Haar Cascade for image & video object classification - OpenCV w/ Python for Image Video Analysis 21 | Summary and Q&A

TL;DR

Learn how to train your own hard cascade for object detection using OpenCV in Python.

Key Insights

• ⌛ The training process for a Cascade in OpenCV can be time-consuming.
• #️⃣ Adjusting the number of stages and the number of samples can help continue the training process.
• 📁 The size of the Cascade file can impact the accuracy and memory requirements.
• 👨‍💻 The trained Cascade can be used for detecting various objects by training with different samples and modifying the code.
• 👻 The small size of the Cascade file allows for efficient recognition of numerous objects in real-world scenarios.
• 🧡 OpenCV provides a wide range of tools and functionalities for computer vision tasks.
• 🔠 Connecting with external APIs like Watson can enhance object recognition capabilities.

Transcript

what is going on everybody Welcome to the Moment of Truth another open CV with Python tutorial video covering how to make your very own hard Cascade for object detection of really any object of your desires so uh where we left off we were training the Cascade which took a pretty long time as you can see we basically started this process here at 8:1... Read More

Questions & Answers

Q: How long does it take to train a Cascade for object detection?

The training process can take a significant amount of time, depending on the number of stages and the size of the training set. In the tutorial, it took over 2 hours to train the Cascade with 10 stages.

Q: What can be done if the training process runs out of samples?

If the training process runs out of samples, the trainer suggests lowering the starting number of stages or using a lower number of samples than available. This allows for continuing the training process beyond the point where it originally stopped.

Q: Can the size of the Cascade file be reduced?

Yes, the size of the Cascade file can be reduced by training with smaller object sizes. However, this may impact the accuracy of detection. Additionally, training with larger object sizes will increase the file size and require more memory.

Q: Are there any other applications for the trained Cascade?

The trained Cascade can be used to detect objects other than watches. By training with different samples and adjusting the code, one can create and use Cascades for different objects of interest.

Summary & Key Takeaways

• The video tutorial covers the process of training a Cascade for object detection using OpenCV in Python.

• The trainer demonstrates adjusting the number of stages for the Cascade and explains how to continue building after running out of samples.

• The tutorial also shows how to use the trained Cascade to detect objects, in this case, a watch, using code examples.

• The trainer provides insights into the process of training and using the Cascade for object detection.