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

scripts/export_clip_text_encoder.py

@@ -0,0 +1,105 @@
+# /// 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()