OpenCV Python ŌĆō How to Convert a Colored Image to a Binary Image?

Images are an essential part of data processing in computer vision, making image file processing a significant application. OpenCV is a vital tool that supports image processing functions, making it possible to process images in real-time with different image processing techniques.

One common problem in image processing is the need to convert colored images to binary images. A binary image is a type of image that contains only two colors, usually black and white. This article will discuss how to convert a colored image to a binary image using OpenCV and Python.

Understanding Colored and Binary Images

Before discussing how to convert a colored image to a binary image, let’s define colored and binary images.

A colored image consists of three color channels- Red, Green, and Blue (RGB). Each color channel is represented by an 8-bit integer, which has values ranging between 0 to 255. The combination of these values creates a unique color for each pixel in the image.

A binary image, on the other hand, consists of only two colors: black and white. In a binary image, black represents the value 0, while white represents the value 255. The image can be seen as a matrix of integers with each element having a value of 0 or 255.

Converting Colored Images to Binary Images

The process of converting a colored image to a binary image involves several steps. Let’s look at each of them.

Step 1 – Importing the necessary libraries

The first step is to import the OpenCV library, which is used for image processing. We also need to import the NumPy library, which is used for mathematical operations on arrays and matrices.

import cv2
import numpy as np

Step 2 – Reading the image

The next step is to read the input image using cv2.imread(). This function reads an image from the specified file path and returns an ndarray.

image = cv2.imread('input-image.jpg')

Step 3 – Preprocessing

Before we convert the colored image to a binary image, we need to preprocess the image. The preprocessing step involves the following:

3.1 – Grayscale conversion

The first step in preprocessing is to convert the colored image to grayscale using cv2.cvtColor(). Grayscale conversion simplifies the image’s color information and reduces the image’s dimensionality while preserving its essential features.

gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

3.2 – Gaussian blurring

The next step is to apply Gaussian blurring to the grayscale image using cv2.GaussianBlur(). Gaussian blurring is a preprocessing technique used to reduce the high-frequency noise present in the image.

blurred = cv2.GaussianBlur(gray, (5, 5), 0)

Step 4 – Thresholding

Thresholding is the process of converting the grayscale image to a binary image by setting a threshold value. Pixels with a value greater than the threshold value are set to white, while pixels with a value less than the threshold value are set to black. OpenCV provides several thresholding techniques that we can use. In this article, we will be using the adaptive thresholding technique.

4.1 – Adaptive thresholding

Adaptive thresholding is an advanced thresholding technique that adjusts the threshold value for small regions in the image. This technique is preferred over global thresholding when different parts of the image have different lighting conditions.

binary = cv2.adaptiveThreshold(blurred, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 2)

Step 5 – Saving the binary image

The final step is to save the binary image using cv2.imwrite(). This function writes an image to a file at the specified path.

cv2.imwrite('binary-image.jpg', binary)

Final Code

Here’s the complete code for converting a colored image to a binary image using OpenCV and Python:

import cv2
import numpy as np

# Reading the image
image = cv2.imread('input-image.jpg')

# Preprocessing
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blurred = cv2.GaussianBlur(gray, (5, 5), 0)

# Thresholding
binary = cv2.adaptiveThreshold(blurred, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 2)

# Saving the binary image
cv2.imwrite('binary-image.jpg', binary)

Conclusion

In conclusion, converting a colored image to a binary image is an essential process in image processing, and OpenCV makes it easy to achieve this task. The article has covered the necessary steps involved in converting a colored image to a binary image. It is essential to note that there are other thresholding techniques that can be used to convert a colored image to a binary image. It is recommended that you explore and experiment with these techniques to find the most suitable one for your image processing task.

OpenCV is a powerful tool that can be used for a wide range of image processing tasks, including filtering, segmentation, feature extraction, and object recognition. The libraries provide functions that support different programming languages, making it easy to integrate with different applications.

Through this article, we have seen how to convert a colored image to a binary image using Python and OpenCV’s essential functions. You can now apply the concepts learned in this article to convert colored images to binary images in your image processing tasks.