Description:
NumPy is a powerful library for numerical computing in Python. It is useful for image processing and manipulation. With NumPy, we can easily perform operations on large arrays of numerical data and manipulate images in a variety of ways. For example, we can create or modify images in a variety of ways, including changing the brightness, contrast, or color balance, or adding filters and effects.
NumPy can also be used to perform more complex tasks, such as feature extraction, image segmentation, and image classification. In addition, NumPy can be used to create 3D models, animations and more.
Images to talk about can be considered as made up of arrays and we can use NumPy to perform different image processing tasks from scratch. In this article, we will learn about image processing that can easily be done using NumPy. So, if you are a beginner and want to understand image processing from the very basics, this stepwise tutorial is going to be quite useful.
Image processing Video
Coding Steps for Images Processing with NumPy
(I) Loading Image
Step 1. Import the NumPy library with a name “np” and import the pyplot function from the matplot library as “plt” name.
Step 2. Add sample image with sample_image variable with image read function of pyplot.
Note: Here we can see image in the raw form. Since mathematically, the images are made up of pixel values. In the above output, we can see some numbers that define the colors in the image and the image is basically the NumPy array. We can also plot the image using the matplotlib library.
Note: The image has 8 rows and 8 columns in three dimensions (Red, Green, Blue): 8x8x3
(II) Color Separation
Step 3. Now, filter the image into three layers of RGB (Red, Green, Blue) with help of the indexing method of the NumPy array. Each image is made of pixel values that represent 3 integers known as the RGB value of its color. We need to pull out the correct slice of the image array to separate the image in these colors.
Output:-
Step 4. Now, do the mathematical operations with numpy arrays.
A. Multiplication:
B. Addition
Step 5. Convert any data type into an array.
Step 6. Do basic indexing and slicing.
Conclusion:
In conclusion, Numpy is an incredibly powerful tool for performing mathematical calculations. It allows you to work with large datasets and perform advanced calculations with ease. It is also used to construct powerful machine-learning models and is an essential tool for data scientists. With its versatile and powerful features, Numpy is a must for any data scientist looking to perform complex mathematics with ease.
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