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OpenCV_4.2.0/opencv_contrib-4.2.0/modules/datasets/samples/tr_svt_benchmark.cpp

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/*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 <iostream>
#include <opencv2/opencv_modules.hpp>
#ifdef HAVE_OPENCV_TEXT
#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 <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);
}
}
}
// std::toupper is int->int
static char char_toupper(char ch)
{
return (char)std::toupper((int)ch);
}
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 << "\" length = " << 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;
}
std::transform(words[j].begin(), words[j].end(), words[j].begin(), char_toupper);
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;
}
#else
int main()
{
std::cerr << "OpenCV was built without text module" << std::endl;
return 0;
}
#endif // HAVE_OPENCV_TEXT
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