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LevMarq made semi-functional.

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Replaced the complex rules OpenCV uses to select lambda with a naive but
fast heuristic. It's imperfect but produces good results. It is still
subject to the same problem as OpenCV - namely, on occasion LevMarq will
make a poor result even worse.
This commit is contained in:
Olexa Bilaniuk
2015-01-14 04:22:48 -05:00
parent 8a5182662e
commit ff91af825d
1 changed files with 73 additions and 42 deletions
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115
modules/calib3d/src/rhorefc.cpp
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@@ -66,7 +66,7 @@
#define CHI_SQ 1.645
#define RLO 0.25
#define RHI 0.75
#define MAXLEVMARQITERS 50
#define MAXLEVMARQITERS 100
#define m 4 /* 4 points required per model */
#define SPRT_T_M 25 /* Guessing 25 match evlauations / 1 model generation */
#define SPRT_M_S 1 /* 1 model per sample */
@@ -1571,10 +1571,20 @@ static inline int sacCanRefine(RHO_HEST_REFC* p){
return p->best.numInl > m;
}
static inline void dumpMat8x8(float (*M)[8]){
for(int i=0;i<8;i++){
printf("\t");
for(int j=0;j<=i;j++){
printf("%14.6e%s", M[i][j], j==i?"\n":", ");
}
}
printf("\n");
}
/**
* Refines the best-so-far homography.
*
* BUG: Totally broken for now. DO NOT ENABLE.
* Refines the best-so-far homography (p->best.H).
*/
static inline void sacRefine(RHO_HEST_REFC* p){
@@ -1591,41 +1601,62 @@ static inline void sacRefine(RHO_HEST_REFC* p){
/* Find initial conditions */
sacCalcJacobianErrors(p->best.H, p->arg.src, p->arg.dst, p->arg.inl, p->arg.N,
p->lm.JtJ, p->lm.Jte, &S);
/*printf("Initial Error: %12.6f\n", S);*/
/*{
for(int j=0;j<8;j++){
for(int k=0;k<8;k++){
printf("%12.6g%s", p->lm.JtJ[j][k], k==7 ? "\n" : ", ");
}
}
}*/
/* Levenberg-Marquardt Loop */
/*Levenberg-Marquardt Loop.*/
for(i=0;i<MAXLEVMARQITERS;i++){
/* The code below becomes an infinite loop when L reeaches infinity. */
while(sacChol8x8Damped(p->lm.JtJ, L, p->lm.tmp1)){
L *= 2.0f;
/**
* Attempt a step given current state
* - Jacobian-x-Jacobian (JtJ)
* - Jacobian-x-error (Jte)
* - Sum of squared errors (S)
* and current parameter
* - Lambda (L)
* .
*
* This is done by solving the system of equations
* Ax = b
* where A (JtJ) and b (Jte) are sourced from our current state, and
* the solution x becomes a step (dH) that is applied to best.H in
* order to compute a candidate homography (newH).
*
* The system above is solved by Cholesky decomposition of a
* sufficently-damped JtJ into a lower-triangular matrix (and its
* transpose), whose inverse is then computed. This inverse (and its
* transpose) then multiply JtJ in order to find dH.
*/
/*printf("Lambda: %f\n", L);*/
while(!sacChol8x8Damped(p->lm.JtJ, L, p->lm.tmp1)){
L *= 2.0f;/*printf("CholFail! Lambda: %f\n", L);*/
}
//sacChol8x8Damped(p->lm.JtJ, L, p->lm.tmp1);
sacTRInv8x8 (p->lm.tmp1, p->lm.tmp1);
sacTRISolve8x8(p->lm.tmp1, p->lm.Jte, dH);
sacSub8x1 (newH, p->best.H, dH);
sacCalcJacobianErrors(newH, p->arg.src, p->arg.dst, p->arg.inl, p->arg.N,
NULL, NULL, &newS);
dS = sacDs (p->lm.JtJ, dH, p->lm.Jte);
R = (S - newS)/(fabs(dS) > DBL_EPSILON ? dS : 1);/* Don't understand */
if(R > 0.75f){
L *= 0.5f;
}else if(R < 0.25f){
L *= 2.0f;
}
/**
* If the new Sum of Square Errors (newS) corresponding to newH is
* lower than the previous one, save the current state and undamp
* sligthly (decrease L). Else damp more (increase L).
*/
if(newS < S){
S = newS;
memcpy(p->best.H, newH, sizeof(newH));
sacCalcJacobianErrors(p->best.H, p->arg.src, p->arg.dst, p->arg.inl, p->arg.N,
p->lm.JtJ, p->lm.Jte, &S);
/*printf("Error: %12.6f\n", S);*/
L *= 0.5;
if(L<FLT_EPSILON){
L = 1;
}
}else{
L *= 2.0;
if(L>1.0f/FLT_EPSILON){
break;
}
}
}
}
@@ -1705,15 +1736,15 @@ static inline void sacCalcJacobianErrors(const float* restrict H,
float dxh11 = x * iW;
float dxh12 = y * iW;
float dxh13 = iW;
float dxh21 = 0.0f;
float dxh22 = 0.0f;
float dxh23 = 0.0f;
/*float dxh21 = 0.0f;*/
/*float dxh22 = 0.0f;*/
/*float dxh23 = 0.0f;*/
float dxh31 = -reprojX*x * iW;
float dxh32 = -reprojX*y * iW;
float dyh11 = 0.0f;
float dyh12 = 0.0f;
float dyh13 = 0.0f;
/*float dyh11 = 0.0f;*/
/*float dyh12 = 0.0f;*/
/*float dyh13 = 0.0f;*/
float dyh21 = x * iW;
float dyh22 = y * iW;
float dyh23 = iW;
@@ -1743,20 +1774,20 @@ static inline void sacCalcJacobianErrors(const float* restrict H,
JtJ[2][1] += dxh12*dxh13 ;/* +0 */
JtJ[2][2] += dxh13*dxh13 ;/* +0 */
/*JtJ[3][0] += ;/* 0+0 */
/*JtJ[3][1] += ;/* 0+0 */
/*JtJ[3][2] += ;/* 0+0 */
/*JtJ[3][0] += ; 0+0 */
/*JtJ[3][1] += ; 0+0 */
/*JtJ[3][2] += ; 0+0 */
JtJ[3][3] += dyh21*dyh21;/* 0+ */
/*JtJ[4][0] += ;/* 0+0 */
/*JtJ[4][1] += ;/* 0+0 */
/*JtJ[4][2] += ;/* 0+0 */
/*JtJ[4][0] += ; 0+0 */
/*JtJ[4][1] += ; 0+0 */
/*JtJ[4][2] += ; 0+0 */
JtJ[4][3] += dyh21*dyh22;/* 0+ */
JtJ[4][4] += dyh22*dyh22;/* 0+ */
/*JtJ[5][0] += ;/* 0+0 */
/*JtJ[5][1] += ;/* 0+0 */
/*JtJ[5][2] += ;/* 0+0 */
/*JtJ[5][0] += ; 0+0 */
/*JtJ[5][1] += ; 0+0 */
/*JtJ[5][2] += ; 0+0 */
JtJ[5][3] += dyh21*dyh23;/* 0+ */
JtJ[5][4] += dyh22*dyh23;/* 0+ */
JtJ[5][5] += dyh23*dyh23;/* 0+ */
@@ -1826,7 +1857,7 @@ static inline float sacDs(const float (*JtJ)[8],
*
* For damping, the diagonal elements are scaled by 1.0 + lambda.
*
* Returns 0 if decomposition successful, and non-zero otherwise.
* Returns zero if decomposition unsuccessful, and non-zero otherwise.
*
* Source: http://en.wikipedia.org/wiki/Cholesky_decomposition#
* The_Cholesky.E2.80.93Banachiewicz_and_Cholesky.E2.80.93Crout_algorithms
@@ -1857,13 +1888,13 @@ static inline int sacChol8x8Damped(const float (*A)[8],
x -= (double)L[j][k] * L[j][k];/* - Sum_{k=0..j-1} Ljk^2 */
}
if(x<0){
return 1;
return 0;
}
L[j][j] = sqrt(x); /* Ljj = sqrt( ... ) */
}
}
return 0;
return 1;
}
/**