Add a face alignment based tracker

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Models were generated with code from
https://github.com/DaWelter/neuralnet-tracker-traincode/releases/tag/v0.1

1 files changed, 917 insertions, 0 deletions

diff --git a/tracker-neuralnet/ftnoir_tracker_neuralnet.cpp b/tracker-neuralnet/ftnoir_tracker_neuralnet.cpp
new file mode 100644
index 00000000..2fad17aa
--- /dev/null
+++ b/tracker-neuralnet/ftnoir_tracker_neuralnet.cpp
@@ -0,0 +1,917 @@
+/* Copyright (c) 2021 Michael Welter <michael@welter-4d.de>
+ *
+ * Permission to use, copy, modify, and/or distribute this software for any
+ * purpose with or without fee is hereby granted, provided that the above
+ * copyright notice and this permission notice appear in all copies.
+ */
+
+#include "ftnoir_tracker_neuralnet.h"
+#include "compat/sleep.hpp"
+#include "compat/math-imports.hpp"
+#include "cv/init.hpp"
+#include <opencv2/core.hpp>
+#include <opencv2/core/hal/interface.h>
+#include <opencv2/core/types.hpp>
+#include <opencv2/calib3d.hpp>
+#include <opencv2/imgcodecs.hpp>
+#include "compat/timer.hpp"
+#include <omp.h>
+
+#ifdef _MSC_VER
+#   pragma warning(disable : 4702)
+#endif
+
+#include <QMutexLocker>
+#include <QDebug>
+#include <QFile>
+
+#include <cstdio>
+#include <cmath>
+#include <algorithm>
+#include <chrono>
+
+// Some demo code for onnx
+// https://github.com/microsoft/onnxruntime/blob/master/csharp/test/Microsoft.ML.OnnxRuntime.EndToEndTests.Capi/C_Api_Sample.cpp
+// https://github.com/leimao/ONNX-Runtime-Inference/blob/main/src/inference.cpp
+
+namespace
+{
+
+using numeric_types::vec3;
+using numeric_types::vec2;
+using numeric_types::mat33;
+
+// Minimal difference if at all going from 1 to 2 threads.
+static constexpr int num_threads = 1;
+
+
+#if _MSC_VER
+std::wstring convert(const QString &s) { return s.toStdWString(); }
+#else
+std::string convert(const QString &s) { return s.toStdString(); }
+#endif
+
+
+float sigmoid(float x)
+{
+    return 1.f/(1.f + std::exp(-x));
+}
+
+
+template<class T>
+cv::Rect_<T> squarize(const cv::Rect_<T> &r)
+{
+    cv::Point_<T> c{r.x + r.width/T(2), r.y + r.height/T(2)};
+    const T sz = std::max(r.height, r.width);
+    return {c.x - sz/T(2), c.y - sz/T(2), sz, sz};
+}
+
+
+int compute_padding(const cv::Rect &r, int w, int h)
+{
+    using std::max;
+    return max({
+        max(-r.x, 0),
+        max(-r.y, 0),
+        max(r.x+r.width-w, 0),
+        max(r.y+r.height-h, 0)
+    });
+}
+
+
+cv::Rect2f unnormalize(const cv::Rect2f &r, int h, int w)
+{
+    auto unnorm = [](float x) -> float { return 0.5*(x+1); };
+    auto tl = r.tl();
+    auto br = r.br();
+    auto x0 = unnorm(tl.x)*w;
+    auto y0 = unnorm(tl.y)*h;
+    auto x1 = unnorm(br.x)*w;
+    auto y1 = unnorm(br.y)*h;
+    return {
+        x0, y0, x1-x0, y1-y0
+    };
+}
+
+cv::Point2f normalize(const cv::Point2f &p, int h, int w)
+{
+    return {
+        p.x/w*2.f-1.f,
+        p.y/h*2.f-1.f
+    };
+}
+
+
+mat33 rotation_from_two_vectors(const vec3 &a, const vec3 &b)
+{
+    vec3 axis = a.cross(b);
+    const float len_a = cv::norm(a);
+    const float len_b = cv::norm(b);
+    const float len_axis = cv::norm(axis);
+    const float sin_angle = std::clamp(len_axis / (len_a * len_b), -1.f, 1.f);
+    const float angle = std::asin(sin_angle);
+    axis *= angle/(1.e-12 + len_axis);
+    mat33 out;
+    cv::Rodrigues(axis, out);
+    return out;
+}
+
+
+/* Computes correction due to head being off screen center.
+    x, y: In screen space, i.e. in [-1,1]
+    focal_length_x: In screen space
+*/
+mat33 compute_rotation_correction(const cv::Point2f &p, float focal_length_x)
+{
+    return rotation_from_two_vectors(
+        {1.f,0.f,0.f}, 
+        {focal_length_x, p.y, p.x});
+}
+
+
+mat33 quaternion_to_mat33(const std::array<float,4> quat)
+{
+    mat33 m;
+    const float w = quat[0];
+    const float i = quat[1];
+    const float j = quat[2];
+    const float k = quat[3];
+    m(0,0) = 1.f - 2.f*(j*j + k*k);
+    m(1,0) =       2.f*(i*j + k*w);
+    m(2,0) =       2.f*(i*k - j*w);
+    m(0,1) =       2.f*(i*j - k*w);
+    m(1,1) = 1.f - 2.f*(i*i + k*k);
+    m(2,1) =       2.f*(j*k + i*w);
+    m(0,2) =       2.f*(i*k + j*w);
+    m(1,2) =       2.f*(j*k - i*w);
+    m(2,2) = 1.f - 2.f*(i*i + j*j);
+    return m;
+}
+
+
+template<class T>
+T iou(const cv::Rect_<T> &a, const cv::Rect_<T> &b)
+{
+    auto i = a & b;
+    return double{i.area()} / (a.area()+b.area()-i.area());
+}
+
+
+} // namespace
+
+
+namespace neuralnet_tracker_ns
+{
+
+
+int enum_to_fps(int value)
+{
+    switch (value)
+    {
+        case fps_30:        return 30;
+        case fps_60:        return 60;
+        default: [[fallthrough]];
+        case fps_default:   return 0;
+    }
+}
+
+
+Localizer::Localizer(Ort::MemoryInfo &allocator_info, Ort::Session &&session) :
+    session{std::move(session)},
+    scaled_frame(input_img_height, input_img_width, CV_8U),
+    input_mat(input_img_height, input_img_width, CV_32F)
+{
+    // Only works when input_mat does not reallocated memory ...which it should not.
+    // Non-owning memory reference to input_mat?
+    // Note: shape = (bach x channels x h x w)
+    const std::int64_t input_shape[4] = { 1, 1, input_img_height, input_img_width };
+    input_val = Ort::Value::CreateTensor<float>(allocator_info, input_mat.ptr<float>(0), input_mat.total(), input_shape, 4);
+
+    const std::int64_t output_shape[2] = { 1, 5 };
+    output_val = Ort::Value::CreateTensor<float>(allocator_info, results.data(), results.size(), output_shape, 2);
+}
+
+
+std::pair<float, cv::Rect2f> Localizer::run(
+    const cv::Mat &frame)
+{
+    auto p = input_mat.ptr(0);
+
+    cv::resize(frame, scaled_frame, { input_img_width, input_img_height }, 0, 0, cv::INTER_AREA);
+    scaled_frame.convertTo(input_mat, CV_32F, 1./255., -0.5);
+
+    assert (input_mat.ptr(0) == p);
+    assert (!input_mat.empty() && input_mat.isContinuous());
+    assert (input_mat.cols == input_img_width && input_mat.rows == input_img_height);
+
+    const char* input_names[] = {"x"};
+    const char* output_names[] = {"logit_box"};
+
+    //Timer t_; t_.start();
+
+    const auto nt = omp_get_num_threads();
+    omp_set_num_threads(num_threads);
+    session.Run(Ort::RunOptions{nullptr}, input_names, &input_val, 1, output_names, &output_val, 1);
+    omp_set_num_threads(nt);
+
+    //qDebug() << "localizer: " << t_.elapsed_ms() << " ms\n";
+
+    const cv::Rect2f roi = unnormalize(cv::Rect2f{
+        results[1],
+        results[2],
+        results[3]-results[1], // Width
+        results[4]-results[2] // Height
+    }, frame.rows, frame.cols);
+    const float score = sigmoid(results[0]);
+
+    return { score, roi };
+}
+
+
+PoseEstimator::PoseEstimator(Ort::MemoryInfo &allocator_info, Ort::Session &&session) :
+    session{std::move(session)},
+    scaled_frame(input_img_height, input_img_width, CV_8U),
+    input_mat(input_img_height, input_img_width, CV_32F)
+{
+    {
+        const std::int64_t input_shape[4] = { 1, 1, input_img_height, input_img_width };
+        input_val = Ort::Value::CreateTensor<float>(allocator_info, input_mat.ptr<float>(0), input_mat.total(), input_shape, 4);
+    }
+
+    {
+        const std::int64_t output_shape[2] = { 1, 3 };
+        output_val[0] = Ort::Value::CreateTensor<float>(
+            allocator_info, &output_coord[0], output_coord.rows, output_shape, 2);
+    }
+
+    {
+        const std::int64_t output_shape[2] = { 1, 4 };
+        output_val[1] = Ort::Value::CreateTensor<float>(
+            allocator_info, &output_quat[0], output_quat.rows, output_shape, 2);
+    }
+
+    {
+        const std::int64_t output_shape[2] = { 1, 4 };
+        output_val[2] = Ort::Value::CreateTensor<float>(
+            allocator_info, &output_box[0], output_box.rows, output_shape, 2);
+    }
+}
+
+
+int PoseEstimator::find_input_intensity_90_pct_quantile() const
+{
+    const int channels[] = { 0 };
+    const int hist_size[] = { 255 };
+    float range[] = { 0, 256 };
+    const float* ranges[] = { range };
+    cv::Mat hist;
+    cv::calcHist(&scaled_frame, 1,  channels, cv::Mat(), hist, 1, hist_size, ranges, true, false);
+    int gray_level = 0;
+    const int num_pixels_quantile = scaled_frame.total()*0.9f;
+    int num_pixels_accum = 0;
+    for (int i=0; i<hist_size[0]; ++i)
+    {
+        num_pixels_accum += hist.at<float>(i);
+        if (num_pixels_accum > num_pixels_quantile)
+        {
+            gray_level = i;
+            break;
+        }
+    }
+    return gray_level;
+}
+
+
+std::optional<PoseEstimator::Face> PoseEstimator::run(
+    const cv::Mat &frame, const cv::Rect &box)
+{
+    cv::Mat cropped;
+    
+    const int patch_size = std::max(box.width, box.height)*1.05;
+    const cv::Point2f patch_center = {
+        std::clamp<float>(box.x + 0.5f*box.width, 0.f, frame.cols),
+        std::clamp<float>(box.y + 0.5f*box.height, 0.f, frame.rows)
+    };
+    cv::getRectSubPix(frame, {patch_size, patch_size}, patch_center, cropped);
+
+    // Will get failure if patch_center is outside image boundaries.
+    // Have to catch this case.
+    if (cropped.rows != patch_size || cropped.cols != patch_size)
+        return {};
+    
+    auto p = input_mat.ptr(0);
+
+    cv::resize(cropped, scaled_frame, { input_img_width, input_img_height }, 0, 0, cv::INTER_AREA);
+
+    // Automatic brightness amplification.
+    const int brightness = find_input_intensity_90_pct_quantile();
+    const double alpha = brightness<127 ? 0.5/std::max(5,brightness) : 1./255;
+    const double beta = -0.5;
+
+    scaled_frame.convertTo(input_mat, CV_32F, alpha, beta);
+
+    assert (input_mat.ptr(0) == p);
+    assert (!input_mat.empty() && input_mat.isContinuous());
+    assert (input_mat.cols == input_img_width && input_mat.rows == input_img_height);
+
+    const char* input_names[] = {"x"};
+    const char* output_names[] = {"pos_size", "quat", "box"};
+
+    //Timer t_; t_.start();
+
+    const auto nt = omp_get_num_threads();
+    omp_set_num_threads(num_threads);
+    session.Run(Ort::RunOptions{nullptr}, input_names, &input_val, 1, output_names, output_val, 3);
+    omp_set_num_threads(nt);
+
+    // FIXME: Execution time fluctuates wildly. 19 to 26 ms. Why???
+    //        The instructions are always the same. Maybe a memory allocation
+    //        issue. The ONNX api suggests that tensor are allocated in an
+    //        arena. Does that matter? Maybe the issue is something else?
+
+    //qDebug() << "pose net: " << t_.elapsed_ms() << " ms\n";
+
+    // Perform coordinate transformation.
+    // From patch-local normalized in [-1,1] to
+    // frame unnormalized pixel coordinates.
+
+    const cv::Point2f center = patch_center + 
+        (0.5f*patch_size)*cv::Point2f{output_coord[0], output_coord[1]};
+
+    const float size = patch_size*0.5f*output_coord[2];
+
+    // Following Eigen which uses quat components in the order w, x, y, z.
+    const std::array<float,4> rotation = { 
+        output_quat[3], 
+        output_quat[0], 
+        output_quat[1], 
+        output_quat[2] };
+
+    const cv::Rect2f outbox = {
+        patch_center.x + (0.5f*patch_size)*output_box[0],
+        patch_center.y + (0.5f*patch_size)*output_box[1],
+        0.5f*patch_size*(output_box[2]-output_box[0]),
+        0.5f*patch_size*(output_box[3]-output_box[1])
+    };
+
+    return std::optional<Face>({
+        rotation, outbox, center, size
+    });
+}
+
+
+cv::Mat PoseEstimator::last_network_input() const
+{
+    cv::Mat ret;
+    if (!input_mat.empty())
+    {
+        input_mat.convertTo(ret, CV_8U, 255., 127.);
+        cv::cvtColor(ret, ret, cv::COLOR_GRAY2RGB);
+    }
+    return ret;
+}
+
+
+bool neuralnet_tracker::detect()
+{
+    // Note: BGR colors!
+    if (!last_localizer_roi || !last_roi ||
+        iou(*last_localizer_roi,*last_roi)<0.25)
+    {
+        auto [p, rect] = localizer->run(grayscale);
+        if (p > 0.5)
+        {
+            last_localizer_roi = rect;
+            last_roi = rect;
+        }
+    }
+
+    if (!last_roi)
+        return false;
+
+    auto face = poseestimator->run(grayscale, *last_roi);
+    
+    if (!face)
+    {
+        last_roi.reset();
+        return false;
+    }
+
+    last_roi = face->box;
+
+    Affine pose = compute_pose(*face);
+
+    draw_gizmos(frame, *face, pose);
+
+    {
+        QMutexLocker lck(&mtx);
+        this->pose_ = pose;
+    }
+
+    return true;
+}
+
+
+Affine neuralnet_tracker::compute_pose(const PoseEstimator::Face &face) const
+{
+    const mat33 rot_correction = compute_rotation_correction(
+        normalize(face.center, frame.rows, frame.cols),
+        intrinsics.focal_length_w);
+
+    const mat33 m = rot_correction * quaternion_to_mat33(face.rotation);
+
+    /*
+         
+       hhhhhh  <- head size (meters)
+      \      | -----------------------
+       \     |                         \
+        \    |                          |
+         \   |                          |- tz (meters)
+          ____ <- face.size / width     |
+           \ |  |                       |
+            \|  |- focal length        /
+               ------------------------
+    */
+
+    // Compute the location the network outputs in 3d space.
+    const vec3 face_world_pos = image_to_world(face.center.x, face.center.y, face.size, head_size_mm);
+
+    // But this is in general not the location of the rotation joint in the neck.
+    // So we need an extra offset. Which we determine by solving
+    // z,y,z-pos = head_joint_loc + R_face * offset
+
+    const vec3 pos = face_world_pos
+        + m * vec3{
+            static_cast<float>(s.offset_fwd), 
+            static_cast<float>(s.offset_up),
+            static_cast<float>(s.offset_right)};
+
+    return { m, pos };
+}
+
+
+void neuralnet_tracker::draw_gizmos(
+    cv::Mat frame,
+    const PoseEstimator::Face &face,
+    const Affine& pose) const
+{
+    if (last_roi) 
+    {
+        const int col = 255;
+        cv::rectangle(frame, *last_roi, cv::Scalar(0, 255, 0), /*thickness=*/1);
+    }
+    if (last_localizer_roi)
+    {
+        const int col = 255;
+        cv::rectangle(frame, *last_localizer_roi, cv::Scalar(col, 0, 255-col), /*thickness=*/1);
+    }
+
+    if (face.size>=1.f)
+        cv::circle(frame, static_cast<cv::Point>(face.center), int(face.size), cv::Scalar(255,255,255), 2);
+    cv::circle(frame, static_cast<cv::Point>(face.center), 3, cv::Scalar(255,255,255), -1);
+
+    auto draw_coord_line = [&](int i, const cv::Scalar& color)
+    {
+        const float vx = -pose.R(2,i);
+        const float vy = -pose.R(1,i);
+        static constexpr float len = 100.f;
+        cv::Point q = face.center + len*cv::Point2f{vx, vy};
+        cv::line(frame, static_cast<cv::Point>(face.center), static_cast<cv::Point>(q), color, 2);
+    };
+    draw_coord_line(0, {0, 0, 255});
+    draw_coord_line(1, {0, 255, 0});
+    draw_coord_line(2, {255, 0, 0});
+
+    if (s.show_network_input)
+    {
+        cv::Mat netinput = poseestimator->last_network_input();
+        if (!netinput.empty())
+        {
+            const int w = std::min(netinput.cols, frame.cols);
+            const int h = std::min(netinput.rows, frame.rows);
+            cv::Rect roi(0, 0, w, h);
+            netinput(roi).copyTo(frame(roi));
+        }
+    }
+    {
+        // Draw the computed joint position
+        auto xy = world_to_image(pose.t);
+        cv::circle(frame, cv::Point(xy[0],xy[1]), 5, cv::Scalar(0,0,255), -1);
+    }
+
+    char buf[128];
+    ::snprintf(buf, sizeof(buf), "%d Hz, Max: %d ms", clamp(int(fps), 0, 9999), int(max_frame_time*1000.));
+    cv::putText(frame, buf, cv::Point(10, frame.rows-10), cv::FONT_HERSHEY_PLAIN, 1, cv::Scalar(0, 255, 0), 1);
+}
+
+
+neuralnet_tracker::neuralnet_tracker()
+{
+    opencv_init();
+    cv::setNumThreads(num_threads);
+}
+
+
+neuralnet_tracker::~neuralnet_tracker()
+{
+    requestInterruption();
+    wait();
+    // fast start/stop causes breakage
+    portable::sleep(1000);
+}
+
+
+module_status neuralnet_tracker::start_tracker(QFrame* videoframe)
+{
+    videoframe->show();
+    videoWidget = std::make_unique<cv_video_widget>(videoframe);
+    layout = std::make_unique<QHBoxLayout>();
+    layout->setContentsMargins(0, 0, 0, 0);
+    layout->addWidget(videoWidget.get());
+    videoframe->setLayout(layout.get());
+    videoWidget->show();
+    start();
+    return status_ok();
+}
+
+
+bool neuralnet_tracker::load_and_initialize_model()
+{
+    const QString localizer_model_path_enc =
+        OPENTRACK_BASE_PATH+"/" OPENTRACK_LIBRARY_PATH "/models/head-localizer.onnx";
+    const QString poseestimator_model_path_enc =
+        OPENTRACK_BASE_PATH+"/" OPENTRACK_LIBRARY_PATH "/models/head-pose.onnx";
+
+    try
+    {
+        env = Ort::Env{
+            OrtLoggingLevel::ORT_LOGGING_LEVEL_ERROR,
+            "tracker-neuralnet"
+        };
+        auto opts = Ort::SessionOptions{};
+        // Do thread settings here do anything?
+        // There is a warning which says to control number of threads via
+        // openmp settings. Which is what we do. omp_set_num_threads directly
+        // before running the inference pass.
+        opts.SetIntraOpNumThreads(num_threads);
+        opts.SetInterOpNumThreads(num_threads);
+        opts.SetGraphOptimizationLevel(
+            GraphOptimizationLevel::ORT_ENABLE_EXTENDED);
+
+        opts.EnableCpuMemArena();
+        allocator_info = Ort::MemoryInfo::CreateCpu(OrtArenaAllocator, OrtMemTypeDefault);
+
+        localizer.emplace(
+            allocator_info, 
+            Ort::Session{env, convert(localizer_model_path_enc).c_str(), opts});
+        
+        poseestimator.emplace(
+            allocator_info,
+            Ort::Session{env, convert(poseestimator_model_path_enc).c_str(), opts});
+    }
+    catch (const Ort::Exception &e)
+    {
+        qDebug() << "Failed to initialize the neural network models. ONNX error message: " 
+            << e.what();
+        return false;
+    }
+    return true;
+}
+
+
+bool neuralnet_tracker::open_camera()
+{
+    int fps = enum_to_fps(s.force_fps);
+
+    QMutexLocker l(&camera_mtx);
+
+    camera = video::make_camera(s.camera_name);
+
+    if (!camera)
+        return false;
+
+    video::impl::camera::info args {};
+
+    args.width = 320;
+    args.height = 240;
+
+    if (fps)
+        args.fps = fps;
+
+    if (!camera->start(args))
+    {
+        qDebug() << "neuralnet tracker: can't open camera";
+        return false;
+    }
+    return true;
+}
+
+
+void neuralnet_tracker::set_intrinsics()
+{
+    const int w = grayscale.cols, h = grayscale.rows;
+    const double diag_fov = s.fov * M_PI / 180.;
+    const double fov_w = 2.*atan(tan(diag_fov/2.)/sqrt(1. + h/(double)w * h/(double)w));
+    const double fov_h = 2.*atan(tan(diag_fov/2.)/sqrt(1. + w/(double)h * w/(double)h));
+    const double focal_length_w = 1. / tan(.5 * fov_w);
+    const double focal_length_h = 1. / tan(.5 * fov_h);
+
+    intrinsics.fov_h = fov_h;
+    intrinsics.fov_w = fov_w;
+    intrinsics.focal_length_w = focal_length_w;
+    intrinsics.focal_length_h = focal_length_h;
+}
+
+
+vec3 neuralnet_tracker::image_to_world(float x, float y, float size, float real_size) const
+{
+    // Compute the location the network outputs in 3d space.
+    const float xpos = -(intrinsics.focal_length_w * frame.cols * 0.5f) / size * real_size;
+    const float zpos = (x / frame.cols * 2.f - 1.f) * xpos / intrinsics.focal_length_w;
+    const float ypos = (y / frame.rows * 2.f - 1.f) * xpos / intrinsics.focal_length_h;
+    return {xpos, ypos, zpos};
+}
+
+
+vec2 neuralnet_tracker::world_to_image(const vec3& pos) const
+{
+    const float xscr = pos[2] / pos[0] * intrinsics.focal_length_w;
+    const float yscr = pos[1] / pos[0] * intrinsics.focal_length_h;
+    const float x = (xscr+1.)*0.5f*frame.cols;
+    const float y = (yscr+1.)*0.5f*frame.rows;
+    return {x, y};
+}
+
+
+void neuralnet_tracker::run()
+{
+    if (!open_camera())
+        return;
+
+    if (!load_and_initialize_model())
+        return;
+
+    std::chrono::high_resolution_clock clk;
+
+    while (!isInterruptionRequested())
+    {
+        auto t = clk.now();
+        {
+            QMutexLocker l(&camera_mtx);
+
+            auto [ img, res ] = camera->get_frame();
+
+            if (!res)
+            {
+                l.unlock();
+                portable::sleep(100);
+                continue;
+            }
+
+            auto color = cv::Mat(img.height, img.width, CV_8UC(img.channels), (void*)img.data, img.stride);
+            color.copyTo(frame);
+
+            switch (img.channels)
+            {
+            case 1:
+                grayscale.setTo(color); 
+                break;
+            case 3:
+                cv::cvtColor(color, grayscale, cv::COLOR_BGR2GRAY);
+                break;
+            default:
+                qDebug() << "Can't handle" << img.channels << "color channels";
+                return;
+            }
+        }
+
+        set_intrinsics();
+
+        detect();
+
+        if (frame.rows > 0)
+            videoWidget->update_image(frame);
+        
+        update_fps(
+            std::chrono::duration_cast<std::chrono::milliseconds>(
+                clk.now() - t).count()*1.e-3);
+    }
+}
+
+
+void neuralnet_tracker::update_fps(double dt)
+{
+    const double alpha = dt/(dt + RC);
+
+    if (dt > 1e-6)
+    {
+        fps *= 1 - alpha;
+        fps += alpha * 1./dt;
+    }
+
+    max_frame_time = std::max(max_frame_time, dt);
+}
+
+
+void neuralnet_tracker::data(double *data)
+{
+    Affine tmp = [&]()
+    {
+        QMutexLocker lck(&mtx);
+        return pose_;
+    }();
+
+    const auto& mx = tmp.R.col(0);
+    const auto& my = tmp.R.col(1);
+    const auto& mz = -tmp.R.col(2);
+
+    const float yaw = std::atan2(mx(2), mx(0));
+    const float pitch = -std::atan2(-mx(1), std::sqrt(mx(2)*mx(2)+mx(0)*mx(0)));
+    const float roll = std::atan2(-my(2), mz(2));
+    {
+        constexpr double rad2deg = 180/M_PI;
+        data[Yaw]   = rad2deg * yaw;
+        data[Pitch] = rad2deg * pitch;
+        data[Roll]  = rad2deg * roll;
+
+        // convert to cm
+        data[TX] = -tmp.t[2] * 0.1;
+        data[TY] = tmp.t[1] * 0.1;
+        data[TZ] = -tmp.t[0] * 0.1;
+    }
+}
+
+
+Affine neuralnet_tracker::pose()
+{
+    QMutexLocker lck(&mtx);
+    return pose_;
+}
+
+
+void neuralnet_dialog::make_fps_combobox()
+{
+    for (int k = 0; k < fps_MAX; k++)
+    {
+        const int hz = enum_to_fps(k);
+        const QString name = (hz == 0) ? tr("Default") : QString::number(hz);
+        ui.cameraFPS->addItem(name, k);
+    }
+}
+
+
+neuralnet_dialog::neuralnet_dialog() :
+    trans_calib(1, 2)
+{
+    ui.setupUi(this);
+
+    make_fps_combobox();
+    tie_setting(s.force_fps, ui.cameraFPS);
+
+    for (const auto& str : video::camera_names())
+        ui.cameraName->addItem(str);
+
+    tie_setting(s.camera_name, ui.cameraName);
+    tie_setting(s.fov, ui.cameraFOV);
+    tie_setting(s.offset_fwd, ui.tx_spin);
+    tie_setting(s.offset_up, ui.ty_spin);
+    tie_setting(s.offset_right, ui.tz_spin);
+    tie_setting(s.show_network_input, ui.showNetworkInput);
+
+    connect(ui.buttonBox, SIGNAL(accepted()), this, SLOT(doOK()));
+    connect(ui.buttonBox, SIGNAL(rejected()), this, SLOT(doCancel()));
+    connect(ui.camera_settings, SIGNAL(clicked()), this, SLOT(camera_settings()));
+
+    connect(&s.camera_name, value_::value_changed<QString>(), this, &neuralnet_dialog::update_camera_settings_state);
+
+    update_camera_settings_state(s.camera_name);
+
+    connect(&calib_timer, &QTimer::timeout, this, &neuralnet_dialog::trans_calib_step);
+    calib_timer.setInterval(35);
+    connect(ui.tcalib_button,SIGNAL(toggled(bool)), this, SLOT(startstop_trans_calib(bool)));
+}
+
+
+void neuralnet_dialog::doOK()
+{
+    s.b->save();
+    close();
+}
+
+
+void neuralnet_dialog::doCancel()
+{
+    close();
+}
+
+
+void neuralnet_dialog::camera_settings()
+{
+    if (tracker)
+    {
+        QMutexLocker l(&tracker->camera_mtx);
+        (void)tracker->camera->show_dialog();
+    }
+    else
+        (void)video::show_dialog(s.camera_name);
+}
+
+
+void neuralnet_dialog::update_camera_settings_state(const QString& name)
+{
+    (void)name;
+    ui.camera_settings->setEnabled(true);
+}
+
+
+void neuralnet_dialog::register_tracker(ITracker * x)
+{
+    tracker = static_cast<neuralnet_tracker*>(x);
+    ui.tcalib_button->setEnabled(true);
+}
+
+
+void neuralnet_dialog::unregister_tracker()
+{
+    tracker = nullptr;
+    ui.tcalib_button->setEnabled(false);
+}
+
+
+void neuralnet_dialog::trans_calib_step()
+{
+    if (tracker)
+    {
+        const Affine X_CM = [&]() { 
+            QMutexLocker l(&calibrator_mutex);
+            return tracker->pose();
+        }();
+        trans_calib.update(X_CM.R, X_CM.t);
+        auto [_, nsamples] = trans_calib.get_estimate();
+
+        constexpr int min_yaw_samples = 15;
+        constexpr int min_pitch_samples = 12;
+        constexpr int min_samples = min_yaw_samples+min_pitch_samples;
+
+        // Don't bother counting roll samples. Roll calibration is hard enough
+        // that it's a hidden unsupported feature anyway.
+
+        QString sample_feedback;
+        if (nsamples[0] < min_yaw_samples)
+            sample_feedback = tr("%1 yaw samples. Yaw more to %2 samples for stable calibration.").arg(nsamples[0]).arg(min_yaw_samples);
+        else if (nsamples[1] < min_pitch_samples)
+            sample_feedback = tr("%1 pitch samples. Pitch more to %2 samples for stable calibration.").arg(nsamples[1]).arg(min_pitch_samples);
+        else
+        {
+            const int nsamples_total = nsamples[0] + nsamples[1];
+            sample_feedback = tr("%1 samples. Over %2, good!").arg(nsamples_total).arg(min_samples);
+        }
+        ui.sample_count_display->setText(sample_feedback);
+    }
+    else
+        startstop_trans_calib(false);
+}
+
+
+void neuralnet_dialog::startstop_trans_calib(bool start)
+{
+    QMutexLocker l(&calibrator_mutex);
+    // FIXME: does not work ...  
+    if (start)
+    {
+        qDebug() << "pt: starting translation calibration";
+        calib_timer.start();
+        trans_calib.reset();
+        ui.sample_count_display->setText(QString());
+        // Tracker must run with zero'ed offset for calibration.
+        s.offset_fwd = 0;
+        s.offset_up = 0;
+        s.offset_right = 0;
+    }
+    else
+    {
+        calib_timer.stop();
+        qDebug() << "pt: stopping translation calibration";
+        {
+            auto [tmp, nsamples] = trans_calib.get_estimate();
+            s.offset_fwd = int(tmp[0]);
+            s.offset_up = int(tmp[1]);
+            s.offset_right = int(tmp[2]);
+        }
+    }
+    ui.tx_spin->setEnabled(!start);
+    ui.ty_spin->setEnabled(!start);
+    ui.tz_spin->setEnabled(!start);
+
+    if (start)
+        ui.tcalib_button->setText(tr("Stop calibration"));
+    else
+        ui.tcalib_button->setText(tr("Start calibration"));
+}
+
+
+settings::settings() : opts("neuralnet-tracker") {}
+
+} // neuralnet_tracker_ns
+
+OPENTRACK_DECLARE_TRACKER(neuralnet_tracker, neuralnet_dialog, neuralnet_metadata)