diff --git a/README.md b/README.md
index 751fda2..235eff4 100644
--- a/README.md
+++ b/README.md
@@ -1,24 +1,9 @@
-# height-map
-<img src="images/image_53.png" alt="height map" width="400px"></img>
+# BEV images generator
 
-Currently, this ROS workspace processes LIDAR data to create PNG "bird's eye" height maps for use in deep learning applications. All of the real code currently resides in ```/ros-examples/src/lidar/src/lidar_node.cpp```.
+## Brief overview
+This repository is based on the original one by MacAllister Higgins (https://github.com/mjshiggins/ros-examples) ,but provides HSR colorization of the PointCloud or code the encoding with height, density and intensity (Colour visualisation corresponds to the LiDAR values)
 
-To use this node, first build. You'll need OpenCV dependencies, but that should be included in ROS. If you want to save the images, create a folder in the ROS root workspace (where this readme is located) called ```images```. Everything will automatically be saved there.
+add images here...
 
-To run: ```rosrun lidar lidar_node```
-
-You should see an image window pop up. The point cloud topic is hard coded in lidar_node.cpp. You may need to change that to ```/points_raw```. After this node is up, play your bag file.
-
-# ros-examples
-Want to learn how to use the Robot Operating System (ROS), the Point Cloud Library (PCL), and a bunch of other cool tools to make a self-driving car or other awesome robot? This repository is home to a collection of ROS nodes that process 3D sensor information, specifically as examples for the Udacity/Didi $100k object detection challenge. Learn about obstacle fusion techniques, and use these nodes as a starting point for building your own awesome obstacle detection engine!
-
-For more info, check out the main competition site [here](https://www.udacity.com/didi-challenge).
-
-If you have any issues, create a pull request! I'd love to add your contributions to this repo. Alternatively, shoot me a tweet at [macjshiggins](https://twitter.com/macjshiggins).
-
-# setup
-This is a standard ROS catkin-ized workspace. Don't know what that means? Check out a great intro on ROS [here](http://wiki.ros.org/ROS/Tutorials) or head on over to [Udacity](http://udacity.com) to sign up for the Robotics or Self-Driving Car Engineer Nanodegree.
-
-There's currently only one node for processing LIDAR data. Use "catkin_make" in the root of this repo to build the lidar node, and run with ```rosrun lidar lidar_node``` after installing PCL and ROS (Indigo, preferably).
-
-If you've downloaded any of the datasets for the challenge, you can start using the data with these nodes immediately by running ```rosbag play -l name-of-file.bag```. The "-l" keeps the bag file playing on repeat so that you can keep working on your algorithm without having to mess with the data playback.
+# Setup
+to be continued...
diff --git a/src/lidar/src/lidar_node.cpp b/src/lidar/src/lidar_node.cpp
index 1a14a81..5180379 100644
--- a/src/lidar/src/lidar_node.cpp
+++ b/src/lidar/src/lidar_node.cpp
@@ -17,9 +17,10 @@
 #include <pcl/io/pcd_io.h>
 
 #include <cv_bridge/cv_bridge.h>
-#include <opencv/cv.h>
-#include <opencv/highgui.h>
+#include <opencv2/core.hpp>
+#include <opencv2/highgui.hpp
 #include <opencv2/highgui/highgui.hpp>
+#include <opencv2/highgui/highgui_c.h>
 
 #include <ros/ros.h>
 #include <ros/console.h>
@@ -171,14 +172,14 @@ int main(int argc, char** argv)
   // Setup image
   cv::Mat map(IMAGE_HEIGHT, IMAGE_WIDTH, CV_8UC3, cv::Scalar(0, 0, 0));
   heightmap = &map;
-  cvNamedWindow("Height Map", CV_WINDOW_AUTOSIZE);
+  cvNamedWindow("Height Map", WINDOW_AUTOSIZE);
   cvStartWindowThread();
   cv::imshow("Height Map", *heightmap);
 
   // Setup Image Output Parameters
   fnameCounter = 0;
   lowest = FLT_MAX;
-  compression_params.push_back(CV_IMWRITE_PNG_COMPRESSION);
+  compression_params.push_back(IMWRITE_PNG_COMPRESSION);
   compression_params.push_back(9);
 
   // Setup indicies in point clouds