//--------------------------------------------------------------------------------
int ContourFinder::findContours( ofxCvGrayscaleImage& input,
int minArea,
int maxArea,
int nConsidered,
double hullPress,
bool bFindHoles,
bool bUseApproximation ) {
reset();
// opencv will clober the image it detects contours on, so we want to
// copy it into a copy before we detect contours. That copy is allocated
// if necessary (necessary = (a) not allocated or (b) wrong size)
// so be careful if you pass in different sized images to "findContours"
// there is a performance penalty, but we think there is not a memory leak
// to worry about better to create mutiple contour finders for different
// sizes, ie, if you are finding contours in a 640x480 image but also a
// 320x240 image better to make two ContourFinder objects then to use
// one, because you will get penalized less.
if( inputCopy.width == 0 ) {
inputCopy.allocate( input.width, input.height );
inputCopy = input;
} else {
if( inputCopy.width == input.width && inputCopy.height == input.height )
inputCopy = input;
else {
// we are allocated, but to the wrong size --
// been checked for memory leaks, but a warning:
// be careful if you call this function with alot of different
// sized "input" images!, it does allocation every time
// a new size is passed in....
//inputCopy.clear();
inputCopy.allocate( input.width, input.height );
inputCopy = input;
}
}
CvSeq* contour_list = NULL;
contour_storage = cvCreateMemStorage( 1000 );
storage = cvCreateMemStorage( 1000 );
CvContourRetrievalMode retrieve_mode
= (bFindHoles) ? CV_RETR_LIST : CV_RETR_EXTERNAL;
cvFindContours( inputCopy.getCvImage(), contour_storage, &contour_list,
sizeof(CvContour), retrieve_mode, bUseApproximation ? CV_CHAIN_APPROX_SIMPLE : CV_CHAIN_APPROX_NONE );
CvSeq* contour_ptr = contour_list;
nCvSeqsFound = 0;
// put the contours from the linked list, into an array for sorting
while( (contour_ptr != NULL) ) {
CvBox2D box = cvMinAreaRect2(contour_ptr);
int objectId; // If the contour is an object, then objectId is its ID
objectId = (bTrackObjects)? templates->getTemplateId(box.size.width,box.size.height): -1;
if(objectId != -1 ) { //If the blob is a object
Blob blob = Blob();
blob.id = objectId;
blob.isObject = true;
float area = cvContourArea( contour_ptr, CV_WHOLE_SEQ );
cvMoments( contour_ptr, myMoments );
// this is if using non-angle bounding box
CvRect rect = cvBoundingRect( contour_ptr, 0 );
blob.boundingRect.x = rect.x;
blob.boundingRect.y = rect.y;
blob.boundingRect.width = rect.width;
blob.boundingRect.height = rect.height;
//For anglebounding rectangle
blob.angleBoundingBox=box;
blob.angleBoundingRect.x = box.center.x;
blob.angleBoundingRect.y = box.center.y;
blob.angleBoundingRect.width = box.size.height;
blob.angleBoundingRect.height = box.size.width;
blob.angle = box.angle;
//TEMPORARY INITIALIZATION TO 0, Will be calculating afterwards.This is to prevent sending wrong data
blob.D.x = 0;
blob.D.y = 0;
blob.maccel = 0;
// assign other parameters
blob.area = fabs(area);
blob.hole = area < 0 ? true : false;
blob.length = cvArcLength(contour_ptr);
blob.centroid.x = (myMoments->m10 / myMoments->m00);
blob.centroid.y = (myMoments->m01 / myMoments->m00);
blob.lastCentroid.x = 0;
blob.lastCentroid.y = 0;
// get the points for the blob:
CvPoint pt;
CvSeqReader reader;
cvStartReadSeq( contour_ptr, &reader, 0 );
for( int j=0; j < contour_ptr->total; j++ ) {
CV_READ_SEQ_ELEM( pt, reader );
blob.pts.push_back( ofPoint((float)pt.x, (float)pt.y) );
}
blob.nPts = blob.pts.size();
objects.push_back(blob);
} else if ( bTrackBlobs ) { // SEARCH FOR BLOBS
float area = fabs( cvContourArea(contour_ptr, CV_WHOLE_SEQ) );
if( (area > minArea) && (area < maxArea) ) {
Blob blob=Blob();
float area = cvContourArea( contour_ptr, CV_WHOLE_SEQ );
cvMoments( contour_ptr, myMoments );
// this is if using non-angle bounding box
CvRect rect = cvBoundingRect( contour_ptr, 0 );
blob.boundingRect.x = rect.x;
blob.boundingRect.y = rect.y;
blob.boundingRect.width = rect.width;
blob.boundingRect.height = rect.height;
//Angle Bounding rectangle
blob.angleBoundingRect.x = box.center.x;
blob.angleBoundingRect.y = box.center.y;
blob.angleBoundingRect.width = box.size.height;
blob.angleBoundingRect.height = box.size.width;
blob.angle = box.angle;
// assign other parameters
blob.area = fabs(area);
blob.hole = area < 0 ? true : false;
blob.length = cvArcLength(contour_ptr);
// AlexP
// The cast to int causes errors in tracking since centroids are calculated in
// floats and they migh land between integer pixel values (which is what we really want)
// This not only makes tracking more accurate but also more fluid
blob.centroid.x = (myMoments->m10 / myMoments->m00);
blob.centroid.y = (myMoments->m01 / myMoments->m00);
blob.lastCentroid.x = 0;
blob.lastCentroid.y = 0;
// get the points for the blob:
CvPoint pt;
CvSeqReader reader;
cvStartReadSeq( contour_ptr, &reader, 0 );
for( int j=0; j < min(TOUCH_MAX_CONTOUR_LENGTH, contour_ptr->total); j++ ) {
CV_READ_SEQ_ELEM( pt, reader );
blob.pts.push_back( ofPoint((float)pt.x, (float)pt.y) );
}
blob.nPts = blob.pts.size();
blobs.push_back(blob);
}
}
contour_ptr = contour_ptr->h_next;
}
if ( bTrackFingers ) { // SEARCH FOR FINGERS
CvPoint* PointArray;
int* hull;
int hullsize;
if (contour_list)
contour_list = cvApproxPoly(contour_list, sizeof(CvContour), storage, CV_POLY_APPROX_DP, hullPress, 1 );
for( ; contour_list != 0; contour_list = contour_list->h_next ){
int count = contour_list->total; // This is number point in contour
CvRect rect = cvContourBoundingRect(contour_list, 1);
if ( (rect.width*rect.height) > 300 ){ // Analize the bigger contour
CvPoint center;
center.x = rect.x+rect.width/2;
center.y = rect.y+rect.height/2;
PointArray = (CvPoint*)malloc( count*sizeof(CvPoint) ); // Alloc memory for contour point set.
hull = (int*)malloc(sizeof(int)*count); // Alloc memory for indices of convex hull vertices.
cvCvtSeqToArray(contour_list, PointArray, CV_WHOLE_SEQ); // Get contour point set.
// Find convex hull for curent contour.
cvConvexHull( PointArray,
count,
NULL,
CV_COUNTER_CLOCKWISE,
hull,
&hullsize);
int upper = 640, lower = 0;
for (int j=0; j<hullsize; j++) {
int idx = hull[j]; // corner index
if (PointArray[idx].y < upper)
upper = PointArray[idx].y;
if (PointArray[idx].y > lower)
lower = PointArray[idx].y;
}
float cutoff = lower - (lower - upper) * 0.1f;
// find interior angles of hull corners
for (int j=0; j<hullsize; j++) {
int idx = hull[j]; // corner index
int pdx = idx == 0 ? count - 1 : idx - 1; // predecessor of idx
int sdx = idx == count - 1 ? 0 : idx + 1; // successor of idx
cv::Point v1 = cv::Point(PointArray[sdx].x - PointArray[idx].x, PointArray[sdx].y - PointArray[idx].y);
cv::Point v2 = cv::Point(PointArray[pdx].x - PointArray[idx].x, PointArray[pdx].y - PointArray[idx].y);
float angle = acos( (v1.x*v2.x + v1.y*v2.y) / (norm(v1) * norm(v2)) );
// low interior angle + within upper 90% of region -> we got a finger
if (angle < 1 ){ //&& PointArray[idx].y < cutoff) {
Blob blob = Blob();
//float area = cvContourArea( contour_ptr, CV_WHOLE_SEQ );
//cvMoments( contour_ptr, myMoments );
// this is if using non-angle bounding box
//CvRect rect = cvBoundingRect( contour_ptr, 0 );
blob.boundingRect.x = PointArray[idx].x-5;
blob.boundingRect.y = PointArray[idx].y-5;
blob.boundingRect.width = 10;
blob.boundingRect.height = 10;
//Angle Bounding rectangle
blob.angleBoundingRect.x = PointArray[idx].x-5;
blob.angleBoundingRect.y = PointArray[idx].y-5;
blob.angleBoundingRect.width = 10;
blob.angleBoundingRect.height = 10;
blob.angle = atan2(PointArray[idx].x - center.x , PointArray[idx].y - center.y);
// assign other parameters
//blob.area = fabs(area);
//blob.hole = area < 0 ? true : false;
//blob.length = cvArcLength(contour_ptr);
// AlexP
// The cast to int causes errors in tracking since centroids are calculated in
// floats and they migh land between integer pixel values (which is what we really want)
// This not only makes tracking more accurate but also more fluid
blob.centroid.x = PointArray[idx].x;//(myMoments->m10 / myMoments->m00);
blob.centroid.y = PointArray[idx].y;//(myMoments->m01 / myMoments->m00);
blob.lastCentroid.x = 0;
blob.lastCentroid.y = 0;
fingers.push_back(blob);
}
}
// Free memory.
free(PointArray);
free(hull);
}
}
}
nBlobs = blobs.size();
nFingers = fingers.size();
nObjects = objects.size();
// Free the storage memory.
// Warning: do this inside this function otherwise a strange memory leak
if( contour_storage != NULL )
cvReleaseMemStorage(&contour_storage);
if( storage != NULL )
cvReleaseMemStorage(&storage);
return (bTrackFingers)? nFingers:nBlobs;
}
int fmaFitEllipse(void)
{
long lErrors = 0;
CvPoint points[1000];
CvPoint2D32f fpoints[1000];
CvBox2D box;
CvMemStorage* storage = cvCreateMemStorage(0);
CvContour* contour;
CvSize axis;
IplImage* img = cvCreateImage( cvSize(200,200), IPL_DEPTH_8U, 1 );
for( int k = 0 ; k < 1000; k++ )
{
iplSet( img, 0 );
CvPoint center = { 100, 100 };
double angle = atsInitRandom( 0, 360 );
axis.height = (int)atsInitRandom( 5, 50 );
axis.width = (int)atsInitRandom( 5, 50 );
cvEllipse( img, center, axis, angle, 0, 360, 255, -1 );
cvFindContours( img, storage, (CvSeq**)&contour, sizeof(CvContour) );
cvCvtSeqToArray( (CvSeq*)contour, points );
for( int i = 0; i < contour->total; i++ )
{
fpoints[i].x = (float)points[i].x;
fpoints[i].y = (float)points[i].y;
}
cvFitEllipse( fpoints, contour->total, &box );
//compare boxes
if( fabs( box.center.x - center.x) > 1 || fabs( box.center.y - center.y ) > 1 )
{
lErrors++;
}
if( ( fabs( box.size.width - (axis.width * 2 ) ) > 4 ||
fabs( box.size.height - (axis.height * 2) ) > 4 ) &&
( fabs( box.size.height - (axis.width * 2 ) ) > 4 ||
fabs( box.size.width - (axis.height * 2) ) > 4 ) )
{
lErrors++;
//graphic
/*IplImage* rgb = cvCreateImage( cvSize(200,200), IPL_DEPTH_8U, 3 );
iplSet( rgb, 0 );
cvEllipse( rgb, center, axis, angle, 0, 360, CV_RGB(255,0,0) , 1 );
int window = atsCreateWindow( "proba", cvPoint(0,0), cvSize(200,200) );
cvEllipse( rgb, center, cvSize( box.size.width/2, box.size.height/2) , -box.angle,
0, 360, CV_RGB(0,255,0) , 1 );
//draw center
cvEllipse( rgb, center, cvSize( 0, 0) , 0,
0, 360, CV_RGB(255,255,255) , -1 );
atsDisplayImage( rgb, window, cvPoint(0,0), cvSize(200,200) );
getch();
atsDestroyWindow( window );
//one more
cvFitEllipse( fpoints, contour->total, &box );
*/
}
}
cvReleaseMemStorage( &storage );
if( !lErrors) return trsResult(TRS_OK, "No errors");
else
return trsResult(TRS_FAIL, "Fixed %d errors", lErrors);
}
/***************************************************************************************\
*
* This function compute intermediate polygon between contour1 and contour2
*
* Correspondence between points of contours specify by corr
*
* param = [0,1]; 0 correspondence to contour1, 1 - contour2
*
\***************************************************************************************/
CvSeq* icvBlendContours(CvSeq* contour1,
CvSeq* contour2,
CvSeq* corr,
double param,
CvMemStorage* storage)
{
int j;
CvSeqWriter writer01;
CvSeqReader reader01;
int Ni,Nj; // size of contours
int i; // counter
CvPoint* point1; // array of first contour point
CvPoint* point2; // array of second contour point
CvPoint point_output; // intermediate storage of ouput point
int corr_point;
// Create output sequence.
CvSeq* output = cvCreateSeq(0,
sizeof(CvSeq),
sizeof(CvPoint),
storage );
// Find size of contours.
Ni = contour1->total + 1;
Nj = contour2->total + 1;
point1 = (CvPoint* )malloc( Ni*sizeof(CvPoint) );
point2 = (CvPoint* )malloc( Nj*sizeof(CvPoint) );
// Initialize arrays of point
cvCvtSeqToArray( contour1, point1, CV_WHOLE_SEQ );
cvCvtSeqToArray( contour2, point2, CV_WHOLE_SEQ );
// First and last point mast be equal.
point1[Ni-1] = point1[0];
point2[Nj-1] = point2[0];
// Initializes process of writing to sequence.
cvStartAppendToSeq( output, &writer01);
i = Ni-1; //correspondence to points of contour1
for( ; corr; corr = corr->h_next )
{
//Initializes process of sequential reading from sequence
cvStartReadSeq( corr, &reader01, 0 );
for(j=0; j < corr->total; j++)
{
// Read element from sequence.
CV_READ_SEQ_ELEM( corr_point, reader01 );
// Compute point of intermediate polygon.
point_output.x = cvRound(point1[i].x + param*( point2[corr_point].x - point1[i].x ));
point_output.y = cvRound(point1[i].y + param*( point2[corr_point].y - point1[i].y ));
// Write element to sequence.
CV_WRITE_SEQ_ELEM( point_output, writer01 );
}
i--;
}
// Updates sequence header.
cvFlushSeqWriter( &writer01 );
return output;
}
void icvCalcContoursCorrespondence(CvSeq* contour1,
CvSeq* contour2,
CvSeq** corr,
CvMemStorage* storage)
{
int i,j; // counter of cycles
int Ni,Nj; // size of contours
_CvWork** W; // graph for search minimum of work
CvPoint* point1; // array of first contour point
CvPoint* point2; // array of second contour point
CvPoint2D32f* edges1; // array of first contour edge
CvPoint2D32f* edges2; // array of second contour edge
//CvPoint null_edge = {0,0}; //
CvPoint2D32f small_edge;
//double inf; // infinity
CvSeq* corr01;
CvSeqWriter writer;
char path; //
// Find size of contours
Ni = contour1->total + 1;
Nj = contour2->total + 1;
// Create arrays
W = (_CvWork**)malloc(sizeof(_CvWork*)*Ni);
for(i=0; i<Ni; i++)
{
W[i] = (_CvWork*)malloc(sizeof(_CvWork)*Nj);
}
point1 = (CvPoint* )malloc( Ni*sizeof(CvPoint) );
point2 = (CvPoint* )malloc( Nj*sizeof(CvPoint) );
edges1 = (CvPoint2D32f* )malloc( (Ni-1)*sizeof(CvPoint2D32f) );
edges2 = (CvPoint2D32f* )malloc( (Nj-1)*sizeof(CvPoint2D32f) );
// Initialize arrays of point
cvCvtSeqToArray( contour1, point1, CV_WHOLE_SEQ );
cvCvtSeqToArray( contour2, point2, CV_WHOLE_SEQ );
point1[Ni-1] = point1[0];
point2[Nj-1] = point2[0];
for(i=0; i<Ni-1; i++)
{
edges1[i].x = (float)( point1[i+1].x - point1[i].x );
edges1[i].y = (float)( point1[i+1].y - point1[i].y );
};
for(i=0; i<Nj-1; i++)
{
edges2[i].x = (float)( point2[i+1].x - point2[i].x );
edges2[i].y = (float)( point2[i+1].y - point2[i].y );
};
// Find infinity constant
//inf=1;
/////////////
//Find min path in graph
/////////////
W[0][0].w_east = 0;
W[0][0].w_south = 0;
W[0][0].w_southeast = 0;
W[1][1].w_southeast = _cvStretchingWork( &edges1[0], &edges2[0] );
W[1][1].w_east = inf;
W[1][1].w_south = inf;
W[1][1].path_se = PATH_TO_SE;
W[0][1].w_south = _cvStretchingWork( &null_edge, &edges2[0] );
W[0][1].path_s = 3;
W[1][0].w_east = _cvStretchingWork( &edges2[0], &null_edge );
W[1][0].path_e = PATH_TO_E;
for( i=1; i<Ni; i++ )
{
W[i][0].w_south = inf;
W[i][0].w_southeast = inf;
}
for(j=1; j<Nj; j++)
{
W[0][j].w_east = inf;
W[0][j].w_southeast = inf;
}
for(i=2; i<Ni; i++)
{
j=0;/////////
W[i][j].w_east = W[i-1][j].w_east;
W[i][j].w_east = W[i][j].w_east /*+
_cvBendingWork( &edges1[i-2], &edges1[i-1], &null_edge, &null_edge, NULL )*/;
W[i][j].w_east = W[i][j].w_east + _cvStretchingWork( &edges2[i-1], &null_edge );
W[i][j].path_e = PATH_TO_E;
j=1;//////////
W[i][j].w_south = inf;
_cvWorkEast (i, j, W, edges1, edges2);
W[i][j].w_southeast = W[i-1][j-1].w_east;
W[i][j].w_southeast = W[i][j].w_southeast + _cvStretchingWork( &edges1[i-1], &edges2[j-1] );
small_edge.x = NULL_EDGE*edges1[i-2].x;
small_edge.y = NULL_EDGE*edges1[i-2].y;
W[i][j].w_southeast = W[i][j].w_southeast +
_cvBendingWork( &edges1[i-2], &edges1[i-1], /*&null_edge*/&small_edge, &edges2[j-1]/*, &edges2[Nj-2]*/);
W[i][j].path_se = PATH_TO_E;
}
for(j=2; j<Nj; j++)
{
i=0;//////////
W[i][j].w_south = W[i][j-1].w_south;
W[i][j].w_south = W[i][j].w_south + _cvStretchingWork( &null_edge, &edges2[j-1] );
W[i][j].w_south = W[i][j].w_south /*+
_cvBendingWork( &null_edge, &null_edge, &edges2[j-2], &edges2[j-1], NULL )*/;
W[i][j].path_s = 3;
i=1;///////////
W[i][j].w_east= inf;
_cvWorkSouth(i, j, W, edges1, edges2);
W[i][j].w_southeast = W[i-1][j-1].w_south;
W[i][j].w_southeast = W[i][j].w_southeast + _cvStretchingWork( &edges1[i-1], &edges2[j-1] );
small_edge.x = NULL_EDGE*edges2[j-2].x;
small_edge.y = NULL_EDGE*edges2[j-2].y;
W[i][j].w_southeast = W[i][j].w_southeast +
_cvBendingWork( /*&null_edge*/&small_edge, &edges1[i-1], &edges2[j-2], &edges2[j-1]/*, &edges1[Ni-2]*/);
W[i][j].path_se = 3;
}
for(i=2; i<Ni; i++)
for(j=2; j<Nj; j++)
{
_cvWorkEast (i, j, W, edges1, edges2);
_cvWorkSouthEast(i, j, W, edges1, edges2);
_cvWorkSouth (i, j, W, edges1, edges2);
}
i=Ni-1;
j=Nj-1;
*corr = cvCreateSeq(0,
sizeof(CvSeq),
sizeof(int),
storage );
corr01 = *corr;
cvStartAppendToSeq( corr01, &writer );
if( W[i][j].w_east > W[i][j].w_southeast )
{
if( W[i][j].w_southeast > W[i][j].w_south )
{
path = 3;
}
else
{
path = PATH_TO_SE;
}
}
else
{
if( W[i][j].w_east < W[i][j].w_south )
{
path = PATH_TO_E;
}
else
{
path = 3;
}
}
do
{
CV_WRITE_SEQ_ELEM( j, writer );
switch( path )
{
case PATH_TO_E:
path = W[i][j].path_e;
i--;
cvFlushSeqWriter( &writer );
corr01->h_next = cvCreateSeq( 0,
sizeof(CvSeq),
sizeof(int),
storage );
corr01 = corr01->h_next;
cvStartAppendToSeq( corr01, &writer );
break;
case PATH_TO_SE:
path = W[i][j].path_se;
j--;
i--;
cvFlushSeqWriter( &writer );
corr01->h_next = cvCreateSeq( 0,
sizeof(CvSeq),
sizeof(int),
storage );
corr01 = corr01->h_next;
cvStartAppendToSeq( corr01, &writer );
break;
case 3:
path = W[i][j].path_s;
j--;
break;
}
} while( (i>=0) && (j>=0) );
cvFlushSeqWriter( &writer );
// Free memory
for(i=1; i<Ni; i++)
{
free(W[i]);
}
free(W);
free(point1);
free(point2);
free(edges1);
free(edges2);
}
//--------------------------------------------------------------------------------
int ofxContourFinder::findContours( ofxCvGrayscaleImage& input,
int minArea,
int maxArea,
int nConsidered,
double hullPress,
bool bFindHoles,
bool bUseApproximation) {
// get width/height disregarding ROI
IplImage* ipltemp = input.getCvImage();
width = ipltemp->width;
height = ipltemp->height;
reset();
// opencv will clober the image it detects contours on, so we want to
// copy it into a copy before we detect contours. That copy is allocated
// if necessary (necessary = (a) not allocated or (b) wrong size)
// so be careful if you pass in different sized images to "findContours"
// there is a performance penalty, but we think there is not a memory leak
// to worry about better to create mutiple contour finders for different
// sizes, ie, if you are finding contours in a 640x480 image but also a
// 320x240 image better to make two ofxContourFinder objects then to use
// one, because you will get penalized less.
if( inputCopy.width == 0 ) {
inputCopy.allocate( input.width, input.height );
inputCopy = input;
} else {
if( inputCopy.width == input.width && inputCopy.height == input.height )
inputCopy = input;
else {
// we are allocated, but to the wrong size --
// been checked for memory leaks, but a warning:
// be careful if you call this function with alot of different
// sized "input" images!, it does allocation every time
// a new size is passed in....
inputCopy.clear();
inputCopy.allocate( input.width, input.height );
inputCopy = input;
}
}
CvSeq* contour_list = NULL;
contour_storage = cvCreateMemStorage( 1000 );
storage = cvCreateMemStorage( 1000 );
CvContourRetrievalMode retrieve_mode
= (bFindHoles) ? CV_RETR_LIST : CV_RETR_EXTERNAL;
cvFindContours( inputCopy.getCvImage(), contour_storage, &contour_list,
sizeof(CvContour), retrieve_mode, bUseApproximation ? CV_CHAIN_APPROX_SIMPLE : CV_CHAIN_APPROX_NONE );
CvSeq* contour_ptr = contour_list;
nCvSeqsFound = 0;
// put the contours from the linked list, into an array for sorting
while( (contour_ptr != NULL) ) {
CvBox2D box=cvMinAreaRect2(contour_ptr);
float area = fabs( cvContourArea(contour_ptr, CV_WHOLE_SEQ) );
if( (area > minArea) && (area < maxArea) ) {
ofxBlob blob = ofxBlob();
float area = cvContourArea( contour_ptr, CV_WHOLE_SEQ);
cvMoments( contour_ptr, myMoments );
// this is if using non-angle bounding box
CvRect rect = cvBoundingRect( contour_ptr, 0 );
blob.boundingRect.x = rect.x/width;
blob.boundingRect.y = rect.y/height;
blob.boundingRect.width = rect.width/width;
blob.boundingRect.height = rect.height/height;
//Angle Bounding rectangle
blob.angleBoundingRect.x = box.center.x/width;
blob.angleBoundingRect.y = box.center.y/height;
blob.angleBoundingRect.width = box.size.height/width;
blob.angleBoundingRect.height = box.size.width/height;
blob.angle = box.angle;
// assign other parameters
blob.area = fabs(area);
blob.hole = area < 0 ? true : false;
blob.length = cvArcLength(contour_ptr);
// The cast to int causes errors in tracking since centroids are calculated in
// floats and they migh land between integer pixel values (which is what we really want)
// This not only makes tracking more accurate but also more fluid
blob.centroid.x = (myMoments->m10 / myMoments->m00) / width;
blob.centroid.y = (myMoments->m01 / myMoments->m00) / height;
blob.lastCentroid.x = 0;
blob.lastCentroid.y = 0;
if (blob.nFingers != 0){
blob.nFingers = 0;
blob.fingers.clear();
}
// get the points for the blob:
CvPoint pt;
CvSeqReader reader;
cvStartReadSeq( contour_ptr, &reader, 0 );
for( int j=0; j < min(TOUCH_MAX_CONTOUR_LENGTH, contour_ptr->total); j++ ) {
CV_READ_SEQ_ELEM( pt, reader );
blob.pts.push_back( ofPoint((float)pt.x / width, (float)pt.y / height) );
}
blob.nPts = blob.pts.size();
// Check if it´s a Hand and if it have fingers
//
if (area > 5000){
CvPoint* PointArray;
int* hull;
int hullsize;
CvSeq* contourAprox = cvApproxPoly(contour_ptr, sizeof(CvContour), storage, CV_POLY_APPROX_DP, hullPress, 1 );
int count = contourAprox->total; // This is number point in contour
PointArray = (CvPoint*)malloc( count*sizeof(CvPoint) ); // Alloc memory for contour point set.
hull = (int*)malloc(sizeof(int)*count); // Alloc memory for indices of convex hull vertices.
cvCvtSeqToArray(contourAprox, PointArray, CV_WHOLE_SEQ); // Get contour point set.
// Find convex hull for curent contour.
cvConvexHull( PointArray, count, NULL, CV_COUNTER_CLOCKWISE, hull, &hullsize);
int upper = 1, lower = 0;
for (int j=0; j<hullsize; j++) {
int idx = hull[j]; // corner index
if (PointArray[idx].y < upper)
upper = PointArray[idx].y;
if (PointArray[idx].y > lower)
lower = PointArray[idx].y;
}
float cutoff = lower - (lower - upper) * 0.1f;
// find interior angles of hull corners
for (int j=0; j < hullsize; j++) {
int idx = hull[j]; // corner index
int pdx = idx == 0 ? count - 1 : idx - 1; // predecessor of idx
int sdx = idx == count - 1 ? 0 : idx + 1; // successor of idx
cv::Point v1 = cv::Point(PointArray[sdx].x - PointArray[idx].x, PointArray[sdx].y - PointArray[idx].y);
cv::Point v2 = cv::Point(PointArray[pdx].x - PointArray[idx].x, PointArray[pdx].y - PointArray[idx].y);
float angle = acos( (v1.x*v2.x + v1.y*v2.y) / (norm(v1) * norm(v2)) );
// We got a finger
//
if (angle < 1 ){
ofPoint posibleFinger = ofPoint((float)PointArray[idx].x / width,
(float)PointArray[idx].y / height);
blob.nFingers++;
blob.fingers.push_back( posibleFinger );
}
}
if ( blob.nFingers > 0 ){
// because means that probably it's a hand
ofVec2f fingersAverage;
for (int j = 0; j < blob.fingers.size(); j++){
fingersAverage += blob.fingers[j];
}
fingersAverage /= blob.fingers.size();
if (blob.gotFingers){
blob.palm = (blob.palm + fingersAverage)*0.5;
//blob.palm = fingersAverage;
} else {
blob.palm = fingersAverage;
blob.gotFingers = true; // If got more than three fingers in a road it'll remember
}
}
// Free memory.
free(PointArray);
free(hull);
}
blobs.push_back(blob);
}
contour_ptr = contour_ptr->h_next;
}
nBlobs = blobs.size();
// Free the storage memory.
// Warning: do this inside this function otherwise a strange memory leak
if( contour_storage != NULL )
cvReleaseMemStorage(&contour_storage);
if( storage != NULL )
cvReleaseMemStorage(&storage);
free(contour_ptr);
return nBlobs;
}
static int aGestureRecognition(void)
{
IplImage *image, *imagew, *image_rez, *mask_rez, *image_hsv, *img_p[2],*img_v,
*init_mask_ver = 0, *final_mask_ver = 0;
CvPoint3D32f *pp, p;
CvPoint pt;
CvSize2D32f fsize;
CvPoint3D32f center, cf;
IplImage *image_mask, *image_maskw;
CvSize size;
CvHistogram *hist, *hist_mask;
int width, height;
int k_points, k_indexs;
int warpFlag, interpolate;
int hdim[2] = {20, 20};
double coeffs[3][3], rect[2][2], rez = 0, eps_rez = 2.5, rez_h;
float *thresh[2];
float hv[3];
float reps, aeps, ww;
float line[6], in[3][3], h[3][3];
float cx, cy, fx, fy;
static char num[4];
char *name_image;
char *name_range_image;
char *name_verify_data;
char *name_init_mask_very;
char *name_final_mask_very;
CvSeq *numbers;
CvSeq *points;
CvSeq *indexs;
CvMemStorage *storage;
CvRect hand_roi, hand_roi_trans;
int i,j, lsize, block_size = 1000, flag;
int code;
FILE *filin, *fil_ver;
/* read tests params */
code = TRS_OK;
/* define input information */
strcpy (num, "001");
lsize = strlen(data_path)+12;
name_verify_data = (char*)trsmAlloc(lsize);
name_range_image = (char*)trsmAlloc(lsize);
name_image = (char*)trsmAlloc(lsize);
name_init_mask_very = (char*)trsmAlloc(lsize);
name_final_mask_very = (char*)trsmAlloc(lsize);
/* define input range_image file path */
strcpy(name_range_image, data_path);
strcat(name_range_image, "rpts");
strcat(name_range_image, num);
strcat(name_range_image, ".txt");
/* define input image file path */
strcpy(name_image, data_path);
strcat(name_image, "real");
strcat(name_image, num);
strcat(name_image, ".bmp");
/* define verify data file path */
strcpy(name_verify_data, data_path);
strcat(name_verify_data, "very");
strcat(name_verify_data, num);
strcat(name_verify_data, ".txt");
/* define verify init mask file path */
strcpy(name_init_mask_very, data_path);
strcat(name_init_mask_very, "imas");
strcat(name_init_mask_very, num);
strcat(name_init_mask_very, ".bmp");
/* define verify final mask file path */
strcpy(name_final_mask_very, data_path);
strcat(name_final_mask_very, "fmas");
strcat(name_final_mask_very, num);
strcat(name_final_mask_very, ".bmp");
filin = fopen(name_range_image,"r");
fil_ver = fopen(name_verify_data,"r");
fscanf( filin, "\n%d %d\n", &width, &height);
printf("width=%d height=%d reading testing data...", width,height);
OPENCV_CALL( storage = cvCreateMemStorage ( block_size ) );
OPENCV_CALL( points = cvCreateSeq( CV_SEQ_POINT3D_SET, sizeof(CvSeq),
sizeof(CvPoint3D32f), storage ) );
OPENCV_CALL (indexs = cvCreateSeq( CV_SEQ_POINT_SET, sizeof(CvSeq),
sizeof(CvPoint), storage ) );
pp = 0;
/* read input image from file */
image = atsCreateImageFromFile( name_image );
if(image == NULL) {code = TRS_FAIL; goto m_exit;}
/* read input 3D points from input file */
for (i = 0; i < height; i++)
{
for (j = 0; j < width; j++)
{
fscanf( filin, "%f %f %f\n", &p.x, &p.y, &p.z);
if(/*p.x != 0 || p.y != 0 ||*/ p.z != 0)
{
OPENCV_CALL(cvSeqPush(points, &p));
pt.x = j; pt.y = i;
OPENCV_CALL(cvSeqPush(indexs, &pt));
}
}
}
k_points = points->total;
k_indexs = indexs->total;
/* convert sequence to array */
pp = (CvPoint3D32f*)trsmAlloc(k_points * sizeof(CvPoint3D32f));
OPENCV_CALL(cvCvtSeqToArray(points, pp ));
/* find 3D-line */
reps = (float)0.1;
aeps = (float)0.1;
ww = (float)0.08;
OPENCV_CALL( cvFitLine3D(pp, k_points, CV_DIST_WELSCH, &ww, reps, aeps, line ));
/* find hand location */
flag = -1;
fsize.width = fsize.height = (float)0.22; // (hand size in m)
numbers = NULL;
OPENCV_CALL( cvFindHandRegion (pp, k_points, indexs,line, fsize,
flag,¢er,storage, &numbers));
/* read verify data */
fscanf( fil_ver, "%f %f %f\n", &cf.x, &cf.y, &cf.z);
rez+= cvSqrt((center.x - cf.x)*(center.x - cf.x)+(center.y - cf.y)*(center.y - cf.y)+
(center.z - cf.z)*(center.z - cf.z))/3.;
/* create hand mask */
size.height = height;
size.width = width;
OPENCV_CALL( image_mask = cvCreateImage(size, IPL_DEPTH_8U, 1) );
OPENCV_CALL( cvCreateHandMask(numbers, image_mask, &hand_roi) );
/* read verify initial image mask */
init_mask_ver = atsCreateImageFromFile( name_init_mask_very );
if(init_mask_ver == NULL) {code = TRS_FAIL; goto m_exit;}
rez+= iplNorm(init_mask_ver, image_mask, IPL_L2) / (width*height+0.);
/* calculate homographic transformation matrix */
cx = (float)(width / 2.);
cy = (float)(height / 2.);
fx = fy = (float)571.2048;
/* define intrinsic camera parameters */
in[0][1] = in[1][0] = in[2][0] = in[2][1] = 0;
in[0][0] = fx; in[0][2] = cx;
in[1][1] = fy; in[1][2] = cy;
in[2][2] = 1;
OPENCV_CALL( cvCalcImageHomography(line, ¢er, in, h) );
rez_h = 0;
for(i=0;i<3;i++)
{
fscanf( fil_ver, "%f %f %f\n", &hv[0], &hv[1], &hv[2]);
for(j=0;j<3;j++)
{
rez_h+=(hv[j] - h[i][j])*(hv[j] - h[i][j]);
}
}
rez+=sqrt(rez_h)/9.;
/* image unwarping */
size.width = image->width;
size.height = image->height;
OPENCV_CALL( imagew = cvCreateImage(size, IPL_DEPTH_8U,3) );
OPENCV_CALL( image_maskw = cvCreateImage(size, IPL_DEPTH_8U,1) );
iplSet(image_maskw, 0);
cvSetImageROI(image, hand_roi);
cvSetImageROI(image_mask, hand_roi);
/* convert homographic transformation matrix from float to double */
for(i=0;i<3;i++)
for(j=0;j<3;j++)
coeffs[i][j] = (double)h[i][j];
/* get bounding rectangle for image ROI */
iplGetPerspectiveBound(image, coeffs, rect);
width = (int)(rect[1][0] - rect[0][0]);
height = (int)(rect[1][1] - rect[0][1]);
hand_roi_trans.x = (int)rect[0][0];hand_roi_trans.y = (int)rect[0][1];
hand_roi_trans.width = width; hand_roi_trans.height = height;
cvMaxRect(&hand_roi, &hand_roi_trans, &hand_roi);
iplSetROI((IplROI*)image->roi, 0, hand_roi.x, hand_roi.y,
hand_roi.width,hand_roi.height);
iplSetROI((IplROI*)image_mask->roi, 0, hand_roi.x, hand_roi.y,
hand_roi.width,hand_roi.height);
warpFlag = IPL_WARP_R_TO_Q;
/* interpolate = IPL_INTER_CUBIC; */
/* interpolate = IPL_INTER_NN; */
interpolate = IPL_INTER_LINEAR;
iplWarpPerspective(image, imagew, coeffs, warpFlag, interpolate);
iplWarpPerspective(image_mask, image_maskw, coeffs, warpFlag, IPL_INTER_NN);
/* set new image and mask ROI after transformation */
iplSetROI((IplROI*)imagew->roi,0, (int)rect[0][0], (int)rect[0][1],(int)width,(int)height);
iplSetROI((IplROI*)image_maskw->roi,0, (int)rect[0][0], (int)rect[0][1],(int)width,(int)height);
/* copy image ROI to new image and resize */
size.width = width; size.height = height;
image_rez = cvCreateImage(size, IPL_DEPTH_8U,3);
mask_rez = cvCreateImage(size, IPL_DEPTH_8U,1);
iplCopy(imagew,image_rez);
iplCopy(image_maskw,mask_rez);
/* convert rezult image from RGB to HSV */
image_hsv = iplCreateImageHeader(3, 0, IPL_DEPTH_8U, "HSV", "HSV",
IPL_DATA_ORDER_PIXEL, IPL_ORIGIN_TL,IPL_ALIGN_DWORD,
image_rez->width, image_rez->height, NULL, NULL, NULL, NULL);
iplAllocateImage(image_hsv, 0, 0 );
strcpy(image_rez->colorModel, "RGB");
strcpy(image_rez->channelSeq, "RGB");
image_rez->roi = NULL;
iplRGB2HSV(image_rez, image_hsv);
/* convert to three images planes */
img_p[0] = cvCreateImage(size, IPL_DEPTH_8U,1);
img_p[1] = cvCreateImage(size, IPL_DEPTH_8U,1);
img_v = cvCreateImage(size, IPL_DEPTH_8U,1);
cvCvtPixToPlane(image_hsv, img_p[0], img_p[1], img_v, NULL);
/* calculate histograms */
hist = cvCreateHist ( 2, hdim, CV_HIST_ARRAY);
hist_mask = cvCreateHist ( 2, hdim, CV_HIST_ARRAY);
/* install histogram threshold */
thresh[0] = (float*) trsmAlloc(2*sizeof(float));
thresh[1] = (float*) trsmAlloc(2*sizeof(float));
thresh[0][0] = thresh[1][0] = -0.5;
thresh[0][1] = thresh[1][1] = 255.5;
cvSetHistThresh( hist, thresh, 1);
cvSetHistThresh( hist_mask, thresh, 1);
cvCalcHist(img_p, hist, 0);
cvCalcHistMask(img_p, mask_rez, hist_mask, 0);
cvCalcProbDensity(hist, hist_mask, hist_mask);
cvCalcBackProject( img_p, mask_rez, hist_mask );
/* read verify final image mask */
final_mask_ver = atsCreateImageFromFile( name_final_mask_very );
if(final_mask_ver == NULL) {code = TRS_FAIL; goto m_exit;}
rez+= iplNorm(final_mask_ver, mask_rez, IPL_L2) / (width*height+0.);
trsWrite( ATS_CON | ATS_SUM, "\n gesture recognition \n");
trsWrite( ATS_CON | ATS_SUM, "result testing error = %f \n",rez);
if(rez > eps_rez) code = TRS_FAIL;
else code = TRS_OK;
m_exit:
cvReleaseImage(&image_mask);
cvReleaseImage(&mask_rez);
cvReleaseImage(&image_rez);
atsReleaseImage(final_mask_ver);
atsReleaseImage(init_mask_ver);
cvReleaseImage(&imagew);
cvReleaseImage(&image_maskw);
cvReleaseImage(&img_p[0]);
cvReleaseImage(&img_p[1]);
cvReleaseImage(&img_v);
cvReleaseHist( &hist);
cvReleaseHist( &hist_mask);
cvReleaseMemStorage ( &storage );
trsFree(pp);
trsFree(name_final_mask_very);
trsFree(name_init_mask_very);
trsFree(name_image);
trsFree(name_range_image);
trsFree(name_verify_data);
fclose(filin);
fclose(fil_ver);
/* _getch(); */
return code;
}
void find_convex_hull(struct ctx *ctx)
{
CvSeq *defects;
CvConvexityDefect *defect_array;
int i;
int x = 0, y = 0;
int dist = 0;
ctx->hull = NULL;
if (!ctx->contour)
return;
ctx->hull = cvConvexHull2(ctx->contour, ctx->hull_st, CV_CLOCKWISE, 0);
if (ctx->hull)
{
/* Get convexity defects of contour w.r.t. the convex hull */
defects = cvConvexityDefects(ctx->contour, ctx->hull,
ctx->defects_st);
if (defects && defects->total)
{
defect_array = (CvConvexityDefect*)calloc(defects->total,
sizeof(CvConvexityDefect));
cvCvtSeqToArray(defects, defect_array, CV_WHOLE_SEQ);
/* Average depth points to get hand center */
for (i = 0; i < defects->total && i < NUM_DEFECTS; i++)
{
x += defect_array[i].depth_point->x;
y += defect_array[i].depth_point->y;
ctx->defects[i] = cvPoint(defect_array[i].depth_point->x,
defect_array[i].depth_point->y);
}
x /= defects->total;
y /= defects->total;
ctx->num_defects = defects->total;
ctx->hand_center = cvPoint(x, y);
/* Compute hand radius as mean of distances of
defects' depth point to hand center */
for (i = 0; i < defects->total; i++)
{
int d = (x - defect_array[i].depth_point->x) *
(x - defect_array[i].depth_point->x) +
(y - defect_array[i].depth_point->y) *
(y - defect_array[i].depth_point->y);
dist += sqrt(d);
}
ctx->hand_radius = dist / defects->total;
free(defect_array);
}
}
}
/*F///////////////////////////////////////////////////////////////////////////////////////
// Name: icvCreateContourTree
// Purpose:
// Create binary tree representation for the contour
// Context:
// Parameters:
// contour - pointer to input contour object.
// storage - pointer to the current storage block
// tree - output pointer to the binary tree representation
// threshold - threshold for the binary tree building
//
//F*/
static CvStatus
icvCreateContourTree( const CvSeq * contour, CvMemStorage * storage,
CvContourTree ** tree, double threshold )
{
CvPoint *pt_p; /* pointer to previos points */
CvPoint *pt_n; /* pointer to next points */
CvPoint *pt1, *pt2; /* pointer to current points */
CvPoint t, tp1, tp2, tp3, tn1, tn2, tn3;
int lpt, flag, i, j, i_tree, j_1, j_3, i_buf;
double s, sp1, sp2, sn1, sn2, s_c, sp1_c, sp2_c, sn1_c, sn2_c, h, hp1, hp2, hn1, hn2,
a, ap1, ap2, an1, an2, b, bp1, bp2, bn1, bn2;
double a_s_c, a_sp1_c;
_CvTrianAttr **ptr_p, **ptr_n, **ptr1, **ptr2; /* pointers to pointers of triangles */
_CvTrianAttr *cur_adr;
int *num_p, *num_n, *num1, *num2; /* numbers of input contour points */
int nm, nmp1, nmp2, nmp3, nmn1, nmn2, nmn3;
int seq_flags = 1, i_end, prev_null, prev2_null;
double koef = 1.5;
double eps = 1.e-7;
double e;
CvStatus status;
int hearder_size;
_CvTrianAttr tree_one, tree_two, *tree_end, *tree_root;
CvSeqWriter writer;
assert( contour != NULL && contour->total >= 4 );
status = CV_OK;
if( contour == NULL )
return CV_NULLPTR_ERR;
if( contour->total < 4 )
return CV_BADSIZE_ERR;
if( !CV_IS_SEQ_POINT_SET( contour ))
return CV_BADFLAG_ERR;
/* Convert Sequence to array */
lpt = contour->total;
pt_p = pt_n = NULL;
num_p = num_n = NULL;
ptr_p = ptr_n = ptr1 = ptr2 = NULL;
tree_end = NULL;
pt_p = (CvPoint *) cvAlloc( lpt * sizeof( CvPoint ));
pt_n = (CvPoint *) cvAlloc( lpt * sizeof( CvPoint ));
num_p = (int *) cvAlloc( lpt * sizeof( int ));
num_n = (int *) cvAlloc( lpt * sizeof( int ));
hearder_size = sizeof( CvContourTree );
seq_flags = CV_SEQ_POLYGON_TREE;
cvStartWriteSeq( seq_flags, hearder_size, sizeof( _CvTrianAttr ), storage, &writer );
ptr_p = (_CvTrianAttr **) cvAlloc( lpt * sizeof( _CvTrianAttr * ));
ptr_n = (_CvTrianAttr **) cvAlloc( lpt * sizeof( _CvTrianAttr * ));
memset( ptr_p, 0, lpt * sizeof( _CvTrianAttr * ));
memset( ptr_n, 0, lpt * sizeof( _CvTrianAttr * ));
if( pt_p == NULL || pt_n == NULL )
return CV_OUTOFMEM_ERR;
if( ptr_p == NULL || ptr_n == NULL )
return CV_OUTOFMEM_ERR;
/* write fild for the binary tree root */
/* start_writer = writer; */
tree_one.pt.x = tree_one.pt.y = 0;
tree_one.sign = 0;
tree_one.area = 0;
tree_one.r1 = tree_one.r2 = 0;
tree_one.next_v1 = tree_one.next_v2 = tree_one.prev_v = NULL;
CV_WRITE_SEQ_ELEM( tree_one, writer );
tree_root = (_CvTrianAttr *) (writer.ptr - writer.seq->elem_size);
if( cvCvtSeqToArray( contour, (char *) pt_p ) == (char *) contour )
return CV_BADPOINT_ERR;
for( i = 0; i < lpt; i++ )
num_p[i] = i;
i = lpt;
flag = 0;
i_tree = 0;
e = 20.; /* initial threshold value */
ptr1 = ptr_p;
ptr2 = ptr_n;
pt1 = pt_p;
pt2 = pt_n;
num1 = num_p;
num2 = num_n;
/* binary tree constraction */
while( i > 4 )
{
if( flag == 0 )
{
ptr1 = ptr_p;
ptr2 = ptr_n;
pt1 = pt_p;
pt2 = pt_n;
num1 = num_p;
num2 = num_n;
flag = 1;
}
else
{
ptr1 = ptr_n;
ptr2 = ptr_p;
pt1 = pt_n;
pt2 = pt_p;
num1 = num_n;
num2 = num_p;
flag = 0;
}
t = pt1[0];
nm = num1[0];
tp1 = pt1[i - 1];
nmp1 = num1[i - 1];
tp2 = pt1[i - 2];
nmp2 = num1[i - 2];
tp3 = pt1[i - 3];
nmp3 = num1[i - 3];
tn1 = pt1[1];
nmn1 = num1[1];
tn2 = pt1[2];
nmn2 = num1[2];
i_buf = 0;
i_end = -1;
CV_MATCH_CHECK( status,
icvCalcTriAttr( contour, t, tp1, nmp1, tn1, nmn1, &s, &s_c, &h, &a,
&b ));
CV_MATCH_CHECK( status,
icvCalcTriAttr( contour, tp1, tp2, nmp2, t, nm, &sp1, &sp1_c, &hp1,
&ap1, &bp1 ));
CV_MATCH_CHECK( status,
icvCalcTriAttr( contour, tp2, tp3, nmp3, tp1, nmp1, &sp2, &sp2_c, &hp2,
&ap2, &bp2 ));
CV_MATCH_CHECK( status,
icvCalcTriAttr( contour, tn1, t, nm, tn2, nmn2, &sn1, &sn1_c, &hn1,
&an1, &bn1 ));
j_3 = 3;
prev_null = prev2_null = 0;
for( j = 0; j < i; j++ )
{
tn3 = pt1[j_3];
nmn3 = num1[j_3];
if( j == 0 )
j_1 = i - 1;
else
j_1 = j - 1;
CV_MATCH_CHECK( status, icvCalcTriAttr( contour, tn2, tn1, nmn1, tn3, nmn3,
&sn2, &sn2_c, &hn2, &an2, &bn2 ));
if( (s_c < sp1_c && s_c < sp2_c && s_c <= sn1_c && s_c <= sn2_c && s_c < e) ||
(((s_c == sp1_c && s_c <= sp2_c) || (s_c == sp2_c && s_c <= sp1_c)) &&
s_c <= sn1_c && s_c <= sn2_c && s_c < e && j > 1 && prev2_null == 0) ||
(s_c < eps && j > 0 && prev_null == 0) )
{
prev_null = prev2_null = 1;
if( s_c < threshold )
{
if( ptr1[j_1] == NULL && ptr1[j] == NULL )
{
if( i_buf > 0 )
ptr2[i_buf - 1] = NULL;
else
i_end = 0;
}
else
{
/* form next vertex */
tree_one.pt = t;
tree_one.sign = (char) (CV_SIGN( s ));
tree_one.r1 = h / a;
tree_one.r2 = b / a;
tree_one.area = fabs( s );
tree_one.next_v1 = ptr1[j_1];
tree_one.next_v2 = ptr1[j];
CV_WRITE_SEQ_ELEM( tree_one, writer );
cur_adr = (_CvTrianAttr *) (writer.ptr - writer.seq->elem_size);
if( ptr1[j_1] != NULL )
ptr1[j_1]->prev_v = cur_adr;
if( ptr1[j] != NULL )
ptr1[j]->prev_v = cur_adr;
if( i_buf > 0 )
ptr2[i_buf - 1] = cur_adr;
else
{
tree_end = (_CvTrianAttr *) writer.ptr;
i_end = 1;
}
i_tree++;
}
}
else
/* form next vertex */
{
tree_one.pt = t;
tree_one.sign = (char) (CV_SIGN( s ));
tree_one.area = fabs( s );
tree_one.r1 = h / a;
tree_one.r2 = b / a;
tree_one.next_v1 = ptr1[j_1];
tree_one.next_v2 = ptr1[j];
CV_WRITE_SEQ_ELEM( tree_one, writer );
cur_adr = (_CvTrianAttr *) (writer.ptr - writer.seq->elem_size);
if( ptr1[j_1] != NULL )
ptr1[j_1]->prev_v = cur_adr;
if( ptr1[j] != NULL )
ptr1[j]->prev_v = cur_adr;
if( i_buf > 0 )
ptr2[i_buf - 1] = cur_adr;
else
{
tree_end = cur_adr;
i_end = 1;
}
i_tree++;
}
}
else
/* the current triangle is'not LMIAT */
{
prev_null = 0;
switch (prev2_null)
{
case 0:
break;
case 1:
{
prev2_null = 2;
break;
}
case 2:
{
prev2_null = 0;
break;
}
}
if( j != i - 1 || i_end == -1 )
ptr2[i_buf] = ptr1[j];
else if( i_end == 0 )
ptr2[i_buf] = NULL;
else
ptr2[i_buf] = tree_end;
pt2[i_buf] = t;
num2[i_buf] = num1[j];
i_buf++;
}
/* go to next vertex */
tp3 = tp2;
tp2 = tp1;
tp1 = t;
t = tn1;
tn1 = tn2;
tn2 = tn3;
nmp3 = nmp2;
nmp2 = nmp1;
nmp1 = nm;
nm = nmn1;
nmn1 = nmn2;
nmn2 = nmn3;
sp2 = sp1;
sp1 = s;
s = sn1;
sn1 = sn2;
sp2_c = sp1_c;
sp1_c = s_c;
s_c = sn1_c;
sn1_c = sn2_c;
ap2 = ap1;
ap1 = a;
a = an1;
an1 = an2;
bp2 = bp1;
bp1 = b;
b = bn1;
bn1 = bn2;
hp2 = hp1;
hp1 = h;
h = hn1;
hn1 = hn2;
j_3++;
if( j_3 >= i )
j_3 = 0;
}
i = i_buf;
e = e * koef;
}
/* constract tree root */
if( i != 4 )
return CV_BADFACTOR_ERR;
t = pt2[0];
tn1 = pt2[1];
tn2 = pt2[2];
tp1 = pt2[3];
nm = num2[0];
nmn1 = num2[1];
nmn2 = num2[2];
nmp1 = num2[3];
/* first pair of the triangles */
CV_MATCH_CHECK( status,
icvCalcTriAttr( contour, t, tp1, nmp1, tn1, nmn1, &s, &s_c, &h, &a, &b ));
CV_MATCH_CHECK( status,
icvCalcTriAttr( contour, tn2, tn1, nmn1, tp1, nmp1, &sn2, &sn2_c, &hn2,
&an2, &bn2 ));
/* second pair of the triangles */
CV_MATCH_CHECK( status,
icvCalcTriAttr( contour, tn1, t, nm, tn2, nmn2, &sn1, &sn1_c, &hn1, &an1,
&bn1 ));
CV_MATCH_CHECK( status,
icvCalcTriAttr( contour, tp1, tn2, nmn2, t, nm, &sp1, &sp1_c, &hp1, &ap1,
&bp1 ));
a_s_c = fabs( s_c - sn2_c );
a_sp1_c = fabs( sp1_c - sn1_c );
if( a_s_c > a_sp1_c )
/* form child vertexs for the root */
{
tree_one.pt = t;
tree_one.sign = (char) (CV_SIGN( s ));
tree_one.area = fabs( s );
tree_one.r1 = h / a;
tree_one.r2 = b / a;
tree_one.next_v1 = ptr2[3];
tree_one.next_v2 = ptr2[0];
tree_two.pt = tn2;
tree_two.sign = (char) (CV_SIGN( sn2 ));
tree_two.area = fabs( sn2 );
tree_two.r1 = hn2 / an2;
tree_two.r2 = bn2 / an2;
tree_two.next_v1 = ptr2[1];
tree_two.next_v2 = ptr2[2];
CV_WRITE_SEQ_ELEM( tree_one, writer );
cur_adr = (_CvTrianAttr *) (writer.ptr - writer.seq->elem_size);
if( s_c > sn2_c )
{
if( ptr2[3] != NULL )
ptr2[3]->prev_v = cur_adr;
if( ptr2[0] != NULL )
ptr2[0]->prev_v = cur_adr;
ptr1[0] = cur_adr;
i_tree++;
CV_WRITE_SEQ_ELEM( tree_two, writer );
cur_adr = (_CvTrianAttr *) (writer.ptr - writer.seq->elem_size);
if( ptr2[1] != NULL )
ptr2[1]->prev_v = cur_adr;
if( ptr2[2] != NULL )
ptr2[2]->prev_v = cur_adr;
ptr1[1] = cur_adr;
i_tree++;
pt1[0] = tp1;
pt1[1] = tn1;
}
else
{
CV_WRITE_SEQ_ELEM( tree_two, writer );
cur_adr = (_CvTrianAttr *) (writer.ptr - writer.seq->elem_size);
if( ptr2[1] != NULL )
ptr2[1]->prev_v = cur_adr;
if( ptr2[2] != NULL )
ptr2[2]->prev_v = cur_adr;
ptr1[0] = cur_adr;
i_tree++;
CV_WRITE_SEQ_ELEM( tree_one, writer );
cur_adr = (_CvTrianAttr *) (writer.ptr - writer.seq->elem_size);
if( ptr2[3] != NULL )
ptr2[3]->prev_v = cur_adr;
if( ptr2[0] != NULL )
ptr2[0]->prev_v = cur_adr;
ptr1[1] = cur_adr;
i_tree++;
pt1[0] = tn1;
pt1[1] = tp1;
}
}
else
{
tree_one.pt = tp1;
tree_one.sign = (char) (CV_SIGN( sp1 ));
tree_one.area = fabs( sp1 );
tree_one.r1 = hp1 / ap1;
tree_one.r2 = bp1 / ap1;
tree_one.next_v1 = ptr2[2];
tree_one.next_v2 = ptr2[3];
tree_two.pt = tn1;
tree_two.sign = (char) (CV_SIGN( sn1 ));
tree_two.area = fabs( sn1 );
tree_two.r1 = hn1 / an1;
tree_two.r2 = bn1 / an1;
tree_two.next_v1 = ptr2[0];
tree_two.next_v2 = ptr2[1];
CV_WRITE_SEQ_ELEM( tree_one, writer );
cur_adr = (_CvTrianAttr *) (writer.ptr - writer.seq->elem_size);
if( sp1_c > sn1_c )
{
if( ptr2[2] != NULL )
ptr2[2]->prev_v = cur_adr;
if( ptr2[3] != NULL )
ptr2[3]->prev_v = cur_adr;
ptr1[0] = cur_adr;
i_tree++;
CV_WRITE_SEQ_ELEM( tree_two, writer );
cur_adr = (_CvTrianAttr *) (writer.ptr - writer.seq->elem_size);
if( ptr2[0] != NULL )
ptr2[0]->prev_v = cur_adr;
if( ptr2[1] != NULL )
ptr2[1]->prev_v = cur_adr;
ptr1[1] = cur_adr;
i_tree++;
pt1[0] = tn2;
pt1[1] = t;
}
else
{
CV_WRITE_SEQ_ELEM( tree_two, writer );
cur_adr = (_CvTrianAttr *) (writer.ptr - writer.seq->elem_size);
if( ptr2[0] != NULL )
ptr2[0]->prev_v = cur_adr;
if( ptr2[1] != NULL )
ptr2[1]->prev_v = cur_adr;
ptr1[0] = cur_adr;
i_tree++;
CV_WRITE_SEQ_ELEM( tree_one, writer );
cur_adr = (_CvTrianAttr *) (writer.ptr - writer.seq->elem_size);
if( ptr2[2] != NULL )
ptr2[2]->prev_v = cur_adr;
if( ptr2[3] != NULL )
ptr2[3]->prev_v = cur_adr;
ptr1[1] = cur_adr;
i_tree++;
pt1[0] = t;
pt1[1] = tn2;
}
}
/* form root */
s = cvContourArea( contour );
tree_root->pt = pt1[1];
tree_root->sign = 0;
tree_root->area = fabs( s );
tree_root->r1 = 0;
tree_root->r2 = 0;
tree_root->next_v1 = ptr1[0];
tree_root->next_v2 = ptr1[1];
tree_root->prev_v = NULL;
ptr1[0]->prev_v = (_CvTrianAttr *) tree_root;
ptr1[1]->prev_v = (_CvTrianAttr *) tree_root;
/* write binary tree root */
/* CV_WRITE_SEQ_ELEM (tree_one, start_writer); */
i_tree++;
/* create Sequence hearder */
*((CvSeq **) tree) = cvEndWriteSeq( &writer );
/* write points for the main segment into sequence header */
(*tree)->p1 = pt1[0];
M_END:
cvFree( &ptr_n );
cvFree( &ptr_p );
cvFree( &num_n );
cvFree( &num_p );
cvFree( &pt_n );
cvFree( &pt_p );
return status;
}
/*
函数：contoursample
功能：轮廓抽样
参数：seq ------ 轮廓点序列
samplearry --- 用于存放抽样点
samplearry ---- 抽样点数
*/
int contoursample(CvSeq * seq , CvPoint *samplearry, int samplenum)
{
int num = 0 ;
for (CvSeq *s = seq ; s !=NULL;s=s->h_next)
num +=s->total;
if ( num < samplenum)
{
return 0;
}
CvPoint *pointarray = (CvPoint *)malloc(num * sizeof(CvPoint));
int accum = 0 ;
for (CvSeq *s =seq ; s!=NULL;s=s->h_next)
{
cvCvtSeqToArray( s, pointarray +accum);
accum +=s->total;
}
// 轮廓点随机打乱
CvRNG rng;
rng = cvRNG(cvGetTickCount());
CvPoint pointtemp;
int tagtemp = -1;
for (int i = 0 ; i < num ; ++i)
{
int index = cvRandInt(&rng)%(num-i)+i;
if(index !=i)
{
pointtemp = pointarray[index];
pointarray[index] = pointarray[i];
pointarray[i] = pointtemp;
}
}
// 如果*samplenum > num 即取样点数远远小于轮廓点数随即抽取samplenum个点节省运算时间
if (num > 3 * samplenum)
{
CvPoint *pointarray2 = (CvPoint *)malloc(3*samplenum * sizeof(CvPoint));
for (int i = 0;i < 3*samplenum;++i)
{
pointarray2[i] = pointarray[i];
}
free(pointarray);
pointarray = pointarray2;
num = 3 * samplenum;
}
// 计算轮廓点与点间距离
int beg = 0,nelement = 0;
pairDistance* pd = (pairDistance*)malloc(sizeof(pairDistance)*((num-1)*num/2));
for (int i = 0 ; i < num ; i++)
{
for (int j = i +1 ; j < num ; ++j)
{
pd[nelement].i = i;
pd[nelement].j = j;
pd[nelement++].distance = (pointarray[i].x -pointarray[j].x) * (pointarray[i].x -pointarray[j].x) +
(pointarray[i].y -pointarray[j].y) * (pointarray[i].y -pointarray[j].y);
}
}
// 排序
quick_sort(pd,0,nelement-1);
// 删除最小距离点对中的其中一个点直到满足samplenum
int nneedremove = num - samplenum;
int *mask = (int *)malloc( num * sizeof(int));
memset(mask,0,num * sizeof(int));
//list<pairDistance>::iterator iter = list_pair.begin();
//list<pairDistance>::iterator iter = list_pair.begin();
while (nneedremove > 0)
{
int index0 = pd[beg].i;
int index1 = pd[beg].j;
if (mask[index0] == 0 && mask[index1] ==0)
{
mask[index1] = 1 ;
nneedremove --;
}
beg++;
}
// 将抽样点存放到samplearry中
int nstartindex = 0 ;
for (int i = 0 ; i < num ; ++i)
{
if (mask[i] ==0)
{
samplearry[nstartindex] = pointarray[i];
nstartindex++;
}
}
free(pointarray);
free(pd);
return 1;
}
void AirCursor::analyzeGrab()
{
cvClearMemStorage(m_cvMemStorage);
// get current depth map from Kinect
const XnDepthPixel* depthMap = m_depthGenerator.GetDepthMap();
// convert 16bit openNI depth map to 8bit IplImage used in opencv processing
int origDepthIndex = 0;
char* depthPtr = m_iplDepthMap->imageData;
char* debugPtr = 0;
if (m_debugImageEnabled) debugPtr = m_iplDebugImage->imageData;
for (unsigned int y = 0; y < DEPTH_MAP_SIZE_Y; y++)
{
for (unsigned int x = 0; x < DEPTH_MAP_SIZE_X; x++)
{
// get current depth value from original depth map
short depth = depthMap[origDepthIndex];
// check that current value is in the allowed range determined by clipping distances,
// and if it is map it to range 0 - 255 so that 255 is the closest value
unsigned char pixel = 0;
if (depth >= NEAR_CLIPPING_DISTANCE && depth <= FAR_CLIPPING_DISTANCE)
{
depth -= NEAR_CLIPPING_DISTANCE;
pixel = 255 - (255.0f * ((float)depth / (FAR_CLIPPING_DISTANCE - NEAR_CLIPPING_DISTANCE)));
}
else {
pixel = 0;
}
m_iplDepthMap->imageData[y * m_iplDepthMap->widthStep + x] = pixel;
*depthPtr = pixel;
if (m_debugImageEnabled)
{
// init debug image with the same depth map
*(debugPtr + 0) = pixel;
*(debugPtr + 1) = pixel;
*(debugPtr + 2) = pixel;
debugPtr += 3;
}
origDepthIndex++;
depthPtr++;
}
}
// calculate region of interest corner points in real world coordinates
XnPoint3D rwPoint1 = m_handPosRealWorld;
rwPoint1.X -= HAND_ROI_SIZE_LEFT;
rwPoint1.Y += HAND_ROI_SIZE_UP;
XnPoint3D rwPoint2 = m_handPosRealWorld;
rwPoint2.X += HAND_ROI_SIZE_RIGHT;
rwPoint2.Y -= HAND_ROI_SIZE_DOWN;
// convert corner points to projective coordinates
XnPoint3D projPoint1, projPoint2;
m_depthGenerator.ConvertRealWorldToProjective(1, &rwPoint1, &projPoint1);
m_depthGenerator.ConvertRealWorldToProjective(1, &rwPoint2, &projPoint2);
// round projected corner points to ints and clip them against the depth map
int ROItopLeftX = qRound(projPoint1.X); int ROItopLeftY = qRound(projPoint1.Y);
int ROIbottomRightX = qRound(projPoint2.X); int ROIbottomRightY = qRound(projPoint2.Y);
if (ROItopLeftX < 0) ROItopLeftX = 0; else if (ROItopLeftX > DEPTH_MAP_SIZE_X - 1) ROItopLeftX = DEPTH_MAP_SIZE_X - 1;
if (ROItopLeftY < 0) ROItopLeftY = 0; else if (ROItopLeftY > DEPTH_MAP_SIZE_Y - 1) ROItopLeftY = DEPTH_MAP_SIZE_Y - 1;
if (ROIbottomRightX < 0) ROIbottomRightX = 0; else if (ROIbottomRightX > DEPTH_MAP_SIZE_X - 1) ROIbottomRightX = DEPTH_MAP_SIZE_X - 1;
if (ROIbottomRightY < 0) ROIbottomRightY = 0; else if (ROIbottomRightY > DEPTH_MAP_SIZE_Y - 1) ROIbottomRightY = DEPTH_MAP_SIZE_Y - 1;
// set region of interest
CvRect rect = cvRect(ROItopLeftX, ROItopLeftY, ROIbottomRightX - ROItopLeftX, ROIbottomRightY - ROItopLeftY);
if(rect.height > 0 && rect.width > 0)
{
cvSetImageROI(m_iplDepthMap, rect);
if (m_debugImageEnabled) cvSetImageROI(m_iplDebugImage, rect);
}
// use depth threshold to isolate hand
// as a center point of thresholding, it seems that it's better to use a point bit below
// the point Nite gives as the hand point
XnPoint3D rwThresholdPoint = m_handPosRealWorld; rwThresholdPoint.Y -= 30;
XnPoint3D projThresholdPoint;
m_depthGenerator.ConvertRealWorldToProjective(1, &rwThresholdPoint, &projThresholdPoint);
int lowerBound = (unsigned char)m_iplDepthMap->imageData[(int)projThresholdPoint.Y * DEPTH_MAP_SIZE_X + (int)projThresholdPoint.X] - DEPTH_THRESHOLD;
if (lowerBound < 0) lowerBound = 0;
cvThreshold( m_iplDepthMap, m_iplDepthMap, lowerBound, 255, CV_THRESH_BINARY );
// color used for drawing the hand in the debug image, green for normal and red for grab.
// color lags one frame from actual grab status but in practice that shouldn't be too big of a problem
int rCol, gCol, bCol;
if(m_grabbing) {
rCol = 255; gCol = 0; bCol = 0;
}
else {
rCol = 0; gCol = 255; bCol = 0;
}
// go through the ROI and paint hand on debug image with current grab status color
if (m_debugImageEnabled)
{
// index of first pixel in the ROI
int startIndex = ROItopLeftY * m_iplDepthMap->widthStep + ROItopLeftX;
depthPtr = &(m_iplDepthMap->imageData[startIndex]);
debugPtr = &(m_iplDebugImage->imageData[startIndex * 3]);
// how much index needs to increase when moving to next line
int vertInc = m_iplDepthMap->widthStep - (ROIbottomRightX - ROItopLeftX);
for (int y = ROItopLeftY; y < ROIbottomRightY; y++)
{
for (int x = ROItopLeftX; x < ROIbottomRightX; x++)
{
if((unsigned char)*depthPtr > 0)
{
*(debugPtr + 0) = rCol / 2;
*(debugPtr + 1) = gCol / 2;
*(debugPtr + 2) = bCol / 2;
}
// next pixel
depthPtr++;
debugPtr += 3;
}
// next line
depthPtr += vertInc;
debugPtr += vertInc * 3;
}
}
// find contours in the hand and draw them on debug image
CvSeq* contours = 0;
cvFindContours(m_iplDepthMap, m_cvMemStorage, &contours, sizeof(CvContour));
if (m_debugImageEnabled)
{
if(contours)
{
cvDrawContours(m_iplDebugImage, contours, cvScalar(rCol, gCol , bCol), cvScalar(rCol, gCol, bCol), 1);
}
}
// go through contours and search for the biggest one
CvSeq* biggestContour = 0;
double biggestArea = 0.0f;
for(CvSeq* currCont = contours; currCont != 0; currCont = currCont->h_next)
{
// ignore small contours which are most likely caused by artifacts
double currArea = cvContourArea(currCont);
if(currArea < CONTOUR_MIN_SIZE) continue;
if(!biggestContour || currArea > biggestArea) {
biggestContour = currCont;
biggestArea = currArea;
}
}
int numOfValidDefects = 0;
if(biggestContour)
{
// calculate convex hull of the biggest contour found which is hopefully the hand
CvSeq* hulls = cvConvexHull2(biggestContour, m_cvMemStorage, CV_CLOCKWISE, 0);
if (m_debugImageEnabled)
{
// calculate convex hull and return it in a different form.
// only required for drawing
CvSeq* hulls2 = cvConvexHull2(biggestContour, m_cvMemStorage, CV_CLOCKWISE, 1);
// draw the convex hull
cvDrawContours(m_iplDebugImage, hulls2, cvScalar(rCol, gCol , bCol), cvScalar(rCol, gCol, bCol), 1);
}
// calculate convexity defects of hand's convex hull
CvSeq* defects = cvConvexityDefects(biggestContour, hulls, m_cvMemStorage);
int numOfDefects = defects->total;
if (numOfDefects > 0)
{
// calculate defect min size in projective coordinates.
// this is done using a vector from current hand position to a point DEFECT_MIN_SIZE amount above it.
// that vector is converted to projective coordinates and it's length is calculated.
XnPoint3D rwTempPoint = m_handPosRealWorld;
rwTempPoint.Y += DEFECT_MIN_SIZE;
XnPoint3D projTempPoint;
m_depthGenerator.ConvertRealWorldToProjective(1, &rwTempPoint, &projTempPoint);
int defectMinSizeProj = m_handPosProjected.Y - projTempPoint.Y;
// convert opencv seq to array
CvConvexityDefect* defectArray;defectArray = (CvConvexityDefect*)malloc(sizeof(CvConvexityDefect) * numOfDefects);
cvCvtSeqToArray(defects, defectArray, CV_WHOLE_SEQ);
for(int i = 0; i < numOfDefects; i++)
{
// ignore too small defects
if((defectArray[i].depth) < defectMinSizeProj)
{
continue;
}
numOfValidDefects++;
if (m_debugImageEnabled)
{
// draw blue point to defect
cvCircle(m_iplDebugImage, *(defectArray[i].depth_point), 5, cvScalar(0, 0, 255), -1);
cvCircle(m_iplDebugImage, *(defectArray[i].start), 5, cvScalar(0, 0, 255), -1);
cvCircle(m_iplDebugImage, *(defectArray[i].end), 5, cvScalar(0, 0, 255), -1);
}
}
free(defectArray);
}
}
if (m_debugImageEnabled)
{
cvResetImageROI(m_iplDebugImage);
// draw white dot on current hand position
cvCircle(m_iplDebugImage, cvPoint(m_handPosProjected.X, m_handPosProjected.Y), 5, cvScalar(255, 255, 255), -1);
// draw gray dot on current center of threshold position
//cvCircle(m_iplDebugImage, cvPoint(projThresholdPoint.X, projThresholdPoint.Y), 5, cvScalar(127, 127, 127), -1);
// draw ROI with green
//cvRectangle(m_iplDebugImage, cvPoint(ROItopLeftX, ROItopLeftY), cvPoint(ROIbottomRightX, ROIbottomRightY), cvScalar(0, 255, 0));
}
// determine current grab status based on defect count
if(numOfValidDefects <= GRAB_MAX_DEFECTS)
{
m_currentGrab = true;
}
else
{
m_currentGrab = false;
}
if (m_debugImageEnabled)
{
// debug strings
QList<QString> debugStrings;
debugStrings.push_back(QString("hand distance: " + QString::number(m_handPosRealWorld.Z) + " mm").toStdString().c_str());
debugStrings.push_back(QString("defects: " + QString::number(numOfValidDefects)).toStdString().c_str());
// convert iplDebugImage to QImage
char* scanLinePtr = m_iplDebugImage->imageData;
for (int y = 0;y < DEPTH_MAP_SIZE_Y; y++) {
memcpy(m_debugImage->scanLine(y), scanLinePtr, DEPTH_MAP_SIZE_X * 3);
scanLinePtr += DEPTH_MAP_SIZE_X * 3;
}
emit debugUpdate(*m_debugImage, debugStrings);
}
}
void save_camera_paramsOriginal( const char* out_filename, int image_count, CvSize img_size,
CvSize board_size, float square_size,
float aspect_ratio, int flags,
const CvMat* camera_matrix, CvMat* dist_coeffs,
const CvMat* extr_params, const CvSeq* image_points_seq,
const CvMat* reproj_errs, double avg_reproj_err )
{
CvFileStorage* fs = cvOpenFileStorage( out_filename, 0, CV_STORAGE_WRITE );
time_t t;
time( &t );
struct tm *t2 = localtime( &t );
char buf[1024];
strftime( buf, sizeof(buf)-1, "%c", t2 );
cvWriteString( fs, "calibration_time", buf );
cvWriteInt( fs, "image_count", image_count );
cvWriteInt( fs, "image_width", img_size.width );
cvWriteInt( fs, "image_height", img_size.height );
cvWriteInt( fs, "board_width", board_size.width );
cvWriteInt( fs, "board_height", board_size.height );
cvWriteReal( fs, "square_size", square_size );
if( flags & CV_CALIB_FIX_ASPECT_RATIO )
cvWriteReal( fs, "aspect_ratio", aspect_ratio );
if( flags != 0 )
{
sprintf( buf, "flags: %s%s%s%s",
flags & CV_CALIB_USE_INTRINSIC_GUESS ? "+use_intrinsic_guess" : "",
flags & CV_CALIB_FIX_ASPECT_RATIO ? "+fix_aspect_ratio" : "",
flags & CV_CALIB_FIX_PRINCIPAL_POINT ? "+fix_principal_point" : "",
flags & CV_CALIB_ZERO_TANGENT_DIST ? "+zero_tangent_dist" : "" );
cvWriteComment( fs, buf, 0 );
}
cvWriteInt( fs, "flags", flags );
cvWrite( fs, "camera_matrix", camera_matrix );
cvWrite( fs, "distortion_coefficients", dist_coeffs );
cvWriteReal( fs, "avg_reprojection_error", avg_reproj_err );
if( reproj_errs )
cvWrite( fs, "per_view_reprojection_errors", reproj_errs );
if( extr_params )
{
cvWriteComment( fs, "a set of 6-tuples (rotation vector + translation vector) for each view", 0 );
cvWrite( fs, "extrinsic_parameters", extr_params );
}
if( image_points_seq )
{
cvWriteComment( fs, "the array of board corners projections used for calibration", 0 );
assert( image_points_seq->total == image_count );
CvMat* image_points = cvCreateMat( 1, image_count*board_size.width*board_size.height, CV_32FC2 );
cvCvtSeqToArray( image_points_seq, image_points->data.fl );
cvWrite( fs, "image_points", image_points );
cvReleaseMat( &image_points );
}
cvReleaseFileStorage( &fs );
}
int findStableMatches( CvSeq *seq, float minRad, float maxRad, CandidatePtrVector& kps, IplImage* bin ) {
// Return value
int retVal = -1;
// Threshold Contour entries size
int elements = seq->total;
if( elements < 8 ) {
return retVal;
}
// Gather statistics
CvRect rect = cvBoundingRect( seq );
int high = ( rect.height < rect.width ? rect.width : rect.height );
int low = ( rect.height < rect.width ? rect.height : rect.width );
// If bounding box is very small simply return
if( low < minRad*2 ) {
return retVal;
}
// Allocate Contour array
CvPoint *group_pos = (CvPoint*) malloc(elements * sizeof(CvPoint));
cvCvtSeqToArray(seq, group_pos, CV_WHOLE_SEQ);
// Calculate arc and downsampling statistics
double arc_length = cvArcLength( seq );
double arc_approx = arc_length / 10;
double rect_approx = 12*(float)high / (float)low;
double downsample = 2 * elements / (rect_approx + arc_approx);
double ds_length = arc_length / 4;
// Perform downsampling
int maxSize = downsample * elements;
int newSize = 0;
CvPoint *dsed = (CvPoint*) malloc(maxSize * sizeof(CvPoint));
dsed[0] = CvPoint( group_pos[0] );
CvPoint last = CvPoint( dsed[0] );
newSize++;
for( int i = 1; i < elements; i++ ) {
double dist_so_far = dist_squared( group_pos[i], last );
if( dist_so_far > ds_length && newSize < maxSize ) {
dsed[newSize] = CvPoint( group_pos[i] );
newSize++;
last = CvPoint( group_pos[i] );
}
}
// Check to make sure reduced Contour size is sufficient [quickfix: todo revise above]
if( newSize < 6 ) {
free(group_pos);
free(dsed);
return -1;
}
// Fit Ellipse
CvPoint2D32f* input = (CvPoint2D32f*)malloc(newSize*sizeof(CvPoint2D32f));
for( int i=0; i<newSize; i++ ) {
input[i].x = dsed[i].x;
input[i].y = dsed[i].y;
}
CvBox2D* box = (CvBox2D*)malloc(sizeof(CvBox2D));
cvFitEllipse( input, newSize, box );
// Threshold size
float esize = PI*box->size.height*box->size.width/4.0f;
if( esize < PI*maxRad*maxRad ) {
// Add
Candidate *kp = new Candidate;
kp->angle = box->angle;
kp->r = box->center.y;
kp->c = box->center.x;
kp->minor = box->size.width/2;
kp->major = box->size.height/2;
kp->magnitude = 0;
kp->method = ADAPTIVE;
kps.push_back( kp );
retVal = 0;
} else {
// Interest point too large
retVal = 1;
}
// Deallocations
free(box);
free(input);
free(group_pos);
free(dsed);
return retVal;
}
IplImage* PlateFinder::FindPlate (IplImage *src) {
IplImage* plate;
IplImage* contourImg = cvCreateImage(cvGetSize(src), IPL_DEPTH_8U, 1); // anh tim contour
IplImage* grayImg = cvCreateImage(cvGetSize(src), IPL_DEPTH_8U, 1); // anh xam
cvCvtColor(src, grayImg, CV_RGB2GRAY);
IplImage* cloneImg = cvCreateImage(cvGetSize(src), IPL_DEPTH_8U, 3);
cloneImg = cvCloneImage(src);
// tien xu ly anh
cvCopy(grayImg, contourImg);
cvNormalize(contourImg, contourImg, 0, 255, CV_MINMAX);
ImageRestoration(contourImg);
IplImage* rectImg = cvCreateImage(cvGetSize(src), IPL_DEPTH_8U, 3);
cvMerge(contourImg, contourImg, contourImg, NULL, rectImg); // tron anh
// tim contour cua buc anh
CvMemStorage *storagePlate = cvCreateMemStorage(0);
CvSeq *contours = cvCreateSeq(CV_SEQ_ELTYPE_POINT, sizeof(CvSeq), sizeof(CvPoint), storagePlate);
cvFindContours(contourImg, storagePlate, &contours, sizeof(CvContour), CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE, cvPoint(0, 0));
//cvShowImage("contourImg", contourImg);
int xmin, ymin, xmax, ymax, w, h, s, r;
int count;
double ratio; // ty le chieu rong tren chieu cao
CvRect rectPlate;
// luu lai cac anh co kha nang la bien so
IplImage** plateArr = new IplImage *[5];
int j = 0;
for (int i = 0; i < 5; i++)
{
plateArr[i] = NULL;
}
while (contours) {
count = contours->total;
CvPoint *PointArray = new CvPoint[count];
cvCvtSeqToArray (contours, PointArray, CV_WHOLE_SEQ);
for (int i = 0; i < count; i++)
{
if (i == 0)
{
xmin = xmax = PointArray[i].x;
ymin = ymax = PointArray[i].y;
}
if (PointArray[i].x > xmax) {
xmax = PointArray[i].x;
}
if (PointArray[i].x < xmin) {
xmin = PointArray[i].x;
}
if (PointArray[i].y > ymax) {
ymax = PointArray[i].y;
}
if (PointArray[i].y < ymin) {
ymin = PointArray[i].y;
}
}
w = xmax - xmin;
h = ymax - ymin;
s = w * h;
cvRectangle (rectImg, cvPoint(xmin, ymin), cvPoint(xmax, ymax), RED);
// loai bo nhung hinh chu nhat co ti le khong dung
if (s != 0) {
r = (contourImg->height * contourImg->width) / s;
} else {
r = 1000;
}
if (w == 0 && h == 0) {
ratio = 0;
} else {
ratio = (double)w/h;
}
if (r > 30 && r < 270) {
// ve ra hcn mau xanh la
cvRectangle (rectImg, cvPoint(xmin, ymin), cvPoint(xmax, ymax), GREEN);
if (ratio > 2.6 && ratio < 7) {
cvRectangle (rectImg, cvPoint(xmin, ymin), cvPoint(xmax, ymax), BLUE);
if (w > 80 && w < 250 && h > 25 && h < 150) {
rectPlate = cvRect (xmin, ymin, w, h);
cvRectangle (cloneImg, cvPoint(rectPlate.x, rectPlate.y),
cvPoint(rectPlate.x + rectPlate.width, rectPlate.y + rectPlate.height), RED, 3);
// cat bien so
plate = cvCreateImage(cvSize(rectPlate.width, rectPlate.height), IPL_DEPTH_8U, 3);
cvSetImageROI(src, rectPlate);
cvCopy(src, plate, NULL);
cvResetImageROI(src);
// luu vao mang cac bien so plateArr
int cnt = CountCharacter(plate);
if (cnt >= 5) {
plateArr[j] = cvCloneImage(plate);
j++;
}
}
}
}
delete []PointArray;
contours = contours->h_next;
}
// sap xep
if (plateArr[0])
{
int w = plateArr[0]->width;
int flag;
for (int i = 1; i < 4; i++)
{
if (plateArr[i] && plateArr[i]->width < w)
{
flag = i;
}
}
plateArr[0] = plateArr[flag];
}
cvShowImage("cloneImg", cloneImg);
//cvShowImage("rectImg", rectImg);
//cvShowImage("plate", plateArr[0]);
cvReleaseImage(&contourImg);
cvReleaseImage(&rectImg);
cvReleaseImage(&plate);
return plateArr[0];
}
bool Classifier::run(const IplImage *frame, CObjectList *objects, bool scored)
{
double xDiff = 0, yDiff = 0;
optical_flow(frame, &xDiff, &yDiff);
totalXDiff += xDiff;
totalYDiff += yDiff;
if (!scored)
return true;
cout << "--------------------------------------" << endl;
cout << "\t\tRun" << endl;
assert((frame != NULL) && (objects != NULL));
printf("Let's go!\n");
for (int i = 0; i < (int)prevObjects.size(); ++i) {
if (prevObjects[i].rect.x > -20 && prevObjects[i].rect.x < frame->width
&& prevObjects[i].rect.y > -20 && prevObjects[i].rect.y < frame->height) {
objects->push_back(prevObjects[i]);
cout << prevObjects[i].label << " is now at (" << prevObjects[i].rect.x << ", " << prevObjects[i].rect.y << ")" << endl;
}
}
//printf("HEY OPTICAL FLOW!!!! %f %f\n", totalXDiff, totalYDiff);
// move old objects
for (int i = 0; i < (int)objects->size(); ++i) {
(*objects)[i].rect.x -= totalXDiff * 3;
(*objects)[i].rect.y -= totalYDiff * 3;
}
cout << "Flow: " << totalXDiff << " " << totalYDiff << endl;
totalYDiff = 0;
totalXDiff = 0;
// Convert to grayscale.
IplImage *gray = cvCreateImage(cvGetSize(frame), IPL_DEPTH_8U, 1);
cvCvtColor(frame, gray, CV_BGR2GRAY);
// Resize by half first, as per the handout.
double scale = 2.0;
IplImage *dst = cvCreateImage(cvSize(gray->width / scale, gray->height / scale), gray->depth, gray->nChannels);
cvResize(gray, dst);
printf("About to do SURF\n");
CvSeq *keypoints = 0, *descriptors = 0;
CvSURFParams params = cvSURFParams(100, SURF_SIZE == 128);
cvExtractSURF(dst, 0, &keypoints, &descriptors, storage, params);
cout << "desc: " << descriptors->total << endl;
if (descriptors->total == 0) return false;
vector<float> desc;
desc.resize(descriptors->total * descriptors->elem_size/sizeof(float));
cvCvtSeqToArray(descriptors, &desc[0]);
vector<CvSURFPoint> keypts;
keypts.resize(keypoints->total);
cvCvtSeqToArray(keypoints, &keypts[0]);
vector<float *> features;
int where = 0;
for (int pt = 0; pt < keypoints->total; ++pt) {
float *f = new float[SURF_SIZE];
for (int j = 0; j < SURF_SIZE; ++j) {
f[j] = desc[where];
++where;
}
features.push_back(f);
}
printf("Done SURF\n");
printf("Clustering...\n");
vector<int> cluster(features.size());
for (int i = 0; i < (int)features.size(); ++i) {
cluster[i] = best_cluster(centers, features[i]);
}
printf("Done clustering...\n");
vector<FoundObject> newObjects;
run_boxscan(dst, cluster, keypts, features, newObjects, objects);
for (int i = 0; i < (int)newObjects.size(); ++i) {
if (newObjects[i].object.rect.x > -20 && newObjects[i].object.rect.x < frame->width
&& newObjects[i].object.rect.y > -20 && newObjects[i].object.rect.y < frame->height) {
objects->push_back(newObjects[i].object);
cout << "Found object: " << newObjects[i].object.label << " at (" ;
cout << newObjects[i].object.rect.x << ", " << newObjects[i].object.rect.y << ")" << endl;
}
}
prevObjects = *objects;
cvReleaseImage(&gray);
return true;
}
bool Classifier::extract(TTrainingFileList& fileList)
{
cout << "Classes:" << endl;
for (int i = 0; i < (int)fileList.classes.size(); i++) {
cout << fileList.classes[i] << " (";
int count = 0;
for (int j = 0; j < (int)fileList.files.size(); j++) {
if (fileList.files[j].label == fileList.classes[i]) {
count += 1;
}
}
cout << count << " samples)" << endl;
}
cout << endl;
IplImage *image;
int maxImages = INT_MAX;
cout << "Processing images..." << endl;
//vector<int> numIpoints(kNumObjectTypes);
int minfiles = min(maxImages, (int)fileList.files.size());
for (int i = 0; i < minfiles; i++)
{
// skip too many others...
if (fileList.files[i].label == "other" && --max_others < 0) continue;
// show progress
if (i % 10 == 0) showProgress(i, minfiles);
image = cvLoadImage(fileList.files[i].filename.c_str(), 0);
if (image == NULL) {
cerr << "ERROR: could not load image " << fileList.files[i].filename.c_str() << endl;
continue;
}
string label = fileList.files[i].label;
CvSeq *keypoints = 0, *descriptors = 0;
CvSURFParams params = cvSURFParams(100, SURF_SIZE == 128);
cvExtractSURF(image, 0, &keypoints, &descriptors, storage, params);
if (descriptors->total == 0) continue;
vector<float> desc;
desc.resize(descriptors->total * descriptors->elem_size/sizeof(float));
cvCvtSeqToArray(descriptors, &desc[0]);
vector<float *> features;
int where = 0;
for (int pt = 0; pt < keypoints->total; ++pt) {
float *f = new float[SURF_SIZE];
for (int j = 0; j < SURF_SIZE; ++j) {
f[j] = desc[where];
++where;
}
features.push_back(f);
}
if (i % test_to_train == 1 || !test_on)
set_train.push(stringToClassInt(label), features);
else set_test.push(stringToClassInt(label), features);
}
cout << endl << "Extracted surf features. " << endl;
cout << "Train Set" << endl;
set_train.recount();
set_train.stats();
cout << "Test Set" << endl;
set_train.recount();
set_test.stats();
return true;
}
