void testRadialInversion(int scale)
{
RGB24Buffer *image = new RGB24Buffer(250 * scale, 400 * scale);
auto operation = [](int i, int j, RGBColor *pixel)
{
i = i / 100;
j = j / 200;
if ( (i % 2) && (j % 2)) *pixel = RGBColor::Green();
if (!(i % 2) && (j % 2)) *pixel = RGBColor::Yellow();
if ( (i % 2) && !(j % 2)) *pixel = RGBColor::Red();
if (!(i % 2) && !(j % 2)) *pixel = RGBColor::Blue();
};
touchOperationElementwize(image, operation);
#if 0
LensDistortionModelParameters deformator;
deformator.setPrincipalX(image->w / 2);
deformator.setPrincipalY(image->h / 2);
deformator.setNormalizingFocal(deformator.principalPoint().l2Metric());
deformator.setTangentialX(0.001);
deformator.setTangentialY(0.001);
deformator.setAspect(1.0);
deformator.setScale(1.0);
deformator.mKoeff.push_back( 0.1);
deformator.mKoeff.push_back(-0.2);
deformator.mKoeff.push_back( 0.3);
#else
LensDistortionModelParameters deformator;
deformator.setMapForward(false);
deformator.setPrincipalX(480);
deformator.setPrincipalY(360);
deformator.setNormalizingFocal(734.29999999999995);
deformator.setTangentialX(0.00);
deformator.setTangentialY(0.00);
deformator.setShiftX(0.00);
deformator.setShiftY(0.00);
deformator.setAspect(1.0);
deformator.setScale (1.0);
deformator.mKoeff.clear();
deformator.mKoeff.push_back( 0);
deformator.mKoeff.push_back( -0.65545);
deformator.mKoeff.push_back( 0);
deformator.mKoeff.push_back( 8.2439);
// deformator.mKoeff.push_back( 0);
// deformator.mKoeff.push_back( 8.01);
#endif
RadialCorrection T(deformator);
PreciseTimer timer;
cout << "Initial deformation... " << endl;
cout << T.mParams << flush;;
cout << "Starting deformation... " << flush;
timer = PreciseTimer::currentTime();
RGB24Buffer *deformed = image->doReverseDeformationBlTyped<RadialCorrection>(&T);
cout << "done in: " << timer.usecsToNow() << "us" << endl;
/* */
int inversionGridStep = 30;
cout << "Starting invertion... " << flush;
RadialCorrection invert = T.invertCorrection(image->h, image->w, inversionGridStep);
cout << "done" << endl;
cout << "Starting backprojection... " << flush;
timer = PreciseTimer::currentTime();
RGB24Buffer *backproject = deformed->doReverseDeformationBlTyped<RadialCorrection>(&invert);
cout << "done in: " << timer.usecsToNow() << "us" << endl;
cout << "done" << endl;
RGB24Buffer *debug = new RGB24Buffer(image->getSize());
/* Show visual */
double dh = (double)image->h / (inversionGridStep - 1);
double dw = (double)image->w / (inversionGridStep - 1);
for (int i = 0; i < inversionGridStep; i++)
{
for (int j = 0; j < inversionGridStep; j++)
{
Vector2dd point(dw * j, dh * i);
debug->drawCrosshare1(point, RGBColor::Yellow());
Vector2dd deformed = T.mapToUndistorted(point); /* this could be cached */
Vector2dd backproject = invert.mapToUndistorted(deformed);
debug->drawCrosshare1(backproject, RGBColor::Green());
}
}
BMPLoader().save("input.bmp" , image);
BMPLoader().save("debug.bmp" , debug);
BMPLoader().save("forward.bmp" , deformed);
BMPLoader().save("backproject.bmp", backproject);
delete_safe(image);
delete_safe(debug);
delete_safe(deformed);
delete_safe(backproject);
}
//int main (int /*argC*/, char *argV[])
TEST(FaceRecognition1, main)
{
const double UP_FACTOR = 5;
const double UP_STEP = 1.2;
// const double DOWN_FACTOR = 0.25;
// const double DOWN_STEP = 1.0 / 1.2;
fstream patternFile;
patternFile.open("boost-result.txt", fstream::in);
VJAdaBoostedClassifier *classifier = new VJAdaBoostedClassifier();
patternFile >> (*classifier);
for (unsigned i = 0; i < classifier->children.size(); i++)
{
char name[100];
snprintf2buf(name, "pattern%d.bmp", i);
VJSimpleClassifier* partClass = classifier->children.at(i);
RGB24Buffer *buffer = partClass->drawPattern();
(BMPLoader()).save(string(name), buffer);
delete buffer;
}
// classifier->print();
patternFile.close();
G12Buffer *inputPicture = BufferFactory::getInstance()->loadG12Bitmap(argV[1]);
if (inputPicture == NULL)
{
printf("Problem loading input %s\n", argV[1]);
}
RGB24Buffer *output = new RGB24Buffer(inputPicture);
G12IntegralBuffer *integral = new G12IntegralBuffer(inputPicture);
int total = 0;
int positive = 0;
double factor;
for (factor = 1.0; factor < UP_FACTOR; factor *= UP_STEP)
{
VJAdaBoostedClassifier *scaled = classifier->scale(factor);
scaled->initLimits();
double x = scaled->leftMargin;
double y = scaled->topMargin;
double patw = scaled->rightMargin + scaled->leftMargin;
double path = scaled->topMargin + scaled->bottomMargin;
int hlimit = inputPicture->h - scaled->bottomMargin;
int wlimit = inputPicture->w - scaled->rightMargin;
for (; y < hlimit ; y += factor)
{
for (; x < wlimit ; x += factor)
{
bool hasMatch = scaled->applyToPoint(integral, y, x);
if (hasMatch)
{
//printf("Match Found! at %d %d level %d\n",j ,i, l);
int h = path;
int w = patw;
output->drawCrosshare1(x, y , RGBColor(0xFF0000));
output->drawCrosshare1(x + w, y , RGBColor(0x7F0000));
output->drawCrosshare1(x, y + h , RGBColor(0x00FF00));
output->drawCrosshare1(x + w, y + h , RGBColor(0x007F00));
positive++;
}
total++;
}
}
delete scaled;
}
delete integral;
printf("Found %d faces among %d objects %2.2lf%%\n", positive, total, (double) 100.0 * positive / total );
(BMPLoader()).save("output.bmp", output);
// return 0;
}
