From 17db4d9c369ee8633b05e1d19b6fbb3d61007598 Mon Sep 17 00:00:00 2001 From: Mike Turvey Date: Sun, 26 Mar 2017 09:34:08 -0700 Subject: Adding octavioradii OctavioRadii converges to the right radius!!!! --- src/poser_octavioradii.c | 542 +++++++++++++++++++++++++ src/poser_turveytori.c | 2 +- winbuild/libsurvive/libsurvive.vcxproj | 1 + winbuild/libsurvive/libsurvive.vcxproj.filters | 3 + 4 files changed, 547 insertions(+), 1 deletion(-) create mode 100644 src/poser_octavioradii.c diff --git a/src/poser_octavioradii.c b/src/poser_octavioradii.c new file mode 100644 index 0000000..84d69fb --- /dev/null +++ b/src/poser_octavioradii.c @@ -0,0 +1,542 @@ +#include +#include +#include + +typedef struct +{ + int something; + //Stuff +} OctavioRadiiData; + +#include +#include +#include "linmath.h" +#include +#include +#include + +#define PTS 32 +#define MAX_CHECKS 40000 +#define MIN_HITS_FOR_VALID 10 + +FLT hmd_points[PTS * 3]; +FLT hmd_norms[PTS * 3]; +FLT hmd_point_angles[PTS * 2]; +int hmd_point_counts[PTS * 2]; +int best_hmd_target = 0; +int LoadData(char Camera, const char * FileData); + +//Values used for RunTest() +FLT LighthousePos[3] = { 0, 0, 0 }; +FLT LighthouseQuat[4] = { 1, 0, 0, 0 }; + +FLT RunTest(int print); +void PrintOpti(); + +#define MAX_POINT_PAIRS 100 + +typedef struct +{ + FLT x; + FLT y; + FLT z; +} Point; + +typedef struct +{ + Point point; // location of the sensor on the tracked object; + Point normal; // unit vector indicating the normal for the sensor + double theta; // "horizontal" angular measurement from lighthouse radians + double phi; // "vertical" angular measurement from lighthouse in radians. +} TrackedSensor; + +typedef struct +{ + size_t numSensors; + TrackedSensor sensor[0]; +} TrackedObject; + +typedef struct +{ + unsigned char index1; + unsigned char index2; + FLT KnownDistance; +} PointPair; + +static FLT distance(Point a, Point b) +{ + FLT x = a.x - b.x; + FLT y = a.y - b.y; + FLT z = a.z - b.z; + return FLT_SQRT(x*x + y*y + z*z); +} + +typedef struct +{ + FLT HorizAngle; + FLT VertAngle; +} SensorAngles; + +#define SQUARED(x) ((x)*(x)) + +static FLT calculateFitnessOld(SensorAngles *angles, FLT *radii, PointPair *pairs, size_t numPairs) +{ + FLT fitness = 0; + for (size_t i = 0; i < numPairs; i++) + { + FLT estimatedDistanceBetweenPoints = + SQUARED(radii[pairs[i].index1]) + + SQUARED(radii[pairs[i].index2]) + - 2 * radii[pairs[i].index1] * radii[pairs[i].index2] + * FLT_SIN(angles[pairs[i].index1].HorizAngle) * FLT_SIN(angles[pairs[i].index2].HorizAngle) + * FLT_COS(angles[pairs[i].index1].VertAngle - angles[pairs[i].index2].VertAngle) + + FLT_COS(angles[pairs[i].index1].VertAngle) * FLT_COS(angles[pairs[i].index2].VertAngle); + + fitness += SQUARED(estimatedDistanceBetweenPoints - pairs[i].KnownDistance); + } + + return FLT_SQRT(fitness); +} + +// angles is an array of angles between a sensor pair +// pairs is an array of point pairs +// radii is the guess at the radii of those angles +static FLT calculateFitnessOld2(SensorAngles *angles, FLT *radii, PointPair *pairs, size_t numPairs) +{ + FLT fitness = 0; + for (size_t i = 0; i < numPairs; i++) + { + // These are the vectors that represent the direction for the two points. + // TODO: optimize by precomputing the tangent. + FLT v1[3], v2[3], diff[3]; + + v1[0] = 1; + v2[0] = 1; + v1[1] = tan(angles[pairs[i].index1].HorizAngle); // can be precomputed + v2[1] = tan(angles[pairs[i].index2].HorizAngle); // can be precomputed + v1[2] = tan(angles[pairs[i].index1].VertAngle); // can be precomputed + v2[2] = tan(angles[pairs[i].index2].VertAngle); // can be precomputed + + // Now, normalize the vectors + normalize3d(v1, v1); // can be precomputed + normalize3d(v2, v2); // can be precomputed + + // Now, given the specified radii, find where the new points are + scale3d(v1, v1, radii[pairs[i].index1]); + scale3d(v2, v2, radii[pairs[i].index2]); + + // Cool, now find the vector between these two points + // TODO: optimize the following two funcs into one. + sub3d(diff, v1, v2); + + FLT distance = magnitude3d(diff); + + FLT t1 = magnitude3d(v1); + FLT t2 = magnitude3d(v2); + + + + FLT estimatedDistanceBetweenPoints = + + SQUARED(radii[pairs[i].index1]) + + SQUARED(radii[pairs[i].index2]) + - 2 * radii[pairs[i].index1] * radii[pairs[i].index2] + * FLT_SIN(angles[pairs[i].index1].HorizAngle) * FLT_SIN(angles[pairs[i].index2].HorizAngle) + * FLT_COS(angles[pairs[i].index1].VertAngle - angles[pairs[i].index2].VertAngle) + + FLT_COS(angles[pairs[i].index1].VertAngle) * FLT_COS(angles[pairs[i].index2].VertAngle); + + + //fitness += SQUARED(estimatedDistanceBetweenPoints - pairs[i].KnownDistance); + fitness += SQUARED(distance - pairs[i].KnownDistance); + } + + return FLT_SQRT(fitness); +} + +static FLT angleBetweenSensors(SensorAngles *a, SensorAngles *b) +{ + FLT angle = FLT_ACOS(FLT_COS(a->VertAngle - b->VertAngle)*FLT_COS(a->HorizAngle - b->HorizAngle)); + //FLT angle2 = FLT_ACOS(FLT_COS(b->phi - a->phi)*FLT_COS(b->theta - a->theta)); + + return angle; +} + +// angles is an array of angles between a sensor pair +// pairs is an array of point pairs +// radii is the guess at the radii of those angles +static FLT calculateFitness(SensorAngles *angles, FLT *radii, PointPair *pairs, size_t numPairs) +{ + FLT fitness = 0; + for (size_t i = 0; i < numPairs; i++) + { + + FLT angle = angleBetweenSensors(&angles[pairs[i].index1], &angles[pairs[i].index2]); + + // now we have a side-angle-side triangle, and we need to find the third side. + + // The Law of Cosines says: a^2 = b^2 + c^2 ? 2bc * cosA, + // where A is the angle opposite side a. + + // Transform this to: + // a = sqrt(b^2 + c^2 - 2bc * cosA) and we know the length of the missing side! + + FLT b2 = (SQUARED(radii[pairs[i].index1])); + FLT c2 = (SQUARED(radii[pairs[i].index2])); + FLT bc2 = (2 * radii[pairs[i].index1] * radii[pairs[i].index2]); + FLT cosA = (FLT_COS(angle)); + + FLT angleInDegrees = angle * 180 / LINMATHPI; + + FLT dist = sqrt( (SQUARED(radii[pairs[i].index1])) + + (SQUARED(radii[pairs[i].index2])) - + ( (2 * radii[pairs[i].index1] * radii[pairs[i].index2]) * + (FLT_COS(angle)))); + + + FLT fitnessAdder = SQUARED(dist - pairs[i].KnownDistance); + + if (isnan(fitnessAdder)) + { + int a = 0; + } + + //printf(" %2d %f\n", i, fitnessAdder); + + //fitness += SQUARED(estimatedDistanceBetweenPoints - pairs[i].KnownDistance); + fitness += SQUARED(dist - pairs[i].KnownDistance); + } + + //fitness = 1 / fitness; + return FLT_SQRT(fitness); +} + +#define MAX_RADII 32 + +// note gradientOut will be of the same degree as numRadii +static void getGradient(FLT *gradientOut, SensorAngles *angles, FLT *radii, size_t numRadii, PointPair *pairs, size_t numPairs, const FLT precision) +{ + FLT baseline = calculateFitness(angles, radii, pairs, numPairs); + + for (size_t i = 0; i < numRadii; i++) + { + FLT tmpPlus[MAX_RADII]; + memcpy(tmpPlus, radii, sizeof(*radii) * numRadii); + tmpPlus[i] += precision; + gradientOut[i] = -(calculateFitness(angles, tmpPlus, pairs, numPairs) - baseline); + } + + return; +} + +static void normalizeAndMultiplyVector(FLT *vectorToNormalize, size_t count, FLT desiredMagnitude) +{ + FLT distanceIn = 0; + + for (size_t i = 0; i < count; i++) + { + distanceIn += SQUARED(vectorToNormalize[i]); + } + distanceIn = FLT_SQRT(distanceIn); + + + FLT scale = desiredMagnitude / distanceIn; + + for (size_t i = 0; i < count; i++) + { + vectorToNormalize[i] *= scale; + } + + return; +} + + +static RefineEstimateUsingGradientDescentRadii(FLT *estimateOut, SensorAngles *angles, FLT *initialEstimate, size_t numRadii, PointPair *pairs, size_t numPairs, FILE *logFile) +{ + int i = 0; + FLT lastMatchFitness = calculateFitness(angles, initialEstimate, pairs, numPairs); + if (estimateOut != initialEstimate) + { + memcpy(estimateOut, initialEstimate, sizeof(*estimateOut) * numRadii); + } + + + // The values below are somewhat magic, and definitely tunable + // The initial vlue of g will represent the biggest step that the gradient descent can take at first. + // bigger values may be faster, especially when the initial guess is wildly off. + // The downside to a bigger starting guess is that if we've picked a good guess at the local minima + // if there are other local minima, we may accidentally jump to such a local minima and get stuck there. + // That's fairly unlikely with the lighthouse problem, from expereince. + // The other downside is that if it's too big, we may have to spend a few iterations before it gets down + // to a size that doesn't jump us out of our minima. + // The terminal value of g represents how close we want to get to the local minima before we're "done" + // The change in value of g for each iteration is intentionally very close to 1. + // in fact, it probably could probably be 1 without any issue. The main place where g is decremented + // is in the block below when we've made a jump that results in a worse fitness than we're starting at. + // In those cases, we don't take the jump, and instead lower the value of g and try again. + for (FLT g = 0.4; g > 0.00001; g *= 0.9999) + { + i++; + + + + FLT point1[MAX_RADII]; + memcpy(point1, estimateOut, sizeof(*point1) * numRadii); + + // let's get 3 iterations of gradient descent here. + FLT gradient1[MAX_RADII]; + getGradient(gradient1, angles, point1, numRadii, pairs, numPairs, g / 1000 /*somewhat arbitrary*/); + normalizeAndMultiplyVector(gradient1, numRadii, g); + + FLT point2[MAX_RADII]; + for (size_t i = 0; i < numRadii; i++) + { + point2[i] = point1[i] + gradient1[i]; + } + FLT gradient2[MAX_RADII]; + getGradient(gradient2, angles, point2, numRadii, pairs, numPairs, g / 1000 /*somewhat arbitrary*/); + normalizeAndMultiplyVector(gradient2, numRadii, g); + + FLT point3[MAX_RADII]; + for (size_t i = 0; i < numRadii; i++) + { + point3[i] = point2[i] + gradient2[i]; + } + + // remember that gradient descent has a tendency to zig-zag when it encounters a narrow valley? + // Well, solving the lighthouse problem presents a very narrow valley, and the zig-zag of a basic + // gradient descent is kinda horrible here. Instead, think about the shape that a zig-zagging + // converging gradient descent makes. Instead of using the gradient as the best indicator of + // the direction we should follow, we're looking at one side of the zig-zag pattern, and specifically + // following *that* vector. As it turns out, this works *amazingly* well. + + FLT specialGradient[MAX_RADII]; + for (size_t i = 0; i < numRadii; i++) + { + specialGradient[i] = point3[i] - point1[i]; + } + + // The second parameter to this function is very much a tunable parameter. Different values will result + // in a different number of iterations before we get to the minimum. Numbers between 3-10 seem to work well + // It's not clear what would be optimum here. + normalizeAndMultiplyVector(specialGradient, numRadii, g / 4); + + + FLT point4[MAX_RADII]; + for (size_t i = 0; i < numRadii; i++) + { + point4[i] = point3[i] + specialGradient[i]; + } + + + FLT newMatchFitness = calculateFitness(angles, point4, pairs, numPairs); + + + if (newMatchFitness < lastMatchFitness) + { + //if (logFile) + //{ + // writePoint(logFile, lastPoint.x, lastPoint.y, lastPoint.z, 0xFFFFFF); + //} + + lastMatchFitness = newMatchFitness; + memcpy(estimateOut, point4, sizeof(*estimateOut) * numRadii); + +#ifdef RADII_DEBUG + printf("+ %d %0.9f (%0.9f) \n", i, newMatchFitness, g); +#endif + g = g * 1.05; + } + else + { +//#ifdef RADII_DEBUG + // printf("-"); + printf("- %d %0.9f (%0.9f) [%0.9f] \n", i, newMatchFitness, g, estimateOut[0]); +//#endif + // if it wasn't a match, back off on the distance we jump + g *= 0.7; + + } + +#ifdef RADII_DEBUG + FLT avg = 0; + FLT diffFromAvg[MAX_RADII]; + + for (size_t m = 0; m < numRadii; m++) + { + avg += estimateOut[m]; + } + avg = avg / numRadii; + + for (size_t m = 0; m < numRadii; m++) + { + diffFromAvg[m] = estimateOut[m] - avg;; + } + printf("[avg:%f] ", avg); + + for (size_t x = 0; x < numRadii; x++) + { + printf("%f, ", diffFromAvg[x]); + //printf("%f, ", estimateOut[x]); + } + printf("\n"); + + +#endif + + + } + printf("\ni=%d\n", i); +} + +void SolveForLighthouseRadii(Point *objPosition, FLT *objOrientation, TrackedObject *obj) +{ + FLT estimate[MAX_RADII]; + + for (size_t i = 0; i < MAX_RADII; i++) + { + estimate[i] = 2.2; + } + + SensorAngles angles[MAX_RADII]; + PointPair pairs[MAX_POINT_PAIRS]; + + size_t pairCount = 0; + + //obj->numSensors = 5; // TODO: HACK!!!! + + for (size_t i = 0; i < obj->numSensors; i++) + { + angles[i].HorizAngle = obj->sensor[i].theta; + angles[i].VertAngle = obj->sensor[i].phi; + } + + for (size_t i = 0; i < obj->numSensors - 1; i++) + { + for (size_t j = i + 1; j < obj->numSensors; j++) + { + pairs[pairCount].index1 = i; + pairs[pairCount].index2 = j; + pairs[pairCount].KnownDistance = distance(obj->sensor[i].point, obj->sensor[j].point); + pairCount++; + } + } + + + RefineEstimateUsingGradientDescentRadii(estimate, angles, estimate, obj->numSensors, pairs, pairCount, NULL); + + // we should now have an estimate of the radii. + + for (size_t i = 0; i < obj->numSensors; i++) + { + printf("radius[%d]: %f\n", i, estimate[i]); + } + // (FLT *estimateOut, SensorAngles *angles, FLT *initialEstimate, size_t numRadii, PointPair *pairs, size_t numPairs, FILE *logFile) + + return; +} + +int PoserOctavioRadii( SurviveObject * so, PoserData * pd ) +{ + PoserType pt = pd->pt; + SurviveContext * ctx = so->ctx; + OctavioRadiiData * dd = so->PoserData; + + if( !dd ) so->PoserData = dd = malloc( sizeof(OctavioRadiiData) ); + + switch( pt ) + { + case POSERDATA_IMU: + { + PoserDataIMU * imu = (PoserDataIMU*)pd; + //printf( "IMU:%s (%f %f %f) (%f %f %f)\n", so->codename, imu->accel[0], imu->accel[1], imu->accel[2], imu->gyro[0], imu->gyro[1], imu->gyro[2] ); + break; + } + case POSERDATA_LIGHT: + { + PoserDataLight * l = (PoserDataLight*)pd; + //printf( "LIG:%s %d @ %f rad, %f s (AC %d) (TC %d)\n", so->codename, l->sensor_id, l->angle, l->length, l->acode, l->timecode ); + break; + } + case POSERDATA_FULL_SCENE: + { + TrackedObject *to; + + PoserDataFullScene * fs = (PoserDataFullScene*)pd; + + to = malloc(sizeof(TrackedObject) + (SENSORS_PER_OBJECT * sizeof(TrackedSensor))); + + //FLT lengths[SENSORS_PER_OBJECT][NUM_LIGHTHOUSES][2]; + //FLT angles[SENSORS_PER_OBJECT][NUM_LIGHTHOUSES][2]; //2 Axes (Angles in LH space) + //FLT synctimes[SENSORS_PER_OBJECT][NUM_LIGHTHOUSES]; + + //to->numSensors = so->nr_locations; + { + int sensorCount = 0; + + for (int i = 0; i < so->nr_locations; i++) + { + if (fs->lengths[i][0][0] != -1 && fs->lengths[i][0][1] != -1) //lh 0 + { + to->sensor[sensorCount].normal.x = so->sensor_normals[i * 3 + 0]; + to->sensor[sensorCount].normal.y = so->sensor_normals[i * 3 + 1]; + to->sensor[sensorCount].normal.z = so->sensor_normals[i * 3 + 2]; + to->sensor[sensorCount].point.x = so->sensor_locations[i * 3 + 0]; + to->sensor[sensorCount].point.y = so->sensor_locations[i * 3 + 1]; + to->sensor[sensorCount].point.z = so->sensor_locations[i * 3 + 2]; + to->sensor[sensorCount].theta = fs->angles[i][0][0] + LINMATHPI / 2; // lighthouse 0, angle 0 (horizontal) + to->sensor[sensorCount].phi = fs->angles[i][0][1] + LINMATHPI / 2; // lighthosue 0, angle 1 (vertical) + sensorCount++; + } + } + + to->numSensors = sensorCount; + + Point position; + FLT orientation[4]; + + SolveForLighthouseRadii(&position, &orientation, to); + } + { + int sensorCount = 0; + int lh = 1; + + for (int i = 0; i < so->nr_locations; i++) + { + if (fs->lengths[i][lh][0] != -1 && fs->lengths[i][lh][1] != -1) + { + to->sensor[sensorCount].normal.x = so->sensor_normals[i * 3 + 0]; + to->sensor[sensorCount].normal.y = so->sensor_normals[i * 3 + 1]; + to->sensor[sensorCount].normal.z = so->sensor_normals[i * 3 + 2]; + to->sensor[sensorCount].point.x = so->sensor_locations[i * 3 + 0]; + to->sensor[sensorCount].point.y = so->sensor_locations[i * 3 + 1]; + to->sensor[sensorCount].point.z = so->sensor_locations[i * 3 + 2]; + to->sensor[sensorCount].theta = fs->angles[i][lh][0] + LINMATHPI / 2; // lighthouse 0, angle 0 (horizontal) + to->sensor[sensorCount].phi = fs->angles[i][lh][1] + LINMATHPI / 2; // lighthosue 0, angle 1 (vertical) + sensorCount++; + } + } + + to->numSensors = sensorCount; + + Point position; + FLT orientation[4]; + + SolveForLighthouseRadii(&position, &orientation, to); + } + //printf( "Full scene data.\n" ); + break; + } + case POSERDATA_DISASSOCIATE: + { + free( dd ); + so->PoserData = 0; + //printf( "Need to disassociate.\n" ); + break; + } + } + return 0; +} + + +REGISTER_LINKTIME( PoserOctavioRadii ); + diff --git a/src/poser_turveytori.c b/src/poser_turveytori.c index e9e5b7a..4e602f3 100644 --- a/src/poser_turveytori.c +++ b/src/poser_turveytori.c @@ -299,7 +299,7 @@ void estimateToroidalAndPoloidalAngleOfPoint( FLT angleBetweenSensors(TrackedSensor *a, TrackedSensor *b) { FLT angle = FLT_ACOS(FLT_COS(a->phi - b->phi)*FLT_COS(a->theta - b->theta)); - FLT angle2 = FLT_ACOS(FLT_COS(b->phi - a->phi)*FLT_COS(b->theta - a->theta)); + //FLT angle2 = FLT_ACOS(FLT_COS(b->phi - a->phi)*FLT_COS(b->theta - a->theta)); return angle; } diff --git a/winbuild/libsurvive/libsurvive.vcxproj b/winbuild/libsurvive/libsurvive.vcxproj index 05fec8c..c794382 100644 --- a/winbuild/libsurvive/libsurvive.vcxproj +++ b/winbuild/libsurvive/libsurvive.vcxproj @@ -153,6 +153,7 @@ + diff --git a/winbuild/libsurvive/libsurvive.vcxproj.filters b/winbuild/libsurvive/libsurvive.vcxproj.filters index 8bb09b2..e7d44e2 100644 --- a/winbuild/libsurvive/libsurvive.vcxproj.filters +++ b/winbuild/libsurvive/libsurvive.vcxproj.filters @@ -87,6 +87,9 @@ Source Files + + Source Files + -- cgit v1.2.3