OpenCV Tutorial

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

OpenCV consists of a series of C functions and a small number of C++ classes, and also 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 image and video processing, analysis, and machine learning.

OpenCV is designed to provide a simple and easy-to-use basic computer vision library, helping developers quickly build complex vision applications.

What you need to know before learning this tutorial

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

Before learning the OpenCV tutorial, you need to have a basic understanding of Python. If you are not yet familiar with Python, you can read our tutorial:

History

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

  • In 1999, the OpenCV project was initiated by Intel Research to promote research and applications in 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

Read and display an image with OpenCV:

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, e.g., "cat.jpg" or "C:/images/dog.png"
img = cv.imread("path/to/image")

# If the image path is incorrect or the file does not exist, cv.imread() returns 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; it can be customized
# img is the image data to be displayed
cv.imshow("Display window", img)

# Wait for key input
# 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()

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