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#define STB_IMAGE_IMPLEMENTATION
#include"stb_image.h"
#include<string>
#include<iostream>
#include<fstream>
#include <sstream>
#include<cmath>
#include <vector>
#include <algorithm>
#include"template.h"
#include"spellchecker.h"
// for debug
#ifndef ALLOW_DEBUG_MSG // set to show debug message (in console)
#define ALLOW_DEBUG_MSG false
#endif
#ifndef ALLOW_DEBUG_FILE_STORAGE // set to save every image generated (including segmentation cache)
#define ALLOW_DEBUG_FILE_STORAGE false
#endif
static int debug_alp_count = 0;
/*****************************************************************************\
*** ***\
*** Text Extractor and Recognition ***\
*** This can help to to split alphaberts from an image. ***\
*** by Kanch at http://akakanch.com ***\
*** kanchisme@gmail.com ***\
*** ***\
*** ***\
****************************************************************************\*/
/* ==============================================Types Defination============================================ */
#ifndef FEATURE_DEFENDED
/* Basic types */
typedef uint8_t BASE;
typedef BASE*** MATRIX;
typedef BASE** ROW;
typedef BASE** COLUMN;
typedef BASE* PIXEL;
typedef BASE* ALPHABERTS;
typedef BASE CHANNEL;
typedef MATRIX IMAGE;
typedef std::vector<int> LIST;
typedef ROW IMAGE2D;
typedef PIXEL ROW2D;
/* struct stores image basic information */
struct ImageData{
int width;
int height;
int channel;
};
/* struct stores image matrix and its basic information */
struct ImagePack{
IMAGE image;
ImageData properties;
};
struct ImagePack2D{
IMAGE2D image;
ImageData properties;
};
/* structures to stores Features */
struct Features{
std::vector<int> x_sum;
std::vector<int> y_sum;
std::vector<int> ninesampling; // for nine sampling
int circle; // record if there is a circle in the image
int width;
int height;
double wh_ratio;
std::string label;
};
typedef std::vector<ImagePack> LISTIMAGEPACK;
typedef std::vector<ImagePack2D> LISTIMAGEPACK2D;
typedef std::vector<LISTIMAGEPACK> DLISTIMAGEPACK;
typedef std::vector<ImagePack>::iterator LISTIMAGEPACKITERATOR;
typedef std::vector<ImagePack2D>::iterator LISTIMAGEPACK2DITERATOR;
typedef std::vector<LISTIMAGEPACK>::iterator DLISTIMAGEPACKITERATOR;
#endif
void save_string(const std::string data,const std::string path);
/* ==============================================Helper Function============================================ */
/*Transfer anyvalue to string, for compatiable of Android app.
source:https://stackoverflow.com/questions/22774009/android-ndk-stdto-string-support*/
template <typename T>
std::string to_string(T value){
std::ostringstream os ;
os << value ;
return os.str() ;
}
void pause(bool pause=true){
while(pause){
;
}
}
/* used to display debug message,
you can turn it off by setting macro ALLOW_DEBUG_MSG to 0 */
template <typename T>
void klog(T msg,const bool newline=true){
if(ALLOW_DEBUG_MSG){
std::cout<<">>>>>"<<msg;
if(newline){
std::cout<<std::endl;
}
}
}
template <typename T>
void printVector(std::vector<T> v,std::string name=""){
if(ALLOW_DEBUG_MSG){
std::cout<<"Vector "<<name<<" : ";
for(int i=0;i<v.size();++i){
std::cout<<v[i]<<",";
}
std::cout<<std::endl;
}
}
/* Calucate average of a given array (row vector) */
BASE avg(const PIXEL p,const uint8_t channels){
int sum = 0;
for(int i=0;i<channels;++i){
sum+=p[i];
}
return sum/channels;
}
/* sum up an array */
long sum(const PIXEL p,const uint8_t channels){
long sum = 0;
for(int i=0;i<channels;++i){
sum+=p[i];
}
return sum;
}
/* save image to file */
void save2File(const IMAGE image,const ImageData &id,const std::string des="image.txt"){
std::string data = "";
for(int i=0;i<id.height;++i){
std::string rowstr = "";
for(int j=0;j<id.width;++j){
std::string pixel = "";
for(int c=0;c<id.channel;c++){
pixel += to_string(int(image[i][j][c])) + ",";
}
pixel = "[" + pixel.substr(0,pixel.length()-1) + "]";
rowstr += pixel + ",";
}
rowstr = "[" + rowstr.substr(0,rowstr.length()-1) + "]";
data += rowstr + "\r\n";
}
data = "[" + data.substr(0,data.length()-1) + "]";
std::fstream fs;
fs.open(des.c_str(),std::ios_base::out);
fs<<data;
fs.close();
}
/* print Matrix to console */
/* can print pixelize and depixelize */
void printMatrix(const MATRIX mat3d,const ImageData&id,const IMAGE2D mat2d=nullptr){
if(ALLOW_DEBUG_MSG){
std::cout<<"[\n";
if(id.channel > 0){
MATRIX mat = mat3d;
for(int i =0;i<id.height;++i){
std::cout<<"[";
for(int j=0;j<id.width;++j){
std::cout<<"[";
for(int c=0;c<id.channel;c++){
std::cout<< int(mat[i][j][c]) <<",";
}
std::cout<<"\b] " ;
}
std::cout<<"\b] "<<std::endl;
}
}else{
IMAGE2D mat = mat2d;
for(int i =0;i<id.height;++i){
std::cout<<"[";
for(int j=0;j<id.width;++j){
std::cout<< int(mat[i][j]) <<",";
}
std::cout<<"\b] "<<std::endl;
}
}
std::cout<<"]"<<std::endl;
}
}
/* perform mean normailize to pictures */
const IMAGE normalize(IMAGE image,const ImageData& id,BASE* mean){
long long sumx = 0;
for(int i=0;i<id.height;++i){
for(int j=0;j<id.width;++j){
sumx += sum( image[i][j],id.channel );
}
}
BASE avgv = BASE(sumx/(id.height*id.width));
*mean = avgv;
for(int i =0;i<id.height;++i){
for(int j=0;j<id.width;++j){
image[i][j][0] -= avgv;
}
}
return image;
}
/* this function can set all pixels which values large than a specified value to 1 */
const IMAGE set_largeThan2Value(IMAGE image,const ImageData& id,const uint8_t value,const int threshold){
for(int i=0;i<id.height;++i){
for(int j=0;j<id.width;++j){
if( int(image[i][j][0]) >= threshold ){
image[i][j][0] = value;
}
}
}
return image;
}
/* this function can set all pixels which values less than a specified value to 1 */
const IMAGE set_lessThan2Value(IMAGE image,const ImageData& id,const uint8_t value,const int threshold){
for(int i=0;i<id.height;++i){
for(int j=0;j<id.width;++j){
if( int(image[i][j][0]) <= threshold ){
image[i][j][0] = value;
}
}
}
return image;
}
/* this function will enhancing the image (maxiumize the gray pixel value) */
/* this function should be use only after the normailization and background deleted */
/* for one channel image */
const IMAGE enhanceImage(IMAGE image,const ImageData& id){
for(int i=0;i<id.height;++i){
for(int j=0;j<id.width;++j){
if(int(image[i][j][0])>0){
image[i][j][0] = 255;
}
}
}
return image;
}
const IMAGE reverseImageBit(IMAGE image,const ImageData& id){
for(int i=0;i<id.height;++i){
for(int j=0;j<id.width;++j){
if(int(image[i][j][0]) == 0){
image[i][j][0] = 1;
}else{
image[i][j][0] = 0;
}
}
}
return image;
}
/* Tranfera row vector into a matrix */
const IMAGE to_Martix(uint8_t* img,const ImageData & id){
MATRIX mat = new COLUMN[id.height];
for(int i= 0;i<id.height;++i){
ROW row = new PIXEL[id.width];
for(int j=0;j<id.width;++j){
PIXEL pixel = new CHANNEL[id.channel];
for(int c=0;c<id.channel;c++){
pixel[c] = int(img[ 3*(i*id.width + j) +c ]);
}
row[j] = pixel;
}
mat[i] = row;
}
return mat;
}
/* set background to 1, text to 0 */
const IMAGE Thresholding(IMAGE& img,const ImageData& id){
std::string hist = "";
//first count 0 to 255 pixel value count
std::vector<int> pixelcount(256);
for(int i=0;i<id.height;++i){
for(int j=0;j<id.width;++j){
++pixelcount[ img[i][j][0] ];
}
}
for(auto&a : pixelcount){
hist += to_string(a);
hist += ",";
}
save_string( "[" + hist.substr(0,hist.length() -1 ) + "]" ,"cache/hist.txt");
// start split backgroud color and forecolor
int minvalue = 10;
for(std::vector<int>::iterator it=pixelcount.begin()+1;it<pixelcount.end();++it){
if(*it >= pixelcount[0]){
minvalue = std::distance(pixelcount.begin(),it)/5*4;
break;
}
}
// set background to 1, data to 1
img = set_lessThan2Value(img,id,1,minvalue);
img = set_largeThan2Value(img,id,0,minvalue);
return img;
}
/* get a slice of an matrix, like a[:,:] in Python */
/* return `nullptr` if invailed data dectected */
/* double nagative value in row or column stands for all select all rows and columns */
const MATRIX sliceSubMatrix3D(MATRIX mat,const ImageData&id,const int rs=0,const int re=0,const int cs=0,const int ce=0){
int row_start,row_end,col_start,col_end;
row_start = rs;
row_end = re;
col_start = cs;
col_end = ce;
if( rs > re || cs > ce || ( (rs>=0)^(re>=0) ) || ( (cs>=0)^(ce>=0) ) ){
return nullptr;
}
if( row_end < 0 && row_start < 0 ){ // all rows
row_start = 0;
row_end = id.height;
}
if( col_end < 0 && col_start < 0 ){ // all columns
col_start = 0;
col_end = id.width;
}
int height = row_end - row_start;
int width = col_end - col_start;
MATRIX submatrix = new COLUMN[height];
int colbase = 0;
for(int i=0;i<id.height;++i){
if( i >= row_start && i < row_end){
ROW row = new PIXEL[width];
int base = 0;
for(int j=0;j<id.width;++j){
if( j >=col_start && j < col_end ){
PIXEL pixel = new BASE[id.channel];
for(int c=0;c<id.channel;c++){
pixel[c] = mat[i][j][c];
}
row[base] = pixel;
base++;
}
}
submatrix[colbase] = row;
colbase++;
}
}
return submatrix;
}
/* Delete resource taken up by matrix */
void deleteMatrix(MATRIX mat,const ImageData& id){
for(int i=0;i<id.height;++i){
for(int j=0;j<id.width;++j){
delete[] mat[i][j];
}
delete[] mat[i];
}
delete[] mat;
}
/* Grayscale an image */
/* only 1 color channel will be left after this function */
const ImagePack to_grayScale(const IMAGE image,const ImageData& id){
IMAGE grayscale = new COLUMN[id.height];
for(int i=0;i<id.height;++i){
ROW row = new PIXEL[id.width];
for(int j=0;j<id.width;++j){
int buf = int(image[i][j][0]*0.11 + image[i][j][1]*0.59 + image[i][j][2]*0.3 );
PIXEL p = new CHANNEL[1]{ BASE(buf) };
row[j] = p;
}
grayscale[i] = row;
}
ImagePack result = {grayscale,{id.width,id.height,1}};
return result;
}
/* make pixel in image from a array to single BASE variable */
const ImagePack2D depixelize(const IMAGE image,const ImageData& id){
ImagePack2D i2d;
i2d.properties.width = id.width;
i2d.properties.height = id.height;
i2d.properties.channel = 0;
if(id.channel != 1){
i2d.image = nullptr;
return i2d;
}
IMAGE2D buf = new ROW2D[id.height];
for(int i=0;i<id.height;++i){
ROW2D row = new BASE[id.width];
for(int j=0;j<id.width;++j){
row[j] = image[i][j][0];
}
buf[i] = row;
}
i2d.image = buf;
return i2d;
}
/* make an matrix avaliable to show as image in python */
const std::string numpylize(const IMAGE mat3d,const ImageData& id,const IMAGE2D mat2d=nullptr){
std::string numpylizedstring = "";
if(ALLOW_DEBUG_FILE_STORAGE){
if(id.channel > 0){
MATRIX mat = mat3d;
for(int i =0;i<id.height;++i){
numpylizedstring += "[";
for(int j=0;j<id.width;++j){
numpylizedstring += "[";
for(int c=0;c<id.channel;c++){
numpylizedstring += to_string(int(mat[i][j][c])) + ",";
}
numpylizedstring.pop_back();
numpylizedstring += "],";
}
numpylizedstring.pop_back();
numpylizedstring += "],";
}
}else{
IMAGE2D mat = mat2d;
for(int i =0;i<id.height;++i){
numpylizedstring += "[";
for(int j=0;j<id.width;++j){
numpylizedstring += to_string(int(mat[i][j])) + ",";
}
numpylizedstring.pop_back();
numpylizedstring += "],";
}
}
numpylizedstring.pop_back();
}
numpylizedstring += "]";
return "[" + numpylizedstring;
}
/* save string to file */
void save_string(const std::string data,const std::string path){
if(ALLOW_DEBUG_FILE_STORAGE){
std::fstream fs;
fs.open(path.c_str(),std::ios_base::out);
fs<<data;
fs.close();
}
}
/* type cast: from C++ native int to uint8_t */
uint8_t * cast2uint8_t(const int * src,const int len){
uint8_t * des = new uint8_t[len];
for(int i=0;i<len;++i){
des[i] = src[i];
}
return des;
}
std::vector<std::string> split_string(const std::string& data,const std::string&sym){
std::vector<std::string> result;
int s=0;
std::string subresult = "";
for(int i=0;i<data.length();++i){
if( data[i] == sym[0] ){
bool found = true;
for(int j=1;j<sym.length();++j){
if( sym[j] != data[i+j] ){
found = false;
break;
}
}
if(found){
subresult = data.substr(s,i-s);
result.push_back(subresult);
i += sym.length();
s = i;
}
}
}
if(s != data.length()){
result.push_back( data.substr(s,data.length()-s) );
}
return result;
}
/* strinfy the result list */
/* format: [array of image data]@width@height<!> */
/* <!> this symbol is used to split differnt images */
const std::string strinfy(DLISTIMAGEPACK dpack,int&sum){
std::string result = "";
for(int i=0;i<dpack.size();++i){ // extract line container
LISTIMAGEPACK linecontainer = dpack[i];
for(int j=0;j<linecontainer.size();++j){ // extract alpberts image on every line
// we're processing ImagePack here
std::string buf = "[";
std::string properties = to_string(linecontainer[j].properties.width) + "@"
+ to_string(linecontainer[j].properties.height) + "<!>";
//start processing evert channel's pixel now,only work for 1 channel image
// that is grayscale image
//int mmm=0;
if( linecontainer[j].properties.channel ==1 ){
for(int z=0;z<linecontainer[j].properties.height;z++){
for(int l=0;l<linecontainer[j].properties.width;l++){
buf += to_string(int(linecontainer[j].image[z][l][0])) + ",";
}
}
buf = buf.substr(0,buf.length() - 1) + "]@";
buf += properties;
result += buf;
sum++;
}
}
}
return result;
}
/* delete empty line of an image */
const IMAGE delteEmptyline(IMAGE image,ImageData&id,const int del_target=1){
int countx = 0;
int del_line=0;
// scan on top
for(int i=0;i<id.height;++i){
for(int j=0;j<id.width;++j){
if( int(image[i][j][0]) == del_target ){
++countx;
}
}
if(countx != id.width){
break;
}else{
++del_line;
countx = 0;
}
}
image = sliceSubMatrix3D(image,id,del_line,id.height,-1,-1);
id.height -= del_line;
// scan on bottom
countx=0;
del_line=0;
for(int i=id.height-1;i>=0;i--){
for(int j=0;j<id.width;++j){
if( int(image[i][j][0]) == del_target ){
++countx;
}
}
if(countx != id.width){
break;
}else{
++del_line;
countx = 0;
}
}
image = sliceSubMatrix3D(image,id,0,id.height-del_line,-1,-1);
id.height -= del_line;
// scan on the left
countx=0;
del_line=0;
for(int i=0;i<id.width;++i){
for(int j=0;j<id.height;++j){
if( int(image[j][i][0]) == del_target ){
++countx;
}
}
if(countx != id.height){
break;
}else{
++del_line;
countx = 0;
}
}
image = sliceSubMatrix3D(image,id,-1,-1,del_line,id.width);
id.width -= del_line;
// scan on the right
countx=0;
del_line=0;
for(int i=id.width-1;i>=0;++i){
for(int j=0;j<id.height;++j){
if( int(image[j][i][0]) == del_target ){
++countx;
}
}
if(countx != id.height){
break;
}else{
++del_line;
countx = 0;
}
}
image = sliceSubMatrix3D(image,id,-1,-1,0,id.width-del_line);
id.width -= del_line;
return image;
}
/* image feature extractor: 投影区块匹配 */
Features feature_extractor_projectionmatch(IMAGE img,ImageData&id,std::string label){
Features feature;
std::vector<int> xsum(id.width);
std::vector<int> ysum;
for(int i=0;i<id.height;++i){
int ycount = 0;
for(int j=0;j<id.width;++j){
if( int(img[i][j][0]) == 0 ){ // 0 for data , 1 for background
++ycount;
++(xsum[j]);
}
}
ysum.push_back(ycount);
}
feature.x_sum = xsum;
feature.y_sum = ysum;
feature.width = id.width;
feature.height = id.height;
feature.wh_ratio = double(id.width)/id.height;
feature.label = label;
return feature;
}
/* image feature extractor: nine special points sampling */
Features feature_extractor_9Sampling(IMAGE img,ImageData&id,Features& fea){
//we're going to select 9 special point to use as our third feature
// these 9 points are top-left, top-center, top-right, also with mid-height and bottom
// then, we're going to arrange them in the order of scaning
fea.ninesampling.push_back( img[ 0 ][ 0 ][0] ); // top-left
fea.ninesampling.push_back( img[ 0 ][ int(id.width/2) ][0] ); // top-center
fea.ninesampling.push_back( img[ 0 ][ id.width-1 ][0] ); //top-right
fea.ninesampling.push_back( img[ int(id.height/2) ][ 0 ][0] ); //mid-l
fea.ninesampling.push_back( img[ int(id.height/2) ][ int(id.width/2) ][0] ); //mid-c
fea.ninesampling.push_back( img[ int(id.height/2) ][ id.width-1 ][0] ); //mid-r
fea.ninesampling.push_back( img[ id.height-1 ][ 0 ][0] ); //bottom-l
fea.ninesampling.push_back( img[ id.height-1 ][ int(id.width/2) ][0] ); //bottom-c
fea.ninesampling.push_back( img[ id.height-1 ][ id.width-1 ][0] ); //bottom-r
return fea;
}
/* get the weight feature of the picture */
void feature_circle(IMAGE img,ImageData&id,Features&fea){
// scan from left to right
int lcount = 0;
for(int i=0;i<id.height;++i){
int bufx = 0;
bool nof = false;
for(int j=0;j<id.width;++j){
if(img[i][j][0] == 0 && !nof){
++bufx;
nof = true;
}else if(img[i][j][0]== 1 && nof){
nof = false;
}
}
if(bufx >= 2){
++lcount;
}
}
//scan from top to bottom
int vcount = 0;
for(int i=0;i<id.width;++i){
int bufx = 0;
bool nof = false;
for(int j=0;j<id.height;++j){
if(img[j][i][0] == 0 && !nof){
++bufx;
nof = true;
}else if(img[j][i][0]== 1 && nof){
nof = false;
}
}
if(bufx >= 2){
++vcount;
}
}
//make sure the point exist
if(lcount > 0 && vcount > 0){
fea.circle = 1;
return;
}
fea.circle = 0;
}
/* compute cosine */
const double cosine(std::vector<int> v1,std::vector<int> v2){
int sum = 0;
int sumv1=0,sumv2=0;
// reverse value (1<>0) in v1 and v2
for(int i=0;i<v1.size();++i){
v1[i] = std::abs(--v1[i]);
v2[i] = std::abs(--v2[i]);
}
for(int i=0;i<v1.size();++i){
sum += v1[i]*v2[i];
sumv1 += v1[i];
sumv2 += v2[i];
}
return sum*1.0/(sqrt(sumv1)*sqrt(sumv2));
}
/* this function can resize an image to the size of template */
const IMAGE resizeToTemplate(IMAGE& img, ImageData& id,const Features& temp){
// we use this formula:
// dest[dx,dy] = src[dx*src_width/dest_width,dy*src_height/dest_height]
// dest[dx,dy] = src[dx*px,dy*py]
double px = id.width/temp.width;
double py = id.height/temp.height;
IMAGE newimg = new COLUMN[temp.height];
for(int i=0;i<temp.height;++i){
newimg[i] = new PIXEL[temp.width];
for(int j=0;j<temp.width;++j){
newimg[i][j] = new CHANNEL[1];
newimg[i][j][0] = img[int(i*py)][int(j*px)][0];
}
}
return newimg;
}
/* parse feature structure to string */
/* feature format: x1,x2@y1,y2@scale@height,width@label@9points#next_feature_section@xweight,vertial_ratio */
std::string feature2string(Features f){
std::string buf = "";
for(int i=0;i<f.x_sum.size();++i){
buf += to_string(f.x_sum[i]) + ",";
}
buf = buf.substr(0,buf.length()-1) + "@";
for(int i=0;i<f.y_sum.size();++i){
buf += to_string(f.y_sum[i]) + ",";
}
buf = buf.substr(0,buf.length()-1) + "@";
buf += to_string(f.wh_ratio) + "@";
buf += to_string(f.height) + ",";
buf += to_string(f.width) + "@";
buf += f.label + "@";
for(int i=0;i<f.ninesampling.size();++i){
buf += to_string(f.ninesampling[i]) + ",";
}
buf = buf.substr(0,buf.length()-1) + "@";
buf += to_string(f.circle) ;
return buf;
}
#ifndef FEATURE_DEFENDED
/* converting string to features,s = x1,x2@y1,y2@scale@height,width@label */
Features string2feature(std::string s){
Features f;
// get x_sum_str,y_sum_str,tatio_str,height_str,width_str,label
std::vector<std::string> dataframe = split_string(s,"@");
//parse x_sum
std::vector<std::string> values = split_string(dataframe[0],",");
std::vector<int> xsum;
for(int i=0;i<values.size();++i){
xsum.push_back( atoi( values[i].c_str() ) );
}
//parse y_sum
values = split_string(dataframe[1],",");
std::vector<int> ysum;
for(int i=0;i<values.size();++i){
ysum.push_back( atoi( values[i].c_str() ) );
}
f.x_sum = xsum;
f.y_sum = ysum;
//parse ratio
f.wh_ratio = atof(dataframe[2].c_str());
// parse width and height
std::vector<std::string> hw = split_string(dataframe[3],",");
f.height = atoi(hw[0].c_str());
f.width = atoi(hw[1].c_str());
f.label = dataframe[4];
// set 9 special feature
values = split_string(dataframe[5],",");
std::vector<int> nsps;
for(int i=0;i<values.size();++i){
nsps.push_back(atoi(values[i].c_str()));
}
f.ninesampling = nsps;
// set ratio feature
f.circle = atoi(dataframe[6].c_str());
return f;
}
#endif
/* function below is used for prediction */
const std::vector<Features> parseTemplateData(std::string data){
std::vector<Features> templatelist;
std::vector<std::string> datalist = split_string(data,"#");
for(int i=0;i<datalist.size();++i){
templatelist.push_back( string2feature(datalist[i]) );
}
return templatelist;
}
/* minus two feature vectors with the consideration of different font size */
const std::vector<double> minusWithScale(const std::vector<int> x,const std::vector<int> templatex,const double payoff){
std::vector<double> result;
// compress longer vector to align with the shorter one
if( x.size() >= templatex.size() ){
// compress x
const double mapping_size = x.size()/templatex.size();
for(int i=0;i<templatex.size();++i){
int base = int(i*mapping_size);
int upper = int(base+mapping_size);
int sumx = 0;
for(int j=base;j<upper;++j){
sumx += x[j];
}
sumx /= (upper-base);
result.push_back( sumx*payoff - templatex[i] );
}
}else{
// compress template
const double mapping_size =templatex.size()/x.size();
for(int i=0;i<x.size();++i){
int base = int(i*mapping_size);
int upper = int(base+mapping_size);
int sumx = 0;
for(int j=base;j<int(base+mapping_size);++j){
sumx += templatex[j];
}
sumx /= (upper-base);
result.push_back( sumx*payoff - x[i] );
}
}
printVector(x,"x");
printVector(templatex,"templatex");
printVector(result,"minusWithScale");
return result;
}
/* */
const double meanSquaredError(const std::vector<double> con,const double csum){
double error = 0.0;
std::string logx="";
for(int i=0;i<con.size();++i){
double xx = con[i]/csum;
error += (xx*xx);
}
return error/con.size();
}
const double payoff(const int a,const int b){
return ((a>=b?b:a)*1.0) / ((a>=b?a:b)*1.0) ;
}
/* compute similarity based on width and height ratio*/
const double error_feature_WHR(const Features img,const Features temp){
return std::abs( img.wh_ratio - temp.wh_ratio );
}
/* compute similarity */
inline const double similarity(const Features img,const Features temp){
std::vector<double> x_diff = minusWithScale( img.x_sum , temp.x_sum , payoff( temp.height,img.height ) );
std::vector<double> y_diff = minusWithScale( img.y_sum , temp.y_sum , payoff( temp.width,img.width ) );
double x_error = meanSquaredError( x_diff , temp.height<=img.height?img.height:temp.height );
double y_error = meanSquaredError( y_diff , temp.width<=img.width?img.width:temp.width );
const double efwhr = error_feature_WHR(img,temp);
const double simNSPS = cosine(temp.ninesampling,img.ninesampling);
//std::cout<<"\n\t\tx_err="<<x_error<<"\ty_err="<<y_error<<"\tcos="<<simNSPS<<"\twh_ratio="<<efwhr<<"\n";
return 0.65*(x_error + y_error) + 0.12*(1-simNSPS) + 0.23*efwhr ;
//return 0.65*(x_error + y_error) + 0.12*(1-simNSPS) + 0.13*efwhr + 0.1*std::abs(img.circle-temp.circle);
}
void exportImage(ImagePack & imp2d,std::string filename){
ImagePack2D d2line = depixelize(imp2d.image,imp2d.properties);
save_string( numpylize( nullptr ,d2line.properties, d2line.image ) ,"cache/" + filename);
}
/* predict which alphaberts it is */
std::string predictAlphberts(ImagePack img,const std::vector<Features> &templatedata){
// get feature
//feature_circle(img.image,img.properties,f);
double minv = std::numeric_limits<double>::max(); // stores max similarity
std::string current="*"; // stores char which matches to the max similarity
// loop computing cos with the template data
for(auto& temp: templatedata){
IMAGE imagex =resizeToTemplate(img.image,img.properties,temp);
ImageData id = ImageData{temp.width,temp.height,1};
ImagePack s= ImagePack{imagex,id};
Features f = feature_extractor_projectionmatch(imagex,id,"");
f = feature_extractor_9Sampling(imagex,id,f);
double x = similarity(f,temp);
if( x <= minv ){
minv =x;
current = temp.label;
}
}
// return data
return current;
}
/* ==============================================Extract text fucntion here================================== */
DLISTIMAGEPACK extractText(uint8_t* img,const ImageData & id){
const IMAGE image = to_Martix(img,id);
save_string( numpylize(image,id) ,"cache/raw_image.txt");
ImagePack grayscaleimgpack = to_grayScale(image,id);
//Thresholding(image,id);
ImagePack2D a = depixelize(grayscaleimgpack.image ,grayscaleimgpack.properties);
save_string(numpylize(nullptr,a.properties,a.image),"cache/grayscale.txt");
/* 1 for background, 0 for data */
grayscaleimgpack.image = set_lessThan2Value(grayscaleimgpack.image,grayscaleimgpack.properties,1,180); //50
grayscaleimgpack.image = set_largeThan2Value(grayscaleimgpack.image,grayscaleimgpack.properties,0,180); //185
a = depixelize(grayscaleimgpack.image ,grayscaleimgpack.properties);
save_string(numpylize(nullptr,a.properties,a.image),"cache/thresholding.txt");
int *ones_on_y = new int[grayscaleimgpack.properties.height];
for(int i=0;i<grayscaleimgpack.properties.height;++i){
int buf = 0;
for(int j=0;j<grayscaleimgpack.properties.width;++j){
if(grayscaleimgpack.image[i][j][0] > 0){
++buf;
}
}
ones_on_y[i] = buf;
}
LIST sentence_boundary;
int start,end;
for(int i=0;i<grayscaleimgpack.properties.height-1;++i){
if( ( ones_on_y[i] == 0) && (ones_on_y[i+1] > 0) ){ //top boundary of sentence
start = i;
}else if((ones_on_y[i] > 0) && (ones_on_y[i+1] == 0)){ // bottom boundary of sentence
end = i+1;
sentence_boundary.push_back(start);
sentence_boundary.push_back(end);
}
}
int linecount = sentence_boundary.size()/2;
LISTIMAGEPACK sentences;
for(int i=0;i<linecount;++i){
ImagePack im;
im.image = sliceSubMatrix3D(grayscaleimgpack.image,grayscaleimgpack.properties,
sentence_boundary[2*i],sentence_boundary[2*i+1],-1,-1);
im.properties.width = grayscaleimgpack.properties.width;
im.properties.height = sentence_boundary[2*i+1] - sentence_boundary[2*i];
im.properties.channel = 1;
sentences.push_back(im);
ImagePack2D d2line = depixelize(im.image,im.properties);
//save_string( numpylize( nullptr ,d2line.properties, d2line.image ) ,"cache/line" + to_string(i+1) + ".txt");
}
DLISTIMAGEPACK alphaberts;
for(int i =0;i<sentences.size();++i){
LISTIMAGEPACK alpha;
ImagePack sen = sentences[i];
LIST zeros_on_sentence;
for(int j=0;j<sen.properties.width;++j ){
int buf=0;
for(int c=0;c<sen.properties.height;c++){
if(sen.image[c][j][0] > 0){
++buf;
}
}
zeros_on_sentence.push_back(buf);
}
start = end = 0;
for(int j=0;j<zeros_on_sentence.size();++j){
if( ( zeros_on_sentence[j] == 0) && (zeros_on_sentence[j+1] > 0) ){ //left boundary of alphaberts
start = j;
}else if((zeros_on_sentence[j] > 0) && (zeros_on_sentence[j+1] == 0)){ // right boundary of alplaberts
end = j+1;
IMAGE imx = sliceSubMatrix3D(sen.image,sen.properties,-1,-1,start,end);
ImagePack imp = {imx,{end-start,sen.properties.height,1}};
imp.image = reverseImageBit(imp.image,imp.properties);
ImagePack2D d2line = depixelize(imp.image,imp.properties);
//save_string( numpylize( nullptr ,d2line.properties, d2line.image ) ,"cache/alp_"+ to_string(i+1) + to_string(j+1) + ".txt");
alpha.push_back(imp);
}
}
alphaberts.push_back(alpha);
}
//delete resource
delete[] ones_on_y;
deleteMatrix(image,id);
deleteMatrix(grayscaleimgpack.image,grayscaleimgpack.properties);
return alphaberts;
}
/* this function will extract all words out of the picture */
/* words extracted can be used in extractText for future recognize */
DLISTIMAGEPACK extractWord(uint8_t* img,const ImageData & id){
const IMAGE image = to_Martix(img,id);
save_string( numpylize(image,id) ,"cache/raw_image.txt");
ImagePack grayscaleimgpack = to_grayScale(image,id);
/* 1 for background, 0 for data */
Thresholding(grayscaleimgpack.image,grayscaleimgpack.properties);
//grayscaleimgpack.image = set_lessThan2Value(grayscaleimgpack.image,grayscaleimgpack.properties,1,180);
//grayscaleimgpack.image = set_largeThan2Value(grayscaleimgpack.image,grayscaleimgpack.properties,0,180);
exportImage(grayscaleimgpack,"thresholding_img.txt" );
int *ones_on_y = new int[grayscaleimgpack.properties.height];
for(int i=0;i<grayscaleimgpack.properties.height;++i){
int buf = 0;
for(int j=0;j<grayscaleimgpack.properties.width;++j){
if(grayscaleimgpack.image[i][j][0] > 0){
++buf;
}
}
ones_on_y[i] = buf;
}
LIST sentence_boundary;
int start,end;
for(int i=0;i<grayscaleimgpack.properties.height-1;++i){
if( ( ones_on_y[i] == 0) && (ones_on_y[i+1] > 0) ){ //top boundary of sentence
start = i;
}else if((ones_on_y[i] > 0) && (ones_on_y[i+1] == 0)){ // bottom boundary of sentence
end = i+1;
sentence_boundary.push_back(start);
sentence_boundary.push_back(end);
}
}
int linecount = sentence_boundary.size()/2;
LISTIMAGEPACK sentences;
for(int i=0;i<linecount;++i){
ImagePack im;
im.image = sliceSubMatrix3D(grayscaleimgpack.image,grayscaleimgpack.properties,
sentence_boundary[2*i],sentence_boundary[2*i+1],-1,-1);
im.properties.width = grayscaleimgpack.properties.width;
im.properties.height = sentence_boundary[2*i+1] - sentence_boundary[2*i];
im.properties.channel = 1;
sentences.push_back(im);
ImagePack2D d2line = depixelize(im.image,im.properties);
//save_string( numpylize( nullptr ,d2line.properties, d2line.image ) ,"cache/line" + to_string(i+1) + ".txt");
}
DLISTIMAGEPACK words;
for(int i =0;i<sentences.size();++i){
LISTIMAGEPACK alpha;
ImagePack sen = sentences[i];
LIST zeros_on_sentence;
for(int j=0;j<sen.properties.width;++j ){
int buf=0;
for(int c=0;c<sen.properties.height;c++){
if(sen.image[c][j][0] > 0){
++buf;
}
}
zeros_on_sentence.push_back(buf);
}
// get avereage space length
//get left and right boundary first
int left_line_boundary = 0;
for(int j=0;j<zeros_on_sentence.size();--j){
if(zeros_on_sentence[j] != 0){
break;
}
++left_line_boundary;
}
int right_line_boundary = 0;
for(int j=zeros_on_sentence.size()-1;j >= 0;--j){
if(zeros_on_sentence[j] != 0){
break;
}
++right_line_boundary;
}
int average = 0;
int count_space = 0;
int lenxxx = 0;
bool loopzero = false;
for(int j=left_line_boundary;j<zeros_on_sentence.size()-right_line_boundary;++j){
if(zeros_on_sentence[j] == 0){
if(!loopzero){
start = j;
lenxxx = 1;
loopzero = true;
++count_space;
}else{
++lenxxx;
}
}else{
if(loopzero){
loopzero = false;
average += lenxxx;
}
}
}
average = (average/count_space) + 2; // change this number if it works badly
//std::cout<<"average space length="<<average<<std::endl;
// start extract words
start = end = 0;
bool no_split = true;
for(int j=0;j<zeros_on_sentence.size();++j){
if( ( zeros_on_sentence[j] == 0) && (zeros_on_sentence[j+1] > 0) ){ //left boundary of words
if(no_split){
start = j;
}
}else if((zeros_on_sentence[j] > 0) && (zeros_on_sentence[j+1] == 0)){ // right boundary of alplaberts
end = j+1;
no_split = true;
int ending_checking_pos = ((j+average)>(zeros_on_sentence.size()))?(zeros_on_sentence.size()):(j+average);
for(int k=j+1;k<ending_checking_pos;k++){
if(zeros_on_sentence[k] > 0){
no_split = false;
break;
}
}
if(no_split){
IMAGE imx = sliceSubMatrix3D(sen.image,sen.properties,-1,-1,start,end);
ImagePack imp = {imx,{end-start,sen.properties.height,1}};
//imp.image = reverseImageBit(imp.image,imp.properties);
alpha.push_back(imp);
no_split = true;
}
}
}
words.push_back(alpha);
}
//delete resource
delete[] ones_on_y;
deleteMatrix(image,id);
deleteMatrix(grayscaleimgpack.image,grayscaleimgpack.properties);
return words;
}
DLISTIMAGEPACK extractTextFromWord(const IMAGE word,const ImageData & id){
DLISTIMAGEPACK alphaberts;
LISTIMAGEPACK alpha;
ImagePack sen = ImagePack{word,id};
exportImage(sen,"extractTextFromWord_raw.txt" );
LIST zeros_on_sentence;
for(int j=0;j<sen.properties.width;++j ){
int buf=0;
for(int c=0;c<sen.properties.height;c++){
if(sen.image[c][j][0] > 0){
++buf;
}
}
zeros_on_sentence.push_back(buf);
}
int start=0, end = 0;
bool beg_found = false;
for(int j=0;j<zeros_on_sentence.size();++j){
if( ( zeros_on_sentence[j] > 0 ) && (!beg_found) ){ //left boundary of alphaberts
start = j;
beg_found = true;
}else if(((zeros_on_sentence[j] == 0) || (j==zeros_on_sentence.size()-1)) && beg_found){ // right boundary of alplaberts
end = j;
IMAGE imx = sliceSubMatrix3D(sen.image,sen.properties,-1,-1,start,end);
ImagePack imp = {imx,{end-start,sen.properties.height,1}};
imp.image = reverseImageBit(imp.image,imp.properties);
beg_found = false;
//printMatrix(imp.image,imp.properties);
//exportImage(imp,"alp_alp_from_wordsExtracted_"+ to_string(debug_alp_count++) + ".txt");
alpha.push_back(imp);
}
}
alphaberts.push_back(alpha);
return alphaberts;
}
/* ==============================================Recognize text fucntion here================================== */
/* recognize a single image */
std::string recognize(DLISTIMAGEPACK& data){
//start recognize
std::string result = "";
const std::vector<Features> templatedata = parseTemplateData(pcadata);
for(DLISTIMAGEPACKITERATOR i=data.begin();i<data.end();++i){
LISTIMAGEPACK alpha = *i;
for(LISTIMAGEPACKITERATOR j=alpha.begin();j<alpha.end();++j){
//delete empty line
(*j).image = delteEmptyline((*j).image,(*j).properties);
ImagePack2D d2line = depixelize((*j).image,(*j).properties);
std::string re = predictAlphberts(*j,templatedata);
result += re;
//printMatrix((*j).image,(*j).properties);
}
}
return result;
}
/* recognize a data with the original format, like words and space */
/* input must be words list */
std::string recognizeWithFormat(const DLISTIMAGEPACK& data){
std::string result = "";
initSpellChecker();
for(auto& line: data){
for(auto& wordimg: line){
DLISTIMAGEPACK word = extractTextFromWord(wordimg.image,wordimg.properties);
std::string wordx = recognize(word);
wordx = suggest(wordx);// correct word here
result += wordx + " ";
}
result += "\r\n";
}
return result;
}