//图像预处理第6步：分割，并在分割出来的字符外面画框以标识
void CChildView::OnImgprcDivide()
{
m_charRect=CharSegment(m_hDIB);
//在屏幕上显示位图
CDC* pDC=GetDC();
DisplayDIB(pDC,m_hDIB);
DrawFrame(pDC,m_hDIB,m_charRect,2,RGB(20,60,200));
}
// 阈值分割
vector<CharSegment> OCR::segment(Plate plate) {
Mat input = plate.plateImg;
vector<CharSegment> output;
Mat thresholdImage;
threshold(input, thresholdImage, 60, 255, CV_THRESH_BINARY_INV);
if (DEBUG)
imshow("Threshold plate", thresholdImage);
Mat img_contours;
thresholdImage.copyTo(img_contours);
// 找到可能的车牌的轮廓
vector< vector< Point> > contours;
findContours(img_contours,
contours, // 检测的轮廓数组，每一个轮廓用一个point类型的vector表示
CV_RETR_EXTERNAL, // 表示只检测外轮廓
CV_CHAIN_APPROX_NONE); // 轮廓的近似办法，这里存储所有的轮廓点
// 在白色的图上画出蓝色的轮廓
cv::Mat result;
thresholdImage.copyTo(result);
cvtColor(result, result, CV_GRAY2RGB);
cv::drawContours(result, contours,
-1, // 所有的轮廓都画出
cv::Scalar(255, 0, 0), // 颜色
1); // 线粗
// 对每个轮廓检测和提取最小区域的有界矩形区域
vector<vector<Point> >::iterator itc = contours.begin();
char res[20];
int i = 0;
// 若没有达到设定的宽高比要求，移去该区域
while (itc != contours.end())
{
Rect mr = boundingRect(Mat(*itc));
rectangle(result, mr, Scalar(0, 255, 0));
// 裁剪图像
Mat auxRoi(thresholdImage, mr);
if (verifySizes(auxRoi)) {
auxRoi = preprocessChar(auxRoi);
output.push_back(CharSegment(auxRoi, mr));
//保存每个字符图片
sprintf(res, "PlateNumber%d.jpg", i);
i++;
imwrite(res, auxRoi);
rectangle(result, mr, Scalar(0, 125, 255));
}
++itc;
}
if (DEBUG)
cout << "Num chars: " << output.size() << "\n";
if (DEBUG)
imshow("SEgmented Chars", result);
return output;
}
//Segment the chars from plate
vector<CharSegment> OCR::segment(Plate plate){
Mat input=plate.plateImg;
vector<CharSegment> output;
//Threshold input image
Mat img_threshold;
//To make char image clearly
// threshold(input, img_threshold, 60, 255, CV_THRESH_BINARY_INV); //Spain
// threshold(input, img_threshold, 150~160, 255, CV_THRESH_BINARY); //China
// TODO: IMPORTANT
threshold(input, img_threshold, 175, 255, CV_THRESH_BINARY); //China
if(debug) {
imshow("OCR_Threshold_Binary", img_threshold);
}
Mat img_contours;
img_threshold.copyTo(img_contours);
//Find contours of possibles characters
vector< vector< Point> > contours;
findContours(img_contours,
contours, // a vector of contours
CV_RETR_EXTERNAL, // retrieve the external contours
CV_CHAIN_APPROX_NONE); // all pixels of each contours
// Draw blue contours on a white image
cv::Mat result;
img_threshold.copyTo(result);
cvtColor(result, result, CV_GRAY2RGB);
cv::drawContours(result,
contours,
-1, // draw all contours
cv::Scalar(255,0,0), // in BLUE
1); // with a thickness of 1
//Start to iterate to each contour founded
vector<vector<Point> >::iterator itc = contours.begin();
//Remove patch that are no inside limits of aspect ratio and area.
while (itc!=contours.end()) {
//Create bounding rect of object
Rect mr = boundingRect(Mat(*itc));
rectangle(result, mr, Scalar(0,255,0)); //Possible chars in GREEN
//Crop image
Mat auxRoi(img_threshold, mr);
if(verifySizes(auxRoi)){
auxRoi=preprocessChar(auxRoi);
output.push_back(CharSegment(auxRoi, mr));
rectangle(result, mr, Scalar(0,0,255)); //Possible chars in RED
}
++itc;
}
if(debug)
{
cout << "OCR number of chars: " << output.size() << "\n";
imshow("OCR Chars", result);
cvWaitKey(0);
}
return output;
}
