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#include "opencv2/datasets/tr_icdar.hpp"
#include <opencv2/core.hpp>
#include "opencv2/text.hpp"
#include "opencv2/imgproc.hpp"
#include "opencv2/imgcodecs.hpp"
#include <cstdio>
#include <cstdlib> // atoi
#include <iostream>
#include <string>
#include <vector>
using namespace std;
using namespace cv;
using namespace cv::datasets;
using namespace cv::text;
//Calculate edit distance between two words
size_t edit_distance(const string& A, const string& B);
size_t min(size_t x, size_t y, size_t z);
bool isRepetitive(const string& s);
bool sort_by_lenght(const string &a, const string &b);
//Draw ER's in an image via floodFill
void er_draw(vector<Mat> &channels, vector<vector<ERStat> > ®ions, vector<Vec2i> group, Mat& segmentation);
size_t min(size_t x, size_t y, size_t z)
{
return x < y ? min(x,z) : min(y,z);
}
size_t edit_distance(const string& A, const string& B)
{
size_t NA = A.size();
size_t NB = B.size();
vector< vector<size_t> > M(NA + 1, vector<size_t>(NB + 1));
for (size_t a = 0; a <= NA; ++a)
M[a][0] = a;
for (size_t b = 0; b <= NB; ++b)
M[0][b] = b;
for (size_t a = 1; a <= NA; ++a)
for (size_t b = 1; b <= NB; ++b)
{
size_t x = M[a-1][b] + 1;
size_t y = M[a][b-1] + 1;
size_t z = M[a-1][b-1] + (A[a-1] == B[b-1] ? 0 : 1);
M[a][b] = min(x,y,z);
}
return M[A.size()][B.size()];
}
bool sort_by_lenght(const string &a, const string &b){return (a.size()>b.size());}
bool isRepetitive(const string& s)
{
int count = 0;
for (int i=0; i<(int)s.size(); i++)
{
if ((s[i] == 'i') ||
(s[i] == 'l') ||
(s[i] == 'I'))
count++;
}
if (count > ((int)s.size()+1)/2)
{
return true;
}
return false;
}
void er_draw(vector<Mat> &channels, vector<vector<ERStat> > ®ions, vector<Vec2i> group, Mat& segmentation)
{
for (int r=0; r<(int)group.size(); r++)
{
ERStat er = regions[group[r][0]][group[r][1]];
if (er.parent != NULL) // deprecate the root region
{
int newMaskVal = 255;
int flags = 4 + (newMaskVal << 8) + FLOODFILL_FIXED_RANGE + FLOODFILL_MASK_ONLY;
floodFill(channels[group[r][0]],segmentation,Point(er.pixel%channels[group[r][0]].cols,er.pixel/channels[group[r][0]].cols),
Scalar(255),0,Scalar(er.level),Scalar(0),flags);
}
}
}
int main(int argc, char *argv[])
{
const char *keys =
"{ help h usage ? | | show this message }"
"{ path p |true| path to dataset root folder }"
"{ ws wordspotting| | evaluate \"word spotting\" results }"
"{ lex lexicon |1 | 0:no-lexicon, 1:100-words, 2:full-lexicon }";
CommandLineParser parser(argc, argv, keys);
string path(parser.get<string>("path"));
if (parser.has("help") || path=="true")
{
parser.printMessage();
return -1;
}
bool is_word_spotting = parser.has("ws");
int selected_lex = parser.get<int>("lex");
if ((selected_lex < 0) || (selected_lex > 2))
{
parser.printMessage();
printf("Unsupported lex value.\n");
return -1;
}
// loading train & test images description
Ptr<TR_icdar> dataset = TR_icdar::create();
dataset->load(path);
vector<double> f1Each;
unsigned int correctNum = 0;
unsigned int returnedNum = 0;
unsigned int returnedCorrectNum = 0;
vector< Ptr<Object> >& test = dataset->getTest();
unsigned int num = 0;
for (vector< Ptr<Object> >::iterator itT=test.begin(); itT!=test.end(); ++itT)
{
TR_icdarObj *example = static_cast<TR_icdarObj *>((*itT).get());
num++;
printf("processed image: %u, name: %s\n", num, example->fileName.c_str());
vector<string> empty_lexicon;
vector<string> *lex;
switch (selected_lex)
{
case 0:
lex = &empty_lexicon;
break;
case 2:
lex = &example->lexFull;
break;
default:
lex = &example->lex100;
break;
}
correctNum += example->words.size();
unsigned int correctNumEach = example->words.size();
// Take care of dontcare regions t.value == "###"
for (size_t w=0; w<example->words.size(); w++)
{
string w_upper = example->words[w].value;
transform(w_upper.begin(), w_upper.end(), w_upper.begin(), ::toupper);
if ((find (lex->begin(), lex->end(), w_upper) == lex->end()) &&
(is_word_spotting) && (selected_lex != 0))
example->words[w].value = "###";
if ( (example->words[w].value == "###") || (example->words[w].value.size()<3) )
{
correctNum --;
correctNumEach --;
}
}
unsigned int returnedNumEach = 0;
unsigned int returnedCorrectNumEach = 0;
Mat image = imread((path+"/test/"+example->fileName).c_str());
/*Text Detection*/
// Extract channels to be processed individually
vector<Mat> channels;
Mat grey;
cvtColor(image,grey,COLOR_RGB2GRAY);
// Notice here we are only using grey channel, see textdetection.cpp for example with more channels
channels.push_back(grey);
channels.push_back(255-grey);
// Create ERFilter objects with the 1st and 2nd sworde default classifiers
Ptr<ERFilter> er_filter1 = createERFilterNM1(loadClassifierNM1("trained_classifierNM1.xml"),8,0.00015f,0.13f,0.2f,true,0.1f);
Ptr<ERFilter> er_filter2 = createERFilterNM2(loadClassifierNM2("trained_classifierNM2.xml"),0.5);
vector<vector<ERStat> > regions(channels.size());
// Apply the default cascade classifier to each independent channel (could be done in parallel)
for (int c=0; c<(int)channels.size(); c++)
{
er_filter1->run(channels[c], regions[c]);
er_filter2->run(channels[c], regions[c]);
}
// Detect character groups
vector< vector<Vec2i> > nm_region_groups;
vector<Rect> nm_boxes;
erGrouping(image, channels, regions, nm_region_groups, nm_boxes, ERGROUPING_ORIENTATION_HORIZ);
/*Text Recognition (OCR)*/
Ptr<OCRTesseract> ocr = OCRTesseract::create();
bool ocr_is_tesseract = true;
vector<string> final_words;
vector<Rect> final_boxes;
vector<float> final_confs;
for (int i=0; i<(int)nm_boxes.size(); i++)
{
Mat group_img = Mat::zeros(image.rows+2, image.cols+2, CV_8UC1);
er_draw(channels, regions, nm_region_groups[i], group_img);
if (ocr_is_tesseract)
{
group_img(nm_boxes[i]).copyTo(group_img);
copyMakeBorder(group_img,group_img,15,15,15,15,BORDER_CONSTANT,Scalar(0));
} else {
group_img(Rect(1,1,image.cols,image.rows)).copyTo(group_img);
}
string output;
vector<Rect> boxes;
vector<string> words;
vector<float> confidences;
ocr->run(grey, group_img, output, &boxes, &words, &confidences, OCR_LEVEL_WORD);
output.erase(remove(output.begin(), output.end(), '\n'), output.end());
//cout << "OCR output = \"" << output << "\" lenght = " << output.size() << endl;
if (output.size() < 3)
continue;
for (int j=0; j<(int)boxes.size(); j++)
{
if (ocr_is_tesseract)
{
boxes[j].x += nm_boxes[i].x-15;
boxes[j].y += nm_boxes[i].y-15;
}
float min_confidence = (ocr_is_tesseract)? (float)51. : (float)0.;
float min_confidence4 = (ocr_is_tesseract)? (float)60. : (float)0.;
//cout << " word = " << words[j] << "\t confidence = " << confidences[j] << endl;
if ((words[j].size() < 2) || (confidences[j] < min_confidence) ||
((words[j].size()==2) && (words[j][0] == words[j][1])) ||
((words[j].size()< 4) && (confidences[j] < min_confidence4)) ||
isRepetitive(words[j]))
{
continue;
}
std::transform(words[j].begin(), words[j].end(), words[j].begin(), ::toupper);
/* Increase confidence of predicted words matching a word in the lexicon */
if (lex->size() > 0)
{
if (find(lex->begin(), lex->end(), words[j]) == lex->end())
confidences[j] = 200;
}
final_words.push_back(words[j]);
final_boxes.push_back(boxes[j]);
final_confs.push_back(confidences[j]);
}
}
/* Non Maximal Suppression using OCR confidence */
float thr = 0.5;
for (size_t i=0; i<final_words.size(); )
{
int to_delete = -1;
for (size_t j=i+1; j<final_words.size(); )
{
to_delete = -1;
Rect intersection = final_boxes[i] & final_boxes[j];
float IoU = (float)intersection.area() / (final_boxes[i].area() + final_boxes[j].area() - intersection.area());
if ((IoU > thr) || (intersection.area() > 0.8*final_boxes[i].area()) || (intersection.area() > 0.8*final_boxes[j].area()))
{
// if regions overlap more than thr delete the one with lower confidence
to_delete = (final_confs[i] < final_confs[j]) ? i : j;
if (to_delete == (int)j )
{
final_words.erase(final_words.begin()+j);
final_boxes.erase(final_boxes.begin()+j);
final_confs.erase(final_confs.begin()+j);
continue;
} else {
break;
}
}
j++;
}
if (to_delete == (int)i )
{
final_words.erase(final_words.begin()+i);
final_boxes.erase(final_boxes.begin()+i);
final_confs.erase(final_confs.begin()+i);
continue;
}
i++;
}
/* Predicted words which are not in the lexicon are filtered
or changed to match one (when edit distance ratio < 0.34)*/
float max_edit_distance_ratio = (float)0.34;
for (size_t j=0; j<final_boxes.size(); j++)
{
if (lex->size() > 0)
{
if (find(lex->begin(), lex->end(), final_words[j]) == lex->end())
{
int best_match = -1;
int best_dist = final_words[j].size();
for (size_t l=0; l<lex->size(); l++)
{
int dist = edit_distance(lex->at(l),final_words[j]);
if (dist < best_dist)
{
best_match = l;
best_dist = dist;
}
}
if (best_dist/final_words[j].size() < max_edit_distance_ratio)
final_words[j] = lex->at(best_match);
else
continue;
}
}
if ((find (lex->begin(), lex->end(), final_words[j])
== lex->end()) && (is_word_spotting) && (selected_lex != 0))
continue;
// Output final recognition in csv format compatible with the ICDAR Competition
/*cout << final_boxes[j].tl().x << ","
<< final_boxes[j].tl().y << ","
<< min(final_boxes[j].br().x,image.cols-2)
<< "," << final_boxes[j].tl().y << ","
<< min(final_boxes[j].br().x,image.cols-2) << ","
<< min(final_boxes[j].br().y,image.rows-2) << ","
<< final_boxes[j].tl().x << ","
<< min(final_boxes[j].br().y,image.rows-2) << ","
<< final_words[j] << endl ;*/
returnedNum++;
returnedNumEach++;
bool matched = false;
for (vector<word>::iterator it=example->words.begin(); it!=example->words.end(); ++it)
{
word &t = (*it);
// ICDAR protocol accepts recognition up to the first non alphanumeric char
string alnum_value = t.value;
for (size_t c=0; c<alnum_value.size(); c++)
{
if (!isalnum(alnum_value[c]))
{
alnum_value = alnum_value.substr(0,c);
break;
}
}
std::transform(t.value.begin(), t.value.end(), t.value.begin(), ::toupper);
if (((t.value==final_words[j]) || (alnum_value==final_words[j])) &&
!(final_boxes[j].tl().x > t.x+t.width || final_boxes[j].br().x < t.x ||
final_boxes[j].tl().y > t.y+t.height || final_boxes[j].br().y < t.y))
{
matched = true;
returnedCorrectNum++;
returnedCorrectNumEach++;
//cout << "OK!" << endl;
break;
}
}
if (!matched) // Take care of dontcare regions t.value == "###"
for (vector<word>::iterator it=example->words.begin(); it!=example->words.end(); ++it)
{
word &t = (*it);
std::transform(t.value.begin(), t.value.end(), t.value.begin(), ::toupper);
if ((t.value == "###") &&
!(final_boxes[j].tl().x > t.x+t.width || final_boxes[j].br().x < t.x ||
final_boxes[j].tl().y > t.y+t.height || final_boxes[j].br().y < t.y))
{
matched = true;
returnedNum--;
returnedNumEach--;
//cout << "DontCare!" << endl;
break;
}
}
//if (!matched) cout << "FAIL." << endl;
}
double p = 0.0;
if (0 != returnedNumEach)
{
p = 1.0*returnedCorrectNumEach/returnedNumEach;
}
double r = 0.0;
if (0 != correctNumEach)
{
r = 1.0*returnedCorrectNumEach/correctNumEach;
}
double f1 = 0.0;
if (0 != p+r)
{
f1 = 2*(p*r)/(p+r);
}
if ( (correctNumEach == 0) && (returnedNumEach == 0) )
{
p = 1.;
r = 1.;
f1 = 1.;
}
//printf("|%f|%f|%f|\n",r,p,f1);
f1Each.push_back(f1);
}
double p = 1.0*returnedCorrectNum/returnedNum;
double r = 1.0*returnedCorrectNum/correctNum;
double f1 = 2*(p*r)/(p+r);
printf("\n-------------------------------------------------------------------------\n");
printf("ICDAR2015 -- Challenge 2: \"Focused Scene Text\" -- Task 4 \"End-to-End\"\n");
if (is_word_spotting) printf(" Word spotting results -- ");
else printf(" End-to-End recognition results -- ");
switch (selected_lex)
{
case 0:
printf("generic recognition (no given lexicon)\n");
break;
case 2:
printf("weakly contextualized lexicon (624 words)\n");
break;
default:
printf("strongly contextualized lexicon (100 words)\n");
break;
}
printf(" Recall: %f | Precision: %f | F-score: %f\n", r, p, f1);
printf("-------------------------------------------------------------------------\n\n");
/*double mf1 = 0.0;
for (vector<double>::iterator it=f1Each.begin(); it!=f1Each.end(); ++it)
{
mf1 += *it;
}
mf1 /= f1Each.size();
printf("mean f1: %f\n", mf1);*/
return 0;
}