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:QtQt5.9.05.9mingw53_32in
C:QtQt5.9.0Toolsmingw530_32in

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_contribmodules
  • 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modulesvideoiosrccap_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:opencvx86mingwin

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(0);
    destroyWindow("Image");
    return 0;
}
ShowImage.cpp

.pro配置文件:

TEMPLATE = app
CONFIG += console c++11
CONFIG -= app_bundle
CONFIG -= qt

SOURCES += main.cpp

INCLUDEPATH += D:opencvinclude
CONFIG(debug, debug | release) {
    LIBS += D:opencvx86mingwinlibopencv_world340d.dll
} else {
    LIBS += -LD:opencvx86mingwlib -lopencv_world340
}
ShowImage.pro

效果:

测试2：

  1 /*
  2  * textdetection.cpp
  3  *
  4  * A demo program of End-to-end Scene Text Detection and Recognition:
  5  * Shows the use of the Tesseract OCR API with the Extremal Region Filter algorithm described in:
  6  * Neumann L., Matas J.: Real-Time Scene Text Localization and Recognition, CVPR 2012
  7  *
  8  * Created on: Jul 31, 2014
  9  *     Author: Lluis Gomez i Bigorda <lgomez AT cvc.uab.es>
 10  */
 11 
 12 #include "opencv2/text.hpp"
 13 #include "opencv2/core/utility.hpp"
 14 #include "opencv2/highgui.hpp"
 15 #include "opencv2/imgproc.hpp"
 16 
 17 #include <iostream>
 18 
 19 using namespace std;
 20 using namespace cv;
 21 using namespace cv::text;
 22 
 23 //Calculate edit distance between two words
 24 size_t edit_distance(const string& A, const string& B);
 25 size_t min(size_t x, size_t y, size_t z);
 26 bool   isRepetitive(const string& s);
 27 bool   sort_by_lenght(const string &a, const string &b);
 28 //Draw ER's in an image via floodFill
 29 void   er_draw(vector<Mat> &channels, vector<vector<ERStat> > &regions, vector<Vec2i> group, Mat& segmentation);
 30 
 31 //Perform text detection and recognition and evaluate results using edit distance
 32 int main1(int argc, char* argv[])
 33 {
 34     cout << endl << argv[0] << endl << endl;
 35     cout << "A demo program of End-to-end Scene Text Detection and Recognition: " << endl;
 36     cout << "Shows the use of the Tesseract OCR API with the Extremal Region Filter algorithm described in:" << endl;
 37     cout << "Neumann L., Matas J.: Real-Time Scene Text Localization and Recognition, CVPR 2012" << endl << endl;
 38 
 39     Mat image;
 40 
 41     if(argc>1)
 42         image  = imread(argv[1]);
 43     else
 44     {
 45         cout << "    Usage: " << argv[0] << " <input_image> [<gt_word1> ... <gt_wordN>]" << endl;
 46         return(0);
 47     }
 48 
 49     cout << "IMG_W=" << image.cols << endl;
 50     cout << "IMG_H=" << image.rows << endl;
 51 
 52     /*Text Detection*/
 53 
 54     // Extract channels to be processed individually
 55     vector<Mat> channels;
 56 
 57     Mat grey;
 58     cvtColor(image,grey,COLOR_RGB2GRAY);
 59 
 60     // Notice here we are only using grey channel, see textdetection.cpp for example with more channels
 61     channels.push_back(grey);
 62     channels.push_back(255-grey);
 63 
 64     double t_d = (double)getTickCount();
 65     // Create ERFilter objects with the 1st and 2nd stage default classifiers
 66     Ptr<ERFilter> er_filter1 = createERFilterNM1(loadClassifierNM1("trained_classifierNM1.xml"),8,0.00015f,0.13f,0.2f,true,0.1f);
 67     Ptr<ERFilter> er_filter2 = createERFilterNM2(loadClassifierNM2("trained_classifierNM2.xml"),0.5);
 68 
 69     vector<vector<ERStat> > regions(channels.size());
 70     // Apply the default cascade classifier to each independent channel (could be done in parallel)
 71     for (int c=0; c<(int)channels.size(); c++)
 72     {
 73         er_filter1->run(channels[c], regions[c]);
 74         er_filter2->run(channels[c], regions[c]);
 75     }
 76     cout << "TIME_REGION_DETECTION = " << ((double)getTickCount() - t_d)*1000/getTickFrequency() << endl;
 77 
 78     Mat out_img_decomposition= Mat::zeros(image.rows+2, image.cols+2, CV_8UC1);
 79     vector<Vec2i> tmp_group;
 80     for (int i=0; i<(int)regions.size(); i++)
 81     {
 82         for (int j=0; j<(int)regions[i].size();j++)
 83         {
 84             tmp_group.push_back(Vec2i(i,j));
 85         }
 86         Mat tmp= Mat::zeros(image.rows+2, image.cols+2, CV_8UC1);
 87         er_draw(channels, regions, tmp_group, tmp);
 88         if (i > 0)
 89             tmp = tmp / 2;
 90         out_img_decomposition = out_img_decomposition | tmp;
 91         tmp_group.clear();
 92     }
 93 
 94     double t_g = (double)getTickCount();
 95     // Detect character groups
 96     vector< vector<Vec2i> > nm_region_groups;
 97     vector<Rect> nm_boxes;
 98     erGrouping(image, channels, regions, nm_region_groups, nm_boxes,ERGROUPING_ORIENTATION_HORIZ);
 99     cout << "TIME_GROUPING = " << ((double)getTickCount() - t_g)*1000/getTickFrequency() << endl;
100 
101 
102 
103     /*Text Recognition (OCR)*/
104 
105     double t_r = (double)getTickCount();
106     Ptr<OCRTesseract> ocr = OCRTesseract::create();
107     cout << "TIME_OCR_INITIALIZATION = " << ((double)getTickCount() - t_r)*1000/getTickFrequency() << endl;
108     string output;
109 
110     Mat out_img;
111     Mat out_img_detection;
112     Mat out_img_segmentation = Mat::zeros(image.rows+2, image.cols+2, CV_8UC1);
113     image.copyTo(out_img);
114     image.copyTo(out_img_detection);
115     float scale_img  = 600.f/image.rows;
116     float scale_font = (float)(2-scale_img)/1.4f;
117     vector<string> words_detection;
118 
119     t_r = (double)getTickCount();
120 
121     for (int i=0; i<(int)nm_boxes.size(); i++)
122     {
123 
124         rectangle(out_img_detection, nm_boxes[i].tl(), nm_boxes[i].br(), Scalar(0,255,255), 3);
125 
126         Mat group_img = Mat::zeros(image.rows+2, image.cols+2, CV_8UC1);
127         er_draw(channels, regions, nm_region_groups[i], group_img);
128         Mat group_segmentation;
129         group_img.copyTo(group_segmentation);
130         //image(nm_boxes[i]).copyTo(group_img);
131         group_img(nm_boxes[i]).copyTo(group_img);
132         copyMakeBorder(group_img,group_img,15,15,15,15,BORDER_CONSTANT,Scalar(0));
133 
134         vector<Rect>   boxes;
135         vector<string> words;
136         vector<float>  confidences;
137         ocr->run(group_img, output, &boxes, &words, &confidences, OCR_LEVEL_WORD);
138 
139         output.erase(remove(output.begin(), output.end(), '
'), output.end());
140         //cout << "OCR output = "" << output << "" length = " << output.size() << endl;
141         if (output.size() < 3)
142             continue;
143 
144         for (int j=0; j<(int)boxes.size(); j++)
145         {
146             boxes[j].x += nm_boxes[i].x-15;
147             boxes[j].y += nm_boxes[i].y-15;
148 
149             //cout << "  word = " << words[j] << "	 confidence = " << confidences[j] << endl;
150             if ((words[j].size() < 2) || (confidences[j] < 51) ||
151                     ((words[j].size()==2) && (words[j][0] == words[j][1])) ||
152                     ((words[j].size()< 4) && (confidences[j] < 60)) ||
153                     isRepetitive(words[j]))
154                 continue;
155             words_detection.push_back(words[j]);
156             rectangle(out_img, boxes[j].tl(), boxes[j].br(), Scalar(255,0,255),3);
157             Size word_size = getTextSize(words[j], FONT_HERSHEY_SIMPLEX, (double)scale_font, (int)(3*scale_font), NULL);
158             rectangle(out_img, boxes[j].tl()-Point(3,word_size.height+3), boxes[j].tl()+Point(word_size.width,0), Scalar(255,0,255),-1);
159             putText(out_img, words[j], boxes[j].tl()-Point(1,1), FONT_HERSHEY_SIMPLEX, scale_font, Scalar(255,255,255),(int)(3*scale_font));
160             out_img_segmentation = out_img_segmentation | group_segmentation;
161         }
162 
163     }
164 
165     cout << "TIME_OCR = " << ((double)getTickCount() - t_r)*1000/getTickFrequency() << endl;
166 
167 
168     /* Recognition evaluation with (approximate) Hungarian matching and edit distances */
169 
170     if(argc>2)
171     {
172         int num_gt_characters   = 0;
173         vector<string> words_gt;
174         for (int i=2; i<argc; i++)
175         {
176             string s = string(argv[i]);
177             if (s.size() > 0)
178             {
179                 words_gt.push_back(string(argv[i]));
180                 //cout << " GT word " << words_gt[words_gt.size()-1] << endl;
181                 num_gt_characters += (int)(words_gt[words_gt.size()-1].size());
182             }
183         }
184 
185         if (words_detection.empty())
186         {
187             //cout << endl << "number of characters in gt = " << num_gt_characters << endl;
188             cout << "TOTAL_EDIT_DISTANCE = " << num_gt_characters << endl;
189             cout << "EDIT_DISTANCE_RATIO = 1" << endl;
190         }
191         else
192         {
193 
194             sort(words_gt.begin(),words_gt.end(),sort_by_lenght);
195 
196             int max_dist=0;
197             vector< vector<int> > assignment_mat;
198             for (int i=0; i<(int)words_gt.size(); i++)
199             {
200                 vector<int> assignment_row(words_detection.size(),0);
201                 assignment_mat.push_back(assignment_row);
202                 for (int j=0; j<(int)words_detection.size(); j++)
203                 {
204                     assignment_mat[i][j] = (int)(edit_distance(words_gt[i],words_detection[j]));
205                     max_dist = max(max_dist,assignment_mat[i][j]);
206                 }
207             }
208 
209             vector<int> words_detection_matched;
210 
211             int total_edit_distance = 0;
212             int tp=0, fp=0, fn=0;
213             for (int search_dist=0; search_dist<=max_dist; search_dist++)
214             {
215                 for (int i=0; i<(int)assignment_mat.size(); i++)
216                 {
217                     int min_dist_idx =  (int)distance(assignment_mat[i].begin(),
218                                         min_element(assignment_mat[i].begin(),assignment_mat[i].end()));
219                     if (assignment_mat[i][min_dist_idx] == search_dist)
220                     {
221                         //cout << " GT word "" << words_gt[i] << "" best match "" << words_detection[min_dist_idx] << "" with dist " << assignment_mat[i][min_dist_idx] << endl;
222                         if(search_dist == 0)
223                             tp++;
224                         else { fp++; fn++; }
225 
226                         total_edit_distance += assignment_mat[i][min_dist_idx];
227                         words_detection_matched.push_back(min_dist_idx);
228                         words_gt.erase(words_gt.begin()+i);
229                         assignment_mat.erase(assignment_mat.begin()+i);
230                         for (int j=0; j<(int)assignment_mat.size(); j++)
231                         {
232                             assignment_mat[j][min_dist_idx]=INT_MAX;
233                         }
234                         i--;
235                     }
236                 }
237             }
238 
239             for (int j=0; j<(int)words_gt.size(); j++)
240             {
241                 //cout << " GT word "" << words_gt[j] << "" no match found" << endl;
242                 fn++;
243                 total_edit_distance += (int)words_gt[j].size();
244             }
245             for (int j=0; j<(int)words_detection.size(); j++)
246             {
247                 if (find(words_detection_matched.begin(),words_detection_matched.end(),j) == words_detection_matched.end())
248                 {
249                     //cout << " Detection word "" << words_detection[j] << "" no match found" << endl;
250                     fp++;
251                     total_edit_distance += (int)words_detection[j].size();
252                 }
253             }
254 
255 
256             //cout << endl << "number of characters in gt = " << num_gt_characters << endl;
257             cout << "TOTAL_EDIT_DISTANCE = " << total_edit_distance << endl;
258             cout << "EDIT_DISTANCE_RATIO = " << (float)total_edit_distance / num_gt_characters << endl;
259             cout << "TP = " << tp << endl;
260             cout << "FP = " << fp << endl;
261             cout << "FN = " << fn << endl;
262         }
263     }
264 
265 
266 
267     //resize(out_img_detection,out_img_detection,Size(image.cols*scale_img,image.rows*scale_img),0,0,INTER_LINEAR_EXACT);
268     //imshow("detection", out_img_detection);
269     //imwrite("detection.jpg", out_img_detection);
270     //resize(out_img,out_img,Size(image.cols*scale_img,image.rows*scale_img),0,0,INTER_LINEAR_EXACT);
271     namedWindow("recognition",WINDOW_NORMAL);
272     imshow("recognition", out_img);
273     waitKey(0);
274     //imwrite("recognition.jpg", out_img);
275     //imwrite("segmentation.jpg", out_img_segmentation);
276     //imwrite("decomposition.jpg", out_img_decomposition);
277 
278     return 0;
279 }
280 
281 size_t min(size_t x, size_t y, size_t z)
282 {
283     return x < y ? min(x,z) : min(y,z);
284 }
285 
286 size_t edit_distance(const string& A, const string& B)
287 {
288     size_t NA = A.size();
289     size_t NB = B.size();
290 
291     vector< vector<size_t> > M(NA + 1, vector<size_t>(NB + 1));
292 
293     for (size_t a = 0; a <= NA; ++a)
294         M[a][0] = a;
295 
296     for (size_t b = 0; b <= NB; ++b)
297         M[0][b] = b;
298 
299     for (size_t a = 1; a <= NA; ++a)
300         for (size_t b = 1; b <= NB; ++b)
301         {
302             size_t x = M[a-1][b] + 1;
303             size_t y = M[a][b-1] + 1;
304             size_t z = M[a-1][b-1] + (A[a-1] == B[b-1] ? 0 : 1);
305             M[a][b] = min(x,y,z);
306         }
307 
308     return M[A.size()][B.size()];
309 }
310 
311 bool isRepetitive(const string& s)
312 {
313     int count = 0;
314     for (int i=0; i<(int)s.size(); i++)
315     {
316         if ((s[i] == 'i') ||
317                 (s[i] == 'l') ||
318                 (s[i] == 'I'))
319             count++;
320     }
321     if (count > ((int)s.size()+1)/2)
322     {
323         return true;
324     }
325     return false;
326 }
327 
328 
329 void er_draw(vector<Mat> &channels, vector<vector<ERStat> > &regions, vector<Vec2i> group, Mat& segmentation)
330 {
331     for (int r=0; r<(int)group.size(); r++)
332     {
333         ERStat er = regions[group[r][0]][group[r][1]];
334         if (er.parent != NULL) // deprecate the root region
335         {
336             int newMaskVal = 255;
337             int flags = 4 + (newMaskVal << 8) + FLOODFILL_FIXED_RANGE + FLOODFILL_MASK_ONLY;
338             floodFill(channels[group[r][0]],segmentation,Point(er.pixel%channels[group[r][0]].cols,er.pixel/channels[group[r][0]].cols),
339                       Scalar(255),0,Scalar(er.level),Scalar(0),flags);
340         }
341     }
342 }
343 
344 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 extsamples中

效果:

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原文地址：https://www.cnblogs.com/hupeng1234/p/8593287.html