1 files changed, 31 insertions, 37 deletions

diff --git a/intern/libmv/libmv/tracking/kalman_filter.h b/intern/libmv/libmv/tracking/kalman_filter.h
index 9841f0e912c..b1312d0e027 100644
--- a/intern/libmv/libmv/tracking/kalman_filter.h
+++ b/intern/libmv/libmv/tracking/kalman_filter.h
@@ -19,13 +19,14 @@
 // IN THE SOFTWARE.
 
 #ifndef LIBMV_TRACKING_KALMAN_FILTER_H_
+#define LIBMV_TRACKING_KALMAN_FILTER_H_
 
 #include "libmv/numeric/numeric.h"
 
 namespace mv {
 
 // A Kalman filter with order N and observation size K.
-template<typename T, int N, int K>
+template <typename T, int N, int K>
 class KalmanFilter {
  public:
   struct State {
@@ -38,54 +39,47 @@ class KalmanFilter {
                const T* observation_data,
                const T* process_covariance_data,
                const T* default_measurement_covariance_data)
-       : state_transition_matrix_(
-             Eigen::Matrix<T, N, N, Eigen::RowMajor>(state_transition_data)),
-         observation_matrix_(
-             Eigen::Matrix<T, K, N, Eigen::RowMajor>(observation_data)),
-         process_covariance_(
-             Eigen::Matrix<T, N, N, Eigen::RowMajor>(process_covariance_data)),
-         default_measurement_covariance_(
-             Eigen::Matrix<T, K, K, Eigen::RowMajor>(
-                 default_measurement_covariance_data)) {
-  }
+      : state_transition_matrix_(
+            Eigen::Matrix<T, N, N, Eigen::RowMajor>(state_transition_data)),
+        observation_matrix_(
+            Eigen::Matrix<T, K, N, Eigen::RowMajor>(observation_data)),
+        process_covariance_(
+            Eigen::Matrix<T, N, N, Eigen::RowMajor>(process_covariance_data)),
+        default_measurement_covariance_(Eigen::Matrix<T, K, K, Eigen::RowMajor>(
+            default_measurement_covariance_data)) {}
 
-  KalmanFilter(
-     const Eigen::Matrix<T, N, N> &state_transition_matrix,
-     const Eigen::Matrix<T, K, N> &observation_matrix,
-     const Eigen::Matrix<T, N, N> &process_covariance,
-     const Eigen::Matrix<T, K, K> &default_measurement_covariance)
-       : state_transition_matrix_(state_transition_matrix),
-         observation_matrix_(observation_matrix),
-         process_covariance_(process_covariance),
-         default_measurement_covariance_(default_measurement_covariance) {
-  }
+  KalmanFilter(const Eigen::Matrix<T, N, N>& state_transition_matrix,
+               const Eigen::Matrix<T, K, N>& observation_matrix,
+               const Eigen::Matrix<T, N, N>& process_covariance,
+               const Eigen::Matrix<T, K, K>& default_measurement_covariance)
+      : state_transition_matrix_(state_transition_matrix),
+        observation_matrix_(observation_matrix),
+        process_covariance_(process_covariance),
+        default_measurement_covariance_(default_measurement_covariance) {}
 
   // Advances the system according to the current state estimate.
-  void Step(State *state) const {
+  void Step(State* state) const {
     state->mean = state_transition_matrix_ * state->mean;
-    state->covariance = state_transition_matrix_ *
-                        state->covariance *
-                        state_transition_matrix_.transpose() +
+    state->covariance = state_transition_matrix_ * state->covariance *
+                            state_transition_matrix_.transpose() +
                         process_covariance_;
   }
 
   // Updates a state with a new measurement.
-  void Update(const Eigen::Matrix<T, K, 1> &measurement_mean,
-              const Eigen::Matrix<T, K, K> &measurement_covariance,
-              State *state) const {
+  void Update(const Eigen::Matrix<T, K, 1>& measurement_mean,
+              const Eigen::Matrix<T, K, K>& measurement_covariance,
+              State* state) const {
     // Calculate the innovation, which is a distribution over prediction error.
-    Eigen::Matrix<T, K, 1> innovation_mean = measurement_mean -
-                                             observation_matrix_ *
-                                             state->mean;
+    Eigen::Matrix<T, K, 1> innovation_mean =
+        measurement_mean - observation_matrix_ * state->mean;
     Eigen::Matrix<T, K, K> innovation_covariance =
-        observation_matrix_ *
-        state->covariance *
-        observation_matrix_.transpose() +
+        observation_matrix_ * state->covariance *
+            observation_matrix_.transpose() +
         measurement_covariance;
 
     // Calculate the Kalman gain.
     Eigen::Matrix<T, 6, 2> kalman_gain = state->covariance *
-                                         observation_matrix_.transpose() * 
+                                         observation_matrix_.transpose() *
                                          innovation_covariance.inverse();
 
     // Update the state mean and covariance.
@@ -95,8 +89,8 @@ class KalmanFilter {
                         state->covariance;
   }
 
-  void Update(State *state,
-              const Eigen::Matrix<T, K, 1> &measurement_mean) const {
+  void Update(State* state,
+              const Eigen::Matrix<T, K, 1>& measurement_mean) const {
     Update(state, measurement_mean, default_measurement_covariance_);
   }