From d753062c6ce6df9b6a7f027ffcdad6d13530fb6c Mon Sep 17 00:00:00 2001 From: Duncan Tourolle Date: Fri, 12 Jun 2026 15:29:01 +0200 Subject: [PATCH] Initial commit: scene-actor-extraction pipeline Source (KPN++ pipeline nodes, ArcFace embedders, SCRFD/YuNet detectors, gallery builder), build scripts, and eval artifacts. - external/KPN as a git submodule (gitea.tourolle.paris/dtourolle/KPN) - ONNX models tracked via Git LFS (models/*.onnx) - generated outputs, TensorRT engines, reference repos, and media ignored --- .gitattributes | 1 + .gitignore | 67 + .gitmodules | 3 + CMakeLists.txt | 148 ++ README.md | 107 ++ eval/gt.json | 362 +++++ eval/predictions_mbf.json | 1562 ++++++++++++++++++++++ eval/predictions_r18.json | 1562 ++++++++++++++++++++++ eval/predictions_r50.json | 1562 ++++++++++++++++++++++ eval/report.md | 20 + external/KPN | 1 + models/arcface_r18.onnx | 3 + models/arcface_w600k_mbf.onnx | 3 + models/arcface_w600k_r50.onnx | 3 + models/face_detection_yunet_2023mar.onnx | 3 + models/scrfd_500m_bnkps.onnx | 3 + scripts/build_trt_engines.sh | 51 + scripts/download_models.sh | 71 + scripts/make_gallery.py | 291 ++++ scripts/movienet_eval.py | 144 ++ scripts/movienet_prep.py | 199 +++ scripts/movienet_score.py | 144 ++ src/arcface_embedder.hpp | 132 ++ src/build_gallery.cpp | 85 ++ src/config.hpp | 72 + src/embed_faces.cpp | 251 ++++ src/face_utils.hpp | 36 + src/ffmpeg_decoder.hpp | 231 ++++ src/gallery/gallery_builder.cpp | 115 ++ src/gallery/gallery_builder.hpp | 36 + src/gallery/gallery_calibration.hpp | 118 ++ src/gallery/gallery_store.cpp | 58 + src/gallery/gallery_store.hpp | 20 + src/main.cpp | 213 +++ src/nodes/debug_renderer_node.hpp | 150 +++ src/nodes/embedder_node.hpp | 66 + src/nodes/face_aligner_node.hpp | 39 + src/nodes/face_detector_node.hpp | 61 + src/nodes/face_tracker_node.hpp | 243 ++++ src/nodes/frame_source_node.hpp | 144 ++ src/nodes/identity_matcher_node.hpp | 154 +++ src/nodes/preview_node.hpp | 140 ++ src/nodes/result_sink_node.hpp | 204 +++ src/nodes/scene_tracker_node.hpp | 92 ++ src/ort_provider.hpp | 146 ++ src/scene_preview.cpp | 165 +++ src/scrfd_decoder.hpp | 230 ++++ src/trt_arcface_embedder.hpp | 202 +++ src/trt_scrfd_decoder.hpp | 263 ++++ src/types.hpp | 124 ++ 50 files changed, 10100 insertions(+) create mode 100644 .gitattributes create mode 100644 .gitignore create mode 100644 .gitmodules create mode 100644 CMakeLists.txt create mode 100644 README.md create mode 100644 eval/gt.json create mode 100644 eval/predictions_mbf.json create mode 100644 eval/predictions_r18.json create mode 100644 eval/predictions_r50.json create mode 100644 eval/report.md create mode 160000 external/KPN create mode 100644 models/arcface_r18.onnx create mode 100644 models/arcface_w600k_mbf.onnx create mode 100644 models/arcface_w600k_r50.onnx create mode 100644 models/face_detection_yunet_2023mar.onnx create mode 100644 models/scrfd_500m_bnkps.onnx create mode 100755 scripts/build_trt_engines.sh create mode 100755 scripts/download_models.sh create mode 100755 scripts/make_gallery.py create mode 100644 scripts/movienet_eval.py create mode 100644 scripts/movienet_prep.py create mode 100644 scripts/movienet_score.py create mode 100644 src/arcface_embedder.hpp create mode 100644 src/build_gallery.cpp create mode 100644 src/config.hpp create mode 100644 src/embed_faces.cpp create mode 100644 src/face_utils.hpp create mode 100644 src/ffmpeg_decoder.hpp create mode 100644 src/gallery/gallery_builder.cpp create mode 100644 src/gallery/gallery_builder.hpp create mode 100644 src/gallery/gallery_calibration.hpp create mode 100644 src/gallery/gallery_store.cpp create mode 100644 src/gallery/gallery_store.hpp create mode 100644 src/main.cpp create mode 100644 src/nodes/debug_renderer_node.hpp create mode 100644 src/nodes/embedder_node.hpp create mode 100644 src/nodes/face_aligner_node.hpp create mode 100644 src/nodes/face_detector_node.hpp create mode 100644 src/nodes/face_tracker_node.hpp create mode 100644 src/nodes/frame_source_node.hpp create mode 100644 src/nodes/identity_matcher_node.hpp create mode 100644 src/nodes/preview_node.hpp create mode 100644 src/nodes/result_sink_node.hpp create mode 100644 src/nodes/scene_tracker_node.hpp create mode 100644 src/ort_provider.hpp create mode 100644 src/scene_preview.cpp create mode 100644 src/scrfd_decoder.hpp create mode 100644 src/trt_arcface_embedder.hpp create mode 100644 src/trt_scrfd_decoder.hpp create mode 100644 src/types.hpp diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..0f278d5 --- /dev/null +++ b/.gitattributes @@ -0,0 +1 @@ +models/*.onnx filter=lfs diff=lfs merge=lfs -text diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..ee6109d --- /dev/null +++ b/.gitignore @@ -0,0 +1,67 @@ +# Build +build/ +cmake-build-*/ +CMakeCache.txt +CMakeFiles/ +*.cmake +Makefile +install_manifest.txt +compile_commands.json + +# Compiled objects +*.o +*.a +*.so +*.dylib + +# Video files +*.mp4 +*.mkv +*.avi +*.mov + +# ONNX models in models/ are tracked via Git LFS (see .gitattributes). +# Any stray ONNX elsewhere is generated/downloaded and not tracked. +external/*.onnx + +# Generated TensorRT engines (rebuilt by ORT / scripts/build_trt_engines.sh) +trt_cache/ + +# Gallery JSON files (generated) +gallery.json +gallery_*.json + +# Per-movie analysis output (generated by scene_analyze) +death_of_stalin.json + +# Per-frame debug images +images/ + +# Annotations output +annotations.json +*_annotations.json + +# MovieNet evaluation data +movienet-ps/ + +# Eval probe images (data, regenerable) +eval/probe/ + +# Local reference repos kept for inspiration (each has its own .git) +inspiration/ + +# Python +__pycache__/ +*.pyc +*.pyo +.venv/ +venv/ + +# Editor / OS +.vscode/ +.idea/ +.claude/settings.local.json +*.swp +*.swo +.DS_Store +Thumbs.db diff --git a/.gitmodules b/.gitmodules new file mode 100644 index 0000000..31df759 --- /dev/null +++ b/.gitmodules @@ -0,0 +1,3 @@ +[submodule "external/KPN"] + path = external/KPN + url = git@gitea.tourolle.paris:dtourolle/KPN.git diff --git a/CMakeLists.txt b/CMakeLists.txt new file mode 100644 index 0000000..a8674df --- /dev/null +++ b/CMakeLists.txt @@ -0,0 +1,148 @@ +cmake_minimum_required(VERSION 3.21) +project(scene_actor_extraction VERSION 0.1.0 LANGUAGES CXX) + +set(CMAKE_CXX_STANDARD 20) +set(CMAKE_CXX_STANDARD_REQUIRED ON) +set(CMAKE_CXX_EXTENSIONS OFF) + +# ── Dependencies ────────────────────────────────────────────────────────────── + +# KPN++ (pipeline backbone) +set(KPN_BUILD_TESTS OFF CACHE BOOL "" FORCE) +set(KPN_BUILD_PYTHON OFF CACHE BOOL "" FORCE) +set(KPN_BUILD_EXAMPLES OFF CACHE BOOL "" FORCE) +option(SAE_WEB_DEBUG "Enable KPN web debug UI (localhost:9090)" OFF) +if(SAE_WEB_DEBUG) + set(KPN_WEB_DEBUG ON CACHE BOOL "" FORCE) +endif() +add_subdirectory(external/KPN) + +# OpenCV (video decode, image ops, DNN inference, face detection) +find_package(OpenCV 4 REQUIRED COMPONENTS + core imgproc imgcodecs videoio dnn objdetect highgui) + +# ONNX Runtime (SCRFD detector — cv::dnn cannot handle dynamic Shape nodes) +find_library(ORT_LIB onnxruntime REQUIRED + HINTS /usr/lib /usr/local/lib) +find_path(ORT_INCLUDE onnxruntime_cxx_api.h + PATH_SUFFIXES onnxruntime + HINTS /usr/include /usr/local/include + REQUIRED) +add_library(onnxruntime UNKNOWN IMPORTED) +set_target_properties(onnxruntime PROPERTIES + IMPORTED_LOCATION "${ORT_LIB}" + INTERFACE_INCLUDE_DIRECTORIES "${ORT_INCLUDE}") +message(STATUS "ONNX Runtime: ${ORT_LIB} headers: ${ORT_INCLUDE}") + +# TensorRT + CUDA runtime (raw-TRT ArcFace embedder; activated by --arcface-engine). +find_library(NVINFER_LIB nvinfer + HINTS /usr/lib /usr/local/lib /opt/tensorrt/lib) +find_path(NVINFER_INCLUDE NvInfer.h + HINTS /usr/include /usr/local/include /opt/tensorrt/include) +find_library(CUDART_LIB cudart + HINTS /opt/cuda/lib64 /usr/local/cuda/lib64 /usr/lib) +find_path(CUDART_INCLUDE cuda_runtime_api.h + HINTS /opt/cuda/targets/x86_64-linux/include /opt/cuda/include + /usr/local/cuda/include /usr/include) +if(NOT (NVINFER_LIB AND NVINFER_INCLUDE AND CUDART_LIB AND CUDART_INCLUDE)) + message(FATAL_ERROR + "TensorRT or CUDA runtime not found " + "(nvinfer=${NVINFER_LIB} headers=${NVINFER_INCLUDE} " + "cudart=${CUDART_LIB} headers=${CUDART_INCLUDE})") +endif() +add_library(trt_runtime INTERFACE) +target_include_directories(trt_runtime INTERFACE + "${NVINFER_INCLUDE}" "${CUDART_INCLUDE}") +target_link_libraries(trt_runtime INTERFACE + "${NVINFER_LIB}" "${CUDART_LIB}") +message(STATUS "TensorRT: ${NVINFER_LIB} CUDA runtime: ${CUDART_LIB}") + +# FFmpeg (NVDEC hardware video decode + swscale colour conversion) +find_package(PkgConfig REQUIRED) +pkg_check_modules(AVFORMAT REQUIRED libavformat) +pkg_check_modules(AVCODEC REQUIRED libavcodec) +pkg_check_modules(AVUTIL REQUIRED libavutil) +pkg_check_modules(SWSCALE REQUIRED libswscale) + +add_library(ffmpeg_libs INTERFACE) +target_compile_options(ffmpeg_libs INTERFACE + ${AVFORMAT_CFLAGS_OTHER} ${AVCODEC_CFLAGS_OTHER} + ${AVUTIL_CFLAGS_OTHER} ${SWSCALE_CFLAGS_OTHER}) +target_include_directories(ffmpeg_libs INTERFACE + ${AVFORMAT_INCLUDE_DIRS} ${AVCODEC_INCLUDE_DIRS} + ${AVUTIL_INCLUDE_DIRS} ${SWSCALE_INCLUDE_DIRS}) +target_link_libraries(ffmpeg_libs INTERFACE + ${AVFORMAT_LIBRARIES} ${AVCODEC_LIBRARIES} + ${AVUTIL_LIBRARIES} ${SWSCALE_LIBRARIES}) +message(STATUS "FFmpeg: avformat=${AVFORMAT_VERSION} avcodec=${AVCODEC_VERSION}") + +# nlohmann/json (gallery + output serialisation) +include(FetchContent) +FetchContent_Declare( + nlohmann_json + GIT_REPOSITORY https://github.com/nlohmann/json.git + GIT_TAG v3.11.3 + GIT_SHALLOW TRUE +) +FetchContent_MakeAvailable(nlohmann_json) + +# ── Model paths ─────────────────────────────────────────────────────────────── +set(SAE_MODELS_DIR "${CMAKE_SOURCE_DIR}/models" + CACHE PATH "Directory containing ONNX model files") + +# ── Shared library: gallery store ───────────────────────────────────────────── +add_library(sae_gallery STATIC + src/gallery/gallery_store.cpp + src/gallery/gallery_builder.cpp +) +target_include_directories(sae_gallery PUBLIC src) +target_link_libraries(sae_gallery PUBLIC + kpn + ${OpenCV_LIBS} + nlohmann_json::nlohmann_json + onnxruntime + trt_runtime + ffmpeg_libs +) +target_compile_definitions(sae_gallery PUBLIC + SAE_MODELS_DIR="${SAE_MODELS_DIR}" +) + +# ── embed_faces — image → embedding JSON (used by gallery builder scripts) ──── +add_executable(embed_faces src/embed_faces.cpp) +target_link_libraries(embed_faces PRIVATE + kpn + ${OpenCV_LIBS} + nlohmann_json::nlohmann_json + onnxruntime + trt_runtime + ffmpeg_libs +) +target_compile_definitions(embed_faces PRIVATE SAE_MODELS_DIR="${SAE_MODELS_DIR}") + +# ── analyze — main analysis binary ─────────────────────────────────────────── +add_executable(scene_analyze src/main.cpp) +target_link_libraries(scene_analyze PRIVATE sae_gallery) + +# ── 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) +target_compile_definitions(scene_analyze_debug PRIVATE SAE_DEBUG=1) + +# ── scene_preview — live annotated display while analysing ─────────────────── +add_executable(scene_preview src/scene_preview.cpp) +target_link_libraries(scene_preview PRIVATE sae_gallery) + +# ── build_gallery — offline gallery construction tool ──────────────────────── +add_executable(build_gallery src/build_gallery.cpp) +target_link_libraries(build_gallery PRIVATE sae_gallery) + +# ── Optional: web debug UI for pipeline introspection ──────────────────────── +if(SAE_WEB_DEBUG) + kpn_target_enable_web_debug(scene_analyze) + kpn_target_enable_web_debug(scene_analyze_debug) + kpn_target_enable_web_debug(scene_preview) +endif() + +message(STATUS "OpenCV ${OpenCV_VERSION} found") +message(STATUS "Models dir: ${SAE_MODELS_DIR}") diff --git a/README.md b/README.md new file mode 100644 index 0000000..0cc6c85 --- /dev/null +++ b/README.md @@ -0,0 +1,107 @@ +# Scene Actor Extraction + +Identifies actors in movie files and produces X-ray-style scene annotations compatible with [Jellyfin](https://jellyfin.org/). Built on a KPN++ pipeline with ArcFace embeddings and a tracked-identity matcher. + +## How it works + +1. **Build a gallery** — download actor headshots from TMDB/IMDB, embed them with ArcFace (`build_gallery` / `scripts/make_gallery.py`). +2. **Analyze a movie** — `scene_analyze` decodes frames at configurable FPS, detects faces (YuNet/SCRFD), tracks them across cuts, matches identities against the gallery using calibrated similarity, and writes time-window JSON. +3. **Output** — minimal mode produces Jellyfin-ready actor name + time-window JSON; standard mode adds per-frame bbox, similarity, and track data. + +## Dependencies + +| Dependency | Role | +|---|---| +| KPN++ | Pipeline backbone (nodes, networks) | +| OpenCV 4 | Video decode, image ops, DNN inference, YuNet face detection | +| ONNX Runtime | SCRFD face detector (dynamic shape nodes unsupported by cv::dnn) | +| nlohmann/json | JSON I/O | + +## Build + +```bash +cmake -B build -DCMAKE_BUILD_TYPE=Release +cmake --build build -j$(nproc) +``` + +Optional flags: + +| Flag | Default | Effect | +|---|---|---| +| `-DSAE_WEB_DEBUG=ON` | OFF | Enables KPN web debug UI at `localhost:9090` | + +## Models + +Download the required ONNX models: + +```bash +bash scripts/download_models.sh +``` + +Models are placed in `external/`: +- `arcface_w600k_r50.onnx` — primary ArcFace embedder +- `arcface_w600k_mbf.onnx`, `arcface_r18.onnx` — lighter alternatives +- `face_detection_yunet_2023mar.onnx` — YuNet face detector +- `scrfd_500m_bnkps.onnx` — SCRFD face detector + +## Binaries + +| Binary | Description | +|---|---| +| `scene_analyze` | Main analysis pipeline, writes JSON output | +| `scene_analyze_debug` | Same as above + per-frame annotated JPEGs (`SAE_DEBUG=1`) | +| `scene_preview` | Live OpenCV display window while analysing | +| `build_gallery` | Offline gallery builder from a directory of images | +| `embed_faces` | Standalone embedder used by gallery scripts | + +### `scene_analyze` + +```bash +./build/scene_analyze --gallery gallery.json --input movie.mp4 [options] +``` + +Key options: + +| Flag | Default | Description | +|---|---|---| +| `--fps` | 1 | Frames per second to sample (5–10 recommended for tracking) | +| `--prob-threshold` | 0.5 | Minimum calibrated match probability | +| `--match-threshold` | — | Raw cosine similarity threshold (fallback) | +| `--extinction` | 5s | How long a track persists after last detection | +| `--track-alpha` | — | IoU vs. embedding weight in Hungarian assignment | +| `--track-min-iou` | — | Minimum IoU gate for spatial assignment | +| `--track-max-embed` | — | Maximum embedding distance gate | +| `--track-max-missing` | — | Frames a track survives without a detection | +| `--track-min-frames` | 3 | Observations before a track's mean embedding is used for matching | + +### Gallery builder + +```bash +python3 scripts/make_gallery.py --tmdb-bearer --movie-id --output gallery.json +``` + +Fetches cast images from TMDB and embeds them via `embed_faces`. + +## Output format + +**Minimal** (default) — Jellyfin-ready: +```json +[ + { "actor": "Name", "start": 12.0, "end": 45.5 } +] +``` + +**Standard** — per-frame detail with bounding boxes, similarity scores, and track IDs. + +## Pipeline topology + +``` +frame_source → face_detector → face_aligner → embedder + → face_tracker → identity_matcher → scene_tracker → result_sink +``` + +Debug/preview branches fan out automatically from `identity_matcher`. + +## Evaluation + +Scripts in `eval/` and `scripts/movienet_*.py` support benchmarking against the MovieNet dataset. diff --git a/eval/gt.json b/eval/gt.json new file mode 100644 index 0000000..6602e7e --- /dev/null +++ b/eval/gt.json @@ -0,0 +1,362 @@ +[ + { + "crop": "eval/probe/nm0001589_0000.jpg", + "imdb_id": "nm0001589", + "actor_name": "Michael Palin", + "source_frame": "tt0079470/shot_0119_img_0.jpg" + }, + { + "crop": "eval/probe/nm0001589_0001.jpg", + "imdb_id": "nm0001589", + "actor_name": "Michael Palin", + "source_frame": "tt0079470/shot_0125_img_2.jpg" + }, + { + "crop": "eval/probe/nm0001589_0002.jpg", + "imdb_id": "nm0001589", + "actor_name": "Michael Palin", + "source_frame": "tt0079470/shot_0128_img_1.jpg" + }, + { + "crop": "eval/probe/nm0001589_0003.jpg", + "imdb_id": "nm0001589", + "actor_name": "Michael Palin", + "source_frame": "tt0079470/shot_0225_img_0.jpg" + }, + { + "crop": "eval/probe/nm0001589_0004.jpg", + "imdb_id": "nm0001589", + "actor_name": "Michael Palin", + "source_frame": "tt0079470/shot_0306_img_2.jpg" + }, + { + "crop": "eval/probe/nm0001589_0005.jpg", + "imdb_id": "nm0001589", + "actor_name": "Michael Palin", + "source_frame": "tt0079470/shot_0308_img_1.jpg" + }, + { + "crop": "eval/probe/nm0001589_0006.jpg", + "imdb_id": "nm0001589", + "actor_name": "Michael Palin", + "source_frame": "tt0079470/shot_0311_img_1.jpg" + }, + { + "crop": "eval/probe/nm0001589_0007.jpg", + "imdb_id": "nm0001589", + "actor_name": "Michael Palin", + "source_frame": "tt0079470/shot_0315_img_0.jpg" + }, + { + "crop": "eval/probe/nm0001589_0008.jpg", + "imdb_id": "nm0001589", + "actor_name": "Michael Palin", + "source_frame": "tt0079470/shot_0317_img_1.jpg" + }, + { + "crop": "eval/probe/nm0001589_0009.jpg", + "imdb_id": "nm0001589", + "actor_name": "Michael Palin", + "source_frame": "tt0079470/shot_0319_img_1.jpg" + }, + { + "crop": "eval/probe/nm0000114_0000.jpg", + "imdb_id": "nm0000114", + "actor_name": "Steve Buscemi", + "source_frame": "tt0101410/shot_0072_img_0.jpg" + }, + { + "crop": "eval/probe/nm0000114_0001.jpg", + "imdb_id": "nm0000114", + "actor_name": "Steve Buscemi", + "source_frame": "tt0101410/shot_0074_img_2.jpg" + }, + { + "crop": "eval/probe/nm0000114_0002.jpg", + "imdb_id": "nm0000114", + "actor_name": "Steve Buscemi", + "source_frame": "tt0101410/shot_0078_img_0.jpg" + }, + { + "crop": "eval/probe/nm0000114_0003.jpg", + "imdb_id": "nm0000114", + "actor_name": "Steve Buscemi", + "source_frame": "tt0101410/shot_0079_img_2.jpg" + }, + { + "crop": "eval/probe/nm0000114_0004.jpg", + "imdb_id": "nm0000114", + "actor_name": "Steve Buscemi", + "source_frame": "tt0101410/shot_0080_img_0.jpg" + }, + { + "crop": "eval/probe/nm0000114_0005.jpg", + "imdb_id": "nm0000114", + "actor_name": "Steve Buscemi", + "source_frame": "tt0101410/shot_0638_img_0.jpg" + }, + { + "crop": "eval/probe/nm0000114_0006.jpg", + "imdb_id": "nm0000114", + "actor_name": "Steve Buscemi", + "source_frame": "tt0101410/shot_0641_img_0.jpg" + }, + { + "crop": "eval/probe/nm0000114_0007.jpg", + "imdb_id": "nm0000114", + "actor_name": "Steve Buscemi", + "source_frame": "tt0105236/shot_0017_img_1.jpg" + }, + { + "crop": "eval/probe/nm0000114_0008.jpg", + "imdb_id": "nm0000114", + "actor_name": "Steve Buscemi", + "source_frame": "tt0105236/shot_0022_img_2.jpg" + }, + { + "crop": "eval/probe/nm0000114_0009.jpg", + "imdb_id": "nm0000114", + "actor_name": "Steve Buscemi", + "source_frame": "tt0105236/shot_0024_img_0.jpg" + }, + { + "crop": "eval/probe/nm0005042_0000.jpg", + "imdb_id": "nm0005042", + "actor_name": "Jason Isaacs", + "source_frame": "tt0119081/shot_0097_img_2.jpg" + }, + { + "crop": "eval/probe/nm0005042_0001.jpg", + "imdb_id": "nm0005042", + "actor_name": "Jason Isaacs", + "source_frame": "tt0119081/shot_0100_img_0.jpg" + }, + { + "crop": "eval/probe/nm0005042_0002.jpg", + "imdb_id": "nm0005042", + "actor_name": "Jason Isaacs", + "source_frame": "tt0119081/shot_0108_img_0.jpg" + }, + { + "crop": "eval/probe/nm0005042_0003.jpg", + "imdb_id": "nm0005042", + "actor_name": "Jason Isaacs", + "source_frame": "tt0119081/shot_0110_img_0.jpg" + }, + { + "crop": "eval/probe/nm0005042_0004.jpg", + "imdb_id": "nm0005042", + "actor_name": "Jason Isaacs", + "source_frame": "tt0119081/shot_0121_img_0.jpg" + }, + { + "crop": "eval/probe/nm0005042_0005.jpg", + "imdb_id": "nm0005042", + "actor_name": "Jason Isaacs", + "source_frame": "tt0119081/shot_0123_img_0.jpg" + }, + { + "crop": "eval/probe/nm0005042_0006.jpg", + 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------------------ | ------------ | ------------ | ------------ | +| Steve Buscemi | 100% (9/9) | 100% (9/9) | 100% (9/9) | +| Michael Palin | 88% (7/8) | 50% (4/8) | 62% (5/8) | +| Jason Isaacs | 100% (10/10) | 100% (10/10) | 100% (10/10) | +| Paddy Considine | 50% (5/10) | 50% (5/10) | 60% (6/10) | +| Olga Kurylenko | 89% (8/9) | 67% (6/9) | 100% (9/9) | +| Andrea Riseborough | 88% (7/8) | 62% (5/8) | 75% (6/8) | diff --git a/external/KPN b/external/KPN new file mode 160000 index 0000000..79916f1 --- /dev/null +++ b/external/KPN @@ -0,0 +1 @@ +Subproject commit 79916f1da131465aa7a712dab8f7d1fc76aa5ba7 diff --git a/models/arcface_r18.onnx b/models/arcface_r18.onnx new file mode 100644 index 0000000..621e7d3 --- /dev/null +++ b/models/arcface_r18.onnx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e52f648f6f57356118717df38c7fdfe562166ebf48fb34624ddb18b498a19f53 +size 48064813 diff --git a/models/arcface_w600k_mbf.onnx b/models/arcface_w600k_mbf.onnx new file mode 100644 index 0000000..d81d20d --- /dev/null +++ b/models/arcface_w600k_mbf.onnx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9cc6e4a75f0e2bf0b1aed94578f144d15175f357bdc05e815e5c4a02b319eb4f +size 13616099 diff --git a/models/arcface_w600k_r50.onnx b/models/arcface_w600k_r50.onnx new file mode 100644 index 0000000..571d2bb --- /dev/null +++ b/models/arcface_w600k_r50.onnx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4c06341c33c2ca1f86781dab0e829f88ad5b64be9fba56e56bc9ebdefc619e43 +size 174383860 diff --git a/models/face_detection_yunet_2023mar.onnx b/models/face_detection_yunet_2023mar.onnx new file mode 100644 index 0000000..2d8804a --- /dev/null +++ b/models/face_detection_yunet_2023mar.onnx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8f2383e4dd3cfbb4553ea8718107fc0423210dc964f9f4280604804ed2552fa4 +size 232589 diff --git a/models/scrfd_500m_bnkps.onnx b/models/scrfd_500m_bnkps.onnx new file mode 100644 index 0000000..a8a550a --- /dev/null +++ b/models/scrfd_500m_bnkps.onnx @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5e4447f50245bbd7966bd6c0fa52938c61474a04ec7def48753668a9d8b4ea3a +size 2524817 diff --git a/scripts/build_trt_engines.sh b/scripts/build_trt_engines.sh new file mode 100755 index 0000000..eed6ab7 --- /dev/null +++ b/scripts/build_trt_engines.sh @@ -0,0 +1,51 @@ +#!/usr/bin/env bash +# Pre-build TensorRT engines for ArcFace and SCRFD with the same shape profiles +# the runtime nodes use. First-run ORT engine builds take 30–90 s per model and +# block the pipeline; this script does it offline so cold starts are instant. +# +# Profiles must match src/arcface_embedder.hpp and src/scrfd_decoder.hpp: +# ArcFace : min=1x3x112x112 opt=Nx3x112x112 max=Nx3x112x112 (N = embed batch) +# SCRFD : 1x3x640x640 (fixed; we letterbox to this) +# +# These trtexec-built engines are *not* picked up by the ORT TRT EP cache — +# ORT uses its own engine format. The point of this script is: +# (a) sanity-check that the ONNX models build under TRT at all; +# (b) measure pure inference latency without ORT overhead. +# Run scene_analyze normally and ORT will populate ./trt_cache itself. +set -euo pipefail + +ROOT="$(cd "$(dirname "$0")/.." && pwd)" +MODELS="$ROOT/models" +OUT="$ROOT/trt_cache" +mkdir -p "$OUT" + +EMBED_BATCH="${EMBED_BATCH:-4}" +ARCFACE_MODEL="${ARCFACE_MODEL:-$MODELS/arcface_w600k_r50.onnx}" +SCRFD_MODEL="${SCRFD_MODEL:-$MODELS/scrfd_500m_bnkps.onnx}" + +run() { echo "+ $*"; "$@"; } + +echo "== ArcFace ==" +run trtexec \ + --onnx="$ARCFACE_MODEL" \ + --fp16 \ + --minShapes=input.1:1x3x112x112 \ + --optShapes=input.1:${EMBED_BATCH}x3x112x112 \ + --maxShapes=input.1:${EMBED_BATCH}x3x112x112 \ + --saveEngine="$OUT/arcface.$(basename "$ARCFACE_MODEL" .onnx).b${EMBED_BATCH}.fp16.engine" \ + --useCudaGraph + +echo +echo "== SCRFD ==" +run trtexec \ + --onnx="$SCRFD_MODEL" \ + --fp16 \ + --minShapes=input.1:1x3x640x640 \ + --optShapes=input.1:1x3x640x640 \ + --maxShapes=input.1:1x3x640x640 \ + --saveEngine="$OUT/scrfd.$(basename "$SCRFD_MODEL" .onnx).640.fp16.engine" \ + --useCudaGraph + +echo +echo "Engines saved under: $OUT" +echo "Look for 'mean: ... ms' in each section for per-call latency." diff --git a/scripts/download_models.sh b/scripts/download_models.sh new file mode 100755 index 0000000..ed2f3d4 --- /dev/null +++ b/scripts/download_models.sh @@ -0,0 +1,71 @@ +#!/usr/bin/env bash +# Download ONNX models required by scene_analyze and build_gallery. +# Run from the project root: bash scripts/download_models.sh + +set -euo pipefail + +MODELS_DIR="${1:-models}" +mkdir -p "$MODELS_DIR" + +# ── YuNet face detection ────────────────────────────────────────────────────── +YUNET_URL="https://github.com/opencv/opencv_zoo/raw/main/models/face_detection_yunet/face_detection_yunet_2023mar.onnx" +YUNET_FILE="$MODELS_DIR/face_detection_yunet_2023mar.onnx" +if [ ! -f "$YUNET_FILE" ]; then + echo "Downloading YuNet…" + curl -L "$YUNET_URL" -o "$YUNET_FILE" +else + echo "YuNet already present: $YUNET_FILE" +fi + +# ── ArcFace face recognition (buffalo_l / w600k_r50) ───────────────────────── +# This model is part of InsightFace's buffalo_l pack. +# We download and unpack only the recognition model. +ARCFACE_FILE="$MODELS_DIR/arcface_w600k_r50.onnx" +if [ ! -f "$ARCFACE_FILE" ]; then + echo "Downloading ArcFace (buffalo_l)…" + TMP_ZIP=$(mktemp /tmp/buffalo_l.XXXXXX.zip) + curl -L "https://github.com/deepinsight/insightface/releases/download/v0.7/buffalo_l.zip" \ + -o "$TMP_ZIP" + # The zip contains: 1k3d68.onnx 2d106det.onnx det_10g.onnx genderage.onnx w600k_r50.onnx + unzip -jo "$TMP_ZIP" "w600k_r50.onnx" -d "$MODELS_DIR" + mv "$MODELS_DIR/w600k_r50.onnx" "$ARCFACE_FILE" + rm "$TMP_ZIP" +else + echo "ArcFace already present: $ARCFACE_FILE" +fi + +# ── ArcFace face recognition (buffalo_s / w600k_mbf — MobileFaceNet) ───────── +# Lighter backbone (13 MB vs 174 MB for R50) — same 512-dim output, faster inference. +ARCFACE_MBF_FILE="$MODELS_DIR/arcface_w600k_mbf.onnx" +if [ ! -f "$ARCFACE_MBF_FILE" ]; then + echo "Downloading ArcFace MobileFaceNet (buffalo_s)…" + TMP_ZIP=$(mktemp /tmp/buffalo_s.XXXXXX.zip) + curl -L "https://github.com/deepinsight/insightface/releases/download/v0.7/buffalo_s.zip" \ + -o "$TMP_ZIP" + unzip -jo "$TMP_ZIP" "w600k_mbf.onnx" -d "$MODELS_DIR" + mv "$MODELS_DIR/w600k_mbf.onnx" "$ARCFACE_MBF_FILE" + rm "$TMP_ZIP" +else + echo "ArcFace MBF already present: $ARCFACE_MBF_FILE" +fi + +# ── SCRFD-500MF face detection (InsightFace buffalo_sc) ─────────────────────── +# buffalo_sc.zip contains det_500m.onnx (SCRFD-500MF with 5 keypoints). +# If the unzip fails (file not found in archive), download manually from: +# https://huggingface.co/deepinsight/insightface/resolve/main/models/buffalo_sc/det_500m.onnx +SCRFD_FILE="$MODELS_DIR/scrfd_500m_bnkps.onnx" +if [ ! -f "$SCRFD_FILE" ]; then + echo "Downloading SCRFD-500MF (buffalo_sc)…" + TMP_ZIP=$(mktemp /tmp/buffalo_sc.XXXXXX.zip) + curl -L "https://github.com/deepinsight/insightface/releases/download/v0.7/buffalo_sc.zip" \ + -o "$TMP_ZIP" + unzip -jo "$TMP_ZIP" "det_500m.onnx" -d "$MODELS_DIR" + mv "$MODELS_DIR/det_500m.onnx" "$SCRFD_FILE" + rm "$TMP_ZIP" +else + echo "SCRFD-500MF already present: $SCRFD_FILE" +fi + +echo "" +echo "Models ready in $MODELS_DIR/:" +ls -lh "$MODELS_DIR" diff --git a/scripts/make_gallery.py b/scripts/make_gallery.py new file mode 100755 index 0000000..89a6996 --- /dev/null +++ b/scripts/make_gallery.py @@ -0,0 +1,291 @@ +#!/usr/bin/env python3 +"""make_gallery.py — fetch actor images for a movie and build gallery.json. + +Fetches the cast from TMDB, downloads actor profile images, runs the C++ +embed_faces binary (SCRFD + ArcFace, same models as scene_analyze) to produce +embeddings, then writes gallery.json. + +Requirements: + pip install requests Pillow + +Usage: + # By IMDB movie ID (most natural — resolves to TMDB automatically): + python scripts/make_gallery.py \\ + --tmdb-key YOUR_KEY \\ + --imdb-id tt0137523 \\ + --output gallery.json + + # Or directly with a TMDB movie ID: + python scripts/make_gallery.py \\ + --tmdb-key YOUR_KEY \\ + --movie-id 550 \\ + --output gallery.json + + # Additional options: + # --embed-bin build/embed_faces path to embed_faces binary + # --models-dir models/ directory with ONNX models + # --max-actors 20 how many cast members to include + # --images-per-actor 3 profile images to download per actor + # --image-dir /tmp/gallery_imgs where to cache downloaded images + +Get a free TMDB API key at: https://www.themoviedb.org/settings/api +""" + +import argparse +import json +import os +import subprocess +import sys +import tempfile +import time +from pathlib import Path + +import io + +import requests +from PIL import Image + +TMDB_BASE = "https://api.themoviedb.org/3" +TMDB_IMG = "https://image.tmdb.org/t/p/original" + +# ── TMDB helpers ────────────────────────────────────────────────────────────── + +def tmdb_get(path: str, token: str, **params) -> dict: + url = TMDB_BASE + path + if token.startswith("eyJ"): + headers = {"Authorization": f"Bearer {token}", "Accept": "application/json"} + r = requests.get(url, params=params, headers=headers, timeout=10) + else: + params["api_key"] = token + r = requests.get(url, params=params, headers={"Accept": "application/json"}, timeout=10) + r.raise_for_status() + return r.json() + + +def tmdb_id_from_imdb(imdb_id: str, key: str) -> int: + data = tmdb_get(f"/find/{imdb_id}", key, external_source="imdb_id") + results = data.get("movie_results", []) + if not results: + raise ValueError(f"No TMDB movie found for IMDB ID {imdb_id}") + return results[0]["id"] + + +def fetch_cast(movie_id: int, key: str, max_actors: int) -> list[dict]: + """Return list of {id, name, imdb_id, profile_images: [...url...]}.""" + credits = tmdb_get(f"/movie/{movie_id}/credits", key) + cast = credits.get("cast", [])[:max_actors] + + actors = [] + for member in cast: + person_id = member["id"] + + # Get IMDB ID for this person + ext = tmdb_get(f"/person/{person_id}/external_ids", key) + imdb_id = ext.get("imdb_id") or f"tmdb_{person_id}" + + # Get profile images (sorted by vote_average desc by TMDB) + images_data = tmdb_get(f"/person/{person_id}/images", key) + profiles = images_data.get("profiles", []) + image_urls = [TMDB_IMG + p["file_path"] for p in profiles if p.get("file_path")] + + if not image_urls: + print(f" [warn] no images for {member['name']}, skipping", file=sys.stderr) + continue + + actors.append({ + "id": person_id, + "name": member["name"], + "imdb_id": imdb_id, + "profile_images": image_urls, + }) + time.sleep(0.05) # be polite to TMDB + + return actors + + +# ── Image download ──────────────────────────────────────────────────────────── + +def download_images(actor: dict, dest_dir: Path, n: int) -> list[Path]: + """Download up to n profile images for an actor into dest_dir.""" + dest_dir.mkdir(parents=True, exist_ok=True) + paths = [] + for i, url in enumerate(actor["profile_images"][:n]): + out = dest_dir / f"{i:02d}.jpg" + if out.exists() and out.stat().st_size > 1024: + paths.append(out) + continue + try: + r = requests.get(url, timeout=15) + r.raise_for_status() + Image.open(io.BytesIO(r.content)).convert("RGB").save(out, "JPEG") + paths.append(out) + except Exception as e: + print(f" [warn] download failed: {url}: {e}", file=sys.stderr) + return paths + + +# ── Embedding via embed_faces binary ───────────────────────────────────────── + +def embed_images(image_paths: list[Path], embed_bin: str, + detector: str, arcface: str) -> list[dict | None]: + """ + Call the C++ embed_faces binary on a list of images. + Returns a list of result dicts (or None if no face / error) per image. + """ + if not image_paths: + return [] + + cmd = [ + embed_bin, + "--detector", detector, + "--arcface", arcface, + ] + [str(p) for p in image_paths] + + try: + proc = subprocess.run(cmd, capture_output=True, text=True, check=True) + except subprocess.CalledProcessError as e: + print(f"[error] embed_faces failed:\n{e.stderr}", file=sys.stderr) + return [None] * len(image_paths) + + try: + results = json.loads(proc.stdout) + except json.JSONDecodeError as e: + print(f"[error] embed_faces output is not valid JSON: {e}", file=sys.stderr) + return [None] * len(image_paths) + + return results + + +# ── Gallery assembly ───────────────────────────────────────────────────────── + +def build_gallery(movie_id: int, key: str, embed_bin: str, + detector: str, arcface: str, + max_actors: int, images_per_actor: int, + image_root: Path) -> dict: + """Fetch cast, download images, embed, return gallery dict.""" + print(f"Fetching cast for TMDB movie {movie_id}…", file=sys.stderr) + actors = fetch_cast(movie_id, key, max_actors) + print(f"Found {len(actors)} actors with images", file=sys.stderr) + + gallery_actors = [] + + for actor in actors: + safe_name = actor["name"].replace(" ", "_") + actor_dir = image_root / f"{actor['imdb_id']}_{safe_name}" + + print(f"\n{actor['name']} ({actor['imdb_id']})", file=sys.stderr) + image_paths = download_images(actor, actor_dir, images_per_actor) + if not image_paths: + print(" no images downloaded, skipping", file=sys.stderr) + continue + + print(f" embedding {len(image_paths)} image(s)…", file=sys.stderr) + results = embed_images(image_paths, embed_bin, detector, arcface) + + embeddings = [] + source_images = [] + for path, res in zip(image_paths, results): + if res is None or res.get("embedding") is None: + reason = res.get("error", "unknown") if res else "binary error" + print(f" [skip] {path.name}: {reason}", file=sys.stderr) + continue + embeddings.append(res["embedding"]) + source_images.append(path.name) + print(f" [ok] {path.name} conf={res.get('confidence', 0):.2f}", + file=sys.stderr) + + if not embeddings: + print(" no valid embeddings, skipping actor", file=sys.stderr) + continue + + gallery_actors.append({ + "imdb_id": actor["imdb_id"], + "name": actor["name"], + "source_images": source_images, + "embeddings": embeddings, + }) + print(f" → {len(embeddings)} embedding(s) stored", file=sys.stderr) + + return {"actors": gallery_actors} + + +# ── Entry point ─────────────────────────────────────────────────────────────── + +def main(): + parser = argparse.ArgumentParser( + description="Fetch TMDB cast images and build gallery.json via embed_faces") + parser.add_argument("--tmdb-key", required=True, + help="TMDB Bearer token (API Read Access Token from themoviedb.org/settings/api)") + group = parser.add_mutually_exclusive_group(required=True) + group.add_argument("--imdb-id", + help="IMDB movie ID, e.g. tt0137523 — looked up via TMDB automatically") + group.add_argument("--movie-id", type=int, + help="TMDB movie ID (alternative to --imdb-id)") + parser.add_argument("--output", required=True, help="Output gallery.json path") + parser.add_argument("--embed-bin", default="build/embed_faces", + help="Path to embed_faces binary (default: build/embed_faces)") + parser.add_argument("--models-dir", default="models", + help="Directory containing ONNX models (default: models/)") + parser.add_argument("--arcface", default=None, + help="Path to ArcFace ONNX model (overrides --models-dir selection)") + parser.add_argument("--max-actors", type=int, default=20, + help="Maximum number of cast members to include (default: 20)") + parser.add_argument("--images-per-actor",type=int, default=3, + help="Profile images to download per actor (default: 3)") + parser.add_argument("--image-dir", default=None, + help="Where to store downloaded images (default: /images)") + parser.add_argument("--keep-images", action="store_true", + help="Do not delete downloaded images after embedding") + args = parser.parse_args() + + # Resolve paths + embed_bin = str(Path(args.embed_bin).resolve()) + models_dir = Path(args.models_dir) + detector = str(models_dir / "scrfd_500m_bnkps.onnx") + arcface = args.arcface if args.arcface else str(models_dir / "arcface_w600k_r50.onnx") + output = Path(args.output) + image_root = Path(args.image_dir) if args.image_dir else output.parent / "images" + + # Validate + if not Path(embed_bin).is_file(): + sys.exit(f"embed_faces binary not found: {embed_bin}\n" + f"Build it first: cmake --build build --target embed_faces") + for model, name in [(detector, "SCRFD"), (arcface, "ArcFace")]: + if not Path(model).is_file(): + sys.exit(f"{name} model not found: {model}\n" + f"Run: bash scripts/download_models.sh") + + # Resolve movie ID + movie_id = args.movie_id + if movie_id is None: + print(f"Resolving IMDB ID {args.imdb_id} → TMDB…", file=sys.stderr) + movie_id = tmdb_id_from_imdb(args.imdb_id, args.tmdb_key) + print(f"TMDB movie ID: {movie_id}", file=sys.stderr) + + # Build gallery + gallery = build_gallery( + movie_id = movie_id, + key = args.tmdb_key, + embed_bin = embed_bin, + detector = detector, + arcface = arcface, + max_actors = args.max_actors, + images_per_actor = args.images_per_actor, + image_root = image_root, + ) + + n_actors = len(gallery["actors"]) + n_embeddings = sum(len(a["embeddings"]) for a in gallery["actors"]) + print(f"\nGallery: {n_actors} actors, {n_embeddings} total embeddings", + file=sys.stderr) + + if n_actors == 0: + sys.exit("No actors could be processed — check models and images.") + + output.parent.mkdir(parents=True, exist_ok=True) + output.write_text(json.dumps(gallery, indent=2) + "\n") + print(f"Saved: {output}", file=sys.stderr) + + +if __name__ == "__main__": + main() diff --git a/scripts/movienet_eval.py b/scripts/movienet_eval.py new file mode 100644 index 0000000..3084c48 --- /dev/null +++ b/scripts/movienet_eval.py @@ -0,0 +1,144 @@ +#!/usr/bin/env python3 +""" +movienet_eval.py — embed probe crops and match against a gallery. + +Usage: + python scripts/movienet_eval.py \ + --gallery gallery_r50.json \ + --arcface models/arcface_w600k_r50.onnx \ + --gt eval/gt.json \ + --output eval/predictions_r50.json \ + [--yunet models/face_detection_yunet_2023mar.onnx] \ + [--embed-bin build/embed_faces] + +Input (--gt): list of {"crop": , "imdb_id": , "actor_name": } +Output: list of {"crop", "gt", "pred", "similarity", "detection_failed", "all_scores"} +""" + +import argparse +import json +import math +import subprocess +import sys +from pathlib import Path + + +def load_gallery(path: str) -> dict[str, dict]: + """Return {imdb_id: {"name": str, "embeddings": [[float]]}}.""" + with open(path) as f: + data = json.load(f) + return {a["imdb_id"]: {"name": a["name"], "embeddings": a["embeddings"]} + for a in data["actors"]} + + +def dot(a: list[float], b: list[float]) -> float: + return sum(x * y for x, y in zip(a, b)) + + +def embed_images(paths: list[Path], embed_bin: str, yunet: str, arcface: str) -> list[dict | None]: + if not paths: + return [] + cmd = [embed_bin, "--yunet", yunet, "--arcface", arcface] + [str(p) for p in paths] + try: + proc = subprocess.run(cmd, capture_output=True, text=True, check=True) + except subprocess.CalledProcessError as e: + print(f"[error] embed_faces failed:\n{e.stderr}", file=sys.stderr) + return [None] * len(paths) + try: + return json.loads(proc.stdout) + except json.JSONDecodeError as e: + print(f"[error] embed_faces JSON parse error: {e}", file=sys.stderr) + return [None] * len(paths) + + +def match(embedding: list[float], gallery: dict[str, dict]) -> tuple[str, float, dict[str, float]]: + """Return (best_imdb_id, best_similarity, {imdb_id: similarity}).""" + scores: dict[str, float] = {} + for imdb_id, actor in gallery.items(): + # max similarity across all reference embeddings for this actor + scores[imdb_id] = max(dot(embedding, ref) for ref in actor["embeddings"]) + best_id = max(scores, key=lambda k: scores[k]) + return best_id, scores[best_id], scores + + +def main(): + p = argparse.ArgumentParser() + p.add_argument("--gallery", required=True) + p.add_argument("--arcface", required=True) + p.add_argument("--gt", required=True) + p.add_argument("--output", required=True) + p.add_argument("--yunet", default="models/face_detection_yunet_2023mar.onnx") + p.add_argument("--embed-bin", default="build/embed_faces") + args = p.parse_args() + + gallery = load_gallery(args.gallery) + print(f"[eval] gallery: {len(gallery)} actors", file=sys.stderr) + + with open(args.gt) as f: + gt_entries = json.load(f) + print(f"[eval] probe crops: {len(gt_entries)}", file=sys.stderr) + + # Batch all crops in one embed_faces call to amortise startup cost + crop_paths = [Path(e["crop"]) for e in gt_entries] + missing = [p for p in crop_paths if not p.exists()] + if missing: + print(f"[warn] {len(missing)} crop(s) not found on disk, skipping", file=sys.stderr) + + results_raw = embed_images( + [p for p in crop_paths if p.exists()], + args.embed_bin, args.yunet, args.arcface + ) + + # Re-index results back to original list (missing files get None) + raw_iter = iter(results_raw) + embed_results: list[dict | None] = [] + for p in crop_paths: + embed_results.append(next(raw_iter) if p.exists() else None) + + predictions = [] + n_det_fail = 0 + n_correct = 0 + + for entry, result in zip(gt_entries, embed_results): + detection_failed = result is None or result.get("embedding") is None + if detection_failed: + n_det_fail += 1 + predictions.append({ + "crop": entry["crop"], + "gt": entry["imdb_id"], + "pred": None, + "similarity": None, + "detection_failed": True, + "all_scores": {}, + }) + continue + + pred_id, sim, all_scores = match(result["embedding"], gallery) + correct = pred_id == entry["imdb_id"] + if correct: + n_correct += 1 + + predictions.append({ + "crop": entry["crop"], + "gt": entry["imdb_id"], + "pred": pred_id, + "similarity": sim, + "detection_failed": False, + "all_scores": all_scores, + }) + + n_total = len(gt_entries) + n_evaluated = n_total - n_det_fail + rank1 = n_correct / n_evaluated * 100 if n_evaluated else 0 + print(f"[eval] detection failures: {n_det_fail}/{n_total}", file=sys.stderr) + print(f"[eval] rank-1 accuracy: {rank1:.1f}% ({n_correct}/{n_evaluated})", file=sys.stderr) + + out_path = Path(args.output) + out_path.parent.mkdir(parents=True, exist_ok=True) + with open(out_path, "w") as f: + json.dump(predictions, f, indent=2) + print(f"[eval] written → {out_path}", file=sys.stderr) + + +if __name__ == "__main__": + main() diff --git a/scripts/movienet_prep.py b/scripts/movienet_prep.py new file mode 100644 index 0000000..331cec1 --- /dev/null +++ b/scripts/movienet_prep.py @@ -0,0 +1,199 @@ +#!/usr/bin/env python3 +""" +movienet_prep.py — extract probe crops from MovieNet-PS for actors in our gallery. + +Usage: + python scripts/movienet_prep.py \ + --movienet \ + --gallery gallery.json \ + --output eval/ \ + [--split Train_app10] \ + [--margin 0.2] \ + [--max-per-actor 50] + +MovieNet-PS format (annotation.zip + Image.zip): + annotation/test/train_test/Train_app.mat — N annotations per actor + Train[i] = [imdb_id (nm...), count, [[img_path, bbox[x,y,w,h], label], ...]] + Image//shot_XXXX_img_Y.jpg — source frames + +Output: + eval/probe/ face crops (jpg) + eval/gt.json [{crop, imdb_id, actor_name, source_frame}] +""" + +import argparse +import json +import sys +import zipfile +from io import BytesIO +from pathlib import Path + +try: + import cv2 + import numpy as np + import scipy.io as sio +except ImportError as e: + print(f"[error] missing dependency: {e}", file=sys.stderr) + print("Install: pip install opencv-python scipy numpy", file=sys.stderr) + sys.exit(1) + + +# ── MovieNet-PS loader ──────────────────────────────────────────────────────── + +def load_movienet_annotations(movienet_root: Path, split: str) -> list[dict]: + """ + Parse a MovieNet-PS Train_app.mat split. + Returns flat list of {"imdb_id", "img_path", "bbox": [x,y,w,h]}. + img_path is relative to Image/ inside Image.zip, e.g. tt0047396/shot_0004_img_1.jpg + """ + mat_path = movienet_root / "annotation" / "test" / "train_test" / f"{split}.mat" + if not mat_path.exists(): + # try extracting from annotation.zip + zip_path = movienet_root / "annotation.zip" + if not zip_path.exists(): + raise FileNotFoundError(f"annotation.zip not found in {movienet_root}") + inner = f"annotation/test/train_test/{split}.mat" + print(f"[prep] extracting {inner} from annotation.zip…", file=sys.stderr) + with zipfile.ZipFile(zip_path) as z: + z.extract(inner, movienet_root) + mat_path = movienet_root / inner + + data = sio.loadmat(str(mat_path))["Train"] + annotations = [] + for row in data: + imdb_id = str(row[0].flat[0]) # e.g. "nm0000023" + entries = row[2] # array of [path, bbox, label] + for entry in entries: + img_path = str(entry[0].flat[0]) # e.g. "tt0032138/shot_0003_img_1.jpg" + bbox = [float(v) for v in entry[1].flat] # [x, y, w, h] + annotations.append({"imdb_id": imdb_id, "img_path": img_path, "bbox": bbox}) + return annotations + + +# ── Gallery loader ──────────────────────────────────────────────────────────── + +def load_gallery_ids(gallery_path: str) -> dict[str, str]: + """Return {imdb_id: actor_name} for all actors in the gallery.""" + with open(gallery_path) as f: + data = json.load(f) + return {a["imdb_id"]: a["name"] for a in data["actors"]} + + +# ── Crop + save ─────────────────────────────────────────────────────────────── + +def crop_face(img: "np.ndarray", bbox: list[float], margin: float) -> "np.ndarray | None": + h, w = img.shape[:2] + x, y, bw, bh = bbox + # expand by margin + pad_x = bw * margin + pad_y = bh * margin + x1 = max(0, int(x - pad_x)) + y1 = max(0, int(y - pad_y)) + x2 = min(w, int(x + bw + pad_x)) + y2 = min(h, int(y + bh + pad_y)) + crop = img[y1:y2, x1:x2] + return crop if crop.size > 0 else None + + +# ── Main ────────────────────────────────────────────────────────────────────── + +def main(): + p = argparse.ArgumentParser() + p.add_argument("--movienet", required=True, help="MovieNet-PS root directory") + p.add_argument("--gallery", required=True, help="gallery.json (for actor list)") + p.add_argument("--output", default="eval", help="output directory") + p.add_argument("--split", default="Train_app10", + help="annotation split to use (default: Train_app10)") + p.add_argument("--margin", type=float, default=0.2, + help="bbox expansion factor (default 0.2 = 20%%)") + p.add_argument("--max-per-actor", type=int, default=50, + help="cap probe crops per actor (default 50)") + args = p.parse_args() + + movienet_root = Path(args.movienet) + out_dir = Path(args.output) + probe_dir = out_dir / "probe" + probe_dir.mkdir(parents=True, exist_ok=True) + + gallery_ids = load_gallery_ids(args.gallery) + print(f"[prep] gallery actors: {len(gallery_ids)}", file=sys.stderr) + + annotations = load_movienet_annotations(movienet_root, args.split) + print(f"[prep] total annotations in split: {len(annotations)}", file=sys.stderr) + + matched = [a for a in annotations if a["imdb_id"] in gallery_ids] + print(f"[prep] annotations matching gallery: {len(matched)}", file=sys.stderr) + + if not matched: + print("[error] no overlap between MovieNet and gallery — check IMDb ID format", file=sys.stderr) + sys.exit(1) + + # Build set of image paths we actually need, then extract from Image.zip in one pass + needed_paths = {a["img_path"] for a in matched} + image_zip = movienet_root / "Image.zip" + frame_cache: dict[str, np.ndarray] = {} + + print(f"[prep] extracting {len(needed_paths)} frames from Image.zip…", file=sys.stderr) + with zipfile.ZipFile(image_zip) as zf: + for img_path in needed_paths: + zip_entry = f"Image/{img_path}" + try: + data = zf.read(zip_entry) + arr = np.frombuffer(data, dtype=np.uint8) + img = cv2.imdecode(arr, cv2.IMREAD_COLOR) + if img is not None: + frame_cache[img_path] = img + except KeyError: + pass # file missing from zip, skip silently + + print(f"[prep] frames loaded: {len(frame_cache)}/{len(needed_paths)}", file=sys.stderr) + + per_actor_count: dict[str, int] = {} + gt_entries = [] + n_failed = 0 + + for ann in matched: + imdb_id = ann["imdb_id"] + img_path = ann["img_path"] + count = per_actor_count.get(imdb_id, 0) + if count >= args.max_per_actor: + continue + + img = frame_cache.get(img_path) + if img is None: + n_failed += 1 + continue + + crop = crop_face(img, ann["bbox"], args.margin) + if crop is None: + n_failed += 1 + continue + + crop_name = f"{imdb_id}_{count:04d}.jpg" + crop_path = probe_dir / crop_name + cv2.imwrite(str(crop_path), crop) + + per_actor_count[imdb_id] = count + 1 + gt_entries.append({ + "crop": str(crop_path), + "imdb_id": imdb_id, + "actor_name": gallery_ids[imdb_id], + "source_frame": img_path, + }) + + gt_path = out_dir / "gt.json" + with open(gt_path, "w") as f: + json.dump(gt_entries, f, indent=2) + + print(f"[prep] crops saved: {len(gt_entries)}", file=sys.stderr) + print(f"[prep] crop failures: {n_failed}", file=sys.stderr) + print(f"[prep] actors covered: {len(per_actor_count)}/{len(gallery_ids)}", file=sys.stderr) + for imdb_id, name in sorted(gallery_ids.items()): + n = per_actor_count.get(imdb_id, 0) + status = f"{n} crops" if n else "NO MATCH" + print(f" {name:30s} {status}", file=sys.stderr) + print(f"[prep] gt.json → {gt_path}", file=sys.stderr) + + +if __name__ == "__main__": + main() diff --git a/scripts/movienet_score.py b/scripts/movienet_score.py new file mode 100644 index 0000000..c8eb1aa --- /dev/null +++ b/scripts/movienet_score.py @@ -0,0 +1,144 @@ +#!/usr/bin/env python3 +""" +movienet_score.py — compare model predictions against ground truth. + +Usage: + python scripts/movienet_score.py \ + --gt eval/gt.json \ + --predictions eval/predictions_r50.json:R50:167MB \ + eval/predictions_r18.json:R18:46MB \ + eval/predictions_mbf.json:MBF:13MB \ + [--output eval/report.md] + +Each --predictions value is :