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obs-StreamFX/source/filters/filter-nv-face-tracking.cpp

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/*
* Modern effects for a modern Streamer
* Copyright (C) 2017 Michael Fabian Dirks
*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 2 of the License, or
* (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA
*/
#include "filter-nv-face-tracking.hpp"
#include <algorithm>
#include <filesystem>
#include <util/platform.h>
#include "nvidia/cuda/nvidia-cuda-context.hpp"
#include "obs/gs/gs-helper.hpp"
#include "obs/obs-tools.hpp"
#define ST_I18N "Filter.NVIDIA.FaceTracking"
#define ST_I18N_ROI ST_I18N ".ROI"
#define ST_I18N_ROI_ZOOM ST_I18N_ROI ".Zoom"
#define ST_I18N_ROI_OFFSET ST_I18N_ROI ".ROI.Offset"
#define ST_I18N_ROI_OFFSET_X ST_I18N_ROI_OFFSET ".X"
#define ST_I18N_ROI_OFFSET_Y ST_I18N_ROI_OFFSET ".Y"
#define ST_I18N_ROI_STABILITY ST_I18N_ROI ".Stability"
#define ST_KEY_ROI_ZOOM "ROI.Zoom"
#define ST_KEY_ROI_OFFSET_X "ROI.Offset.X"
#define ST_KEY_ROI_OFFSET_Y "ROI.Offset.Y"
#define ST_KEY_ROI_STABILITY "ROI.Stability"
using namespace streamfx::filter::nvidia;
void ar_feature_deleter(NvAR_FeatureHandle v)
{
face_tracking_factory::get()->get_ar()->destroy(v);
}
face_tracking_instance::face_tracking_instance(obs_data_t* settings, obs_source_t* self)
: obs::source_instance(settings, self),
_rt_is_fresh(false), _rt(),
_cfg_zoom(1.0), _cfg_offset({0., 0.}), _cfg_stability(1.0),
_geometry(), _filters(), _values(),
_cuda(::nvidia::cuda::obs::get()), _cuda_stream(),
_ar_library(face_tracking_factory::get()->get_ar()), _ar_loaded(false), _ar_feature(), _ar_is_tracking(false),
_ar_bboxes_confidence(), _ar_bboxes_data(), _ar_bboxes(), _ar_texture(), _ar_texture_cuda_fresh(false),
_ar_texture_cuda(), _ar_texture_cuda_mem(), _ar_image(), _ar_image_bgr(), _ar_image_temp()
{
#ifdef ENABLE_PROFILING
// Profiling
_profile_capture = streamfx::util::profiler::create();
_profile_capture_realloc = streamfx::util::profiler::create();
_profile_capture_copy = streamfx::util::profiler::create();
_profile_ar_realloc = streamfx::util::profiler::create();
_profile_ar_copy = streamfx::util::profiler::create();
_profile_ar_transfer = streamfx::util::profiler::create();
_profile_ar_run = streamfx::util::profiler::create();
_profile_ar_calc = streamfx::util::profiler::create();
#endif
{ // Create render target, vertex buffer, and CUDA stream.
auto gctx = streamfx::obs::gs::context{};
_rt = std::make_shared<streamfx::obs::gs::rendertarget>(GS_RGBA_UNORM, GS_ZS_NONE);
_geometry = std::make_shared<streamfx::obs::gs::vertex_buffer>(uint32_t(4), uint8_t(1));
auto cctx = _cuda->get_context()->enter();
_cuda_stream = std::make_shared<::nvidia::cuda::stream>(::nvidia::cuda::stream_flags::NON_BLOCKING, 0);
}
{ // Asynchronously load Face Tracking.
async_initialize();
}
{ // Set up initial tracking data.
_values.center[0] = _values.center[1] = .5;
_values.size[0] = _values.size[1] = 1.;
refresh_region_of_interest();
}
}
face_tracking_instance::~face_tracking_instance()
{
// Kill pending tasks.
streamfx::threadpool()->pop(_async_initialize);
streamfx::threadpool()->pop(_async_track);
_ar_loaded.store(false);
std::unique_lock<std::mutex> alk{_ar_lock};
_ar_library->image_dealloc(&_ar_image_temp);
_ar_library->image_dealloc(&_ar_image_bgr);
}
void face_tracking_instance::async_initialize(std::shared_ptr<void> ptr)
{
struct async_data {
std::shared_ptr<obs_weak_source_t> source;
std::string models_path;
};
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);
{
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();
}
_async_initialize = streamfx::threadpool()->push(
std::bind(&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> remote_work =
std::shared_ptr<obs_source_t>(obs_weak_source_get_source(data->source.get()), obs::obs_source_deleter);
if (!remote_work) { // If that failed, the source we are working for was deleted - abort now.
return;
}
// Update the current CUDA context for working.
streamfx::obs::gs::context gctx;
auto cctx = _cuda->get_context()->enter();
// 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) {
DLOG_WARNING("<%s> Unable to enable Temporal tracking mode.", obs_source_get_name(remote_work.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<uint8_t>(static_cast<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) {
DLOG_ERROR("<%s> Failed to load Face Tracking feature.", obs_source_get_name(_self));
_ar_loaded = false;
return;
} else {
_ar_loaded = true;
}
_async_initialize.reset();
}
}
void face_tracking_instance::async_track(std::shared_ptr<void> ptr)
{
struct async_data {
std::shared_ptr<obs_weak_source_t> source;
};
if (!_ar_loaded)
return;
if (!ptr) {
// Check if we can track.
if (_ar_is_tracking)
return; // Can't track a new frame right now.
#ifdef ENABLE_PROFILING
streamfx::obs::gs::debug_marker gdm{streamfx::obs::gs::debug_color_convert, "Start Asynchronous Tracking"};
#endif
// Don't push additional tracking frames while processing one.
_ar_is_tracking = true;
// 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);
// Check if things exist as planned.
if (!_ar_texture || (_ar_texture->get_width() != _size.first) || (_ar_texture->get_height() != _size.second)) {
#ifdef ENABLE_PROFILING
auto prof = _profile_capture_realloc->track();
streamfx::obs::gs::debug_marker marker{streamfx::obs::gs::debug_color_allocate, "Reallocate GPU Buffer"};
#endif
_ar_texture =
std::make_shared<streamfx::obs::gs::texture>(_size.first, _size.second, GS_RGBA_UNORM, uint32_t(1),
nullptr, streamfx::obs::gs::texture::flags::None);
_ar_texture_cuda_fresh = false;
}
{ // Copy texture
#ifdef ENABLE_PROFILING
auto prof = _profile_capture_copy->track();
streamfx::obs::gs::debug_marker marker{streamfx::obs::gs::debug_color_copy, "Copy Capture",
obs_source_get_name(_self)};
#endif
gs_copy_texture(_ar_texture->get_object(), _rt->get_texture()->get_object());
}
// Push work
_async_track = streamfx::threadpool()->push(
std::bind(&face_tracking_instance::async_track, this, std::placeholders::_1), data);
} else {
// Prevent conflicts.
std::unique_lock<std::mutex> alk{_ar_lock};
if (!_ar_loaded)
return;
// Try and acquire a strong source reference.
std::shared_ptr<async_data> data = std::static_pointer_cast<async_data>(ptr);
std::shared_ptr<obs_source_t> remote_work =
std::shared_ptr<obs_source_t>(obs_weak_source_get_source(data->source.get()), obs::obs_source_deleter);
if (!remote_work) { // If that failed, the source we are working for was deleted - abort now.
return;
}
// Acquire GS context.
streamfx::obs::gs::context gctx{};
// Update the current CUDA context for working.
auto cctx = _cuda->get_context()->enter();
// Refresh any now broken buffers.
if (!_ar_texture_cuda_fresh) {
#ifdef ENABLE_PROFILING
auto prof = _profile_ar_realloc->track();
streamfx::obs::gs::debug_marker marker{streamfx::obs::gs::debug_color_allocate,
"%s: Reallocate CUDA Buffers", obs_source_get_name(_self)};
#endif
// Assign new texture and allocate new memory.
std::size_t pitch = _ar_texture->get_width() * 4ul;
_ar_texture_cuda = std::make_shared<::nvidia::cuda::gstexture>(_ar_texture);
_ar_texture_cuda_mem = std::make_shared<::nvidia::cuda::memory>(pitch * _ar_texture->get_height());
if (auto res = _ar_library->image_init(&_ar_image, static_cast<unsigned int>(_ar_texture->get_width()),
static_cast<unsigned int>(_ar_texture->get_height()),
static_cast<int>(pitch),
reinterpret_cast<void*>(_ar_texture_cuda_mem->get()), NVCV_RGBA,
NVCV_U8, NVCV_INTERLEAVED, NVCV_CUDA);
res != NVCV_SUCCESS) {
DLOG_ERROR("<%s> Failed to allocate image for tracking.", obs_source_get_name(_self));
return;
}
// Reallocate transposed buffer.
_ar_library->image_dealloc(&_ar_image_temp);
_ar_library->image_dealloc(&_ar_image_bgr);
if (auto res = _ar_library->image_alloc(&_ar_image_bgr, _ar_image.width, _ar_image.height, NVCV_BGR,
NVCV_U8, NVCV_INTERLEAVED, NVCV_CUDA, 0);
res != NVCV_SUCCESS) {
DLOG_ERROR("<%s> Failed to allocate image for color conversion.", obs_source_get_name(_self));
return;
}
// Synchronize Streams.
_cuda_stream->synchronize();
// 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) {
DLOG_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 ENABLE_PROFILING
auto prof = _profile_ar_copy->track();
#endif
::nvidia::cuda::memcpy2d_v2_t mc;
mc.src_x_in_bytes = 0;
mc.src_y = 0;
mc.src_memory_type = ::nvidia::cuda::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::memory_type::DEVICE;
mc.dst_host = 0;
mc.dst_device = reinterpret_cast<::nvidia::cuda::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::result res = _cuda->get_cuda()->cuMemcpy2DAsync(&mc, _cuda_stream->get());
res != ::nvidia::cuda::result::SUCCESS) {
DLOG_ERROR("<%s> Failed to prepare buffers for tracking.", obs_source_get_name(_self));
return;
}
}
{ // Convert from RGBA 32-bit to BGR 24-bit.
#ifdef ENABLE_PROFILING
auto prof = _profile_ar_transfer->track();
#endif
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) {
DLOG_ERROR("<%s> Failed to convert from RGBX 32-bit to BGR 24-bit.", obs_source_get_name(_self));
return;
}
// Synchronize Streams.
_cuda_stream->synchronize();
_cuda->get_context()->synchronize();
}
{ // Track any faces.
#ifdef ENABLE_PROFILING
auto prof = _profile_ar_run->track();
#endif
if (NvCV_Status res = _ar_library->run(_ar_feature.get()); res != NVCV_SUCCESS) {
DLOG_ERROR("<%s> Failed to run tracking.", obs_source_get_name(_self));
return;
}
}
// Are we tracking anything, and confident enough in the tracking?
if ((_ar_bboxes.num_boxes == 0) || (_ar_bboxes_confidence.at(0) < 0.3333)) {
// If not, just return to full frame.
std::unique_lock<std::mutex> tlk{_values.lock};
_values.center[0] = .5;
_values.center[1] = .5;
_values.size[0] = 1.;
_values.size[1] = 1.;
_values.velocity[0] = 0;
_values.velocity[1] = 0;
} else {
// If yes, begin tracking.
#ifdef ENABLE_PROFILING
auto prof = _profile_ar_calc->track();
#endif
double_t sx = static_cast<double_t>(_ar_image_bgr.width);
double_t sy = static_cast<double_t>(_ar_image_bgr.height);
double_t aspect = double_t(sx) / double_t(sy);
double_t fps = 0.;
{
obs_video_info ovi;
obs_get_video_info(&ovi);
fps = static_cast<double_t>(ovi.fps_num) / static_cast<double_t>(ovi.fps_den);
}
// Store values and center.
double_t bsx = _ar_bboxes.boxes[0].width;
double_t bsy = _ar_bboxes.boxes[0].height;
double_t bcx = _ar_bboxes.boxes[0].x + bsx / 2.0;
double_t bcy = _ar_bboxes.boxes[0].y + bsy / 2.0;
// Zoom, Aspect Ratio, Offset
bsy = streamfx::util::math::lerp<double_t>(sy, bsy, _cfg_zoom);
bsy = std::clamp(bsy, 10 * aspect, static_cast<double_t>(_size.second));
bsx = bsy * aspect;
bcx += _ar_bboxes.boxes[0].width * _cfg_offset.first;
bcy += _ar_bboxes.boxes[0].height * _cfg_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.
bcx = std::clamp(bcx, (bsx / 2.), sx - (bsx / 2.));
bcy = std::clamp(bcy, (bsy / 2.), sy - (bsy / 2.));
{ // Update target values.
std::unique_lock<std::mutex> tlk{_values.lock};
_values.velocity[0] = -_values.center[0];
_values.velocity[1] = -_values.center[1];
_values.center[0] = bcx / sx;
_values.center[1] = bcy / sy;
_values.velocity[0] += _values.center[0];
_values.velocity[1] += _values.center[1];
_values.velocity[0] *= fps;
_values.velocity[1] *= fps;
_values.size[0] = bsx / sx;
_values.size[1] = bsy / sy;
}
}
_async_track.reset();
// Allow new frames to be queued again.
_ar_is_tracking = false;
}
}
void face_tracking_instance::refresh_geometry()
{ // Update Region of Interest Geometry.
auto v0 = _geometry->at(0);
auto v1 = _geometry->at(1);
auto v2 = _geometry->at(2);
auto v3 = _geometry->at(3);
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.);
float_t hsx = static_cast<float_t>(_filters.size[0].get() / 2.);
float_t hsy = static_cast<float_t>(_filters.size[1].get() / 2.);
vec4_set(v0.uv[0], static_cast<float_t>(_filters.center[0].get() - hsx),
static_cast<float_t>(_filters.center[1].get() - hsy), 0., 0.);
vec4_set(v1.uv[0], static_cast<float_t>(_filters.center[0].get() - hsx),
static_cast<float_t>(_filters.center[1].get() + hsy), 0., 0.);
vec4_set(v2.uv[0], static_cast<float_t>(_filters.center[0].get() + hsx),
static_cast<float_t>(_filters.center[1].get() - hsy), 0., 0.);
vec4_set(v3.uv[0], static_cast<float_t>(_filters.center[0].get() + hsx),
static_cast<float_t>(_filters.center[1].get() + hsy), 0., 0.);
_geometry->update(true);
}
void face_tracking_instance::refresh_region_of_interest()
{
std::unique_lock<std::mutex> tlk(_values.lock);
double_t kalman_q = streamfx::util::math::lerp<double_t>(1.0, 1e-6, _cfg_stability);
double_t kalman_r =
streamfx::util::math::lerp<double_t>(std::numeric_limits<double_t>::epsilon(), 1e+2, _cfg_stability);
_filters.center[0] = streamfx::util::math::kalman1D<double_t>{kalman_q, kalman_r, 1., _values.center[0]};
_filters.center[1] = streamfx::util::math::kalman1D<double_t>{kalman_q, kalman_r, 1., _values.center[1]};
_filters.size[0] = streamfx::util::math::kalman1D<double_t>{kalman_q, kalman_r, 1., _values.size[0]};
_filters.size[1] = streamfx::util::math::kalman1D<double_t>{kalman_q, kalman_r, 1., _values.size[1]};
}
void face_tracking_instance::load(obs_data_t* data)
{
update(data);
}
void face_tracking_instance::migrate(obs_data_t* data, uint64_t version) {}
void face_tracking_instance::update(obs_data_t* data)
{
_cfg_zoom = obs_data_get_double(data, ST_KEY_ROI_ZOOM) / 100.0;
_cfg_offset.first = obs_data_get_double(data, ST_KEY_ROI_OFFSET_X) / 100.0;
_cfg_offset.second = obs_data_get_double(data, ST_KEY_ROI_OFFSET_Y) / 100.0;
_cfg_stability = obs_data_get_double(data, ST_KEY_ROI_STABILITY) / 100.0;
// Refresh the Region Of Interest
refresh_region_of_interest();
}
void face_tracking_instance::video_tick(float_t seconds)
{
// If we aren't yet ready to do work, abort for now.
if (!_ar_loaded) {
return;
}
// Update the input size.
if (obs_source_t* src = obs_filter_get_target(_self); src != nullptr) {
_size.first = obs_source_get_base_width(src);
_size.second = obs_source_get_base_height(src);
}
// Update filters and geometry
{
std::unique_lock<std::mutex> tlk(_values.lock);
_filters.center[0].filter(_values.center[0]);
_filters.center[1].filter(_values.center[1]);
_filters.size[0].filter(_values.size[0]);
_filters.size[1].filter(_values.size[1]);
_values.center[0] += _values.velocity[0] * seconds;
_values.center[1] += _values.velocity[1] * seconds;
}
refresh_geometry();
_rt_is_fresh = false;
}
void face_tracking_instance::video_render(gs_effect_t*)
{
obs_source_t* filter_parent = obs_filter_get_parent(_self);
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 || !_size.first || !_size.second || !_ar_loaded) {
obs_source_skip_video_filter(_self);
return;
}
#ifdef ENABLE_PROFILING
streamfx::obs::gs::debug_marker gdmp{streamfx::obs::gs::debug_color_source, "NVIDIA Face Tracking '%s'...",
obs_source_get_name(_self)};
streamfx::obs::gs::debug_marker gdmp2{streamfx::obs::gs::debug_color_source, "... on '%s'",
obs_source_get_name(obs_filter_get_parent(_self))};
#endif
if (!_rt_is_fresh) { // Capture the filter stack "below" us.
#ifdef ENABLE_PROFILING
auto prof = _profile_capture->track();
#endif
{
#ifdef ENABLE_PROFILING
streamfx::obs::gs::debug_marker gdm{streamfx::obs::gs::debug_color_cache, "Cache"};
#endif
if (obs_source_process_filter_begin(_self, GS_RGBA, OBS_NO_DIRECT_RENDERING)) {
auto op = _rt->render(_size.first, _size.second);
vec4 clr = {0., 0., 0., 0.};
gs_ortho(0., 1., 0., 1., -1., 1.);
gs_clear(GS_CLEAR_COLOR, &clr, 0., 0);
gs_enable_color(true, true, true, true);
gs_enable_blending(false);
auto old_fbsrgb = gs_framebuffer_srgb_enabled();
gs_enable_framebuffer_srgb(gs_get_linear_srgb());
obs_source_process_filter_tech_end(_self, default_effect, 1, 1, "Draw");
gs_enable_framebuffer_srgb(old_fbsrgb);
} else {
obs_source_skip_video_filter(_self);
return;
}
}
// Probably spawn new work.
async_track(nullptr);
_rt_is_fresh = true;
}
{ // Draw Texture
#ifdef ENABLE_PROFILING
streamfx::obs::gs::debug_marker gdm{streamfx::obs::gs::debug_color_render, "Render"};
#endif
gs_effect_set_texture(gs_effect_get_param_by_name(default_effect, "image"), _rt->get_texture()->get_object());
gs_load_vertexbuffer(_geometry->update(false));
while (gs_effect_loop(default_effect, "Draw")) {
gs_draw(gs_draw_mode::GS_TRISTRIP, 0, 0);
}
gs_load_vertexbuffer(nullptr);
}
}
#ifdef ENABLE_PROFILING
bool face_tracking_instance::button_profile(obs_properties_t* props, obs_property_t* property)
{
DLOG_INFO("%-22s: %-10s %-10s %-10s %-10s %-10s", "Task", "Total", "Count", "Average", "99.9%ile", "95.0%ile");
std::pair<std::string, std::shared_ptr<streamfx::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) {
DLOG_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<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;
}
#endif
face_tracking_factory::face_tracking_factory()
{
// Try and load CUDA.
_cuda = ::nvidia::cuda::obs::get();
// Try and load AR.
_ar = std::make_shared<::nvidia::ar::ar>();
// Info
_info.id = S_PREFIX "filter-nvidia-face-tracking";
_info.type = OBS_SOURCE_TYPE_FILTER;
_info.output_flags = OBS_SOURCE_VIDEO | OBS_SOURCE_CUSTOM_DRAW;
set_resolution_enabled(false);
finish_setup();
register_proxy("streamfx-nvidia-face-tracking");
}
face_tracking_factory::~face_tracking_factory() {}
const char* face_tracking_factory::get_name()
{
return D_TRANSLATE(ST_I18N);
}
void face_tracking_factory::get_defaults2(obs_data_t* data)
{
obs_data_set_default_double(data, ST_KEY_ROI_ZOOM, 50.0);
obs_data_set_default_double(data, ST_KEY_ROI_OFFSET_X, 0.0);
obs_data_set_default_double(data, ST_KEY_ROI_OFFSET_Y, -15.0);
obs_data_set_default_double(data, ST_KEY_ROI_STABILITY, 50.0);
}
obs_properties_t* face_tracking_factory::get_properties2(face_tracking_instance* data)
{
obs_properties_t* pr = obs_properties_create();
{
auto grp = obs_properties_create();
obs_properties_add_group(pr, ST_I18N_ROI, D_TRANSLATE(ST_I18N_ROI), OBS_GROUP_NORMAL, grp);
{
auto p = obs_properties_add_float_slider(grp, ST_KEY_ROI_STABILITY, D_TRANSLATE(ST_I18N_ROI_STABILITY), 0,
100.0, 0.01);
obs_property_float_set_suffix(p, " %");
}
{
auto p =
obs_properties_add_float_slider(grp, ST_KEY_ROI_ZOOM, D_TRANSLATE(ST_I18N_ROI_ZOOM), 0, 200.0, 0.01);
obs_property_float_set_suffix(p, " %");
}
{
auto grp2 = obs_properties_create();
obs_properties_add_group(grp, ST_I18N_ROI_OFFSET, D_TRANSLATE(ST_I18N_ROI_OFFSET), OBS_GROUP_NORMAL, grp2);
{
auto p = obs_properties_add_float_slider(grp2, ST_KEY_ROI_OFFSET_X, D_TRANSLATE(ST_I18N_ROI_OFFSET_X),
-50.0, 50.0, 0.01);
obs_property_float_set_suffix(p, " %");
}
{
auto p = obs_properties_add_float_slider(grp2, ST_KEY_ROI_OFFSET_Y, D_TRANSLATE(ST_I18N_ROI_OFFSET_Y),
-50.0, 50.0, 0.01);
obs_property_float_set_suffix(p, " %");
}
}
}
#ifdef ENABLE_PROFILING
{
obs_properties_add_button2(
pr, "Profile", "Profile",
[](obs_properties_t* props, obs_property_t* property, void* data) {
return reinterpret_cast<face_tracking_instance*>(data)->button_profile(props, property);
},
data);
}
#endif
return pr;
}
std::shared_ptr<::nvidia::ar::ar> face_tracking_factory::get_ar()
{
return _ar;
}
std::shared_ptr<face_tracking_factory> _filter_nvidia_face_tracking_factory_instance = nullptr;
void streamfx::filter::nvidia::face_tracking_factory::initialize()
{
try {
_filter_nvidia_face_tracking_factory_instance = std::make_shared<filter::nvidia::face_tracking_factory>();
} catch (const std::exception& ex) {
DLOG_ERROR("<NVIDIA Face Tracking Filter> %s", ex.what());
}
}
void streamfx::filter::nvidia::face_tracking_factory::finalize()
{
_filter_nvidia_face_tracking_factory_instance.reset();
}
std::shared_ptr<face_tracking_factory> streamfx::filter::nvidia::face_tracking_factory::get()
{
return _filter_nvidia_face_tracking_factory_instance;
}
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