Opencv + opencv_contrib + Tesseract 之Qt开发环境搭建

1.软件包准备

• opencv源码包地址:                官网  github
• opencv_contrib源码包地址:   github
• Tesseract源码包地址:            github
• cmake.exe 下载地址:             官网
• qt  下载地址:                          官网

注意: opencv和open_contrib包的版本号要一致(比如都是3.4.0)

Tesseract源码安装参考: Win10 使用MinGW-w64编译Tesseract4.0

2. 在环境变量PATH中添加:

C:\Qt\Qt5.9.0\5.9\mingw53_32\bin
C:\Qt\Qt5.9.0\Tools\mingw530_32\bin

一方面方便日后在cmd中直接使用gcc、g++,qmake和mingw32-make

另一方面，方便下一步cmake查找Qt相关配置

3. 使用cmake生成解决方案

如果提示:

直接将 "CMAKE_SH"项删除即可。

修改配置如下:

• CMAKE_BUILD_TYPE: Debug或者Release
• CMAKE_INSTALL_PREFIX: 指定程序安装位置
• ENABLE_CXX11: 支持c11特性
• WITH_QT
• WITH_OPENGL
• OPENCV_EXTRA_MODULES_PATH:  若使用opencv_contrib模块，则在此处填写解压后的路径，如 F:\opencv_contrib\modules\
• Tesseract_INCLUDE_DIR:  Tesseract头文件所在路径
• Tesseract_LIBRARY:  Tesseract lib文件所在路径
• Lept_LIBRARY:  leptonica lib文件所在路径 (很重要, 一定要配置，否则可能找不到Tesseract)

建议取消勾选:

• BUILD_DOCS ：生成文档，需要安装Doxygen。官网提供了在线文档和离线文档。
• BUILD_PERF_TESTS: 性能测试相关
• BUILD_TESTS: 测试相关
• BUILD_opencv_ts ：一些单元测试代码。
• INSTALL_TESTS ：与开发无关。

配置截图:

Tesseract 相关:

Qt 相关: 

# 模块相关配置
OpenCV modules:
    To be built:                 aruco bgsegm bioinspired calib3d ccalib core datasets dnn dpm face features2d flann fuzzy highgui img_hash imgcodecs imgproc line_descriptor ml objdetect optflow phase_unwrapping photo plot reg rgbd saliency shape stereo stitching structured_light superres surface_matching text tracking video videoio videostab world xfeatures2d ximgproc xobjdetect xphoto
    Disabled:                    js python2 python_bindings_generator cvv
    Disabled by dependency:      -
    Unavailable:                 cnn_3dobj cudaarithm cudabgsegm cudacodec cudafeatures2d cudafilters cudaimgproc cudalegacy cudaobjdetect cudaoptflow cudastereo cudawarping cudev dnn_modern freetype hdf java matlab ovis python3 python3 sfm ts viz
    Applications:                apps
    Documentation:               NO
    Non-free algorithms:         NO

BUILD_opencv_world : 将.lib或者.dll文件统一整合进一个world文件中，方便使用。但若想只使用一部分模块可不勾选以减少体积

重要:

如果勾选BUILD_opencv_world, 就需要取消勾选BUILD_opencv_cvv,否则会出现以下错误

4.安装:

5. 遇到的问题:

1) 'sprintf_instead_use_StringCbPrintfA_or_StringCchPrintfA' was not declared in this scope .

解决方法: 修改opencv源码目录中modules\videoio\src\cap_dshow.cpp, 找到#include "DShow.h"，然后在其上面添加一行

release版本:

#define NO_DSHOW_STRSAFE

debug版本:

#define STRSAFE_NO_DEPRECATE

如下图:

2)

解决方法:

关闭预编译头, 取消勾选"ENABLE_PRECOMPILED_HEADERS"

参考: https://wiki.qt.io/How_to_setup_Qt_and_openCV_on_Windows

6.目录结构

7. 向环境变量PATH中加入dll所在路径

D:\opencv\x86\mingw\bin

8. 测试

打开Qtcreator，创建一个c++项目。

测试1:

代码:

#include <opencv2/opencv.hpp>
using namespace cv;

int main()
{
    Mat im = imread("lena.png");
    namedWindow("Image");
    imshow("Image", im);
    waitKey();
    destroyWindow("Image");
    return ;
}

ShowImage.cpp

.pro配置文件:

TEMPLATE = app
CONFIG += console c++
CONFIG -= app_bundle
CONFIG -= qt

SOURCES += main.cpp

INCLUDEPATH += D:\opencv\include
CONFIG(debug, debug | release) {
    LIBS += D:\opencv\x86\mingw\bin\libopencv_world340d.dll
} else {
    LIBS += -LD:\opencv\x86\mingw\lib -lopencv_world340
}

ShowImage.pro

效果:

测试2：

 /*
  * textdetection.cpp
  *
  * A demo program of End-to-end Scene Text Detection and Recognition:
  * Shows the use of the Tesseract OCR API with the Extremal Region Filter algorithm described in:
  * Neumann L., Matas J.: Real-Time Scene Text Localization and Recognition, CVPR 2012
  *
  * Created on: Jul 31, 2014
  *     Author: Lluis Gomez i Bigorda <lgomez AT cvc.uab.es>
  */

 #include "opencv2/text.hpp"
 #include "opencv2/core/utility.hpp"
 #include "opencv2/highgui.hpp"
 #include "opencv2/imgproc.hpp"

 #include <iostream>

 using namespace std;
 using namespace cv;
 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);

 //Perform text detection and recognition and evaluate results using edit distance
 int main1(int argc, char* argv[])
 {
     cout << endl << argv[] << endl << endl;
     cout << "A demo program of End-to-end Scene Text Detection and Recognition: " << endl;
     cout << "Shows the use of the Tesseract OCR API with the Extremal Region Filter algorithm described in:" << endl;
     cout << "Neumann L., Matas J.: Real-Time Scene Text Localization and Recognition, CVPR 2012" << endl << endl;

     Mat image;

     if(argc>)
         image  = imread(argv[]);
     else
     {
         cout << "    Usage: " << argv[] << " <input_image> [<gt_word1> ... <gt_wordN>]" << endl;
         return();
     }

     cout << "IMG_W=" << image.cols << endl;
     cout << "IMG_H=" << image.rows << endl;

     /*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(-grey);

     double t_d = (double)getTickCount();
     // Create ERFilter objects with the 1st and 2nd stage default classifiers
     Ptr<ERFilter> er_filter1 = createERFilterNM1(loadClassifierNM1("trained_classifierNM1.xml"),,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=; c<(int)channels.size(); c++)
     {
         er_filter1->run(channels[c], regions[c]);
         er_filter2->run(channels[c], regions[c]);
     }
     cout << "TIME_REGION_DETECTION = " << ((double)getTickCount() - t_d)*/getTickFrequency() << endl;

     Mat out_img_decomposition= Mat::zeros(image.rows+, image.cols+, CV_8UC1);
     vector<Vec2i> tmp_group;
     for (int i=; i<(int)regions.size(); i++)
     {
         for (int j=; j<(int)regions[i].size();j++)
         {
             tmp_group.push_back(Vec2i(i,j));
         }
         Mat tmp= Mat::zeros(image.rows+, image.cols+, CV_8UC1);
         er_draw(channels, regions, tmp_group, tmp);
         if (i > )
             tmp = tmp / ;
         out_img_decomposition = out_img_decomposition | tmp;
         tmp_group.clear();
     }

     double t_g = (double)getTickCount();
     // 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);
     cout << "TIME_GROUPING = " << ((double)getTickCount() - t_g)*/getTickFrequency() << endl;

     /*Text Recognition (OCR)*/

     double t_r = (double)getTickCount();
     Ptr<OCRTesseract> ocr = OCRTesseract::create();
     cout << "TIME_OCR_INITIALIZATION = " << ((double)getTickCount() - t_r)*/getTickFrequency() << endl;
     string output;

     Mat out_img;
     Mat out_img_detection;
     Mat out_img_segmentation = Mat::zeros(image.rows+, image.cols+, CV_8UC1);
     image.copyTo(out_img);
     image.copyTo(out_img_detection);
     float scale_img  = .f/image.rows;
     float scale_font = (float)(-scale_img)/1.4f;
     vector<string> words_detection;

     t_r = (double)getTickCount();

     for (int i=; i<(int)nm_boxes.size(); i++)
     {

         rectangle(out_img_detection, nm_boxes[i].tl(), nm_boxes[i].br(), Scalar(,,), );

         Mat group_img = Mat::zeros(image.rows+, image.cols+, CV_8UC1);
         er_draw(channels, regions, nm_region_groups[i], group_img);
         Mat group_segmentation;
         group_img.copyTo(group_segmentation);
         //image(nm_boxes[i]).copyTo(group_img);
         group_img(nm_boxes[i]).copyTo(group_img);
         copyMakeBorder(group_img,group_img,,,,,BORDER_CONSTANT,Scalar());

         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() < )
             continue;

         for (int j=; j<(int)boxes.size(); j++)
         {
             boxes[j].x += nm_boxes[i].x-;
             boxes[j].y += nm_boxes[i].y-;

             //cout << "  word = " << words[j] << "\t confidence = " << confidences[j] << endl;
             if ((words[j].size() < ) || (confidences[j] < ) ||
                     ((words[j].size()==) && (words[j][] == words[j][])) ||
                     ((words[j].size()< ) && (confidences[j] < )) ||
                     isRepetitive(words[j]))
                 continue;
             words_detection.push_back(words[j]);
             rectangle(out_img, boxes[j].tl(), boxes[j].br(), Scalar(,,),);
             Size word_size = getTextSize(words[j], FONT_HERSHEY_SIMPLEX, (double)scale_font, (int)(*scale_font), NULL);
             rectangle(out_img, boxes[j].tl()-Point(,word_size.height+), boxes[j].tl()+Point(word_size.width,), Scalar(,,),-);
             putText(out_img, words[j], boxes[j].tl()-Point(,), FONT_HERSHEY_SIMPLEX, scale_font, Scalar(,,),(int)(*scale_font));
             out_img_segmentation = out_img_segmentation | group_segmentation;
         }

     }

     cout << "TIME_OCR = " << ((double)getTickCount() - t_r)*/getTickFrequency() << endl;

     /* Recognition evaluation with (approximate) Hungarian matching and edit distances */

     if(argc>)
     {
         int num_gt_characters   = ;
         vector<string> words_gt;
         for (int i=; i<argc; i++)
         {
             string s = string(argv[i]);
             if (s.size() > )
             {
                 words_gt.push_back(string(argv[i]));
                 //cout << " GT word " << words_gt[words_gt.size()-1] << endl;
                 num_gt_characters += (int)(words_gt[words_gt.size()-].size());
             }
         }

         if (words_detection.empty())
         {
             //cout << endl << "number of characters in gt = " << num_gt_characters << endl;
             cout << "TOTAL_EDIT_DISTANCE = " << num_gt_characters << endl;
             cout << "EDIT_DISTANCE_RATIO = 1" << endl;
         }
         else
         {

             sort(words_gt.begin(),words_gt.end(),sort_by_lenght);

             int max_dist=;
             vector< vector<int> > assignment_mat;
             for (int i=; i<(int)words_gt.size(); i++)
             {
                 vector<int> assignment_row(words_detection.size(),);
                 assignment_mat.push_back(assignment_row);
                 for (int j=; j<(int)words_detection.size(); j++)
                 {
                     assignment_mat[i][j] = (int)(edit_distance(words_gt[i],words_detection[j]));
                     max_dist = max(max_dist,assignment_mat[i][j]);
                 }
             }

             vector<int> words_detection_matched;

             int total_edit_distance = ;
             int tp=, fp=, fn=;
             for (int search_dist=; search_dist<=max_dist; search_dist++)
             {
                 for (int i=; i<(int)assignment_mat.size(); i++)
                 {
                     int min_dist_idx =  (int)distance(assignment_mat[i].begin(),
                                         min_element(assignment_mat[i].begin(),assignment_mat[i].end()));
                     if (assignment_mat[i][min_dist_idx] == search_dist)
                     {
                         //cout << " GT word \"" << words_gt[i] << "\" best match \"" << words_detection[min_dist_idx] << "\" with dist " << assignment_mat[i][min_dist_idx] << endl;
                         if(search_dist == )
                             tp++;
                         else { fp++; fn++; }

                         total_edit_distance += assignment_mat[i][min_dist_idx];
                         words_detection_matched.push_back(min_dist_idx);
                         words_gt.erase(words_gt.begin()+i);
                         assignment_mat.erase(assignment_mat.begin()+i);
                         for (int j=; j<(int)assignment_mat.size(); j++)
                         {
                             assignment_mat[j][min_dist_idx]=INT_MAX;
                         }
                         i--;
                     }
                 }
             }

             for (int j=; j<(int)words_gt.size(); j++)
             {
                 //cout << " GT word \"" << words_gt[j] << "\" no match found" << endl;
                 fn++;
                 total_edit_distance += (int)words_gt[j].size();
             }
             for (int j=; j<(int)words_detection.size(); j++)
             {
                 if (find(words_detection_matched.begin(),words_detection_matched.end(),j) == words_detection_matched.end())
                 {
                     //cout << " Detection word \"" << words_detection[j] << "\" no match found" << endl;
                     fp++;
                     total_edit_distance += (int)words_detection[j].size();
                 }
             }

             //cout << endl << "number of characters in gt = " << num_gt_characters << endl;
             cout << "TOTAL_EDIT_DISTANCE = " << total_edit_distance << endl;
             cout << "EDIT_DISTANCE_RATIO = " << (float)total_edit_distance / num_gt_characters << endl;
             cout << "TP = " << tp << endl;
             cout << "FP = " << fp << endl;
             cout << "FN = " << fn << endl;
         }
     }

     //resize(out_img_detection,out_img_detection,Size(image.cols*scale_img,image.rows*scale_img),0,0,INTER_LINEAR_EXACT);
     //imshow("detection", out_img_detection);
     //imwrite("detection.jpg", out_img_detection);
     //resize(out_img,out_img,Size(image.cols*scale_img,image.rows*scale_img),0,0,INTER_LINEAR_EXACT);
     namedWindow("recognition",WINDOW_NORMAL);
     imshow("recognition", out_img);
     waitKey();
     //imwrite("recognition.jpg", out_img);
     //imwrite("segmentation.jpg", out_img_segmentation);
     //imwrite("decomposition.jpg", out_img_decomposition);

     return ;
 }

 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 + , vector<size_t>(NB + ));

     for (size_t a = ; a <= NA; ++a)
         M[a][] = a;

     for (size_t b = ; b <= NB; ++b)
         M[][b] = b;

     for (size_t a = ; a <= NA; ++a)
         for (size_t b = ; b <= NB; ++b)
         {
             size_t x = M[a-][b] + ;
             size_t y = M[a][b-] + ;
             size_t z = M[a-][b-] + (A[a-] == B[b-] ?  : );
             M[a][b] = min(x,y,z);
         }

     return M[A.size()][B.size()];
 }

 bool isRepetitive(const string& s)
 {
     int count = ;
     for (int i=; i<(int)s.size(); i++)
     {
         if ((s[i] == 'i') ||
                 (s[i] == 'l') ||
                 (s[i] == 'I'))
             count++;
     }
     if (count > ((int)s.size()+)/)
     {
         return true;
     }
     return false;
 }

 void er_draw(vector<Mat> &channels, vector<vector<ERStat> > &regions, vector<Vec2i> group, Mat& segmentation)
 {
     for (int r=; r<(int)group.size(); r++)
     {
         ERStat er = regions[group[r][]][group[r][]];
         if (er.parent != NULL) // deprecate the root region
         {
             int newMaskVal = ;
             int flags =  + (newMaskVal << ) + FLOODFILL_FIXED_RANGE + FLOODFILL_MASK_ONLY;
             floodFill(channels[group[r][]],segmentation,Point(er.pixel%channels[group[r][]].cols,er.pixel/channels[group[r][]].cols),
                       Scalar(),,Scalar(er.level),Scalar(),flags);
         }
     }
 }

 bool   sort_by_lenght(const string &a, const string &b){return (a.size()>b.size());}

end_to_end_recognition.cpp

测试用到的文件end_to_end_recognition.cpp、scenetext01.jpg、trained_classifierNM1.xml、trained_classifierNM2.xml都位于opencv_contrib源码包目录下的modules\text\samples中

效果: