void HOGFeatures<T>::pyramid(const Mat& im, vectorMat& pyrafeatures) {
// calculate the scaling factor
Size_<float> imsize = im.size();
nscales_ = 1 + floor(log(min(imsize.height, imsize.width)/(5.0f*(float)binsize_))/log(sfactor_));
vectorMat pyraimages;
pyraimages.resize(nscales_);
pyrafeatures.clear();
pyrafeatures.resize(nscales_);
pyraimages.resize(nscales_);
scales_.clear();
scales_.resize(nscales_);
// perform the non-power of two scaling
// TODO: is this the most intuitive way to represent scaling?
#ifdef _OPENMP
#pragma omp parallel for
#endif
for (unsigned int i = 0; i < interval_; ++i) {
Mat scaled;
resize(im, scaled, imsize * (1.0f/pow(sfactor_,(int)i)));
pyraimages[i] = scaled;
scales_[i] = pow(sfactor_,(int)i)*binsize_;
// perform subsequent power of two scaling
for (unsigned int j = i+interval_; j < nscales_; j+=interval_) {
Mat scaled2;
pyrDown(scaled, scaled2);
pyraimages[j] = scaled2;
scales_[j] = 2 * scales_[j-interval_];
scaled2.copyTo(scaled);
}
}
// perform the actual feature computation, in parallel if possible
#ifdef _OPENMP
#pragma omp parallel for
#endif
for (unsigned int n = 0; n < nscales_; ++n) {
Mat feature;
Mat padded;
switch (im.depth()) {
case CV_32F: features<float>(pyraimages[n], feature); break;
case CV_64F: features<double>(pyraimages[n], feature); break;
case CV_8U: features<uint8_t>(pyraimages[n], feature); break;
case CV_16U: features<uint16_t>(pyraimages[n], feature); break;
default: CV_Error(cv::Error::StsUnsupportedFormat, "Unsupported image type"); break;
}
//copyMakeBorder(feature, padded, 3, 3, 3*flen_, 3*flen_, BORDER_CONSTANT, 0);
//boundaryOcclusionFeature(padded, flen_, 3);
pyrafeatures[n] = feature;
}
}
/*! @brief set the filters
*
* given a set of filters, split each filter channel into a plane,
* in preparation for convolution
*
* @param filters the filters
*/
void SpatialConvolutionEngine::setFilters(const vectorMat& filters) {
const size_t N = filters.size();
filters_.clear();
filters_.resize(N);
// split each filter into separate channels, and create a filter engine
const size_t C = flen_;
for (size_t n = 0; n < N; ++n) {
vectorMat filtervec;
vectorFilterEngine filter_engines(C);
split(filters[n].reshape(C), filtervec);
// the first N-1 filters have zero-padding
for (size_t m = 0; m < C-1; ++m) {
Ptr<FilterEngine> fe = createLinearFilter(type_, type_,
filtervec[m], Point(-1,-1), 0, BORDER_CONSTANT, -1, Scalar(0,0,0,0));
filter_engines[m] = fe;
}
// the last filter has one-padding
Ptr<FilterEngine> fe = createLinearFilter(type_, type_,
filtervec[C-1], Point(-1,-1), 0, BORDER_CONSTANT, -1, Scalar(1,1,1,1));
filter_engines[C-1] = fe;
filters_[n] = filter_engines;
}
}
void HOGFeatures<T>::setFilters(const vectorMat& filters) {
const int N = filters.size();
filters_.clear();
filters_.resize(N);
// split each filter into separate channels, and create a filter engine
const int C = flen_;//filters[0].cols/filters[0].rows;
for (int n = 0; n < N; ++n) {
vectorMat filtervec;
std::vector<Ptr<FilterEngine> > filter_engines(C);
split(filters[n].reshape(C), filtervec);
// the first N-1 filters have zero-padding
for (int m = 0; m < C-1; ++m) {
Ptr<FilterEngine> fe = createLinearFilter(DataType<T>::type, DataType<T>::type,
filtervec[m], Point(-1,-1), 0, BORDER_CONSTANT, -1, Scalar(0,0,0,0));
filter_engines[m] = fe;
}
// the last filter has one-padding
Ptr<FilterEngine> fe = createLinearFilter(DataType<T>::type, DataType<T>::type,
filtervec[C-1], Point(-1,-1), 0, BORDER_CONSTANT, -1, Scalar(1,1,1,1));
filter_engines[C-1] = fe;
filters_[n] = filter_engines;
}
}
void DynamicProgram<T>::reducePickIndex(const vectorMat& in, const Mat& idx, Mat& out) {
// error checking
int K = in.size();
if (K == 1) { in[0].copyTo(out); return; }
double minv, maxv;
minMaxLoc(idx, &minv, &maxv);
assert(minv >= 0 && maxv < K);
for (int k = 0; k < K; ++k) assert(in[k].size() == idx.size());
// allocate the output array
out.create(in[0].size(), in[0].type());
// perform the indexing
int M = in[0].rows;
int N = in[0].cols;
vector<const IT*> in_ptr(K);
if (in[0].isContinuous()) { N = M*N; M = 1; }
for (int m = 0; m < M; ++m) {
IT* out_ptr = out.ptr<IT>(m);
const int* idx_ptr = idx.ptr<int>(m);
for (int k = 0; k < K; ++k) in_ptr[k] = in[k].ptr<IT>(m);
for (int n = 0; n < N; ++n) {
out_ptr[n] = in_ptr[idx_ptr[n]][n];
}
}
}
void Part::toVector(vectorMat& vec) {
// if root, allocate space for all of the filters
if (isRoot()) vec.resize((ndescendants_+1) * nmixtures_);
// add my filters to the vector, then my children's, etc
int os = pos_ * nmixtures_;
for (unsigned int n = 0; n < nmixtures_; ++n) vec[os+n] = filters_[n];
for (unsigned int c = 0; c < children_.size(); ++c) children_[c].toVector(vec);
}
/*! @brief Calculate the responses of a set of features to a set of filter experts
*
* A response represents the likelihood of the part appearing at each location of
* the feature map. Parts are support vector machines (SVMs) represented as filters.
* The convolution of a filter with a feature produces a probability density function
* (pdf) of part location
* @param features the input features (at different scales, and by extension, size)
* @param responses the vector of responses (pdfs) to return
*/
void SpatialConvolutionEngine::pdf(const vectorMat& features, vector2DMat& responses) {
// preallocate the output
const size_t M = features.size();
const size_t N = filters_.size();
responses.resize(M, vectorMat(N));
// iterate
#ifdef _OPENMP
#pragma omp parallel for
#endif
for (size_t n = 0; n < N; ++n) {
for (size_t m = 0; m < M; ++m) {
Mat response;
convolve(features[m], filters_[n], response, flen_);
responses[m][n] = response;
}
}
}
void HOGFeatures<T>::pdf(const vectorMat& features, vector2DMat& responses) {
// preallocate the output
int M = features.size();
int N = filters_.size();
responses.resize(M, vectorMat(N));
// iterate
#ifdef _OPENMP
omp_set_num_threads(8);
#pragma omp parallel for
#endif
for (int n = 0; n < N; ++n) {
for (int m = 0; m < M; ++m) {
Mat response;
convolve(features[m], filters_[n], response, flen_);
responses[m][n] = response;
}
}
}
static void reduceMax(const vectorMat& in, cv::Mat& maxv, cv::Mat& maxi) {
// TODO: flatten the input into a multi-channel matrix for faster indexing
// error checking
const unsigned int K = in.size();
if (K == 1) {
// just return
in[0].copyTo(maxv);
maxi = cv::Mat::zeros(in[0].size(), cv::DataType<int>::type);
return;
}
assert (K > 1);
for (unsigned int k = 1; k < K; ++k) assert(in[k].size() == in[k-1].size());
// allocate the output matrices
maxv.create(in[0].size(), in[0].type());
maxi.create(in[0].size(), cv::DataType<int>::type);
unsigned int M = in[0].rows;
unsigned int N = in[0].cols;
std::vector<const T*> in_ptr(K);
if (in[0].isContinuous()) {
N = M*N;
M = 1;
}
for (unsigned int m = 0; m < M; ++m) {
T* maxv_ptr = maxv.ptr<T>(m);
int* maxi_ptr = maxi.ptr<int>(m);
for (unsigned int k = 0; k < K; ++k) in_ptr[k] = in[k].ptr<T>(m);
for (unsigned int n = 0; n < N; ++n) {
T v = -std::numeric_limits<T>::infinity();
int i = 0;
for (unsigned int k = 0; k < K; ++k) if (in_ptr[k][n] > v) {
i = k;
v = in_ptr[k][n];
}
maxi_ptr[n] = i;
maxv_ptr[n] = v;
}
}
}
/*! @brief return the filters for all mixtures of a part
*
* @param out the filters to return
*/
void filters(vectorMat& out) const {
out.clear();
for (unsigned int m = 0; m < filterid_[self_].size(); ++m) {
out.push_back((*filtersw_)[(*filterid_)[self_][m]]);
}
}
