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

Smart Vision Technology Quality Control
https://github.com/williyam-m/flipkart_grid_6_0

cnn django efficientdet flipkart flipkart-grid flipkartgrid hackathon keras machine-learning mobilenetv2 mobilenetv2-model ocr opencv pytesseract python sqlite support-vector-machine svm tensorflow tesseract

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Smart Vision Technology Quality Control

• Host: GitHub
• URL: https://github.com/williyam-m/flipkart_grid_6_0
• Owner: williyam-m
• License: mit
• Created: 2024-09-23T15:17:24.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-12-21T08:57:24.000Z (over 1 year ago)
• Last Synced: 2025-04-23T04:14:03.943Z (about 1 year ago)
• Topics: cnn, django, efficientdet, flipkart, flipkart-grid, flipkartgrid, hackathon, keras, machine-learning, mobilenetv2, mobilenetv2-model, ocr, opencv, pytesseract, python, sqlite, support-vector-machine, svm, tensorflow, tesseract
• Language: Python
• Homepage:
• Size: 14.9 MB
• Stars: 0
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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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