//getPlateType
//判断车牌的类型，1为蓝牌，2为黄牌，0为未知，默认蓝牌
//通过像素中蓝色所占比例的多少来判断，大于0.3为蓝牌，否则为黄牌
int CCharsSegment::getPlateType(Mat input)
{
Mat img;
input.copyTo(img);
img = histeq(img);
double countBlue = 0;
double countWhite = 0;
int nums = img.rows*img.cols;
for(int i=0; i < img.rows; i++)
{
for(int j=0; j < img.cols; j++)
{
Vec3b intensity = img.at<Vec3b>(i,j);
int blue = int(intensity.val[0]);
int green = int(intensity.val[1]);
int red = int(intensity.val[2]);
if(blue > m_ColorThreshold && green > 10 && red > 10)
countBlue++;
if(blue > m_ColorThreshold && green > m_ColorThreshold && red > m_ColorThreshold)
countWhite++;
}
}
double percentBlue = countBlue/nums;
double percentWhite = countWhite/nums;
if (percentBlue - m_BluePercent > 0 && percentWhite - m_WhitePercent > 0)
return 1;
else
return 2;
return 0;
}
//! EasyPR的getFeatures回调函数
//! 本函数是生成直方图均衡特征的回调函数
void getHisteqFeatures(const Mat& image, Mat& features)
{
features = histeq(image);
}
vector<Plate> DetectRegions::segment(Mat input){
vector<Plate> output;
//convert image to gray
Mat img_gray; //= *new Mat(input.size().width,input.size().height, CV_8UC1);
cvtColor(input, img_gray, CV_BGR2GRAY);
blur(img_gray, img_gray, Size(5,5));
//Finde vertical lines. Car plates have high density of vertical lines
Mat img_sobel;
Sobel(img_gray, img_sobel, CV_8U, 1, 0, 3, 1, 0, BORDER_DEFAULT);
if(showSteps)
imshow("Sobel", img_sobel);
//threshold image
Mat img_threshold;
threshold(img_sobel, img_threshold, 0, 255, CV_THRESH_OTSU+CV_THRESH_BINARY);
if(showSteps)
imshow("Threshold", img_threshold);
//Morphplogic operation close
Mat element = getStructuringElement(MORPH_RECT, Size(17, 3) );
morphologyEx(img_threshold, img_threshold, CV_MOP_CLOSE, element);
if(showSteps)
imshow("Close", img_threshold);
//Find contours of possibles plates
vector< vector< Point> > contours;
findContours(img_threshold,
contours, // a vector of contours
CV_RETR_EXTERNAL, // retrieve the external contours
CV_CHAIN_APPROX_NONE); // all pixels of each contours
//Start to iterate to each contour founded
vector<vector<Point> >::iterator itc= contours.begin();
vector<RotatedRect> rects;
//Remove patch that are no inside limits of aspect ratio and area.
while (itc!=contours.end()) {
//Create bounding rect of object
RotatedRect mr= minAreaRect(Mat(*itc));
if( !verifySizes(mr)){
itc= contours.erase(itc);
}else{
++itc;
rects.push_back(mr);
}
}
// Draw blue contours on a white image
cv::Mat result;
input.copyTo(result);
cv::drawContours(result,contours,
-1, // draw all contours
cv::Scalar(255,0,0), // in blue
1); // with a thickness of 1
for(int i=0; i< rects.size(); i++){
//For better rect cropping for each posible box
//Make floodfill algorithm because the plate has white background
//And then we can retrieve more clearly the contour box
circle(result, rects[i].center, 3, Scalar(0,255,0), -1);
//get the min size between width and height
float minSize=(rects[i].size.width < rects[i].size.height)?rects[i].size.width:rects[i].size.height;
minSize=minSize-minSize*0.5;
//initialize rand and get 5 points around center for floodfill algorithm
srand ( time(NULL) );
//Initialize floodfill parameters and variables
Mat mask;
mask.create(input.rows + 2, input.cols + 2, CV_8UC1);
mask= Scalar::all(0);
int loDiff = 30;
int upDiff = 30;
int connectivity = 4;
int newMaskVal = 255;
int NumSeeds = 10;
Rect ccomp;
int flags = connectivity + (newMaskVal << 8 ) + CV_FLOODFILL_FIXED_RANGE + CV_FLOODFILL_MASK_ONLY;
for(int j=0; j<NumSeeds; j++){
Point seed;
seed.x=rects[i].center.x+rand()%(int)minSize-(minSize/2);
seed.y=rects[i].center.y+rand()%(int)minSize-(minSize/2);
circle(result, seed, 1, Scalar(0,255,255), -1);
int area = floodFill(input, mask, seed, Scalar(255,0,0), &ccomp, Scalar(loDiff, loDiff, loDiff), Scalar(upDiff, upDiff, upDiff), flags);
}
if(showSteps)
imshow("MASK", mask);
//cvWaitKey(0);
//Check new floodfill mask match for a correct patch.
//Get all points detected for get Minimal rotated Rect
vector<Point> pointsInterest;
Mat_<uchar>::iterator itMask= mask.begin<uchar>();
Mat_<uchar>::iterator end= mask.end<uchar>();
for( ; itMask!=end; ++itMask)
if(*itMask==255)
pointsInterest.push_back(itMask.pos());
RotatedRect minRect = minAreaRect(pointsInterest);
if(verifySizes(minRect)){
// rotated rectangle drawing
Point2f rect_points[4]; minRect.points( rect_points );
for( int j = 0; j < 4; j++ )
line( result, rect_points[j], rect_points[(j+1)%4], Scalar(0,0,255), 1, 8 );
//Get rotation matrix
float r= (float)minRect.size.width / (float)minRect.size.height;
float angle=minRect.angle;
if(r<1)
angle=90+angle;
Mat rotmat= getRotationMatrix2D(minRect.center, angle,1);
//Create and rotate image
Mat img_rotated;
warpAffine(input, img_rotated, rotmat, input.size(), CV_INTER_CUBIC);
//Crop image
Size rect_size=minRect.size;
if(r < 1)
swap(rect_size.width, rect_size.height);
Mat img_crop;
getRectSubPix(img_rotated, rect_size, minRect.center, img_crop);
Mat resultResized;
resultResized.create(33,144, CV_8UC3);
resize(img_crop, resultResized, resultResized.size(), 0, 0, INTER_CUBIC);
//Equalize croped image
Mat grayResult;
cvtColor(resultResized, grayResult, CV_BGR2GRAY);
blur(grayResult, grayResult, Size(3,3));
grayResult=histeq(grayResult);
if(saveRegions){
stringstream ss(stringstream::in | stringstream::out);
ss << "tmp/" << filename << "_" << i << ".jpg";
imwrite(ss.str(), grayResult);
}
output.push_back(Plate(grayResult,minRect.boundingRect()));
}
}
if(showSteps)
imshow("Contours", result);
return output;
}
