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opencv_contrib
Commit ffcda05c authored by Vadim Pisarevsky's avatar Vadim Pisarevsky
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Merge pull request #59 from lluisgomez/master 

update to new OpenCV 3.0 ml API and adds parallell_for_ OCR
parents 62eda671 820f2e08
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Showing with 104 additions and 41 deletions
modules/text/samples/trained_classifierNM1.xml
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modules/text/samples/trained_classifierNM2.xml
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modules/text/samples/trained_classifier_erGrouping.xml
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modules/text/samples/webcam_demo.cpp
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......@@ -45,6 +45,35 @@ public:
Parallel_extractCSER & operator=(const Parallel_extractCSER &a);
};
//OCR recognition is done in parallel for different detections
class Parallel_OCR: public cv::ParallelLoopBody
{
private:
vector<Mat> &detections;
vector<string> &outputs;
vector< vector<Rect> > &boxes;
vector< vector<string> > &words;
vector< vector<float> > &confidences;
vector< OCRTesseract* > &ocrs;
public:
Parallel_OCR(vector<Mat> &_detections, vector<string> &_outputs, vector< vector<Rect> > &_boxes,
vector< vector<string> > &_words, vector< vector<float> > &_confidences,
vector< OCRTesseract* > &_ocrs)
: detections(_detections), outputs(_outputs), boxes(_boxes), words(_words),
confidences(_confidences), ocrs(_ocrs)
{}
virtual void operator()( const cv::Range &r ) const
{
for (int c=r.start; c < r.end; c++)
{
ocrs[c%ocrs.size()]->run(detections[c], outputs[c], &boxes[c], &words[c], &confidences[c], OCR_LEVEL_WORD);
}
}
Parallel_OCR & operator=(const Parallel_OCR &a);
};
//Discard wrongly recognised strings
bool isRepetitive(const string& s);
......@@ -87,10 +116,16 @@ int main(int argc, char* argv[])
er_filters2.push_back(er_filter2);
}
//Initialize OCR engine
//double t_r = getTickCount();
OCRTesseract *ocr_tess = new OCRTesseract();
//Initialize OCR engine (we initialize 10 instances in order to work several recognitions in parallel)
int num_ocrs = 10;
vector<OCRTesseract*> ocrs;
for (int o=0; o<num_ocrs; o++)
{
OCRTesseract* ocr = new OCRTesseract();
ocrs.push_back(ocr);
}
//cout << "TIME_OCR_INITIALIZATION_ALT = "<< ((double)getTickCount() - t_r)*1000/getTickFrequency() << endl;
......@@ -182,7 +217,9 @@ int main(int argc, char* argv[])
float scale_img = (float)(600.f/frame.rows);
float scale_font = (float)(2-scale_img)/1.4f;
vector<string> words_detection;
string output;
float min_confidence1 = 51.f, min_confidence2 = 60.f;
vector<Mat> detections;
//t_r = getTickCount();
......@@ -195,42 +232,51 @@ int main(int argc, char* argv[])
er_draw(channels, regions, nm_region_groups[i], group_img);
group_img(nm_boxes[i]).copyTo(group_img);
copyMakeBorder(group_img,group_img,15,15,15,15,BORDER_CONSTANT,Scalar(0));
detections.push_back(group_img);
}
vector<string> outputs((int)detections.size());
vector< vector<Rect> > boxes((int)detections.size());
vector< vector<string> > words((int)detections.size());
vector< vector<float> > confidences((int)detections.size());
// parallel process detections in batches of ocrs.size()
for (int i=0; i<(int)detections.size(); i=i+(int)ocrs.size())
{
Range r;
if (i+(int)ocrs.size() <= (int)detections.size())
r = Range(i,i+(int)ocrs.size());
else
r = Range(i,(int)detections.size());
vector<Rect> boxes;
vector<string> words;
vector<float> confidences;
parallel_for_(r, Parallel_OCR(detections, outputs, boxes, words, confidences, ocrs));
}
float min_confidence1 = 0.f, min_confidence2 = 0.f;
for (int i=0; i<(int)detections.size(); i++)
{
if (RECOGNITION == 0)
{
ocr_tess->run(group_img, output, &boxes, &words, &confidences, OCR_LEVEL_WORD);
min_confidence1 = 51.f;
min_confidence2 = 60.f;
}
output.erase(remove(output.begin(), output.end(), '\n'), output.end());
outputs[i].erase(remove(outputs[i].begin(), outputs[i].end(), '\n'), outputs[i].end());
//cout << "OCR output = \"" << output << "\" lenght = " << output.size() << endl;
if (output.size() < 3)
if (outputs[i].size() < 3)
continue;
for (int j=0; j<(int)boxes.size(); j++)
for (int j=0; j<(int)boxes[i].size(); j++)
{
boxes[j].x += nm_boxes[i].x-15;
boxes[j].y += nm_boxes[i].y-15;
boxes[i][j].x += nm_boxes[i].x-15;
boxes[i][j].y += nm_boxes[i].y-15;
//cout << " word = " << words[j] << "\t confidence = " << confidences[j] << endl;
if ((words[j].size() < 2) || (confidences[j] < min_confidence1) ||
((words[j].size()==2) && (words[j][0] == words[j][1])) ||
((words[j].size()< 4) && (confidences[j] < min_confidence2)) ||
isRepetitive(words[j]))
if ((words[i][j].size() < 2) || (confidences[i][j] < min_confidence1) ||
((words[i][j].size()==2) && (words[i][j][0] == words[i][j][1])) ||
((words[i][j].size()< 4) && (confidences[i][j] < min_confidence2)) ||
isRepetitive(words[i][j]))
continue;
words_detection.push_back(words[j]);
rectangle(out_img, boxes[j].tl(), boxes[j].br(), Scalar(255,0,255),3);
Size word_size = getTextSize(words[j], FONT_HERSHEY_SIMPLEX, (double)scale_font, (int)(3*scale_font), NULL);
rectangle(out_img, boxes[j].tl()-Point(3,word_size.height+3), boxes[j].tl()+Point(word_size.width,0), Scalar(255,0,255),-1);
putText(out_img, words[j], boxes[j].tl()-Point(1,1), FONT_HERSHEY_SIMPLEX, scale_font, Scalar(255,255,255),(int)(3*scale_font));
words_detection.push_back(words[i][j]);
rectangle(out_img, boxes[i][j].tl(), boxes[i][j].br(), Scalar(255,0,255),3);
Size word_size = getTextSize(words[i][j], FONT_HERSHEY_SIMPLEX, (double)scale_font, (int)(3*scale_font), NULL);
rectangle(out_img, boxes[i][j].tl()-Point(3,word_size.height+3), boxes[i][j].tl()+Point(word_size.width,0), Scalar(255,0,255),-1);
putText(out_img, words[i][j], boxes[i][j].tl()-Point(1,1), FONT_HERSHEY_SIMPLEX, scale_font, Scalar(255,255,255),(int)(3*scale_font));
}
}
......
modules/text/src/erfilter.cpp
View file @ ffcda05c
......@@ -62,6 +62,7 @@ namespace text
using namespace cv::ml;
using namespace std;
using namespace cv::ml;
// Deletes a tree of ERStat regions starting at root. Used only
// internally to this implementation.
......@@ -1017,7 +1018,14 @@ ERClassifierNM1::ERClassifierNM1(const string& filename)
{
if (ifstream(filename.c_str()))
{
boost = StatModel::load<Boost>( filename.c_str() );
if( boost.empty() )
{
cout << "Could not read the classifier " << filename.c_str() << endl;
CV_Error(Error::StsBadArg, "Could not read the default classifier!");
}
}
else
CV_Error(Error::StsBadArg, "Default classifier file not found!");
}
......@@ -1025,14 +1033,12 @@ ERClassifierNM1::ERClassifierNM1(const string& filename)
double ERClassifierNM1::eval(const ERStat& stat)
{
//Classify
float arr[] = {0,(float)(stat.rect.width)/(stat.rect.height), // aspect ratio
Mat sample = (Mat_<float>(1,4) << (float)(stat.rect.width)/(stat.rect.height), // aspect ratio
sqrt((float)(stat.area))/stat.perimeter, // compactness
(float)(1-stat.euler), //number of holes
stat.med_crossings};
vector<float> sample (arr, arr + sizeof(arr) / sizeof(arr[0]) );
stat.med_crossings);
float votes = boost->predict( Mat(sample), noArray(), StatModel::RAW_OUTPUT );
float votes = boost->predict( sample, noArray(), DTrees::PREDICT_SUM | StatModel::RAW_OUTPUT);
// Logistic Correction returns a probability value (in the range(0,1))
return (double)1-(double)1/(1+exp(-2*votes));
......@@ -1043,7 +1049,14 @@ double ERClassifierNM1::eval(const ERStat& stat)
ERClassifierNM2::ERClassifierNM2(const string& filename)
{
if (ifstream(filename.c_str()))
{
boost = StatModel::load<Boost>( filename.c_str() );
if( boost.empty() )
{
cout << "Could not read the classifier " << filename.c_str() << endl;
CV_Error(Error::StsBadArg, "Could not read the default classifier!");
}
}
else
CV_Error(Error::StsBadArg, "Default classifier file not found!");
}
......@@ -1051,15 +1064,13 @@ ERClassifierNM2::ERClassifierNM2(const string& filename)
double ERClassifierNM2::eval(const ERStat& stat)
{
//Classify
float arr[] = {0,(float)(stat.rect.width)/(stat.rect.height), // aspect ratio
Mat sample = (Mat_<float>(1,7) << (float)(stat.rect.width)/(stat.rect.height), // aspect ratio
sqrt((float)(stat.area))/stat.perimeter, // compactness
(float)(1-stat.euler), //number of holes
stat.med_crossings, stat.hole_area_ratio,
stat.convex_hull_ratio, stat.num_inflexion_points};
stat.convex_hull_ratio, stat.num_inflexion_points);
vector<float> sample (arr, arr + sizeof(arr) / sizeof(arr[0]) );
float votes = boost->predict( Mat(sample), noArray(), StatModel::RAW_OUTPUT );
float votes = boost->predict( sample, noArray(), DTrees::PREDICT_SUM | StatModel::RAW_OUTPUT);
// Logistic Correction returns a probability value (in the range(0,1))
return (double)1-(double)1/(1+exp(-2*votes));
......@@ -2231,7 +2242,14 @@ MaxMeaningfulClustering::MaxMeaningfulClustering(unsigned char _method, unsigned
minProbability = _minProbability;
if (ifstream(filename.c_str()))
group_boost = StatModel::load<Boost>(filename.c_str());
{
group_boost = StatModel::load<Boost>( filename.c_str() );
if( group_boost.empty() )
{
cout << "Could not read the classifier " << filename.c_str() << endl;
CV_Error(Error::StsBadArg, "Could not read the default classifier!");
}
}
else
CV_Error(Error::StsBadArg, "erGrouping: Default classifier file not found!");
}
......@@ -2542,7 +2560,6 @@ double MaxMeaningfulClustering::probability(vector<int> &cluster)
return 0.;
vector<float> sample;
sample.push_back(0);
sample.push_back((float)cluster.size());
Mat diameters ( (int)cluster.size(), 1, CV_32F, 1 );
......@@ -2724,7 +2741,7 @@ double MaxMeaningfulClustering::probability(vector<int> &cluster)
sample.push_back((float)mean[0]);
sample.push_back((float)std[0]);
float votes_group = group_boost->predict( Mat(sample), noArray(), StatModel::RAW_OUTPUT );
float votes_group = group_boost->predict( Mat(sample), noArray(), DTrees::PREDICT_SUM | StatModel::RAW_OUTPUT);
return (double)1-(double)1/(1+exp(-2*votes_group));
}
......@@ -3022,7 +3039,7 @@ static void erGroupingGK(InputArray _image, InputArrayOfArrays _src, vector<vect
// assert correct image type
CV_Assert( channel.type() == CV_8UC1 );
CV_Assert( !regions.at(c).empty() );
//CV_Assert( !regions.at(c).empty() );
if ( regions.at(c).size() < 3 )
continue;
......
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