In this article, we will learn how to binarize an image using NumPy, and of course, we will use OpenCV for reading the image both in grayscale and RGB.
To understand what binary is — binary is something that is made of two things. In computer terminology, binary is just 0 and 1. If we were to relate the same in the images, it is said as black and white images where —
- 0 signifies Black
- 1 signifies White.
At the initial stages of learning image processing, we often think of a grayscale image as a binary image. Although it is not. But slowly when we pick up the subject, we realize how wrong we were. So, moving ahead, we will learn how to binarize the image with both using the library and without using the library (NumPy is used for matrix operations just to avoid the slowness of the program when used the regular for loops). Besides this, we will also use Matplotlib to plot the results.
The binary operation works really well for the grayscale images. The problem with the color (RGB) images is that each pixel is a vector representing 3 unique values one for Red, one for Green, and one for Blue.
A typical grayscale image’s matrix would look like —
array([[162, 162, 162, ..., 170, 155, 128],
[162, 162, 162, ..., 170, 155, 128],
[162, 162, 162, ..., 170, 155, 128],
...,
[ 43, 43, 50, ..., 104, 100, 98],
[ 44, 44, 55, ..., 104, 105, 108],
[ 44, 44, 55, ..., 104, 105, 108]], dtype=uint8)A typical RGB image’s matrix would seem like —
array([[[226, 137, 125], ..., [200, 99, 90]],
[[226, 137, 125], ..., [200, 99, 90]],
[[226, 137, 125], ..., [200, 99, 90]],
...,
[[ 84, 18, 60], ..., [177, 62, 79]],
[[ 82, 22, 57], ..., [185, 74, 81]],
[[ 82, 22, 57], ..., [185, 74, 81]]], dtype=uint8)If we were to separate R, G, and B pixels from the above matrix. We get.
R matrix
array([[226, 226, 223, ..., 230, 221, 200],
[226, 226, 223, ..., 230, 221, 200],
[226, 226, 223, ..., 230, 221, 200],
...,
[ 84, 84, 92, ..., 173, 172, 177],
[ 82, 82, 96, ..., 179, 181, 185],
[ 82, 82, 96, ..., 179, 181, 185]], dtype=uint8)G matrix
array([[137, 137, 137, ..., 148, 130, 99],
[137, 137, 137, ..., 148, 130, 99],
[137, 137, 137, ..., 148, 130, 99],
...,
[ 18, 18, 27, ..., 73, 68, 62],
[ 22, 22, 32, ..., 70, 71, 74],
[ 22, 22, 32, ..., 70, 71, 74]], dtype=uint8)B matrix
array([[125, 125, 133, ..., 122, 110, 90],
[125, 125, 133, ..., 122, 110, 90],
[125, 125, 133, ..., 122, 110, 90],
...,
[ 60, 60, 58, ..., 84, 76, 79],
[ 57, 57, 62, ..., 79, 81, 81],
[ 57, 57, 62, ..., 79, 81, 81]], dtype=uint8)Whatever operation we compute on the grayscale image, we will need to compute the same on the RGB image but for 3 times separating R, G, and B pixels and finally merging them as a proper RGB image.
