add windows vs project
This commit is contained in:
jackyu
@@ -0,0 +1,19 @@
|
||||
//
|
||||
// Created by 庾金科 on 21/10/2017.
|
||||
//
|
||||
|
||||
#include "../include/CNNRecognizer.h"
|
||||
|
||||
namespace pr{
|
||||
CNNRecognizer::CNNRecognizer(std::string prototxt,std::string caffemodel){
|
||||
net = cv::dnn::readNetFromCaffe(prototxt, caffemodel);
|
||||
}
|
||||
|
||||
label CNNRecognizer::recognizeCharacter(cv::Mat charImage){
|
||||
if(charImage.channels()== 3)
|
||||
cv::cvtColor(charImage,charImage,cv::COLOR_BGR2GRAY);
|
||||
cv::Mat inputBlob = cv::dnn::blobFromImage(charImage, 1/255.0, cv::Size(CHAR_INPUT_W,CHAR_INPUT_H), cv::Scalar(0,0,0),false);
|
||||
net.setInput(inputBlob,"data");
|
||||
return net.forward();
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,133 @@
|
||||
//
|
||||
// Created by 庾金科 on 02/10/2017.
|
||||
//
|
||||
|
||||
|
||||
|
||||
#include "FastDeskew.h"
|
||||
|
||||
namespace pr{
|
||||
|
||||
|
||||
const int ANGLE_MIN = 30 ;
|
||||
const int ANGLE_MAX = 150 ;
|
||||
const int PLATE_H = 36;
|
||||
const int PLATE_W = 136;
|
||||
|
||||
int angle(float x,float y)
|
||||
{
|
||||
return atan2(x,y)*180/3.1415;
|
||||
}
|
||||
|
||||
std::vector<float> avgfilter(std::vector<float> angle_list,int windowsSize) {
|
||||
std::vector<float> angle_list_filtered(angle_list.size() - windowsSize + 1);
|
||||
for (int i = 0; i < angle_list.size() - windowsSize + 1; i++) {
|
||||
float avg = 0.00f;
|
||||
for (int j = 0; j < windowsSize; j++) {
|
||||
avg += angle_list[i + j];
|
||||
}
|
||||
avg = avg / windowsSize;
|
||||
angle_list_filtered[i] = avg;
|
||||
}
|
||||
|
||||
return angle_list_filtered;
|
||||
}
|
||||
|
||||
|
||||
void drawHist(std::vector<float> seq){
|
||||
cv::Mat image(300,seq.size(),CV_8U);
|
||||
image.setTo(0);
|
||||
|
||||
for(int i = 0;i<seq.size();i++)
|
||||
{
|
||||
float l = *std::max_element(seq.begin(),seq.end());
|
||||
|
||||
int p = int(float(seq[i])/l*300);
|
||||
|
||||
cv::line(image,cv::Point(i,300),cv::Point(i,300-p),cv::Scalar(255,255,255));
|
||||
}
|
||||
cv::imshow("vis",image);
|
||||
}
|
||||
|
||||
cv::Mat correctPlateImage(cv::Mat skewPlate,float angle,float maxAngle)
|
||||
{
|
||||
|
||||
cv::Mat dst;
|
||||
|
||||
cv::Size size_o(skewPlate.cols,skewPlate.rows);
|
||||
|
||||
|
||||
int extend_padding = 0;
|
||||
// if(angle<0)
|
||||
extend_padding = static_cast<int>(skewPlate.rows*tan(cv::abs(angle)/180* 3.14) );
|
||||
// else
|
||||
// extend_padding = static_cast<int>(skewPlate.rows/tan(cv::abs(angle)/180* 3.14) );
|
||||
|
||||
// std::cout<<"extend:"<<extend_padding<<std::endl;
|
||||
|
||||
cv::Size size(skewPlate.cols + extend_padding ,skewPlate.rows);
|
||||
|
||||
float interval = abs(sin((angle /180) * 3.14)* skewPlate.rows);
|
||||
// std::cout<<interval<<std::endl;
|
||||
|
||||
cv::Point2f pts1[4] = {cv::Point2f(0,0),cv::Point2f(0,size_o.height),cv::Point2f(size_o.width,0),cv::Point2f(size_o.width,size_o.height)};
|
||||
if(angle>0) {
|
||||
cv::Point2f pts2[4] = {cv::Point2f(interval, 0), cv::Point2f(0, size_o.height),
|
||||
cv::Point2f(size_o.width, 0), cv::Point2f(size_o.width - interval, size_o.height)};
|
||||
cv::Mat M = cv::getPerspectiveTransform(pts1,pts2);
|
||||
cv::warpPerspective(skewPlate,dst,M,size);
|
||||
|
||||
|
||||
}
|
||||
else {
|
||||
cv::Point2f pts2[4] = {cv::Point2f(0, 0), cv::Point2f(interval, size_o.height), cv::Point2f(size_o.width-interval, 0),
|
||||
cv::Point2f(size_o.width, size_o.height)};
|
||||
cv::Mat M = cv::getPerspectiveTransform(pts1,pts2);
|
||||
cv::warpPerspective(skewPlate,dst,M,size,cv::INTER_CUBIC);
|
||||
|
||||
}
|
||||
return dst;
|
||||
}
|
||||
cv::Mat fastdeskew(cv::Mat skewImage,int blockSize){
|
||||
|
||||
|
||||
const int FILTER_WINDOWS_SIZE = 5;
|
||||
std::vector<float> angle_list(180);
|
||||
memset(angle_list.data(),0,angle_list.size()*sizeof(int));
|
||||
|
||||
cv::Mat bak;
|
||||
skewImage.copyTo(bak);
|
||||
if(skewImage.channels() == 3)
|
||||
cv::cvtColor(skewImage,skewImage,cv::COLOR_RGB2GRAY);
|
||||
|
||||
if(skewImage.channels() == 1)
|
||||
{
|
||||
cv::Mat eigen;
|
||||
|
||||
cv::cornerEigenValsAndVecs(skewImage,eigen,blockSize,5);
|
||||
for( int j = 0; j < skewImage.rows; j+=blockSize )
|
||||
{ for( int i = 0; i < skewImage.cols; i+=blockSize )
|
||||
{
|
||||
float x2 = eigen.at<cv::Vec6f>(j, i)[4];
|
||||
float y2 = eigen.at<cv::Vec6f>(j, i)[5];
|
||||
int angle_cell = angle(x2,y2);
|
||||
angle_list[(angle_cell + 180)%180]+=1.0;
|
||||
|
||||
}
|
||||
}
|
||||
}
|
||||
std::vector<float> filtered = avgfilter(angle_list,5);
|
||||
|
||||
int maxPos = std::max_element(filtered.begin(),filtered.end()) - filtered.begin() + FILTER_WINDOWS_SIZE/2;
|
||||
if(maxPos>ANGLE_MAX)
|
||||
maxPos = (-maxPos+90+180)%180;
|
||||
if(maxPos<ANGLE_MIN)
|
||||
maxPos-=90;
|
||||
maxPos=90-maxPos;
|
||||
cv::Mat deskewed = correctPlateImage(bak, static_cast<float>(maxPos),60.0f);
|
||||
return deskewed;
|
||||
}
|
||||
|
||||
|
||||
|
||||
}//namespace pr
|
||||
@@ -0,0 +1,205 @@
|
||||
//
|
||||
// Created by 庾金科 on 22/09/2017.
|
||||
//
|
||||
|
||||
#include "FineMapping.h"
|
||||
namespace pr{
|
||||
|
||||
const int FINEMAPPING_H = 50;
|
||||
const int FINEMAPPING_W = 120;
|
||||
const int PADDING_UP_DOWN = 30;
|
||||
void drawRect(cv::Mat image,cv::Rect rect)
|
||||
{
|
||||
cv::Point p1(rect.x,rect.y);
|
||||
cv::Point p2(rect.x+rect.width,rect.y+rect.height);
|
||||
cv::rectangle(image,p1,p2,cv::Scalar(0,255,0),1);
|
||||
}
|
||||
|
||||
|
||||
FineMapping::FineMapping(std::string prototxt,std::string caffemodel) {
|
||||
net = cv::dnn::readNetFromCaffe(prototxt, caffemodel);
|
||||
|
||||
}
|
||||
|
||||
cv::Mat FineMapping::FineMappingHorizon(cv::Mat FinedVertical,int leftPadding,int rightPadding)
|
||||
{
|
||||
|
||||
// if(FinedVertical.channels()==1)
|
||||
// cv::cvtColor(FinedVertical,FinedVertical,cv::COLOR_GRAY2BGR);
|
||||
cv::Mat inputBlob = cv::dnn::blobFromImage(FinedVertical, 1/255.0, cv::Size(66,16),
|
||||
cv::Scalar(0,0,0),false);
|
||||
|
||||
net.setInput(inputBlob,"data");
|
||||
cv::Mat prob = net.forward();
|
||||
int front = static_cast<int>(prob.at<float>(0,0)*FinedVertical.cols);
|
||||
int back = static_cast<int>(prob.at<float>(0,1)*FinedVertical.cols);
|
||||
front -= leftPadding ;
|
||||
if(front<0) front = 0;
|
||||
back +=rightPadding;
|
||||
if(back>FinedVertical.cols-1) back=FinedVertical.cols - 1;
|
||||
cv::Mat cropped = FinedVertical.colRange(front,back).clone();
|
||||
return cropped;
|
||||
|
||||
|
||||
}
|
||||
std::pair<int,int> FitLineRansac(std::vector<cv::Point> pts,int zeroadd = 0 )
|
||||
{
|
||||
std::pair<int,int> res;
|
||||
if(pts.size()>2)
|
||||
{
|
||||
cv::Vec4f line;
|
||||
cv::fitLine(pts,line,cv::DIST_HUBER,0,0.01,0.01);
|
||||
float vx = line[0];
|
||||
float vy = line[1];
|
||||
float x = line[2];
|
||||
float y = line[3];
|
||||
int lefty = static_cast<int>((-x * vy / vx) + y);
|
||||
int righty = static_cast<int>(((136- x) * vy / vx) + y);
|
||||
res.first = lefty+PADDING_UP_DOWN+zeroadd;
|
||||
res.second = righty+PADDING_UP_DOWN+zeroadd;
|
||||
return res;
|
||||
}
|
||||
res.first = zeroadd;
|
||||
res.second = zeroadd;
|
||||
return res;
|
||||
}
|
||||
|
||||
cv::Mat FineMapping::FineMappingVertical(cv::Mat InputProposal,int sliceNum,int upper,int lower,int windows_size){
|
||||
|
||||
|
||||
cv::Mat PreInputProposal;
|
||||
cv::Mat proposal;
|
||||
|
||||
cv::resize(InputProposal,PreInputProposal,cv::Size(FINEMAPPING_W,FINEMAPPING_H));
|
||||
if(InputProposal.channels() == 3)
|
||||
cv::cvtColor(PreInputProposal,proposal,cv::COLOR_BGR2GRAY);
|
||||
else
|
||||
PreInputProposal.copyTo(proposal);
|
||||
|
||||
// proposal = PreInputProposal;
|
||||
|
||||
// this will improve some sen
|
||||
cv::Mat kernal = cv::getStructuringElement(cv::MORPH_ELLIPSE,cv::Size(1,3));
|
||||
// cv::erode(proposal,proposal,kernal);
|
||||
|
||||
|
||||
float diff = static_cast<float>(upper-lower);
|
||||
diff/=static_cast<float>(sliceNum-1);
|
||||
cv::Mat binary_adaptive;
|
||||
std::vector<cv::Point> line_upper;
|
||||
std::vector<cv::Point> line_lower;
|
||||
int contours_nums=0;
|
||||
|
||||
for(int i = 0 ; i < sliceNum ; i++)
|
||||
{
|
||||
std::vector<std::vector<cv::Point> > contours;
|
||||
float k =lower + i*diff;
|
||||
cv::adaptiveThreshold(proposal,binary_adaptive,255,cv::ADAPTIVE_THRESH_MEAN_C,cv::THRESH_BINARY,windows_size,k);
|
||||
cv::Mat draw;
|
||||
binary_adaptive.copyTo(draw);
|
||||
cv::findContours(binary_adaptive,contours,cv::RETR_EXTERNAL,cv::CHAIN_APPROX_SIMPLE);
|
||||
for(auto contour: contours)
|
||||
{
|
||||
cv::Rect bdbox =cv::boundingRect(contour);
|
||||
float lwRatio = bdbox.height/static_cast<float>(bdbox.width);
|
||||
int bdboxAera = bdbox.width*bdbox.height;
|
||||
if (( lwRatio>0.7&&bdbox.width*bdbox.height>100 && bdboxAera<300)
|
||||
|| (lwRatio>3.0 && bdboxAera<100 && bdboxAera>10))
|
||||
{
|
||||
|
||||
cv::Point p1(bdbox.x, bdbox.y);
|
||||
cv::Point p2(bdbox.x + bdbox.width, bdbox.y + bdbox.height);
|
||||
line_upper.push_back(p1);
|
||||
line_lower.push_back(p2);
|
||||
contours_nums+=1;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
std:: cout<<"contours_nums "<<contours_nums<<std::endl;
|
||||
|
||||
if(contours_nums<41)
|
||||
{
|
||||
cv::bitwise_not(InputProposal,InputProposal);
|
||||
cv::Mat kernal = cv::getStructuringElement(cv::MORPH_ELLIPSE,cv::Size(1,5));
|
||||
cv::Mat bak;
|
||||
cv::resize(InputProposal,bak,cv::Size(FINEMAPPING_W,FINEMAPPING_H));
|
||||
cv::erode(bak,bak,kernal);
|
||||
if(InputProposal.channels() == 3)
|
||||
cv::cvtColor(bak,proposal,cv::COLOR_BGR2GRAY);
|
||||
else
|
||||
proposal = bak;
|
||||
int contours_nums=0;
|
||||
|
||||
for(int i = 0 ; i < sliceNum ; i++)
|
||||
{
|
||||
std::vector<std::vector<cv::Point> > contours;
|
||||
float k =lower + i*diff;
|
||||
cv::adaptiveThreshold(proposal,binary_adaptive,255,cv::ADAPTIVE_THRESH_MEAN_C,cv::THRESH_BINARY,windows_size,k);
|
||||
// cv::imshow("image",binary_adaptive);
|
||||
// cv::waitKey(0);
|
||||
cv::Mat draw;
|
||||
binary_adaptive.copyTo(draw);
|
||||
cv::findContours(binary_adaptive,contours,cv::RETR_EXTERNAL,cv::CHAIN_APPROX_SIMPLE);
|
||||
for(auto contour: contours)
|
||||
{
|
||||
cv::Rect bdbox =cv::boundingRect(contour);
|
||||
float lwRatio = bdbox.height/static_cast<float>(bdbox.width);
|
||||
int bdboxAera = bdbox.width*bdbox.height;
|
||||
if (( lwRatio>0.7&&bdbox.width*bdbox.height>120 && bdboxAera<300)
|
||||
|| (lwRatio>3.0 && bdboxAera<100 && bdboxAera>10))
|
||||
{
|
||||
|
||||
cv::Point p1(bdbox.x, bdbox.y);
|
||||
cv::Point p2(bdbox.x + bdbox.width, bdbox.y + bdbox.height);
|
||||
line_upper.push_back(p1);
|
||||
line_lower.push_back(p2);
|
||||
contours_nums+=1;
|
||||
}
|
||||
}
|
||||
}
|
||||
// std:: cout<<"contours_nums "<<contours_nums<<std::endl;
|
||||
}
|
||||
|
||||
cv::Mat rgb;
|
||||
cv::copyMakeBorder(PreInputProposal, rgb, 30, 30, 0, 0, cv::BORDER_REPLICATE);
|
||||
// cv::imshow("rgb",rgb);
|
||||
// cv::waitKey(0);
|
||||
//
|
||||
|
||||
|
||||
std::pair<int, int> A;
|
||||
std::pair<int, int> B;
|
||||
A = FitLineRansac(line_upper, -2);
|
||||
B = FitLineRansac(line_lower, 2);
|
||||
int leftyB = A.first;
|
||||
int rightyB = A.second;
|
||||
int leftyA = B.first;
|
||||
int rightyA = B.second;
|
||||
int cols = rgb.cols;
|
||||
int rows = rgb.rows;
|
||||
// pts_map1 = np.float32([[cols - 1, rightyA], [0, leftyA],[cols - 1, rightyB], [0, leftyB]])
|
||||
// pts_map2 = np.float32([[136,36],[0,36],[136,0],[0,0]])
|
||||
// mat = cv2.getPerspectiveTransform(pts_map1,pts_map2)
|
||||
// image = cv2.warpPerspective(rgb,mat,(136,36),flags=cv2.INTER_CUBIC)
|
||||
std::vector<cv::Point2f> corners(4);
|
||||
corners[0] = cv::Point2f(cols - 1, rightyA);
|
||||
corners[1] = cv::Point2f(0, leftyA);
|
||||
corners[2] = cv::Point2f(cols - 1, rightyB);
|
||||
corners[3] = cv::Point2f(0, leftyB);
|
||||
std::vector<cv::Point2f> corners_trans(4);
|
||||
corners_trans[0] = cv::Point2f(136, 36);
|
||||
corners_trans[1] = cv::Point2f(0, 36);
|
||||
corners_trans[2] = cv::Point2f(136, 0);
|
||||
corners_trans[3] = cv::Point2f(0, 0);
|
||||
cv::Mat transform = cv::getPerspectiveTransform(corners, corners_trans);
|
||||
cv::Mat quad = cv::Mat::zeros(36, 136, CV_8UC3);
|
||||
cv::warpPerspective(rgb, quad, transform, quad.size());
|
||||
return quad;
|
||||
|
||||
}
|
||||
|
||||
|
||||
}
|
||||
|
||||
|
||||
@@ -0,0 +1 @@
|
||||
////
|
||||
@@ -0,0 +1,61 @@
|
||||
//
|
||||
// Created by 庾金科 on 20/09/2017.
|
||||
//
|
||||
#include "../include/PlateDetection.h"
|
||||
|
||||
#include "util.h"
|
||||
|
||||
namespace pr{
|
||||
|
||||
|
||||
PlateDetection::PlateDetection(std::string filename_cascade){
|
||||
cascade.load(filename_cascade);
|
||||
|
||||
};
|
||||
|
||||
|
||||
void PlateDetection::plateDetectionRough(cv::Mat InputImage,std::vector<pr::PlateInfo> &plateInfos,int min_w,int max_w){
|
||||
|
||||
cv::Mat processImage;
|
||||
|
||||
cv::cvtColor(InputImage,processImage,cv::COLOR_BGR2GRAY);
|
||||
|
||||
|
||||
std::vector<cv::Rect> platesRegions;
|
||||
// std::vector<PlateInfo> plates;
|
||||
cv::Size minSize(min_w,min_w/4);
|
||||
cv::Size maxSize(max_w,max_w/4);
|
||||
// cv::imshow("input",InputImage);
|
||||
// cv::waitKey(0);
|
||||
cascade.detectMultiScale( processImage, platesRegions,
|
||||
1.1, 3, cv::CASCADE_SCALE_IMAGE,minSize,maxSize);
|
||||
for(auto plate:platesRegions)
|
||||
{
|
||||
// extend rects
|
||||
// x -= w * 0.14
|
||||
// w += w * 0.28
|
||||
// y -= h * 0.6
|
||||
// h += h * 1.1;
|
||||
int zeroadd_w = static_cast<int>(plate.width*0.28);
|
||||
int zeroadd_h = static_cast<int>(plate.height*1.2);
|
||||
int zeroadd_x = static_cast<int>(plate.width*0.14);
|
||||
int zeroadd_y = static_cast<int>(plate.height*0.6);
|
||||
plate.x-=zeroadd_x;
|
||||
plate.y-=zeroadd_y;
|
||||
plate.height += zeroadd_h;
|
||||
plate.width += zeroadd_w;
|
||||
cv::Mat plateImage = util::cropFromImage(InputImage,plate);
|
||||
PlateInfo plateInfo(plateImage,plate);
|
||||
plateInfos.push_back(plateInfo);
|
||||
|
||||
}
|
||||
}
|
||||
// std::vector<pr::PlateInfo> PlateDetection::plateDetectionRough(cv::Mat InputImage,cv::Rect roi,int min_w,int max_w){
|
||||
// cv::Mat roi_region = util::cropFromImage(InputImage,roi);
|
||||
// return plateDetectionRough(roi_region,min_w,max_w);
|
||||
// }
|
||||
|
||||
|
||||
|
||||
|
||||
}//namespace pr
|
||||
@@ -0,0 +1,402 @@
|
||||
//
|
||||
// Created by 庾金科 on 16/10/2017.
|
||||
//
|
||||
|
||||
#include "../include/PlateSegmentation.h"
|
||||
#include "../include/niBlackThreshold.h"
|
||||
|
||||
|
||||
//#define DEBUG
|
||||
namespace pr{
|
||||
|
||||
PlateSegmentation::PlateSegmentation(std::string prototxt,std::string caffemodel) {
|
||||
net = cv::dnn::readNetFromCaffe(prototxt, caffemodel);
|
||||
}
|
||||
cv::Mat PlateSegmentation::classifyResponse(const cv::Mat &cropped){
|
||||
cv::Mat inputBlob = cv::dnn::blobFromImage(cropped, 1/255.0, cv::Size(22,22), cv::Scalar(0,0,0),false);
|
||||
net.setInput(inputBlob,"data");
|
||||
return net.forward();
|
||||
}
|
||||
|
||||
void drawHist(float* seq,int size,const char* name){
|
||||
cv::Mat image(300,size,CV_8U);
|
||||
image.setTo(0);
|
||||
float* start =seq;
|
||||
float* end = seq+size;
|
||||
float l = *std::max_element(start,end);
|
||||
for(int i = 0;i<size;i++)
|
||||
{
|
||||
int p = int(float(seq[i])/l*300);
|
||||
cv::line(image,cv::Point(i,300),cv::Point(i,300-p),cv::Scalar(255,255,255));
|
||||
}
|
||||
cv::resize(image,image,cv::Size(600,100));
|
||||
cv::imshow(name,image);
|
||||
}
|
||||
|
||||
inline void computeSafeMargin(int &val,const int &rows){
|
||||
val = std::min(val,rows);
|
||||
val = std::max(val,0);
|
||||
}
|
||||
|
||||
cv::Rect boxFromCenter(const cv::Point center,int left,int right,int top,int bottom,cv::Size bdSize)
|
||||
{
|
||||
cv::Point p1(center.x - left ,center.y - top);
|
||||
cv::Point p2( center.x + right, center.y + bottom);
|
||||
p1.x = std::max(0,p1.x);
|
||||
p1.y = std::max(0,p1.y);
|
||||
p2.x = std::min(p2.x,bdSize.width-1);
|
||||
p2.y = std::min(p2.y,bdSize.height-1);
|
||||
cv::Rect rect(p1,p2);
|
||||
return rect;
|
||||
}
|
||||
|
||||
cv::Rect boxPadding(cv::Rect rect,int left,int right,int top,int bottom,cv::Size bdSize)
|
||||
{
|
||||
|
||||
cv::Point center(rect.x+(rect.width>>1),rect.y + (rect.height>>1));
|
||||
int rebuildLeft = (rect.width>>1 )+ left;
|
||||
int rebuildRight = (rect.width>>1 )+ right;
|
||||
int rebuildTop = (rect.height>>1 )+ top;
|
||||
int rebuildBottom = (rect.height>>1 )+ bottom;
|
||||
return boxFromCenter(center,rebuildLeft,rebuildRight,rebuildTop,rebuildBottom,bdSize);
|
||||
|
||||
}
|
||||
|
||||
|
||||
|
||||
void PlateSegmentation:: refineRegion(cv::Mat &plateImage,const std::vector<int> &candidatePts,const int padding,std::vector<cv::Rect> &rects){
|
||||
int w = candidatePts[5] - candidatePts[4];
|
||||
int cols = plateImage.cols;
|
||||
int rows = plateImage.rows;
|
||||
for(int i = 0 ; i < candidatePts.size() ; i++)
|
||||
{
|
||||
int left = 0;
|
||||
int right = 0 ;
|
||||
|
||||
if(i == 0 ){
|
||||
left= candidatePts[i];
|
||||
right = left+w+padding;
|
||||
}
|
||||
else {
|
||||
left = candidatePts[i] - padding;
|
||||
right = left + w + padding * 2;
|
||||
}
|
||||
|
||||
computeSafeMargin(right,cols);
|
||||
computeSafeMargin(left,cols);
|
||||
cv::Rect roi(left,0,right - left,rows-1);
|
||||
cv::Mat roiImage;
|
||||
plateImage(roi).copyTo(roiImage);
|
||||
|
||||
if (i>=1)
|
||||
{
|
||||
|
||||
cv::Mat roi_thres;
|
||||
// cv::threshold(roiImage,roi_thres,0,255,cv::THRESH_OTSU|cv::THRESH_BINARY);
|
||||
|
||||
niBlackThreshold(roiImage,roi_thres,255,cv::THRESH_BINARY,15,0.3,BINARIZATION_NIBLACK);
|
||||
|
||||
std::vector<std::vector<cv::Point>> contours;
|
||||
cv::findContours(roi_thres,contours,cv::RETR_LIST,cv::CHAIN_APPROX_SIMPLE);
|
||||
cv::Point boxCenter(roiImage.cols>>1,roiImage.rows>>1);
|
||||
|
||||
cv::Rect final_bdbox;
|
||||
cv::Point final_center;
|
||||
int final_dist = INT_MAX;
|
||||
|
||||
|
||||
for(auto contour:contours)
|
||||
{
|
||||
cv::Rect bdbox = cv::boundingRect(contour);
|
||||
cv::Point center(bdbox.x+(bdbox.width>>1),bdbox.y + (bdbox.height>>1));
|
||||
int dist = (center.x - boxCenter.x)*(center.x - boxCenter.x);
|
||||
if(dist<final_dist && bdbox.height > rows>>1)
|
||||
{ final_dist =dist;
|
||||
final_center = center;
|
||||
final_bdbox = bdbox;
|
||||
}
|
||||
}
|
||||
|
||||
//rebuild box
|
||||
if(final_bdbox.height/ static_cast<float>(final_bdbox.width) > 3.5 && final_bdbox.width*final_bdbox.height<10)
|
||||
final_bdbox = boxFromCenter(final_center,8,8,(rows>>1)-3 , (rows>>1) - 2,roiImage.size());
|
||||
else {
|
||||
if(i == candidatePts.size()-1)
|
||||
final_bdbox = boxPadding(final_bdbox, padding/2, padding, padding/2, padding/2, roiImage.size());
|
||||
else
|
||||
final_bdbox = boxPadding(final_bdbox, padding, padding, padding, padding, roiImage.size());
|
||||
|
||||
|
||||
// std::cout<<final_bdbox<<std::endl;
|
||||
// std::cout<<roiImage.size()<<std::endl;
|
||||
#ifdef DEBUG
|
||||
cv::imshow("char_thres",roi_thres);
|
||||
|
||||
cv::imshow("char",roiImage(final_bdbox));
|
||||
cv::waitKey(0);
|
||||
#endif
|
||||
|
||||
|
||||
}
|
||||
|
||||
|
||||
final_bdbox.x += left;
|
||||
|
||||
rects.push_back(final_bdbox);
|
||||
//
|
||||
|
||||
}
|
||||
else
|
||||
{
|
||||
rects.push_back(roi);
|
||||
}
|
||||
|
||||
// else
|
||||
// {
|
||||
//
|
||||
// }
|
||||
|
||||
// cv::GaussianBlur(roiImage,roiImage,cv::Size(7,7),3);
|
||||
//
|
||||
// cv::imshow("image",roiImage);
|
||||
// cv::waitKey(0);
|
||||
|
||||
|
||||
}
|
||||
|
||||
|
||||
|
||||
}
|
||||
void avgfilter(float *angle_list,int size,int windowsSize) {
|
||||
float *filterd = new float[size];
|
||||
for(int i = 0 ; i < size ; i++) filterd [i] = angle_list[i];
|
||||
// memcpy(filterd,angle_list,size);
|
||||
|
||||
cv::Mat kernal_gaussian = cv::getGaussianKernel(windowsSize,3,CV_32F);
|
||||
float *kernal = (float*)kernal_gaussian.data;
|
||||
// kernal+=windowsSize;
|
||||
int r = windowsSize/2;
|
||||
|
||||
|
||||
|
||||
|
||||
for (int i = 0; i < size; i++) {
|
||||
float avg = 0.00f;
|
||||
for (int j = 0; j < windowsSize; j++) {
|
||||
if(i+j-r>0&&i+j+r<size-1)
|
||||
avg += filterd[i + j-r]*kernal[j];
|
||||
}
|
||||
// avg = avg / windowsSize;
|
||||
angle_list[i] = avg;
|
||||
|
||||
}
|
||||
|
||||
delete filterd;
|
||||
}
|
||||
|
||||
void PlateSegmentation::templateMatchFinding(const cv::Mat &respones,int windowsWidth,std::pair<float,std::vector<int>> &candidatePts){
|
||||
int rows = respones.rows;
|
||||
int cols = respones.cols;
|
||||
|
||||
|
||||
|
||||
float *data = (float*)respones.data;
|
||||
float *engNum_prob = data;
|
||||
float *false_prob = data+cols;
|
||||
float *ch_prob = data+cols*2;
|
||||
|
||||
avgfilter(engNum_prob,cols,5);
|
||||
avgfilter(false_prob,cols,5);
|
||||
// avgfilter(ch_prob,cols,5);
|
||||
std::vector<int> candidate_pts(7);
|
||||
#ifdef DEBUG
|
||||
drawHist(engNum_prob,cols,"engNum_prob");
|
||||
drawHist(false_prob,cols,"false_prob");
|
||||
drawHist(ch_prob,cols,"ch_prob");
|
||||
cv::waitKey(0);
|
||||
#endif
|
||||
|
||||
|
||||
|
||||
|
||||
int cp_list[7];
|
||||
float loss_selected = -1;
|
||||
|
||||
for(int start = 0 ; start < 20 ; start+=2)
|
||||
for(int width = windowsWidth-5; width < windowsWidth+5 ; width++ ){
|
||||
for(int interval = windowsWidth/2; interval < windowsWidth; interval++)
|
||||
{
|
||||
int cp1_ch = start;
|
||||
int cp2_p0 = cp1_ch+ width;
|
||||
int cp3_p1 = cp2_p0+ width + interval;
|
||||
int cp4_p2 = cp3_p1 + width;
|
||||
int cp5_p3 = cp4_p2 + width+1;
|
||||
int cp6_p4 = cp5_p3 + width+2;
|
||||
int cp7_p5= cp6_p4+ width+2;
|
||||
|
||||
int md1 = (cp1_ch+cp2_p0)>>1;
|
||||
int md2 = (cp2_p0+cp3_p1)>>1;
|
||||
int md3 = (cp3_p1+cp4_p2)>>1;
|
||||
int md4 = (cp4_p2+cp5_p3)>>1;
|
||||
int md5 = (cp5_p3+cp6_p4)>>1;
|
||||
int md6 = (cp6_p4+cp7_p5)>>1;
|
||||
|
||||
|
||||
|
||||
|
||||
if(cp7_p5>=cols)
|
||||
continue;
|
||||
float loss = ch_prob[cp1_ch]+
|
||||
engNum_prob[cp2_p0] +engNum_prob[cp3_p1]+engNum_prob[cp4_p2]+engNum_prob[cp5_p3]+engNum_prob[cp6_p4] +engNum_prob[cp7_p5]
|
||||
+ (false_prob[md2]+false_prob[md3]+false_prob[md4]+false_prob[md5]+false_prob[md5] + false_prob[md6]);
|
||||
// float loss = ch_prob[cp1_ch]*3 -(false_prob[cp3_p1]+false_prob[cp4_p2]+false_prob[cp5_p3]+false_prob[cp6_p4]+false_prob[cp7_p5]);
|
||||
|
||||
|
||||
|
||||
|
||||
if(loss>loss_selected)
|
||||
{
|
||||
loss_selected = loss;
|
||||
cp_list[0]= cp1_ch;
|
||||
cp_list[1]= cp2_p0;
|
||||
cp_list[2]= cp3_p1;
|
||||
cp_list[3]= cp4_p2;
|
||||
cp_list[4]= cp5_p3;
|
||||
cp_list[5]= cp6_p4;
|
||||
cp_list[6]= cp7_p5;
|
||||
}
|
||||
}
|
||||
}
|
||||
candidate_pts[0] = cp_list[0];
|
||||
candidate_pts[1] = cp_list[1];
|
||||
candidate_pts[2] = cp_list[2];
|
||||
candidate_pts[3] = cp_list[3];
|
||||
candidate_pts[4] = cp_list[4];
|
||||
candidate_pts[5] = cp_list[5];
|
||||
candidate_pts[6] = cp_list[6];
|
||||
|
||||
candidatePts.first = loss_selected;
|
||||
candidatePts.second = candidate_pts;
|
||||
|
||||
};
|
||||
|
||||
|
||||
void PlateSegmentation::segmentPlateBySlidingWindows(cv::Mat &plateImage,int windowsWidth,int stride,cv::Mat &respones){
|
||||
|
||||
|
||||
cv::resize(plateImage,plateImage,cv::Size(136,36));
|
||||
|
||||
cv::Mat plateImageGray;
|
||||
cv::cvtColor(plateImage,plateImageGray,cv::COLOR_BGR2GRAY);
|
||||
|
||||
int height = plateImage.rows - 1;
|
||||
int width = plateImage.cols - 1;
|
||||
|
||||
for(int i = 0 ; i < plateImage.cols - windowsWidth +1 ; i +=stride)
|
||||
{
|
||||
cv::Rect roi(i,0,windowsWidth,height);
|
||||
cv::Mat roiImage = plateImageGray(roi);
|
||||
cv::Mat response = classifyResponse(roiImage);
|
||||
respones.push_back(response);
|
||||
}
|
||||
|
||||
|
||||
|
||||
|
||||
respones = respones.t();
|
||||
// std::pair<float,std::vector<int>> images ;
|
||||
//
|
||||
//
|
||||
// std::cout<<images.first<<" ";
|
||||
// for(int i = 0 ; i < images.second.size() ; i++)
|
||||
// {
|
||||
// std::cout<<images.second[i]<<" ";
|
||||
//// cv::line(plateImageGray,cv::Point(images.second[i],0),cv::Point(images.second[i],36),cv::Scalar(255,255,255),1); //DEBUG
|
||||
// }
|
||||
|
||||
// int w = images.second[5] - images.second[4];
|
||||
|
||||
// cv::line(plateImageGray,cv::Point(images.second[5]+w,0),cv::Point(images.second[5]+w,36),cv::Scalar(255,255,255),1); //DEBUG
|
||||
// cv::line(plateImageGray,cv::Point(images.second[5]+2*w,0),cv::Point(images.second[5]+2*w,36),cv::Scalar(255,255,255),1); //DEBUG
|
||||
|
||||
|
||||
// RefineRegion(plateImageGray,images.second,5);
|
||||
|
||||
// std::cout<<w<<std::endl;
|
||||
|
||||
// std::cout<<<<std::endl;
|
||||
|
||||
// cv::resize(plateImageGray,plateImageGray,cv::Size(600,100));
|
||||
|
||||
|
||||
|
||||
}
|
||||
|
||||
// void filterGaussian(cv::Mat &respones,float sigma){
|
||||
//
|
||||
// }
|
||||
|
||||
|
||||
void PlateSegmentation::segmentPlatePipline(PlateInfo &plateInfo,int stride,std::vector<cv::Rect> &Char_rects){
|
||||
cv::Mat plateImage = plateInfo.getPlateImage(); // get src image .
|
||||
cv::Mat plateImageGray;
|
||||
cv::cvtColor(plateImage,plateImageGray,cv::COLOR_BGR2GRAY);
|
||||
//do binarzation
|
||||
//
|
||||
std::pair<float,std::vector<int>> sections ; // segment points variables .
|
||||
|
||||
cv::Mat respones; //three response of every sub region from origin image .
|
||||
segmentPlateBySlidingWindows(plateImage,DEFAULT_WIDTH,1,respones);
|
||||
templateMatchFinding(respones,DEFAULT_WIDTH/stride,sections);
|
||||
|
||||
// std::cout<<sections<<std::endl;
|
||||
|
||||
refineRegion(plateImageGray,sections.second,5,Char_rects);
|
||||
#ifdef DEBUG
|
||||
for(int i = 0 ; i < sections.second.size() ; i++)
|
||||
{
|
||||
std::cout<<sections.second[i]<<" ";
|
||||
cv::line(plateImageGray,cv::Point(sections.second[i],0),cv::Point(sections.second[i],36),cv::Scalar(255,255,255),1); //DEBUG
|
||||
}
|
||||
cv::imshow("plate",plateImageGray);
|
||||
cv::waitKey(0);
|
||||
#endif
|
||||
// cv::waitKey(0);
|
||||
|
||||
}
|
||||
|
||||
void PlateSegmentation::ExtractRegions(PlateInfo &plateInfo,std::vector<cv::Rect> &rects){
|
||||
cv::Mat plateImage = plateInfo.getPlateImage();
|
||||
for(int i = 0 ; i < rects.size() ; i++){
|
||||
cv::Mat charImage;
|
||||
plateImage(rects[i]).copyTo(charImage);
|
||||
if(charImage.channels())
|
||||
cv::cvtColor(charImage,charImage,cv::COLOR_BGR2GRAY);
|
||||
// cv::imshow("image",charImage);
|
||||
// cv::waitKey(0);
|
||||
cv::equalizeHist(charImage,charImage);
|
||||
//
|
||||
|
||||
//
|
||||
|
||||
|
||||
std::pair<CharType,cv::Mat> char_instance;
|
||||
if(i == 0 ){
|
||||
|
||||
char_instance.first = CHINESE;
|
||||
|
||||
|
||||
} else if(i == 1){
|
||||
char_instance.first = LETTER;
|
||||
}
|
||||
else{
|
||||
char_instance.first = LETTER_NUMS;
|
||||
}
|
||||
char_instance.second = charImage;
|
||||
plateInfo.appendPlateChar(char_instance);
|
||||
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
}//namespace pr
|
||||
@@ -0,0 +1,26 @@
|
||||
//
|
||||
// Created by 庾金科 on 22/10/2017.
|
||||
//
|
||||
|
||||
#include "../include/Recognizer.h"
|
||||
|
||||
namespace pr{
|
||||
void GeneralRecognizer::SegmentBasedSequenceRecognition(PlateInfo &plateinfo){
|
||||
|
||||
|
||||
for(auto char_instance:plateinfo.plateChars)
|
||||
{
|
||||
|
||||
|
||||
std::pair<CharType,cv::Mat> res;
|
||||
cv::Mat code_table= recognizeCharacter(char_instance.second);
|
||||
res.first = char_instance.first;
|
||||
code_table.copyTo(res.second);
|
||||
plateinfo.appendPlateCoding(res);
|
||||
|
||||
}
|
||||
|
||||
|
||||
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,79 @@
|
||||
//
|
||||
// Created by 庾金科 on 04/04/2017.
|
||||
//
|
||||
|
||||
#include <opencv2/opencv.hpp>
|
||||
|
||||
namespace util{
|
||||
|
||||
template <class T> void swap ( T& a, T& b )
|
||||
{
|
||||
T c(a); a=b; b=c;
|
||||
}
|
||||
|
||||
template <class T> T min(T& a,T& b )
|
||||
{
|
||||
return a>b?b:a;
|
||||
|
||||
}
|
||||
|
||||
cv::Mat cropFromImage(const cv::Mat &image,cv::Rect rect){
|
||||
int w = image.cols-1;
|
||||
int h = image.rows-1;
|
||||
rect.x = std::max(rect.x,0);
|
||||
rect.y = std::max(rect.y,0);
|
||||
rect.height = std::min(rect.height,h-rect.y);
|
||||
rect.width = std::min(rect.width,w-rect.x);
|
||||
cv::Mat temp(rect.size(), image.type());
|
||||
cv::Mat cropped;
|
||||
temp = image(rect);
|
||||
temp.copyTo(cropped);
|
||||
return cropped;
|
||||
|
||||
}
|
||||
|
||||
cv::Mat cropBox2dFromImage(const cv::Mat &image,cv::RotatedRect rect)
|
||||
{
|
||||
cv::Mat M, rotated, cropped;
|
||||
float angle = rect.angle;
|
||||
cv::Size rect_size(rect.size.width,rect.size.height);
|
||||
if (rect.angle < -45.) {
|
||||
angle += 90.0;
|
||||
swap(rect_size.width, rect_size.height);
|
||||
}
|
||||
M = cv::getRotationMatrix2D(rect.center, angle, 1.0);
|
||||
cv::warpAffine(image, rotated, M, image.size(), cv::INTER_CUBIC);
|
||||
cv::getRectSubPix(rotated, rect_size, rect.center, cropped);
|
||||
return cropped;
|
||||
}
|
||||
|
||||
cv::Mat calcHist(const cv::Mat &image)
|
||||
{
|
||||
cv::Mat hsv;
|
||||
std::vector<cv::Mat> hsv_planes;
|
||||
cv::cvtColor(image,hsv,cv::COLOR_BGR2HSV);
|
||||
cv::split(hsv,hsv_planes);
|
||||
cv::Mat hist;
|
||||
int histSize = 256;
|
||||
float range[] = {0,255};
|
||||
const float* histRange = {range};
|
||||
|
||||
cv::calcHist( &hsv_planes[0], 1, 0, cv::Mat(), hist, 1, &histSize, &histRange,true, true);
|
||||
return hist;
|
||||
|
||||
}
|
||||
float computeSimilir(const cv::Mat &A,const cv::Mat &B)
|
||||
{
|
||||
|
||||
cv::Mat histA,histB;
|
||||
histA = calcHist(A);
|
||||
histB = calcHist(B);
|
||||
return cv::compareHist(histA,histB,CV_COMP_CORREL);
|
||||
|
||||
}
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
}//namespace util
|
||||
Reference in New Issue
Block a user