void sceneTextDetector::set_Channels_Group(Mat image, int mode, int type, string xm, double prob)
{
//-----------------------------------------------------------------------------CSER
/*computeNMChannels(image, channels, mode);
int cn = (int)channels.size();
for (int c = 0; c < cn - 1; c++)
channels.push_back(255 - channels[c]);
for (int c = 0; c<(int)channels.size(); c++)
{
er_filter1->run(channels[c], regions[c]);
er_filter2->run(channels[c], regions[c]);
}*/
Mat grey;
vector<vector<ERStat> > regions(2);
cvtColor(image, grey, COLOR_RGB2GRAY);
channels.clear();
channels.push_back(grey);
channels.push_back(255 - grey);
regions[0].clear();
regions[1].clear();
nm_region_groups.clear();
nm_boxes.clear();
parallel_for_(cv::Range(0, (int)channels.size()), Parallel_extractCSER(channels, regions, er_filters1, er_filters2));
//-----------------------------------------------------------------------------MSER
/* vector<vector<Point> > contours;
vector<Rect> bboxes;
Mat grey;
cvtColor(image, grey, COLOR_RGB2GRAY);
channels.clear();
channels.push_back(grey);
channels.push_back(255 - grey);
regions.resize(channels.size());
nm_region_groups.clear();
nm_boxes.clear();
Ptr<MSER> mser = MSER::create(21, (int)(0.00002*grey.cols*grey.rows), (int)(0.05*grey.cols*grey.rows), 1, 0.7);
mser->detectRegions(grey, contours, bboxes);
if (contours.size() > 0)
MSERsToERStats(grey, contours, regions);*/
//-----------------------------------------------------------------------------
erGrouping(image, channels, regions, nm_region_groups, nm_boxes, type, xm, prob);
}
bool OCRTess::detectAndRecog() {
UMat grey = UMat::zeros(this->img.rows + 2, this->img.cols + 2, CV_8UC1);
cvtColor(this->img.clone(), grey, COLOR_RGB2GRAY);
vector<UMat> channels;
channels.clear();
channels.push_back(grey);
Mat m = 255 - grey.getMat(ACCESS_READ | ACCESS_WRITE);
channels.push_back(m.getUMat(ACCESS_READ));
vector<vector<ERStat>> regions(2);
regions[0].clear();
regions[1].clear();
switch (this->REGION) {
case REG_CSER: {
parallel_for_(Range(0, (int) channels.size()), Parallel_extractCSER(channels, regions, this->erf1, this->erf2));
break;
}
case REG_MSER: {
vector<vector<Point> > contours;
vector<Rect> bboxes;
Ptr<MSER> mser = MSER::create(21, (int) (0.00002 * grey.cols * grey.rows), (int) (0.05 * grey.cols * grey.rows), 1, 0.7);
mser->detectRegions(grey, contours, bboxes);
if (contours.size() > 0)
MSERsToERStats(grey, contours, regions);
break;
}
default: {
break;
}
}
/*Text Recognition (OCR)*/
vector<vector<Vec2i> > nm_region_groups;
vector<Rect> nm_boxes;
switch (this->GROUP) {
case 0:
erGrouping(this->img, channels, regions, nm_region_groups, nm_boxes, ERGROUPING_ORIENTATION_HORIZ);
break;
case 1:
default:
erGrouping(this->img, channels, regions, nm_region_groups, nm_boxes, ERGROUPING_ORIENTATION_ANY, DIR + TR_GRP, 0.5);
break;
}
if (!nm_boxes.size() || nm_boxes.size() > 1) return false;
vector<string> words_detection;
float min_confidence1 = 51.f, min_confidence2 = 60.f;
vector<UMat> detections;
for (int i = 0; i < (int) nm_boxes.size(); i++) {
// rectangle(this->out, nm_boxes[i].tl(), nm_boxes[i].br(), Scalar(255, 255, 0), 3);
UMat group_img = UMat::zeros(this->img.rows + 2, this->img.cols + 2, CV_8UC1);
er_draw(channels, regions, nm_region_groups[i], group_img);
group_img = group_img(nm_boxes[i]);
copyMakeBorder(group_img.clone(), group_img, 15, 15, 15, 15, BORDER_CONSTANT, Scalar(0));
detections.push_back(group_img);
}
vector<string> outputs((int) detections.size());
vector<vector<Rect> > boxes((int) detections.size());
vector<vector<string> > words((int) detections.size());
vector<vector<float> > confidences((int) detections.size());
if (!detections.size() || detections.size() > 1) return false;
for (int i = 0; i < (int) detections.size(); i = i + this->num) {
Range r;
if (i + this->num <= (int) detections.size()) r = Range(i, i + this->num);
else r = Range(i, (int) detections.size());
parallel_for_(r, Parallel_OCR<OCRTesseract>(detections, outputs, boxes, words, confidences, this->ocrs));
}
for (int i = 0; i < (int) detections.size(); i++) {
outputs[i].erase(remove(outputs[i].begin(), outputs[i].end(), '\n'), outputs[i].end());
if (outputs[i].size() < 3) {
continue;
}
for (int j = 0; j < (int) boxes[i].size(); j++) {
boxes[i][j].x += nm_boxes[i].x - 15;
boxes[i][j].y += nm_boxes[i].y - 15;
if ((words[i][j].size() < 2) || (confidences[i][j] < min_confidence1) ||
((words[i][j].size() == 2) && (words[i][j][0] == words[i][j][1])) ||
((words[i][j].size() < 4) && (confidences[i][j] < min_confidence2)) ||
isRepetitive(words[i][j]))
continue;
words_detection.push_back(words[i][j]);
// rectangle(this->out, boxes[i][j].tl(), boxes[i][j].br(), Scalar(255, 0, 255), 3);
// Size word_size = getTextSize(words[i][j], FONT_HERSHEY_SIMPLEX, (double) scale_font, (int) (3 * scale_font), NULL);
// rectangle(this->out, boxes[i][j].tl() - Point(3, word_size.height + 3), boxes[i][j].tl() + Point(word_size.width, 0), Scalar(255, 0, 255), -1);
// putText(this->out, words[i][j], boxes[i][j].tl() - Point(1, 1), FONT_HERSHEY_SIMPLEX, scale_font, Scalar(255, 255, 255), (int) (3 * scale_font));
}
}
if (!words_detection.size() || words_detection.size() > 1) return false;
return (words_detection[0].compare(WORD) == 0);
}
