#include "ros/ros.h"
#include <image_transport/image_transport.h>
#include <cv_bridge/cv_bridge.h>
#include <sensor_msgs/image_encodings.h>

#include <opencv/cv.h>

#include <sstream>

using namespace cv;
using namespace std;

class Traite_image {
	public:
		const static int SENSITIVITY_VALUE = 30;
		const static int BLUR_SIZE = 10;
        const int HORIZONTAL_BORDER_CROP = 20; // In pixels. Crops the border to reduce the black borders from stabilisation being too noticeable.


		Mat prev;
		Mat last_T;
		bool first = true;
		int resize_f = 1;

		int theObject[2] = {0,0};
		Rect objectBoundingRectangle = Rect(0,0,0,0);

		ros::NodeHandle n;

		image_transport::ImageTransport it;
		image_transport::Publisher      pub;
		image_transport::Subscriber     sub;


		Traite_image() : n("~"),it(n) {
			pub = it.advertise("/image_out", 1);
			sub = it.subscribe("/usb_cam/image_raw", 1, [this](const sensor_msgs::ImageConstPtr& img) -> void { this->on_image(img);},ros::VoidPtr(),image_transport::TransportHints("compressed"));
		}


		// This processes an image and publishes the result.
		void on_image(const sensor_msgs::ImageConstPtr& msg) {

			cv_bridge::CvImageConstPtr bridge_input;
			try {
				bridge_input = cv_bridge::toCvShare(msg,sensor_msgs::image_encodings::RGB8);
			}
			catch (Exception& e) {
				std::ostringstream errstr;
				errstr << "cv_bridge exception caught: " << e.what();
				return;
			}

			//Mat& input  = const_cast<Mat&>(bridge_input->image);
			const Mat& input = bridge_input->image;
			Mat next;
			resize(input, next, Size(input.size().width/resize_f, input.size().height/resize_f));
			Mat output;// = input.clone(); //   (input.rows, input.cols, CV_32FC2);
			//ROS_INFO("got input");
			if (first) {
				prev = next.clone();
				first = false;
				ROS_INFO("first done");
			}

			Mat next_stab;
			stabiliseImg(prev, next, next_stab);
            Rect myROI(next_stab.size().width/4, next_stab.size().height/4, next_stab.size().width/2, next_stab.size().height/2);
            Mat next_stab_cropped = next_stab(myROI);
            Mat prev_cropped = prev(myROI);
			searchForMovement(prev_cropped, next_stab_cropped, output);


			pub.publish(cv_bridge::CvImage(msg->header, "rgb8", output).toImageMsg());
			// bridge_input is handled by a smart-pointer. No explicit delete needed.

			//ROS_INFO("pub");

			prev = next.clone();
		}

		//int to string helper function
		string intToString(int number){

			//this function has a number input and string output
			std::stringstream ss;
			ss << number;
			return ss.str();
		}

		void stabiliseImg(Mat prev, Mat cur, Mat &output){
			Mat cur_grey, prev_grey;
			cvtColor(cur, cur_grey, COLOR_BGR2GRAY);
			cvtColor(prev, prev_grey, COLOR_BGR2GRAY);

			// vector from prev to cur
			vector <Point2f> prev_corner, cur_corner;
			vector <Point2f> prev_corner2, cur_corner2;
			vector <uchar> status;
			vector <float> err;

			goodFeaturesToTrack(prev_grey, prev_corner, 200, 0.01, 30);
			calcOpticalFlowPyrLK(prev_grey, cur_grey, prev_corner, cur_corner, status, err);

			// weed out bad matches
			for(size_t i=0; i < status.size(); i++) {
				if(status[i]) {
					prev_corner2.push_back(prev_corner[i]);
					cur_corner2.push_back(cur_corner[i]);
				}
			}

			Mat T = estimateRigidTransform(prev_corner2, cur_corner2, true); // false = rigid transform, no scaling/shearing

			if(T.data == NULL) {
				last_T.copyTo(T);
			}
			T.copyTo(last_T);

			Mat cur2;

			warpAffine(cur, cur2, T, cur.size(),INTER_NEAREST|WARP_INVERSE_MAP);

			cur2.copyTo(output);
		}

		void searchForMovement(Mat prev, Mat cur, Mat &output){
			Mat cur_grey, prev_grey;
			cur.copyTo(output);
			cvtColor(prev, prev_grey, COLOR_BGR2GRAY);
			cvtColor(cur, cur_grey, COLOR_BGR2GRAY);

			// Subtract the 2 last frames and threshold them
			Mat thres;
			absdiff(prev_grey,cur_grey,thres);
			threshold(thres, thres, SENSITIVITY_VALUE, 255, THRESH_BINARY);
			// Blur to eliminate noise
			blur(thres, thres, Size(BLUR_SIZE, BLUR_SIZE));
			threshold(thres, thres, SENSITIVITY_VALUE, 255, THRESH_BINARY);

			//notice how we use the '&' operator for objectDetected and output. This is because we wish
			//to take the values passed into the function and manipulate them, rather than just working with a copy.
			//eg. we draw to the output to be displayed in the main() function.
			bool objectDetected = false;
			Mat temp;
			thres.copyTo(temp);
			//these two vectors needed for output of findContours
			vector< vector<Point> > contours;
			vector<Vec4i> hierarchy;
			//find contours of filtered image using openCV findContours function
			//findContours(temp,contours,hierarchy,CV_RETR_CCOMP,CV_CHAIN_APPROX_SIMPLE );// retrieves all contours
			findContours(temp,contours,hierarchy,CV_RETR_EXTERNAL,CV_CHAIN_APPROX_SIMPLE );// retrieves external contours

			//if contours vector is not empty, we have found some objects
			if(contours.size()>0)objectDetected=true;
			else objectDetected = false;

			if(objectDetected){
				//the largest contour is found at the end of the contours vector
				//we will simply assume that the biggest contour is the object we are looking for.
				vector< vector<Point> > largestContourVec;
				largestContourVec.push_back(contours.at(contours.size()-1));
				//make a bounding rectangle around the largest contour then find its centroid
				//this will be the object's final estimated position.
				objectBoundingRectangle = boundingRect(largestContourVec.at(0));
				int xpos = objectBoundingRectangle.x+objectBoundingRectangle.width/2;
				int ypos = objectBoundingRectangle.y+objectBoundingRectangle.height/2;

				//update the objects positions by changing the 'theObject' array values
				theObject[0] = xpos , theObject[1] = ypos;
			}
			//make some temp x and y variables so we dont have to type out so much
			int x = theObject[0];
			int y = theObject[1];

			//draw some crosshairs around the object
			circle(output,Point(x,y),20,Scalar(0,255,0),2);
			line(output,Point(x,y),Point(x,y-25),Scalar(0,255,0),2);
			line(output,Point(x,y),Point(x,y+25),Scalar(0,255,0),2);
			line(output,Point(x,y),Point(x-25,y),Scalar(0,255,0),2);
			line(output,Point(x,y),Point(x+25,y),Scalar(0,255,0),2);

			//write the position of the object to the screen
			putText(output,"Tracking object at (" + intToString(x)+","+intToString(y)+")",Point(x,y),1,1,Scalar(255,0,0),2);
		}

		inline bool isFlowCorrect(Point2f u)
		{
			return !cvIsNaN(u.x) && !cvIsNaN(u.y) && fabs(u.x) < 1e9 && fabs(u.y) < 1e9;
		}

		Vec3b computeColor(float fx, float fy)
		{
			static bool first = true;

			// relative lengths of color transitions:
			// these are chosen based on perceptual similarity
			// (e.g. one can distinguish more shades between red and yellow
			//  than between yellow and green)
			const int RY = 15;
			const int YG = 6;
			const int GC = 4;
			const int CB = 11;
			const int BM = 13;
			const int MR = 6;
			const int NCOLS = RY + YG + GC + CB + BM + MR;
			static Vec3i colorWheel[NCOLS];

			if (first)
			{
				int k = 0;

				for (int i = 0; i < RY; ++i, ++k)
					colorWheel[k] = Vec3i(255, 255 * i / RY, 0);

				for (int i = 0; i < YG; ++i, ++k)
					colorWheel[k] = Vec3i(255 - 255 * i / YG, 255, 0);

				for (int i = 0; i < GC; ++i, ++k)
					colorWheel[k] = Vec3i(0, 255, 255 * i / GC);

				for (int i = 0; i < CB; ++i, ++k)
					colorWheel[k] = Vec3i(0, 255 - 255 * i / CB, 255);

				for (int i = 0; i < BM; ++i, ++k)
					colorWheel[k] = Vec3i(255 * i / BM, 0, 255);

				for (int i = 0; i < MR; ++i, ++k)
					colorWheel[k] = Vec3i(255, 0, 255 - 255 * i / MR);

				first = false;
			}

			const float rad = sqrt(fx * fx + fy * fy);
			const float a = atan2(-fy, -fx) / (float)CV_PI;

			const float fk = (a + 1.0f) / 2.0f * (NCOLS - 1);
			const int k0 = static_cast<int>(fk);
			const int k1 = (k0 + 1) % NCOLS;
			const float f = fk - k0;

			Vec3b pix;

			for (int b = 0; b < 3; b++)
			{
				const float col0 = colorWheel[k0][b] / 255.f;
				const float col1 = colorWheel[k1][b] / 255.f;

				float col = (1 - f) * col0 + f * col1;

				if (rad <= 1)
					col = 1 - rad * (1 - col); // increase saturation with radius
				else
					col *= .75; // out of range

				pix[2 - b] = static_cast<uchar>(255.f * col);
			}

			return pix;
		}

		void drawOpticalFlow(const Mat_<Point2f>& flow, Mat& dst, float maxmotion = -1)
		{
			dst.create(flow.size(), CV_8UC3);
			dst.setTo(Scalar::all(0));

			// determine motion range:
			float maxrad = maxmotion;

			if (maxmotion <= 0)
			{
				maxrad = 1;
				for (int y = 0; y < flow.rows; ++y)
				{
					for (int x = 0; x < flow.cols; ++x)
					{
						Point2f u = flow(y, x);

						if (!isFlowCorrect(u))
							continue;

						maxrad = max(maxrad, sqrt(u.x * u.x + u.y * u.y));
					}
				}
			}

			for (int y = 0; y < flow.rows; ++y)
			{
				for (int x = 0; x < flow.cols; ++x)
				{
					Point2f u = flow(y, x);

					if (isFlowCorrect(u))
						dst.at<Vec3b>(y, x) = computeColor(u.x / maxrad, u.y / maxrad);
				}
			}
		}

};


int main(int argc, char **argv)
{
	ros::init(argc, argv, "test_opencv");
	Traite_image dataset=Traite_image();
	ros::spin();

	return 0;
}