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tlds_kr.cpp
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tlds_kr.cpp
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#include <opencv2/opencv.hpp>
#include <opencv2/plot.hpp>
#include "opencv2/highgui.hpp"
#include <iostream>
#include <vector>
#include <string>
#include "opencv2/videoio.hpp"
#include <opencv2/imgcodecs.hpp>
#include <opencv2/videoio/videoio.hpp>
#include <opencv2/highgui/highgui.hpp>
#include "opencv2/core/core.hpp"
#include "opencv2/features2d/features2d.hpp"
#include "opencv2/opencv_modules.hpp"
#include "opencv2/calib3d/calib3d.hpp"
#include <opencv2/opencv.hpp>
#include <opencv2/tracking.hpp>
#include <opencv2/core/ocl.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/calib3d/calib3d.hpp>
#include <opencv2/videoio.hpp>
#include "opencv2/objdetect/objdetect.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include <ctime>
#include <time.h>
#include <cstdio>
#include <sys/stat.h>
#include <sys/time.h>
#include <math.h>
#include <iomanip>
#include <chrono>
#include <fstream>
#include <cmath>
#include <cstdlib>
extern "C" {
#include <stdio.h>
}
using namespace std;
using namespace cv;
#define VIDEO_FILE_NAME "/home/rohit/adas/car_lane_sign_detection/New_sensor_data/new16.mp4"
#define TEMPLATE_PATH "/home/rohit/adas/TrafficLight-Detection/arrow/arrow_2.jpeg"
#define CASCADE_FILE_NAME "/home/rohit/adas/TrafficLight-Detection/cascade_87_24*12.xml"
#define SKIPFRAME 5
#define AUTOSTART 1
#define DISPLAYALL 1
#define HSV_VALUE 1
#define MEAN_VALUE 0
#define RED_COLOR 1
#define GREEN_COLOR 1
#define SHOWIMAGE 0
#define NUMOFTHRESHOLDFRAMES 1
#define DEBUG_FLAG 1
#define SAVE_DEBUG_TO_TEXT 0
#define DEBUG_GLOBAL_VAR 0
string template_path = "/home/rohit/adas/TrafficLight-Detection/arrow/arrow_2.jpeg";
int numberofthresholdframes;
int leftthresholdframes;
static unsigned long long get_localTimeStamp()
{
time_t timestamp;
unsigned long long secTime, msecTime;
struct timeval tv;
struct tm lt;
gettimeofday(&tv, NULL);
localtime_r(&tv.tv_sec, <);
secTime = mktime(<) + lt.tm_gmtoff;
msecTime = ((unsigned long long)secTime * (unsigned long long)1000) + ((unsigned long long)tv.tv_usec / (unsigned long long)1000);
return msecTime;
}
//=======================================================variable declaration=================================================
//++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
CascadeClassifier traffic_light;
Mat leftSideROI, rightSideROI, middleROI, middleROI1, middleROI2 ;
vector<Vec3f> red_circles, green_circles;
Rect detected_light;
Mat templ;
int framecount = 0;
int beepcount = 0;
int thresholdcount = 0;
int left_thresholdcount = 0;
bool isUsingDetector = true;
bool isStart = false;
bool isRedColor = false;
bool isGreenColor = false;
bool ifRedHappened = false;
bool isFourPattern = false;
bool isThreePattern = false;
bool isLightDetected = false;
bool arrowPresent = false;
int segmentationType = -1;
int red_fail_count = 0;
int detected_frames_count = 0;
int color_seg_fail_count = 0;
const int frame_width = 704;
const int frame_height = 480;
char* pYuvBuf;
char* pMemContent;
char timebuf[512];
char genbuf[512];
struct timeval tv;
struct tm stTime;
time_t lastTime = 0;
int len;
int value1 = 0, value2 = 0, value3 = 0, value4 = 0;
int load_dataset=0;
VideoCapture cap;
void getVideoData()
{
cap.open(VIDEO_FILE_NAME);
}
void loadDetector()
{
traffic_light.load(CASCADE_FILE_NAME);
}
Mat framePreprocessing(Mat& frame)
{
Mat imageROI;
//printf("7. frame preprocessing function ++++#########\n");
//printf(" frame size before framePreprocessing: %dX%d\n",frame.size().width, frame.size().height);
resize(frame, frame, Size(704, 480));
imageROI = frame(Rect(frame.cols*0.25, frame.rows*0.10, frame.cols*0.50, frame.rows*0.38));
//printf("7. Size of the Image ROI: %dX%d\n\n",imageROI.size().width, imageROI.size().height);
//imageROI.copyTo(frame);
Mat frameGray;
//namedWindow("image_ROI", WINDOW_NORMAL);
//resizeWindow("image_ROI", Size(300, 200));
//imshow("image_ROI", imageROI);
printf("isStart______: %d\n", isStart);
#if DEBUG_FLAG
printf("1. Size of the Image ROI: %dX%d\n\n",imageROI.size().width, imageROI.size().height);
// ostringstream name5;
// name5 << "/app/sd/BlackVue/Record/imageROI_"<<framecount<<".jpg";
// imwrite(name5.str(), imageROI);
#endif
#if HSV_VALUE
GaussianBlur( imageROI, imageROI, Size(3, 3), 0 );
return imageROI;
#endif
#if DEBUG_GLOBAL_VAR
return NULL;
#endif
}
int getAverage(Mat& frame)
{
double res;
for (int i = 0; i < frame.rows; i++){
for (int j = 0; j < frame.cols; j++){
res += frame.at<uchar>(i, j);
// printf("color, x, y: %d, %d, %d\n", color, j, i);
}
}
int corow = frame.cols * frame.rows; // image's full pixel number
res /= corow;
return res;
}
/*int segmentColor_night(Mat& imageROI)
{
printf("GOT HERE======================!!!!!!!!!!!!!!!!=========================\n");
Mat red_image, green_image, lower_red_hue_range, upper_red_hue_range, saturated_red_image, saturated_green_image;
Mat red_hue_image, green_hue_image, lower_green_hue_range, upper_green_hue_range;
// For Red light: Threshold the HSV image, keep only the red pixels
red_image = imageROI.clone();
green_image = imageROI.clone();
#if DEBUG_FLAG
printf("6. Finding out which color is present.\n");
#endif
// Red color segmentation --------------------------------------------------------------------
Mat leftRed, rightRed;
cvtColor(red_image, red_image, CV_BGR2HSV);
inRange(red_image, Scalar(0, 100, 100), Scalar(5, 255, 255), lower_red_hue_range);
inRange(red_image, Scalar(160, 100, 100), Scalar(180, 255, 255), upper_red_hue_range);
addWeighted(lower_red_hue_range, 1.0, upper_red_hue_range, 1.0, 0.0, red_hue_image);
GaussianBlur(red_hue_image, red_hue_image, Size(5, 5), 0, 0);
value1 = getAverage(red_hue_image);
namedWindow("Red Combined threshold images", WINDOW_NORMAL);
resizeWindow("Red Combined threshold images", 120, 90);
imshow("Red Combined threshold images", red_hue_image);
#if DEBUG_FLAG
printf("Red image value: %d\n", value1);
#endif
// Red color segmentation --------------------------------------------------------------------
// xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
// Green color segmentation --------------------------------------------------------------------
Mat leftGreen, rightGreen;
cvtColor(green_image, green_image, CV_BGR2HSV);
inRange(green_image, Scalar(50, 100, 90), Scalar(70, 255, 255), lower_green_hue_range);
inRange(green_image, Scalar(70, 100, 90), Scalar(90, 255, 255), upper_green_hue_range);
addWeighted(lower_green_hue_range, 1.0, upper_green_hue_range, 1.0, 0.0, green_hue_image);
GaussianBlur(green_hue_image, green_hue_image, Size(5, 5), 0, 0);
value2 = getAverage(green_hue_image);
namedWindow("Green Combined threshold images", WINDOW_NORMAL);
resizeWindow("Green Combined threshold images", 120, 90);
imshow("Green Combined threshold images", green_hue_image);
#if DEBUG_FLAG
printf("Green image value: %d \n", value2);
#endif
// Green color segmentation -------------------------------------------------------------------
if ((value1 > 10) && (value2 > 10)){ // green and red circle
return 1;
}
if ((value1 > 10) && (value2 < 10)){ // red cirle
return 2;
}
if ((value2 > 10) && (value1 < 10)){ // green circle
return 0;
}
if ((value1 == 0 && value2 == 0)){ // none
return -1;
}
return -1;
}*/
vector<int> segmentColor_night(Mat& imageROI) // , bool contour)
{
Mat red_image, green_image, lower_red_hue_range, upper_red_hue_range, saturated_red_image, saturated_green_image;
Mat red_hue_image, green_hue_image, lower_green_hue_range, upper_green_hue_range;
// For Red light: Threshold the HSV image, keep only the red pixels
red_image = imageROI.clone();
green_image = imageROI.clone();
vector<int> res;
#if DEBUG_FLAG
printf("6. Finding out which color is present.\n");
#endif
// Red color segmentation ----------------------------------------------------------------------
Mat leftRed, rightRed;
cvtColor(red_image, red_image, COLOR_BGR2HSV);
inRange(red_image, Scalar(0, 100, 100), Scalar(8, 255, 255), lower_red_hue_range);
inRange(red_image, Scalar(150, 100, 100), Scalar(180, 255, 255), upper_red_hue_range);
addWeighted(lower_red_hue_range, 1.0, upper_red_hue_range, 1.0, 0.0, red_hue_image);
GaussianBlur(red_hue_image, red_hue_image, Size(5, 5), 0, 0);
value1 = getAverage(red_hue_image);
imshow("Red Combined threshold images", red_hue_image);
#if DEBUG_FLAG
printf("Red image value: %d\n", value1);
#endif
// Red color segmentation ----------------------------------------------------------------------
// xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
// Green color segmentation --------------------------------------------------------------------
Mat leftGreen, rightGreen;
cvtColor(green_image, green_image, COLOR_BGR2HSV);
inRange(green_image, Scalar(45, 75, 90), Scalar(90, 255, 255), green_hue_image);
// inRange(green_image, Scalar(50, 100, 90), Scalar(70, 255, 255), lower_green_hue_range);
// inRange(green_image, Scalar(70, 100, 90), Scalar(95, 255, 255), upper_green_hue_range);
// addWeighted(lower_green_hue_range, 1.0, upper_green_hue_range, 1.0, 0.0, green_hue_image);
GaussianBlur(green_hue_image, green_hue_image, Size(5, 5), 0, 0);
value2 = getAverage(green_hue_image);
imshow("Green Combined threshold images", green_hue_image);
#if DEBUG_FLAG
printf("Green image value: %d \n", value2);
#endif
// Green color segmentation --------------------------------------------------------------------
res.push_back(value1);
res.push_back(value2);
return res;
}
int segmentDecision_night(vector<int> &top, vector<int> &mid_top, vector<int> &mid, vector<int> &mid_bot, vector<int> &bot)
{
int red_sum = 0, green_sum = 0;
int red_arr[4] = {}, green_arr[4] = {};
red_arr[0] = top[0];
green_arr[0] = top[1];
red_sum += top[0];
green_sum += top[1];
#if DEBUG_FLAG
printf("Red vs green on TOP: %dx%d\n", red_arr[0], green_arr[0]);
#endif
red_arr[1] = mid_top[0];
green_arr[1] = mid_top[1];
red_sum += mid_top[0];
green_sum += mid_top[1];
#if DEBUG_FLAG
printf("Red vs green on MID_TOP: %dx%d\n", red_arr[1], green_arr[1]);
#endif
red_arr[2] = mid[0];
green_arr[2] = mid[1];
red_sum += mid[0];
green_sum += mid[1];
#if DEBUG_FLAG
printf("Red vs green on MID: %dx%d\n", red_arr[2], green_arr[2]);
#endif
red_arr[3] = mid_bot[0];
green_arr[3] = mid_bot[1];
red_sum += mid_bot[0];
green_sum += mid_bot[1];
#if DEBUG_FLAG
printf("Red vs green on MID_BOT: %dx%d\n", red_arr[3], green_arr[3]);
#endif
red_arr[4] = bot[0];
green_arr[4] = bot[1];
red_sum += bot[0];
green_sum += bot[1];
#if DEBUG_FLAG
printf("Red vs green on BOT: %dx%d\n\n", red_arr[4], green_arr[4]);
#endif
#if DEBUG_FLAG
printf("Red vs green total: %d x %d\n\n", red_sum, green_sum);
#endif
if ((red_sum > 10) && (green_sum > 10)){ // green and red circle
return 1;
}
if ((red_sum > 10) && (green_sum < 10)){ // red cirle
return 2;
}
if ((green_sum > 10) && (red_sum < 10)){ // green circle
return 0;
}
if ((red_sum == 0 && green_sum == 0)){ // none
return -1;
}
return -1;
}
int colorDecision_night(int segmentationType)
{
if (segmentationType == 1){
#if DEBUG_FLAG
printf("7. Segmented light area is on both sides: Turn left and RED LIGHT. \n\n");
#endif
isRedColor = true;
ifRedHappened = true;
}
else if (segmentationType == 2)
{
#if DEBUG_FLAG
printf("7. Segmented light area is on leftside : RED LIGHT. \n\n");
#endif
isRedColor = true;
ifRedHappened = true;
return 0;
}
else if (segmentationType == 0)
{
#if DEBUG_FLAG
printf("7. Segmented light area is rightside : GREEN LIGHT. \n\n");
#endif
isGreenColor = true;
isRedColor = false;
}
else
{
#if DEBUG_FLAG
printf("7. Color segmentation failed !!!!!\n\n");
#endif
return -1;
}
if (isGreenColor == true && isRedColor == false && ifRedHappened == true)
{
thresholdcount++;
if (thresholdcount > numberofthresholdframes)
{
#if DEBUG_FLAG
printf("8. Light change occured and the alarm started.\n\n");
#endif
return 1;
}
}
return 0;
}
int segmentColor(Mat& lightROI)
{
Mat red_image, green_image, lower_red_hue_range, upper_red_hue_range, saturated_red_image, saturated_green_image;
Mat red_hue_image, green_hue_image, lower_green_hue_range, upper_green_hue_range;
// For Red light: Threshold the HSV image, keep only the red pixels
red_image = lightROI.clone();
green_image = lightROI.clone();
vector<Mat> channel;
#if DEBUG_FLAG
printf("12. Finding out which color is present.\n");
#endif
// Red color segmentation --------------------------------------------------------------------
Mat leftRed, rightRed, topRed, bottomRed;
cvtColor(red_image, red_image, CV_BGR2HSV);
inRange(red_image, Scalar(0, 100, 100), Scalar(15, 255, 255), lower_red_hue_range);
inRange(red_image, Scalar(150, 100, 100), Scalar(180, 255, 255), upper_red_hue_range);
addWeighted(lower_red_hue_range, 1.0, upper_red_hue_range, 1.0, 0.0, red_hue_image);
GaussianBlur(red_hue_image, red_hue_image, Size(5, 5), 0, 0);
Rect leftRedPart(0, 0, red_hue_image.cols / 2, red_hue_image.rows );
red_hue_image(Rect(leftRedPart.x, leftRedPart.y, leftRedPart.width, leftRedPart.height)).copyTo(leftRed);
imshow("leftRed", leftRed);
// Rect topRedPart(0, 0, red_hue_image.cols , red_hue_image.rows / 2 );
// red_hue_image(Rect(topRedPart.x, topRedPart.y, topRedPart.width, topRedPart.height)).copyTo(topRed);
// imshow("topRed", topRed);
inRange(red_image, Scalar(0, 100, 100), Scalar(15, 255, 255), lower_red_hue_range);
inRange(red_image, Scalar(150, 100, 100), Scalar(180, 255, 255), upper_red_hue_range);
addWeighted(lower_red_hue_range, 1.0, upper_red_hue_range, 1.0, 0.0, red_hue_image);
GaussianBlur(red_hue_image, red_hue_image, Size(5, 5), 0, 0);
Rect rightRedPart(red_hue_image.cols / 2, 0, red_hue_image.cols - red_hue_image.cols / 2, red_hue_image.rows );
red_hue_image(Rect(rightRedPart.x, rightRedPart.y, rightRedPart.width, rightRedPart.height)).copyTo(rightRed);
// Rect bottomRedPart(0, red_hue_image.rows / 2, red_hue_image.cols, red_hue_image.rows / 2 );
// red_hue_image(Rect(bottomRedPart.x, bottomRedPart.y, bottomRedPart.width, bottomRedPart.height)).copyTo(bottomRed);
GaussianBlur(rightRed, rightRed, Size(3, 3), 0 );
value1 = getAverage(leftRed);
value2 = getAverage(rightRed);
leftRed.release();
rightRed.release();
// GaussianBlur(bottomRed, bottomRed, Size(3, 3), 0 );
// value1 = getAverage(topRed);
// value2 = getAverage(bottomRed);
// topRed.release();
// bottomRed.release();
if (value1 < 6 && red_fail_count < 3)
{
red_fail_count++;
}
else if ((value1 < 6 && red_fail_count == 3 && isGreenColor == false) || segmentationType == 1)
{
red_image = lightROI.clone();
cvtColor(red_image, saturated_red_image, CV_BGR2HSV);
split(saturated_red_image, channel);
channel[1] *= 1.2;
merge(channel, saturated_red_image);
cvtColor(red_image, red_image, CV_BGR2HSV);
#if DEBUG_FLAG
printf("=X=X=X=X=X=X=X=X=X=X=X=X=X=X=X=X=X===UPDATED COLOR RANGE\n");
#endif
inRange(red_image, Scalar(0, 100, 100), Scalar(15, 255, 255), lower_red_hue_range);
inRange(red_image, Scalar(150, 100, 100), Scalar(180, 255, 255), upper_red_hue_range);
addWeighted(lower_red_hue_range, 1.0, upper_red_hue_range, 1.0, 0.0, red_hue_image);
GaussianBlur(red_hue_image, red_hue_image, Size(5, 5), 0, 0);
Rect rightRedPart(red_hue_image.cols / 2, 0, red_hue_image.cols - red_hue_image.cols / 2, red_hue_image.rows );
red_hue_image(Rect(rightRedPart.x, rightRedPart.y, rightRedPart.width, rightRedPart.height)).copyTo(rightRed);
// Rect bottomRedPart(0, red_hue_image.rows / 2, red_hue_image.cols, red_hue_image.rows / 2 );
// red_hue_image(Rect(bottomRedPart.x, bottomRedPart.y, bottomRedPart.width, bottomRedPart.height)).copyTo(bottomRed);
// inRange(saturated_red_image, Scalar(0, 87, 100), Scalar(25, 255, 255), lower_red_hue_range);
// inRange(saturated_red_image, Scalar(155, 87, 100), Scalar(180, 255, 255), upper_red_hue_range);
// addWeighted(lower_red_hue_range, 1.0, upper_red_hue_range, 1.0, 0.0, red_hue_image);
// GaussianBlur(red_hue_image, red_hue_image, Size(5, 5), 0, 0);
// Rect leftRedPart(0, 0, red_hue_image.cols / 2, red_hue_image.rows );
// red_hue_image(Rect(leftRedPart.x, leftRedPart.y, leftRedPart.width, leftRedPart.height)).copyTo(leftRed);
GaussianBlur(rightRed, rightRed, Size(3, 3), 0 );
value1 = getAverage(leftRed);
value2 = getAverage(rightRed);
leftRed.release();
rightRed.release();
// value1 = getAverage(topRed);
// value2 = getAverage(bottomRed);
// topRed.release();
// bottomRed.release();
}
else if (red_fail_count > 0)
{
red_fail_count--;
}
#if DEBUG_FLAG
printf(" Red image left vs right: %d vs %d\n", value1, value2);
#endif
// Red color segmentation --------------------------------------------------------------------
// xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
// Green color segmentation --------------------------------------------------------------------
cvtColor(green_image, saturated_green_image, CV_BGR2HSV);
split(saturated_green_image, channel);
channel[1] *= 1.2;
merge(channel, saturated_green_image);
Mat leftGreen, rightGreen, topGreen, bottomGreen;
inRange(saturated_green_image, Scalar(45, 70, 100), Scalar(70, 255, 255), lower_green_hue_range);
inRange(saturated_green_image, Scalar(70, 70, 100), Scalar(90, 255, 255), upper_green_hue_range);
addWeighted(lower_green_hue_range, 1.0, upper_green_hue_range, 1.0, 0.0, green_hue_image);
GaussianBlur(green_hue_image, green_hue_image, Size(5, 5), 0, 0);
Rect rightGreenPart(green_hue_image.cols / 2, 0, green_hue_image.cols - green_hue_image.cols / 2, red_hue_image.rows );
green_hue_image(Rect(rightGreenPart.x, rightGreenPart.y, rightGreenPart.width, rightGreenPart.height)).copyTo(rightGreen);
imshow("rightGreen", rightGreen);
// Rect bottomGreenPart(0, green_hue_image.rows / 2, green_hue_image.cols , red_hue_image.rows / 2 );
// green_hue_image(Rect(bottomGreenPart.x, bottomGreenPart.y, bottomGreenPart.width, bottomGreenPart.height)).copyTo(bottomGreen);
// imshow("bottomGreen", bottomGreen);
cvtColor(green_image, green_image, CV_BGR2HSV);
inRange(green_image, Scalar(45, 100, 100), Scalar(70, 255, 255), lower_green_hue_range);
inRange(green_image, Scalar(70, 100, 100), Scalar(90, 255, 255), upper_green_hue_range);
addWeighted(lower_green_hue_range, 1.0, upper_green_hue_range, 1.0, 0.0, green_hue_image);
GaussianBlur(green_hue_image, green_hue_image, Size(5, 5), 0, 0);
Rect leftGreenPart(0, 0, green_hue_image.cols / 2, green_hue_image.rows );
green_hue_image(Rect(leftGreenPart.x, leftGreenPart.y, leftGreenPart.width, leftGreenPart.height)).copyTo(leftGreen);
// Rect topGreenPart(0, 0, green_hue_image.cols , green_hue_image.rows / 2 );
// green_hue_image(Rect(topGreenPart.x, topGreenPart.y, topGreenPart.width, topGreenPart.height)).copyTo(topGreen);
GaussianBlur(leftGreen, leftGreen, Size(3, 3), 0, 0);
value3 = getAverage(leftGreen);
value4 = getAverage(rightGreen);
leftGreen.release();
rightGreen.release();
// GaussianBlur(topGreen, topGreen, Size(3, 3), 0, 0);
// value3 = getAverage(topGreen);
// value4 = getAverage(bottomGreen);
// topGreen.release();
// bottomGreen.release();
#if DEBUG_FLAG
printf(" Green image left vs right: %d vs %d\n", value3, value4);
#endif
// Green color segmentation --------------------------------------------------------------------
if ((value1 > value2) && (value3 < value4) && (value1 >= 6) && (value4 >= 6) && (value2 <= 6) && (value3 <= 6)){ // green and red circle
return 1;
}
if ((value1 > value2) && (value1 >= 6) && (value2 <= 6)){ // red cirle
return 2;
}
if ((value3 < value4) && (value4 >= 6) && (value3 <= 6)){ // green circle
return 0;
}
if (((value1 == 0 && value2 == 0) && (value3 == 0 && value4 == 0)) || ((value1 < value2) || (value3 > value4))){ // none
return -1;
}
return -1;
}
bool isArrow(Mat& lightROI, string template_path, double minMatchQuality){
Mat result;
Mat img = lightROI;
templ = imread(TEMPLATE_PATH);
resize(templ, templ, Size(7, 7));
// resize(lightROI, lightROI, Size(704, 480));
/// Create windows
//namedWindow("image_window", WINDOW_NORMAL);
Mat img_display;
img.copyTo(img_display);
int result_cols = img.cols - templ.cols + 1;
int result_rows = img.rows - templ.rows + 1;
result.create(result_rows, result_cols, CV_32FC1);
/// Do the Matching and Normalize
matchTemplate(img, templ, result, 5);
/// Create Trackbar
double minVal; double maxVal; Point minLoc; Point maxLoc;
Point matchLoc;
minMaxLoc(result, &minVal, &maxVal, &minLoc, &maxLoc, Mat());
matchLoc = maxLoc;
printf("maxVal: %f\n", maxVal);
printf("minVal: %f\n", minVal);
/// For SQDIFF and SQDIFF_NORMED, the best matches are lower values. For all the other methods, the higher the better
if (maxVal > minMatchQuality){
Mat rect;
/// Show me what you got
rectangle(img, matchLoc, Point(matchLoc.x + templ.cols, matchLoc.y + templ.rows), Scalar::all(0), 2, 8, 0 );
img_display(Rect(matchLoc.x, matchLoc.y, templ.cols, templ.rows)).copyTo(rect);
//namedWindow("detected", WINDOW_NORMAL);
//resizeWindow("detected", Size(500, 500));
//imshow("detected", rect);
//imshow("image_window", img_display );
waitKey(1);
printf("detected the arrow\n");
return true;
}
else{
// rectangle( img_display, matchLoc, Point( matchLoc.x + templ.cols , matchLoc.y + templ.rows ), Scalar::all(0), 2, 8, 0 );
// rectangle( result, matchLoc, Point( matchLoc.x + templ.cols , matchLoc.y + templ.rows ), Scalar::all(0), 2, 8, 0 );
// resizeWindow("image_window", 120, 90);
//imshow("image_window", img_display );
//imshow( "result_window", result );
//MatchingMethod( 0, 0 );
waitKey(1);
printf("arrow detection failed!!!!!!\n");
}
return false;
}
bool compare_rect(const cv::Rect & a, const cv::Rect &b)
{
return a.width > b.width;
}
int colorDecision(int segmentationType, bool fourPattern)
{
if (segmentationType == 1){
#if DEBUG_FLAG
printf("12. Segmented light area is on both sides: Turn left and RED LIGHT. \n\n");
#endif
isRedColor = true;
ifRedHappened = true;
}
else if (!fourPattern)
{
arrowPresent = false;
if(segmentationType == 2)
{
#if DEBUG_FLAG
printf("12. Segmented light area is on leftside : RED LIGHT. \n\n");
#endif
isRedColor = true;
ifRedHappened = true;
}
else if (segmentationType == 0)
{
#if DEBUG_FLAG
printf("12. Segmented light area is rightside : GREEN LIGHT\n\n");
#endif
isGreenColor = true;
isRedColor = false;
}
else
{
#if DEBUG_FLAG
printf("12. ThreePattern: Color segmentation failed !!!!!\n\n");
#endif
return -1;
}
if (isGreenColor == true && isRedColor == false && ifRedHappened == true)
{
thresholdcount++;
if (thresholdcount > numberofthresholdframes)
{
#if DEBUG_FLAG
printf("13. Light change occured and the alarm started.\n\n");
#endif
return 1;
}
}
}
else if (fourPattern == true)
{
if(segmentationType == 2)
{
#if DEBUG_FLAG
printf("12. Segmented light area is on leftside and arrow is present : RED LIGHT AND ARROW. \n\n");
#endif
isRedColor = true;
ifRedHappened = true;
left_thresholdcount++;
if (left_thresholdcount > leftthresholdframes){
#if DEBUG_FLAG
printf("13. Left arrow number of frames passed a threshold.\n\n");
#endif
arrowPresent = true;
}
}
else if (segmentationType == 0)
{
#if DEBUG_FLAG
printf("12. Segmented light area is rightside and arrow is present: GREEN LIGHT AND ARROW.\n\n");
#endif
isGreenColor = true;
isRedColor = false;
left_thresholdcount++;
if (left_thresholdcount > leftthresholdframes){
#if DEBUG_FLAG
printf("13. Left arrow number of frames passed a threshold.\n\n");
#endif
arrowPresent = true;
}
}
else
{
#if DEBUG_FLAG
printf("12. FourPattern: Color segmentation failed !!!!!\n\n");
#endif
return -1;
}
if (isGreenColor == true && isRedColor == false && ifRedHappened == true)
{
thresholdcount++;
if (thresholdcount > numberofthresholdframes)
{
#if DEBUG_FLAG
printf("13. Light change occured and the alarm started.\n\n");
#endif
return 1;
}
}
}
else
{
#if DEBUG_FLAG
printf("20. Failed to go somewhere in color decision function!!!!!!!!!!!\n\n");
#endif
}
return 0;
}
Mat detect_TLDS_light(Mat& imageROI)
{
// namedWindow("imageROI", WINDOW_NORMAL);
// resizeWindow("imageROI", 120, 90);
// imshow("imageROI", imageROI);
Mat lightROI;
vector<Rect> light;
#if DEBUG_FLAG
printf("2. Detecting the traffic light.\n\n");
#endif
traffic_light.detectMultiScale( imageROI, light, 1.05, 3, 0|2 );
#if DEBUG_FLAG
printf("3. Number of traffic light signs: %zu\n", light.size());
#endif
if(light.size() > 0)
{
sort(light.begin(), light.end(), compare_rect);
detected_light = light[0];
imageROI(Rect(detected_light.x, detected_light.y, detected_light.width, detected_light.height)).copyTo(lightROI);
#if DEBUG_FLAG
printf(" Size of the detected traffic light: %dX%d\n", detected_light.size().width, detected_light.size().height);
//rectangle(lightROI, detected_light, Scalar(0, 0, 255));
ostringstream name4;
name4 << "/app/sd/BlackVue/Record/detected_light_"<<framecount<<".jpg";
imwrite(name4.str(), lightROI);
#endif
isLightDetected = true;
}
else
{
isLightDetected = false;
}
if (light.size() > 0){
namedWindow("lightROI", WINDOW_NORMAL);
resizeWindow("lightROI", 120, 90);
imshow("lightROI", lightROI);
}
return lightROI;
}
void StartTLDS()
{
isStart = true;
isUsingDetector = true;
thresholdcount = 0;
left_thresholdcount = 0;
red_fail_count = 0;
detected_frames_count = 0;
color_seg_fail_count = 0;
}
void EndTLDS()
{
isStart = false;
red_fail_count = 0;
detected_frames_count = 0;
color_seg_fail_count = 0;
}
int main()
{
#if DEBUG_FLAG
printf("2. Inside the tlds function.\n\n");
#endif
#if SAVE_DEBUG_TO_TEXT
freopen("/app/sd/Blackvue/Record/debug_tlds.txt","a",stdout);
#endif
int gps_speed = 0;
int max_speed = 0;
if (max_speed >= 0) //tlds only works when speed = 0
{
if ((gps_speed==0) && (gps_speed<=max_speed)) // tlds starts when car stops, speed = 0
{
#if DEBUG_FLAG
printf("3. GPS SPEED and MAX SPEED satisfy the conditions.\n\n");
printf("TLDS starts.\n\n");
#endif
if (isStart == false)
{
StartTLDS();
numberofthresholdframes=2;
leftthresholdframes=2;
}
}
else
{
#if DEBUG_FLAG
printf("TLDS stops.\n\n");
#endif
if(isStart == true)
{
EndTLDS();
numberofthresholdframes=2;
}
}
}
Mat imageROI, lightROI;
int flag_alarm = 0;
loadDetector();
#if DEBUG_FLAG
printf("Finish loading dataset. \n");
#endif
getVideoData();
#if AUTOSTART
StartTLDS();
#endif
bool fourPattern;
Mat frame;
vector<int> whole_image, top, middle_top, middle, middle_bot, bot;
while (cap.read(frame))
{
//Mat frame_template = frame.clone();
//resize(frame_template, frame_template, Size(704, 408));
if (framecount % SKIPFRAME == 0)
{
auto start_time = std::chrono::high_resolution_clock::now();
if (isStart == true)
{
Mat frame_template = frame.clone();
resize(frame_template, frame_template, Size(704, 480));
imageROI = frame_template(Rect(frame_template.cols*0.25, frame_template.rows*0.10, frame_template.cols*0.50, frame_template.rows*0.38));
imshow("image_ROI", imageROI);
#if DISPLAYALL
namedWindow("detection", WINDOW_NORMAL);
resizeWindow("detection", 500, 500);
imshow("detection", frame);
#endif
// int v = 0;
// Mat hsv, valueImage ;
// vector<Mat> channels;
// cvtColor(imageROI, valueImage, CV_BGR2HSV);
// split(valueImage, channels);
// hsv = channels[2];
// v = getAverage(hsv);
// Mat black_hue;
// inRange(frame_hsv, Scalar(0, 0, 0), Scalar(180, 255, 40), black_hue);
// GaussianBlur(black_hue, black_hue, Size(5, 5), 0, 0);
// int black = getAverage(black_hue);
Mat frame_hsv;
cvtColor(imageROI, frame_hsv, COLOR_BGR2HSV);
vector<Mat> channel;
split(frame_hsv, channel);
int value = getAverage(channel[2]);
channel.clear();
Mat black_hue;
inRange(frame_hsv, Scalar(0, 0, 0), Scalar(180, 255, 40), black_hue);