How to Create a Trackbar as the HSV Color Palette using OpenCV Python?

When it comes to image processing and computer vision tasks, OpenCV is one of the most widely used libraries. Python, being one of the most popular languages, has a great integration with OpenCV. One of the many things you can do with OpenCV is creating a HSV color palette using trackbars. In this article, we will guide you through the process of creating a trackbar as the HSV color palette using OpenCV Python.

What is HSV Color Space?

Color space is an abstract mathematical model describing colors as tuples of numbers. HSV (Hue, Saturation, Value) is a color space that represents colors in terms of their shade, saturation, and brightness. Hue is the color type (red, green, blue, etc.) Saturation is the intensity or purity of the color, and Value is the brightness. In OpenCV, colors are often represented in the HSV color space.

Why Create a HSV Color Palette with Trackbars?

We can create a HSV color palette with trackbars to give the user the ability to change the HSV values of the image in real-time. This is incredibly useful because it allows us to fine-tune the values to get the desired output. It is also a great GUI experience for users who need to interact with the image and understand its color properties.

How to Create a Trackbar as the HSV Color Palette in OpenCV Python

Here are the steps to create a trackbar as the HSV color palette in OpenCV Python:

1. Import libraries: We will first import the necessary Python libraries, including OpenCV and NumPy.

import cv2
import numpy as np

2. Create a blank image: We will create a blank image of 250 pixels by 300 pixels.

img = np.zeros((250, 300, 3), np.uint8)

The “0” value sets the pixel color value to black. Setting the image size to 250×300, we also set the third parameter to 3, which means that the image is a 3-channel image (RGB) or a BGR format that OpenCV uses.

3. Create the trackbars: We will create three trackbars for hue, saturation, and value. The maximum value for each trackbar is 180 because HSV values range from 0 to 180.

cv2.namedWindow("image")

cv2.createTrackbar("H", "image", 0, 180, lambda x: None)
cv2.createTrackbar("S", "image", 0, 255, lambda x: None)
cv2.createTrackbar("V", "image", 0, 255, lambda x: None)

The cv2.namedWindow() function creates a window with the given window name. cv2.createTrackbar() function creates a trackbar for the given name (H, S, V), in the specified window (“image”). The arguments following the window name are the default value, maximum value, and a lambda function with an unused argument x. The lambda function has to be there but does nothing useful because it is called implicitly when the user moves the trackbar. The first argument of the lambda function is ‘x’, which is the new value of the trackbar.

4. Create the while loop: We will create an infinite while loop to keep the image displayed until the user closes it.

while True:
    cv2.imshow("image", img)
    key = cv2.waitKey(1)
    if key == 27:
        break

In the while loop, we display the image using the cv2.imshow() function. The cv2.waitKey() function holds the image on the screen until the user presses a key. The “1” argument specifies a time delay of one millisecond. If the user presses the escape key (key code 27), we break out of the loop.

5. Get the trackbar positions: Inside the while loop, we will get the positions of the trackbars and update the image accordingly.

    h = cv2.getTrackbarPos("H", "image")
    s = cv2.getTrackbarPos("S", "image")
    v = cv2.getTrackbarPos("V", "image")

    img[:] = [h, s, v]

The cv2.getTrackbarPos() function receives the name of the trackbar we set in step 3 and the name of the window. It then returns the current position of the trackbar. We update the color of the image by assigning a list containing the positions of the Hue, Saturation, and Value trackbars.

Conclusion

In conclusion, creating a trackbar as the HSV color palette using OpenCV Python is straightforward. Wecreated a blank image, created three trackbars for Hue, Saturation, and Value, and created an infinite while loop to keep the image displayed until the user closes it. Inside the while loop, we got the positions of the trackbars and updated the image accordingly. This provides an excellent GUI experience for users who may need to fine-tune the HSV values of an image to achieve their desired output.

Learning how to create a trackbar as the HSV color palette in OpenCV Python opens up a world of possibilities for image processing and computer vision tasks. With this knowledge, one can easily manipulate the HSV values of images and perform various operations like color thresholding, image segmentation, color filtering, and much more.

In conclusion, mastering OpenCV Python can be challenging, but with the right learning resources and practice, anyone can become an expert. Creating a trackbar as the HSV color palette is just one of the many things you can do with OpenCV Python. We hope this article has been insightful enough to get you started in your journey towards mastering OpenCV Python.