Introduction to Image Processing
This entire series of tutorials covers the basics of image processing using OpenCV library with python to the advanced deep learning. In this tutorial you will get to about introduction to image processing
Let us look into the course curriculum.
The entire tutorials include the concepts of basic image, NumPy basics of storing the images, OpenCV operations, video processing with OpenCV, object detection, object tracking, deep learning of OpenCV using Keras and Tensorflow with a wide range of real-time project application explanation.
You will need to work on an IDE to write, edit and run your codes. If you are advanced you can continue with your own preferred IDE, but if you are beginner learning image processing it is better to use the Jupyter lab which is preferred by me in the complete tutorials of image processing.
Initially, one must download the anaconda from here https://www.anaconda.com/products/individual and install them on your operating system accordingly.
Later, open the Jupyter notebook in the Anaconda.
Now, install the requirements to work with the image processing that includes the list of libraries and set up the environment to save and run the files accordingly.
Finally, you can activate the environment in the anaconda command prompt and run the Jupyter lab to open up the Jupyter notebook on your browser.
What is an Image?
Now, let us know about the basic concept of an image. Generally, in the real world, we look at the images with different colors, but the computer read the images in the form of data. This data is in the form of pixels.
What is a Pixel?
In a digital image, the pixel is the smallest element that can represent an image with combination and control the colours and effects on the image. Generally, on the large screens, the representation of the image is done as the rectangular pixels, but the structure of pixel range in different shapes.
Representation of a Grayscale Image in an Array
Considering a grayscale image, which is in the black and white format.
The image is storage is done in the array with different pixel values present in the image. For an 8 bit image, the pixel values range from 0 to 255. So, the normalisation of the 256 values is done between 0 and 1. Thus in the array, the storages of values lie between 0 to 1 with floating digits.
The colour image is defined with the combination of the primary colours – Red, Green, Blue (RGB). Each primary colour with different intensity values will represent the image with different colour variations.
Reading a Colour Image
The representation of the colour image is done with the combination of three primary colours as discussed above. The colour image will have three dimensions that include:
- Colour Channels
That means the reading of the colour image will be like (560,720,3), which means 560-pixel height, 720-pixel width and 3 colour channels. The colour image is stored in the 3-dimensional array, with the values that represent the grayscale image in three different colour intensity channels.
Numpy is the numerical python library, that plays a key role in storing the values in the arrays up to n dimensions. To know much about Numpy you can read this tutorial Introduction to Numpy.
And few operations of the Numpy library like slicing and indexing are very much important concepts to start the journey in the image processing. If you are a beginner and interested to learn about the indexing and slicing you can go through this tutorial Indexing and Slicing in Numpy.
This is all about the introduction to the image processing with the basic concepts, that play a major role to work with concepts in the upcoming tutorials. For any sort of doubts or questions, you can reach us through the comment box. If you like this tutorial, hit the like button located on the left side top position at the beginning of this tutorial.