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

#include <opencv/cv.h>

#include <sstream>

using namespace std;

class Traite_image {
	public:
		const static int SENSITIVITY_VALUE = 20;
		const static int BLUR_Size = 9;
		const static int CLOSE_SIZE = 30;
		const static int ERODE_SIZE = 2;
		const static int NB_FRAME_DROP = 1;


		vector<cv::Mat> prevs;
		cv::Mat last_T;
		bool first = true;
		int resize_f = 1;

		int theObject[2] = {0,0};

		ros::NodeHandle n;

		image_transport::ImageTransport it;
		image_transport::Publisher      pub_img;
		image_transport::Publisher      pub_thres;
        ros::Publisher					pub_cmd;
		image_transport::Subscriber     sub;


		Traite_image() : n("~"),it(n) {
			pub_img = it.advertise("/papillon/image_out", 1);
			pub_thres = it.advertise("/papillon/thres_out", 1);
			pub_cmd = n.advertise<papillon::BoundingBox>("/papillon/bbox", 1);
			sub = it.subscribe("/bebop/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 (cv::Exception& e) {
				std::ostringstream errstr;
				errstr << "cv_bridge exception caught: " << e.what();
				return;
			}

			//cv::Mat& input  = const_cast<cv::Mat&>(bridge_input->image);
			const cv::Mat& input = bridge_input->image;
			cv::Mat next;
			resize(input, next, cv::Size(input.size().width/resize_f, input.size().height/resize_f));
			cv::Mat output;// = input.clone(); //   (input.rows, input.cols, CV_32FC2);
			if (first) {
				for (int i = 0; i < NB_FRAME_DROP; ++i) {
					prevs.push_back(next.clone());
				}
				first = false;
				ROS_INFO("first done");
			}

			cv::Mat next_stab;
			stabiliseImg(prevs.back(), next, next_stab);
			int crop_ratio = 6;
			float crop_x = next_stab.size().width/crop_ratio;
			float crop_y = next_stab.size().height/crop_ratio;
			float crop_w = next_stab.size().width*(1-2.0/crop_ratio);
			float crop_h = next_stab.size().height*(1-2.0/crop_ratio);
			cv::Rect myROI(crop_x, crop_y, crop_w, crop_h);
			cv::Mat next_stab_cropped = next_stab(myROI);
			cv::Mat prev_cropped = prevs.back()(myROI);
			cv::Mat closed_thres;
			searchForMovement(prev_cropped, next_stab_cropped, output, closed_thres);


			pub_img.publish(cv_bridge::CvImage(msg->header, "rgb8", output).toImageMsg());
			pub_thres.publish(cv_bridge::CvImage(msg->header, "mono8", closed_thres).toImageMsg());
			// bridge_input is handled by a smart-pointer. No explicit delete needed.

            //droneTracking(Rect(Point(0,0), output.size()));

			prevs.pop_back();
			prevs.insert(prevs.begin(), 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(cv::Mat prev, cv::Mat cur, cv::Mat &output){
			cv::Mat cur_grey, prev_grey;
			cv::cvtColor(cur, cur_grey, cv::COLOR_BGR2GRAY);
			cv::cvtColor(prev, prev_grey, cv::COLOR_BGR2GRAY);

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

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

			// weed out bad cv::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]);
				}
			}

			cv::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);

			cv::Mat cur2;

			cv::warpAffine(cur, cur2, T, cur.size(),cv::INTER_CUBIC|cv::WARP_INVERSE_MAP);

			cur2.copyTo(output);
		}

		void searchForMovement(cv::Mat prev, cv::Mat cur, cv::Mat &output, cv::Mat &out2){
			cv::Mat cur_grey, prev_grey;
			cur.copyTo(output);
			cv::cvtColor(prev, prev_grey, cv::COLOR_BGR2GRAY);
			cv::cvtColor(cur, cur_grey, cv::COLOR_BGR2GRAY);
			cv::GaussianBlur(prev_grey, prev_grey, cv::Size(BLUR_Size,BLUR_Size), 3.0);
			cv::GaussianBlur(cur_grey, cur_grey, cv::Size(BLUR_Size,BLUR_Size), 3.0);
			//blur(prev_grey, prev_grey, cv::Size(BLUR_Size, BLUR_Size));
			//blur(cur_grey, cur_grey, cv::Size(BLUR_Size, BLUR_Size));

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

			cv::Mat element = getStructuringElement( cv::MORPH_ELLIPSE,
                                   cv::Size( 2*ERODE_SIZE + 1, 2*ERODE_SIZE+1 ),
                                   cv::Point( ERODE_SIZE, ERODE_SIZE ) );
			// Apply the dilation operation
			cv::erode(thres, thres, element );

			thres.copyTo(out2);


			cv::threshold(thres, thres, SENSITIVITY_VALUE, 255, cv::THRESH_BINARY);

			cv::Mat closed_thres;
			cv::Mat structuringElement = getStructuringElement(cv::MORPH_ELLIPSE, cv::Size(CLOSE_SIZE, CLOSE_SIZE));
			cv::morphologyEx( thres, closed_thres, cv::MORPH_CLOSE, structuringElement );
			// dilated_thres.copyTo(output);


			//closed_thres.copyTo(output);

			//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;
			cv::Mat temp;
			closed_thres.copyTo(temp);
			//these two vectors needed for output of findContours
			vector< vector<cv::Point> > contours;
			vector<cv::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
			cv::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 esticv::Mated position.
				vector<cv::Rect> nc_rects; // Non connected rectangles
				for(size_t i=0; i<contours.size();i++)
				{
					nc_rects.push_back(cv::boundingRect(contours[i]));
					//cv::rectangle(output, objectBoundingRectangle, cv::Scalar(0, 255, 0), 2);
				}

				vector<cv::Rect> c_rects; // Connected rectangles
				cleanBBoxes(nc_rects, cur.size(), c_rects);
				if (c_rects.size() > 0) {
					for (const auto& rect : c_rects)
						cv::rectangle(output, rect, cv::Scalar(0, 255, 0), 2);

					cv::Rect objBRect = c_rects.front();
					//cv::rectangle(output, objBRect, cv::Scalar(0, 255, 0), 2);
					papillon::BoundingBox bbox = papillon::BoundingBox();
					bbox.x = objBRect.x / (float)cur.size().width;
					bbox.y = objBRect.y / (float)cur.size().height;
					bbox.width = objBRect.width / (float)cur.size().width;
					bbox.height = objBRect.height / (float)cur.size().height;
					pub_cmd.publish(bbox);
				}
			}
			//make some temp x and y variables so we dont have to type out so much
			//~ int x = objectBoundingRectangle.x;
			//~ int y = objectBoundingRectangle.y;
			//~ int width = objectBoundingRectangle.width;
			//~ int height = objectBoundingRectangle.height;

			//draw a rectangle around the object
			//rectangle(output, Point(x,y), Point(x+width, y+height), cv::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,cv::Scalar(255,0,0),2);
		}


		void cleanBBoxes(vector<cv::Rect> nc_rects, cv::Size img, vector<cv::Rect> &c_rects) {
			int max = 0;
			for (auto r : nc_rects) {
				cv::Point center = rectCenter(r);
				if (r.width > img.width / 2 || r.height > img.height / 8)
					continue;

				if (center.y > img.height * 2 / 3 || center.y < img.height / 3)
					continue;

				int r_area = r.area();
				if (max < r_area) {
					max = r_area;
					c_rects.insert(c_rects.begin(), r);
				} else {
					c_rects.push_back(r);
				}
			}
		}

		cv::Point rectCenter(cv::Rect r) {
			return cv::Point(r.x + r.width/2, r.y + r.height / 2);
		}

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


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

	return 0;
}