int main(int argc, char** argv){
if(argc < 3){
std::cout << "usage:"<<std::endl;
std::cout << "composite [foregroundImagename] [backgroundImagename]" << std::endl;
std::cout << "or" <<std::endl;
std::cout << "composite [foregroundImagename] [backgroundImagename] [outputIMagename]" << std::endl;
exit(-1);
}
FileIO::getInstance().readFromFileToImage(foreImage, argv[1]);
FileIO::getInstance().readFromFileToImage(backImage, argv[2]);
imageToBeOutput = foreImage.over(backImage,0,0);
display_data = new GLubyte[imageToBeOutput.getWidth()*imageToBeOutput.getHeight()*4];
imageToBeOutput.displayOutput(display_data);
if(argc>3){
FileIO::getInstance().writeImageToFile(imageToBeOutput,argv[3]);
}
glutInit(&argc, argv);
glutInitDisplayMode(GLUT_SINGLE|GLUT_RGBA);
glutInitWindowSize(imageToBeOutput.getWidth(), imageToBeOutput.getHeight());
glutInitWindowPosition(0, 0);
glutCreateWindow(argv[0]);
init();
glutDisplayFunc(display);
glutMainLoop();
return 0;
}
//callback in glut loop
void display(void)
{
std::cout << imageToBeOutput.getWidth() << " " << imageToBeOutput.getHeight() << " " << imageToBeOutput.getChannels()<<std::endl;
glClear(GL_COLOR_BUFFER_BIT);
glDrawPixels(imageToBeOutput.getWidth(), imageToBeOutput.getHeight(), GL_RGBA, GL_UNSIGNED_BYTE, display_data);
glFlush();
}
bool compareImage_basic(const MyImage &img_logo, const MyImage &img_pic)
{
int *his_logo = getHistogram_H(img_logo, 0, 0, img_logo.getHeight(), img_logo.getWidth());
int *his_pic = getHistogram_H(img_pic, 0, 0, img_pic.getHeight(), img_pic.getWidth());
print_arr(his_logo, H_N);
print_arr(his_pic, H_N);
int total_pixel = img_logo.getWidth() * img_logo.getHeight();
retainMajority(his_logo, 0.9, H_N);
TRACE("After retaining majority\n");
print_arr(his_logo, H_N);
filter(his_logo, his_pic, H_N);
TRACE("After filtering\n");
print_arr(his_pic, H_N);
double *norm_his_logo = normalize(his_logo, H_N);
double *norm_his_pic = normalize(his_pic, H_N);
double diff = differ(norm_his_logo, norm_his_pic, H_N);
TRACE("ecu diff: %lf\n", diff);
delete his_logo;
delete his_pic;
delete norm_his_logo;
delete norm_his_pic;
return diff <= THRESHOLD;
}
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;
}
void sendSuccessiveBits(void* storage){
TwoByte* st = (TwoByte*) storage;
int h = workingImage.getHeight();
int w = workingImage.getWidth();
int len = h*w*3;
TwoByte *newst = new TwoByte[len];
memset(newst, 0x00, sizeof(TwoByte)*len);
TwoByte offset = 1<<12;
for(int k = 12; k >= 0; --k){
for(int i = 0; i < len; ++i){
TwoByte temp = st[i] + offset;
temp &= (1<<k);
newst[i] |= temp;
}
decodeProgressiveModeBitApproximation(newst, offset);
Sleep(latency);
}
delete[] newst;
MessageBox(NULL, "DECODING DONE", "Status", NULL);
}
//
// FUNCTION: WndProc(HWND, unsigned, WORD, LONG)
//
// PURPOSE: Processes messages for the main window.
//
// WM_COMMAND - process the application menu
// WM_PAINT - Paint the main window
// WM_DESTROY - post a quit message and return
//
//
LRESULT CALLBACK WndProc(HWND hWnd, UINT message, WPARAM wParam, LPARAM lParam)
{
int wmId, wmEvent;
PAINTSTRUCT ps;
HDC hdc;
TCHAR szHello[MAX_LOADSTRING];
LoadString(hInst, IDS_HELLO, szHello, MAX_LOADSTRING);
switch (message)
{
case WM_COMMAND:
wmId = LOWORD(wParam);
wmEvent = HIWORD(wParam);
// Parse the menu selections:
switch (wmId)
{
case IDM_ABOUT:
DialogBox(hInst, (LPCTSTR)IDD_ABOUTBOX, hWnd, (DLGPROC)About);
break;
case IDM_EXIT:
DestroyWindow(hWnd);
break;
default:
return DefWindowProc(hWnd, message, wParam, lParam);
}
break;
case WM_PAINT:
{
hdc = BeginPaint(hWnd, &ps);
// TODO: Add any drawing code here...
RECT rt;
GetClientRect(hWnd, &rt);
// Top Text
char text[1000];
// Image Header setup
BITMAPINFO bmi;
CBitmap bitmap;
memset(&bmi,0,sizeof(bmi));
bmi.bmiHeader.biSize = sizeof(bmi.bmiHeader);
bmi.bmiHeader.biWidth = myImage.getWidth();
bmi.bmiHeader.biHeight = -myImage.getHeight(); // Use negative height. DIB is top-down.
bmi.bmiHeader.biPlanes = 1;
bmi.bmiHeader.biBitCount = 24;
bmi.bmiHeader.biCompression = BI_RGB;
bmi.bmiHeader.biSizeImage = myImage.getWidth()*myImage.getHeight();
// Draw Processed image
sprintf(text, "\n %s -> display", myImage.getImagePath());
DrawText(hdc, text, strlen(text), &rt, DT_LEFT);
sprintf(text, "\n %s -> Gray Scaled -> YUV -> DCT -> Quant [ %d ] -> Dequant [ %d ] -> IDCT [ %d Co-Efficient(s) ] ", myImage.getImagePath(), myImage.getQuant(), myImage.getQuant(), myImage.getCoEff());
DrawText(hdc, text, strlen(text), &rt, DT_RIGHT);
// Draw image
SetDIBitsToDevice(hdc,
100,100,myImage.getWidth(),myImage.getHeight(),
0,0,0,myImage.getHeight(),
myImage.getBytesRGBSTART(),&bmi,DIB_RGB_COLORS);
SetDIBitsToDevice(hdc,
300+myImage.getWidth()+50,100,myImage.getWidth(),myImage.getHeight(),
0,0,0,myImage.getHeight(),
myImage.getBytesRGBEND(),&bmi,DIB_RGB_COLORS);
EndPaint(hWnd, &ps);
}
break;
case WM_DESTROY:
PostQuitMessage(0);
break;
default:
return DefWindowProc(hWnd, message, wParam, lParam);
}
return 0;
}
/** @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;
}
void sendSpectralSelection(void* storage){
TwoByte* st = (TwoByte*) storage;
int h = workingImage.getHeight();
int w = workingImage.getWidth();
int len = h*w*3;
TwoByte *newst = new TwoByte[len];
memset(newst, 0x00, sizeof(TwoByte)*len);
int xx, yy;
int t = 0;
for(int k= 0; k <= 14; ++k){
if((k%7)%2 == 0){
if(k == 7 || k==14){
xx = 7;
yy = k-7;
}
else{
yy = k%7;
xx = k-yy;
}
}
else{
xx = k%7;
yy = k-xx;
}
if(yy > xx){
t = yy - xx;
for(int i = 0; i <= t; ++i){
for(int y = 0; y < h; y+=8){
for(int x = 0; x < w; x+=8){
int index = ((y + yy)*w + (x + xx))*3;
newst[index] = st[index];
newst[index + 1] = st[index + 1];
newst[index + 2] = st[index + 2];
}
}
decodeProgressiveModeSpectralSelection(newst);
Sleep(latency);
yy--;xx++;
}
}
else{
t = xx - yy;
for(int i = 0; i <= t; ++i){
for(int y = 0; y < h; y+=8){
for(int x = 0; x < w; x+=8){
int index = ((y + yy)*w + (x + xx))*3;
newst[index] = st[index];
newst[index + 1] = st[index + 1];
newst[index + 2] = st[index + 2];
}
}
decodeProgressiveModeSpectralSelection(newst);
Sleep(latency);
yy++;xx--;
}
}
}
delete[] newst;
MessageBox(NULL, "DECODING DONE", "Status", NULL);
}
bool compareImage_v2(MyImage &img_logo, MyImage &img_pic)
{
int row_n = img_pic.getHeight() / BLOCK_SIZE;
int col_n = img_pic.getWidth() / BLOCK_SIZE;
int *his_logo = getHistogram(img_logo, 0, 0, img_logo.getHeight(), img_logo.getWidth());
//int *his_logo = getHistogram_H(img_logo, 0, 0, img_logo.getHeight(), img_logo.getWidth());
//int *his_pic = getHistogram_H(img_pic, 0, 0, img_pic.getHeight(), img_pic.getWidth());
//print_arr(his_logo, histo_size);
//print_arr(his_logo, histo_size_h);
//delete his_pic;
double *norm_logo = normalize(his_logo, histo_size);
//double *norm_logo = normalize(his_logo, histo_size_h);
//print_arr_d(norm_logo, histo_size);
//print_arr_d(norm_logo, histo_size_h);
int **block_histos = new int*[row_n * col_n];
int total_blc = 0;
for(int i = 0; i < img_pic.getHeight(); i += BLOCK_SIZE) {
for(int j = 0; j < img_pic.getWidth(); j += BLOCK_SIZE) {
//TRACE("i: %d, j: %d\n", i, j);
block_histos[total_blc++] = getHistogram(img_pic, i, j, i + BLOCK_SIZE, j + BLOCK_SIZE);
// print_arr(block_histos[total_blc - 1], H_N);
}
}
int window_size = min(row_n, col_n);
//double min_diff = 1000.0;
//int min_x = -1, min_y = -1, min_size = -1;
std::priority_queue<Box, std::vector<Box>, CompareBox> best_boxes;
int max_heap_size = 5;
while(window_size > 0) {
for(int row = 0; row <= row_n - window_size; row++) {
for(int col = 0; col <= col_n - window_size; col++) {
int *local_histo = new int[histo_size];
//int *local_histo = new int[histo_size_h];
for(int x = 0; x < histo_size; x++) local_histo[x] = 0;
//for(int x = 0; x < histo_size_h; x++) local_histo[x] = 0;
for(int x = row; x < row + window_size; x++) {
for(int y = col; y < col + window_size; y++) {
int block_index = x * col_n + y;
for(int z = 0; z < histo_size; z++)
//for(int z = 0; z < histo_size_h; z++)
local_histo[z] += block_histos[block_index][z];
}
}
double *norm_local = normalize(local_histo, histo_size);
//double *norm_local = normalize(local_histo, histo_size_h);
//print_arr_d(norm_local, H_N);
//print_arr_d(norm_logo, H_N);
double diff = differ(norm_local, norm_logo, histo_size);
//double diff = differ(norm_local, norm_logo, histo_size_h);
//TRACE("row: %d, col: %d, size: %d, diff: %lf\n", row, col, window_size, diff);
/*
if(row == 3 && col == 6 && window_size == 2) {
print_arr_d(norm_local, histo_size);
TRACE("diff: %lf\n", diff);
}
*/
if(best_boxes.size() == max_heap_size && best_boxes.top().diff > diff) {
delete best_boxes.top().histogram;
best_boxes.pop();
}
if(best_boxes.size() < max_heap_size) {
Box new_box = {row, col, window_size, diff, local_histo};
//TRACE("r: %d, c: %d, size: %d, diff: %lf\n", new_box.row, new_box.col, new_box.len, new_box.diff);
best_boxes.push(new_box);
}
else
delete local_histo;
delete norm_local;
}
}
window_size--;
}
//TRACE("row: %d, col: %d, size: %d, diff: %lf\n", min_y, min_x, min_size, min_diff);
//int length = min_size * BLOCK_SIZE;
img_pic.RGBtoGray();
img_logo.RGBtoGray();
unsigned char **pyramid = create_img_pyr(img_logo, 0);
int min_err = (1 << 31) - 1;
double min_diff = 10.0;
//printf("here\n");
while(!best_boxes.empty()) {
Box best_box = best_boxes.top();
TRACE("row: %d, col: %d, size: %d, diff: %lf\n", best_box.row, best_box.col, best_box.len, best_box.diff);
//printf("row: %d, col: %d, size: %d, diff: %lf\n", best_box.row, best_box.col, best_box.len, best_box.diff);
//print_arr(best_box.histogram, histo_size);
//print_arr(best_box.histogram, histo_size_h);
img_pic.DrawBox(best_box.row * BLOCK_SIZE, best_box.col * BLOCK_SIZE, (best_box.row + best_box.len)* BLOCK_SIZE, (best_box.col + best_box.len) * BLOCK_SIZE);
best_boxes.pop();
if(best_boxes.empty())
min_err = diff_pic(pyramid[best_box.len - 1], best_box.len, img_pic, best_box.row * BLOCK_SIZE, best_box.col * BLOCK_SIZE, (best_box.row + best_box.len)* BLOCK_SIZE, (best_box.col + best_box.len) * BLOCK_SIZE);
//TRACE("cur_err: %d\n", cur_err);
//printf("cur_err: %d\n", cur_err);
//if(cur_err < min_err) min_err = cur_err;
if(best_box.diff < min_diff) min_diff = best_box.diff;
delete best_box.histogram;
}
TRACE("min_square_error: %d\n", min_err);
TRACE("min_diff: %f\n", min_diff);
delete his_logo;
delete norm_logo;
//delete [] block_histos;
for(int i = 0; i < total_blc; i++) {
delete block_histos[i];
}
for(int i = 0; i < 9; i++) {
delete pyramid[i];
}
delete pyramid;
delete block_histos;
if(min_diff <= 0.02) return true;
if(min_diff >= 0.33) return false;
return min_err < 400000;
}
int diff_pic(unsigned char *logo_img_sample, int pyr_i, const MyImage &img_pic, int start_r, int start_c, int end_r, int end_c)
{
unsigned char *Ybuf_pic = img_pic.getYbuf();
int block_unit = 8;
int block_len = pyr_i * BLOCK_SIZE;
int error = 0;
for(int i = start_r; i < end_r; i += block_unit) {
for(int j = start_c; j < end_c; j += block_unit) {
int min_mse = (1 << 31) - 1;
for(int row = 0; row < block_len; row += block_unit) {
for(int col = 0; col < block_len; col += block_unit) {
int block_err = 0;
for(int blc_i = 0; blc_i < block_unit; blc_i++) {
for(int blc_j = 0; blc_j < block_unit; blc_j++) {
int diff = logo_img_sample[(row + blc_i) * block_len + col + blc_j] - Ybuf_pic[(i + blc_i) * img_pic.getWidth() + j + blc_j];
block_err += diff * diff;
}
}
if(block_err < min_mse) min_mse = block_err;
}
}
error += min_mse;
}
}
return error / (block_len / 8) / (block_len / 8);
}
