Picture *OpenCVPicture::distort(RNG &rng, batchType type) {
OpenCVPicture *pic = new OpenCVPicture(*this);
if (epoch <= 800 and type == TRAINBATCH) {
// 2x2 identity matrix:
// Generate an affine distortion matrix
float c00 = 1, c01 = 0, c10 = 0, c11 = 1;
c00 *= 1 + rng.uniform(-0.2, 0.2); // x stretch
c11 *= 1 + rng.uniform(-0.2, 0.2); // y stretch
if (rng.randint(2) == 0) // Horizontal flip
c00 *= -1;
int r = rng.randint(3);
float alpha = rng.uniform(-0.2, 0.2);
if (r == 0) // Slant
matrixMul2x2inPlace(c00, c01, c10, c11, 1, 0, alpha, 1);
if (r == 1) // Slant
matrixMul2x2inPlace(c00, c01, c10, c11, 1, alpha, 0, 1);
if (r == 2) // Rotate
matrixMul2x2inPlace(c00, c01, c10, c11, cos(alpha), -sin(alpha),
sin(alpha), cos(alpha));
pic->affineTransform(c00, c01, c10, c11);
pic->jiggle(rng, 16);
pic->colorDistortion(rng, 25.5, 0.15, 2.4, 2.4);
}
return pic;
}
Picture *OpenCVPicture::distort(RNG &rng, batchType type) {
OpenCVPicture *pic = new OpenCVPicture(*this);
pic->loadDataWithoutScaling(0);
float c00 = 1, c01 = 0, // 2x2 identity matrix---starting point for
// calculating affine distortion matrix
c10 = 0, c11 = 1;
float r, alpha, beta, s = 1;
if (type == TRAINBATCH) {
r = rng.uniform(-0.1, 0.1);
alpha = rng.uniform(0, 2 * 3.1415926535);
beta = rng.uniform(-0.2, 0.2) + alpha;
} else {
r = 0;
alpha = rng.uniform(0, 2 * 3.1415926535);
beta = alpha;
}
c00 = (1 + r) * cos(alpha);
c01 = (1 + r) * sin(alpha);
c10 = -(1 - r) * sin(beta);
c11 = (1 - r) * cos(beta);
if (rng.randint(2) == 0) {
c00 *= -1;
c01 *= -1;
} // Horizontal flip
pic->affineTransform(c00, c01, c10, c11);
pic->jiggle(rng, 300);
return pic;
}
void loadDataThread(std::vector<Picture *> *pictures, int flags, int k, int n) {
for (; k < pictures->size(); k += n) {
OpenCVPicture *pic = dynamic_cast<OpenCVPicture *>(pictures->at(k));
// pic->loadDataWithoutScaling(flags);
pic->loadDataWithoutScalingRemoveModalColor();
std::cout << "!" << std::flush;
}
}
KagglePlanktonLabeledDataSet::KagglePlanktonLabeledDataSet
(std::string classesListFile, std::string dataDirectory, batchType type_, int backgroundCol) {
name=dataDirectory;
type=type_;
{
std::ifstream f(classesListFile.c_str());
std::string cl;
int ctr=0;
while (f >> cl)
classes[cl]=ctr++;
}
nClasses=classes.size();
for (auto &kv : classes) {
for (auto &file : globVector(dataDirectory+kv.first+"/*.jpg")) {
OpenCVPicture* pic = new OpenCVPicture(file,-1,backgroundCol,kv.second);
pic->loadDataWithoutScaling();
nFeatures=pic->mat.channels();
pic->scale=powf(powf(pic->mat.rows,2)+powf(pic->mat.cols,2),0.5);
pictures.push_back(pic);
}
}
}
KagglePlanktonUnlabeledDataSet::KagglePlanktonUnlabeledDataSet
(std::string classesListFile, std::string dataDirectory, int backgroundCol) {
name=dataDirectory;
header="image";
type=UNLABELEDBATCH;
{
std::ifstream f(classesListFile.c_str());
std::string cl;
int ctr=0;
while (f >> cl) {
classes[cl]=ctr++;
header=header+","+cl;
}
}
nClasses=classes.size();
for (auto &file : globVector(std::string(dataDirectory+"*.jpg"))) {
OpenCVPicture* pic = new OpenCVPicture(file,-1,backgroundCol,0);
pic->loadDataWithoutScaling();
nFeatures=pic->mat.channels();
pic->scale=powf(powf(pic->mat.rows,2)+powf(pic->mat.cols,2),0.5);
pictures.push_back(pic);
}
}
