void testerConversionNoirEtBlanc(const int& seuilNoirEtBlanc)
{
genererImageTest();
Image imageTest(QImage("ImageTest.png"), QImage());
imageTest.convertirImageNoirEtBlanc(seuilNoirEtBlanc);
imageTest.getImageConvertie().save("ImageTestConversionNoirEtBlanc.png");
}
void testerConversionNiveauxDeGris(const int& nombreNiveauxDeGris)
{
genererImageTest();
Image imageTest(QImage("ImageTest.png"), QImage());
imageTest.convertirImageNiveauxDeGris(nombreNiveauxDeGris);
imageTest.getImageConvertie().save("ImageTestConversionNiveauxDeGris.png");
}
void testerConversionTeintesSaturees(const int& nombreNiveauxDeGris,
const int& nombreTeintesSaturees, const int& seuilSaturation)
{
genererImageTest();
Image imageTest(QImage("ImageTest.png"), QImage());
imageTest.convertirImageTeintesSaturees(nombreNiveauxDeGris, nombreTeintesSaturees,
seuilSaturation);
imageTest.getImageConvertie().save("ImageTestConversionTeintesSaturees.png");
}
void siftDesctor::covdet_keypoints_and_descriptors(cv::Mat &img, std::vector<std::vector<float>> &frames, std::vector<std::vector<float>> &desctor, bool rootSIFT, bool verbose){
bool display_image = 0;
vl_size numCols, numRows;
numCols = img.cols; //61
numRows = img.rows; //74
img = (cv::Mat_<float>)img/255.0;
// This is for debugging, checking the image was correctly loaded.
if (display_image) {
std::string window_name = "Image";
namedWindow(window_name, cv::WINDOW_AUTOSIZE );// Create a window for display.
imshow(window_name, img); // Show our image inside it.
cv::waitKey(0); // Wait for a keystroke in the window
}
cv::Mat imageTest(img.rows, img.cols, CV_32FC1);
cv::Mat imgT(img.cols, img.rows, CV_32FC1);
img.copyTo(imageTest);
imgT = img.t();
imgT = imgT.reshape(1,1);
//std::cout << imgT << std::endl;
float *image = new float[(int)imgT.cols];
for(int i = 0; i < imgT.cols; i++){
image[i] = (float)imgT.at<float>(i);
//std::cout << image[i] << " ";
}
typedef enum _VlCovDetDescriptorType{
VL_COVDET_DESC_SIFT
} VlCovDetDescriporType;
VlCovDetMethod method = VL_COVDET_METHOD_DOG;
//vl_bool estimateAffineShape = VL_FALSE;
//vl_bool estimateOrientation = VL_FALSE;
vl_bool doubleImage = VL_TRUE;
vl_index octaveResolution = -1;
double edgeThreshold = 10;
double peakThreshold = 0.01;
double lapPeakThreshold = -1;
int descriptorType = -1;
vl_index patchResolution = -1;
double patchRelativeExtent = -1;
double patchRelativeSmoothing = -1;
double boundaryMargin = 2.0;
if (descriptorType < 0) descriptorType = VL_COVDET_DESC_SIFT;
switch (descriptorType){
case VL_COVDET_DESC_SIFT:
if (patchResolution < 0) patchResolution = 15;
if (patchRelativeExtent < 0) patchRelativeExtent = 10;
if (patchRelativeSmoothing <0) patchRelativeSmoothing = 1;
if (verbose){
printf("vl_covdet: VL_COVDET_DESC_SIFT: patchResolution=%lld, patchRelativeExtent=%g, patchRelativeSmoothing=%g\n", patchResolution, patchRelativeExtent, patchRelativeSmoothing);
}
}
if (1) {
//clock_t t_start = clock();
// create a detector object: VL_COVDET_METHOD_HESSIAN
VlCovDet * covdet = vl_covdet_new(method);
// set various parameters (optional)
vl_covdet_set_transposed(covdet, VL_TRUE);
vl_covdet_set_first_octave(covdet, doubleImage? -1 : 0);
if (octaveResolution >= 0) vl_covdet_set_octave_resolution(covdet, octaveResolution);
if (peakThreshold >= 0) vl_covdet_set_peak_threshold(covdet, peakThreshold);
if (edgeThreshold >= 0) vl_covdet_set_edge_threshold(covdet, edgeThreshold);
if (lapPeakThreshold >= 0) vl_covdet_set_laplacian_peak_threshold(covdet, lapPeakThreshold);
//vl_covdet_set_target_num_features(covdet, target_num_features);
//vl_covdet_set_use_adaptive_suppression(covdet, use_adaptive_suppression);
if(verbose){
VL_PRINTF("vl_covdet: doubling image: %s, image size: numRows = %d, numCols = %d\n", VL_YESNO(vl_covdet_get_first_octave(covdet) < 0), numCols, numRows);
}
// process the image and run the detector, im.shape(1) is column, im.shape(0) is row
//see http://www.vlfeat.org/api/covdet_8h.html#affcedda1fdc7ed72d223e0aab003024e for detail
vl_covdet_put_image(covdet, image, numRows, numCols);
if (verbose) {
VL_PRINTF("vl_covdet: detector: %s\n", vl_enumeration_get_by_value(vlCovdetMethods, method)->name);
VL_PRINTF("vl_covdet: peak threshold: %g, edge threshold: %g\n", vl_covdet_get_peak_threshold(covdet),vl_covdet_get_edge_threshold(covdet));
}
vl_covdet_detect(covdet);
if (verbose) {
vl_size numFeatures = vl_covdet_get_num_features(covdet) ;
VL_PRINTF("vl_covdet: %d features suppressed as duplicate (threshold: %g)\n", vl_covdet_get_num_non_extrema_suppressed(covdet), vl_covdet_get_non_extrema_suppression_threshold(covdet));
VL_PRINTF("vl_covdet: detected %d features", numFeatures);
}
//drop feature on the margin(optimal)
if(boundaryMargin > 0){
vl_covdet_drop_features_outside(covdet, boundaryMargin);
if(verbose){
vl_size numFeatures = vl_covdet_get_num_features(covdet);
VL_PRINTF("vl_covdet: kept %d inside the boundary margin (%g)\n", numFeatures, boundaryMargin);
}
}
/* affine adaptation if needed */
bool estimateAffineShape = true;
if (estimateAffineShape) {
if (verbose) {
vl_size numFeaturesBefore = vl_covdet_get_num_features(covdet) ;
VL_PRINTF("vl_covdet: estimating affine shape for %d features", numFeaturesBefore);
}
vl_covdet_extract_affine_shape(covdet) ;
if (verbose) {
vl_size numFeaturesAfter = vl_covdet_get_num_features(covdet) ;
VL_PRINTF("vl_covdet: %d features passed affine adaptation\n", numFeaturesAfter);
}
}
// compute the orientation of the features (optional)
//vl_covdet_extract_orientations(covdet);
// get feature descriptors
vl_size numFeatures = vl_covdet_get_num_features(covdet);
VlCovDetFeature const * feature = (VlCovDetFeature const *)vl_covdet_get_features(covdet);
VlSiftFilt * sift = vl_sift_new(16, 16, 1, 3, 0);
vl_size patchSide = 2 * patchResolution + 1;
double patchStep = (double)patchRelativeExtent / patchResolution;
float tempDesc[128];
//float * desc;
if (verbose) {
VL_PRINTF("vl_covdet: descriptors: type = sift, resolution = %d, extent = %g, smoothing = %g\n", patchResolution,patchRelativeExtent, patchRelativeSmoothing);
}
//std::vector<float> points(6 * numFeatures);
//std::vector<float> desc(dimension * numFeatures);
std::vector<float> desc(128);
std::vector<float> frame(6);
//std::vector<float> patch(patchSide * patchSide);
//std::vector<float> patchXY(2 * patchSide * patchSide);
float *patch = (float*)malloc(sizeof(float)*patchSide*patchSide);
float *patchXY = (float*)malloc(2*sizeof(float)*patchSide*patchSide);
vl_sift_set_magnif(sift, 3.0);
for (vl_index i = 0; i < (signed)numFeatures; i++) {
frame.clear();
frame.push_back(feature[i].frame.y);
frame.push_back(feature[i].frame.x);
frame.push_back(feature[i].frame.a22);
frame.push_back(feature[i].frame.a12);
frame.push_back(feature[i].frame.a21);
frame.push_back(feature[i].frame.a11);
frames.push_back(frame);
//std::cout << std::setiosflags(std::ios::fixed);
//std::cout << std::setprecision(6) << feature[i].frame.y << ", " << feature[i].frame.x << ", " << feature[i].frame.a22 << ", "<< feature[i].frame.a12 << ", "<< feature[i].frame.a21 << ", " << feature[i].frame.a11 << ", "<< std::endl;
vl_covdet_extract_patch_for_frame(covdet,
patch,
patchResolution,
patchRelativeExtent,
patchRelativeSmoothing,
feature[i].frame);
vl_imgradient_polar_f(patchXY, patchXY+1,
2, 2 * patchSide,
patch, patchSide, patchSide, patchSide);
vl_sift_calc_raw_descriptor(sift,
patchXY,
tempDesc,
(int)patchSide, (int)patchSide,
(double)(patchSide - 1) / 2, (double)(patchSide - 1) / 2,
(double)patchRelativeExtent / (3.0 * (4 + 1) / 2) / patchStep,
VL_PI / 2);
flip_descriptor(desc, tempDesc);
// sift or rootsift
if(rootSIFT == true){
std::vector<float> rootdesc = rootsift(desc);
desctor.push_back(rootdesc);
}else{
desctor.push_back(desc);
}
/*std::cout << std::setiosflags(std::ios::fixed);
for(vl_index j = 0; j < 128; j++){
std::cout << std::setprecision(6) << desc[j] << " ";
}
std::cout << "\n" << std::endl;*/
}
vl_sift_delete(sift);
vl_covdet_delete(covdet);
//delete covdet;
delete patch;
delete patchXY;
}
}
void genererImageTest()
{
// Définition de l'image de test
QImage imageTest(640, 480, QImage::Format_RGB32);
const int largeurSpectre = 600;
const int hauteurSpectre = 50;
const int hauteurSpectreTeinte = 230;
// Fond blanc
QColor couleurBlanc = Qt::white;
for (int x = 0; x < imageTest.width(); x++)
{
for (int y = 0; y < imageTest.height(); y++)
{
imageTest.setPixel(x, y, couleurBlanc.rgb());
}
}
// Spectre de gris
QColor couleurGris = Qt::black;
for (int x = 0; x < largeurSpectre; x++)
{
for (int y = 0; y < hauteurSpectre; y++)
{
int valeurGris = (int) round(x * (255.0 / largeurSpectre));
couleurGris.setRgb(valeurGris, valeurGris, valeurGris);
imageTest.setPixel(x + 20, y + 20, couleurGris.rgb());
}
}
// Spectre de saturation
QColor couleurSaturation = Qt::red;
for (int x = 0; x < largeurSpectre; x++)
{
for (int y = 0; y < hauteurSpectre; y++)
{
int valeurSaturation = (int) round(x * (255.0 / largeurSpectre));
couleurSaturation = QColor::fromHsv(couleurSaturation.hue(), valeurSaturation, 255);
imageTest.setPixel(x + 20, y + 90, couleurSaturation.rgb());
}
}
// Spectre de brillance
QColor couleurBrillance = Qt::red;
for (int x = 0; x < largeurSpectre; x++)
{
for (int y = 0; y < hauteurSpectre; y++)
{
int valeurBrillance = (int) round(x * (255.0 / largeurSpectre));
couleurBrillance = QColor::fromHsv(couleurBrillance.hue(), 255, valeurBrillance);
imageTest.setPixel(x + 20, y + 160, couleurBrillance.rgb());
}
}
// Spectre de teinte
QColor couleurTeinte = Qt::black;
for (int x = 0; x < largeurSpectre; x++)
{
for (int y = 0; y < hauteurSpectreTeinte; y++)
{
int valeurTeinte = (int) round(x * (360.0 / largeurSpectre)) % 360;
couleurTeinte = QColor::fromHsv(valeurTeinte, 255, 255);
imageTest.setPixel(x + 20, y + 230, couleurTeinte.rgb());
}
}
imageTest.save("ImageTest.png");
}
