added tr_svt benchmark

modules/datasets/CMakeLists.txt

 set(the_description "datasets framework")
-ocv_define_module(datasets opencv_core opencv_face opencv_ml opencv_flann)
+ocv_define_module(datasets opencv_core opencv_face opencv_ml opencv_flann opencv_text)
 
 ocv_warnings_disable(CMAKE_CXX_FLAGS /wd4267) # flann, Win64

modules/datasets/samples/tr_svt_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_svt.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 xml files }";
+    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_svt> dataset = TR_svt::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_svtObj *example = static_cast<TR_svtObj *>((*itT).get());
+
+        num++;
+        printf("processed image: %u, name: %s\n", num, example->fileName.c_str());
+
+        correctNum += example->tags.size();
+/*    printf("\ntags:\n");
+    for (vector<tag>::iterator it=example->tags.begin(); it!=example->tags.end(); ++it)
+    {
+        tag &t = (*it);
+        printf("%s\nx: %u, y: %u, width: %u, height: %u\n",
+               t.value.c_str(), t.x, t.y, t.x+t.width, t.y+t.height);
+    }*/
+        unsigned int correctNumEach = example->tags.size();
+        unsigned int returnedNumEach = 0;
+        unsigned int returnedCorrectNumEach = 0;
+
+        Mat image = imread((path+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 stage 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();
+        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);
+            group_img(nm_boxes[i]).copyTo(group_img);
+            copyMakeBorder(group_img,group_img,15,15,15,15,BORDER_CONSTANT,Scalar(0));
+
+            string output;
+            vector<Rect>   boxes;
+            vector<string> words;
+            vector<float>  confidences;
+            ocr->run(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++)
+            {
+                boxes[j].x += nm_boxes[i].x-15;
+                boxes[j].y += nm_boxes[i].y-15;
+
+                //cout << "  word = " << words[j] << "\t confidence = " << confidences[j] << endl;
+                if ((words[j].size() < 2) || (confidences[j] < 51) ||
+                    ((words[j].size()==2) && (words[j][0] == words[j][1])) ||
+                    ((words[j].size()< 4) && (confidences[j] < 60)) ||
+                     isRepetitive(words[j]))
+                {
+                    continue;
+                }
+
+                if (find(example->lex.begin(), example->lex.end(), words[j]) == example->lex.end())
+                {
+                    continue;
+                }
+
+                returnedNum++;
+                returnedNumEach++;
+                /*printf("%s\nx: %u, y: %u, width: %u, height: %u\n",
+                        words[j].c_str(), boxes[j].tl().x, boxes[j].tl().y, boxes[j].br().x, boxes[j].br().y);*/
+                for (vector<tag>::iterator it=example->tags.begin(); it!=example->tags.end(); ++it)
+                {
+                    tag &t = (*it);
+                    if (t.value==words[j] &&
+                        !(boxes[j].tl().x > t.x+t.width || boxes[j].br().x < t.x ||
+                          boxes[j].tl().y > t.y+t.height || boxes[j].br().y < t.y))
+                    {
+                        returnedCorrectNum++;
+                        returnedCorrectNumEach++;
+                        break;
+                    }
+                }
+            }
+        }
+        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);
+        }
+        //printf("|%f|\n", 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("f1: %f\n", f1);*/
+
+    double f1 = 0.0;
+    for (vector<double>::iterator it=f1Each.begin(); it!=f1Each.end(); ++it)
+    {
+        f1 += *it;
+    }
+    f1 /= f1Each.size();
+    printf("mean f1: %f\n", f1);
+
+    return 0;
+}