https://github.com/djdhairya/car-parking-detection
https://github.com/djdhairya/car-parking-detection
cnn cv2 deep deep-learning epoch flask keras numpy numpy-arrays opencv pickle tensorflow
Last synced: 3 months ago
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- Host: GitHub
- URL: https://github.com/djdhairya/car-parking-detection
- Owner: djdhairya
- License: mit
- Created: 2025-03-17T20:07:36.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2025-03-17T20:14:56.000Z (3 months ago)
- Last Synced: 2025-03-17T21:25:04.889Z (3 months ago)
- Topics: cnn, cv2, deep, deep-learning, epoch, flask, keras, numpy, numpy-arrays, opencv, pickle, tensorflow
- Language: Jupyter Notebook
- Homepage:
- Size: 13.5 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
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README
# Car Parking Detection
This project is a web application that detects and counts free and occupied parking spaces in a video feed. It uses a pre-trained Convolutional Neural Network (CNN) model to classify whether a parking space is occupied by a car or not. The application is built using Flask, OpenCV, and TensorFlow/Keras.
## Features
- **Real-time Video Processing:** Detects cars in real-time from a video feed.
- **Parking Space Detection:** Classifies each predefined parking space as "car" or "no car".
- **Live Video Stream:** Displays the video feed with overlaid parking space information.
- **API for Space Count:** Provides an API endpoint to get the current count of free and occupied spaces.
## Technologies Used
- **Flask:** A micro web framework for Python.
- **OpenCV:** A library for real-time computer vision.
- **TensorFlow/Keras:** A deep learning framework used for training and running the CNN model.
- **NumPy:** A library for numerical computations in Python.
- **Pickle:** Used to load the saved parking space positions.
## Setup Instructions
### 1. Clone the Repository
```bash
git clone https://github.com/djdhairya/Car-Parking-Detection.git
```
### 2. Install Dependencies
Make sure you have Python installed. Then, install the required Python packages:
```bash
pip install -r requirements.txt
```
### 3. Prepare the Model and Data
- **Model:** Ensure the trained model (`model/model_final.h5`) is placed in the project directory.
- **Parking Positions:** The parking space positions are stored in `model/carposition.pkl`. Ensure this file is also in the project directory.
- **Video Feed:** Place your test video (`video/car_test.mp4`) in the project directory.
### 4. Run the Application
Start the Flask application:
```bash
python app.py
```
The application will be available at `http://127.0.0.1:5000/`.
### 5. Access the Application
- **Homepage:** View the video stream at `http://127.0.0.1:5000/`.
- **Video Feed:** The video feed can be accessed at `http://127.0.0.1:5000/video_feed`.
- **Space Count API:** Get the current free and occupied space count via `http://127.0.0.1:5000/space_count`.
## Project Structure
- `app.py`: The main Flask application file.
- `model_final.h5`: The pre-trained CNN model used for detecting cars in parking spaces.
- `carposition.pkl`: Pickled list of parking space positions.
- `car_test.mp4`: The test video file.
- `templates/index.html`: The HTML template for the web interface.
- `requirements.txt`: A list of Python dependencies.
## How It Works
1. **Video Processing:** The application reads frames from the video feed.
2. **Parking Space Detection:** Each frame is processed to extract regions corresponding to predefined parking spaces. The CNN model classifies these regions as either "car" or "no car".
3. **Overlay Information:** The application overlays the classification results on the video frame and counts the number of free and occupied spaces.
4. **API Responses:** The `/space_count` endpoint provides real-time data about parking space availability.
## Dependencies
- Flask
- OpenCV
- TensorFlow/Keras
- NumPy
- Pickle
## File Structure
```
├── model/
│ ├── model_final.h5
│ └── carposition.pkl
├── templates/
│ └── index.htm
├── train_data/
│ ├── test/
│ └── train/
├── video/
│ └── car_test.mp4
├── app.py
├── car_parking_detection.ipynb
├── car1.png
├── datacollection.py
├── test.py
├── requirements.txt
├── README.md