This repository contains code that performs instance segmentation using DensePose, which is part of Facebook's Detectron project. Instance segmentation involves detecting and segmenting individual objects within an image or video.
- Detectron2
- OpenCV
- NumPy
- Google Colab patches
To run the code, you need to have the required dependencies installed. You can install them using the following command:
pip install detectron2 opencv-python numpy- Clone the DensePose repository by running the following command:
git clone https://github.com/facebookresearch/detectron2- Navigate to the detectron2 directory.
- Install the necessary packages by running:
pip install -e .- Run the provided code in a Jupyter Notebook or similar environment.
The code includes the following steps:
- Load the DensePose model for object detection or instance segmentation, depending on the use case.
- Load an image or video file.
- Use the loaded model to perform instance segmentation on the input.
- Visualize the segmentation results using OpenCV and NumPy.
- Display the output with the segmented instances highlighted.
You can customize the code to suit your specific needs. For example, you can modify the confidence threshold for instance segmentation, adjust the visualization settings, or use different pre-trained models available in the Detectron2 model zoo.
The code in this repository is based on the DensePose implementation within the Detectron2 project by Facebook Research. The original repository can be found at:
- Detectron2 GitHub Repository (https://github.com/facebookresearch/detectron2)
- Detectron2: Object Detection and Segmentation Library (https://github.com/facebookresearch/detectron2)
- DensePose: Dense Human Pose Estimation In The Wild (https://github.com/facebookresearch/DensePose)
Please note that this README.md assumes the user is familiar with using Python, libraries like Detectron2 and OpenCV, and running code in a Jupyter Notebook or similar environment.