
In this session, Introduction about the Google Colab is explained and executing the codes in google colab, mounting the google drive and accessing files in the google colab is discussed. Some basics about the Numpy is given.
In this session, a detailed explanation on gray images is done and the data component of the Gray Image is explained. Image Pixel values in a gray Image are explained and focus is on understanding the Image values and what they signify.
In this image, the differences between the 2D and 3D images is explained. The data of the colour images is explained and the three colour channels i.e., blue, green and red are separated and explained in detail. Converting image colour spaces is also explained.
In this session, the Image Threshold concepts are explained and examples are given with different images to show the steps to convert a gray image or colour image into a binary image by setting a threshold. This concept is the basic step in further analysis of the images using complex algorithms.
In this session, the colour detection is explained in detail for any random images. The colour detection is used in various places to detect and identify the required area or object in an image. Hence examples are given by using different images and objects with different colour are extracted from the image.
In this Session, the drawing functions in OpenCV are discussed. Various shapes can be drawn using the openCV functions and also the text insertion in an image is also discussed and examples are given so that you can understand the concepts clearly
This course is a practical explanation on using the Google Colab for executing the Image Processing algorithms using OpenCV module available in Python. The course starts with explanation about the Google Colab and executing few basic codes in Python and then the basics of Image Processing are explained. Working with gray Images and Colour Images is taken up next and conversion from colour to gray is also explained. Then the Image Threshold and colour detection is explained by taking random images as inputs. The drawing tools are explained using which the images can be marked, lines, polygons and shapes can be drawn using the functions available in python.
This course will explain the concepts of Image Processing and learn how to access the Image data for a 2D and a 3D Image and this course can be used a foundation to build more complex algorithms in Image Processing. The image data for 2D and 3D image is explained and the red, blue and green channel in the image are extracted to understand exactly what a colour image consists of. This helps the students to learn the algorithms better and apply it in any further image processing. The Image Threshold and colour detection concepts are also explained by taking the image data as example which ensures that you understand the concepts very clearly.