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#include "precomp.hpp"
#include "opencv2/imgproc.hpp"
#include "opencv2/ml.hpp"
#include <iostream>
#include <fstream>
#include <set>
namespace cv
{
namespace text
{
using namespace std;
using namespace cv::ml;
/* OCR BeamSearch Decoder */
void OCRBeamSearchDecoder::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 OCRBeamSearchDecoder::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(mask.type() == CV_8UC1);
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();
}
CV_WRAP String OCRBeamSearchDecoder::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 String OCRBeamSearchDecoder::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 OCRBeamSearchDecoder::ClassifierCallback::eval( InputArray image, vector< vector<double> >& recognition_probabilities, vector<int>& oversegmentation)
{
CV_Assert(( image.getMat().type() == CV_8UC3 ) || ( image.getMat().type() == CV_8UC1 ));
if (!recognition_probabilities.empty())
{
for (size_t i=0; i<recognition_probabilities.size(); i++)
recognition_probabilities[i].clear();
}
recognition_probabilities.clear();
oversegmentation.clear();
}
struct beamSearch_node {
double score;
vector<int> segmentation;
bool expanded;
// TODO calculating score of its child would be much faster if we store the last column
// of their "root" path.
};
bool beam_sort_function ( beamSearch_node a, beamSearch_node b );
bool beam_sort_function ( beamSearch_node a, beamSearch_node b )
{
return (a.score > b.score);
}
class OCRBeamSearchDecoderImpl CV_FINAL : public OCRBeamSearchDecoder
{
public:
//Default constructor
OCRBeamSearchDecoderImpl( Ptr<OCRBeamSearchDecoder::ClassifierCallback> _classifier,
const string& _vocabulary,
InputArray transition_probabilities_table,
InputArray emission_probabilities_table,
decoder_mode _mode,
int _beam_size)
{
classifier = _classifier;
step_size = classifier->getStepSize();
win_size = classifier->getWindowSize();
emission_p = emission_probabilities_table.getMat();
vocabulary = _vocabulary;
mode = _mode;
beam_size = _beam_size;
transition_probabilities_table.getMat().copyTo(transition_p);
for (int i=0; i<transition_p.rows; i++)
{
for (int j=0; j<transition_p.cols; j++)
{
if (transition_p.at<double>(i,j) == 0)
transition_p.at<double>(i,j) = -DBL_MAX;
else
transition_p.at<double>(i,j) = log(transition_p.at<double>(i,j));
}
}
}
~OCRBeamSearchDecoderImpl() CV_OVERRIDE
{
}
void run( Mat& src,
Mat& mask,
string& out_sequence,
vector<Rect>* component_rects,
vector<string>* component_texts,
vector<float>* component_confidences,
int component_level) CV_OVERRIDE
{
CV_Assert(mask.type() == CV_8UC1);
//nothing to do with a mask here
run( src, out_sequence, component_rects, component_texts, component_confidences,
component_level);
}
void run( Mat& src,
string& out_sequence,
vector<Rect>* component_rects,
vector<string>* component_texts,
vector<float>* component_confidences,
int component_level) CV_OVERRIDE
{
CV_Assert( (src.type() == CV_8UC1) || (src.type() == CV_8UC3) );
CV_Assert( (src.cols > 0) && (src.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();
if(src.type() == CV_8UC3)
{
cvtColor(src,src,COLOR_RGB2GRAY);
}
// TODO if input is a text line (not a word) we may need to split into words here!
// do sliding window classification along a cropped word image
classifier->eval(src, recognition_probabilities, oversegmentation);
// if the number of oversegmentation points found is less than 2 we can not do nothing!!
if (oversegmentation.size() < 2) return;
//NMS of recognitions
double last_best_p = 0;
int last_best_idx = -1;
for (size_t i=0; i<recognition_probabilities.size(); )
{
double best_p = 0;
int best_idx = -1;
for (size_t j=0; j<recognition_probabilities[i].size(); j++)
{
if (recognition_probabilities[i][j] > best_p)
{
best_p = recognition_probabilities[i][j];
best_idx = (int)j;
}
}
if ((i>0) && (best_idx == last_best_idx)
&& (oversegmentation[i]*step_size < oversegmentation[i-1]*step_size + win_size) )
{
if (last_best_p > best_p)
{
//remove i'th elements and do not increment i
recognition_probabilities.erase (recognition_probabilities.begin()+i);
oversegmentation.erase (oversegmentation.begin()+i);
continue;
} else {
//remove (i-1)'th elements and do not increment i
recognition_probabilities.erase (recognition_probabilities.begin()+i-1);
oversegmentation.erase (oversegmentation.begin()+i-1);
last_best_idx = best_idx;
last_best_p = best_p;
continue;
}
}
last_best_idx = best_idx;
last_best_p = best_p;
i++;
}
/*Now we go with the beam search algorithm to optimize the recognition score*/
//convert probabilities to log probabilities
for (size_t i=0; i<recognition_probabilities.size(); i++)
{
for (size_t j=0; j<recognition_probabilities[i].size(); j++)
{
if (recognition_probabilities[i][j] == 0)
recognition_probabilities[i][j] = -DBL_MAX;
else
recognition_probabilities[i][j] = log(recognition_probabilities[i][j]);
}
}
// initialize the beam with all possible character's pairs
int generated_chids = 0;
for (size_t i=0; i<recognition_probabilities.size()-1; i++)
{
for (size_t j=i+1; j<recognition_probabilities.size(); j++)
{
beamSearch_node node;
node.segmentation.push_back((int)i);
node.segmentation.push_back((int)j);
node.score = score_segmentation(node.segmentation, out_sequence);
vector< vector<int> > childs = generate_childs( node.segmentation );
node.expanded = true;
beam.push_back( node );
if (!childs.empty())
update_beam( childs );
generated_chids += (int)childs.size();
}
}
while (generated_chids != 0)
{
generated_chids = 0;
for (size_t i=0; i<beam.size(); i++)
{
vector< vector<int> > childs;
if (!beam[i].expanded)
{
childs = generate_childs( beam[i].segmentation );
beam[i].expanded = true;
}
if (!childs.empty())
update_beam( childs );
generated_chids += (int)childs.size();
}
}
// Done! Get the best prediction found into out_sequence
double lp = score_segmentation( beam[0].segmentation, out_sequence );
// fill other (dummy) output parameters
component_rects->push_back(Rect(0,0,src.cols,src.rows));
component_texts->push_back(out_sequence);
component_confidences->push_back((float)exp(lp));
return;
}
private:
int win_size;
int step_size;
vector< beamSearch_node > beam;
vector< vector<double> > recognition_probabilities;
vector<int> oversegmentation;
vector< vector<int> > generate_childs( vector<int> &segmentation )
{
vector< vector<int> > childs;
for (size_t i=segmentation[segmentation.size()-1]+1; i<oversegmentation.size(); i++)
{
int seg_point = (int)i;
if (find(segmentation.begin(), segmentation.end(), seg_point) == segmentation.end())
{
vector<int> child = segmentation;
child.push_back(seg_point);
childs.push_back(child);
}
}
return childs;
}
void update_beam ( vector< vector<int> > &childs )
{
string out_sequence;
double min_score = -DBL_MAX; //min score value to be part of the beam
if ((int)beam.size() >= beam_size)
min_score = beam[beam_size-1].score; //last element has the lowest score
for (size_t i=0; i<childs.size(); i++)
{
double score = score_segmentation(childs[i], out_sequence);
if (score > min_score)
{
beamSearch_node node;
node.score = score;
node.segmentation = childs[i];
node.expanded = false;
beam.push_back(node);
sort(beam.begin(),beam.end(),beam_sort_function);
if ((int)beam.size() > beam_size)
{
beam.erase(beam.begin()+beam_size,beam.end());
min_score = beam[beam.size()-1].score;
}
}
}
}
double score_segmentation( vector<int> &segmentation, string& outstring )
{
// Score Heuristics:
// No need to use Viterbi to know a given segmentation is bad
// e.g.: in some cases we discard a segmentation because it includes a very large character
// in other cases we do it because the overlapping between two chars is too large
// TODO Add more heuristics (e.g. penalize large inter-character variance)
Mat interdist ((int)segmentation.size()-1, 1, CV_32F, 1);
for (size_t i=0; i<segmentation.size()-1; i++)
{
interdist.at<float>((int)i,0) = (float)oversegmentation[segmentation[(int)i+1]]*step_size
- (float)oversegmentation[segmentation[(int)i]]*step_size;
if ((float)interdist.at<float>((int)i,0)/win_size > 2.25) // TODO explain how did you set this thrs
{
return -DBL_MAX;
}
if ((float)interdist.at<float>((int)i,0)/win_size < 0.15) // TODO explain how did you set this thrs
{
return -DBL_MAX;
}
}
Scalar m, std;
meanStdDev(interdist, m, std);
//double interdist_std = std[0];
//TODO Extracting start probs from lexicon (if we have it) may boost accuracy!
vector<double> start_p(vocabulary.size());
for (int i=0; i<(int)vocabulary.size(); i++)
start_p[i] = log(1.0/vocabulary.size());
Mat V = Mat::ones((int)segmentation.size(),(int)vocabulary.size(),CV_64FC1);
V = V * -DBL_MAX;
vector<string> path(vocabulary.size());
// Initialize base cases (t == 0)
for (int i=0; i<(int)vocabulary.size(); i++)
{
V.at<double>(0,i) = start_p[i] + recognition_probabilities[segmentation[0]][i];
path[i] = vocabulary.at(i);
}
// Run Viterbi for t > 0
for (int t=1; t<(int)segmentation.size(); t++)
{
vector<string> newpath(vocabulary.size());
for (int i=0; i<(int)vocabulary.size(); i++)
{
double max_prob = -DBL_MAX;
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) + recognition_probabilities[segmentation[t]][i];
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 = -DBL_MAX;
int best_idx = 0;
for (int i=0; i<(int)vocabulary.size(); i++)
{
double prob = V.at<double>((int)segmentation.size()-1,i);
if ( prob > max_prob)
{
max_prob = prob;
best_idx = i;
}
}
outstring = path[best_idx];
return (max_prob / (segmentation.size()-1));
}
};
Ptr<OCRBeamSearchDecoder> OCRBeamSearchDecoder::create( Ptr<OCRBeamSearchDecoder::ClassifierCallback> _classifier,
const string& _vocabulary,
InputArray transition_p,
InputArray emission_p,
decoder_mode _mode,
int _beam_size)
{
return makePtr<OCRBeamSearchDecoderImpl>(_classifier, _vocabulary, transition_p, emission_p, _mode, _beam_size);
}
Ptr<OCRBeamSearchDecoder> OCRBeamSearchDecoder::create(Ptr<OCRBeamSearchDecoder::ClassifierCallback> _classifier,
const String& _vocabulary,
InputArray transition_p,
InputArray emission_p,
int _mode,
int _beam_size)
{
return makePtr<OCRBeamSearchDecoderImpl>(_classifier, _vocabulary, transition_p, emission_p, (decoder_mode)_mode, _beam_size);
}
Ptr<OCRBeamSearchDecoder> OCRBeamSearchDecoder::create(const String& _filename,
const String& _vocabulary,
InputArray transition_p,
InputArray emission_p,
int _mode,
int _beam_size)
{
return makePtr<OCRBeamSearchDecoderImpl>(loadOCRBeamSearchClassifierCNN(_filename), _vocabulary, transition_p, emission_p, (decoder_mode)_mode, _beam_size);
}
class OCRBeamSearchClassifierCNN CV_FINAL : public OCRBeamSearchDecoder::ClassifierCallback
{
public:
//constructor
OCRBeamSearchClassifierCNN(const std::string& filename);
// Destructor
~OCRBeamSearchClassifierCNN() CV_OVERRIDE {}
void eval( InputArray src, vector< vector<double> >& recognition_probabilities, vector<int>& oversegmentation ) CV_OVERRIDE;
int getWindowSize() {return window_size;}
int getStepSize() {return step_size;}
void setStepSize(int _step_size) {step_size = _step_size;}
protected:
void normalizeAndZCA(Mat& patches);
double eval_feature(Mat& feature, double* prob_estimates);
private:
int window_size; // window size
int step_size; // sliding window step
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 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)
};
OCRBeamSearchClassifierCNN::OCRBeamSearchClassifierCNN (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!");
nr_feature = weights.rows;
nr_class = weights.cols;
patch_size = cvRound(sqrt((float)kernels.cols));
window_size = 4*patch_size;
step_size = 4;
quad_size = 12;
num_quads = 25;
num_tiles = 25;
alpha = 0.5; // used in non-linear activation function z = max(0, |D*a| - alpha)
}
void OCRBeamSearchClassifierCNN::eval( InputArray _src, vector< vector<double> >& recognition_probabilities, vector<int>& oversegmentation)
{
CV_Assert(( _src.getMat().type() == CV_8UC3 ) || ( _src.getMat().type() == CV_8UC1 ));
if (!recognition_probabilities.empty())
{
for (size_t i=0; i<recognition_probabilities.size(); i++)
recognition_probabilities[i].clear();
}
recognition_probabilities.clear();
oversegmentation.clear();
Mat src = _src.getMat();
if(src.type() == CV_8UC3)
{
cvtColor(src,src,COLOR_RGB2GRAY);
}
resize(src,src,Size(window_size*src.cols/src.rows,window_size),0,0,INTER_LINEAR_EXACT);
int seg_points = 0;
Mat quad;
Mat tmp;
Mat img;
int sz = src.cols - window_size;
int sz_window_quad = window_size - quad_size;
int sz_half_quad = (int)(quad_size/2-1);
int sz_quad_patch = quad_size - patch_size;
// begin sliding window loop foreach detection window
for (int x_c = 0; x_c <= sz; x_c += step_size)
{
img = src(Rect(Point(x_c,0),Size(window_size,window_size)));
int patch_count = 0;
vector< vector<double> > data_pool(9);
int quad_id = 1;
for (int q_x = 0; q_x <= sz_window_quad; q_x += sz_half_quad)
{
for (int q_y = 0; q_y <= sz_window_quad; q_y += sz_half_quad)
{
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 <= sz_quad_patch; w_x++)
{
for (int w_y = 0; w_y <= sz_quad_patch; w_y++)
{
quad(Rect(w_x,w_y,patch_size,patch_size)).convertTo(tmp, CV_64F);
tmp = tmp.reshape(0,1);
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);
if ( (predict_label < 0) || (predict_label > nr_class) )
CV_Error(Error::StsOutOfRange, "OCRBeamSearchClassifierCNN::eval Error: unexpected prediction in eval_feature()");
vector<double> recognition_p(p, p+nr_class);
recognition_probabilities.push_back(recognition_p);
oversegmentation.push_back(seg_points);
seg_points++;
}
}
// normalize for contrast and apply ZCA whitening to a set of image patches
void OCRBeamSearchClassifierCNN::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 OCRBeamSearchClassifierCNN::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<OCRBeamSearchDecoder::ClassifierCallback> loadOCRBeamSearchClassifierCNN(const String& filename)
{
return makePtr<OCRBeamSearchClassifierCNN>(std::string(filename));
}
}
}