Introduction to Computer Vision with OpenCV in Python

OpenCV, short for Open Source Computer Vision Library, is an open-source computer vision and machine learning software library. It’s a significant library in the field of computer vision, with powerful capabilities ranging from basic image processing tasks to complex machine learning algorithms. In this tutorial, we will explore the basics of OpenCV with Python, including installation, image reading, image processing, and edge detection.

This article is perfect for Python programmers at all levels, whether you’re just getting started with computer vision or looking to enhance your skills. So let’s dive in!

Installation of OpenCV
Reading, Writing, and Displaying Images
Image Processing
Edge Detection

Installation of OpenCV

Before working with OpenCV, you need to install it. OpenCV can be easily installed from pip. In your terminal, simply type:

pip install opencv-python

Now, to validate if OpenCV is installed correctly, type the following into your Python shell:

import cv2
print(cv2.__version__)

If OpenCV is successfully installed, it should print out the version number.

Reading, Writing, and Displaying Images

Once we have OpenCV installed, we can start using it to read and display images.

Reading an image in OpenCV is straightforward. The method imread() is used to read an image. You just need to provide the path of your image file.

# Read an image using OpenCV
import cv2
image = cv2.imread("C:/Users/PythonTimes/Desktop/path/to/your/image.jpg")

If the image is read successfully, it will be stored in the ‘image’ variable as a NumPy array.

To display the image, the OpenCV function imshow() can be used. Here’s how you do it:

cv2.imshow('image', image)
cv2.waitKey(0) 
cv2.destroyAllWindows()

The waitKey(0) allows us to close the image window by pressing any key, and destroyAllWindows() closes all windows we created.

In order to save an image, we use the OpenCV function imwrite(). The following code writes the image to a new file:

cv2.imwrite('output.jpg', image)

This will save the image in JPEG format in the path specified.

Image Processing

With OpenCV, image processing techniques can be applied to images. Let’s explore a few of them.

Changing Colorspaces

OpenCV allows you to convert images from one color space to another, such as BGR to Grayscale, BGR to HSV and vice versa. For this, we use the function cvtColor().

For example, here’s how we can convert an image from BGR to Grayscale color space:

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

Image Thresholding

Image Thresholding is a simple, yet effective, method of segmenting an image. Here, we set a threshold value, and then all the pixel values of the image above that threshold value are assigned one value (may be white), and all the values below that threshold value are assigned another value (may be black).

ret, threshold_image = cv2.threshold(gray_image, 127, 255, cv2.THRESH_BINARY)

Here, the function cv2.threshold() is used for thresholding. The second argument is the threshold value which is used to classify the pixel values.

Edge Detection

Edge detection is an important concept in computer vision, and the Canny edge detection technique is an often-used, multi-stage algorithm which helps us detect a wide range of edges in images.

Here’s how to perform a basic edge detection using Canny algorithm in OpenCV:

edges = cv2.Canny(image, 100, 200)

The function cv2.Canny() performs the edge detection. The second and the third arguments are, respectively, the minVal and maxVal. Any edges with intensity gradient more than maxVal are sure to be edges and those below minVal are sure to be non-edges, so discarded.

In conclusion, we’ve seen that OpenCV is a powerful tool for performing various operations on images. We’ve gone through how to install and use OpenCV in Python, image reading, writing, displaying, some basic image processing techniques, and an introduction to edge detection. Remember, this guide is just touching the surface of what’s possible with OpenCV. We encourage you to explore it further and unlock its full potential.

Remember, this guide is a gateway to your journey with OpenCV and Python. The more you explore and practice, the more proficient you will get. Happy coding!

References

OpenCV documentation: https://docs.opencv.org/master/
OpenCV Python Tutorials: https://opencv-python-tutroals.readthedocs.io/en/latest/index.html
Computer Vision I: https://www.coursera.org/specializations/computer-vision

Disclaimer: Due to differences between different operating systems and also different setups, some instructions might not work properly for everyone. Please see official OpenCV documentation for thorough guidelines.

Introduction To Computer Vision With Opencv