// 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;
}
void DetectRegions::part2( const cv::Mat& input,
std::vector<img_Plate>& output,
cv::Mat& img_threshold,
const std::string& out_id )
{
cv::Mat my_input;
input.copyTo(my_input);
//Find contours of possibles plates
std::vector< std::vector< cv::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
CV_CHAIN_APPROX_SIMPLE );
//Start to iterate to each contour founded
std::vector< std::vector<cv::Point> >::iterator itc = contours.begin();
std::vector<cv::RotatedRect> rects;
cv::Mat my_input_rect;
input.copyTo(my_input_rect);
//Remove patch that are no inside limits of aspect ratio and area.
while (itc != contours.end())
{
//Create bounding rect of object
cv::RotatedRect mr = minAreaRect(cv::Mat(*itc));
if (!verifySizes(mr))
{
itc = contours.erase(itc);
// rotated rectangle drawing
cv::Point2f rect_points[4];
mr.points( rect_points );
for (int j = 0; j < 4; ++j)
line( my_input_rect, rect_points[j], rect_points[ (j + 1) % 4 ],
cv::Scalar(255,0,0), 1, 8 );
}
else
{
++itc;
rects.push_back(mr);
// rotated rectangle drawing
cv::Point2f rect_points[4];
mr.points( rect_points );
for (int j = 0; j < 4; ++j)
line( my_input_rect, rect_points[j], rect_points[ (j + 1) % 4 ],
cv::Scalar(0,255,0), 2, 8 );
}
}
D_IMG_SAVE( my_input_rect, "img_" << out_id << "Rect.png" );
// 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
// 3 ); // with a thickness of 1
D_IMG_SAVE( result, "04_img_" << out_id << "Contours.png" );
// std::cerr << "rects.size : " << rects.size() << std::endl;
std::vector<cv::Mat> Mats;
for (unsigned 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, cv::Scalar(0,255,0), -1);
//get the min size between width and height
// float minSize = ( (rects[i].size.width < rects[i].size.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
cv::Mat mask;
mask.create(input.rows + 2, input.cols + 2, CV_8UC1);
mask = cv::Scalar::all(0);
int loDiff = 30;
int upDiff = 30;
int connectivity = 4;
int newMaskVal = 255;
// int NumSeeds = 100;
cv::Rect ccomp;
int flags = connectivity + (newMaskVal << 8) + CV_FLOODFILL_FIXED_RANGE + CV_FLOODFILL_MASK_ONLY;
int max_size = rects[i].size.width * rects[i].size.height;
cv::Rect b_rect = rects[i].boundingRect();
int min_x = b_rect.x;
int min_y = b_rect.y;
int max_x = min_x + b_rect.width;
int max_y = min_y + b_rect.height;
for (int local_y = min_y; local_y < max_y; local_y += 5)
for (int local_x = min_x; local_x < max_x; local_x += 5)
{
cv::Point seed;
seed.x = local_x;
seed.y = local_y;
if (Collision( contours[i], seed ))
{
cv::Mat tmp_mask;
tmp_mask.create( input.rows + 2, input.cols + 2, CV_8UC1 );
tmp_mask = cv::Scalar::all(0);
int area = floodFill( input, tmp_mask, seed,
cv::Scalar(255,0,0), &ccomp,
cv::Scalar(loDiff, loDiff, loDiff),
cv::Scalar(upDiff, upDiff, upDiff), flags );
{
cv::Point c( ccomp.x + ccomp.width / 2,
ccomp.y + ccomp.height / 2 );
cv::Size s( ccomp.width, ccomp.height );
cv::RotatedRect tmp_rect( c, s, 0 );
// rotated rectangle drawing
cv::Point2f rect_points[4];
tmp_rect.points( rect_points );
for (int j = 0; j < 4; ++j)
line( my_input, rect_points[j], rect_points[ (j + 1) % 4 ],
cv::Scalar(0,255,255), 1, 8 );
}
bool rect_invalid = ( ccomp.x < min_x || ccomp.x > max_x ||
ccomp.y < min_y || ccomp.y > max_y );
cv::Point left_top( min_x, min_y );
cv::Point right_top( max_x, min_y );
cv::Point left_bottom( min_x, max_y );
cv::Point right_bottom( max_x, max_y );
if (area > max_size)
{
circle( result, seed, 1, cv::Scalar(255,0,0), -1 );
circle( my_input, seed, 1, cv::Scalar(255,0,0), -1 );
}
else if (rect_invalid)
{
circle( result, seed, 1, cv::Scalar(255,0,0), -1 );
circle( my_input, seed, 1, cv::Scalar(255,0,0), -1 );
}
else
{
circle( result, seed, 1, cv::Scalar(0,255,0), -1 );
circle( my_input, seed, 1, cv::Scalar(0,255,0), -1 );
floodFill( input, mask, seed,
cv::Scalar(255,0,0), &ccomp,
cv::Scalar(loDiff, loDiff, loDiff),
cv::Scalar(upDiff, upDiff, upDiff), flags );
}
}
else
{
circle( result, seed, 1, cv::Scalar(255,0,0), -1 );
circle( my_input, seed, 1, cv::Scalar(255,0,0), -1 );
}
} // for (int j = 0; j < NumSeeds; ++j)
{
// rotated rectangle drawing
cv::Point2f rect_points[4];
rects[i].points( rect_points );
for (int j = 0; j < 4; ++j)
line( my_input, rect_points[j], rect_points[ (j + 1) % 4 ],
cv::Scalar(255,255,255), 2, 8 );
D_IMG_SAVE( mask, "img_" << out_id << "" << i << "_01_Mask.png" );
}
//cvWaitKey(0);
//Check new floodfill mask match for a correct patch.
//Get all points detected for get Minimal rotated Rect
std::vector<cv::Point> pointsInterest;
cv::Mat_<uchar>::iterator itMask = mask.begin<uchar>();
cv::Mat_<uchar>::iterator end = mask.end<uchar>();
for (; itMask != end; ++itMask)
if (*itMask == 255)
pointsInterest.push_back(itMask.pos());
if (pointsInterest.size() < 2)
continue;
cv::RotatedRect minRect = minAreaRect(pointsInterest);
if (verifySizes(minRect))
{
// rotated rectangle drawing
cv::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], cv::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;
cv::Mat rotmat = getRotationMatrix2D(minRect.center, angle,1);
//Create and rotate image
cv::Mat img_rotated;
warpAffine(input, img_rotated, rotmat, input.size(), CV_INTER_CUBIC);
//Crop image
cv::Size rect_size = minRect.size;
if (r < 1)
std::swap( rect_size.width, rect_size.height );
cv::Mat img_crop;
getRectSubPix(img_rotated, rect_size, minRect.center, img_crop);
D_IMG_SAVE( img_crop, "img_" << out_id << "" << i << "_02_crop.png" );
cv::Mat resultResized;
resultResized.create(33,144, CV_8UC3);
resize(img_crop, resultResized, resultResized.size(), 0, 0, cv::INTER_CUBIC);
D_IMG_SAVE( resultResized, "img_" << out_id << "" << i << "_03_resultResized.png" );
output.push_back( img_Plate( resultResized, minRect.boundingRect() ) );
// //Equalize croped image
// cv::Mat grayResult;
// cvtColor(resultResized, grayResult, CV_BGR2GRAY);
// // blur(grayResult, grayResult, Size(3,3));
// grayResult = histeq(grayResult);
// D_IMG_SAVE( grayResult, "img_" << out_id << "" << i << "_04_grayResult.png" );
// output.push_back( Plate( grayResult, minRect.boundingRect() ) );
} // if (verifySizes(minRect))
} // for (int i = 0; i < rects.size(); ++i)
D_IMG_SAVE( result, "10_img_" << out_id << "Contours.png" );
D_IMG_SAVE( my_input, "11_img_" << out_id << "my_input.png" );
}
//! 字符分割与排序
int CCharsSegment::charsSegment(Mat input, vector<Mat>& resultVec)
{
if( !input.data )
{ return -3; }
//判断车牌颜色以此确认threshold方法
int plateType = getPlateType(input);
cvtColor(input, input, CV_RGB2GRAY);
//Threshold input image
Mat img_threshold;
if (1 == plateType)
threshold(input, img_threshold, 10, 255, CV_THRESH_OTSU+CV_THRESH_BINARY);
else
threshold(input, img_threshold, 10, 255, CV_THRESH_OTSU+CV_THRESH_BINARY_INV);
if(m_debug)
{
stringstream ss(stringstream::in | stringstream::out);
ss << "image/tmp/debug_char_threshold" << ".jpg";
imwrite(ss.str(), img_threshold);
}
//去除车牌上方的柳钉以及下方的横线等干扰
clearLiuDing(img_threshold);
if(m_debug)
{
stringstream ss(stringstream::in | stringstream::out);
ss << "image/tmp/debug_char_clearLiuDing" << ".jpg";
imwrite(ss.str(), img_threshold);
}
Mat img_contours;
img_threshold.copyTo(img_contours);
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
//Start to iterate to each contour founded
vector<vector<Point> >::iterator itc= contours.begin();
vector<Rect> vecRect;
//Remove patch that are no inside limits of aspect ratio and area.
//将不符合特定尺寸的图块排除出去
while (itc != contours.end())
{
Rect mr = boundingRect(Mat(*itc));
Mat auxRoi(img_threshold, mr);
if (verifySizes(auxRoi))
{
vecRect.push_back(mr);
}
++itc;
}
if (vecRect.size() == 0)
return -3;
vector<Rect> sortedRect;
//对符合尺寸的图块按照从左到右进行排序
SortRect(vecRect, sortedRect);
int specIndex = 0;
//获得指示城市的特定Rect,如苏A的"A"
specIndex = GetSpecificRect(sortedRect);
if(m_debug)
{
if (specIndex < sortedRect.size())
{
Mat specMat(img_threshold, sortedRect[specIndex]);
stringstream ss(stringstream::in | stringstream::out);
ss << "image/tmp/debug_specMat" <<".jpg";
imwrite(ss.str(), specMat);
}
}
//根据特定Rect向左反推出中文字符
//这样做的主要原因是根据findContours方法很难捕捉到中文字符的准确Rect，因此仅能
//退过特定算法来指定
Rect chineseRect;
if (specIndex < sortedRect.size())
chineseRect = GetChineseRect(sortedRect[specIndex]);
else
return -3;
if(m_debug)
{
Mat chineseMat(img_threshold, chineseRect);
stringstream ss(stringstream::in | stringstream::out);
ss << "image/tmp/debug_chineseMat" <<".jpg";
imwrite(ss.str(), chineseMat);
}
//新建一个全新的排序Rect
//将中文字符Rect第一个加进来，因为它肯定是最左边的
//其余的Rect只按照顺序去6个，车牌只可能是7个字符！这样可以避免阴影导致的“1”字符
vector<Rect> newSortedRect;
newSortedRect.push_back(chineseRect);
RebuildRect(sortedRect, newSortedRect, specIndex);
if (newSortedRect.size() == 0)
return -3;
for (int i = 0; i < newSortedRect.size(); i++)
{
Rect mr = newSortedRect[i];
Mat auxRoi(img_threshold, mr);
if (1)
{
auxRoi = preprocessChar(auxRoi);
if(m_debug)
{
stringstream ss(stringstream::in | stringstream::out);
ss << "image/tmp/debug_char_auxRoi_" << i <<".jpg";
imwrite(ss.str(), auxRoi);
}
resultVec.push_back(auxRoi);
}
}
return 0;
}
//! 定位车牌图像
//! src 原始图像
//! resultVec 一个Mat的向量，存储所有抓取到的图像
//! 成功返回0，否则返回-1
int CPlateLocate::plateLocate(Mat src, vector<Mat>& resultVec)
{
Mat src_blur, src_gray;
Mat grad;
int scale = SOBEL_SCALE;
int delta = SOBEL_DELTA;
int ddepth = SOBEL_DDEPTH;
if( !src.data )
{ return -1; }
//高斯均衡。Size中的数字影响车牌定位的效果。
GaussianBlur( src, src_blur, Size(m_GaussianBlurSize, m_GaussianBlurSize),
0, 0, BORDER_DEFAULT );
/// Convert it to gray
cvtColor( src_blur, src_gray, CV_RGB2GRAY );
/// Generate grad_x and grad_y
Mat grad_x, grad_y;
Mat abs_grad_x, abs_grad_y;
/// Gradient X
//Scharr( src_gray, grad_x, ddepth, 1, 0, scale, delta, BORDER_DEFAULT );
Sobel( src_gray, grad_x, ddepth, 1, 0, 3, scale, delta, BORDER_DEFAULT );
convertScaleAbs( grad_x, abs_grad_x );
/// Gradient Y
//Scharr( src_gray, grad_y, ddepth, 0, 1, scale, delta, BORDER_DEFAULT );
Sobel( src_gray, grad_y, ddepth, 0, 1, 3, scale, delta, BORDER_DEFAULT );
convertScaleAbs( grad_y, abs_grad_y );
/// Total Gradient (approximate)
addWeighted( abs_grad_x, SOBEL_X_WEIGHT, abs_grad_y, SOBEL_Y_WEIGHT, 0, grad );
Mat img_threshold;
threshold(grad, img_threshold, 0, 255, CV_THRESH_OTSU+CV_THRESH_BINARY);
//threshold(grad, img_threshold, 75, 255, CV_THRESH_BINARY);
Mat element = getStructuringElement(MORPH_RECT, Size(m_MorphSizeWidth, m_MorphSizeHeight) );
morphologyEx(img_threshold, img_threshold, CV_MOP_CLOSE, element);
//Find 轮廓 of possibles plates
vector< vector< Point> > contours;
findContours(img_threshold,
contours, // a vector of contours
CV_RETR_EXTERNAL, // 提取外部轮廓
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.
int t = 0;
while (itc != contours.end())
{
//Create bounding rect of object
RotatedRect mr = minAreaRect(Mat(*itc));
//large the rect for more
if( !verifySizes(mr))
{
itc = contours.erase(itc);
}
else
{
++itc;
rects.push_back(mr);
}
}
for(int i=0; i< rects.size(); i++)
{
RotatedRect minRect = rects[i];
if(verifySizes(minRect))
{
// rotated rectangle drawing
// Get rotation matrix
// 旋转这部分代码确实可以将某些倾斜的车牌调整正，
// 但是它也会误将更多正的车牌搞成倾斜！所以综合考虑，还是不使用这段代码。
// 2014-08-14,由于新到的一批图片中发现有很多车牌是倾斜的，因此决定再次尝试
// 这段代码。
float r = (float)minRect.size.width / (float)minRect.size.height;
float angle = minRect.angle;
Size rect_size = minRect.size;
if (r < 1)
{
angle = 90 + angle;
swap(rect_size.width, rect_size.height);
}
//如果抓取的方块旋转超过m_angle角度，则不是车牌，放弃处理
if (angle - m_angle < 0 && angle + m_angle > 0)
{
//Create and rotate image
Mat rotmat = getRotationMatrix2D(minRect.center, angle, 1);
Mat img_rotated;
warpAffine(src, img_rotated, rotmat, src.size(), CV_INTER_CUBIC);
Mat resultMat;
resultMat = showResultMat(img_rotated, rect_size, minRect.center);
resultVec.push_back(resultMat);
}
}
}
return 0;
}
void RegionDetector::getMask()
{
//find contours
vector <vector<Point> > contours;
cv::findContours(img_temp,contours,
CV_RETR_EXTERNAL, //retrive external contours
CV_CHAIN_APPROX_NONE); // all pixels of each contour
//extract rectangle of minimal area
//Start to iterate to each contour found
vector <vector<Point> >::iterator itc = contours.begin();
vector <RotatedRect> rects;
//Remove patch that has no inside limits of aspect ratio and area
while (itc!=contours.end())
{
//create bounding rect of object
RotatedRect rr = minAreaRect(Mat(*itc));
if (!verifySizes(rr)) {
itc = contours.erase(itc);
} else {
std::cout<<"bene";
itc++;
rects.push_back(rr);
}
}
std::cout << rects.size();
// Draw blue contours on a white image
Mat result;
img_temp.copyTo(result);
cv::drawContours(result,contours,
-1, // draw all contours
cv::Scalar(255),// in blue
1); // thickness
imshow("nn",result);
for(int i=0; i< rects.size(); i++)
{
std::cout<<"true\n";
//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(img_temp.rows + 2, img_temp.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(img_temp, mask, seed, Scalar(255,0,0), &ccomp, Scalar(loDiff, loDiff, loDiff), Scalar(upDiff, upDiff, upDiff), flags);
}
imshow("MASK", mask);
}
}
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;
}
//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;
}
