Add benchmark for ICDAR2015 dataset using OCRTesseract and ERFilter classes. Gives word spotting f-score 0.642082 with strongly contextualized lexicon (100 words).
lluis
10 years ago
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306aed4078
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1 changed files with 505 additions and 0 deletions
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/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2014, Itseez Inc, all rights reserved.
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// Third party copyrights are property of their respective owners.
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Itseez Inc or contributors be liable for any direct,
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// loss of use, data, or profits; or business interruption) however caused
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#include "opencv2/datasets/tr_icdar.hpp" |
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#include <opencv2/core.hpp> |
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#include "opencv2/text.hpp" |
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#include "opencv2/imgproc.hpp" |
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#include "opencv2/imgcodecs.hpp" |
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#include <cstdio> |
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#include <cstdlib> // atoi |
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#include <iostream> |
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#include <string> |
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#include <vector> |
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using namespace std; |
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using namespace cv; |
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using namespace cv::datasets; |
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using namespace cv::text; |
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//Calculate edit distance between two words
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size_t edit_distance(const string& A, const string& B); |
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size_t min(size_t x, size_t y, size_t z); |
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bool isRepetitive(const string& s); |
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bool sort_by_lenght(const string &a, const string &b); |
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//Draw ER's in an image via floodFill
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void er_draw(vector<Mat> &channels, vector<vector<ERStat> > ®ions, vector<Vec2i> group, Mat& segmentation); |
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size_t min(size_t x, size_t y, size_t z) |
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{ |
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return x < y ? min(x,z) : min(y,z); |
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} |
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size_t edit_distance(const string& A, const string& B) |
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{ |
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size_t NA = A.size(); |
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size_t NB = B.size(); |
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vector< vector<size_t> > M(NA + 1, vector<size_t>(NB + 1)); |
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for (size_t a = 0; a <= NA; ++a) |
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M[a][0] = a; |
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for (size_t b = 0; b <= NB; ++b) |
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M[0][b] = b; |
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for (size_t a = 1; a <= NA; ++a) |
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for (size_t b = 1; b <= NB; ++b) |
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{ |
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size_t x = M[a-1][b] + 1; |
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size_t y = M[a][b-1] + 1; |
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size_t z = M[a-1][b-1] + (A[a-1] == B[b-1] ? 0 : 1); |
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M[a][b] = min(x,y,z); |
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} |
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return M[A.size()][B.size()]; |
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} |
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bool sort_by_lenght(const string &a, const string &b){return (a.size()>b.size());} |
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bool isRepetitive(const string& s) |
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{ |
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int count = 0; |
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for (int i=0; i<(int)s.size(); i++) |
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{ |
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if ((s[i] == 'i') || |
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(s[i] == 'l') || |
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(s[i] == 'I')) |
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count++; |
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} |
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if (count > ((int)s.size()+1)/2) |
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{ |
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return true; |
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} |
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return false; |
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} |
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void er_draw(vector<Mat> &channels, vector<vector<ERStat> > ®ions, vector<Vec2i> group, Mat& segmentation) |
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{ |
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for (int r=0; r<(int)group.size(); r++) |
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{ |
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ERStat er = regions[group[r][0]][group[r][1]]; |
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if (er.parent != NULL) // deprecate the root region
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{ |
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int newMaskVal = 255; |
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int flags = 4 + (newMaskVal << 8) + FLOODFILL_FIXED_RANGE + FLOODFILL_MASK_ONLY; |
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floodFill(channels[group[r][0]],segmentation,Point(er.pixel%channels[group[r][0]].cols,er.pixel/channels[group[r][0]].cols), |
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Scalar(255),0,Scalar(er.level),Scalar(0),flags); |
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} |
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} |
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} |
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int main(int argc, char *argv[]) |
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{ |
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const char *keys = |
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"{ help h usage ? | | show this message }" |
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"{ path p |true| path to dataset root folder }" |
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"{ ws wordspotting| | evaluate \"word spotting\" results }" |
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"{ lex lexicon |1 | 0:no-lexicon, 1:100-words, 2:full-lexicon }"; |
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CommandLineParser parser(argc, argv, keys); |
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string path(parser.get<string>("path")); |
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if (parser.has("help") || path=="true") |
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{ |
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parser.printMessage(); |
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return -1; |
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} |
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bool is_word_spotting = parser.has("ws"); |
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int selected_lex = parser.get<int>("lex"); |
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if ((selected_lex < 0) || (selected_lex > 2)) |
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{ |
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parser.printMessage(); |
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printf("Unsupported lex value.\n"); |
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return -1; |
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} |
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// loading train & test images description
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Ptr<TR_icdar> dataset = TR_icdar::create(); |
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dataset->load(path); |
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vector<double> f1Each; |
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unsigned int correctNum = 0; |
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unsigned int returnedNum = 0; |
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unsigned int returnedCorrectNum = 0; |
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vector< Ptr<Object> >& test = dataset->getTest(); |
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unsigned int num = 0; |
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for (vector< Ptr<Object> >::iterator itT=test.begin(); itT!=test.end(); ++itT) |
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{ |
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TR_icdarObj *example = static_cast<TR_icdarObj *>((*itT).get()); |
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num++; |
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printf("processed image: %u, name: %s\n", num, example->fileName.c_str()); |
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vector<string> empty_lexicon; |
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vector<string> *lex; |
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switch (selected_lex) |
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{ |
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case 0: |
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lex = &empty_lexicon; |
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break; |
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case 2: |
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lex = &example->lexFull; |
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break; |
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default: |
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lex = &example->lex100; |
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break; |
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} |
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correctNum += example->words.size(); |
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unsigned int correctNumEach = example->words.size(); |
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// Take care of dontcare regions t.value == "###"
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for (size_t w=0; w<example->words.size(); w++) |
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{ |
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string w_upper = example->words[w].value; |
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transform(w_upper.begin(), w_upper.end(), w_upper.begin(), ::toupper); |
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if ((find (lex->begin(), lex->end(), w_upper) == lex->end()) && |
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(is_word_spotting) && (selected_lex != 0)) |
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example->words[w].value = "###"; |
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if ( (example->words[w].value == "###") || (example->words[w].value.size()<3) ) |
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{ |
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correctNum --; |
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correctNumEach --; |
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} |
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} |
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unsigned int returnedNumEach = 0; |
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unsigned int returnedCorrectNumEach = 0; |
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Mat image = imread((path+"/test/"+example->fileName).c_str()); |
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/*Text Detection*/ |
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// Extract channels to be processed individually
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vector<Mat> channels; |
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Mat grey; |
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cvtColor(image,grey,COLOR_RGB2GRAY); |
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// Notice here we are only using grey channel, see textdetection.cpp for example with more channels
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channels.push_back(grey); |
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channels.push_back(255-grey); |
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// Create ERFilter objects with the 1st and 2nd sworde default classifiers
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Ptr<ERFilter> er_filter1 = createERFilterNM1(loadClassifierNM1("trained_classifierNM1.xml"),8,0.00015f,0.13f,0.2f,true,0.1f); |
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Ptr<ERFilter> er_filter2 = createERFilterNM2(loadClassifierNM2("trained_classifierNM2.xml"),0.5); |
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vector<vector<ERStat> > regions(channels.size()); |
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// Apply the default cascade classifier to each independent channel (could be done in parallel)
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for (int c=0; c<(int)channels.size(); c++) |
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{ |
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er_filter1->run(channels[c], regions[c]); |
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er_filter2->run(channels[c], regions[c]); |
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} |
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// Detect character groups
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vector< vector<Vec2i> > nm_region_groups; |
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vector<Rect> nm_boxes; |
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erGrouping(image, channels, regions, nm_region_groups, nm_boxes, ERGROUPING_ORIENTATION_HORIZ); |
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/*Text Recognition (OCR)*/ |
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Ptr<OCRTesseract> ocr = OCRTesseract::create(); |
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bool ocr_is_tesseract = true; |
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vector<string> final_words; |
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vector<Rect> final_boxes; |
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vector<float> final_confs; |
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for (int i=0; i<(int)nm_boxes.size(); i++) |
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{ |
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Mat group_img = Mat::zeros(image.rows+2, image.cols+2, CV_8UC1); |
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er_draw(channels, regions, nm_region_groups[i], group_img); |
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if (ocr_is_tesseract) |
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{ |
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group_img(nm_boxes[i]).copyTo(group_img); |
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copyMakeBorder(group_img,group_img,15,15,15,15,BORDER_CONSTANT,Scalar(0)); |
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} else { |
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group_img(Rect(1,1,image.cols,image.rows)).copyTo(group_img); |
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} |
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string output; |
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vector<Rect> boxes; |
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vector<string> words; |
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vector<float> confidences; |
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ocr->run(grey, group_img, output, &boxes, &words, &confidences, OCR_LEVEL_WORD); |
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output.erase(remove(output.begin(), output.end(), '\n'), output.end()); |
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//cout << "OCR output = \"" << output << "\" lenght = " << output.size() << endl;
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if (output.size() < 3) |
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continue; |
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for (int j=0; j<(int)boxes.size(); j++) |
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{ |
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if (ocr_is_tesseract) |
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{ |
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boxes[j].x += nm_boxes[i].x-15; |
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boxes[j].y += nm_boxes[i].y-15; |
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} |
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float min_confidence = (ocr_is_tesseract)? 51. : 0.; |
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float min_confidence4 = (ocr_is_tesseract)? 60. : 0.; |
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//cout << " word = " << words[j] << "\t confidence = " << confidences[j] << endl;
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if ((words[j].size() < 2) || (confidences[j] < min_confidence) || |
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((words[j].size()==2) && (words[j][0] == words[j][1])) || |
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((words[j].size()< 4) && (confidences[j] < min_confidence4)) || |
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isRepetitive(words[j])) |
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{ |
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continue; |
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} |
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std::transform(words[j].begin(), words[j].end(), words[j].begin(), ::toupper); |
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/* Increase confidence of predicted words matching a word in the lexicon */ |
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if (lex->size() > 0) |
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{ |
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if (find(lex->begin(), lex->end(), words[j]) == lex->end()) |
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confidences[j] = 200; |
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} |
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final_words.push_back(words[j]); |
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final_boxes.push_back(boxes[j]); |
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final_confs.push_back(confidences[j]); |
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} |
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} |
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/* Non Maximal Suppression using OCR confidence */ |
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float thr = 0.5; |
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for (size_t i=0; i<final_words.size(); ) |
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{ |
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int to_delete = -1; |
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for (size_t j=i+1; j<final_words.size(); ) |
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{ |
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to_delete = -1; |
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Rect intersection = final_boxes[i] & final_boxes[j]; |
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float IoU = (float)intersection.area() / (final_boxes[i].area() + final_boxes[j].area() - intersection.area()); |
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if ((IoU > thr) || (intersection.area() > 0.8*final_boxes[i].area()) || (intersection.area() > 0.8*final_boxes[j].area())) |
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{ |
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// if regions overlap more than thr delete the one with lower confidence
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to_delete = (final_confs[i] < final_confs[j]) ? i : j; |
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if (to_delete == (int)j ) |
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{ |
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final_words.erase(final_words.begin()+j); |
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final_boxes.erase(final_boxes.begin()+j); |
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final_confs.erase(final_confs.begin()+j); |
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continue; |
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} else { |
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break; |
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} |
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} |
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j++; |
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} |
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if (to_delete == (int)i ) |
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{ |
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final_words.erase(final_words.begin()+i); |
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final_boxes.erase(final_boxes.begin()+i); |
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final_confs.erase(final_confs.begin()+i); |
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continue; |
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} |
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i++; |
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} |
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/* Predicted words which are not in the lexicon are filtered
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or changed to match one (when edit distance ratio < 0.34)*/ |
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float max_edit_distance_ratio = 0.34; |
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for (size_t j=0; j<final_boxes.size(); j++) |
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{ |
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if (lex->size() > 0) |
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{ |
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if (find(lex->begin(), lex->end(), final_words[j]) == lex->end()) |
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{ |
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int best_match = -1; |
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int best_dist = final_words[j].size(); |
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for (size_t l=0; l<lex->size(); l++) |
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{ |
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int dist = edit_distance(lex->at(l),final_words[j]); |
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if (dist < best_dist) |
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{ |
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best_match = l; |
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best_dist = dist; |
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} |
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} |
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if (best_dist/final_words[j].size() < max_edit_distance_ratio) |
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final_words[j] = lex->at(best_match); |
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else |
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continue; |
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} |
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} |
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if ((find (lex->begin(), lex->end(), final_words[j]) |
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== lex->end()) && (is_word_spotting) && (selected_lex != 0)) |
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continue; |
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// Output final recognition in csv format compatible with the ICDAR Competition
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/*cout << final_boxes[j].tl().x << ","
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<< final_boxes[j].tl().y << "," |
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<< min(final_boxes[j].br().x,image.cols-2) |
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<< "," << final_boxes[j].tl().y << "," |
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<< min(final_boxes[j].br().x,image.cols-2) << "," |
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<< min(final_boxes[j].br().y,image.rows-2) << "," |
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<< final_boxes[j].tl().x << "," |
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<< min(final_boxes[j].br().y,image.rows-2) << "," |
||||||
|
<< final_words[j] << endl ;*/ |
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|
|
||||||
|
returnedNum++; |
||||||
|
returnedNumEach++; |
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|
|
||||||
|
bool matched = false; |
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|
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
|
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|
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; |
||||||
|
} |
||||||
Loading…
Reference in new issue