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#OPENCV CONVERT IMAGE FORMAT HOW TO#
#OPENCV CONVERT IMAGE FORMAT SKIN#
#OPENCV CONVERT IMAGE FORMAT CODE#

The H Component is very similar in both the images which indicates the color information is intact even under illumination changes.

Mostly used in compression ( of Cr and Cb components ) for TV Transmission.įigure 5 : Hue ( H ), Saturation ( S ) and Value ( V ) components in HSV color space.

Separates the luminance and chrominance components into different channels.

This color space has the following properties.

Cb = B – Y ( how far is the blue component from Luma ).

Cr = R – Y ( how far is the red component from Luma ).

Y – Luminance or Luma component obtained from RGB after gamma correction.

The YCrCb color space is derived from the RGB color space and has the following three compoenents.

The respective values of Green, Orange and Red ( which are the extremes of the A Component ) has not changed in the B Component and similarly the respective values of Blue and Yellow ( which are the extremes of the B Component ) has not changed in the A component.

The A and B components which contain the color information did not undergo massive changes.

It is pretty clear from the figure that the change in illumination has mostly affected the L component.

We can convert between different colorspaces using the OpenCV function cvtColor() as will be shown later.įigure 3 : The Lightness ( L ), and color components ( A, B ) in LAB Color space. It will get loaded in BGR format by default.

Instead, we will develop a basic intuition and learn some important properties which will be useful in making decisions later on. We will not describe the theory behind them as it can be found on Wikipedia. In this section, we will cover some important color spaces used in computer vision. The right threshold values for segmentation.Then we will jump into some analytics and use a systematic way to choose:.

We will apply a simple color segmentation algorithm as done by Mark and ponder over its weaknesses.

#OPENCV CONVERT IMAGE FORMAT HOW TO#

First we will see how to read an image in OpenCV and convert it into different color spaces and see what new information do the different channels of each color space provide us.

Let’s see how we can help him build a robust color detection system for his robot.

#OPENCV CONVERT IMAGE FORMAT SKIN#

We face this problem in many computer vision applications involving color based segmentation like skin tone detection, traffic light recognition etc. Like many other amateur computer vision enthusiasts, he was not taking into account the effect of different lighting conditions while doing color segmentation. He asked me for help and I immediately understood where he was going wrong.

#OPENCV CONVERT IMAGE FORMAT CODE#

While his color segmentation code worked pretty well during evenings in his room, it fell apart during daytime outside his room! He was trying to use color segmentation to find the current state of the cube. So, when a few days back my friend, Mark, told me about his idea of building a computer vision based automated Rubik’s cube solver, I was intrigued. This invention now known as the Rubik’s Cube took the world by storm selling more than 350 million by January 2009. In 1975, the Hungarian Patent HU170062 introduced a puzzle with just one right solution out of 43,252,003,274,489,856,000 (43 quintillion) possibilities. We will also share demo code in C++ and Python. In this tutorial, we will learn about popular colorspaces used in Computer Vision and use it for color based segmentation.