Metadata-Version: 2.1
Name: car_speed_detection
Version: 0.7.8
Summary: Speed detection library for automobile
Home-page: UNKNOWN
Author: Shao-chieh Lien
Author-email: shaochiehlien@gmail.com
License: MIT
Description: # Camera-based Car Speed Detection for Autonomous Driving
        ![Using our library with optical flow to detect the speed for the car](https://github.com/ShaoChiehLien/Car-Speed-Detection/blob/main/GIFforReadme.gif)
        
        Car-Speed-Detection provides a python library to detect the speed of the driving 
        car itself by the video stream from the dashboard camera installed on the car.
        
        Car-Speed-Detection separates the speed detection process into three steps, 
        preprocessing, training, and speed detection. By using Gunnar-Farneback optical flow
        algorithm along with the pipeline we developed, we are able to extract each frame into
        a small size matrix depends on developers preference. We use the Artifitial Neural 
        Network (ANN) to train our model with the preprocessed matrix acquired from preprocessing
        function. Developers could use the trained model to detect the speed of the car
        at each frame using our speed detection function.
        
        ## Getting Started
        ### Installation
        Car-Speed-Detection is available on [PyPI](https://pypi.org/project/car-speed-detection/) and can be
        installed via [`pip`](https://pypi.org/project/pip/). See
        [car-speed-detection.readthedocs.io](https://car-speed-detection.readthedocs.io/en/latest/)
        to learn about the API and Q&A of our library.
        
        ```shell
        pip install car-speed-detection
        ```
        
        ### Read, Preprocess, Train, and Detect the Car Speed
        The [Car-Speed-Detection](https://pypi.org/project/car-speed-detection/) library consists of the
        following parts:
        - Read (Read the mp4 video and output each frame into a designated directory)
        - Preprocess (Preprocess each frame and output a feature set for training)
        - Train (Train the model using the feature set and Artifitial Neural Network)
        - Speed Detection (Detect the speed using the model and video)
        
        ## API and Example Code
        Take a look at the [API](https://car-speed-detection.readthedocs.io/en/latest/API.html#) to know more about
        the Application Programming Interface and [Sample](https://car-speed-detection.readthedocs.io/en/latest/Example%20Code.html) for further information on how to use our library.
        
        ## Result
        In our [example code](https://car-speed-detection.readthedocs.io/en/latest/Example%20Code.html), we are able to train the model with MSE error less than 2 using the training video provided by comma.ai. We separate the video into 75% for training and 25% for testing so the result woud
        be fair. The ANN model has also substaintially small amount of parameters (< 45,000), which yeild a lower latency 
        compare to [other solutions](https://ucladatares.medium.com/predicting-speed-from-video-frames-dissecting-the-comma-ai-challenge-5da697b55886).
        
        ## Bugs Report
        Issues and bugs can be reported by emailing lienshaochieh@gmail.com
        
        At a minimum, the report must contain the following:
        * Description of the program.
        * Expected Result.
        * Actual Result.
        * Steps to reproduce the issue.
        
        Please do not use the GitHub issue tracker to submit bugs reports. The
        issue tracker is intended to make feature requests.
        
        ## Acknowledge
        This project is managed by Shao-Chieh Lien, the software architect and students at Purdue University. 
        
        Meenakshi Pavithran and Christopher Crocker contributed substaintially in this project. Meenakshi is in charge or 
        paper writing and Christopher Croker is in charge of [training and testing data generation](https://github.com/CrockerC/carla_recording).
        
Keywords: python,car speed detection,software-based speedometer,dashboard camera,optical flow,machine learning,keras
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Description-Content-Type: text/markdown
