https://github.com/alekseyscorpi/fruits-vegetables-cv
PET CV project. The application simulates the operation of smart scales, which themselves classify the product and offer options to the customer.
https://github.com/alekseyscorpi/fruits-vegetables-cv
classification computervision cv2 detection fruits-and-vegetables nano pillow psycopg2 python python3 python311 small yolov8
Last synced: about 2 months ago
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PET CV project. The application simulates the operation of smart scales, which themselves classify the product and offer options to the customer.
- Host: GitHub
- URL: https://github.com/alekseyscorpi/fruits-vegetables-cv
- Owner: AlekseyScorpi
- Created: 2024-10-13T09:45:23.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-03-29T11:40:28.000Z (about 1 year ago)
- Last Synced: 2025-04-16T01:54:01.424Z (about 1 year ago)
- Topics: classification, computervision, cv2, detection, fruits-and-vegetables, nano, pillow, psycopg2, python, python3, python311, small, yolov8
- Language: Python
- Homepage:
- Size: 21.9 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 🍏 Python FRUITS-VEGETABLES SMART SCALES PET PROJECT
🚀 **PET project**, featuring:
- 📸 **YOLO (nano/small) models** for detecting fruits and vegetables (YOLOv8)
- 🖥️ **Qt application** (simulation of smart scales for automatic product detection)
- 🗄️ **Database** (PostgreSQL in a Docker container)
👉 Repository with the container: [fruits-vegetables-cv-docker](https://github.com/AlekseyScorpi/fruits-vegetables-cv-docker)
---
## 🛠 Installation Guide
### 1️⃣ Create and activate a virtual environment:
```bash
python -m venv venv
source venv/bin/activate # Linux/macOS
venv\Scripts\activate # Windows
```
### 2️⃣ Install dependencies:
```bash
pip install -r requirements.txt
```
### 3️⃣ Configure the `.env` file:
Create a `.env` file and add the following parameters:
| Variable | Example Value | Description |
|---------------------|-------------------------|----------|
| `DEBUG` | `TRUE` or `FALSE` | Debug mode |
| `MODEL` | `NANO` or `SMALL` | YOLO version |
| `DATABASE_DBNAME` | `smart_scales_db` | Database name |
| `DATABASE_USERNAME`| `smart_scales_user` | Database user |
| `DATABASE_PASSWORD`| `your_password` | Database password |
| `DATABASE_HOST` | `localhost` or IP | Database host |
| `DATABASE_PORT` | `5432` (or `5433`) | Database port |
🔹 **Note**:
The parameters `DBNAME`, `USERNAME`, and `PASSWORD` are set in the PostgreSQL container. Details can be found in the repository [fruits-vegetables-cv-docker](https://github.com/AlekseyScorpi/fruits-vegetables-cv-docker).
---
## 🚀 Running the Application
Simply execute the following command:
```bash
python main.py
```
---
## 🏆 About the Models
📖 More details about our models and the problem-solving approach can be found in our article:
📝 **[elibrary.ru/item.asp?id=80257050](https://elibrary.ru/item.asp?id=80257050)** (in Russian)
📂 **Training logs and model weights 1-YOLOv8 (small), 2-YOLOv8 (nano), 3-YOLOv11 (nano), 4-YOLOv11 (small)**:
📎 **[Google Drive](https://drive.google.com/file/d/1d2SuxCLBYriJ4DaPlrhQIz8JvasaTVLF/view?usp=drive_link)**
---
## ✉ Contact
📧 **Email**: timoshin_aleksey02@mail.ru
🐙 **GitHub**: [AlekseyScorpi](https://github.com/AlekseyScorpi)