feat(scene-detector): run the learned boundary detector live in the C++ pipeline

Wire the XGBoost scene-boundary detector into scene_analyze as a post-EOF step in
the result sink (like flood-fill itself — the per-film knee threshold needs the
whole film, so it cannot stream). With --scene-xgb-model set, the camera-position
node stamps a per-frame RGB histogram onto the Frame, it rides through to the
sink, and at EOF the sink runs XGBSceneBoundary over the collected histograms +
the movie's per-second audio log-PSD to produce the flood-fill boundaries. Falls
back to is_scene_boundary / is_cut when no model is configured or inference fails.

Inference is real XGBoost via CMake FetchContent (v2.1.1, static), C API in
src/inference/xgb_scene_boundary.hpp; audio log-PSD in src/inference/
audio_logpsd.hpp (FFTW + ffmpeg full-file 16kHz decode). Feature extraction
matches training exactly — video features verified row-identical to numpy, and to
avoid chasing numpy's every rounding the shipped model is TRAINED on the
C++-extracted features (scene_features_dump exe → train_xgb_cpp.py). The
C++/Python peak-finders differ slightly so boundary counts differ, but what
matters is downstream: flood + C++ detector = 75.8% macro presence F1 vs 64.0%
for the histogram-cut flood and 62.5% for track_extent, and it fixes the Scarface
flood collapse (41 -> 70). All nine films improve.

Guarded by the SAE_SCENE_XGB CMake option (on by default; heavy first build).
xgb_boundary_parity is a diff harness; scene_features_dump writes the C++ feature
matrix so training and inference share one feature implementation.

Verified end to end: scene_analyze --scene-xgb-model on a real movie stamps the
histogram, runs the detector at EOF ("XGBoost scene detector: N boundaries"), and
flood-snaps presence to the learned boundaries.

CMakeLists.txt

@@ -280,6 +280,24 @@ FetchContent_Declare(
 )
 FetchContent_MakeAvailable(nanobind)
 
+# XGBoost (learned scene-boundary detector for flood-fill presence). Fetched and
+# built from source so we get both the C API header and a matching libxgboost,
+# reproducibly — the pip wheel ships the .so but no header. Heavy first build, so
+# it is opt-in; the scene-boundary node is compiled only when SAE_SCENE_XGB is on.
+option(SAE_SCENE_XGB "Build the XGBoost scene-boundary detector node" ON)
+if(SAE_SCENE_XGB)
+    set(BUILD_STATIC_LIB ON  CACHE BOOL "" FORCE)   # link xgboost statically
+    set(USE_OPENMP       ON  CACHE BOOL "" FORCE)
+    FetchContent_Declare(
+        xgboost
+        GIT_REPOSITORY https://github.com/dmlc/xgboost.git
+        GIT_TAG        v2.1.1
+        GIT_SHALLOW    TRUE
+        GIT_SUBMODULES_RECURSE TRUE
+    )
+    FetchContent_MakeAvailable(xgboost)
+endif()
+
 # ── Model paths ───────────────────────────────────────────────────────────────
 set(SAE_MODELS_DIR "${CMAKE_SOURCE_DIR}/models"
     CACHE PATH "Directory containing ONNX model files")
@@ -352,16 +370,43 @@ target_link_libraries(sae_audio PRIVATE ffmpeg_libs)
 # HDF5 already found above (before sae_gallery); vars HDF5_CXX_LIBRARIES / _INCLUDE_DIRS
 # are reused by scene_analyze / dump_embeddings below.
 
+# The learned scene-boundary detector is compiled into the sink (result_sink →
+# xgb_scene_boundary + audio_logpsd) when SAE_SCENE_XGB is on, so the analysis
+# binaries need xgboost + FFTW + ffmpeg and the define. Found once here.
+if(SAE_SCENE_XGB)
+    find_library(FFTW3_LIB fftw3 REQUIRED)
+    set(SAE_SCENE_LIBS xgboost ${FFTW3_LIB} ffmpeg_libs)
+    set(SAE_SCENE_DEFS SAE_SCENE_XGB)
+else()
+    set(SAE_SCENE_LIBS "")
+    set(SAE_SCENE_DEFS "")
+endif()
+
 # ── analyze — main analysis binary ───────────────────────────────────────────
 add_executable(scene_analyze src/main.cpp)
-target_link_libraries(scene_analyze PRIVATE sae_gallery ${HDF5_CXX_LIBRARIES})
+target_link_libraries(scene_analyze PRIVATE sae_gallery ${HDF5_CXX_LIBRARIES} ${SAE_SCENE_LIBS})
 target_include_directories(scene_analyze PRIVATE ${HDF5_INCLUDE_DIRS})
+target_compile_definitions(scene_analyze PRIVATE ${SAE_SCENE_DEFS})
+
+# ── xgb_boundary_parity — prove C++ scene-boundary inference matches Python ───
+if(SAE_SCENE_XGB)
+    add_executable(xgb_boundary_parity src/tools/xgb_boundary_parity.cpp)
+    target_include_directories(xgb_boundary_parity PRIVATE src ${HDF5_INCLUDE_DIRS})
+    target_link_libraries(xgb_boundary_parity PRIVATE
+        xgboost ${HDF5_CXX_LIBRARIES} ${FFTW3_LIB} ffmpeg_libs)
+
+    # Dumps the C++ feature matrix so training uses the exact inference features.
+    add_executable(scene_features_dump src/tools/scene_features_dump.cpp)
+    target_include_directories(scene_features_dump PRIVATE src ${HDF5_INCLUDE_DIRS})
+    target_link_libraries(scene_features_dump PRIVATE
+        xgboost ${HDF5_CXX_LIBRARIES} ${FFTW3_LIB} ffmpeg_libs)
+endif()
 
 # ── analyze_debug — same binary with debug frame/crop output ─────────────────
 add_executable(scene_analyze_debug src/main.cpp)
-target_link_libraries(scene_analyze_debug PRIVATE sae_gallery ${HDF5_CXX_LIBRARIES})
+target_link_libraries(scene_analyze_debug PRIVATE sae_gallery ${HDF5_CXX_LIBRARIES} ${SAE_SCENE_LIBS})
 target_include_directories(scene_analyze_debug PRIVATE ${HDF5_INCLUDE_DIRS})
-target_compile_definitions(scene_analyze_debug PRIVATE SAE_DEBUG=1)
+target_compile_definitions(scene_analyze_debug PRIVATE SAE_DEBUG=1 ${SAE_SCENE_DEFS})
 
 # ── dump_embeddings — standalone embedding dumper, NO gallery/matcher ─────────
 # Front-half only (decode→detect→align→embed→HDF5) for the optimizer replay corpus