Merge pull request #328 from lluisgomez:icdar_dataset

modules/datasets/include/opencv2/datasets/tr_icdar.hpp

+/*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

modules/datasets/samples/tr_icdar.cpp

+/*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;
+}

modules/datasets/samples/tr_icdar_benchmark.cpp

+/*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> > &regions, 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> > &regions, 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;
+}

modules/datasets/src/tr_icdar.cpp

+/*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);
+}
+
+}
+}