void sendDecodeSequentialMode(void* storage){
TwoByte* st = (TwoByte*) storage;
int h = workingImage.getHeight();
int w = workingImage.getWidth();
Byte* imgd = workingImage.getImageData();
unsigned int q = 1<<quantizationLevel;
TwoByte* tblock = new TwoByte[192];
Byte* idctblock = new Byte[192];
for(int y = 0; y < h; y+=8){
for(int x = 0; x < w; x+=8){
for(int yy= 0; yy < 8; yy++){
for(int xx = 0; xx < 8; xx++){
int src = ((y + yy)*w + (x + xx))*3;
int dest = (yy*8 + xx)*3;
tblock[dest] = st[src]*q;
tblock[dest + 1] = st[src + 1]*q;
tblock[dest + 2] = st[src + 2]*q;
}
}
doIDCT(tblock, idctblock);
for(int yy= 0; yy < 8; yy++){
for(int xx = 0; xx < 8; xx++){
int dest = ((y + yy)*w + (x + xx))*3;
int src = (yy*8 + xx)*3;
imgd[dest] = idctblock[src];
imgd[dest + 1] = idctblock[src + 1];
imgd[dest + 2] = idctblock[src + 2];
}
}
drawImage(&workingImage, nextStart);
Sleep(latency);
}
}
delete[] tblock;
delete[] idctblock;
MessageBox(NULL, "DECODING DONE", "Status", NULL);
}
void decodeProgressiveModeSpectralSelection(TwoByte* st){
int h = workingImage.getHeight();
int w = workingImage.getWidth();
Byte* imgd = workingImage.getImageData();
unsigned int q = 1<<quantizationLevel;
TwoByte* tblock = new TwoByte[192];
Byte* idctblock = new Byte[192];
memset(tblock, 0x00, sizeof(TwoByte)*192);
memset(idctblock, 0x00, sizeof(Byte)*192);
for(int y = 0; y < h; y+=8){
for(int x = 0; x < w; x+=8){
for(int yy= 0; yy < 8; yy++){
for(int xx = 0; xx < 8; xx++){
int src = ((y + yy)*w + (x + xx))*3;
int dest = (yy*8 + xx)*3;
tblock[dest] = st[src]*q;
tblock[dest + 1] = st[src + 1]*q;
tblock[dest + 2] = st[src + 2]*q;
}
}
doIDCT(tblock, idctblock);
for(int yy= 0; yy < 8; yy++){
for(int xx = 0; xx < 8; xx++){
int dest = ((y + yy)*w + (x + xx))*3;
int src = (yy*8 + xx)*3;
imgd[dest] = idctblock[src];
imgd[dest + 1] = idctblock[src + 1];
imgd[dest + 2] = idctblock[src + 2];
}
}
}
}
delete[] tblock;
delete[] idctblock;
drawImage(&workingImage, nextStart);
}
void encode(TwoByte *store){
int h = originalImage.getHeight();
int w = originalImage.getWidth();
Byte* imgd = originalImage.getImageData();
Byte* tblock = new Byte[192];
double* dctblock = new double[192];
memset(tblock, 0x00, sizeof(Byte)*192);
memset(dctblock, 0x00, sizeof(double)*192);
unsigned int q = 1<<quantizationLevel;
for(int y = 0; y < h; y+=8){
for(int x = 0; x < w; x+=8){
for(int yy= 0; yy < 8; yy++){
for(int xx = 0; xx < 8; xx++){
int src = ((y+yy)*w + (x + xx))*3;
int dest = (yy*8 + xx)*3;
tblock[dest] = imgd[src];
tblock[dest + 1] = imgd[src + 1];
tblock[dest + 2] = imgd[src + 2];
}
}
doDCT(tblock, dctblock);
for(int yy= 0; yy < 8; yy++){
for(int xx = 0; xx < 8; xx++){
int dest = ((y+yy)*w + (x + xx))*3;
int src = (yy*8 + xx)*3;
store[dest] = (TwoByte)(dctblock[src]/q);
store[dest + 1] = (TwoByte)(dctblock[src + 1]/q);
store[dest + 2] = (TwoByte)(dctblock[src + 2]/q);
}
}
}
}
delete[] tblock;
delete[] dctblock;
}
/** @function main */
int main( int argc, char** argv )
{
//300 350
double probability = 0.6;
std::vector<string> sourceVector = saveFileName(argv[1]);
std::vector<string> searchVector = saveFileName(argv[2]);
double matched = 0;
for(std::vector<string>::iterator sourceIterator = sourceVector.begin(); sourceIterator != sourceVector.end(); sourceIterator++ ){
for(std::vector<string>::iterator searchIterator = searchVector.begin(); searchIterator != searchVector.end(); searchIterator++ ){
if (*sourceIterator == *searchIterator) {
matched++;
break;
}
}
}
probability *= (matched / sourceVector.size());
MyImage *image = new MyImage();
image->setWidth(352);
image->setHeight(288);
image->setImagePath(argv[1]);
if(!image->ReadImage()){
std::cout << "Error reading MyImage" << std::endl;
}
printf("%s", argv[1]);
FILE *f = fopen(argv[1], "rb");
if (!f) {
printf("error\n");
exit(1);
}
unsigned char pixels[352 * 288 * 3];
fread(pixels, sizeof(unsigned char), 352*288 * 3, f);
fclose(f);
cv::Mat object(Size(352, 288), CV_8UC3, pixels);
namedWindow("image", CV_WINDOW_AUTOSIZE);
imshow("image", object);
if( !object.data )
{
std::cout<< "Error reading object " << std::endl;
return -1;
}
cv::Mat tmp, alpha;
threshold(object, object, 255, 0, THRESH_TOZERO_INV);
cv::Rect myROI(100, 100, 150, 150);
int minHessian = 1000;
cv::SurfFeatureDetector detector( minHessian );
std::vector<cv::KeyPoint> kp_object;
detector.upright = false;
detector.detect( object, kp_object );
cv::SurfDescriptorExtractor extractor;
cv::Mat des_object;
extractor.compute( object, kp_object, des_object );
cv::FlannBasedMatcher matcher;
cv::namedWindow("Good Matches");
std::vector<cv::Point2f> obj_corners(4);
//Get the corners from the object
obj_corners[0] = cvPoint(0,0);
obj_corners[1] = cvPoint( object.cols, 0 );
obj_corners[2] = cvPoint( object.cols, object.rows );
obj_corners[3] = cvPoint( 0, object.rows );
char key = 'a';
int framecount = 0;
while (key != 27)
{
if (framecount < 5)
{
framecount++;
continue;
}
cv::Mat des_image, img_matches;
std::vector<cv::KeyPoint> kp_image;
std::vector<std::vector<cv::DMatch > > matches;
std::vector<cv::DMatch > good_matches;
std::vector<cv::Point2f> obj;
std::vector<cv::Point2f> scene;
std::vector<cv::Point2f> scene_corners(4);
cv::Mat H;
MyImage *sceneMyPic = new MyImage();
sceneMyPic->setWidth(352);
sceneMyPic->setHeight(288);
sceneMyPic->setImagePath(argv[2]);
sceneMyPic->ReadImage();
cv::Mat image(Size(352, 288), CV_8UC3, sceneMyPic->getImageData());
detector.detect( image, kp_image );
extractor.compute( image, kp_image, des_image );
matcher.knnMatch(des_object, des_image, matches, 2);
for(int i = 0; i < cv::min(des_image.rows-1,(int) matches.size()); i++) //THIS LOOP IS SENSITIVE TO SEGFAULTS
{
if((matches[i][0].distance < 0.8*(matches[i][1].distance)) && ((int) matches[i].size()<=2 && (int) matches[i].size()>0))
{
good_matches.push_back(matches[i][0]);
}
}
drawMatches( object, kp_object, image, kp_image, good_matches, img_matches, cv::Scalar::all(-1), cv::Scalar::all(-1), std::vector<char>(), cv::DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );
if (good_matches.size() >= 4)
{
Point2f averagePoint;
for( int i = 0; i < good_matches.size(); i++ )
{
obj.push_back( kp_object[ good_matches[i].queryIdx ].pt );
scene.push_back( kp_image[ good_matches[i].trainIdx ].pt );
averagePoint.x += kp_image[ good_matches[i].trainIdx ].pt.x;
averagePoint.y += kp_image[ good_matches[i].trainIdx ].pt.y;
}
averagePoint.x /= good_matches.size();
averagePoint.y /= good_matches.size();
int inRange = 0;
int delta = 60;
for( int i = 0; i < good_matches.size(); i++ ){
int x =kp_image[ good_matches[i].trainIdx ].pt.x;
int y =kp_image[ good_matches[i].trainIdx ].pt.y;
if ((x > (averagePoint.x - delta) && x < (averagePoint.x + delta)) && (y > (averagePoint.y - delta) && (y < (averagePoint.y + delta)))) {
inRange++;
}
}
if (probability + (double)inRange / good_matches.size() > 0.8) {
printf("found\n");
}else{
MyImage *objectPic = new MyImage();
objectPic->setWidth(352);
objectPic->setHeight(288);
objectPic->setImagePath(argv[1]);
objectPic->ReadImage();
cv::Mat objectImage(Size(352, 288), CV_8UC3, objectPic->getImageData());
cv::Mat smallerQueryImage;
resize(objectImage, smallerQueryImage, Size(16, 16), 0,0, INTER_CUBIC);
cvtColor(objectImage, objectImage, COLOR_RGB2HSV);
for (int x = 0; x < 352; x += 16) {
for (int y = 0; y < 288; y += 16) {
Rect region(Point(x, y), Size(16, 16));
cv::Mat subSampleOfScene = image(region);
cvtColor(subSampleOfScene, subSampleOfScene, COLOR_RGB2HSV);
int h_bins = 50; int s_bins = 60;
int histSize[] = { h_bins, s_bins };
float h_ranges[] = { 0, 180 };
float s_ranges[] = { 0, 256 };
const float* ranges[] = { h_ranges, s_ranges };
int channels[] = { 0, 1 };
MatND hist_base;
MatND hist_test;
calcHist( &smallerQueryImage, 1, channels, Mat(), hist_base, 2, histSize, ranges, true, false );
normalize( hist_base, hist_base, 0, 1, NORM_MINMAX, -1, Mat() );
calcHist( &subSampleOfScene, 1, channels, Mat(), hist_test, 2, histSize, ranges, true, false );
normalize( hist_test, hist_test, 0, 1, NORM_MINMAX, -1, Mat() );
double base_test1 = compareHist(hist_base, hist_test, CV_COMP_CORREL);
if (base_test1 > 0.1) {
probability += base_test1;
}
}
}
if (probability > 0.8) {
printf("found with confidence: %f\n", probability);
}
else{
printf("not found\n");
}
}
line(img_matches, cv::Point2f(averagePoint.x - 40 + sceneMyPic->getWidth(), averagePoint.y - 40), cv::Point2f(averagePoint.x + 40 + sceneMyPic->getWidth(), averagePoint.y - 40), cv::Scalar(0, 255, 0), 4);
line(img_matches, cv::Point2f(averagePoint.x + 40 + sceneMyPic->getWidth(), averagePoint.y - 40), cv::Point2f(averagePoint.x + 40 + sceneMyPic->getWidth(), averagePoint.y + 40), cv::Scalar(0, 255, 0), 4);
line(img_matches, cv::Point2f(averagePoint.x + 40 + sceneMyPic->getWidth(), averagePoint.y + 40), cv::Point2f(averagePoint.x - 40 + sceneMyPic->getWidth(), averagePoint.y + 40), cv::Scalar(0, 255, 0), 4);
line(img_matches, cv::Point2f(averagePoint.x - 40 + sceneMyPic->getWidth(), averagePoint.y + 40), cv::Point2f(averagePoint.x - 40 + sceneMyPic->getWidth(), averagePoint.y - 40), cv::Scalar(0, 255, 0), 4);
}
imshow("Good matches", img_matches);
key = cv::waitKey(0);
}
return 0;
}
