y2023/vision/aprilrobotics.cc

#include "y2023/vision/aprilrobotics.h"

#include "y2023/vision/vision_util.h"

DEFINE_bool(
    debug, false,
    "If true, dump a ton of debug and crash on the first valid detection.");

DEFINE_double(min_decision_margin, 75.0,
              "Minimum decision margin (confidence) for an apriltag detection");
DEFINE_int32(pixel_border, 3,
             "Size of image border within which to reject detected corners");

namespace y2023 {
namespace vision {

namespace chrono = std::chrono;

AprilRoboticsDetector::AprilRoboticsDetector(aos::EventLoop *event_loop,
                                             std::string_view channel_name)
    : calibration_data_(event_loop),
      image_size_(0, 0),
      ftrace_(),
      image_callback_(event_loop, channel_name,
                      [&](cv::Mat image_color_mat,
                          const aos::monotonic_clock::time_point eof) {
                        HandleImage(image_color_mat, eof);
                      },
                      chrono::milliseconds(5)),
      target_map_sender_(
          event_loop->MakeSender<frc971::vision::TargetMap>("/camera")),
      image_annotations_sender_(
          event_loop->MakeSender<foxglove::ImageAnnotations>("/camera")) {
  tag_family_ = tag16h5_create();
  tag_detector_ = apriltag_detector_create();

  apriltag_detector_add_family_bits(tag_detector_, tag_family_, 1);
  tag_detector_->nthreads = 6;
  tag_detector_->wp = workerpool_create(tag_detector_->nthreads);
  tag_detector_->qtp.min_white_black_diff = 5;
  tag_detector_->debug = FLAGS_debug;

  std::string hostname = aos::network::GetHostname();

  // Check team string is valid
  calibration_ = FindCameraCalibration(
      calibration_data_.constants(), event_loop->node()->name()->string_view());

  extrinsics_ = CameraExtrinsics(calibration_);

  intrinsics_ = CameraIntrinsics(calibration_);
  // Create an undistort projection matrix using the intrinsics
  projection_matrix_ = cv::Mat::zeros(3, 4, CV_64F);
  intrinsics_.rowRange(0, 3).colRange(0, 3).copyTo(
      projection_matrix_.rowRange(0, 3).colRange(0, 3));

  dist_coeffs_ = CameraDistCoeffs(calibration_);

  image_callback_.set_format(frc971::vision::ImageCallback::Format::GRAYSCALE);
}

AprilRoboticsDetector::~AprilRoboticsDetector() {
  apriltag_detector_destroy(tag_detector_);
  free(tag_family_);
}

void AprilRoboticsDetector::SetWorkerpoolAffinities() {
  for (int i = 0; i < tag_detector_->wp->nthreads; i++) {
    cpu_set_t affinity;
    CPU_ZERO(&affinity);
    CPU_SET(i, &affinity);
    pthread_setaffinity_np(tag_detector_->wp->threads[i], sizeof(affinity),
                           &affinity);
    struct sched_param param;
    param.sched_priority = 20;
    int res = pthread_setschedparam(tag_detector_->wp->threads[i], SCHED_FIFO,
                                    &param);
    PCHECK(res == 0) << "Failed to set priority of threadpool threads";
  }
}

void AprilRoboticsDetector::HandleImage(cv::Mat image_grayscale,
                                        aos::monotonic_clock::time_point eof) {
  image_size_ = image_grayscale.size();

  std::vector<Detection> detections = DetectTags(image_grayscale, eof);

  auto builder = target_map_sender_.MakeBuilder();
  std::vector<flatbuffers::Offset<frc971::vision::TargetPoseFbs>> target_poses;
  for (const auto &detection : detections) {
    target_poses.emplace_back(BuildTargetPose(detection, builder.fbb()));
  }
  const auto target_poses_offset = builder.fbb()->CreateVector(target_poses);
  auto target_map_builder = builder.MakeBuilder<frc971::vision::TargetMap>();
  target_map_builder.add_target_poses(target_poses_offset);
  target_map_builder.add_monotonic_timestamp_ns(eof.time_since_epoch().count());
  builder.CheckOk(builder.Send(target_map_builder.Finish()));
}

flatbuffers::Offset<frc971::vision::TargetPoseFbs>
AprilRoboticsDetector::BuildTargetPose(const Detection &detection,
                                       flatbuffers::FlatBufferBuilder *fbb) {
  const auto T =
      Eigen::Translation3d(detection.pose.t->data[0], detection.pose.t->data[1],
                           detection.pose.t->data[2]);
  const auto position_offset =
      frc971::vision::CreatePosition(*fbb, T.x(), T.y(), T.z());

  // Aprilrobotics stores the rotation matrix in row-major order
  const auto orientation = Eigen::Quaterniond(
      Eigen::Matrix<double, 3, 3, Eigen::RowMajor>(detection.pose.R->data));
  const auto orientation_offset = frc971::vision::CreateQuaternion(
      *fbb, orientation.w(), orientation.x(), orientation.y(), orientation.z());

  return frc971::vision::CreateTargetPoseFbs(
      *fbb, detection.det.id, position_offset, orientation_offset,
      detection.det.decision_margin, detection.pose_error,
      detection.distortion_factor);
}

void AprilRoboticsDetector::UndistortDetection(
    apriltag_detection_t *det) const {
  // 4 corners
  constexpr size_t kRows = 4;
  // 2d points
  constexpr size_t kCols = 2;

  cv::Mat distorted_points(kRows, kCols, CV_64F, det->p);
  cv::Mat undistorted_points = cv::Mat::zeros(kRows, kCols, CV_64F);

  // Undistort the april tag points
  cv::undistortPoints(distorted_points, undistorted_points, intrinsics_,
                      dist_coeffs_, cv::noArray(), projection_matrix_);

  // Copy the undistorted points into det
  for (size_t i = 0; i < kRows; i++) {
    for (size_t j = 0; j < kCols; j++) {
      det->p[i][j] = undistorted_points.at<double>(i, j);
    }
  }
}

double AprilRoboticsDetector::ComputeDistortionFactor(
    const std::vector<cv::Point2f> &orig_corners,
    const std::vector<cv::Point2f> &corners) {
  CHECK_EQ(orig_corners.size(), 4ul);
  CHECK_EQ(corners.size(), 4ul);

  double avg_distance = 0.0;
  for (size_t i = 0; i < corners.size(); i++) {
    avg_distance += cv::norm(orig_corners[i] - corners[i]);
  }
  avg_distance /= corners.size();

  // Normalize avg_distance by dividing by the image size
  double distortion_factor =
      avg_distance /
      static_cast<double>(image_size_.width * image_size_.height);
  return distortion_factor;
}

std::vector<cv::Point2f> AprilRoboticsDetector::MakeCornerVector(
    const apriltag_detection_t *det) {
  std::vector<cv::Point2f> corner_points;
  corner_points.emplace_back(det->p[0][0], det->p[0][1]);
  corner_points.emplace_back(det->p[1][0], det->p[1][1]);
  corner_points.emplace_back(det->p[2][0], det->p[2][1]);
  corner_points.emplace_back(det->p[3][0], det->p[3][1]);

  return corner_points;
}

std::vector<AprilRoboticsDetector::Detection> AprilRoboticsDetector::DetectTags(
    cv::Mat image, aos::monotonic_clock::time_point eof) {
  const aos::monotonic_clock::time_point start_time =
      aos::monotonic_clock::now();

  image_u8_t im = {
      .width = image.cols,
      .height = image.rows,
      .stride = image.cols,
      .buf = image.data,
  };
  const uint32_t min_x = FLAGS_pixel_border;
  const uint32_t max_x = image.cols - FLAGS_pixel_border;

  ftrace_.FormatMessage("Starting detect\n");
  zarray_t *detections = apriltag_detector_detect(tag_detector_, &im);
  ftrace_.FormatMessage("Done detecting\n");

  std::vector<Detection> results;

  auto builder = image_annotations_sender_.MakeBuilder();
  std::vector<flatbuffers::Offset<foxglove::PointsAnnotation>> foxglove_corners;

  for (int i = 0; i < zarray_size(detections); i++) {
    apriltag_detection_t *det;
    zarray_get(detections, i, &det);

    if (det->decision_margin > FLAGS_min_decision_margin) {
      // TODO<jim>: Should we check for top/bottom of image?
      if (det->p[0][0] < min_x || det->p[0][0] > max_x ||
          det->p[1][0] < min_x || det->p[1][0] > max_x ||
          det->p[2][0] < min_x || det->p[2][0] > max_x ||
          det->p[3][0] < min_x || det->p[3][0] > max_x) {
        VLOG(1) << "Rejecting detection because corner is outside pixel border";
        // Send rejected corner points in red
        std::vector<cv::Point2f> rejected_corner_points = MakeCornerVector(det);
        foxglove_corners.push_back(frc971::vision::BuildPointsAnnotation(
            builder.fbb(), eof, rejected_corner_points,
            std::vector<double>{1.0, 0.0, 0.0, 0.5}));
        continue;
      }
      VLOG(1) << "Found tag number " << det->id << " hamming: " << det->hamming
              << " margin: " << det->decision_margin;

      // First create an apriltag_detection_info_t struct using your known
      // parameters.
      apriltag_detection_info_t info;
      info.det = det;
      info.tagsize = 0.1524;

      info.fx = intrinsics_.at<double>(0, 0);
      info.fy = intrinsics_.at<double>(1, 1);
      info.cx = intrinsics_.at<double>(0, 2);
      info.cy = intrinsics_.at<double>(1, 2);

      // Send original corner points in green
      std::vector<cv::Point2f> orig_corner_points = MakeCornerVector(det);
      foxglove_corners.push_back(frc971::vision::BuildPointsAnnotation(
          builder.fbb(), eof, orig_corner_points,
          std::vector<double>{0.0, 1.0, 0.0, 0.5}));

      UndistortDetection(det);

      const aos::monotonic_clock::time_point before_pose_estimation =
          aos::monotonic_clock::now();

      apriltag_pose_t pose;
      double pose_error = estimate_tag_pose(&info, &pose);

      const aos::monotonic_clock::time_point after_pose_estimation =
          aos::monotonic_clock::now();
      VLOG(1) << "Took "
              << chrono::duration<double>(after_pose_estimation -
                                          before_pose_estimation)
                     .count()
              << " seconds for pose estimation";
      VLOG(1) << "Pose err: " << pose_error;

      // Send undistorted corner points in pink
      std::vector<cv::Point2f> corner_points = MakeCornerVector(det);
      foxglove_corners.push_back(frc971::vision::BuildPointsAnnotation(
          builder.fbb(), eof, corner_points,
          std::vector<double>{1.0, 0.75, 0.8, 1.0}));

      double distortion_factor =
          ComputeDistortionFactor(orig_corner_points, corner_points);

      results.emplace_back(Detection{.det = *det,
                                     .pose = pose,
                                     .pose_error = pose_error,
                                     .distortion_factor = distortion_factor});
    }
  }

  foxglove::ImageAnnotations::Builder annotation_builder(*builder.fbb());
  const auto corners_offset = builder.fbb()->CreateVector(foxglove_corners);
  annotation_builder.add_points(corners_offset);
  builder.CheckOk(builder.Send(annotation_builder.Finish()));

  apriltag_detections_destroy(detections);

  const aos::monotonic_clock::time_point end_time = aos::monotonic_clock::now();

  if (FLAGS_debug) {
    timeprofile_display(tag_detector_->tp);
  }

  VLOG(1) << "Took " << chrono::duration<double>(end_time - start_time).count()
          << " seconds to detect overall";

  return results;
}

}  // namespace vision
}  // namespace y2023