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https://github.com/nvsriram/keepervision
ECE Final Year Design Project: Using computer vision, image processing, and machine learning, KeeperVision physically guides goalkeepers in real-time to the best position to defend against any incoming shots.
https://github.com/nvsriram/keepervision
aws-rds-mysql aws-s3 computer-vision flask machine-learning yolov8
Last synced: 5 days ago
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ECE Final Year Design Project: Using computer vision, image processing, and machine learning, KeeperVision physically guides goalkeepers in real-time to the best position to defend against any incoming shots.
- Host: GitHub
- URL: https://github.com/nvsriram/keepervision
- Owner: nvsriram
- License: mit
- Created: 2023-07-19T18:30:08.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-05-07T17:54:40.000Z (7 months ago)
- Last Synced: 2024-05-07T20:27:29.944Z (7 months ago)
- Topics: aws-rds-mysql, aws-s3, computer-vision, flask, machine-learning, yolov8
- Language: Python
- Homepage: https://uwaterloo.ca/capstone-design/project-abstracts/2024-capstone-design-projects/2024-electrical-computer-engineering-capstone-designs#40
- Size: 138 MB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
## 💡Motivation
Soccer is the most popular sport with hundreds of millions of players and fans around the world. Many times, a key save can be the difference between winning and losing a game. KeeperVision is a training tool which aims to help goalkeepers in tackling their greatest hurdle - positioning. Using computer vision, image processing, and machine learning, KeeperVision physically guides goalkeepers in real-time to the best position to defend against any incoming shots.
## 🚀 Demo
https://github.com/nvsriram/KeeperVision/assets/50625504/f4d87b97-4a0c-4a7a-9e95-1bd64e781f94
## 🌐 API Reference
### `api/register/`
GET
Response
OK
Code
Content
✅
200
{"id": <player_id>}
❌
404
{"message": "Player <username> does not exist."}
POST
Body
{"email": <email>}
Response
OK
Code
Content
✅
200
{"id": <player_id>}
❌
400
{"message": "<some error message like duplicate key Integrity error>"}
### `api/session/`
GET
Response
OK
Code
Content
✅
200
{"player_id": <player_id>, "session_stats": <list of session_stats in desc order of session_end>}
❌
404
{"message": "Player <username> does not exist."}
POST
Body
{"session_stats": <session_stats JSON with all the fields>}
Files
File Name
Description
initial_image
Image file containing goalkeeper's initial position before session starts
final_image
Image file containing goalkeeper's final position at the end of session
Response
OK
Code
Content
✅
200
{"id": <session_id>}
❌
400
{"message": "<some error message like duplicate key Integrity error>"}
❌
404
{"message": "Player <username> does not exist."}
### `api/predict`
POST
Files
File Name
Description
image
Image file to be processed
Response
OK
Code
Content
✅
200
{"idx": <idx corresponding to direction to move>, "x": <offset in x direction>, "y": <offset in y direction>}
## 📈 Block Diagram
## 💻 Instructions on running locally
1. Ensure python is installed
2. Run `pip install -r requirements.txt` to install all dependencies
**NOTE:** Recommended to use venv before installing dependencies. See below for details on how to set it up
3. Run the Flask app using `python app.py`. This will run the app on debug mode on the server's IP
**NOTE**: Ensure the Flask app and the client are in the same WLAN to be able to connect to the server's IP as it is otherwise considered a private IP address
**[Optional]** To use venv:
1. Run `python -m venv ` to create virtual environment
2. To activate the virtual environment:
- If on Windows, run: `/Scripts/activate`
- If on MacOS, run: `source /bin/activate`
3. Run Step 2 to install all dependencies