Mask R-CNN Car Damage Prediction

This project is a web-based application that utilizes a pre-trained Mask R-CNN model to predict and classify different types of car damage from images. The model is trained to detect and label scratches, dents, shatters, and dislocations on car bodies.

Features

• Upload an image of a car and detect damage.

• Classify the type of damage (scratch, dent, shatter, dislocation).

• Visualize the damage with bounding boxes and masks.

  Demo

Project Structure

• app.py: The main Flask application that serves the web interface and handles image processing requests.
• custom.py: Contains the custom configuration for the Mask R-CNN model.
• templates/index.html: The HTML template for the web interface.
• static/: Contains static files like CSS and images.
• logs/: Directory for storing logs and trained model files.

Installation

1. Clone the Repository

git clone https://github.com/the_hrsr/mask_rcnn_car_damage_prediction.git cd mask_rcnn_car_damage_prediction

2. Create and Activate a Virtual Environment

python -m venv env source env/bin/activate # On Windows: .\env\Scripts\activate

3. Install Dependencies

pip install -r requirements.txt

4. Train the model

Train the model using jupyter notebook and replace the h5 file.

5. Run the Application

Run the Application

The application will start and run on http://127.0.0.1:5000/.

Usage

Open your browser and navigate to http://127.0.0.1:5000/.

Click the "Choose Image" button to upload an image of a car.

Click "Upload" to submit the image. The application will display the detected damages with bounding boxes and masks.

Workflow

Bounding Boxes

ROIs

Anchors

Troubleshooting

If you encounter issues with the Flask server, ensure that all dependencies are installed correctly. If you receive a KeyError: 'file' error, check the form submission in the frontend and ensure the file input is correctly named.