Merge pull request #328 from lluisgomez:icdar_dataset
Vadim Pisarevsky
9 years ago
commit
f9dab6164c
4 changed files with 869 additions and 0 deletions
@ -0,0 +1,87 @@ |
||||
/*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) 2014, Itseez 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 Itseez Inc 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*/
|
||||
|
||||
#ifndef OPENCV_DATASETS_TR_ICDAR_HPP |
||||
#define OPENCV_DATASETS_TR_ICDAR_HPP |
||||
|
||||
#include <string> |
||||
#include <vector> |
||||
|
||||
#include "opencv2/datasets/dataset.hpp" |
||||
|
||||
#include <opencv2/core.hpp> |
||||
|
||||
namespace cv |
||||
{ |
||||
namespace datasets |
||||
{ |
||||
|
||||
//! @addtogroup datasets_tr
|
||||
//! @{
|
||||
|
||||
struct word |
||||
{ |
||||
std::string value; |
||||
int height, width, x, y; |
||||
}; |
||||
|
||||
struct TR_icdarObj : public Object |
||||
{ |
||||
std::string fileName; |
||||
std::vector<std::string> lex100; |
||||
std::vector<std::string> lexFull; |
||||
std::vector<word> words; |
||||
}; |
||||
|
||||
class CV_EXPORTS TR_icdar : public Dataset |
||||
{ |
||||
public: |
||||
virtual void load(const std::string &path) = 0; |
||||
|
||||
static Ptr<TR_icdar> create(); |
||||
}; |
||||
|
||||
//! @}
|
||||
|
||||
} |
||||
} |
||||
|
||||
#endif |
||||
@ -0,0 +1,101 @@ |
||||
/*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) 2014, Itseez 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 Itseez Inc 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 "opencv2/datasets/tr_icdar.hpp" |
||||
|
||||
#include <opencv2/core.hpp> |
||||
|
||||
#include <cstdio> |
||||
#include <cstdlib> // atoi |
||||
|
||||
#include <string> |
||||
#include <vector> |
||||
|
||||
using namespace std; |
||||
using namespace cv; |
||||
using namespace cv::datasets; |
||||
|
||||
int main(int argc, char *argv[]) |
||||
{ |
||||
const char *keys = |
||||
"{ help h usage ? | | show this message }" |
||||
"{ path p |true| path to dataset root folder }"; |
||||
CommandLineParser parser(argc, argv, keys); |
||||
string path(parser.get<string>("path")); |
||||
if (parser.has("help") || path=="true") |
||||
{ |
||||
parser.printMessage(); |
||||
return -1; |
||||
} |
||||
|
||||
// loading train & test images description
|
||||
Ptr<TR_icdar> dataset = TR_icdar::create(); |
||||
dataset->load(path); |
||||
|
||||
// ***************
|
||||
// dataset. train & test contains images description.
|
||||
// For example, let output the last element in train set and it's description.
|
||||
// And their sizes.
|
||||
printf("train size: %u\n", (unsigned int)dataset->getTrain().size()); |
||||
printf("test size: %u\n", (unsigned int)dataset->getTest().size()); |
||||
|
||||
TR_icdarObj *example = static_cast<TR_icdarObj *>(dataset->getTrain().back().get()); |
||||
printf("last element:\nfile name: %s", example->fileName.c_str()); |
||||
printf("\nlex100: "); |
||||
for (vector<string>::iterator it=example->lex100.begin(); it!=example->lex100.end(); ++it) |
||||
{ |
||||
printf("%s,", (*it).c_str()); |
||||
} |
||||
printf("\nlexFULL: "); |
||||
for (vector<string>::iterator it=example->lexFull.begin(); it!=example->lexFull.end(); ++it) |
||||
{ |
||||
printf("%s,", (*it).c_str()); |
||||
} |
||||
printf("\nwords:\n"); |
||||
for (vector<word>::iterator it=example->words.begin(); it!=example->words.end(); ++it) |
||||
{ |
||||
word &t = (*it); |
||||
printf("%s\nheight: %u, width: %u, x: %u, y: %u\n", |
||||
t.value.c_str(), t.height, t.width, t.x, t.y); |
||||
} |
||||
|
||||
return 0; |
||||
} |
||||
@ -0,0 +1,505 @@ |
||||
/*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) 2014, Itseez 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 Itseez Inc 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 "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; |
||||
} |
||||
@ -0,0 +1,176 @@ |
||||
/*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) 2014, Itseez 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 Itseez Inc 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 "opencv2/datasets/tr_icdar.hpp" |
||||
#include "opencv2/datasets/util.hpp" |
||||
|
||||
#include <sstream> |
||||
#include <fstream> |
||||
|
||||
|
||||
namespace cv |
||||
{ |
||||
namespace datasets |
||||
{ |
||||
|
||||
using namespace std; |
||||
|
||||
class TR_icdarImp : public TR_icdar |
||||
{ |
||||
public: |
||||
TR_icdarImp() {} |
||||
//TR_icdarImp(const string &path);
|
||||
virtual ~TR_icdarImp() {} |
||||
|
||||
virtual void load(const string &path); |
||||
|
||||
private: |
||||
void loadDataset(const string &path); |
||||
|
||||
void objParseFiles(const string &path, int img_id, vector<Ptr <Object> > &out); |
||||
}; |
||||
|
||||
void TR_icdarImp::objParseFiles(const string &path, int img_id, vector<Ptr <Object> > &out) |
||||
{ |
||||
Ptr<TR_icdarObj> curr(new TR_icdarObj); |
||||
|
||||
stringstream fileName; |
||||
fileName << "img_" << img_id << ".jpg"; |
||||
curr->fileName = fileName.str(); |
||||
|
||||
stringstream gtFileName; |
||||
gtFileName << path << "/gt_img_" << img_id << ".txt"; |
||||
ifstream infile(gtFileName.str().c_str()); |
||||
if (!infile.is_open()) CV_Error(Error::StsBadArg, gtFileName.str().c_str()); |
||||
string line; |
||||
while (getline(infile, line)) |
||||
{ |
||||
//Ignore EOL characters
|
||||
line.erase(remove(line.begin(), line.end(), '\n'), line.end()); |
||||
line.erase(remove(line.begin(), line.end(), '\r'), line.end()); |
||||
//Ignore byte-order marks (BOM first utf character in W$ files)
|
||||
if ( (line[0] == (char)0xEFu) && (line[1] == (char)0xBBu) && (line[2] == (char)0xBFu) ) |
||||
line.erase (line.begin(),line.begin()+3); |
||||
vector<string> fields; |
||||
split(line, fields, ','); |
||||
word w; |
||||
w.value = fields[8]; |
||||
w.x = atoi(fields[0].c_str()); |
||||
w.y = atoi(fields[1].c_str()); |
||||
w.width = atoi(fields[2].c_str()) - atoi(fields[0].c_str()); |
||||
w.height = atoi(fields[7].c_str()) - atoi(fields[1].c_str()); |
||||
curr->words.push_back(w); |
||||
} |
||||
infile.close(); |
||||
|
||||
stringstream lex100FileName; |
||||
lex100FileName << path << "/voc_img_" << img_id << ".txt"; |
||||
infile.open(lex100FileName.str().c_str()); |
||||
if (!infile.is_open()) CV_Error(Error::StsBadArg, lex100FileName.str().c_str()); |
||||
while (getline(infile, line)) |
||||
{ |
||||
//Ignore EOL characters
|
||||
line.erase(remove(line.begin(), line.end(), '\n'), line.end()); |
||||
line.erase(remove(line.begin(), line.end(), '\r'), line.end()); |
||||
//Ignore byte-order marks (BOM first utf character in W$ files)
|
||||
if ( (line[0] == (char)0xEFu) && (line[1] == (char)0xBBu) && (line[2] == (char)0xBFu) ) |
||||
line.erase (line.begin(),line.begin()+3); |
||||
curr->lex100.push_back(line); |
||||
} |
||||
infile.close(); |
||||
|
||||
stringstream lexFullFileName; |
||||
if (path.substr(path.size()-5,4) == string("test")) |
||||
lexFullFileName << path << "ch2_test_vocabulary.txt"; |
||||
else |
||||
lexFullFileName << path << "ch2_training_vocabulary.txt"; |
||||
infile.open(lexFullFileName.str().c_str()); |
||||
if (!infile.is_open()) CV_Error(Error::StsBadArg, lexFullFileName.str().c_str()); |
||||
while (getline(infile, line)) |
||||
{ |
||||
//Ignore EOL characters
|
||||
line.erase(remove(line.begin(), line.end(), '\n'), line.end()); |
||||
line.erase(remove(line.begin(), line.end(), '\r'), line.end()); |
||||
//Ignore byte-order marks (BOM first utf character in W$ files)
|
||||
if ( (line[0] == (char)0xEFu) && (line[1] == (char)0xBBu) && (line[2] == (char)0xBFu) ) |
||||
line.erase (line.begin(),line.begin()+3); |
||||
curr->lexFull.push_back(line); |
||||
} |
||||
infile.close(); |
||||
|
||||
out.push_back(curr); |
||||
} |
||||
|
||||
/*TR_icdarImp::TR_icdarImp(const string &path)
|
||||
{ |
||||
loadDataset(path); |
||||
}*/ |
||||
|
||||
void TR_icdarImp::load(const string &path) |
||||
{ |
||||
loadDataset(path); |
||||
} |
||||
|
||||
void TR_icdarImp::loadDataset(const string &path) |
||||
{ |
||||
train.push_back(vector< Ptr<Object> >()); |
||||
test.push_back(vector< Ptr<Object> >()); |
||||
validation.push_back(vector< Ptr<Object> >()); |
||||
|
||||
string train_path(path + "/train/"); |
||||
string test_path (path + "/test/"); |
||||
|
||||
// loading 229 train images descriptions
|
||||
for (int i=1; i<230; i++) |
||||
objParseFiles(train_path, i, train.back()); |
||||
|
||||
// loading 233 test images descriptions
|
||||
for (int i=1; i<234; i++) |
||||
objParseFiles(test_path, i, test.back()); |
||||
} |
||||
|
||||
Ptr<TR_icdar> TR_icdar::create() |
||||
{ |
||||
return Ptr<TR_icdarImp>(new TR_icdarImp); |
||||
} |
||||
|
||||
} |
||||
} |
||||
Loading…
Reference in new issue