Commit 7031316c authored by Vladislav Sovrasov's avatar Vladislav Sovrasov

text: add text recognition sample

parent 9195d2e6
#include <opencv2/text.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/imgproc.hpp>
#include <iostream>
#include <fstream>
using namespace cv;
using namespace std;
namespace
{
void printHelpStr(const string& progFname)
{
cout << " Demo of text recognition CNN for text detection." << endl
<< " Max Jaderberg et al.: Reading Text in the Wild with Convolutional Neural Networks, IJCV 2015"<<endl<<endl
<< " Usage: " << progFname << " <output_file> <input_image>" << endl
<< " Caffe Model files (textbox.prototxt, TextBoxes_icdar13.caffemodel)"<<endl
<< " must be in the current directory. See the documentation of text::TextDetectorCNN class to get download links." << endl
<< " Obtaining text recognition Caffe Model files in linux shell:" << endl
<< " wget http://nicolaou.homouniversalis.org/assets/vgg_text/dictnet_vgg.caffemodel" << endl
<< " wget http://nicolaou.homouniversalis.org/assets/vgg_text/dictnet_vgg_deploy.prototxt" << endl
<< " wget http://nicolaou.homouniversalis.org/assets/vgg_text/dictnet_vgg_labels.txt" <<endl << endl;
}
bool fileExists (const string& filename)
{
ifstream f(filename.c_str());
return f.good();
}
void textbox_draw(Mat src, vector<Rect>& groups, vector<float>& probs, float thres)
{
for (size_t i = 0; i < groups.size(); i++)
{
if(probs[i] > thres)
{
if (src.type() == CV_8UC3)
{
rectangle(src, groups[i], Scalar( 0, 255, 255 ), 2, LINE_AA);
String label = format("%.2f", probs[i]);
cout << "text box: " << groups[i] << " confidence: " << probs[i] << "\n";
putText(src, label, groups.at(i).tl(), FONT_HERSHEY_PLAIN, 1, Scalar( 0,0,255 ), 1, LINE_AA);
}
else
rectangle(src, groups[i], Scalar( 255 ), 3, 8 );
}
}
}
}
int main(int argc, const char * argv[])
{
if (argc < 2)
{
printHelpStr(argv[0]);
cout << "Insufiecient parameters. Aborting!" << endl;
exit(1);
}
const string modelArch = "textbox.prototxt";
const string moddelWeights = "TextBoxes_icdar13.caffemodel";
if (!fileExists(modelArch) || !fileExists(moddelWeights))
{
printHelpStr(argv[0]);
cout << "Model files not found in the current directory. Aborting!" << endl;
exit(1);
}
Mat image = imread(String(argv[1]), IMREAD_COLOR);
cout << "Starting Text Box Demo" << endl;
Ptr<text::TextDetectorCNN> textSpotter =
text::TextDetectorCNN::create(modelArch, moddelWeights, false);
vector<Rect> bbox;
vector<float> outProbabillities;
textSpotter->detect(image, bbox, outProbabillities);
float prob_threshold = 0.6f;
Mat image_copy = image.clone();
textbox_draw(image_copy, bbox, outProbabillities, prob_threshold);
imshow("Text detection", image_copy);
image_copy = image.clone();
Ptr<text::OCRHolisticWordRecognizer> wordSpotter =
text::OCRHolisticWordRecognizer::create("dictnet_vgg_deploy.prototxt", "dictnet_vgg.caffemodel", "dictnet_vgg_labels.txt");
for(size_t i = 0; i < bbox.size(); i++)
{
if(outProbabillities[i] > prob_threshold)
{
Mat wordImg;
cvtColor(image(bbox[i]), wordImg, COLOR_BGR2GRAY);
string word;
vector<float> confs;
wordSpotter->run(wordImg, word, NULL, NULL, &confs);
rectangle(image_copy, bbox[i], Scalar(0, 255, 255), 1, LINE_AA);
putText(image_copy, word, bbox[i].tl(), FONT_HERSHEY_PLAIN, 1, Scalar(0, 0, 255), 1, LINE_AA);
}
}
imshow("Text recognition", image_copy);
cout << "Recognition finished. Press any key to exit.\n";
waitKey();
return 0;
}
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