custom object detection using python and opencv | object detection using python | keras | tensorflow

Summary

In this tutorial, the creator demonstrates how to build a custom object detection model using Python and OpenCV. Starting with a web interface for training, users capture images of their chosen objects, which are then labeled and used to train the model. After successful training, the model's accuracy is tested with a webcam, detecting items with varying confidence levels. The video emphasizes the importance of adjusting confidence thresholds and refining the code for better performance. Viewers are encouraged to engage with the content, seek help for issues, and explore further customizations for their projects.

Takeaways

• 😀 This video guides viewers on how to create a custom object detection system using Python and OpenCV.
• 📷 To start, users must connect their webcam and select it for image capturing.
• 🖼️ Users need to collect images of custom objects they want to detect, such as bottles and mobile devices.
• 🔧 The script emphasizes the importance of labeling each object class accurately during the image collection process.
• ⏳ After capturing images, users are prompted to train the model, which can take some time.
• ✅ The video demonstrates testing the model's accuracy by showing detected objects and their confidence levels.
• 🛠️ Users are advised to install necessary libraries such as OpenCV and Keras via pip in their Python environment.
• 📂 The model can be exported in formats compatible with Keras and OpenCV for further use.
• 💻 The guide includes steps to set up a project in PyCharm for coding and testing the object detection model.
• 🔍 Finally, viewers are encouraged to adjust the confidence thresholds in the code to improve detection accuracy.

Q & A

What is the primary objective of the video?

The primary objective of the video is to demonstrate how to create a custom object detection model using Python and OpenCV.

What initial steps are required to start the project?

The initial steps include opening Chrome, searching for the Tech Machine website, and selecting 'Image Project' followed by 'Standard Image Model'.

How does the presenter collect images for training the model?

The presenter connects a webcam, positions it towards the objects, and uses the 'hold to record' feature to capture images of the custom objects.

What are the two classes mentioned for training in the video?

The two classes mentioned for training are a 'bottle' and a 'mobile phone'.

What is the significance of training the model?

Training the model is essential to enable it to recognize and accurately classify the specified objects based on the images collected.

What commands does the presenter use to install necessary libraries in PyCharm?

The presenter uses the commands 'pip install opencv-python' and 'pip install Keras' to install the required libraries.

How does the presenter test the accuracy of the model?

The presenter tests the accuracy of the model by using the webcam to check if it correctly detects and identifies the objects placed in front of it.

What method is used to export the trained model?

The presenter exports the trained model by selecting TensorFlow and saving it in Keras format, then downloads the model files.

How does the code help in determining detection probabilities?

The code prints out the probabilities of detected classes, allowing the user to see which object the model is most confident about identifying.

What final adjustments does the presenter suggest for better detection?

The presenter suggests adjusting confidence thresholds, displaying object names clearly on the output, and making necessary code modifications to enhance performance.