv::Mat& image_bgr) {
+    detected_.clear();
+    if (!impl_->session) return;
+
+    // Re-encode if labels changed
+    if (impl_->has_text_input) {
+        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;
+        // position stays zero (no point cloud available)
+        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;
+        // bounding_box_3d stays zero
+        detected_.push_back(obj);
+    }
+}
+
+const std::vector<DetectedObject>& OpenVocab::objects() const {
+    return detected_;
+}
+
+std::string OpenVocab::label_names() const {
+    return impl_->cached_labels.empty() ? config_.labels : impl_->cached_labels;
+}
+
+} // namespace engines

src/engines/open_vocab.h

@@ -0,0 +1,46 @@
+#pragma once
+
+#include "engines/engine.h"
+
+#include <opencv2/core.hpp>
+#include <memory>
+#include <string>
+
+namespace engines {
+
+struct OpenVocabConfig {
+    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)
+
+    // Open-vocabulary text encoder (required when model has two inputs)
+    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 OpenVocabConfig from_env();
+};
+
+class OpenVocab : public Engine {
+public:
+    explicit OpenVocab(const OpenVocabConfig& config);
+    ~OpenVocab() 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
+    const std::vector<DetectedObject>& objects() const override;
+    std::string label_names() const override;
+
+private:
+    OpenVocabConfig config_;
+    struct Impl;
+    std::unique_ptr<Impl> impl_;
+    std::vector<DetectedObject> detected_;
+
+    void encode_labels(const std::string& labels_str);
+};
+
+} // namespace engines

src/engines/zed_native.cc

@@ -92,4 +92,8 @@ const std::vector<DetectedObject>& ZedNative::objects() const {
     return detected_;
 }
 
+std::string ZedNative::label_names() const {
+    return "PERSON,VEHICLE,BAG,ANIMAL,ELECTRONICS,FRUIT_VEGETABLE,SPORT";
+}
+
 } // namespace engines

src/engines/zed_native.h

@@ -1,8 +1,8 @@
 #pragma once
 
-#include <sl/Camera.hpp>
+#include "engines/engine.h"
+
 #include <string>
-#include <vector>
 
 namespace engines {
 
@@ -14,21 +14,14 @@ struct DetectionConfig {
     static DetectionConfig from_env();
 };
 
-struct DetectedObject {
-    int label_id;
-    float confidence;
-    float position[3];        // center x, y, z (mm)
-    float bounding_box_2d[8]; // 4 corners × 2 coords (pixels)
-    float bounding_box_3d[24]; // 8 corners × 3 coords (mm)
-};
-
-class ZedNative {
+class ZedNative : public Engine {
 public:
     explicit ZedNative(const DetectionConfig& config);
 
-    void enable(sl::Camera& camera);
-    void detect(sl::Camera& camera);
-    const std::vector<DetectedObject>& objects() const;
+    void enable(sl::Camera& camera) override;
+    void detect(sl::Camera& camera) override;
+    const std::vector<DetectedObject>& objects() const override;
+    std::string label_names() const override;
 
 private:
     DetectionConfig config_;