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filter-nv-face-tracking: Improve performance with asynchronous tracking

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Through converting the code to a threaded asynchronous approach, the libOBS video renderer no longer has to wait on our tracking code to run, and we can enjoy a little bit of extra calculation time before we actually have to do anything.

However due to the remaining synchronization with the Direct3D11/OpenGL context, it is not entirely safe to spend a full frame tracking as libOBS will then start skipped/dropping frames. Even though the priority of the stream is now increased, this still means that we can't just sit around and have to quickly finish all work.

Related #150
This commit is contained in:
Michael Fabian 'Xaymar' Dirks 2020-04-10 15:40:12 +02:00 committed by Michael Fabian Dirks
parent 4d8ff417e7
commit 04ac0a640f
2 changed files with 429 additions and 337 deletions
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699
source/filters/filter-nv-face-tracking.cpp
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@ -23,6 +23,7 @@
#include <util/platform.h>
#include "nvidia/cuda/nvidia-cuda-context-stack.hpp"
#include "obs/gs/gs-helper.hpp"
#include "obs/obs-tools.hpp"
#include "utility.hpp"
#define ST "Filter.Nvidia.FaceTracking"
@ -37,135 +38,388 @@
#define ST_ROI_STABILITY "Filter.Nvidia.FaceTracking.ROI.Stability"
#define SK_ROI_STABILITY "ROI.Stability"
void nvar_deleter(NvAR_FeatureHandle v)
void ar_feature_deleter(NvAR_FeatureHandle v)
{
filter::nvidia::face_tracking_factory::get()->get_ar()->destroy(v);
}
filter::nvidia::face_tracking_instance::face_tracking_instance(obs_data_t* settings, obs_source_t* self)
: obs::source_instance(settings, self), _width(), _height(), _up_to_date(false), _rt(), _cfg_roi_zoom(1.0),
_cfg_roi_offset({0., 0.}), _cfg_roi_stability(1.0), _roi_center(), _roi_size(), _roi_geom(4, 1),
: obs::source_instance(settings, self),
_rt_is_fresh(false), _rt(),
_cfg_roi_zoom(1.0), _cfg_roi_offset({0., 0.}), _cfg_roi_stability(1.0),
_roi_center(), _roi_size(), _roi_geom(),
_cuda(face_tracking_factory::get()->get_cuda()), _cuda_ctx(face_tracking_factory::get()->get_cuda_context()),
_cuda_stream(), _cuda_mem(), _cuda_flush_cache(true), _ar(face_tracking_factory::get()->get_ar()),
_ar_models_path(), _ar_tracker(), _ar_ready(false), _ar_bboxes_data(), _ar_bboxes(), _ar_bboxes_confidence(),
_ar_image(), _ar_image_bgr(), _ar_image_temp()
_cuda_stream(),
_ar_library(face_tracking_factory::get()->get_ar()), _ar_loaded(false), _ar_feature(), _ar_tracked(true),
_ar_bboxes_data(), _ar_bboxes(), _ar_bboxes_confidence(),
_ar_texture(), _ar_texture_cuda_fresh(false), _ar_texture_cuda(), _ar_texture_cuda_mem(), _ar_image(),
_ar_image_bgr(), _ar_image_temp()
{
// Create rendertarget for parent source storage.
// Create Graphics resources for everything.
{
auto gctx = gs::context{};
_rt = std::make_shared<gs::rendertarget>(GS_RGBA, GS_ZS_NONE);
}
// Figure out where the AR SDK Models are stored.
{
std::filesystem::path models_path = _ar->get_ar_sdk_path();
models_path = models_path.append("models");
models_path = std::filesystem::absolute(models_path);
models_path.concat("\\");
_ar_models_path = models_path.string();
_roi_geom = std::make_shared<gs::vertex_buffer>(4, 1);
}
// Initialize everything.
{
auto cctx = std::make_shared<::nvidia::cuda::context_stack>(_cuda, _cuda_ctx);
_cuda_stream = std::make_shared<::nvidia::cuda::stream>(_cuda);
face_detection_initialize();
auto cctx = std::make_shared<::nvidia::cuda::context_stack>(_cuda, _cuda_ctx);
std::int32_t minPrio, maxPrio;
_cuda->cuCtxGetStreamPriorityRange(&minPrio, &maxPrio);
_cuda_stream = std::make_shared<::nvidia::cuda::stream>(_cuda, ::nvidia::cuda::cu_stream_flags::NON_BLOCKING,
minPrio + ((maxPrio - minPrio) / 2));
}
#ifdef _DEBUG
// Profiling
_profile_capture = util::profiler::create();
_profile_cuda_register = util::profiler::create();
_profile_cuda_copy = util::profiler::create();
_profile_ar_transfer = util::profiler::create();
_profile_ar_run = util::profiler::create();
_profile_capture = util::profiler::create();
_profile_capture_realloc = util::profiler::create();
_profile_capture_copy = util::profiler::create();
_profile_ar_realloc = util::profiler::create();
_profile_ar_copy = util::profiler::create();
_profile_ar_transfer = util::profiler::create();
_profile_ar_run = util::profiler::create();
_profile_ar_calc = util::profiler::create();
#endif
// Asynchronously load Face Tracking.
async_initialize(nullptr);
}
filter::nvidia::face_tracking_instance::~face_tracking_instance()
{
_ar->image_dealloc(&_ar_image_temp);
_ar->image_dealloc(&_ar_image_bgr);
_ar_library->image_dealloc(&_ar_image_temp);
_ar_library->image_dealloc(&_ar_image_bgr);
}
void filter::nvidia::face_tracking_instance::face_detection_initialize()
void filter::nvidia::face_tracking_instance::async_initialize(std::shared_ptr<void> ptr)
{
// Create
NvAR_FeatureHandle fd_inst;
if (NvCV_Status res = _ar->create(NvAR_Feature_FaceDetection, &fd_inst); res != NVCV_SUCCESS) {
throw std::runtime_error("Failed to create Face Detection feature.");
}
_ar_tracker = std::shared_ptr<nvAR_Feature>{fd_inst, nvar_deleter};
struct async_data {
std::shared_ptr<obs_weak_source_t> source;
std::string models_path;
};
// Configuration
if (NvCV_Status res = _ar->set_cuda_stream(fd_inst, NvAR_Parameter_Config(CUDAStream),
reinterpret_cast<CUstream>(_cuda_stream->get()));
res != NVCV_SUCCESS) {
throw std::runtime_error("");
}
if (NvCV_Status res = _ar->set_string(fd_inst, NvAR_Parameter_Config(ModelDir), _ar_models_path.c_str());
res != NVCV_SUCCESS) {
throw std::runtime_error("");
}
if (NvCV_Status res = _ar->set_uint32(fd_inst, NvAR_Parameter_Config(Temporal), 1); res != NVCV_SUCCESS) {
throw std::runtime_error("");
}
if (!ptr) {
// Spawn the work for the threadpool.
std::shared_ptr<async_data> data = std::make_shared<async_data>();
data->source =
std::shared_ptr<obs_weak_source_t>(obs_source_get_weak_source(_self), obs::obs_weak_source_deleter);
// Create Bounding Boxes Data
_ar_bboxes_data.assign(1, {0., 0., 0., 0.});
_ar_bboxes.boxes = _ar_bboxes_data.data();
_ar_bboxes.max_boxes = std::clamp<std::uint8_t>(static_cast<std::uint8_t>(_ar_bboxes_data.size()), 0, 255);
_ar_bboxes.num_boxes = 0;
_ar_bboxes_confidence.resize(_ar_bboxes_data.size());
std::filesystem::path models_path = _ar_library->get_ar_sdk_path();
models_path = models_path.append("models");
models_path = std::filesystem::absolute(models_path);
models_path.concat("\\");
data->models_path = models_path.string();
if (NvCV_Status res =
_ar->set_object(_ar_tracker.get(), NvAR_Parameter_Output(BoundingBoxes), &_ar_bboxes, sizeof(NvAR_BBoxes));
res != NVCV_SUCCESS) {
throw std::runtime_error("Failed to set BoundingBoxes for Face Tracking feature.");
get_global_threadpool()->push(
std::bind(&filter::nvidia::face_tracking_instance::async_initialize, this, std::placeholders::_1), data);
} else {
std::shared_ptr<async_data> data = std::static_pointer_cast<async_data>(ptr);
// Try and acquire a strong source reference.
std::shared_ptr<obs_source_t> ref =
std::shared_ptr<obs_source_t>(obs_weak_source_get_source(data->source.get()), obs::obs_source_deleter);
if (!ref) { // If that failed, the source we are working for was deleted - abort now.
return;
}
// Update the current CUDA context for working.
gs::context gctx;
auto cctx = std::make_shared<::nvidia::cuda::context_stack>(_cuda, _cuda_ctx);
// Create Face Detection feature.
{
NvAR_FeatureHandle fd_inst;
if (NvCV_Status res = _ar_library->create(NvAR_Feature_FaceDetection, &fd_inst); res != NVCV_SUCCESS) {
throw std::runtime_error("Failed to create Face Detection feature.");
}
_ar_feature = std::shared_ptr<nvAR_Feature>{fd_inst, ar_feature_deleter};
}
// Set the correct CUDA stream for processing.
if (NvCV_Status res = _ar_library->set_cuda_stream(_ar_feature.get(), NvAR_Parameter_Config(CUDAStream),
reinterpret_cast<CUstream>(_cuda_stream->get()));
res != NVCV_SUCCESS) {
throw std::runtime_error("Failed to set CUDA stream.");
}
// Set the correct models path.
if (NvCV_Status res =
_ar_library->set_string(_ar_feature.get(), NvAR_Parameter_Config(ModelDir), data->models_path.c_str());
res != NVCV_SUCCESS) {
throw std::runtime_error("Unable to set model path.");
}
// Finally enable Temporal tracking if possible.
if (NvCV_Status res = _ar_library->set_uint32(_ar_feature.get(), NvAR_Parameter_Config(Temporal), 1);
res != NVCV_SUCCESS) {
LOG_WARNING("<%s> Unable to enable Temporal tracking mode.", obs_source_get_name(ref.get()));
}
// Create Bounding Boxes Data
_ar_bboxes_data.assign(1, {0., 0., 0., 0.});
_ar_bboxes.boxes = _ar_bboxes_data.data();
_ar_bboxes.max_boxes = std::clamp<std::uint8_t>(static_cast<std::uint8_t>(_ar_bboxes_data.size()), 0, 255);
_ar_bboxes.num_boxes = 0;
_ar_bboxes_confidence.resize(_ar_bboxes_data.size());
if (NvCV_Status res = _ar_library->set_object(_ar_feature.get(), NvAR_Parameter_Output(BoundingBoxes),
&_ar_bboxes, sizeof(NvAR_BBoxes));
res != NVCV_SUCCESS) {
throw std::runtime_error("Failed to set BoundingBoxes for Face Tracking feature.");
}
if (NvCV_Status res = _ar_library->set_float32_array(
_ar_feature.get(), NvAR_Parameter_Output(BoundingBoxesConfidence), _ar_bboxes_confidence.data(),
static_cast<int>(_ar_bboxes_confidence.size()));
res != NVCV_SUCCESS) {
throw std::runtime_error("Failed to set BoundingBoxesConfidence for Face Tracking feature.");
}
// And finally, load the feature (takes long).
if (NvCV_Status res = _ar_library->load(_ar_feature.get()); res != NVCV_SUCCESS) {
LOG_ERROR("<%s> Failed to load Face Tracking feature.", obs_source_get_name(_self));
_ar_loaded = false;
return;
} else {
_ar_loaded = true;
}
}
if (NvCV_Status res =
_ar->set_float32_array(_ar_tracker.get(), NvAR_Parameter_Output(BoundingBoxesConfidence),
_ar_bboxes_confidence.data(), static_cast<int>(_ar_bboxes_confidence.size()));
res != NVCV_SUCCESS) {
throw std::runtime_error("Failed to set BoundingBoxesConfidence for Face Tracking feature.");
}
// Push to extra thread to not block OBS Studio.
obs_source_addref(_self);
::get_global_threadpool()->push(std::bind(&filter::nvidia::face_tracking_instance::face_detection_initialize_thread,
this, std::placeholders::_1),
nullptr);
}
void filter::nvidia::face_tracking_instance::face_detection_initialize_thread(std::shared_ptr<void> param)
{
auto cctx = std::make_shared<::nvidia::cuda::context_stack>(_cuda, _cuda_ctx);
if (NvCV_Status res = _ar->load(_ar_tracker.get()); res != NVCV_SUCCESS) {
_ar_fail = true;
}
_ar_ready = true;
obs_source_release(_self);
void filter::nvidia::face_tracking_instance::refresh_geometry()
{ // Update Region of Interest Geometry.
std::unique_lock<std::mutex> lock(_roi_lock);
auto v0 = _roi_geom->at(0);
auto v1 = _roi_geom->at(1);
auto v2 = _roi_geom->at(2);
auto v3 = _roi_geom->at(3);
*v0.color = 0xFFFFFFFF;
*v1.color = 0xFFFFFFFF;
*v2.color = 0xFFFFFFFF;
*v3.color = 0xFFFFFFFF;
vec3_set(v3.position, static_cast<float_t>(_size.first), static_cast<float_t>(_size.second), 0.);
vec3_set(v2.position, v3.position->x, 0., 0.);
vec3_set(v1.position, 0., v3.position->y, 0.);
vec3_set(v0.position, 0., 0., 0.);
vec4_set(v0.uv[0],
static_cast<float_t>((_roi_center.first - _roi_size.first / 2.) / static_cast<double_t>(_size.first)),
static_cast<float_t>((_roi_center.second - _roi_size.second / 2.) / static_cast<double_t>(_size.second)),
0., 0.);
vec4_set(v1.uv[0],
static_cast<float_t>((_roi_center.first - _roi_size.first / 2.) / static_cast<double_t>(_size.first)),
static_cast<float_t>((_roi_center.second + _roi_size.second / 2.) / static_cast<double_t>(_size.second)),
0., 0.);
vec4_set(v2.uv[0],
static_cast<float_t>((_roi_center.first + _roi_size.first / 2.) / static_cast<double_t>(_size.first)),
static_cast<float_t>((_roi_center.second - _roi_size.second / 2.) / static_cast<double_t>(_size.second)),
0., 0.);
vec4_set(v3.uv[0],
static_cast<float_t>((_roi_center.first + _roi_size.first / 2.) / static_cast<double_t>(_size.first)),
static_cast<float_t>((_roi_center.second + _roi_size.second / 2.) / static_cast<double_t>(_size.second)),
0., 0.);
_roi_geom->update();
}
void filter::nvidia::face_tracking_instance::create_image_buffer(std::size_t width, std::size_t height)
void filter::nvidia::face_tracking_instance::async_track(std::shared_ptr<void> ptr)
{
auto cctx = std::make_shared<::nvidia::cuda::context_stack>(_cuda, _cuda_ctx);
struct async_data {
std::shared_ptr<obs_weak_source_t> source;
};
// Create CUDA and AR interop.
std::size_t pitch = width * 4;
_cuda_mem = std::make_shared<::nvidia::cuda::memory>(_cuda, pitch * height);
_ar->image_init(&_ar_image, static_cast<unsigned int>(width), static_cast<unsigned int>(height),
static_cast<int>(pitch), reinterpret_cast<void*>(_cuda_mem->get()), NVCV_RGBA, NVCV_U8,
NVCV_INTERLEAVED, NVCV_CUDA);
_ar->image_dealloc(&_ar_image_bgr);
_ar->image_alloc(&_ar_image_bgr, static_cast<unsigned int>(width), static_cast<unsigned int>(height), NVCV_BGR,
NVCV_U8, NVCV_INTERLEAVED, NVCV_CUDA, 0);
if (!ptr) {
// Spawn the work for the threadpool.
std::shared_ptr<async_data> data = std::make_shared<async_data>();
data->source =
std::shared_ptr<obs_weak_source_t>(obs_source_get_weak_source(_self), obs::obs_weak_source_deleter);
if (NvCV_Status res =
_ar->set_object(_ar_tracker.get(), NvAR_Parameter_Input(Image), &_ar_image_bgr, sizeof(NvCVImage));
res != NVCV_SUCCESS) {
throw std::runtime_error("_ar_tracker NvAR_Parameter_Input(Image)");
// Check if things exist as planned.
if (!_ar_texture || (_ar_texture->get_width() != _size.first) || (_ar_texture->get_height() != _size.second)) {
#ifdef _DEBUG
auto prof = _profile_capture_realloc->track();
#endif
gs::debug_marker marker{gs::debug_color_allocate, "Reallocate GPU Buffer"};
_ar_texture =
std::make_shared<gs::texture>(_size.first, _size.second, GS_RGBA, 1, nullptr, gs::texture::flags::None);
_ar_texture_cuda_fresh = false;
}
{ // Copy texture
#ifdef _DEBUG
auto prof = _profile_capture_copy->track();
#endif
gs::debug_marker marker{gs::debug_color_copy, "Copy Capture", obs_source_get_name(_self)};
gs_copy_texture(_ar_texture->get_object(), _rt->get_texture()->get_object());
}
// Push work
get_global_threadpool()->push(
std::bind(&filter::nvidia::face_tracking_instance::async_track, this, std::placeholders::_1), data);
} else {
std::shared_ptr<async_data> data = std::static_pointer_cast<async_data>(ptr);
// Try and acquire a strong source reference.
std::shared_ptr<obs_source_t> ref =
std::shared_ptr<obs_source_t>(obs_weak_source_get_source(data->source.get()), obs::obs_source_deleter);
if (!ref) { // If that failed, the source we are working for was deleted - abort now.
return;
}
// Update the current CUDA context for working.
auto cctx = std::make_shared<::nvidia::cuda::context_stack>(_cuda, _cuda_ctx);
// Refresh any now broken buffers.
if (!_ar_texture_cuda_fresh) {
#ifdef _DEBUG
auto prof = _profile_ar_realloc->track();
#endif
gs::context gctx;
gs::debug_marker marker{gs::debug_color_allocate, "%s: Reallocate CUDA Buffers",
obs_source_get_name(_self)};
// Assign new texture and allocate new memory.
std::size_t pitch = _size.first * 4ul;
_ar_texture_cuda = std::make_shared<::nvidia::cuda::gstexture>(_cuda, _ar_texture);
_ar_texture_cuda_mem = std::make_shared<::nvidia::cuda::memory>(_cuda, pitch * _size.second);
_ar_library->image_init(&_ar_image, static_cast<unsigned int>(_size.first),
static_cast<unsigned int>(_size.second), static_cast<int>(pitch),
reinterpret_cast<void*>(_ar_texture_cuda_mem->get()), NVCV_RGBA, NVCV_U8,
NVCV_INTERLEAVED, NVCV_CUDA);
// Reallocate transposed buffer.
_ar_library->image_dealloc(&_ar_image_bgr);
_ar_library->image_alloc(&_ar_image_bgr, static_cast<unsigned int>(_size.first),
static_cast<unsigned int>(_size.second), NVCV_BGR, NVCV_U8, NVCV_INTERLEAVED,
NVCV_CUDA, 0);
_ar_library->image_dealloc(&_ar_image_temp);
// Finally set the input object.
if (NvCV_Status res = _ar_library->set_object(_ar_feature.get(), NvAR_Parameter_Input(Image),
&_ar_image_bgr, sizeof(NvCVImage));
res != NVCV_SUCCESS) {
LOG_ERROR("<%s> Failed to update input image for tracking.", obs_source_get_name(_self));
return;
}
// And mark the new texture as fresh.
_ar_texture_cuda_fresh = true;
}
{ // Copy from CUDA array to CUDA device memory.
#ifdef _DEBUG
auto prof = _profile_ar_copy->track();
#endif
gs::context gctx;
::nvidia::cuda::cu_memcpy2d_t mc;
mc.src_x_in_bytes = 0;
mc.src_y = 0;
mc.src_memory_type = ::nvidia::cuda::cu_memory_type::ARRAY;
mc.src_host = nullptr;
mc.src_device = 0;
mc.src_array = _ar_texture_cuda->map(_cuda_stream);
mc.src_pitch = static_cast<size_t>(_ar_image.pitch);
mc.dst_x_in_bytes = 0;
mc.dst_y = 0;
mc.dst_memory_type = ::nvidia::cuda::cu_memory_type::DEVICE;
mc.dst_host = 0;
mc.dst_device = reinterpret_cast<::nvidia::cuda::cu_device_ptr_t>(_ar_image.pixels);
mc.dst_array = 0;
mc.dst_pitch = static_cast<size_t>(_ar_image.pitch);
mc.width_in_bytes = static_cast<size_t>(_ar_image.pitch);
mc.height = _ar_image.height;
if (::nvidia::cuda::cu_result res = _cuda->cuMemcpy2D(&mc); res != ::nvidia::cuda::cu_result::SUCCESS) {
LOG_ERROR("<%s> Failed to prepare buffers for tracking.", obs_source_get_name(_self));
return;
}
}
{ // Convert from RGBA 32-bit to BGR 24-bit.
#ifdef _DEBUG
auto prof = _profile_ar_transfer->track();
#endif
gs::context gctx;
if (NvCV_Status res =
_ar_library->image_transfer(&_ar_image, &_ar_image_bgr, 1.0,
reinterpret_cast<CUstream_st*>(_cuda_stream->get()), &_ar_image_temp);
res != NVCV_SUCCESS) {
LOG_ERROR("<%s> Failed to convert from RGBX 32-bit to BGR 24-bit.", obs_source_get_name(_self));
return;
}
}
{ // Track any faces.
#ifdef _DEBUG
auto prof = _profile_ar_run->track();
#endif
gs::context gctx;
if (NvCV_Status res = _ar_library->run(_ar_feature.get()); res != NVCV_SUCCESS) {
LOG_ERROR("<%s> Failed to run tracking.", obs_source_get_name(_self));
return;
}
}
if ((_ar_bboxes.num_boxes == 0) || (_ar_bboxes_confidence.at(0) < 0.5)) {
// Not confident enough or not tracking anything, return to full frame after a bit.
} else {
#ifdef _DEBUG
auto prof = _profile_ar_calc->track();
#endif
double_t aspect = double_t(_size.first) / double_t(_size.second);
// Store values and center.
double_t bbox_w = _ar_bboxes.boxes[0].width;
double_t bbox_h = _ar_bboxes.boxes[0].height;
double_t bbox_cx = _ar_bboxes.boxes[0].x + bbox_w / 2.0;
double_t bbox_cy = _ar_bboxes.boxes[0].y + bbox_h / 2.0;
// Zoom, Aspect Ratio, Offset
bbox_h = util::math::lerp<double_t>(_size.second, bbox_h, _cfg_roi_zoom);
bbox_h = std::clamp(bbox_h, 10 * aspect, static_cast<double_t>(_size.second));
bbox_w = bbox_h * aspect;
bbox_cx += _ar_bboxes.boxes[0].width * _cfg_roi_offset.first;
bbox_cy += _ar_bboxes.boxes[0].height * _cfg_roi_offset.second;
// Fit back into the frame
// - Above code guarantees that height is never bigger than the height of the frame.
// - Which also guarantees that width is never bigger than the width of the frame.
// Only cx and cy need to be adjusted now to always be in the frame.
bbox_cx = std::clamp(bbox_cx, (bbox_w / 2.), static_cast<double_t>(_size.first) - (bbox_w / 2.));
bbox_cy = std::clamp(bbox_cy, (bbox_h / 2.), static_cast<double_t>(_size.second) - (bbox_h / 2.));
// Filter values
auto size_w = _roi_filters[2].filter(bbox_w);
auto size_h = _roi_filters[3].filter(bbox_h);
auto center_x = _roi_filters[0].filter(bbox_cx);
auto center_y = _roi_filters[1].filter(bbox_cy);
// Fix NaN/Infinity
if (std::isfinite(size_w) && std::isfinite(size_h) && std::isfinite(center_x) && std::isfinite(center_y)) {
std::unique_lock<std::mutex> lock(_roi_lock);
_roi_center.first = center_x;
_roi_center.second = center_y;
_roi_size.first = size_w;
_roi_size.second = size_h;
} else {
std::unique_lock<std::mutex> lock(_roi_lock);
roi_refresh();
}
}
_ar_tracked = true;
}
}
@ -182,10 +436,10 @@ void filter::nvidia::face_tracking_instance::roi_refresh()
void filter::nvidia::face_tracking_instance::roi_reset()
{
_roi_center.first = static_cast<double_t>(_width) / 2.;
_roi_center.second = static_cast<double_t>(_height) / 2.;
_roi_size.first = static_cast<double_t>(_width);
_roi_size.second = static_cast<double_t>(_height);
_roi_center.first = static_cast<double_t>(_size.first / 2);
_roi_center.second = static_cast<double_t>(_size.second / 2);
_roi_size.first = static_cast<double_t>(_size.first);
_roi_size.second = static_cast<double_t>(_size.second);
roi_refresh();
}
@ -205,34 +459,23 @@ void filter::nvidia::face_tracking_instance::update(obs_data_t* data)
_cfg_roi_stability = obs_data_get_double(data, SK_ROI_STABILITY) / 100.0;
// Refresh the Region Of Interest
std::unique_lock<std::mutex> lock(_roi_lock);
roi_refresh();
}
void filter::nvidia::face_tracking_instance::video_tick(float_t seconds)
{
if (!_ar_ready)
// If we aren't yet ready to do work, abort for now.
if (!_ar_loaded) {
return;
}
// Update Buffers
std::uint32_t width = obs_source_get_base_width(obs_filter_get_target(_self));
std::uint32_t height = obs_source_get_base_height(obs_filter_get_target(_self));
if (((width != _width) || (height != _height)) && width && height)
try {
// Recreate things.
create_image_buffer(width, height);
_cuda_flush_cache = true;
if (obs_source_t* target = obs_filter_get_target(_self); target != nullptr) {
_size.first = obs_source_get_width(target);
_size.second = obs_source_get_height(target);
}
// Update Width/Height
_width = width;
_height = height;
// Reset ROI.
roi_reset();
} catch (const std::exception& ex) {
LOG_ERROR("Error: %s", ex.what());
}
_up_to_date = false;
_rt_is_fresh = false;
}
void filter::nvidia::face_tracking_instance::video_render(gs_effect_t* effect)
@ -242,189 +485,44 @@ void filter::nvidia::face_tracking_instance::video_render(gs_effect_t* effect)
obs_source_t* filter_target = obs_filter_get_target(_self);
gs_effect_t* default_effect = obs_get_base_effect(OBS_EFFECT_DEFAULT);
if (!filter_parent || !filter_target || !_width || !_height || !_ar_ready) {
if (!filter_parent || !filter_target || !_size.first || !_size.second || !_ar_loaded) {
obs_source_skip_video_filter(_self);
return;
}
if (!_up_to_date) {
{ // Capture the filter stack "below" us.
if (!_rt_is_fresh) { // Capture the filter stack "below" us.
#ifdef _DEBUG
auto prof = _profile_capture->track();
auto prof = _profile_capture->track();
#endif
gs::debug_marker marker{gs::debug_color_render, "%s: Capture", obs_source_get_name(_self)};
if (obs_source_process_filter_begin(_self, _rt->get_color_format(), OBS_ALLOW_DIRECT_RENDERING)) {
auto op = _rt->render(_width, _height);
vec4 clr = {0., 0., 0., 0.};
gs::debug_marker marker{gs::debug_color_capture, "Capture"};
if (obs_source_process_filter_begin(_self, _rt->get_color_format(), OBS_ALLOW_DIRECT_RENDERING)) {
auto op = _rt->render(_size.first, _size.second);
vec4 clr = {0., 0., 0., 0.};
gs_ortho(0, static_cast<float_t>(_width), 0, static_cast<float_t>(_height), 0, 1);
gs_clear(GS_CLEAR_COLOR, &clr, 0, 0);
gs_ortho(0., static_cast<float_t>(_size.first), 0., static_cast<float_t>(_size.second), 0., 1.);
gs_clear(GS_CLEAR_COLOR, &clr, 0., 0.);
obs_source_process_filter_tech_end(_self, default_effect, _width, _height, "Draw");
} else {
obs_source_skip_video_filter(_self);
return;
}
}
{
gs::debug_marker marker{gs::debug_color_render, "%s: Nvidia AR SDK", obs_source_get_name(_self)};
auto cctx = std::make_shared<::nvidia::cuda::context_stack>(_cuda, _cuda_ctx);
if (_cuda_flush_cache) {
#ifdef _DEBUG
auto prof = _profile_cuda_register->track();
#endif
_cuda_rt_cache = std::make_shared<::nvidia::cuda::gstexture>(_cuda, _rt->get_texture());
_cuda_rt_cache->map(_cuda_stream);
_cuda_flush_cache = false;
}
{
#ifdef _DEBUG
auto prof = _profile_cuda_copy->track();
#endif
::nvidia::cuda::cu_memcpy2d_t mc;
mc.src_x_in_bytes = 0;
mc.src_y = 0;
mc.src_memory_type = ::nvidia::cuda::cu_memory_type::ARRAY;
mc.src_host = nullptr;
mc.src_device = 0;
mc.src_array = _cuda_rt_cache->map(_cuda_stream);
mc.src_pitch = static_cast<size_t>(_ar_image.pitch);
mc.dst_x_in_bytes = 0;
mc.dst_y = 0;
mc.dst_memory_type = ::nvidia::cuda::cu_memory_type::DEVICE;
mc.dst_host = 0;
mc.dst_device = reinterpret_cast<::nvidia::cuda::cu_device_ptr_t>(_ar_image.pixels);
mc.dst_array = 0;
mc.dst_pitch = static_cast<size_t>(_ar_image.pitch);
mc.width_in_bytes = static_cast<size_t>(_ar_image.pitch);
mc.height = _ar_image.height;
if (::nvidia::cuda::cu_result res = _cuda->cuMemcpy2D(&mc); res != ::nvidia::cuda::cu_result::SUCCESS) {
obs_source_skip_video_filter(_self);
return;
}
}
{
#ifdef _DEBUG
auto prof = _profile_ar_transfer->track();
#endif
if (NvCV_Status res =
_ar->image_transfer(&_ar_image, &_ar_image_bgr, 1.0,
reinterpret_cast<CUstream_st*>(_cuda_stream->get()), &_ar_image_temp);
res != NVCV_SUCCESS) {
obs_source_skip_video_filter(_self);
return;
}
}
{
#ifdef _DEBUG
auto prof = _profile_ar_run->track();
#endif
if (NvCV_Status res = _ar->run(_ar_tracker.get()); res != NVCV_SUCCESS) {
obs_source_skip_video_filter(_self);
return;
}
}
}
// Recalculate the region of interest.
if (_ar_bboxes.num_boxes > 0) {
double_t aspect = double_t(_width) / double_t(_height);
// Store values and center.
double_t bbox_w = _ar_bboxes.boxes[0].width;
double_t bbox_h = _ar_bboxes.boxes[0].height;
double_t bbox_cx = _ar_bboxes.boxes[0].x + bbox_w / 2.0;
double_t bbox_cy = _ar_bboxes.boxes[0].y + bbox_h / 2.0;
// Zoom, Aspect Ratio, Offset
bbox_h = util::math::lerp<double_t>(_height, bbox_h, _cfg_roi_zoom);
bbox_h = std::clamp(bbox_h, 10 * aspect, static_cast<double_t>(_height));
bbox_w = bbox_h * aspect;
bbox_cx += _ar_bboxes.boxes[0].width * _cfg_roi_offset.first;
bbox_cy += _ar_bboxes.boxes[0].height * _cfg_roi_offset.second;
// Fit back into the frame
// - Above code guarantees that height is never bigger than the height of the frame.
// - Which also guarantees that width is never bigger than the width of the frame.
// Only cx and cy need to be adjusted now to always be in the frame.
bbox_cx = std::clamp(bbox_cx, (bbox_w / 2.), static_cast<double_t>(_width) - (bbox_w / 2.));
bbox_cy = std::clamp(bbox_cy, (bbox_h / 2.), static_cast<double_t>(_height) - (bbox_h / 2.));
// Filter values
auto size_w = _roi_filters[2].filter(bbox_w);
auto size_h = _roi_filters[3].filter(bbox_h);
auto center_x = _roi_filters[0].filter(bbox_cx);
auto center_y = _roi_filters[1].filter(bbox_cy);
// Fix NaN/Infinity
if (std::isfinite(size_w) && std::isfinite(size_h) && std::isfinite(center_x) && std::isfinite(center_y)) {
_roi_center.first = center_x;
_roi_center.second = center_y;
_roi_size.first = size_w;
_roi_size.second = size_h;
} else {
roi_refresh();
}
obs_source_process_filter_tech_end(_self, default_effect, _size.first, _size.second, "Draw");
} else {
// Todo: Time based return to full frame.
obs_source_skip_video_filter(_self);
return;
}
// Update Region of Interest Geometry.
{
auto v0 = _roi_geom.at(0);
auto v1 = _roi_geom.at(1);
auto v2 = _roi_geom.at(2);
auto v3 = _roi_geom.at(3);
*v0.color = 0xFFFFFFFF;
*v1.color = 0xFFFFFFFF;
*v2.color = 0xFFFFFFFF;
*v3.color = 0xFFFFFFFF;
vec3_set(v3.position, static_cast<float_t>(_width), static_cast<float_t>(_height), 0.);
vec3_set(v2.position, v3.position->x, 0., 0.);
vec3_set(v1.position, 0., v3.position->y, 0.);
vec3_set(v0.position, 0., 0., 0.);
vec4_set(
v0.uv[0],
static_cast<float_t>((_roi_center.first - _roi_size.first / 2.) / static_cast<double_t>(_width)),
static_cast<float_t>((_roi_center.second - _roi_size.second / 2.) / static_cast<double_t>(_height)), 0.,
0.);
vec4_set(
v1.uv[0],
static_cast<float_t>((_roi_center.first - _roi_size.first / 2.) / static_cast<double_t>(_width)),
static_cast<float_t>((_roi_center.second + _roi_size.second / 2.) / static_cast<double_t>(_height)), 0.,
0.);
vec4_set(
v2.uv[0],
static_cast<float_t>((_roi_center.first + _roi_size.first / 2.) / static_cast<double_t>(_width)),
static_cast<float_t>((_roi_center.second - _roi_size.second / 2.) / static_cast<double_t>(_height)), 0.,
0.);
vec4_set(
v3.uv[0],
static_cast<float_t>((_roi_center.first + _roi_size.first / 2.) / static_cast<double_t>(_width)),
static_cast<float_t>((_roi_center.second + _roi_size.second / 2.) / static_cast<double_t>(_height)), 0.,
0.);
_roi_geom.update();
if (_ar_tracked) {
async_track(nullptr);
refresh_geometry();
}
_up_to_date = true;
_rt_is_fresh = true;
}
// Draw Texture
gs::debug_marker marker{gs::debug_color_render, "Render"};
gs_effect_set_texture(gs_effect_get_param_by_name(effect ? effect : default_effect, "image"),
_rt->get_texture()->get_object());
gs_load_vertexbuffer(_roi_geom.update());
gs_load_vertexbuffer(_roi_geom->update());
while (gs_effect_loop(effect ? effect : default_effect, "Draw")) {
gs_draw(gs_draw_mode::GS_TRISTRIP, 0, _roi_geom.size());
gs_draw(gs_draw_mode::GS_TRISTRIP, 0, _roi_geom->size());
}
gs_load_vertexbuffer(nullptr);
}
@ -432,32 +530,21 @@ void filter::nvidia::face_tracking_instance::video_render(gs_effect_t* effect)
#ifdef _DEBUG
bool filter::nvidia::face_tracking_instance::button_profile(obs_properties_t* props, obs_property_t* property)
{
LOG_INFO("Profiling (Total/Avg/99.9/95)");
LOG_INFO(" %-12s: %8lldµs %8lldµs %8lldµs %8lldµs", "Capture",
std::chrono::duration_cast<std::chrono::microseconds>(_profile_capture->total_duration()).count(),
static_cast<std::int64_t>(_profile_capture->average_duration() / 1000.0),
std::chrono::duration_cast<std::chrono::microseconds>(_profile_capture->percentile(0.999)).count(),
std::chrono::duration_cast<std::chrono::microseconds>(_profile_capture->percentile(0.95)).count());
LOG_INFO(" %-12s: %8lldµs %8lldµs %8lldµs %8lldµs", "Register",
std::chrono::duration_cast<std::chrono::microseconds>(_profile_cuda_register->total_duration()).count(),
static_cast<std::int64_t>(_profile_cuda_register->average_duration() / 1000.0),
std::chrono::duration_cast<std::chrono::microseconds>(_profile_cuda_register->percentile(0.999)).count(),
std::chrono::duration_cast<std::chrono::microseconds>(_profile_cuda_register->percentile(0.95)).count());
LOG_INFO(" %-12s: %8lldµs %8lldµs %8lldµs %8lldµs", "Copy",
std::chrono::duration_cast<std::chrono::microseconds>(_profile_cuda_copy->total_duration()).count(),
static_cast<std::int64_t>(_profile_cuda_copy->average_duration() / 1000.0),
std::chrono::duration_cast<std::chrono::microseconds>(_profile_cuda_copy->percentile(0.999)).count(),
std::chrono::duration_cast<std::chrono::microseconds>(_profile_cuda_copy->percentile(0.95)).count());
LOG_INFO(" %-12s: %8lldµs %8lldµs %8lldµs %8lldµs", "Transfer",
std::chrono::duration_cast<std::chrono::microseconds>(_profile_ar_transfer->total_duration()).count(),
static_cast<std::int64_t>(_profile_capture->average_duration() / 1000.0),
std::chrono::duration_cast<std::chrono::microseconds>(_profile_ar_transfer->percentile(0.999)).count(),
std::chrono::duration_cast<std::chrono::microseconds>(_profile_ar_transfer->percentile(0.95)).count());
LOG_INFO(" %-12s: %8lldµs %8lldµs %8lldµs %8lldµs", "Run",
std::chrono::duration_cast<std::chrono::microseconds>(_profile_ar_run->total_duration()).count(),
static_cast<std::int64_t>(_profile_ar_run->average_duration() / 1000.0),
std::chrono::duration_cast<std::chrono::microseconds>(_profile_ar_run->percentile(0.999)).count(),
std::chrono::duration_cast<std::chrono::microseconds>(_profile_ar_run->percentile(0.95)).count());
LOG_INFO("%-22s: %-10s %-10s %-10s %-10s %-10s", "Task", "Total", "Count", "Average", "99.9%ile", "95.0%ile");
std::pair<std::string, std::shared_ptr<util::profiler>> profilers[]{
{"Capture", _profile_capture}, {"Reallocate", _profile_capture_realloc},
{"Copy", _profile_capture_copy}, {"AR Reallocate", _profile_ar_realloc},
{"AR Copy", _profile_ar_copy}, {"AR Convert", _profile_ar_transfer},
{"AR Run", _profile_ar_run}, {"AR Calculate", _profile_ar_calc},
};
for (auto& kv : profilers) {
LOG_INFO(" %-20s: %8lldµs %10lld %8lldµs %8lldµs %8lldµs", kv.first.c_str(),
std::chrono::duration_cast<std::chrono::microseconds>(kv.second->total_duration()).count(),
kv.second->count(), static_cast<std::int64_t>(kv.second->average_duration() / 1000.0),
std::chrono::duration_cast<std::chrono::microseconds>(kv.second->percentile(0.999)).count(),
std::chrono::duration_cast<std::chrono::microseconds>(kv.second->percentile(0.95)).count());
}
return false;
}

67
source/filters/filter-nv-face-tracking.hpp
View file

@ -37,10 +37,9 @@
namespace filter::nvidia {
class face_tracking_instance : public obs::source_instance {
// Filter Cache
std::uint32_t _width;
std::uint32_t _height;
bool _up_to_date;
std::shared_ptr<gs::rendertarget> _rt;
bool _rt_is_fresh;
std::shared_ptr<gs::rendertarget> _rt;
std::pair<std::uint32_t, std::uint32_t> _size;
// Settings
double_t _cfg_roi_zoom;
@ -48,52 +47,58 @@ namespace filter::nvidia {
double_t _cfg_roi_stability;
// Region of Interest
util::math::kalman1D<double_t> _roi_filters[4];
std::pair<double_t, double_t> _roi_center;
std::pair<double_t, double_t> _roi_size;
gs::vertex_buffer _roi_geom;
util::math::kalman1D<double_t> _roi_filters[4];
std::mutex _roi_lock;
std::pair<double_t, double_t> _roi_center;
std::pair<double_t, double_t> _roi_size;
std::shared_ptr<gs::vertex_buffer> _roi_geom;
// Nvidia CUDA interop
std::shared_ptr<::nvidia::cuda::cuda> _cuda;
std::shared_ptr<::nvidia::cuda::context> _cuda_ctx;
std::shared_ptr<::nvidia::cuda::stream> _cuda_stream;
std::shared_ptr<::nvidia::cuda::memory> _cuda_mem;
bool _cuda_flush_cache;
std::shared_ptr<::nvidia::cuda::gstexture> _cuda_rt_cache;
std::shared_ptr<::nvidia::cuda::cuda> _cuda;
std::shared_ptr<::nvidia::cuda::context> _cuda_ctx;
std::shared_ptr<::nvidia::cuda::stream> _cuda_stream;
// Nvidia AR interop
std::shared_ptr<::nvidia::ar::ar> _ar;
std::string _ar_models_path;
std::shared_ptr<nvAR_Feature> _ar_tracker;
std::atomic_bool _ar_ready;
std::atomic_bool _ar_fail;
std::vector<NvAR_Rect> _ar_bboxes_data;
NvAR_BBoxes _ar_bboxes;
std::vector<float_t> _ar_bboxes_confidence;
NvCVImage _ar_image;
NvCVImage _ar_image_bgr;
NvCVImage _ar_image_temp;
std::shared_ptr<::nvidia::ar::ar> _ar_library;
std::atomic_bool _ar_loaded;
std::shared_ptr<nvAR_Feature> _ar_feature;
std::atomic_bool _ar_tracked;
std::vector<float_t> _ar_bboxes_confidence;
std::vector<NvAR_Rect> _ar_bboxes_data;
NvAR_BBoxes _ar_bboxes;
std::shared_ptr<gs::texture> _ar_texture;
bool _ar_texture_cuda_fresh;
std::shared_ptr<::nvidia::cuda::gstexture> _ar_texture_cuda;
std::shared_ptr<::nvidia::cuda::memory> _ar_texture_cuda_mem;
NvCVImage _ar_image;
NvCVImage _ar_image_bgr;
NvCVImage _ar_image_temp;
#ifdef _DEBUG
// Profiling
std::shared_ptr<util::profiler> _profile_capture;
std::shared_ptr<util::profiler> _profile_cuda_register;
std::shared_ptr<util::profiler> _profile_cuda_copy;
std::shared_ptr<util::profiler> _profile_capture_realloc;
std::shared_ptr<util::profiler> _profile_capture_copy;
std::shared_ptr<util::profiler> _profile_ar_realloc;
std::shared_ptr<util::profiler> _profile_ar_copy;
std::shared_ptr<util::profiler> _profile_ar_transfer;
std::shared_ptr<util::profiler> _profile_ar_run;
std::shared_ptr<util::profiler> _profile_ar_calc;
#endif
public:
face_tracking_instance(obs_data_t*, obs_source_t*);
virtual ~face_tracking_instance() override;
// Initialize face detection.
void face_detection_initialize();
// Tasks
void async_initialize(std::shared_ptr<void>);
void face_detection_initialize_thread(std::shared_ptr<void> param);
void refresh_geometry();
void async_track(std::shared_ptr<void>);
// Create image buffer.
void create_image_buffer(std::size_t width, std::size_t height);
//void create_image_buffer(std::size_t width, std::size_t height);
void roi_refresh();