https://github.com/ivangael/person-detection-in-video-with-landmark-depth-mask

Real-Time Person Detection with Landmark Detection and Depth Estimation
https://github.com/ivangael/person-detection-in-video-with-landmark-depth-mask

depth-estimation landmark-detection mediapipe object-detection persondetection python torch torchvision ultralytics yolov5

Last synced: about 2 months ago
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Real-Time Person Detection with Landmark Detection and Depth Estimation

• Host: GitHub
• URL: https://github.com/ivangael/person-detection-in-video-with-landmark-depth-mask
• Owner: IvanGael
• Created: 2024-06-11T00:00:23.000Z (7 months ago)
• Default Branch: main
• Last Pushed: 2024-06-11T00:26:46.000Z (7 months ago)
• Last Synced: 2024-06-11T02:38:29.999Z (7 months ago)
• Topics: depth-estimation, landmark-detection, mediapipe, object-detection, persondetection, python, torch, torchvision, ultralytics, yolov5
• Language: Python
• Homepage:
• Size: 4.71 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
## Real-Time Person Detection with Landmark Detection and Depth Estimation

This project is aimed at detecting persons in real-time video streams, drawing landmarks on their bodies, and estimating the depth of each detected person. It utilizes computer vision techniques and deep learning models to achieve these tasks.

## Features

- Real-Time Person Detection: Utilizes YOLOv5, a state-of-the-art object detection model, to detect persons in real-time video streams.

- Landmark Detection: Employs MediaPipe Pose, a powerful pose estimation library, to detect landmarks on the bodies of detected persons.

- Depth Estimation: Utilizes MiDaS, a depth estimation model, to estimate the depth of each detected person.

- Visualization: Draws bounding boxes around detected persons, landmarks on their bodies, and overlays depth maps on the video frames for visualization.

## Prerequisites

Before running the project, ensure you have the following dependencies installed:

- Python 3.x

- OpenCV

- Torch

- NumPy

- Mediapipe

- Timm

You can install the dependencies by running:

````

pip install opencv-python opencv-python-headless torch torchvision matplotlib timm mediapipe

````

## Usage

To run the project, execute the following command:

````

py app.py

````

## Output

The output will be displayed in a new window showing the real-time video stream. Detected persons will be outlined with bounding boxes, landmarks will be drawn on their bodies, and depth maps will be overlayed on the frames.

** With landmarks drawing

![Demo](demo.png)

** Without landmarks drawing

![Demo](demo2.png)

## Acknowledgments

- YOLOv5: Ultralytics/YOLOv5

- MiDaS: Intel-isl/MiDaS

- MediaPipe: Google/MediaPipe

## License

This project is licensed under the MIT License.