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opencv_contrib/modules/text/src/ocr_hmm_decoder.cpp

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
#include "opencv2/imgproc.hpp"
#include "opencv2/ml.hpp"
#include <iostream>
#include <fstream>
#include <queue>
namespace cv
{
namespace text
{
using namespace std;
using namespace cv::ml;
/* OCR HMM Decoder */
void OCRHMMDecoder::run(Mat& image, string& output_text, vector<Rect>* component_rects,
vector<string>* component_texts, vector<float>* component_confidences,
int component_level)
{
CV_Assert( (image.type() == CV_8UC1) || (image.type() == CV_8UC3) );
CV_Assert( (component_level == OCR_LEVEL_TEXTLINE) || (component_level == OCR_LEVEL_WORD) );
output_text.clear();
if (component_rects != NULL)
component_rects->clear();
if (component_texts != NULL)
component_texts->clear();
if (component_confidences != NULL)
component_confidences->clear();
}
void OCRHMMDecoder::run(Mat& image, Mat& mask, string& output_text, vector<Rect>* component_rects,
vector<string>* component_texts, vector<float>* component_confidences,
int component_level)
{
CV_Assert( (image.type() == CV_8UC1) || (image.type() == CV_8UC3) );
CV_Assert( mask.type() == CV_8UC1 );
CV_Assert( (component_level == OCR_LEVEL_TEXTLINE) || (component_level == OCR_LEVEL_WORD) );
output_text.clear();
if (component_rects != NULL)
component_rects->clear();
if (component_texts != NULL)
component_texts->clear();
if (component_confidences != NULL)
component_confidences->clear();
}
CV_WRAP String OCRHMMDecoder::run(InputArray image, int min_confidence, int component_level)
{
std::string output1;
std::string output2;
vector<string> component_texts;
vector<float> component_confidences;
Mat image_m = image.getMat();
run(image_m, output1, NULL, &component_texts, &component_confidences, component_level);
for(unsigned int i = 0; i < component_texts.size(); i++)
{
//cout << "confidence: " << component_confidences[i] << " text:" << component_texts[i] << endl;
if(component_confidences[i] > min_confidence)
{
output2 += component_texts[i];
}
}
return String(output2);
}
CV_WRAP cv::String OCRHMMDecoder::run(InputArray image, InputArray mask, int min_confidence, int component_level)
{
std::string output1;
std::string output2;
vector<string> component_texts;
vector<float> component_confidences;
Mat image_m = image.getMat();
Mat mask_m = mask.getMat();
run(image_m, mask_m, output1, NULL, &component_texts, &component_confidences, component_level);
for(unsigned int i = 0; i < component_texts.size(); i++)
{
cout << "confidence: " << component_confidences[i] << " text:" << component_texts[i] << endl;
if(component_confidences[i] > min_confidence)
{
output2 += component_texts[i];
}
}
return String(output2);
}
void OCRHMMDecoder::ClassifierCallback::eval( InputArray image, vector<int>& out_class, vector<double>& out_confidence)
{
CV_Assert(( image.getMat().type() == CV_8UC3 ) || ( image.getMat().type() == CV_8UC1 ));
out_class.clear();
out_confidence.clear();
}
bool sort_rect_horiz (Rect a,Rect b);
bool sort_rect_horiz (Rect a,Rect b) { return (a.x<b.x); }
class OCRHMMDecoderImpl : public OCRHMMDecoder
{
public:
//Default constructor
OCRHMMDecoderImpl( Ptr<OCRHMMDecoder::ClassifierCallback> _classifier,
const string& _vocabulary,
InputArray transition_probabilities_table,
InputArray emission_probabilities_table,
decoder_mode _mode)
{
classifier = _classifier;
transition_p = transition_probabilities_table.getMat();
emission_p = emission_probabilities_table.getMat();
vocabulary = _vocabulary;
mode = _mode;
}
~OCRHMMDecoderImpl()
{
}
void run( Mat& image,
string& out_sequence,
vector<Rect>* component_rects,
vector<string>* component_texts,
vector<float>* component_confidences,
int component_level)
{
CV_Assert( (image.type() == CV_8UC1) || (image.type() == CV_8UC3) );
CV_Assert( (image.cols > 0) && (image.rows > 0) );
CV_Assert( component_level == OCR_LEVEL_WORD );
out_sequence.clear();
if (component_rects != NULL)
component_rects->clear();
if (component_texts != NULL)
component_texts->clear();
if (component_confidences != NULL)
component_confidences->clear();
// First we split a line into words
vector<Mat> words_mask;
vector<Rect> words_rect;
/// Find contours
vector<vector<Point> > contours;
vector<Vec4i> hierarchy;
Mat tmp;
image.copyTo(tmp);
findContours( tmp, contours, hierarchy, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE, Point(0, 0) );
if (contours.size() < 6)
{
//do not split lines with less than 6 characters
words_mask.push_back(image);
words_rect.push_back(Rect(0,0,image.cols,image.rows));
}
else
{
Mat_<float> vector_w((int)image.cols,1);
reduce(image, vector_w, 0, REDUCE_SUM, -1);
vector<int> spaces;
vector<int> spaces_start;
vector<int> spaces_end;
int space_count=0;
int last_one_idx;
int s_init = 0, s_end=vector_w.cols;
for (int s=0; s<vector_w.cols; s++)
{
if (vector_w.at<float>(0,s) == 0)
s_init = s+1;
else
break;
}
for (int s=vector_w.cols-1; s>=0; s--)
{
if (vector_w.at<float>(0,s) == 0)
s_end = s;
else
break;
}
for (int s=s_init; s<s_end; s++)
{
if (vector_w.at<float>(0,s) == 0)
{
space_count++;
} else {
if (space_count!=0)
{
spaces.push_back(space_count);
spaces_start.push_back(last_one_idx);
spaces_end.push_back(s-1);
}
space_count = 0;
last_one_idx = s;
}
}
Scalar mean_space,std_space;
meanStdDev(Mat(spaces),mean_space,std_space);
int num_word_spaces = 0;
int last_word_space_end = 0;
for (int s=0; s<(int)spaces.size(); s++)
{
if (spaces_end.at(s)-spaces_start.at(s) > mean_space[0]+(mean_space[0]*1.1)) //this 1.1 is a param?
{
if (num_word_spaces == 0)
{
//cout << " we have a word from 0 to " << spaces_start.at(s) << endl;
Mat word_mask;
Rect word_rect = Rect(0,0,spaces_start.at(s),image.rows);
image(word_rect).copyTo(word_mask);
words_mask.push_back(word_mask);
words_rect.push_back(word_rect);
}
else
{
//cout << " we have a word from " << last_word_space_end << " to " << spaces_start.at(s) << endl;
Mat word_mask;
Rect word_rect = Rect(last_word_space_end,0,spaces_start.at(s)-last_word_space_end,image.rows);
image(word_rect).copyTo(word_mask);
words_mask.push_back(word_mask);
words_rect.push_back(word_rect);
}
num_word_spaces++;
last_word_space_end = spaces_end.at(s);
}
}
//cout << " we have a word from " << last_word_space_end << " to " << vector_w.cols << endl << endl << endl;
Mat word_mask;
Rect word_rect = Rect(last_word_space_end,0,vector_w.cols-last_word_space_end,image.rows);
image(word_rect).copyTo(word_mask);
words_mask.push_back(word_mask);
words_rect.push_back(word_rect);
}
for (int w=0; w<(int)words_mask.size(); w++)
{
vector< vector<int> > observations;
vector< vector<double> > confidences;
vector<int> obs;
// First find contours and sort by x coordinate of bbox
words_mask[w].copyTo(tmp);
if (tmp.empty())
continue;
contours.clear();
hierarchy.clear();
/// Find contours
findContours( tmp, contours, hierarchy, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE, Point(0, 0) );
vector<Rect> contours_rect;
for (int i=0; i<(int)contours.size(); i++)
{
contours_rect.push_back(boundingRect(contours[i]));
}
sort(contours_rect.begin(), contours_rect.end(), sort_rect_horiz);
// Do character recognition foreach contour
for (int i=0; i<(int)contours.size(); i++)
{
Mat tmp_mask;
words_mask[w](contours_rect.at(i)).copyTo(tmp_mask);
vector<int> out_class;
vector<double> out_conf;
classifier->eval(tmp_mask,out_class,out_conf);
if (!out_class.empty())
obs.push_back(out_class[0]);
observations.push_back(out_class);
confidences.push_back(out_conf);
//cout << " out class = " << vocabulary[out_class[0]] << endl;
}
//This must be extracted from dictionary, or just assumed to be equal for all characters
vector<double> start_p(vocabulary.size());
for (int i=0; i<(int)vocabulary.size(); i++)
start_p[i] = 1.0/vocabulary.size();
Mat V = Mat::zeros((int)observations.size(),(int)vocabulary.size(),CV_64FC1);
vector<string> path(vocabulary.size());
// Initialize base cases (t == 0)
for (int i=0; i<(int)vocabulary.size(); i++)
{
for (int j=0; j<(int)observations[0].size(); j++)
{
emission_p.at<double>(observations[0][j],obs[0]) = confidences[0][j];
}
V.at<double>(0,i) = start_p[i] * emission_p.at<double>(i,obs[0]);
path[i] = vocabulary.at(i);
}
// Run Viterbi for t > 0
for (int t=1; t<(int)obs.size(); t++)
{
//Dude this has to be done each time!!
emission_p = Mat::eye(62,62,CV_64FC1);
for (int e=0; e<(int)observations[t].size(); e++)
{
emission_p.at<double>(observations[t][e],obs[t]) = confidences[t][e];
}
vector<string> newpath(vocabulary.size());
for (int i=0; i<(int)vocabulary.size(); i++)
{
double max_prob = 0;
int best_idx = 0;
for (int j=0; j<(int)vocabulary.size(); j++)
{
double prob = V.at<double>(t-1,j) * transition_p.at<double>(j,i) * emission_p.at<double>(i,obs[t]);
if ( prob > max_prob)
{
max_prob = prob;
best_idx = j;
}
}
V.at<double>(t,i) = max_prob;
newpath[i] = path[best_idx] + vocabulary.at(i);
}
// Don't need to remember the old paths
path.swap(newpath);
}
double max_prob = 0;
int best_idx = 0;
for (int i=0; i<(int)vocabulary.size(); i++)
{
double prob = V.at<double>((int)obs.size()-1,i);
if ( prob > max_prob)
{
max_prob = prob;
best_idx = i;
}
}
//cout << path[best_idx] << endl;
if (out_sequence.size()>0) out_sequence = out_sequence+" "+path[best_idx];
else out_sequence = path[best_idx];
if (component_rects != NULL)
component_rects->push_back(words_rect[w]);
if (component_texts != NULL)
component_texts->push_back(path[best_idx]);
if (component_confidences != NULL)
component_confidences->push_back((float)max_prob);
}
return;
}
void run( Mat& image,
Mat& mask,
string& out_sequence,
vector<Rect>* component_rects,
vector<string>* component_texts,
vector<float>* component_confidences,
int component_level)
{
CV_Assert( (image.type() == CV_8UC1) || (image.type() == CV_8UC3) );
CV_Assert( mask.type() == CV_8UC1 );
CV_Assert( (image.cols > 0) && (image.rows > 0) );
CV_Assert( (image.cols == mask.cols) && (image.rows == mask.rows) );
CV_Assert( component_level == OCR_LEVEL_WORD );
out_sequence.clear();
if (component_rects != NULL)
component_rects->clear();
if (component_texts != NULL)
component_texts->clear();
if (component_confidences != NULL)
component_confidences->clear();
// First we split a line into words
vector<Mat> words_mask;
vector<Rect> words_rect;
/// Find contours
vector<vector<Point> > contours;
vector<Vec4i> hierarchy;
Mat tmp;
mask.copyTo(tmp);
findContours( tmp, contours, hierarchy, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE, Point(0, 0) );
if (contours.size() < 6)
{
//do not split lines with less than 6 characters
words_mask.push_back(mask);
words_rect.push_back(Rect(0,0,mask.cols,mask.rows));
}
else
{
Mat_<float> vector_w((int)mask.cols,1);
reduce(mask, vector_w, 0, REDUCE_SUM, -1);
vector<int> spaces;
vector<int> spaces_start;
vector<int> spaces_end;
int space_count=0;
int last_one_idx;
int s_init = 0, s_end=vector_w.cols;
for (int s=0; s<vector_w.cols; s++)
{
if (vector_w.at<float>(0,s) == 0)
s_init = s+1;
else
break;
}
for (int s=vector_w.cols-1; s>=0; s--)
{
if (vector_w.at<float>(0,s) == 0)
s_end = s;
else
break;
}
for (int s=s_init; s<s_end; s++)
{
if (vector_w.at<float>(0,s) == 0)
{
space_count++;
} else {
if (space_count!=0)
{
spaces.push_back(space_count);
spaces_start.push_back(last_one_idx);
spaces_end.push_back(s-1);
}
space_count = 0;
last_one_idx = s;
}
}
Scalar mean_space,std_space;
meanStdDev(Mat(spaces),mean_space,std_space);
int num_word_spaces = 0;
int last_word_space_end = 0;
for (int s=0; s<(int)spaces.size(); s++)
{
if (spaces_end.at(s)-spaces_start.at(s) > mean_space[0]+(mean_space[0]*1.1)) //this 1.1 is a param?
{
if (num_word_spaces == 0)
{
//cout << " we have a word from 0 to " << spaces_start.at(s) << endl;
Mat word_mask;
Rect word_rect = Rect(0,0,spaces_start.at(s),mask.rows);
mask(word_rect).copyTo(word_mask);
words_mask.push_back(word_mask);
words_rect.push_back(word_rect);
}
else
{
//cout << " we have a word from " << last_word_space_end << " to " << spaces_start.at(s) << endl;
Mat word_mask;
Rect word_rect = Rect(last_word_space_end,0,spaces_start.at(s)-last_word_space_end,mask.rows);
mask(word_rect).copyTo(word_mask);
words_mask.push_back(word_mask);
words_rect.push_back(word_rect);
}
num_word_spaces++;
last_word_space_end = spaces_end.at(s);
}
}
//cout << " we have a word from " << last_word_space_end << " to " << vector_w.cols << endl << endl << endl;
Mat word_mask;
Rect word_rect = Rect(last_word_space_end,0,vector_w.cols-last_word_space_end,mask.rows);
mask(word_rect).copyTo(word_mask);
words_mask.push_back(word_mask);
words_rect.push_back(word_rect);
}
for (int w=0; w<(int)words_mask.size(); w++)
{
vector< vector<int> > observations;
vector< vector<double> > confidences;
vector<int> obs;
// First find contours and sort by x coordinate of bbox
words_mask[w].copyTo(tmp);
if (tmp.empty())
continue;
contours.clear();
hierarchy.clear();
/// Find contours
findContours( tmp, contours, hierarchy, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE, Point(0, 0) );
vector<Rect> contours_rect;
for (int i=0; i<(int)contours.size(); i++)
{
contours_rect.push_back(boundingRect(contours[i]));
}
sort(contours_rect.begin(), contours_rect.end(), sort_rect_horiz);
// Do character recognition foreach contour
for (int i=0; i<(int)contours.size(); i++)
{
vector<int> out_class;
vector<double> out_conf;
//take the center of the char rect and translate it to the real origin
Point char_center = Point(contours_rect.at(i).x+contours_rect.at(i).width/2,
contours_rect.at(i).y+contours_rect.at(i).height/2);
char_center.x += words_rect[w].x;
char_center.y += words_rect[w].y;
int win_size = max(contours_rect.at(i).width,contours_rect.at(i).height);
win_size += (int)(win_size*0.6); // add some pixels in the border TODO: is this a parameter for the user space?
Rect char_rect = Rect(char_center.x-win_size/2,char_center.y-win_size/2,win_size,win_size);
char_rect &= Rect(0,0,image.cols,image.rows);
Mat tmp_image;
image(char_rect).copyTo(tmp_image);
classifier->eval(tmp_image,out_class,out_conf);
if (!out_class.empty())
obs.push_back(out_class[0]);
//cout << " out class = " << vocabulary[out_class[0]] << "(" << out_conf[0] << ")" << endl;
observations.push_back(out_class);
confidences.push_back(out_conf);
}
//This must be extracted from dictionary, or just assumed to be equal for all characters
vector<double> start_p(vocabulary.size());
for (int i=0; i<(int)vocabulary.size(); i++)
start_p[i] = 1.0/vocabulary.size();
Mat V = Mat::zeros((int)observations.size(),(int)vocabulary.size(),CV_64FC1);
vector<string> path(vocabulary.size());
// Initialize base cases (t == 0)
for (int i=0; i<(int)vocabulary.size(); i++)
{
for (int j=0; j<(int)observations[0].size(); j++)
{
emission_p.at<double>(observations[0][j],obs[0]) = confidences[0][j];
}
V.at<double>(0,i) = start_p[i] * emission_p.at<double>(i,obs[0]);
path[i] = vocabulary.at(i);
}
// Run Viterbi for t > 0
for (int t=1; t<(int)obs.size(); t++)
{
//Dude this has to be done each time!!
emission_p = Mat::eye(62,62,CV_64FC1);
for (int e=0; e<(int)observations[t].size(); e++)
{
emission_p.at<double>(observations[t][e],obs[t]) = confidences[t][e];
}
vector<string> newpath(vocabulary.size());
for (int i=0; i<(int)vocabulary.size(); i++)
{
double max_prob = 0;
int best_idx = 0;
for (int j=0; j<(int)vocabulary.size(); j++)
{
double prob = V.at<double>(t-1,j) * transition_p.at<double>(j,i) * emission_p.at<double>(i,obs[t]);
if ( prob > max_prob)
{
max_prob = prob;
best_idx = j;
}
}
V.at<double>(t,i) = max_prob;
newpath[i] = path[best_idx] + vocabulary.at(i);
}
// Don't need to remember the old paths
path.swap(newpath);
}
double max_prob = 0;
int best_idx = 0;
for (int i=0; i<(int)vocabulary.size(); i++)
{
double prob = V.at<double>((int)obs.size()-1,i);
if ( prob > max_prob)
{
max_prob = prob;
best_idx = i;
}
}
//cout << path[best_idx] << endl;
if (out_sequence.size()>0) out_sequence = out_sequence+" "+path[best_idx];
else out_sequence = path[best_idx];
if (component_rects != NULL)
component_rects->push_back(words_rect[w]);
if (component_texts != NULL)
component_texts->push_back(path[best_idx]);
if (component_confidences != NULL)
component_confidences->push_back((float)max_prob);
}
return;
}
};
Ptr<OCRHMMDecoder> OCRHMMDecoder::create( Ptr<OCRHMMDecoder::ClassifierCallback> _classifier,
const string& _vocabulary,
InputArray transition_p,
InputArray emission_p,
decoder_mode _mode)
{
return makePtr<OCRHMMDecoderImpl>(_classifier, _vocabulary, transition_p, emission_p, _mode);
}
Ptr<OCRHMMDecoder> OCRHMMDecoder::create( Ptr<OCRHMMDecoder::ClassifierCallback> _classifier,
const String& _vocabulary,
InputArray transition_p,
InputArray emission_p,
int _mode)
{
return makePtr<OCRHMMDecoderImpl>(_classifier, _vocabulary, transition_p, emission_p, (decoder_mode)_mode);
}
class CV_EXPORTS OCRHMMClassifierKNN : public OCRHMMDecoder::ClassifierCallback
{
public:
//constructor
OCRHMMClassifierKNN(const std::string& filename);
// Destructor
~OCRHMMClassifierKNN() {}
void eval( InputArray mask, vector<int>& out_class, vector<double>& out_confidence );
private:
Ptr<KNearest> knn;
};
OCRHMMClassifierKNN::OCRHMMClassifierKNN (const string& filename)
{
knn = KNearest::create();
if (ifstream(filename.c_str()))
{
Mat hus, labels;
cv::FileStorage storage(filename.c_str(), cv::FileStorage::READ);
storage["hus"] >> hus;
storage["labels"] >> labels;
storage.release();
knn->train(hus, ROW_SAMPLE, labels);
}
else
CV_Error(Error::StsBadArg, "Default classifier data file not found!");
}
void OCRHMMClassifierKNN::eval( InputArray _mask, vector<int>& out_class, vector<double>& out_confidence )
{
CV_Assert( _mask.getMat().type() == CV_8UC1 );
out_class.clear();
out_confidence.clear();
int image_height = 35;
int image_width = 35;
int num_features = 200;
Mat img = _mask.getMat();
Mat tmp;
img.copyTo(tmp);
vector<vector<Point> > contours;
vector<Vec4i> hierarchy;
/// Find contours
findContours( tmp, contours, hierarchy, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE, Point(0, 0) );
if (contours.empty())
return;
int idx = 0;
if (contours.size() > 1)
{
// this is to make sure we have the mask with a single contour
// e.g "i" and "j" have two contours, but it may be also a part of a neighbour character
// we take the larger one and clean the outside in order to have a single contour
int max_area = 0;
for (int cc=0; cc<(int)contours.size(); cc++)
{
int area_c = boundingRect(contours[cc]).area();
if ( area_c > max_area)
{
idx = cc;
max_area = area_c;
}
}
// clean-up the outside of the contour
Mat tmp_c = Mat::zeros(tmp.rows, tmp.cols, CV_8UC1);
drawContours(tmp_c, contours, idx, Scalar(255), FILLED);
img = img & tmp_c;
}
Rect bbox = boundingRect(contours[idx]);
//Crop to fit the exact rect of the contour and resize to a fixed-sized matrix of 35 x 35 pixel, while retaining the centroid of the region and aspect ratio.
Mat mask = Mat::zeros(image_height,image_width,CV_8UC1);
img(bbox).copyTo(tmp);
if (tmp.cols>tmp.rows)
{
int height = image_width*tmp.rows/tmp.cols;
if(height == 0) height = 1;
resize(tmp,tmp,Size(image_width,height));
tmp.copyTo(mask(Rect(0,(image_height-height)/2,image_width,height)));
}
else
{
int width = image_height*tmp.cols/tmp.rows;
if(width == 0) width = 1;
resize(tmp,tmp,Size(width,image_height));
tmp.copyTo(mask(Rect((image_width-width)/2,0,width,image_height)));
}
//find contours again (now resized)
mask.copyTo(tmp);
findContours( tmp, contours, hierarchy, RETR_LIST, CHAIN_APPROX_SIMPLE, Point(0, 0) );
vector<Mat> maps;
for (int i=0; i<8; i++)
{
Mat map = Mat::zeros(image_height,image_width,CV_8UC1);
maps.push_back(map);
}
for (int c=0; c<(int)contours.size(); c++)
{
for (int i=0; i<(int)contours[c].size(); i++)
{
//cout << contours[c][i] << " -- " << contours[c][(i+1)%contours[c].size()] << endl;
double dy = contours[c][i].y - contours[c][(i+1)%contours[c].size()].y;
double dx = contours[c][i].x - contours[c][(i+1)%contours[c].size()].x;
double angle = atan2 (dy,dx) * 180 / 3.14159265;
//cout << " angle = " << angle << endl;
int idx_a = 0;
if ((angle>=157.5)||(angle<=-157.5))
idx_a = 0;
else if ((angle>=-157.5)&&(angle<=-112.5))
idx_a = 1;
else if ((angle>=-112.5)&&(angle<=-67.5))
idx_a = 2;
else if ((angle>=-67.5)&&(angle<=-22.5))
idx_a = 3;
else if ((angle>=-22.5)&&(angle<=22.5))
idx_a = 4;
else if ((angle>=22.5)&&(angle<=67.5))
idx_a = 5;
else if ((angle>=67.5)&&(angle<=112.5))
idx_a = 6;
else if ((angle>=112.5)&&(angle<=157.5))
idx_a = 7;
line(maps[idx_a],contours[c][i],contours[c][(i+1)%contours[c].size()],Scalar(255));
}
}
//On each bitmap a regular 7x7 Gaussian masks are evenly placed
for (int i=0; i<(int)maps.size(); i++)
{
copyMakeBorder(maps[i],maps[i],7,7,7,7,BORDER_CONSTANT,Scalar(0));
GaussianBlur(maps[i], maps[i], Size(7,7), 2, 2);
normalize(maps[i],maps[i],0,255,NORM_MINMAX);
resize(maps[i],maps[i],Size(image_width,image_height));
}
//Generate features for each bitmap
Mat sample = Mat(1,num_features,CV_32FC1);
Mat patch;
for (int i=0; i<(int)maps.size(); i++)
{
for(int y=0; y<image_height; y=y+7)
{
for(int x=0; x<image_width; x=x+7)
{
maps[i](Rect(x,y,7,7)).copyTo(patch);
Scalar mean,std;
meanStdDev(patch,mean,std);
sample.at<float>(0,i*25+((int)x/7)+((int)y/7)*5) = (float)(mean[0]/255);
//cout << " avg " << mean[0] << " in patch " << x << "," << y << " channel " << i << " idx = " << i*25+((int)x/7)+((int)y/7)*5<< endl;
}
}
}
Mat responses,dists,predictions;
knn->findNearest( sample, 11, predictions, responses, dists);
Scalar dist_sum = sum(dists);
Mat class_predictions = Mat::zeros(1,62,CV_64FC1);
vector<vector<int> > equivalency_mat(62);
equivalency_mat[2].push_back(28); // c -> C
equivalency_mat[28].push_back(2); // C -> c
equivalency_mat[8].push_back(34); // i -> I
equivalency_mat[8].push_back(11); // i -> l
equivalency_mat[11].push_back(8); // l -> i
equivalency_mat[11].push_back(34); // l -> I
equivalency_mat[34].push_back(8); // I -> i
equivalency_mat[34].push_back(11); // I -> l
equivalency_mat[9].push_back(35); // j -> J
equivalency_mat[35].push_back(9); // J -> j
equivalency_mat[14].push_back(40); // o -> O
equivalency_mat[14].push_back(52); // o -> 0
equivalency_mat[40].push_back(14); // O -> o
equivalency_mat[40].push_back(52); // O -> 0
equivalency_mat[52].push_back(14); // 0 -> o
equivalency_mat[52].push_back(40); // 0 -> O
equivalency_mat[15].push_back(41); // p -> P
equivalency_mat[41].push_back(15); // P -> p
equivalency_mat[18].push_back(44); // s -> S
equivalency_mat[44].push_back(18); // S -> s
equivalency_mat[20].push_back(46); // u -> U
equivalency_mat[46].push_back(20); // U -> u
equivalency_mat[21].push_back(47); // v -> V
equivalency_mat[47].push_back(21); // V -> v
equivalency_mat[22].push_back(48); // w -> W
equivalency_mat[48].push_back(22); // W -> w
equivalency_mat[23].push_back(49); // x -> X
equivalency_mat[49].push_back(23); // X -> x
equivalency_mat[25].push_back(51); // z -> Z
equivalency_mat[51].push_back(25); // Z -> z
for (int j=0; j<responses.cols; j++)
{
if (responses.at<float>(0,j)<0)
continue;
class_predictions.at<double>(0,(int)responses.at<float>(0,j)) += dists.at<float>(0,j);
for (int e=0; e<(int)equivalency_mat[(int)responses.at<float>(0,j)].size(); e++)
{
class_predictions.at<double>(0,equivalency_mat[(int)responses.at<float>(0,j)][e]) += dists.at<float>(0,j);
dist_sum[0] += dists.at<float>(0,j);
}
}
class_predictions = class_predictions/dist_sum[0];
out_class.push_back((int)predictions.at<float>(0,0));
out_confidence.push_back(class_predictions.at<double>(0,(int)predictions.at<float>(0,0)));
for (int i=0; i<class_predictions.cols; i++)
{
if ((class_predictions.at<double>(0,i) > 0) && (i != out_class[0]))
{
out_class.push_back(i);
out_confidence.push_back(class_predictions.at<double>(0,i));
}
}
}
Ptr<OCRHMMDecoder::ClassifierCallback> loadOCRHMMClassifierNM(const String& filename)
{
return makePtr<OCRHMMClassifierKNN>(std::string(filename));
}
class CV_EXPORTS OCRHMMClassifierCNN : public OCRHMMDecoder::ClassifierCallback
{
public:
//constructor
OCRHMMClassifierCNN(const std::string& filename);
// Destructor
~OCRHMMClassifierCNN() {}
void eval( InputArray image, vector<int>& out_class, vector<double>& out_confidence );
protected:
void normalizeAndZCA(Mat& patches);
double eval_feature(Mat& feature, double* prob_estimates);
private:
int nr_class; // number of classes
int nr_feature; // number of features
Mat feature_min; // scale range
Mat feature_max;
Mat weights; // Logistic Regression weights
Mat kernels; // CNN kernels
Mat M, P; // ZCA Whitening parameters
int window_size; // window size
int quad_size;
int patch_size;
int num_quads; // extract 25 quads (12x12) from each image
int num_tiles; // extract 25 patches (8x8) from each quad
double alpha; // used in non-linear activation function z = max(0, |D*a| - alpha)
};
OCRHMMClassifierCNN::OCRHMMClassifierCNN (const string& filename)
{
if (ifstream(filename.c_str()))
{
FileStorage fs(filename, FileStorage::READ);
// Load kernels bank and withenning params
fs["kernels"] >> kernels;
fs["M"] >> M;
fs["P"] >> P;
// Load Logistic Regression weights
fs["weights"] >> weights;
// Load feature scaling ranges
fs["feature_min"] >> feature_min;
fs["feature_max"] >> feature_max;
fs.release();
}
else
CV_Error(Error::StsBadArg, "Default classifier data file not found!");
// check all matrix dimensions match correctly and no one is empty
CV_Assert( (M.cols > 0) && (M.rows > 0) );
CV_Assert( (P.cols > 0) && (P.rows > 0) );
CV_Assert( (kernels.cols > 0) && (kernels.rows > 0) );
CV_Assert( (weights.cols > 0) && (weights.rows > 0) );
CV_Assert( (feature_min.cols > 0) && (feature_min.rows > 0) );
CV_Assert( (feature_max.cols > 0) && (feature_max.rows > 0) );
nr_feature = weights.rows;
nr_class = weights.cols;
patch_size = (int)sqrt(kernels.cols);
// algorithm internal parameters
window_size = 32;
num_quads = 25;
num_tiles = 25;
quad_size = 12;
alpha = 0.5;
}
void OCRHMMClassifierCNN::eval( InputArray _src, vector<int>& out_class, vector<double>& out_confidence )
{
CV_Assert(( _src.getMat().type() == CV_8UC3 ) || ( _src.getMat().type() == CV_8UC1 ));
out_class.clear();
out_confidence.clear();
Mat img = _src.getMat();
if(img.type() == CV_8UC3)
{
cvtColor(img,img,COLOR_RGB2GRAY);
}
// shall we resize the input image or make a copy ?
resize(img,img,Size(window_size,window_size));
Mat quad;
Mat tmp;
int patch_count = 0;
vector< vector<double> > data_pool(9);
int quad_id = 1;
for (int q_x=0; q_x<=window_size-quad_size; q_x=q_x+(int)(quad_size/2-1))
{
for (int q_y=0; q_y<=window_size-quad_size; q_y=q_y+(int)(quad_size/2-1))
{
Rect quad_rect = Rect(q_x,q_y,quad_size,quad_size);
quad = img(quad_rect);
//start sliding window (8x8) in each tile and store the patch as row in data_pool
for (int w_x=0; w_x<=quad_size-patch_size; w_x++)
{
for (int w_y=0; w_y<=quad_size-patch_size; w_y++)
{
quad(Rect(w_x,w_y,patch_size,patch_size)).copyTo(tmp);
tmp = tmp.reshape(0,1);
tmp.convertTo(tmp, CV_64F);
normalizeAndZCA(tmp);
vector<double> patch;
tmp.copyTo(patch);
if ((quad_id == 1)||(quad_id == 2)||(quad_id == 6)||(quad_id == 7))
data_pool[0].insert(data_pool[0].end(),patch.begin(),patch.end());
if ((quad_id == 2)||(quad_id == 7)||(quad_id == 3)||(quad_id == 8)||(quad_id == 4)||(quad_id == 9))
data_pool[1].insert(data_pool[1].end(),patch.begin(),patch.end());
if ((quad_id == 4)||(quad_id == 9)||(quad_id == 5)||(quad_id == 10))
data_pool[2].insert(data_pool[2].end(),patch.begin(),patch.end());
if ((quad_id == 6)||(quad_id == 11)||(quad_id == 16)||(quad_id == 7)||(quad_id == 12)||(quad_id == 17))
data_pool[3].insert(data_pool[3].end(),patch.begin(),patch.end());
if ((quad_id == 7)||(quad_id == 12)||(quad_id == 17)||(quad_id == 8)||(quad_id == 13)||(quad_id == 18)||(quad_id == 9)||(quad_id == 14)||(quad_id == 19))
data_pool[4].insert(data_pool[4].end(),patch.begin(),patch.end());
if ((quad_id == 9)||(quad_id == 14)||(quad_id == 19)||(quad_id == 10)||(quad_id == 15)||(quad_id == 20))
data_pool[5].insert(data_pool[5].end(),patch.begin(),patch.end());
if ((quad_id == 16)||(quad_id == 21)||(quad_id == 17)||(quad_id == 22))
data_pool[6].insert(data_pool[6].end(),patch.begin(),patch.end());
if ((quad_id == 17)||(quad_id == 22)||(quad_id == 18)||(quad_id == 23)||(quad_id == 19)||(quad_id == 24))
data_pool[7].insert(data_pool[7].end(),patch.begin(),patch.end());
if ((quad_id == 19)||(quad_id == 24)||(quad_id == 20)||(quad_id == 25))
data_pool[8].insert(data_pool[8].end(),patch.begin(),patch.end());
patch_count++;
}
}
quad_id++;
}
}
//do dot product of each normalized and whitened patch
//each pool is averaged and this yields a representation of 9xD
Mat feature = Mat::zeros(9,kernels.rows,CV_64FC1);
for (int i=0; i<9; i++)
{
Mat pool = Mat(data_pool[i]);
pool = pool.reshape(0,(int)data_pool[i].size()/kernels.cols);
for (int p=0; p<pool.rows; p++)
{
for (int f=0; f<kernels.rows; f++)
{
feature.row(i).at<double>(0,f) = feature.row(i).at<double>(0,f) + max(0.0,std::abs(pool.row(p).dot(kernels.row(f)))-alpha);
}
}
}
feature = feature.reshape(0,1);
// data must be normalized within the range obtained during training
double lower = -1.0;
double upper = 1.0;
for (int k=0; k<feature.cols; k++)
{
feature.at<double>(0,k) = lower + (upper-lower) *
(feature.at<double>(0,k)-feature_min.at<double>(0,k))/
(feature_max.at<double>(0,k)-feature_min.at<double>(0,k));
}
double *p = new double[nr_class];
double predict_label = eval_feature(feature,p);
//cout << " Prediction: " << vocabulary[predict_label] << " with probability " << p[0] << endl;
if (predict_label < 0)
CV_Error(Error::StsInternal, "OCRHMMClassifierCNN::eval Error: unexpected prediction in eval_feature()");
out_class.push_back((int)predict_label);
out_confidence.push_back(p[(int)predict_label]);
for (int i = 0; i<nr_class; i++)
{
if ( (i != (int)predict_label) && (p[i] != 0.) )
{
out_class.push_back(i);
out_confidence.push_back(p[i]);
}
}
}
// normalize for contrast and apply ZCA whitening to a set of image patches
void OCRHMMClassifierCNN::normalizeAndZCA(Mat& patches)
{
//Normalize for contrast
for (int i=0; i<patches.rows; i++)
{
Scalar row_mean, row_std;
meanStdDev(patches.row(i),row_mean,row_std);
row_std[0] = sqrt(pow(row_std[0],2)*patches.cols/(patches.cols-1)+10);
patches.row(i) = (patches.row(i) - row_mean[0]) / row_std[0];
}
//ZCA whitening
if ((M.dims == 0) || (P.dims == 0))
{
Mat CC;
calcCovarMatrix(patches,CC,M,COVAR_NORMAL|COVAR_ROWS|COVAR_SCALE);
CC = CC * patches.rows / (patches.rows-1);
Mat e_val,e_vec;
eigen(CC.t(),e_val,e_vec);
e_vec = e_vec.t();
sqrt(1./(e_val + 0.1), e_val);
Mat V = Mat::zeros(e_vec.rows, e_vec.cols, CV_64FC1);
Mat D = Mat::eye(e_vec.rows, e_vec.cols, CV_64FC1);
for (int i=0; i<e_vec.cols; i++)
{
e_vec.col(e_vec.cols-i-1).copyTo(V.col(i));
D.col(i) = D.col(i) * e_val.at<double>(0,e_val.rows-i-1);
}
P = V * D * V.t();
}
for (int i=0; i<patches.rows; i++)
patches.row(i) = patches.row(i) - M;
patches = patches * P;
}
double OCRHMMClassifierCNN::eval_feature(Mat& feature, double* prob_estimates)
{
for(int i=0;i<nr_class;i++)
prob_estimates[i] = 0;
for(int idx=0; idx<nr_feature; idx++)
for(int i=0;i<nr_class;i++)
prob_estimates[i] += weights.at<float>(idx,i)*feature.at<double>(0,idx); //TODO use vectorized dot product
int dec_max_idx = 0;
for(int i=1;i<nr_class;i++)
{
if(prob_estimates[i] > prob_estimates[dec_max_idx])
dec_max_idx = i;
}
for(int i=0;i<nr_class;i++)
prob_estimates[i]=1/(1+exp(-prob_estimates[i]));
double sum=0;
for(int i=0; i<nr_class; i++)
sum+=prob_estimates[i];
for(int i=0; i<nr_class; i++)
prob_estimates[i]=prob_estimates[i]/sum;
return dec_max_idx;
}
Ptr<OCRHMMDecoder::ClassifierCallback> loadOCRHMMClassifierCNN(const String& filename)
{
return makePtr<OCRHMMClassifierCNN>(std::string(filename));
}
/** @brief Utility function to create a tailored language model transitions table from a given list of words (lexicon).
@param vocabulary The language vocabulary (chars when ascii english text).
@param lexicon The list of words that are expected to be found in a particular image.
@param transition_probabilities_table Output table with transition probabilities between character pairs. cols == rows == vocabulary.size().
The function calculate frequency statistics of character pairs from the given lexicon and fills
the output transition_probabilities_table with them.
The transition_probabilities_table can be used as input in the OCRHMMDecoder::create() and OCRBeamSearchDecoder::create() methods.
@note
- (C++) An alternative would be to load the default generic language transition table provided in the text module samples folder (created from ispell 42869 english words list) :
<https://github.com/Itseez/opencv_contrib/blob/master/modules/text/samples/OCRHMM_transitions_table.xml>
*/
void createOCRHMMTransitionsTable(string& vocabulary, vector<string>& lexicon, OutputArray _transitions)
{
CV_Assert( vocabulary.size() > 0 );
CV_Assert( lexicon.size() > 0 );
if ( (_transitions.getMat().cols != (int)vocabulary.size()) ||
(_transitions.getMat().rows != (int)vocabulary.size()) ||
(_transitions.getMat().type() != CV_64F) )
{
_transitions.create((int)vocabulary.size(), (int)vocabulary.size(), CV_64F);
}
Mat transitions = _transitions.getMat();
transitions = Scalar(0);
Mat count_pairs = Mat::zeros(1, (int)vocabulary.size(), CV_64F);
for (size_t w=0; w<lexicon.size(); w++)
{
for (size_t i=0,j=1; i<lexicon[w].size()-1; i++,j++)
{
size_t idx_i = vocabulary.find(lexicon[w][i]);
size_t idx_j = vocabulary.find(lexicon[w][j]);
if ((idx_i == string::npos) || (idx_j == string::npos))
{
CV_Error(Error::StsBadArg, "Found a non-vocabulary char in lexicon!");
}
transitions.at<double>((int)idx_i,(int)idx_j) += 1;
count_pairs.at<double>(0,(int)idx_i) += 1;
}
}
for (int i=0; i<transitions.rows; i++)
{
transitions.row(i) = transitions.row(i) / count_pairs.at<double>(0,i); //normalize
}
return;
}
Mat createOCRHMMTransitionsTable(const String& vocabulary, vector<cv::String>& lexicon)
{
std::string voc(vocabulary);
vector<string> lex;
for(vector<cv::String>::iterator l = lexicon.begin(); l != lexicon.end(); l++)
lex.push_back(std::string(*l));
Mat _transitions;
createOCRHMMTransitionsTable(voc, lex, _transitions);
return _transitions;
}
}
}