How to Use OpenCV Python Computer Vision Teaching Examples

Oct 13, 2020 | Data Science

Are you a budding computer scientist looking to delve into the fascinating world of computer vision? If so, the OpenCV Python examples developed for teaching at Durham University could be just what you need! Under the guidance of Prof. Toby Breckon, these examples serve as a fantastic resource for learning how to process and analyze visual data using Python and OpenCV.

Getting Started with OpenCV Examples

To embark on your computer vision journey, follow these straightforward steps to download and execute the examples.

Step 1: Downloading the Examples

  • You can clone the repository directly using Git:
  • git clone https://github.com/tobybreckon/python-examples-cv.git
  • Navigate to the cloned directory:
  • cd python-examples-cv
  • Run one of the example scripts:
  • python3 .insert_file_name_of_one_of_the_examples.py [optional_video_file]

Step 2: Running the Examples

Most examples can run with either a webcam or an optional video file format supported by OpenCV. For specific functionality, use the command:

python3 .generic_interface.py -h

This will display the help message and optional arguments such as camera selection and image rescaling.

Understanding the Basics

Let’s think of running these examples as operating a video store. You can borrow a video (the script) and play it on your screen (your computer) using a specific way to get it running (the commands). Some scripts may require specific types of videos, much like how a certain player is needed for specific formats at a video store.

Reusable Components

The codebase contains re-usable Python classes that enhance your experience:

  • camera_stream.py: This class provides a seamless flow of camera frames without delays.
  • h_concatenate(): A function that allows you to neatly display images side-by-side, adjusting for different sizes.

Troubleshooting Common Issues

In case you encounter challenges while using the examples, consider the following troubleshooting ideas:

  • Ensure that you have installed all dependencies correctly. Missing libraries can cause your program to fail.
  • If a script does not run as expected, check the video file format. OpenCV supports various formats, so ensure compatibility.
  • Don’t forget that pressing f switches to fullscreen mode, while x will exit any running program.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.

References

For academic referencing of these examples, please ensure you consult the related research papers and provide appropriate citations.

Now that you are equipped with this guide, jump in and start exploring the exciting world of computer vision with OpenCV!

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