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-rw-r--r--intern/libmv/libmv/autotrack/predict_tracks.cc78
1 files changed, 52 insertions, 26 deletions
diff --git a/intern/libmv/libmv/autotrack/predict_tracks.cc b/intern/libmv/libmv/autotrack/predict_tracks.cc
index 3786c1b9a3b..f3411066a07 100644
--- a/intern/libmv/libmv/autotrack/predict_tracks.cc
+++ b/intern/libmv/libmv/autotrack/predict_tracks.cc
@@ -20,8 +20,8 @@
//
// Author: mierle@gmail.com (Keir Mierle)
-#include "libmv/autotrack/marker.h"
#include "libmv/autotrack/predict_tracks.h"
+#include "libmv/autotrack/marker.h"
#include "libmv/autotrack/tracks.h"
#include "libmv/base/vector.h"
#include "libmv/logging/logging.h"
@@ -31,8 +31,8 @@ namespace mv {
namespace {
-using libmv::vector;
using libmv::Vec2;
+using libmv::vector;
// Implied time delta between steps. Set empirically by tweaking and seeing
// what numbers did best at prediction.
@@ -57,6 +57,8 @@ const double dt = 3.8;
// For a typical system having constant velocity. This gives smooth-appearing
// predictions, but they are not always as accurate.
+//
+// clang-format off
const double velocity_state_transition_data[] = {
1, dt, 0, 0, 0, 0,
0, 1, 0, 0, 0, 0,
@@ -65,10 +67,13 @@ const double velocity_state_transition_data[] = {
0, 0, 0, 0, 1, 0,
0, 0, 0, 0, 0, 1
};
+// clang-format on
#if 0
// This 3rd-order system also models acceleration. This makes for "jerky"
// predictions, but that tend to be more accurate.
+//
+// clang-format off
const double acceleration_state_transition_data[] = {
1, dt, dt*dt/2, 0, 0, 0,
0, 1, dt, 0, 0, 0,
@@ -77,9 +82,12 @@ const double acceleration_state_transition_data[] = {
0, 0, 0, 0, 1, dt,
0, 0, 0, 0, 0, 1
};
+// clang-format on
// This system (attempts) to add an angular velocity component. However, it's
// total junk.
+//
+// clang-format off
const double angular_state_transition_data[] = {
1, dt, -dt, 0, 0, 0, // Position x
0, 1, 0, 0, 0, 0, // Velocity x
@@ -88,17 +96,22 @@ const double angular_state_transition_data[] = {
0, 0, 0, 0, 1, 0, // Velocity y
0, 0, 0, 0, 0, 1 // Ignored
};
+// clang-format on
#endif
const double* state_transition_data = velocity_state_transition_data;
// Observation matrix.
+// clang-format off
const double observation_data[] = {
1., 0., 0., 0., 0., 0.,
0., 0., 0., 1., 0., 0.
};
+// clang-format on
// Process covariance.
+//
+// clang-format off
const double process_covariance_data[] = {
35, 0, 0, 0, 0, 0,
0, 5, 0, 0, 0, 0,
@@ -107,14 +120,19 @@ const double process_covariance_data[] = {
0, 0, 0, 0, 5, 0,
0, 0, 0, 0, 0, 5
};
+// clang-format on
// Process covariance.
const double measurement_covariance_data[] = {
- 0.01, 0.00,
- 0.00, 0.01,
+ 0.01,
+ 0.00,
+ 0.00,
+ 0.01,
};
// Initial covariance.
+//
+// clang-format off
const double initial_covariance_data[] = {
10, 0, 0, 0, 0, 0,
0, 1, 0, 0, 0, 0,
@@ -123,6 +141,7 @@ const double initial_covariance_data[] = {
0, 0, 0, 0, 1, 0,
0, 0, 0, 0, 0, 1
};
+// clang-format on
typedef mv::KalmanFilter<double, 6, 2> TrackerKalman;
@@ -138,7 +157,7 @@ bool OrderByFrameLessThan(const Marker* a, const Marker* b) {
}
return a->clip < b->clip;
}
- return a->frame < b-> frame;
+ return a->frame < b->frame;
}
// Predicted must be after the previous markers (in the frame numbering sense).
@@ -146,9 +165,9 @@ void RunPrediction(const vector<Marker*> previous_markers,
Marker* predicted_marker) {
TrackerKalman::State state;
state.mean << previous_markers[0]->center.x(), 0, 0,
- previous_markers[0]->center.y(), 0, 0;
- state.covariance = Eigen::Matrix<double, 6, 6, Eigen::RowMajor>(
- initial_covariance_data);
+ previous_markers[0]->center.y(), 0, 0;
+ state.covariance =
+ Eigen::Matrix<double, 6, 6, Eigen::RowMajor>(initial_covariance_data);
int current_frame = previous_markers[0]->frame;
int target_frame = predicted_marker->frame;
@@ -159,19 +178,18 @@ void RunPrediction(const vector<Marker*> previous_markers,
for (int i = 1; i < previous_markers.size(); ++i) {
// Step forward predicting the state until it is on the current marker.
int predictions = 0;
- for (;
- current_frame != previous_markers[i]->frame;
+ for (; current_frame != previous_markers[i]->frame;
current_frame += frame_delta) {
filter.Step(&state);
predictions++;
- LG << "Predicted point (frame " << current_frame << "): "
- << state.mean(0) << ", " << state.mean(3);
+ LG << "Predicted point (frame " << current_frame << "): " << state.mean(0)
+ << ", " << state.mean(3);
}
// Log the error -- not actually used, but interesting.
Vec2 error = previous_markers[i]->center.cast<double>() -
Vec2(state.mean(0), state.mean(3));
- LG << "Prediction error for " << predictions << " steps: ("
- << error.x() << ", " << error.y() << "); norm: " << error.norm();
+ LG << "Prediction error for " << predictions << " steps: (" << error.x()
+ << ", " << error.y() << "); norm: " << error.norm();
// Now that the state is predicted in the current frame, update the state
// based on the measurement from the current frame.
filter.Update(previous_markers[i]->center.cast<double>(),
@@ -184,8 +202,8 @@ void RunPrediction(const vector<Marker*> previous_markers,
// predict until the target frame.
for (; current_frame != target_frame; current_frame += frame_delta) {
filter.Step(&state);
- LG << "Final predicted point (frame " << current_frame << "): "
- << state.mean(0) << ", " << state.mean(3);
+ LG << "Final predicted point (frame " << current_frame
+ << "): " << state.mean(0) << ", " << state.mean(3);
}
// The x and y positions are at 0 and 3; ignore acceleration and velocity.
@@ -207,7 +225,9 @@ void RunPrediction(const vector<Marker*> previous_markers,
} // namespace
-bool PredictMarkerPosition(const Tracks& tracks, Marker* marker) {
+bool PredictMarkerPosition(const Tracks& tracks,
+ const PredictDirection direction,
+ Marker* marker) {
// Get all markers for this clip and track.
vector<Marker> markers;
tracks.GetMarkersForTrackInClip(marker->clip, marker->track, &markers);
@@ -253,13 +273,13 @@ bool PredictMarkerPosition(const Tracks& tracks, Marker* marker) {
} else if (insert_at != -1) {
// Found existing marker; scan before and after it.
forward_scan_begin = insert_at + 1;
- forward_scan_end = markers.size() - 1;;
+ forward_scan_end = markers.size() - 1;
backward_scan_begin = insert_at - 1;
backward_scan_end = 0;
} else {
// Didn't find existing marker but found an insertion point.
forward_scan_begin = insert_before;
- forward_scan_end = markers.size() - 1;;
+ forward_scan_end = markers.size() - 1;
backward_scan_begin = insert_before - 1;
backward_scan_end = 0;
}
@@ -272,9 +292,17 @@ bool PredictMarkerPosition(const Tracks& tracks, Marker* marker) {
}
bool predict_forward = false;
- if (backward_scan_end <= backward_scan_begin) {
- // TODO(keir): Add smarter handling and detecting of consecutive frames!
- predict_forward = true;
+ switch (direction) {
+ case PredictDirection::AUTO:
+ if (backward_scan_end <= backward_scan_begin) {
+ // TODO(keir): Add smarter handling and detecting of consecutive frames!
+ predict_forward = true;
+ }
+ break;
+
+ case PredictDirection::FORWARD: predict_forward = true; break;
+
+ case PredictDirection::BACKWARD: predict_forward = false; break;
}
const int max_frames_to_predict_from = 20;
@@ -301,9 +329,8 @@ bool PredictMarkerPosition(const Tracks& tracks, Marker* marker) {
return false;
}
LG << "Predicting backward";
- int predict_begin =
- std::min(forward_scan_begin + max_frames_to_predict_from,
- forward_scan_end);
+ int predict_begin = std::min(
+ forward_scan_begin + max_frames_to_predict_from, forward_scan_end);
int predict_end = forward_scan_begin;
vector<Marker*> previous_markers;
for (int i = predict_begin; i >= predict_end; --i) {
@@ -312,7 +339,6 @@ bool PredictMarkerPosition(const Tracks& tracks, Marker* marker) {
RunPrediction(previous_markers, marker);
return false;
}
-
}
} // namespace mv
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