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text: improve DL-based samples

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This commit is contained in:
Vladislav Sovrasov
2017-10-30 15:33:12 +03:00
parent 27961cd8cc
commit fd2e37da56
4 changed files with 81 additions and 58 deletions
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66
modules/text/samples/text_recognition_cnn.cpp
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@@ -1,6 +1,7 @@
#include <opencv2/text.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/dnn.hpp>
#include <iostream>
#include <fstream>
@@ -29,22 +30,27 @@ bool fileExists (const string& filename)
return f.good();
}
void textbox_draw(Mat src, vector<Rect>& groups, vector<float>& probs, float thres)
void textbox_draw(Mat src, std::vector<Rect>& groups, std::vector<float>& probs, std::vector<int>& indexes)
{
for (size_t i = 0; i < groups.size(); i++)
for (size_t i = 0; i < indexes.size(); i++)
{
if(probs[i] > thres)
if (src.type() == CV_8UC3)
{
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 );
Rect currrentBox = groups[indexes[i]];
rectangle(src, currrentBox, Scalar( 0, 255, 255 ), 2, LINE_AA);
String label = format("%.2f", probs[indexes[i]]);
std::cout << "text box: " << currrentBox << " confidence: " << probs[indexes[i]] << "\n";
int baseLine = 0;
Size labelSize = getTextSize(label, FONT_HERSHEY_PLAIN, 1, 1, &baseLine);
int yLeftBottom = std::max(currrentBox.y, labelSize.height);
rectangle(src, Point(currrentBox.x, yLeftBottom - labelSize.height),
Point(currrentBox.x + labelSize.width, yLeftBottom + baseLine), Scalar( 255, 255, 255 ), FILLED);
putText(src, label, Point(currrentBox.x, yLeftBottom), FONT_HERSHEY_PLAIN, 1, Scalar( 0,0,0 ), 1, LINE_AA);
}
else
rectangle(src, groups[i], Scalar( 255 ), 3, 8 );
}
}
@@ -73,33 +79,41 @@ int main(int argc, const char * argv[])
cout << "Starting Text Box Demo" << endl;
Ptr<text::TextDetectorCNN> textSpotter =
text::TextDetectorCNN::create(modelArch, moddelWeights, false);
text::TextDetectorCNN::create(modelArch, moddelWeights);
vector<Rect> bbox;
vector<float> outProbabillities;
textSpotter->detect(image, bbox, outProbabillities);
std::vector<int> indexes;
cv::dnn::NMSBoxes(bbox, outProbabillities, 0.4f, 0.5f, indexes);
float prob_threshold = 0.6f;
Mat image_copy = image.clone();
textbox_draw(image_copy, bbox, outProbabillities, prob_threshold);
textbox_draw(image_copy, bbox, outProbabillities, indexes);
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++)
for(size_t i = 0; i < indexes.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);
}
Mat wordImg;
cvtColor(image(bbox[indexes[i]]), wordImg, COLOR_BGR2GRAY);
string word;
vector<float> confs;
wordSpotter->run(wordImg, word, NULL, NULL, &confs);
Rect currrentBox = bbox[indexes[i]];
rectangle(image_copy, currrentBox, Scalar( 0, 255, 255 ), 2, LINE_AA);
int baseLine = 0;
Size labelSize = getTextSize(word, FONT_HERSHEY_PLAIN, 1, 1, &baseLine);
int yLeftBottom = std::max(currrentBox.y, labelSize.height);
rectangle(image_copy, Point(currrentBox.x, yLeftBottom - labelSize.height),
Point(currrentBox.x + labelSize.width, yLeftBottom + baseLine), Scalar( 255, 255, 255 ), FILLED);
putText(image_copy, word, Point(currrentBox.x, yLeftBottom), FONT_HERSHEY_PLAIN, 1, Scalar( 0,0,0 ), 1, LINE_AA);
}
imshow("Text recognition", image_copy);
cout << "Recognition finished. Press any key to exit.\n";