Mat charProjectFeatures(const Mat& in, int sizeData) {
const int VERTICAL = 0;
const int HORIZONTAL = 1;
SHOW_IMAGE(in, 0);
// cut the cetner, will afect 5% perices.
Mat lowData;
resize(in, lowData, Size(sizeData, sizeData));
SHOW_IMAGE(lowData, 0);
// Histogram features
Mat vhist = ProjectedHistogram(lowData, VERTICAL);
Mat hhist = ProjectedHistogram(lowData, HORIZONTAL);
// Last 10 is the number of moments components
int numCols = vhist.cols + hhist.cols;
Mat out = Mat::zeros(1, numCols, CV_32F);
int j = 0;
for (int i = 0; i < vhist.cols; i++) {
out.at<float>(j) = vhist.at<float>(i);
j++;
}
for (int i = 0; i < hhist.cols; i++) {
out.at<float>(j) = hhist.at<float>(i);
j++;
}
//std::cout << out << std::endl;
return out;
}
void init_vector_field() {
int size = img_height*img_width;
glTex_img = new unsigned char[size * 3];
unsigned char* lic_img = new unsigned char[size];
noise_img = new unsigned char[size];
float *vx = new float[size];
float *vy = new float[size];
gen_noise_img(noise_img, img_width, img_height);
test_gen_vector_field_ByElements(vx, vy, img_width, img_height); //generate vector field from a sparse set of feature elements
//gen_lic(lic_img, noise_img, vx, vy, img_width, img_height);
float *des_vx = 0, *dst_vy = 0;
copyVF(vx, vy, des_vx, dst_vy, img_width, img_height);
normalizeVF(des_vx, dst_vy, img_width, img_height);
gen_lic(lic_img, noise_img, des_vx, dst_vy, img_width, img_height);
gray2rgb(glTex_img, lic_img, img_width, img_height);
// test_draw_streamline_rgb(glTex_img, vx, vy, img_width, img_height);//draw streamlines on the vector field
test_draw_arrow_rgb(glTex_img, vx, vy, img_width, img_height); //draw arrows on the vector field
#ifdef HAVE_OPENCV
#define SHOW_IMAGE(x) {cv::imshow(#x, x); cv::waitKey();}
cv::Mat noiseImg(img_height, img_width, CV_8UC1, noise_img);
cv::Mat licImg(img_height, img_width, CV_8UC1, lic_img);
SHOW_IMAGE(licImg);
#endif
}
void getGrayPlusLBP(const Mat& grayChar, Mat& features)
{
// TODO: check channnels == 1
SHOW_IMAGE(grayChar, 0);
SHOW_IMAGE(255 - grayChar, 0);
// resize to uniform size, like 20x32
bool useResize = false;
bool useConvert = true;
bool useMean = true;
bool useLBP = true;
Mat char_mat;
if (useResize) {
char_mat.create(kGrayCharHeight, kGrayCharWidth, CV_8UC1);
resize(grayChar, char_mat, char_mat.size(), 0, 0, INTER_LINEAR);
}
else {
char_mat = grayChar;
}
SHOW_IMAGE(char_mat, 0);
// convert to float
Mat float_img;
if (useConvert) {
float scale = 1.f / 255;
char_mat.convertTo(float_img, CV_32FC1, scale, 0);
}
else {
float_img = char_mat;
}
SHOW_IMAGE(float_img, 0);
// cut from mean, it can be optional
Mat mean_img;
if (useMean) {
float_img -= mean(float_img);
mean_img = float_img;
}
else {
mean_img = float_img;
}
SHOW_IMAGE(mean_img, 0);
// use lbp to get features, it can be changed to other
Mat originImage = mean_img.clone();
Mat lbpimage = libfacerec::olbp(mean_img);
SHOW_IMAGE(lbpimage, 0);
lbpimage = libfacerec::spatial_histogram(lbpimage, kCharLBPPatterns, kCharLBPGridX, kCharLBPGridY);
// 32x20 + 16x16
hconcat(mean_img.reshape(1, 1), lbpimage.reshape(1, 1), features);
}
void getGrayPlusProject(const Mat& grayChar, Mat& features)
{
// TODO: check channnels == 1
SHOW_IMAGE(grayChar, 0);
SHOW_IMAGE(255 - grayChar, 0);
// resize to uniform size, like 20x32
bool useResize = false;
bool useConvert = true;
bool useMean = true;
bool useLBP = false;
Mat char_mat;
if (useResize) {
char_mat.create(kGrayCharHeight, kGrayCharWidth, CV_8UC1);
resize(grayChar, char_mat, char_mat.size(), 0, 0, INTER_LINEAR);
}
else {
char_mat = grayChar;
}
SHOW_IMAGE(char_mat, 0);
// convert to float
Mat float_img;
if (useConvert) {
float scale = 1.f / 255;
char_mat.convertTo(float_img, CV_32FC1, scale, 0);
}
else {
float_img = char_mat;
}
SHOW_IMAGE(float_img, 0);
// cut from mean, it can be optional
Mat mean_img;
if (useMean) {
float_img -= mean(float_img);
mean_img = float_img;
}
else {
mean_img = float_img;
}
SHOW_IMAGE(mean_img, 0);
// use lbp to get features, it can be changed to other
Mat feautreImg;
if (useLBP) {
Mat lbpimage = libfacerec::olbp(char_mat);
SHOW_IMAGE(lbpimage, 0);
feautreImg = libfacerec::spatial_histogram(lbpimage, kCharLBPPatterns, kCharLBPGridX, kCharLBPGridY);
}
else {
feautreImg = mean_img.reshape(1, 1);
}
SHOW_IMAGE(grayChar, 0);
Mat binaryChar;
threshold(grayChar, binaryChar, 0, 255, CV_THRESH_OTSU + CV_THRESH_BINARY);
SHOW_IMAGE(binaryChar, 0);
Mat projectFeature = charProjectFeatures(binaryChar, 32);
hconcat(feautreImg.reshape(1, 1), projectFeature.reshape(1, 1), features);
}
