523 lines
18 KiB
C++
523 lines
18 KiB
C++
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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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//
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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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//
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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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//
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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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//
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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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// and/or other materials provided with the distribution.
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//
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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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//
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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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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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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 <iostream>
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#include <opencv2/opencv_modules.hpp>
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#ifdef HAVE_OPENCV_TEXT
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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 <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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// std::toupper is int->int
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static char char_toupper(char ch)
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{
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return (char)std::toupper((int)ch);
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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(), char_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 << "\" length = " << 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)? (float)51. : (float)0.;
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float min_confidence4 = (ocr_is_tesseract)? (float)60. : (float)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(), char_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;
|
||
|
|
}
|
||
|
|
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(), char_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(), char_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;
|
||
|
|
}
|
||
|
|
|
||
|
|
#else
|
||
|
|
|
||
|
|
int main()
|
||
|
|
{
|
||
|
|
std::cerr << "OpenCV was built without text module" << std::endl;
|
||
|
|
return 0;
|
||
|
|
}
|
||
|
|
|
||
|
|
#endif // HAVE_OPENCV_TEXT
|