https://github.com/djeada/coin-detection
Training and using a machine learning model locally to accurately identify and count coins in a given image. This project includes data preprocessing, model training, and evaluation to ensure precise coin detection and counting.
https://github.com/djeada/coin-detection
image-recognition opencv python
Last synced: 5 months ago
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Training and using a machine learning model locally to accurately identify and count coins in a given image. This project includes data preprocessing, model training, and evaluation to ensure precise coin detection and counting.
- Host: GitHub
- URL: https://github.com/djeada/coin-detection
- Owner: djeada
- License: mit
- Created: 2022-11-05T17:30:28.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2025-06-13T19:19:24.000Z (7 months ago)
- Last Synced: 2025-06-13T20:28:49.241Z (7 months ago)
- Topics: image-recognition, opencv, python
- Language: Python
- Homepage:
- Size: 1.11 MB
- Stars: 3
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Coin-Detection
Training and using a machine learning model locally to accurately identify and count coins in a given image. This project includes data preprocessing, model training, and evaluation to ensure precise coin detection and counting.
## Demo
I. Example Input Image
II. Example Output Image
## Installation
To set up the project locally, follow these steps:
1. **Clone the repository:**
```bash
git clone https://github.com/djeada/kaggle-house-prices.git
```
2. **Install virtualenv (if it's not already installed)**:
```bash
pip install virtualenv
```
3. **Set up the virtual environment and install dependencies**:
```bash
cd kaggle-house-prices
virtualenv env
source env/bin/activate
pip install -r requirements.txt
```
4. **Run the project**:
```bash
cd src
python -m src.main
```
## Usage
To display the help message and learn about the available options, run the following command:
```bash
python -m src.main -h
```
The output should look like this:
```
usage: main.py [-h] [--path PATH] [--output_dir OUTPUT_DIR] [--r_min R_MIN]
[--r_max R_MAX] [--interactive INTERACTIVE] [--verbose VERBOSE]
[--image_output IMAGE_OUTPUT] [--csv_output CSV_OUTPUT]
options:
-h, --help show this help message and exit
--path PATH Path to the image
--output_dir OUTPUT_DIR
Path to the output directory
--r_min R_MIN Minimum radius of the coins
--r_max R_MAX Maximum radius of the coins
--interactive INTERACTIVE
Interactive mode
--verbose VERBOSE Verbose mode
--image_output IMAGE_OUTPUT
Should the images be displayed
--csv_output CSV_OUTPUT
Should the csv with coin coordinates be saved
```
### Example Command
If your image is located at '/home/user/images/image.png', and you want to specify coin radii between 20 and 30 pixels, display the images, and save the results to a CSV file, use the following command:
```bash
python -m src.main --path /home/user/images/image.png --r_min 20 --r_max 30 --image_output True --csv_output True
```
## Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
Please make sure to update tests as appropriate.
## License
[MIT](https://choosealicense.com/licenses/mit/)