From 008ee53f3013cec97513acfda1f9a3378fb4971b Mon Sep 17 00:00:00 2001
From: julien Lengrand-Lambert <julien@lengrandlambert.fr>
Date: Thu, 27 Oct 2011 17:02:52 +0300
Subject: [PATCH] Update README.markdown

---
 README.markdown | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/README.markdown b/README.markdown
index 1a0a05f..aeb5613 100644
--- a/README.markdown
+++ b/README.markdown
@@ -12,7 +12,7 @@ This project aims at magnifying the power of OpenCV in using the power of Python
 Here is a simple example. Let's imagine we have an RGB image, and we want to display it in HSV space. 
 The Python OpenCV code would be:
 
-`
+```Python
 import cv 
 img = cv.LoadImage('test.jpg')
 imgHSV = cv.CreateImage( cv.GetSize(img), img.depth, img.nChannels)
@@ -20,17 +20,17 @@ cv.CvtColor(img, imgHSV, cv.CV_RGB2HSV)
 cv.NamedWindow("HSV display")
 cv.ShowImage("HSV display", imgHSV)
 cv.WaitKey(0)
-`
+```
 
 The same code using Tippy would give :
 
-`
+```Python
 import cv
 import tippy as tp
 img = cv.LoadImage('test.jpg')
 imgHSV = tp.RGB2HSV(img)
 tp.DisplayWait("HSV display", imgHSV, 0)
-`
+```
 
 Hence, the main objective is to keep the best of OpenCV while reducing the amount of code needed to get things working smoothly. Tippy contains templates in which you simply have to fill the parts corresponding to your precise needs.