void genratesignstate()
{
int V_contri, H_contri, i;
for( i = 0; i < 256; i++){
H_contri = contribution( i & H0, i & SIGNH0, i & H1, i & SIGNH1);
V_contri = contribution( i & V0, i & SIGNV0, i & V1, i & SIGNV1);
sign_states[i] = sign_state(H_contri, V_contri);
XORbit[i] = xorbit_state(H_contri, V_contri);
}
}
void Viewport::applyView( const Viewport& segmentVP, const Viewport& viewVP,
const PixelViewport& pvp, const Vector4i& overdraw )
{
// part of view covered by segment/view channel
Viewport contribution( segmentVP );
contribution.intersect( viewVP );
contribution.transform( viewVP );
// extend by overdraw percentage
EQASSERT( pvp.hasArea( ));
const float xDelta(( static_cast< float >( overdraw.x() + pvp.w ) /
static_cast< float >( pvp.w ) - 1.0f ) *
contribution.w );
contribution.x -= xDelta;
contribution.w += (( static_cast< float >( overdraw.z() + pvp.w ) /
static_cast< float >( pvp.w ) - 1.0f ) *
contribution.w );
contribution.w += xDelta;
const float yDelta(( static_cast< float >( overdraw.y() + pvp.h ) /
static_cast< float >( pvp.h ) - 1.0f ) *
contribution.h );
contribution.y -= yDelta;
contribution.h += (( static_cast< float >( overdraw.w() + pvp.h ) /
static_cast< float >( pvp.h ) - 1.0f ) *
contribution.h );
contribution.h += yDelta;
x = contribution.x + x * contribution.w;
y = contribution.y + y * contribution.h;
w *= contribution.w;
h *= contribution.h;
}
void
StructuralEngngModel :: computeExternalLoadReactionContribution(FloatArray &reactions, TimeStep *tStep, int di)
{
int numRestrDofs = this->giveNumberOfPrescribedDomainEquations(di, EID_MomentumBalance);
reactions.resize(numRestrDofs);
reactions.zero();
FloatArray contribution(numRestrDofs);
reactions.resize( this->giveNumberOfPrescribedDomainEquations(di, EID_MomentumBalance) );
reactions.zero();
this->assembleVector( reactions, tStep, EID_MomentumBalance, ExternalForcesVector, VM_Total,
EModelDefaultPrescribedEquationNumbering(), this->giveDomain(di) );
}
void ifaGroup::buildRowSkip()
{
rowSkip.assign(rowMap.size(), false);
if (itemDims == 0) return;
// Rows with no information about an ability will obtain the
// prior distribution as an ability estimate. This will
// throw off multigroup latent distribution estimates.
for (size_t rx=0; rx < rowMap.size(); rx++) {
bool hasNA = false;
std::vector<int> contribution(itemDims);
for (int ix=0; ix < numItems(); ix++) {
int pick = dataColumn(ix)[ rowMap[rx] ];
if (pick == NA_INTEGER) {
hasNA = true;
continue;
}
const double *ispec = spec[ix];
int dims = ispec[RPF_ISpecDims];
double *iparam = getItemParam(ix);
for (int dx=0; dx < dims; dx++) {
// assume factor loadings are the first item parameters
if (iparam[dx] == 0) continue;
contribution[dx] += 1;
}
}
if (!hasNA) continue;
if (minItemsPerScore == NA_INTEGER) {
mxThrow("You have missing data. You must set minItemsPerScore");
}
for (int ax=0; ax < itemDims; ++ax) {
if (contribution[ax] < minItemsPerScore) {
// We could compute the other scores, but estimation of the
// latent distribution is in the hot code path. We can reconsider
// this choice when we try generating scores instead of the
// score distribution.
rowSkip[rx] = true;
}
}
}
}
void PathTracingRenderer::Job::kernel(uint32_t threadID) {
ArenaAllocator &mem = mems[threadID];
IndependentLightPathSampler &pathSampler = *pathSamplers[threadID];
for (int ly = 0; ly < numPixelY; ++ly) {
for (int lx = 0; lx < numPixelX; ++lx) {
float time = pathSampler.getTimeSample(timeStart, timeEnd);
PixelPosition p = pathSampler.getPixelPositionSample(basePixelX + lx, basePixelY + ly);
float selectWLPDF;
WavelengthSamples wls = WavelengthSamples::createWithEqualOffsets(pathSampler.getWavelengthSample(), pathSampler.getWLSelectionSample(), &selectWLPDF);
LensPosQuery lensQuery(time, wls);
LensPosQueryResult lensResult;
SampledSpectrum We0 = camera->sample(lensQuery, pathSampler.getLensPosSample(), &lensResult);
IDFSample WeSample(p.x / imageWidth, p.y / imageHeight);
IDFQueryResult WeResult;
IDF* idf = camera->createIDF(lensResult.surfPt, wls, mem);
SampledSpectrum We1 = idf->sample(WeSample, &WeResult);
Ray ray(lensResult.surfPt.p, lensResult.surfPt.shadingFrame.fromLocal(WeResult.dirLocal), time);
SampledSpectrum C = contribution(*scene, wls, ray, pathSampler, mem);
SLRAssert(C.hasNaN() == false && C.hasInf() == false && C.hasMinus() == false,
"Unexpected value detected: %s\n"
"pix: (%f, %f)", C.toString().c_str(), px, py);
SampledSpectrum weight = (We0 * We1) * (absDot(ray.dir, lensResult.surfPt.gNormal) / (lensResult.areaPDF * WeResult.dirPDF * selectWLPDF));
SLRAssert(weight.hasNaN() == false && weight.hasInf() == false && weight.hasMinus() == false,
"Unexpected value detected: %s\n"
"pix: (%f, %f)", weight.toString().c_str(), px, py);
sensor->add(p.x, p.y, wls, weight * C);
mem.reset();
}
}
}
void KmerHash::buildKmerTable(string genomePath) {
ifstream genomeFile(genomePath.c_str(), ios::in);
string geneSeq = "";
string line;
while(getline(genomeFile, line)) {
if(line[0] != '>') {
geneSeq += boost::trim_copy(line);
}
}
genomeFile.close();
//int gp = 0;
//long long gid = 0;
//for(; gp < K - 1; gp ++) {
// gid = (gid << 2) + parseBP(geneSeq[gp]);
//}
//for(; gp < geneSeq.size(); gp ++) {
// gid = ((gid << 2) + parseBP(geneSeq[gp])) & mask;
// kmers.push_back(gid);
//}
for(int g = 0; g < NG; g++) {
//cout << "Gene-" << g << " processed..." << endl;
for(int e = 0; e < NE[g]; e++) {
ostringstream oss;
oss << g << "," << e << "," << e;
string exonid = oss.str();
int exonst = geneBoundary[g][e].first;
int exoned = geneBoundary[g][e].second + 1;
int p = 0;
long long id = 0;
double tot = (exoned - exonst - readLength + 1) * (readLength - K + 1);
//cout << "Exon start site: " << exonst << endl;
//for(int i = exonst ; i < exoned; i++) {
// cout << geneSeq[i];
//} cout << endl;
for(; p < K - 1; p ++) {
id = (id << 2) + parseBP(geneSeq[exonst + p]);
}
for(; exonst + p < exoned; p ++) {
id = ((id << 2) + parseBP(geneSeq[exonst + p])) & mask;
double contr = contribution(0, exoned - exonst, p - K + 1, p + 1, exoned - exonst) / tot;
if(kmerTable.count(id) > 0) {
if(kmerTable[id].count(exonid) > 0) {
kmerTable[id][exonid] = kmerTable[id][exonid] + contr;
} else {
kmerTable[id][exonid] = contr;
}
}
}
}
for(int ei = 0; ei < NE[g]; ei++) {
for(int ej = ei + 1; ej < NE[g]; ej++) {
ostringstream oss;
oss << g << "," << ei << "," << ej;
string exonid = oss.str();
int exonedi = geneBoundary[g][ei].second + 1;
int exonstj = geneBoundary[g][ej].first;
int p = 0;
long long id = 0;
double tot = (readLength - 1) * (readLength - K + 1);
for(; p < K - 1; p ++) {
id = (id << 2) + parseBP(geneSeq[exonedi - readLength + 1 + p]);
}
for(; p < readLength - 1; p ++) {
id = ((id << 2) + parseBP(geneSeq[exonedi - readLength + 1 + p])) & mask;
double contr = contribution(0, 2*readLength - 2, p - K + 1, p + 1, 2*readLength - 2) / tot;
if(kmerTable.count(id) > 0) {
if(kmerTable[id].count(exonid) > 0) {
kmerTable[id][exonid] = kmerTable[id][exonid] + contr;
} else {
kmerTable[id][exonid] = contr;
}
}
}
for(p = 0; p < readLength - 1; p ++) {
id = ((id << 2) + parseBP(geneSeq[exonstj + p])) & mask;
double contr = contribution(0, 2*readLength - 2, readLength + p - K, readLength + p, 2*readLength - 2) / tot;
if(kmerTable.count(id) > 0) {
if(kmerTable[id].count(exonid) > 0) {
kmerTable[id][exonid] = kmerTable[id][exonid] + contr;
} else {
kmerTable[id][exonid] = contr;
}
}
}
}
}
}
cout << "End of building kmer table" << endl;
}
void DebugRenderer::Job::kernel(uint32_t threadID) {
ArenaAllocator &mem = mems[threadID];
IndependentLightPathSampler &pathSampler = *pathSamplers[threadID];
for (int ly = 0; ly < numPixelY; ++ly) {
for (int lx = 0; lx < numPixelX; ++lx) {
float time = pathSampler.getTimeSample(timeStart, timeEnd);
PixelPosition p = pathSampler.getPixelPositionSample(basePixelX + lx, basePixelY + ly);
float selectWLPDF;
WavelengthSamples wls = WavelengthSamples::createWithEqualOffsets(pathSampler.getWavelengthSample(), pathSampler.getWLSelectionSample(), &selectWLPDF);
LensPosQuery lensQuery(time, wls);
LensPosQueryResult lensResult;
SampledSpectrum We0 = camera->sample(lensQuery, pathSampler.getLensPosSample(), &lensResult);
(void)We0;
IDFSample WeSample(p.x / imageWidth, p.y / imageHeight);
IDFQueryResult WeResult;
IDF* idf = camera->createIDF(lensResult.surfPt, wls, mem);
SampledSpectrum We1 = idf->sample(WeSample, &WeResult);
(void)We1;
Ray ray(lensResult.surfPt.p, lensResult.surfPt.shadingFrame.fromLocal(WeResult.dirLocal), time);
DebugInfo info = contribution(*scene, wls, ray, pathSampler, mem);
for (int i = 0; i < renderer->m_channels.size(); ++i) {
if (!renderer->m_channels[i])
continue;
auto &chImg = (*chImages)[i];
switch ((ExtraChannel)i) {
case ExtraChannel::GeometricNormal: {
RGB8x3 val;
val.r = (uint8_t)std::clamp((0.5f * info.surfPt.gNormal.x + 0.5f) * 255, 0.0f, 255.0f);
val.g = (uint8_t)std::clamp((0.5f * info.surfPt.gNormal.y + 0.5f) * 255, 0.0f, 255.0f);
val.b = (uint8_t)std::clamp((0.5f * info.surfPt.gNormal.z + 0.5f) * 255, 0.0f, 255.0f);
chImg->set((int)p.x, (int)p.y, val);
break;
}
case ExtraChannel::ShadingNormal: {
RGB8x3 val;
val.r = (uint8_t)std::clamp((0.5f * info.surfPt.shadingFrame.z.x + 0.5f) * 255, 0.0f, 255.0f);
val.g = (uint8_t)std::clamp((0.5f * info.surfPt.shadingFrame.z.y + 0.5f) * 255, 0.0f, 255.0f);
val.b = (uint8_t)std::clamp((0.5f * info.surfPt.shadingFrame.z.z + 0.5f) * 255, 0.0f, 255.0f);
chImg->set((int)p.x, (int)p.y, val);
break;
}
case ExtraChannel::ShadingTangent: {
RGB8x3 val;
val.r = (uint8_t)std::clamp((0.5f * info.surfPt.shadingFrame.x.x + 0.5f) * 255, 0.0f, 255.0f);
val.g = (uint8_t)std::clamp((0.5f * info.surfPt.shadingFrame.x.y + 0.5f) * 255, 0.0f, 255.0f);
val.b = (uint8_t)std::clamp((0.5f * info.surfPt.shadingFrame.x.z + 0.5f) * 255, 0.0f, 255.0f);
chImg->set((int)p.x, (int)p.y, val);
break;
}
case ExtraChannel::Distance: {
Gray8 val;
SLRAssert_NotImplemented();
chImg->set((int)p.x, (int)p.y, val);
break;
}
default:
break;
}
}
mem.reset();
}
}
}
void analyzeVideo(const std::string& folder, const Camera& calibrated_camera, float label_width)
{
// Start with a single hypotheses of the cube.
std::vector<ProbabalisticCube> cube_hypotheses;
cube_hypotheses.push_back(ProbabalisticCube());
for (int frame_i = 0;;)
{
printf("Frame %i\n", frame_i);
char buf[1024];
sprintf(buf, "%s/frame%05d.png", folder.c_str(), frame_i);
cv::Mat3b img = cv::imread(buf, cv::IMREAD_COLOR);
if (img.empty())
{
break;
}
const size_t num_hypotheses_before = cube_hypotheses.size();
printf("Num hypotheses before: %lu\n", num_hypotheses_before);
cube_hypotheses = predict(cube_hypotheses);
const size_t num_hypotheses_after = cube_hypotheses.size();
printf("Num hypotheses after: %lu\n", num_hypotheses_after);
printf("Brancing factor: %f\n", double(num_hypotheses_after) / num_hypotheses_before);
const size_t max_hypotheses = 216;
if (num_hypotheses_after > max_hypotheses)
{
const size_t pruned_num = num_hypotheses_after - max_hypotheses;
const double removed_percentage = pruned_num * 100.0 / num_hypotheses_after;
printf("Pruning to %lu hypotheses, removing %lu (%.1f%%) hypotheses.\n",
max_hypotheses, pruned_num, removed_percentage);
}
prune(cube_hypotheses, max_hypotheses);
printf("Most likely cubes:\n");
for (int i = 0; i < cube_hypotheses.size() && i < 5; ++i)
{
const ProbabalisticCube& cube = cube_hypotheses[i];
const std::string permutation = cube.cube_permutation.to_String();
const double likelihood_percent = exp(cube.log_likelihood) * 100.0;
printf("%d: %s %3.5f%%\n", i, permutation.c_str(), likelihood_percent);
}
std::vector<LabelContour> labels = findLabelContours(img, 12, true);
std::vector<std::vector<cv::Point2f>> detected_corners = findLabelCorners(labels);
std::vector<Camera> all_camera_candidates;
for (const auto& corners : detected_corners)
{
std::vector<Camera> cameras = predictCameraPosesForLabel(calibrated_camera, corners, label_width);
all_camera_candidates.insert(all_camera_candidates.end(), cameras.begin(), cameras.end());
}
std::vector<double> camera_scores;
camera_scores.reserve(all_camera_candidates.size());
{
cv::Mat1f accumulation(img.size(), 0.f);
for (const auto& cam : all_camera_candidates)
{
cv::Mat1f contribution(img.size(), 0.f);
std::vector<cv::Point2f> predicted_corners = projectCubeCorners(cam, label_width);
double score = scorePredictedCorners(predicted_corners, detected_corners);
camera_scores.push_back(score);
for (int i = 0; i < predicted_corners.size(); i += 4)
{
std::vector<cv::Point> corners = {
predicted_corners[i + 0],
predicted_corners[i + 1],
predicted_corners[i + 2],
predicted_corners[i + 3],
};
cv::polylines(contribution, corners, true, cv::Scalar(score * score));
}
accumulation += contribution;
}
double minval;
double maxval;
cv::minMaxLoc(accumulation, &minval, &maxval);
cv::imshow("accumulation", accumulation / maxval);
}
if (!camera_scores.empty())
{
std::vector<double> sorted_camera_scores = camera_scores;
std::sort(sorted_camera_scores.begin(), sorted_camera_scores.end());
std::reverse(sorted_camera_scores.begin(), sorted_camera_scores.end());
printf("Min score: %f max score: %f\n", sorted_camera_scores.back(), sorted_camera_scores.front());
for (int i = 0; i < sorted_camera_scores.size() && i < 5; ++i)
{
double score = sorted_camera_scores[i];
printf("#%d score: %f\n", i + 1, score);
}
}
if (!all_camera_candidates.empty())
{
const size_t index = std::distance(camera_scores.begin(),
std::max_element(camera_scores.begin(), camera_scores.end()));
printf("detected_corners size: %lu index: %lu\n", detected_corners.size(), index);
const Camera& cam = all_camera_candidates[index];
std::vector<cv::Point2f> predicted_corners = projectCubeCorners(cam, label_width);
cv::Mat3b canvas = img * 0.25f;
for (int i = 0; i < predicted_corners.size(); i += 4)
{
std::vector<cv::Point> corners = {
predicted_corners[i + 0],
predicted_corners[i + 1],
predicted_corners[i + 2],
predicted_corners[i + 3],
};
cv::polylines(canvas, corners, true, cv::Scalar(0, 0, 255));
}
{
std::vector<cv::Point> corners = {
detected_corners[index / 9][0],
detected_corners[index / 9][1],
detected_corners[index / 9][2],
detected_corners[index / 9][3],
};
cv::polylines(canvas, corners, true, cv::Scalar(255, 0, 255));
}
cv::imshow("predicted labels", canvas);
}
{
cv::Mat3b canvas = img * 0.25f;
drawLabels(canvas, labels, cv::Scalar(255, 255, 255));
for (const auto& corners : detected_corners)
{
cv::polylines(canvas, cast<cv::Point>(corners), true, cv::Scalar(0, 0, 255));
for (size_t i = 0; i < corners.size(); ++i)
{
char text[12];
sprintf(text, "%lu", i);
cv::putText(canvas, text, corners[i],
cv::FONT_HERSHEY_PLAIN, 1, cv::Scalar(0, 0, 255));
}
}
cv::imshow("detected labels", canvas);
}
if (int key = cv::waitKey(0) & 255)
{
if (key == 27)
{
break; // stop by pressing ESC
}
if (key == 32) // space
{
++frame_i;
}
if (key == 83) // right arrow
{
++frame_i;
}
if (key == 82) // up arrow
{
}
if (key == 81) // left arrow
{
--frame_i;
}
if (key == 84) // down arrow
{
}
if (key != 255)
{
printf("Key: %d\n", key);
fflush(stdout);
}
}
}
}
