How to Convert a Colored Image to HLS in OpenCV using Python?

OpenCV is an open-source library for programming image and video datasets. It simplifies image and video processing by providing a set of easy-to-use functions for loading, manipulating, and saving image and video files. OpenCV also provides many algorithms for color conversion, which enables you to convert images from one color space to another.

One color space that is widely used in computer vision is the HLS (Hue, Lightness, Saturation) color space. The HLS color space is commonly used due to its perceptual uniformity, which means that a small change in any of the HLS components will result in a small change in the color appearance.

In this article, we will show how to convert a colored image to HLS in OpenCV using Python. We will provide sample code and explain the steps involved in the process.

Prerequisites

Before you can convert an image to HLS in OpenCV, you need to make sure that you have installed OpenCV and Python on your computer. You can download OpenCV from the official OpenCV website, and Python can be downloaded from the Python website.

Sample Code

The following code demonstrates how to convert a colored image to HLS in OpenCV using Python:

import cv2

image = cv2.imread('colored_image.jpg')
hls = cv2.cvtColor(image, cv2.COLOR_BGR2HLS)
cv2.imwrite('hls_image.jpg', hls)

In the above code, we first use the cv2.imread() function to read the colored image file from the disk. The image is stored in the image variable as a 3D NumPy array.

Next, we use the cv2.cvtColor() function to convert the image to HLS color space. The first argument to this function is the input image, and the second argument is the conversion code. In our case, we use cv2.COLOR_BGR2HLS as the conversion code to indicate that we want to convert from the BGR color space to the HLS color space.

Finally, we write the converted image to the disk using the cv2.imwrite() function. The first argument is the filename to save the image, and the second argument is the image array.

Explanation

Now let’s explain the above code line by line:

import cv2

The first line imports the OpenCV library into the Python code. This is required to use OpenCV functions in the code.

image = cv2.imread('colored_image.jpg')

The second line uses the cv2.imread() function to read the colored image from the disk. The function takes one argument, which is the filename of the image to be loaded. The image is stored in the image variable as a 3D NumPy array.

hls = cv2.cvtColor(image, cv2.COLOR_BGR2HLS)

The third line uses the cv2.cvtColor() function to convert the image from the BGR color space to the HLS color space. The function takes two arguments: the input image and the conversion code. The conversion code cv2.COLOR_BGR2HLS indicates that we want to convert the image to HLS color space.

cv2.imwrite('hls_image.jpg', hls)

The fourth line uses the cv2.imwrite() function to save the converted image to the disk. The function takes two arguments: the filename to save the image and the image array.

By using the above code, you can easily convert any colored image from BGR to HLS. The resulting image will be saved to the disk with the filename specified in the cv2.imwrite() function.

Conclusion

In this article, we have shown how to convert a colored image to HLS in OpenCV using Python. We have provided sample code and explained the steps involved in the process. We hope that this tutorial has been useful to you, and that you can use this knowledge to build applications that manipulate image and video datasets. If you have any questions or comments, please leave them below.