OpenCV Tutorial

OpenCV (Open Source Computer Vision Library) is an open-source computer vision and machine learning software library.

OpenCV is composed of a series of C functions and a small number of C++ classes, and provides interfaces for languages such as Python, Java, and MATLAB.

OpenCV provides a large number of computer vision algorithms and image processing tools, widely used in the fields of image and video processing, analysis, and machine learning.

OpenCV's design goal is to provide a simple and easy-to-use foundational computer vision library, helping developers quickly build complex vision applications.

Before learning this tutorial, you need to know

OpenCV supports multiple programming languages, but Python and C++ are the two most commonly used. Python syntax is simple and easy to learn, suitable for beginners.

Before starting the OpenCV tutorial, you need to have basic Python knowledge. If you are not familiar with Python, you can read our tutorial:

Development History

The OpenCV project was initially launched by Intel in 1999, focusing on CPU-intensive tasks, as part of a project that included ray tracing and 3D display.

  • In 1999, the OpenCV project was launched by Intel Research, aiming to promote the research and application of computer vision.

  • In 2000, the first version of OpenCV was released.

  • In 2006, OpenCV 1.0 was released.

  • In 2009, OpenCV 2.0 was released, adding support for Python.

  • In 2015, OpenCV 3.0 was released, adding support for deep learning.

  • In 2018, OpenCV 4.0 was released, further optimizing performance and functionality.

    • In 2020: OpenCV 4.x versions were released, further strengthening support for modern computing platforms (such as CUDA, OpenCL), and adding more machine learning and computer vision features.

Example

OpenCV reads and displays an image:

Example

# Import the OpenCV library, and use the alias cv instead of cv2
import cv2 as cv

# Replace "path/to/image" with the actual image path, for example "cat.jpg" or "C:/images/dog.png"
img = cv.imread("path/to/image")

# If the image path is wrong or the file does not exist, cv.imread() will return None
if img is None:
    # Print error message
    print("Error: Could not load image.")
    # Exit the program
    exit()

# "Display window" is the name of the display window, which can be customized
# img is the image data to be displayed
cv.imshow("Display window", img)

# Wait for a key press
# Parameter 0 means wait indefinitely until the user presses any key
# The return value k is the ASCII code value of the key pressed by the user
k = cv.waitKey(0)

# Check whether the user pressed the Esc key (ASCII code 27)
if k == 27:
    # Close all OpenCV windows
    cv.destroyAllWindows()

Related Links

Other Extensions