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https://github.com/williyam-m/flipkart_grid_6_0

Flipkart Grid 6.0 (Robotics)
https://github.com/williyam-m/flipkart_grid_6_0

cnn django keras machine-learning ocr opencv pytesseract python sqlite tensorflow tesseract

Last synced: 15 days ago
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Flipkart Grid 6.0 (Robotics)

Host: GitHub
URL: https://github.com/williyam-m/flipkart_grid_6_0
Owner: williyam-m
License: mit
Created: 2024-09-23T15:17:24.000Z (about 2 months ago)
Default Branch: main
Last Pushed: 2024-10-22T07:50:39.000Z (17 days ago)
Last Synced: 2024-10-23T11:15:50.625Z (16 days ago)
Topics: cnn, django, keras, machine-learning, ocr, opencv, pytesseract, python, sqlite, tensorflow, tesseract
Language: Python
Homepage:
Size: 14.9 MB
Stars: 0
Watchers: 2
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

README

# Smart Vision
**Demo Video Link:** https://youtu.be/EFACJ6n8zPc

![Smart Vision](/static/images/smart-vision-top.png)

**Step-by-Step Guide to Setting Up and Running the Application**

1. **Clone the Repository**
```bash
git clone https://github.com/williyam-m/Flipkart_Grid_6_0.git
```
2. **Create a Virtual Environment**
```bash
python -m venv venv
```
3. **Activate the Virtual Environment**

- **On Windows:**
```bash
venv\Scripts\activate
```
- **On Linux/macOS:**
```bash
source venv/bin/activate
```
4. **Install Required Packages**
```bash
pip install -r requirements.txt
```
5. **Run the Application**

```bash
python manage.py runserver
```

### Freshness Detector

![Smart Vision](/static/images/freshness-detector-img.png)
- Predicts the freshness and identifies the class (type) of fruits and vegetables.
- Utilizes a model I trained using a dataset from Kaggle with MobileNetV2 as the base model in TensorFlow.

### Feature Extractor

![Smart Vision](/static/images/feature-extractor-img.png)
- Extracts product details such as MRP, EAN, manufacture date, and expiry date using OCR powered by Pytesseract.
- Processes the text to validate the expiry date of the product.

### Object Detection

![Smart Vision](/static/images/object-detection-img.png)
- Counts and highlights products within an image.
- Employs the EfficientDet model from TensorFlow Hub.

### Dataset For `Freshness Detector`

**Download the dataset from Kaggle.**

- Link : https://www.kaggle.com/datasets/muhriddinmuxiddinov/fruits-and-vegetables-dataset

This dataset contains images of the following fruits and vegetables items:

**Fresh fruits-** fresh banana, fresh apple, fresh orange, fresh mango and fresh strawberry.

**Rotten fruits-** rotten banana, rotten apple, rotten orange, rotten mango and rotten strawberry.

**Fresh vegetables-** fresh potato, fresh cucumber, fresh carrot, fresh tomato and fresh bell pepper.

**Rotten vegetables-** rotten potato, rotten cucumber, rotten carrot, rotten tomato and rotten bell pepper.

### Pre-trained Model / Architecture for `Object Detection`

- Link : https://tfhub.dev/tensorflow/efficientdet/d0/1

### Tesseract OCR Engine for Optical Character Recognition

- Tesseract : https://github.com/tesseract-ocr/tesseract

- Pytesseract : https://pypi.org/project/pytesseract/

![Smart Vision](/static/images/smart-vision-bottom.png)

## References and Resources

- Django Documentation: https://docs.djangoproject.com/
- Python Official Documentation: https://docs.python.org/3/
- Keras : https://keras.io/
- TensorFlow : https://www.tensorflow.org/
- MobileNetV2 : https://keras.io/api/applications/mobilenet/
- OpenCV : https://opencv.org/
- SQLite : https://www.sqlite.org/index.html