Dmitriy Anisimov
11 years ago
parent
c5bc6e82ea
commit
3be484f12b
2 changed files with 304 additions and 1 deletions
@ -1,4 +1,4 @@ |
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set(the_description "datasets framework") |
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ocv_define_module(datasets opencv_core opencv_face opencv_ml opencv_flann) |
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ocv_define_module(datasets opencv_core opencv_face opencv_ml opencv_flann opencv_text) |
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ocv_warnings_disable(CMAKE_CXX_FLAGS /wd4267) # flann, Win64 |
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@ -0,0 +1,303 @@ |
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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 "opencv2/datasets/tr_svt.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 xml files }"; |
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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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// loading train & test images description
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Ptr<TR_svt> dataset = TR_svt::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_svtObj *example = static_cast<TR_svtObj *>((*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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correctNum += example->tags.size(); |
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/* printf("\ntags:\n");
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for (vector<tag>::iterator it=example->tags.begin(); it!=example->tags.end(); ++it) |
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{ |
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tag &t = (*it); |
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printf("%s\nx: %u, y: %u, width: %u, height: %u\n", |
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t.value.c_str(), t.x, t.y, t.x+t.width, t.y+t.height); |
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}*/ |
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unsigned int correctNumEach = example->tags.size(); |
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unsigned int returnedNumEach = 0; |
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unsigned int returnedCorrectNumEach = 0; |
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Mat image = imread((path+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 stage 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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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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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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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(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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boxes[j].x += nm_boxes[i].x-15; |
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boxes[j].y += nm_boxes[i].y-15; |
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//cout << " word = " << words[j] << "\t confidence = " << confidences[j] << endl;
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if ((words[j].size() < 2) || (confidences[j] < 51) || |
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((words[j].size()==2) && (words[j][0] == words[j][1])) || |
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((words[j].size()< 4) && (confidences[j] < 60)) || |
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isRepetitive(words[j])) |
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{ |
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continue; |
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} |
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if (find(example->lex.begin(), example->lex.end(), words[j]) == example->lex.end()) |
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{ |
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continue; |
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} |
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returnedNum++; |
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returnedNumEach++; |
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/*printf("%s\nx: %u, y: %u, width: %u, height: %u\n",
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words[j].c_str(), boxes[j].tl().x, boxes[j].tl().y, boxes[j].br().x, boxes[j].br().y);*/ |
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for (vector<tag>::iterator it=example->tags.begin(); it!=example->tags.end(); ++it) |
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{ |
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tag &t = (*it); |
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if (t.value==words[j] && |
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!(boxes[j].tl().x > t.x+t.width || boxes[j].br().x < t.x || |
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boxes[j].tl().y > t.y+t.height || boxes[j].br().y < t.y)) |
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{ |
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returnedCorrectNum++; |
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returnedCorrectNumEach++; |
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break; |
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} |
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} |
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} |
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} |
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double p = 0.0; |
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if (0 != returnedNumEach) |
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{ |
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p = 1.0*returnedCorrectNumEach/returnedNumEach; |
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} |
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double r = 0.0; |
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if (0 != correctNumEach) |
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{ |
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r = 1.0*returnedCorrectNumEach/correctNumEach; |
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} |
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double f1 = 0.0; |
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if (0 != p+r) |
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{ |
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f1 = 2*(p*r)/(p+r); |
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} |
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//printf("|%f|\n", f1);
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f1Each.push_back(f1); |
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} |
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/*double p = 1.0*returnedCorrectNum/returnedNum;
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double r = 1.0*returnedCorrectNum/correctNum; |
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double f1 = 2*(p*r)/(p+r); |
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printf("f1: %f\n", f1);*/ |
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double f1 = 0.0; |
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for (vector<double>::iterator it=f1Each.begin(); it!=f1Each.end(); ++it) |
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{ |
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f1 += *it; |
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} |
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f1 /= f1Each.size(); |
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printf("mean f1: %f\n", f1); |
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return 0; |
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} |
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