In this tutorial, we shall discuss the concept of gradients. In the previous tutorial, we have seen the morphological operators. This tutorial is the extension of morphological operators. The concept of gradients will be useful for understanding edge detection, which is necessary for object detection, object tracking, etc.
An image gradient is a directional change in the intensity of colour in an image.
Sobel-Feldman operator can be used for the identification of the intensity in an image.
Gradients cannot be calculated in one single direction. We can calculate them in multiple directions.
The Sobel operator uses the 3*3 kernels, to convolute the original image into various gradients.
You can explore a brief about this operator and the math behind this operator. We shall combine the concepts with other image processing techniques and explore the gradient operators with Open CV.
Now, let us look into the practical in the Jupyter Notebook. Initially import all the required libraries and a function to convert the image into grayscale format.
Now, load the image using the function and display it which will be shown as a grayscale image.
Using the Sobel operator with the OpenCV can get the display of the image gradient in the x-direction.
Similarly, calculate for the y-direction by changing the arguments of the function accordingly. In this analysis, the gradient is shown in the y-direction.
Similarly, we can use the laplacian view of the image, which will show the image in both x and y directions.
The image view in the laplacian form is clear with all the things on it. Although some noise has been present, which can be eliminated for better identification of the image. This concept of the view of image can be used in future explanations and discussions for image processing projects.
We can use the concept of blending images as discussed in the previous tutorials, to get the better image by blending the laplacian view, x-directional view and y-directional view. Different weights can be given in the function of blending to obtain a better image.
The concept of the morphological operator as discussed in the previous tutorial can be applied over this image to reduce the noise and apply the various effects to have a better gradient for the image.
The concept of the threshold can also be used to the image obtained by the Sobel operator. This thresholding can be useful to obtain a better image by reducing the noise and the effects. This view of the gradient will be quite good in the grayscale format.
Similarly, the concept of the gradients can be used for the further image processing projects, to identify the numbers in the puzzle and automatically fill them by merging the various concepts of image processing using OpenCV.
This is all about the end of this tutorial, about the concept of the gradients. For any sort of doubts or questions in this tutorial, you can reach us through the comment box.