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ImageProcessing.mm
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//
// ImageProcessing.m
// ObjectDetectionSwift
//
// Created by Maitham Dib on 22/02/2016.
// Copyright © 2016 HelloOpenCV. All rights reserved.
//
#import "ObjectDetectionSwift-Bridging-Header.h"
#import <Foundation/Foundation.h>
#include <opencv2/core/core.hpp>
#include <opencv2/imgcodecs.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc_c.h>
#import "opencv2/highgui.hpp"
#import <opencv2/videoio/cap_ios.h>
#import <opencv2/objdetect.hpp>
#import <opencv2/imgcodecs/ios.h>
#import "DetectLanes.hpp"
#import "IPM.hpp"
#import "KalmanFilterOneLane.hpp"
#import "OpticFlow.hpp"
#define MAX_NUM_LINES 200
#define USE_PPHT
using namespace std;
using namespace cv;
@interface ImageProcessing(){
cv::CascadeClassifier cascade;
}
@end
@implementation ImageProcessing : NSObject
- (id)init {
self = [super init];
// Get xml files
NSBundle *bundle = [NSBundle mainBundle];
NSString *path = [bundle pathForResource:@"cars3" ofType:@"xml"];
std::string cascadeName = (char *)[path UTF8String];
if(!cascade.load(cascadeName)) {
return nil;
}
return self;
}
- (UIImage *)recognizeCars:(UIImage *)image {
// Store Image into Mat
CGColorSpaceRef colorSpace = CGImageGetColorSpace(image.CGImage);
CGFloat width = image.size.width;
CGFloat height = image.size.height;
cv::Mat mat(height, width, CV_8UC4);
CGContextRef contextRef = CGBitmapContextCreate(mat.data,
width,
height,
8,
mat.step[0],
colorSpace,
kCGImageAlphaNoneSkipLast |
kCGBitmapByteOrderDefault);
CGContextDrawImage(contextRef, CGRectMake(0, 0, width, height), image.CGImage);
CGContextRelease(contextRef);
// Use Haar Cascades to draw out cars
std::vector<cv::Rect> cars;
cascade.detectMultiScale(mat, cars,
1.05, 1,
CV_HAAR_SCALE_IMAGE,
cv::Size(70, 70));
// Draw Rectangles
for (int i = 0; i < cars.size(); i++)
{
cv::Point pt1(cars[i].x + cars[i].width, cars[i].y+ cars[i].height);
cv::Point pt2(cars[i].x, cars[i].y);
cv:: rectangle(mat, pt1, pt2, CvScalar(0,255,0,0),1,8,0);
}
// cv::Mat -> UIImage
UIImage *resultImage = MatToUIImage(mat);
return resultImage;
}
string type2str(int type) {
string r;
uchar depth = type & CV_MAT_DEPTH_MASK;
uchar chans = 1 + (type >> CV_CN_SHIFT);
switch ( depth ) {
case CV_8U: r = "8U"; break;
case CV_8S: r = "8S"; break;
case CV_16U: r = "16U"; break;
case CV_16S: r = "16S"; break;
case CV_32S: r = "32S"; break;
case CV_32F: r = "32F"; break;
case CV_64F: r = "64F"; break;
default: r = "User"; break;
}
r += "C";
r += (chans+'0');
return r;
}
cv::Mat cvMatFromUIImage(UIImage * image)
{
CGColorSpaceRef colorSpace = CGImageGetColorSpace(image.CGImage);
CGFloat cols = image.size.width;
CGFloat rows = image.size.height;
cv::Mat cvMat(rows, cols, CV_8UC4); // 8 bits per component, 4 channels (color channels + alpha)
CGContextRef contextRef = CGBitmapContextCreate(cvMat.data, // Pointer to data
cols, // Width of bitmap
rows, // Height of bitmap
8, // Bits per component
cvMat.step[0], // Bytes per row
colorSpace, // Colorspace
kCGImageAlphaNoneSkipLast |
kCGBitmapByteOrderDefault); // Bitmap info flags
CGContextDrawImage(contextRef, CGRectMake(0, 0, cols, rows), image.CGImage);
CGContextRelease(contextRef);
return cvMat;
}
//cv::Mat getSideLaneForOpticFlow1(cv::Mat originalMat)
//{
// detectLanes laneDetection;
// // get instance of detect lanes
// cv::Mat imgCanny = laneDetection.processImgGetCanny(originalMat);
//
// cv::Mat inversePerspective;
//
// static vector<Vec2f> ppLeft;
// static vector<Vec2f> updatedLineFrameLeft;
// static vector<Vec2f> ppRight;
// vector<Vec2f> updatedLineFrameRight;
//
//
// //INITIALISE KALMAN
// static bool isKalmanLeftInitialised;
// if (!isKalmanLeftInitialised) {
// vector<Vec2f> leftLane = laneDetection.findLeftLane(imgCanny);
// if (leftLane.size()>0) {
// KalmanFilterOneLane leftKF(leftLane);
// ppLeft = leftKF.predictOneLane();
// updatedLineFrameLeft = leftKF.updateOneLane(leftLane);
// isKalmanLeftInitialised=true;
// }
// }
// static bool isKalmanRightInitialised;
// if (!isKalmanRightInitialised) {
// vector<Vec2f> rightLane = laneDetection.findRightLane(imgCanny);
// if (rightLane.size()>0) {
// KalmanFilterOneLane rightKF(rightLane);
// ppRight = rightKF.predictOneLane();
// updatedLineFrameRight = rightKF.updateOneLane(rightLane);
// isKalmanRightInitialised=true;
// }
// }
//
// //Pull Left Lane parameters
// vector<Vec2f> leftLane = laneDetection.findLeftLane(imgCanny);
// if(leftLane.size()==0 && isKalmanLeftInitialised)
// leftLane = ppLeft;
//
// // Pull Right Lane Parameters
// vector<Vec2f> rightLane = laneDetection.findRightLane(imgCanny);
// if(rightLane.size()==0 && isKalmanRightInitialised)
// rightLane = ppRight;
//
//
// if(leftLane.size()>0 && rightLane.size()>0){
// vector<Vec2f> lanesSeperate;
// lanesSeperate.push_back(Vec2f(leftLane[0][0],leftLane[0][1]));
// lanesSeperate.push_back(Vec2f(rightLane[0][0],rightLane[0][1]));
//
// //detect vanishing points of input video
// cv::Point2f vanishingPoints(0,0);
// vanishingPoints = laneDetection.getVanishingPoint(lanesSeperate);
// cout<<"VP1: "<<vanishingPoints.x<<","<<vanishingPoints.y<<endl;
// if (vanishingPoints.x!=0 || vanishingPoints.y!=0 ||vanishingPoints.x<1000||vanishingPoints.y<680)
// {
// //Get end Points of line endPoints
// vector<Vec2f> endPoints = laneDetection.getLaneBeginningAndEndPoints(lanesSeperate);
// trapeziumCoordinates trap = createFourPointsForIPM(vanishingPoints.x, vanishingPoints.y);
// cvtColor(originalMat, originalMat,CV_BGR2GRAY);
// cv::Mat sideLanesimg= getLeftAndRightLaneImage(originalMat, endPoints,vanishingPoints);
//
// // Resize image to fit and also copy in colour image to get Only one Output
// inversePerspective = sideLanesimg.clone();
// }
// }
//
// return inversePerspective;
//}
//cv::Mat getSideLaneForOpticFlow2(cv::Mat originalMat)
//{
// detectLanes laneDetection;
// // get instance of detect lanes
// cv::Mat imgCanny = laneDetection.processImgGetCanny(originalMat);
//
// cv::Mat inversePerspective;
//
// static vector<Vec2f> ppLeft;
// static vector<Vec2f> updatedLineFrameLeft;
// static vector<Vec2f> ppRight;
// vector<Vec2f> updatedLineFrameRight;
//
//
// //INITIALISE KALMAN
// static bool isKalmanLeftInitialised;
// if (!isKalmanLeftInitialised) {
// vector<Vec2f> leftLane = laneDetection.findLeftLane(imgCanny);
// if (leftLane.size()>0) {
// KalmanFilterOneLane leftKF(leftLane);
// ppLeft = leftKF.predictOneLane();
// updatedLineFrameLeft = leftKF.updateOneLane(leftLane);
// isKalmanLeftInitialised=true;
// }
// }
// static bool isKalmanRightInitialised;
// if (!isKalmanRightInitialised) {
// vector<Vec2f> rightLane = laneDetection.findRightLane(imgCanny);
// if (rightLane.size()>0) {
// KalmanFilterOneLane rightKF(rightLane);
// ppRight = rightKF.predictOneLane();
// updatedLineFrameRight = rightKF.updateOneLane(rightLane);
// isKalmanRightInitialised=true;
// }
// }
//
// //Pull Left Lane parameters
// vector<Vec2f> leftLane = laneDetection.findLeftLane(imgCanny);
// if(leftLane.size()==0 && isKalmanLeftInitialised)
// leftLane = ppLeft;
//
// // Pull Right Lane Parameters
// vector<Vec2f> rightLane = laneDetection.findRightLane(imgCanny);
// if(rightLane.size()==0 && isKalmanRightInitialised)
// rightLane = ppRight;
//
//
// if(leftLane.size()>0 && rightLane.size()>0){
// vector<Vec2f> lanesSeperate;
// lanesSeperate.push_back(Vec2f(leftLane[0][0],leftLane[0][1]));
// lanesSeperate.push_back(Vec2f(rightLane[0][0],rightLane[0][1]));
//
// //detect vanishing points of input video
// cv::Point2f vanishingPoints(0,0);
// vanishingPoints = laneDetection.getVanishingPoint(lanesSeperate);
// cout<<"VP1: "<<vanishingPoints.x<<","<<vanishingPoints.y<<endl;
// if (vanishingPoints.x!=0 || vanishingPoints.y!=0 ||vanishingPoints.x<1000||vanishingPoints.y<680)
// {
// //Get end Points of line endPoints
// vector<Vec2f> endPoints = laneDetection.getLaneBeginningAndEndPoints(lanesSeperate);
// trapeziumCoordinates trap = createFourPointsForIPM(vanishingPoints.x, vanishingPoints.y);
// cvtColor(originalMat, originalMat,CV_BGR2GRAY);
// cv::Mat sideLanesimg= getLeftAndRightLaneImage(originalMat, endPoints,vanishingPoints);
//
// // Resize image to fit and also copy in colour image to get Only one Output
// inversePerspective = sideLanesimg.clone();
// }
// }
//
// return inversePerspective;
//}
//
cv::Mat getLeftLaneIpmRobust(cv::Mat &frame, vector<Vec2f> endPoints, cv::Point2f vanishingPoints2){
trapeziumCoordinates trapLeft = createFourPointsForIPMLeftLane(vanishingPoints2.x, vanishingPoints2.y);
int lowerLeftLanePointx = endPoints[0][0];
int lowerLeftLanePointy = endPoints[0][1];
int upperLeftLanePointx = endPoints[1][0];
int upperLeftLanePointy = endPoints[1][1];
int upperLeftIpmPointx = (trapLeft.point3[0][0]/trapLeft.point3[3][0]);
int upperLeftIpmPointy = (trapLeft.point3[1][0]/trapLeft.point3[3][0]);
int lowerLeftIpmPointx = (trapLeft.point4[0][0]/trapLeft.point4[3][0]);
int lowerLeftIpmPointy = (trapLeft.point4[1][0]/trapLeft.point4[3][0]);
int upperLeftDiffx = abs(upperLeftLanePointx - upperLeftIpmPointx);
int lowerLeftDiffx = abs(lowerLeftLanePointx - lowerLeftIpmPointx);
int upperLeftDiffxNonAbs = (upperLeftLanePointx - upperLeftIpmPointx);
int lowerLeftDiffxNonAbs = (lowerLeftLanePointx - lowerLeftIpmPointx);
int upperLeftDiffy = abs(upperLeftLanePointy - upperLeftIpmPointy);
int lowerLeftDiffy = abs(lowerLeftLanePointy - lowerLeftIpmPointy);
if(upperLeftDiffx >150|| lowerLeftDiffx>150 || lowerLeftDiffy> 150|| upperLeftDiffy>150){
cout<<"Reset framecount for Left lane recommended"<<endl;
// shouldvanishingPointBeReset= true;
}else{
// shouldvanishingPointBeReset = false;
}
cv::Mat inversePerspectiveViewOfLeftSide2 = createIPMOfLeftSideLaneRobust(frame, trapLeft);
return inversePerspectiveViewOfLeftSide2;
}
cv::Mat getRightLaneIpmRobust(cv::Mat &frame, vector<Vec2f> endPoints, cv::Point2f vanishingPoints2){
trapeziumCoordinates trapRight = createFourPointsForIPMRightLane(vanishingPoints2.x, vanishingPoints2.y);
int lowerRightLanePointx = endPoints[2][0];
int lowerRightLanePointy = endPoints[2][1];
int upperRightLanePointx = endPoints[3][0];
int upperRightLanePointy = endPoints[3][1];
int upperRightIpmPointx = (trapRight.point3[0][0]/trapRight.point3[3][0]);
int upperRightIpmPointy = (trapRight.point3[1][0]/trapRight.point3[3][0]);
int lowerRightIpmPointx = (trapRight.point4[0][0]/trapRight.point4[3][0]);
int lowerRightIpmPointy = (trapRight.point4[1][0]/trapRight.point4[3][0]);
int upperRightDiffx = abs(upperRightLanePointx - upperRightIpmPointx);
int lowerRightDiffx = abs(lowerRightLanePointx - lowerRightIpmPointx);
int upperRightDiffy = abs(upperRightLanePointy - upperRightIpmPointy);
int lowerRightDiffy = abs(lowerRightLanePointy - lowerRightIpmPointy);
if(upperRightDiffx>150|| lowerRightDiffx>150 || lowerRightDiffy>150 || upperRightDiffy>150 ){
cout<<"Reset framecount for right lane recommended"<<endl;
// shouldvanishingPointBeReset = true;
}else{
// shouldvanishingPointBeReset = false;
}
cv::Mat inversePerspectiveViewOfRightSide2 = createIPMOfRightSideLaneRobust(frame, trapRight);
return inversePerspectiveViewOfRightSide2;
}
cv::Mat mergeLeftAndRightImage(cv::Mat inversePerspectiveViewOfLeftSide2, cv::Mat inversePerspectiveViewOfRightSide2 ){
// string ty = type2str( inversePerspectiveViewOfLeftSide2.type() );
// printf("MatrixLeft: %s %dx%d \n", ty.c_str(), inversePerspectiveViewOfLeftSide2.cols, inversePerspectiveViewOfLeftSide2.rows );
// string ty2 = type2str( inversePerspectiveViewOfRightSide2.type() );
// printf("MatrixRight: %s %dx%d \n", ty2.c_str(), inversePerspectiveViewOfRightSide2.cols, inversePerspectiveViewOfRightSide2.rows );
//
Mat sideLanesimg2(Mat(720, 1280, CV_8UC3));
sideLanesimg2 = cv::Scalar(255); // or the desired uint8_t value from 0-255
// string ty3 = type2str( sideLanesimg2.type() );
// printf("MatrixMerge: %s %dx%d \n", ty3.c_str(), sideLanesimg2.cols, sideLanesimg2.rows );
inversePerspectiveViewOfLeftSide2.copyTo(sideLanesimg2(cv::Rect(0,0, 240,720)));
inversePerspectiveViewOfRightSide2.copyTo(sideLanesimg2(cv::Rect(1040,0, 240,720)));
return sideLanesimg2;
}
- (UIImage *)detectLanesEffeciently:(UIImage *)image :(UIImage *)image2{
//Initialise LaneDetection
detectLanes laneDetection;
// Store Image into Mat1
cv::Mat frame1= cvMatFromUIImage(image);
cv::Mat frame2 = cvMatFromUIImage(image2);
//Get sobel image
cv::Mat imgSobelFrame1 = laneDetection.processSobel(frame1);
cv::Mat imgSobelFrame2 = laneDetection.processSobel(frame2);
// Find both Left and Right Frames Frame 1 & 2
vector<Vec2f> leftLaneFrame1 = laneDetection.findLeftLaneUsingSobel(imgSobelFrame1);
vector<Vec2f> rightLaneFrame1 = laneDetection.findRightLaneUsingSobel(imgSobelFrame1);
vector<Vec2f> leftLaneFrame2 = laneDetection.findLeftLaneUsingSobel(imgSobelFrame2);
vector<Vec2f> rightLaneFrame2 = laneDetection.findRightLaneUsingSobel(imgSobelFrame2);
//Initialise UImage
UIImage *resultImage;
if(leftLaneFrame1.size()>0 && rightLaneFrame1.size()>0 && leftLaneFrame2.size()>0 && rightLaneFrame2.size()>0 ){
// Put lanes in one vector frame 1 & 2
vector<Vec2f> detectedLanes1;
detectedLanes1.push_back(Vec2f(leftLaneFrame1[0][0],leftLaneFrame1[0][1]));
detectedLanes1.push_back(Vec2f(rightLaneFrame1[0][0],rightLaneFrame1[0][1]));
vector<Vec2f> detectedLanes2;
detectedLanes2.push_back(Vec2f(leftLaneFrame2[0][0],leftLaneFrame2[0][1]));
detectedLanes2.push_back(Vec2f(rightLaneFrame2[0][0],rightLaneFrame2[0][1]));
// Get the endPoints for IPM estimation
vector<Vec2f> endPoints1 = laneDetection.getLaneBeginningAndEndPoints(detectedLanes1);
vector<Vec2f> endPoints2 = laneDetection.getLaneBeginningAndEndPoints(detectedLanes2);
// Detect vanishing points of input video
cv::Point2f initialVanishingPoint1(0,0);
initialVanishingPoint1 = laneDetection.getVanishingPoint(detectedLanes1);
cv::Point2f initialVanishingPoint2(0,0);
initialVanishingPoint2 = laneDetection.getVanishingPoint(detectedLanes2);
// get the INVERSE PERSPECTIVE OF SIDE
cv::Mat ipmOfLeftLaneRobust1 = getLeftLaneIpmRobust(frame1, endPoints1, initialVanishingPoint1);
cv::Mat ipmOfRightLaneRobust1 = getRightLaneIpmRobust(frame1, endPoints1, initialVanishingPoint1);
cv::Mat ipmOfLeftLaneRobust2 = getLeftLaneIpmRobust(frame2, endPoints2, initialVanishingPoint2);
cv::Mat ipmOfRightLaneRobust2 = getRightLaneIpmRobust(frame2, endPoints2, initialVanishingPoint2);
cv::Mat leftLaneToDrawOnRobust =ipmOfLeftLaneRobust2.clone();
cv::Mat rightLaneToDrawOnRobust= ipmOfRightLaneRobust2.clone();
// Convert Left to gray for optic Flow and Denoise LEFT LANE
cvtColor(ipmOfLeftLaneRobust1, ipmOfLeftLaneRobust1, CV_BGR2GRAY);
cvtColor(ipmOfLeftLaneRobust2, ipmOfLeftLaneRobust2, CV_BGR2GRAY);
cvtColor(ipmOfRightLaneRobust1, ipmOfRightLaneRobust1, CV_BGR2GRAY);
cvtColor(ipmOfRightLaneRobust2, ipmOfRightLaneRobust2, CV_BGR2GRAY);
cv::Mat ipmOfLeftLaneRobustDest1,ipmOfLeftLaneRobustDest2,ipmOfRightLaneRobustDest1,ipmOfRightLaneRobustDest2;
bilateralFilter(ipmOfLeftLaneRobust1, ipmOfLeftLaneRobustDest1, 8, 16, 4);
bilateralFilter(ipmOfLeftLaneRobust2, ipmOfLeftLaneRobustDest2, 8, 16, 4);
bilateralFilter(ipmOfRightLaneRobust1, ipmOfRightLaneRobustDest1, 8, 16, 4);
bilateralFilter(ipmOfRightLaneRobust2, ipmOfRightLaneRobustDest2, 8, 16, 4);
cv::Mat opticFlowImageLeft = getDenseOpticFlowRobustLeft(ipmOfLeftLaneRobustDest1, ipmOfLeftLaneRobustDest2, leftLaneToDrawOnRobust);
cv::Mat opticFlowImageRight = getDenseOpticFlowRobustRight(ipmOfRightLaneRobustDest1, ipmOfRightLaneRobustDest2, rightLaneToDrawOnRobust);
// string ty = type2str( opticFlowImageLeft.type() );
// printf("MatrixLeft: %s %dx%d \n", ty.c_str(), opticFlowImageLeft.cols, opticFlowImageLeft.rows );
// string ty2 = type2str( opticFlowImageRight.type() );
// printf("MatrixRight: %s %dx%d \n", ty2.c_str(), opticFlowImageRight.cols, opticFlowImageRight.rows );
cvtColor(opticFlowImageLeft, opticFlowImageLeft, CV_BGR2RGB);
cvtColor(opticFlowImageRight, opticFlowImageRight, CV_BGR2RGB);
// string ty3 = type2str( opticFlowImageLeft.type() );
// printf("MatrixLeft: %s %dx%d \n", ty3.c_str(), opticFlowImageLeft.cols, opticFlowImageLeft.rows );
// string ty4 = type2str( opticFlowImageRight.type() );
// printf("MatrixRight: %s %dx%d \n", ty4.c_str(), opticFlowImageRight.cols, opticFlowImageRight.rows );
//
cv::Mat mergedDenseOpticRobust= mergeLeftAndRightImage(opticFlowImageLeft, opticFlowImageRight);
// apply optic flow to both and
cv::resize(frame2, frame2, CvSize(800,720));
// string ty5 = type2str(frame2.type() );
// printf("MatrixRight: %s %dx%d \n", ty5.c_str(), frame2.cols, frame2.rows );
//
cvtColor(frame2, frame2, CV_BGR2RGB);
frame2.copyTo(mergedDenseOpticRobust(cv::Rect(240,0, 800,720)));
resultImage = MatToUIImage(mergedDenseOpticRobust);
}else{
cout<<"vanishingPointNotFound"<<endl;
laneDetection.drawBoundingBoxes(frame2);
resultImage = MatToUIImage(frame2);
}
return resultImage;
}
- (UIImage *)detectLanesGetOpticFlow:(UIImage *)image{
//Initialise LaneDetection
detectLanes laneDetection;
// Store Image into Mat1
cv::Mat currentFrame= cvMatFromUIImage(image);
cv::Mat previousFrame =currentFrame.clone() ;
//Initialise UImage
UIImage *resultImage;
//Get sobel image
cv::Mat imgSobelFrame1 = laneDetection.processSobel(currentFrame);
cv::Mat imgSobelFrame2 = laneDetection.processSobel(previousFrame);
// Find both Left and Right Frames Frame 1 & 2
vector<Vec2f> leftLaneFrame1 = laneDetection.findLeftLaneUsingSobel(imgSobelFrame1);
vector<Vec2f> rightLaneFrame1 = laneDetection.findRightLaneUsingSobel(imgSobelFrame1);
vector<Vec2f> leftLaneFrame2 = laneDetection.findLeftLaneUsingSobel(imgSobelFrame2);
vector<Vec2f> rightLaneFrame2 = laneDetection.findRightLaneUsingSobel(imgSobelFrame2);
if(leftLaneFrame1.size()>0 && rightLaneFrame1.size()>0 && leftLaneFrame2.size()>0 && rightLaneFrame2.size()>0 ){
// Put lanes in one vector frame 1 & 2
vector<Vec2f> detectedLanes1;
detectedLanes1.push_back(Vec2f(leftLaneFrame1[0][0],leftLaneFrame1[0][1]));
detectedLanes1.push_back(Vec2f(rightLaneFrame1[0][0],rightLaneFrame1[0][1]));
vector<Vec2f> detectedLanes2;
detectedLanes2.push_back(Vec2f(leftLaneFrame2[0][0],leftLaneFrame2[0][1]));
detectedLanes2.push_back(Vec2f(rightLaneFrame2[0][0],rightLaneFrame2[0][1]));
// Get the endPoints for IPM estimation
vector<Vec2f> endPoints1 = laneDetection.getLaneBeginningAndEndPoints(detectedLanes1);
vector<Vec2f> endPoints2 = laneDetection.getLaneBeginningAndEndPoints(detectedLanes2);
// Detect vanishing points of input video
cv::Point2f initialVanishingPoint1(0,0);
initialVanishingPoint1 = laneDetection.getVanishingPoint(detectedLanes1);
cv::Point2f initialVanishingPoint2(0,0);
initialVanishingPoint2 = laneDetection.getVanishingPoint(detectedLanes2);
// get the INVERSE PERSPECTIVE OF SIDE
cv::Mat ipmOfLeftLaneRobust1 = getLeftLaneIpmRobust(currentFrame, endPoints1, initialVanishingPoint1);
cv::Mat ipmOfRightLaneRobust1 = getRightLaneIpmRobust(currentFrame, endPoints1, initialVanishingPoint1);
cv::Mat ipmOfLeftLaneRobust2 = getLeftLaneIpmRobust(previousFrame, endPoints2, initialVanishingPoint2);
cv::Mat ipmOfRightLaneRobust2 = getRightLaneIpmRobust(previousFrame, endPoints2, initialVanishingPoint2);
cv::Mat leftLaneToDrawOnRobust =ipmOfLeftLaneRobust2.clone();
cv::Mat rightLaneToDrawOnRobust= ipmOfRightLaneRobust2.clone();
// Convert Left to gray for optic Flow and Denoise LEFT LANE
cvtColor(ipmOfLeftLaneRobust1, ipmOfLeftLaneRobust1, CV_BGR2GRAY);
cvtColor(ipmOfLeftLaneRobust2, ipmOfLeftLaneRobust2, CV_BGR2GRAY);
cvtColor(ipmOfRightLaneRobust1, ipmOfRightLaneRobust1, CV_BGR2GRAY);
cvtColor(ipmOfRightLaneRobust2, ipmOfRightLaneRobust2, CV_BGR2GRAY);
cv::Mat ipmOfLeftLaneRobustDest1,ipmOfLeftLaneRobustDest2,ipmOfRightLaneRobustDest1,ipmOfRightLaneRobustDest2;
bilateralFilter(ipmOfLeftLaneRobust1, ipmOfLeftLaneRobustDest1, 8, 16, 4);
bilateralFilter(ipmOfLeftLaneRobust2, ipmOfLeftLaneRobustDest2, 8, 16, 4);
bilateralFilter(ipmOfRightLaneRobust1, ipmOfRightLaneRobustDest1, 8, 16, 4);
bilateralFilter(ipmOfRightLaneRobust2, ipmOfRightLaneRobustDest2, 8, 16, 4);
cv::Mat opticFlowImageLeft= getDenseOpticFlowRobustLeft(ipmOfLeftLaneRobustDest1, ipmOfLeftLaneRobustDest2, leftLaneToDrawOnRobust);
cv::Mat opticFlowImageRight= getDenseOpticFlowRobustRight(ipmOfRightLaneRobustDest1, ipmOfRightLaneRobustDest2, rightLaneToDrawOnRobust);
cvtColor(opticFlowImageLeft, opticFlowImageLeft, CV_BGR2RGB);
cvtColor(opticFlowImageRight, opticFlowImageRight, CV_BGR2RGB);
cv::Mat mergedDenseOpticRobust= mergeLeftAndRightImage(opticFlowImageLeft, opticFlowImageRight);
cv::Mat matBuffer = currentFrame.clone();
laneDetection.drawLeftLinesOnImage(matBuffer, leftLaneFrame1);
laneDetection.drawRightLinesOnImage(matBuffer, rightLaneFrame1);
// apply optic flow to both and
cv::resize(matBuffer, matBuffer, CvSize(800,720));
// string ty5 = type2str(frame2.type() );
// printf("MatrixRight: %s %dx%d \n", ty5.c_str(), frame2.cols, frame2.rows );
cvtColor(matBuffer, matBuffer, CV_BGR2RGB);
matBuffer.copyTo(mergedDenseOpticRobust(cv::Rect(240,0, 800,720)));
resultImage = MatToUIImage(mergedDenseOpticRobust);
previousFrame=currentFrame;
}else{
cout<<"vanishingPointNotFound"<<endl;
laneDetection.drawBoundingBoxes(currentFrame);
resultImage = MatToUIImage(currentFrame);
}
return resultImage;
}
- (UIImage *)carryOutInversePerspectiveMap:(UIImage *)image {
// Store Image into Mat
CGColorSpaceRef colorSpace = CGImageGetColorSpace(image.CGImage);
CGFloat width = image.size.width;
CGFloat height = image.size.height;
cv::Mat mat(height, width, CV_8UC4);
CGContextRef contextRef = CGBitmapContextCreate(mat.data,
width,
height,
8,
mat.step[0],
colorSpace,
kCGImageAlphaNoneSkipLast |
kCGBitmapByteOrderDefault);
CGContextDrawImage(contextRef, CGRectMake(0, 0, width, height), image.CGImage);
CGContextRelease(contextRef);
//*****************************************************************************Express region of Interest
cv::Rect roi(0,0.4*height,width,0.6*height);
mat = mat(roi);
// The 4-points at the input image
vector<Point2f> origPoints;
origPoints.push_back( Point2f(0, height) );
origPoints.push_back( Point2f(width, height) );
origPoints.push_back( Point2f(width/2+30, 140) );
origPoints.push_back( Point2f(width/2-50, 140) );
// The 4-points correspondences in the destination image
vector<Point2f> dstPoints;
dstPoints.push_back( Point2f(0, height) );
dstPoints.push_back( Point2f(width, height) );
dstPoints.push_back( Point2f(width, 0) );
dstPoints.push_back( Point2f(0, 0) );
IPM ipm( cv::Size(width, height), cv::Size(width, height), origPoints, dstPoints);
Mat inputImgGray;
Mat outputImg;
// Color Conversion
if(mat.channels() == 3)
cvtColor(mat, inputImgGray, CV_BGR2GRAY);
else
cvtColor(mat, inputImgGray, CV_BGR2GRAY);
// mat.copyTo(inputImgGray);
// Process
clock_t begin = clock();
ipm.applyHomography( mat, outputImg );
clock_t end = clock();
double elapsed_secs = double(end - begin) / CLOCKS_PER_SEC;
printf("%.2f (ms)\r", 1000*elapsed_secs);
ipm.drawPoints(origPoints, mat );
UIImage *resultImage = MatToUIImage(outputImg);
return resultImage;
}
@end