Requirements¶

• OpenCV
• TensorFlow
• Numpy
• Pandas
• Click

Recognition Process¶

• Get a grayscale version of the frame;
• Select a ROI;
• Apply a Gaussian Adaptive Threshold;
• Apply Median Blur to reduce noise;
• Apply Dilation also to reduce noise;
• Apply an AND operation on 3 consecutive frames to reduce noise;
• Find ROI Contours;
• Generate a square image that is white inside the contour and black outside, for each contour found.
• Resize image using linear interpolation to a 28x28 image, for each contour found;
• Run an Deep Convolutional Neural Network trained for MNIST dataset on each image. For more information on the model check here.
• Concatenate de digits and print the number.

Credits¶

Tools used in rendering this package:

• Cookiecutter
• cookiecutter-pypackage

Stable release¶

To install Number Recognizer, first install OpenCV and TensorFlow then run this command in your terminal:

$ pip install number-recognizer

This is the preferred method to install Number Recognizer, as it will always install the most recent stable release.

If you don’t have pip installed, this Python installation guide can guide you through the process.

From sources¶

The sources for Number Recognizer can be downloaded from the Github repo.

First install OpenCV and TensorFlow.

You can either clone the public repository:

$ git clone git://github.com/condereis/number-recognizer

Or download the tarball:

$ curl  -OL https://github.com/condereis/number-recognizer/tarball/master

Once you have a copy of the source, you can install it with:

$ python setup.py install

Types of Contributions¶

Get Started!¶

Ready to contribute? Here’s how to set up number-recognizer for local development.

1. Fork the number-recognizer repo on GitHub.

2. Clone your fork locally:

   $ git clone git@github.com:your_name_here/number-recognizer.git
   

3. Install your local copy into a virtualenv. Assuming you have virtualenvwrapper installed, this is how you set up your fork for local development:

   $ mkvirtualenv number-recognizer
   $ cd number-recognizer/
   $ python setup.py develop
   

4. Create a branch for local development:

   $ git checkout -b name-of-your-bugfix-or-feature
   

   Now you can make your changes locally.

5. When you’re done making changes, check that your changes pass flake8 and the tests, including testing other Python versions with tox:

   $ flake8 recognizer tests
   $ python setup.py test or py.test
   $ tox
   

   To get flake8 and tox, just pip install them into your virtualenv.

6. Commit your changes and push your branch to GitHub:

   $ git add .
   $ git commit -m "Your detailed description of your changes."
   $ git push origin name-of-your-bugfix-or-feature
   

7. Submit a pull request through the GitHub website.

Pull Request Guidelines¶

Before you submit a pull request, check that it meets these guidelines:

1. The pull request should include tests.
2. If the pull request adds functionality, the docs should be updated. Put your new functionality into a function with a docstring, and add the feature to the list in README.rst.
3. The pull request should work for Python 2.6, 2.7, 3.3, 3.4 and 3.5, and for PyPy. Check https://travis-ci.org/condereis/number-recognizer/pull_requests and make sure that the tests pass for all supported Python versions.

Tips¶

To run a subset of tests:

$ python -m unittest tests.test_recognizer