Project Title: Python Object Detection using OpenCV and TensorFlow Lite Interpreter
Description: This project is an implementation of object detection in Python, leveraging the power of OpenCV (Open Source Computer Vision Library) and TensorFlow Lite Interpreter. Object detection is a computer vision task that involves identifying and locating objects within an image or video. In this repository, we provide a Python script that utilizes pre-trained models from TensorFlow Lite to perform real-time object detection. The TensorFlow Lite Interpreter is used for efficient inference on embedded devices. Additionally, the project utilizes a file named 'coco_labels.txt' which contains the labels (classes) used for object detection.
Key Features:
Utilizes OpenCV for image processing and manipulation. Integrates TensorFlow Lite Interpreter for efficient inference on embedded devices. Implements object detection using pre-trained models, enabling real-time performance. Provides a straightforward Python script for easy integration and usage. Offers flexibility for customizing and fine-tuning detection parameters. Dependencies:
OpenCV: Open Source Computer Vision Library for image processing tasks. TensorFlow Lite Interpreter: Lightweight TensorFlow runtime designed for mobile and edge devices. Python 3.x: Programming language used for scripting and development. Usage:
Clone the repository to your local machine. Ensure all dependencies are installed using pip install -r requirements.txt. Run the Python script object_detection.py. Optionally, modify the script to adjust detection parameters or use custom models. Contributions: Contributions to this project are welcome! Feel free to submit pull requests for bug fixes, enhancements, or additional features. Please adhere to the project's coding style and guidelines.
License: This project is licensed under the MIT License.
Disclaimer: This project is for educational and demonstration purposes only. While efforts have been made to ensure accurate object detection, no guarantees are provided regarding the performance or reliability of the system in all scenarios.
References: OpenCV Documentation: https://docs.opencv.org/ TensorFlow Lite Documentation: https://www.tensorflow.org/lite
Author: Christian Putzu