How to make the blackboard text appear clearer using MATLAB?
What are the sequence of filters I should put if I want the final image to be more clearer with a digital type look. I mean only two distinct colors, one for the board and one for the chalk writing.
Solution 1:
When it comes to identifying text in images you better use Stroke Width Transform.
Here's a little result I obtained on your image (the basic transform + connected component w/o filtering):
My mex implementation based on code from here
#include "mex.h"
#include <vector>
#include <map>
#include <set>
#include <algorithm>
#include <math.h>
using namespace std;
#define PI 3.14159265
struct Point2d {
int x;
int y;
float SWT;
};
struct Point2dFloat {
float x;
float y;
};
struct Ray {
Point2d p;
Point2d q;
std::vector<Point2d> points;
};
void strokeWidthTransform(const float * edgeImage,
const float * gradientX,
const float * gradientY,
bool dark_on_light,
float * SWTImage,
int h, int w,
std::vector<Ray> & rays) {
// First pass
float prec = .05f;
for( int row = 0; row < h; row++ ){
const float* ptr = edgeImage + row*w;
for ( int col = 0; col < w; col++ ){
if (*ptr > 0) {
Ray r;
Point2d p;
p.x = col;
p.y = row;
r.p = p;
std::vector<Point2d> points;
points.push_back(p);
float curX = (float)col + 0.5f;
float curY = (float)row + 0.5f;
int curPixX = col;
int curPixY = row;
float G_x = gradientX[ col + row*w ];
float G_y = gradientY[ col + row*w ];
// normalize gradient
float mag = sqrt( (G_x * G_x) + (G_y * G_y) );
if (dark_on_light){
G_x = -G_x/mag;
G_y = -G_y/mag;
} else {
G_x = G_x/mag;
G_y = G_y/mag;
}
while (true) {
curX += G_x*prec;
curY += G_y*prec;
if ((int)(floor(curX)) != curPixX || (int)(floor(curY)) != curPixY) {
curPixX = (int)(floor(curX));
curPixY = (int)(floor(curY));
// check if pixel is outside boundary of image
if (curPixX < 0 || (curPixX >= w) || curPixY < 0 || (curPixY >= h)) {
break;
}
Point2d pnew;
pnew.x = curPixX;
pnew.y = curPixY;
points.push_back(pnew);
if ( edgeImage[ curPixY*w+ curPixX ] > 0) {
r.q = pnew;
// dot product
float G_xt = gradientX[ curPixY*w + curPixX ];
float G_yt = gradientY[ curPixY*w + curPixX ];
mag = sqrt( (G_xt * G_xt) + (G_yt * G_yt) );
if (dark_on_light){
G_xt = -G_xt/mag;
G_yt = -G_yt/mag;
} else {
G_xt = G_xt/mag;
G_yt = G_yt/mag;
}
if (acos(G_x * -G_xt + G_y * -G_yt) < PI/2.0 ) {
float length = sqrt( ((float)r.q.x - (float)r.p.x)*((float)r.q.x - (float)r.p.x) + ((float)r.q.y - (float)r.p.y)*((float)r.q.y - (float)r.p.y));
for (std::vector<Point2d>::iterator pit = points.begin(); pit != points.end(); pit++) {
float* pSWT = SWTImage + w * pit->y + pit->x;
if (*pSWT < 0) {
*pSWT = length;
} else {
*pSWT = std::min(length, *pSWT);
}
}
r.points = points;
rays.push_back(r);
}
break;
}
}
}
}
ptr++;
}
}
}
bool Point2dSort(const Point2d &lhs, const Point2d &rhs) {
return lhs.SWT < rhs.SWT;
}
void SWTMedianFilter(float * SWTImage, int h, int w,
std::vector<Ray> & rays, float maxWidth = -1 ) {
for (std::vector<Ray>::iterator rit = rays.begin(); rit != rays.end(); rit++) {
for (std::vector<Point2d>::iterator pit = rit->points.begin(); pit != rit->points.end(); pit++) {
pit->SWT = SWTImage[ w*pit->y + pit->x ];
}
std::sort(rit->points.begin(), rit->points.end(), &Point2dSort);
//std::nth_element( rit->points.begin(), rit->points.end(), rit->points.size()/2, &Point2dSort );
float median = (rit->points[rit->points.size()/2]).SWT;
if ( maxWidth > 0 && median >= maxWidth ) {
median = -1;
}
for (std::vector<Point2d>::iterator pit = rit->points.begin(); pit != rit->points.end(); pit++) {
SWTImage[ w*pit->y + pit->x ] = std::min(pit->SWT, median);
}
}
}
typedef std::vector< std::set<int> > graph_t; // graph as a list of neighbors per node
void connComp( const graph_t& g, std::vector<int>& c, int i, int l ) {
// starting from node i labe this conn-comp with label l
if ( i < 0 || i > g.size() ) {
return;
}
std::vector< int > stack;
// push i
stack.push_back(i);
c[i] = l;
while ( ! stack.empty() ) {
// pop
i = stack.back();
stack.pop_back();
// go over all nieghbors
for ( std::set<int>::const_iterator it = g[i].begin(); it != g[i].end(); it++ ) {
if ( c[*it] < 0 ) {
stack.push_back( *it );
c[ *it ] = l;
}
}
}
}
int findNextToLabel( const graph_t& g, const vector<int>& c ) {
for ( int i = 0 ; i < c.size(); i++ ) {
if ( c[i] < 0 ) {
return i;
}
}
return c.size();
}
int connected_components(const graph_t& g, vector<int>& c) {
// check for empty graph!
if ( g.empty() ) {
return 0;
}
int i = 0;
int num_conn = 0;
do {
connComp( g, c, i, num_conn );
num_conn++;
i = findNextToLabel( g, c );
} while ( i < g.size() );
return num_conn;
}
std::vector< std::vector<Point2d> >
findLegallyConnectedComponents(const float* SWTImage, int h, int w,
std::vector<Ray> & rays) {
std::map<int, int> Map;
std::map<int, Point2d> revmap;
std::vector<std::vector<Point2d> > components; // empty
int num_vertices = 0, idx = 0;
graph_t g;
// Number vertices for graph. Associate each point with number
for( int row = 0; row < h; row++ ){
for (int col = 0; col < w; col++ ){
idx = col + w * row;
if (SWTImage[idx] > 0) {
Map[idx] = num_vertices;
Point2d p;
p.x = col;
p.y = row;
revmap[num_vertices] = p;
num_vertices++;
std::set<int> empty;
g.push_back(empty);
}
}
}
if ( g.empty() ) {
return components; // nothing to do with an empty graph...
}
for( int row = 0; row < h; row++ ){
for (int col = 0; col < w; col++ ){
idx = col + w * row;
if ( SWTImage[idx] > 0) {
// check pixel to the right, right-down, down, left-down
int this_pixel = Map[idx];
float thisVal = SWTImage[idx];
if (col+1 < w) {
float right = SWTImage[ w*row + col + 1 ];
if (right > 0 && (thisVal/right <= 3.0 || right/thisVal <= 3.0)) {
g[this_pixel].insert( Map[ w*row + col + 1 ] );
g[ Map[ w*row + col + 1 ] ].insert( this_pixel );
//boost::add_edge(this_pixel, map.at(row * SWTImage->width + col + 1), g);
}
}
if (row+1 < h) {
if (col+1 < w) {
float right_down = SWTImage[ w*(row+1) + col + 1 ];
if (right_down > 0 && (thisVal/right_down <= 3.0 || right_down/thisVal <= 3.0)) {
g[ this_pixel ].insert( Map[ w*(row+1) + col + 1 ] );
g[ Map[ w*(row+1) + col + 1 ] ].insert(this_pixel);
// boost::add_edge(this_pixel, map.at((row+1) * SWTImage->width + col + 1), g);
}
}
float down = SWTImage[ w*(row+1) + col ];
if (down > 0 && (thisVal/down <= 3.0 || down/thisVal <= 3.0)) {
g[ this_pixel ].insert( Map[ w*(row+1) + col ] );
g[ Map[ w*(row+1) + col ] ].insert( this_pixel );
//boost::add_edge(this_pixel, map.at((row+1) * SWTImage->width + col), g);
}
if (col-1 >= 0) {
float left_down = SWTImage[ w*(row+1) + col - 1 ];
if (left_down > 0 && (thisVal/left_down <= 3.0 || left_down/thisVal <= 3.0)) {
g[ this_pixel ].insert( Map[ w*(row+1) + col - 1 ] );
g[ Map[ w*(row+1) + col - 1 ] ].insert( this_pixel );
//boost::add_edge(this_pixel, map.at((row+1) * SWTImage->width + col - 1), g);
}
}
}
}
}
}
std::vector<int> c(num_vertices, -1);
int num_comp = connected_components(g, c);
components.reserve(num_comp);
//std::cout << "Before filtering, " << num_comp << " components and " << num_vertices << " vertices" << std::endl;
for (int j = 0; j < num_comp; j++) {
std::vector<Point2d> tmp;
components.push_back( tmp );
}
for (int j = 0; j < num_vertices; j++) {
Point2d p = revmap[j];
(components[c[j]]).push_back(p);
}
return components;
}
enum {
EIN = 0,
GXIN,
GYIN,
DOLFIN,
MAXWIN,
NIN };
void mexFunction( int nout, mxArray* pout[], int nin, const mxArray* pin[] ) {
//
// make sure images are input in transposed so that they are arranged row-major in memory
//
mxAssert( nin == NIN, "wrong number of inputs" );
mxAssert( nout > 1, "only one output" );
int h = mxGetN( pin[EIN] ); // inputs are transposed!
int w = mxGetM( pin[EIN] );
mxAssert( mxIsClass( pin[EIN], mxSINGLE_CLASS ) && h == mxGetN( pin[EIN] ) && w == mxGetM( pin[EIN] ), "edge map incorrect");
mxAssert( mxIsClass( pin[GXIN], mxSINGLE_CLASS ) && h == mxGetN( pin[GXIN] ) && w == mxGetM( pin[GXIN] ), "edge map incorrect");
mxAssert( mxIsClass( pin[GYIN], mxSINGLE_CLASS ) && h == mxGetN( pin[GYIN] ) && w == mxGetM( pin[GYIN] ), "edge map incorrect");
const float * edgeImage = (float*) mxGetData( pin[EIN] );
const float * gradientX = (float*) mxGetData( pin[GXIN] );
const float * gradientY = (float*) mxGetData( pin[GYIN] );
bool dark_on_light = mxGetScalar( pin[DOLFIN] ) != 0 ;
float maxWidth = mxGetScalar( pin[MAXWIN] );
// allocate output
pout[0] = mxCreateNumericMatrix( w, h, mxSINGLE_CLASS, mxREAL );
float * SWTImage = (float*) mxGetData( pout[0] );
// set SWT to -1
for ( int i = 0 ; i < w*h; i++ ) {
SWTImage[i] = -1;
}
std::vector<Ray> rays;
strokeWidthTransform ( edgeImage, gradientX, gradientY, dark_on_light, SWTImage, h, w, rays );
SWTMedianFilter ( SWTImage, h, w, rays, maxWidth );
// connected components
if ( nout > 1 ) {
// Calculate legally connect components from SWT and gradient image.
// return type is a vector of vectors, where each outer vector is a component and
// the inner vector contains the (y,x) of each pixel in that component.
std::vector<std::vector<Point2d> > components = findLegallyConnectedComponents(SWTImage, h, w, rays);
pout[1] = mxCreateNumericMatrix( w, h, mxSINGLE_CLASS, mxREAL );
float* pComp = (float*) mxGetData( pout[1] );
for ( int i = 0 ; i < w*h; i++ ) {
pComp[i] = 0;
}
for ( int ci = 0 ; ci < components.size(); ci++ ) {
for ( std::vector<Point2d>::iterator it = components[ci].begin() ; it != components[ci].end(); it++ ) {
pComp[ w * it->y + it->x ] = ci + 1;
}
}
}
}
Matlab function calling stroke-width-transform (SWT) mex-file:
function [swt swtcc] = SWT( img, dol, maxWidth )
if size( img, 3 ) == 3
img = rgb2gray(img);
end
img = im2single(img);
edgeMap = single( edge( img, 'canny', .15 ) );
img = imfilter( img, fspecial('gauss',[5 5], 0.3*(2.5-1)+.8) );
gx = imfilter( img, fspecial('prewitt')' ); %//'
gy = imfilter( img, fspecial('prewitt') );
gx = single(medfilt2( gx, [3 3] ));
gy = single(medfilt2( gy, [3 3] ));
[swt swtcc] = swt_mex( edgeMap.', gx.', gy.', dol, maxWidth ); %//'
swt = swt'; %//'
swtcc = double(swtcc'); %//'
Solution 2:
Try this :
I = imread('...'); % Your board image
ThreshConstant = 1; % Try to vary this constant.
bw = im2bw(I , ThreshConstant * graythresh(I)); % Black-white image
SegmentedImg = I.*repmat(uint8(bw), [1 1 3]);
Just do imshow(bw); and you will have a 2 color image normally well segmented.
If the threshold is too strong, try to turn around 0.5 to 1.5 with ThreshConstant.