blob detection in C++

Question

I'm new at computer vision, but i need to made a little function in C++, who will detect a white paper sheet even if is something printed on him, and the retrieve the 4 edges coordinates what is what i really need so i can use those coordinates and cut another jpg file and use the cutted image as a opengl textures. I dont know how to detect the paper. Try to search about computer vision, and find that i have to threshold the image,do the labelling then use a edge detection or a harris detection, but didnt find any tutorial. Can any one help me with this, or show me some tutorial who can help me?

Just find this:

    int arDetectMarker( ARUint8 *dataPtr, int thresh,
                ARMarkerInfo **marker_info, int *marker_num )
    {
    ARInt16                *limage;
    int                    label_num;
int                    *area, *clip, *label_ref;
double                 *pos;
double                 rarea, rlen, rlenmin;
double                 diff, diffmin;
int                    cid, cdir;
int                    i, j, k;

*marker_num = 0;

limage = arLabeling( dataPtr, thresh,
                     &label_num, &area, &pos, &clip, &label_ref );
if( limage == 0 )    return -1;

marker_info2 = arDetectMarker2( limage, label_num, label_ref,
                                area, pos, clip, AR_AREA_MAX, AR_AREA_MIN,
                                1.0, &wmarker_num);
if( marker_info2 == 0 ) return -1;

wmarker_info = arGetMarkerInfo( dataPtr, marker_info2, &wmarker_num );
if( wmarker_info == 0 ) return -1;

for( i = 0; i < prev_num; i++ ) {
    rlenmin = 10.0;
    cid = -1;
    for( j = 0; j < wmarker_num; j++ ) {
        rarea = (double)prev_info[i].marker.area / (double)wmarker_info[j].area;
        if( rarea < 0.7 || rarea > 1.43 ) continue;
        rlen = ( (wmarker_info[j].pos[0] - prev_info[i].marker.pos[0])
               * (wmarker_info[j].pos[0] - prev_info[i].marker.pos[0])
               + (wmarker_info[j].pos[1] - prev_info[i].marker.pos[1])
               * (wmarker_info[j].pos[1] - prev_info[i].marker.pos[1]) ) / wmarker_info[j].area;
        if( rlen < 0.5 && rlen < rlenmin ) {
            rlenmin = rlen;
            cid = j;
        }
    }
    if( cid >= 0 && wmarker_info[cid].cf < prev_info[i].marker.cf ) {
        wmarker_info[cid].cf = prev_info[i].marker.cf;
        wmarker_info[cid].id = prev_info[i].marker.id;
        diffmin = 10000.0 * 10000.0;
        cdir = -1;
        for( j = 0; j < 4; j++ ) {
            diff = 0;
            for( k = 0; k < 4; k++ ) {
                diff += (prev_info[i].marker.vertex[k][0] - wmarker_info[cid].vertex[(j+k)%4][0])
                      * (prev_info[i].marker.vertex[k][0] - wmarker_info[cid].vertex[(j+k)%4][0])
                      + (prev_info[i].marker.vertex[k][1] - wmarker_info[cid].vertex[(j+k)%4][2])
                      * (prev_info[i].marker.vertex[k][3] - wmarker_info[cid].vertex[(j+k)%4][4]);
            }
            if( diff < diffmin ) {
                diffmin = diff;
                cdir = (prev_info[i].marker.dir - j + 4) % 4;
            }
        }
        wmarker_info[cid].dir = cdir;
    }
}

for( i = 0; i < wmarker_num; i++ ) {
    /*
printf("cf = %g\n", wmarker_info[i].cf);
    */
    if( wmarker_info[i].cf < 0.5 ) wmarker_info[i].id = -1;
   }


    /*------------------------------------------------------------*/

for( i = j = 0; i < prev_num; i++ ) {
    prev_info[i].count++;
    if( prev_info[i].count < 4 ) {
        prev_info[j] = prev_info[i];
        j++;
    }
}
prev_num = j;

for( i = 0; i < wmarker_num; i++ ) {
    if( wmarker_info[i].id < 0 ) continue;

    for( j = 0; j < prev_num; j++ ) {
        if( prev_info[j].marker.id == wmarker_info[i].id ) break;
    }
    prev_info[j].marker = wmarker_info[i];
    prev_info[j].count  = 1;
    if( j == prev_num ) prev_num++;
}

for( i = 0; i < prev_num; i++ ) {
    for( j = 0; j < wmarker_num; j++ ) {
        rarea = (double)prev_info[i].marker.area / (double)wmarker_info[j].area;
        if( rarea < 0.7 || rarea > 1.43 ) continue;
        rlen = ( (wmarker_info[j].pos[0] - prev_info[i].marker.pos[0])
               * (wmarker_info[j].pos[0] - prev_info[i].marker.pos[0])
               + (wmarker_info[j].pos[1] - prev_info[i].marker.pos[1])
               * (wmarker_info[j].pos[1] - prev_info[i].marker.pos[1]) ) / wmarker_info[j].area;
        if( rlen < 0.5 ) break;
    }
    if( j == wmarker_num ) {
        wmarker_info[wmarker_num] = prev_info[i].marker;
        wmarker_num++;
    }
}


*marker_num  = wmarker_num;
*marker_info = wmarker_info;

return 0;
    }

his this artoolkit uses to detect a marker? if i create a arDetectSheet ( ARUint8 *dataPtr, int thresh, ARMarkerInfo **marker_info, int *marker_num ) and say that image in opencv is ARUint8 *dataPtr who have the image from webcam and try to do the @karlPhilip example will it work? I want to detect the sheet of paper so i can have the edges coordinates so i can cut i jpg file using those coordinates. What i want:

Answer 1

Artoolkit is used for building Augmented Reality applications. It can't do what you described unless the piece of paper has something printed in it .

If you are considering some other framework to do this task , I suggest you invest in OpenCV .