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KnowYourPlates

Module that allows to recognize license plates from images basing on image processing algorithms.

Getting started

Requirements

Module uses several python packages:

  • OpenCV - open source computer vision and machine learning software library
  • pytesseract - optical character recognition (OCR) tool for python
  • NumPy - fundamental package for scientific computing with Python
  • imutils - series of convenience functions to make basic image processing functions
  • Pillow - Python Image Library
  • Matplotlib - Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms

Be sure to have them installed before using KnowYourPlates package:

pip install opencv-contrib-python
pip install pytesseract
pip install numpy
pip install imutils
pip install Pillow
pip install matplotlib

Installation

Install this package with python package installer pip:

pip install KnowYourPlates

Usage

To recognize license plate from the image, import this package to the project and use license_plate_recognition function with path to the image as an argument. Example code:

# run.py
import argparse
from knowyourplates import alpr

ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required=True,
                help="Path to the image")
args = vars(ap.parse_args())

recognized_text = alpr.license_plate_recognition(
    img_path=args['image'],
    new_size=None,
    blurring_method=alpr.bilateral_filter,
    binarization_method=alpr.adaptive_threshold
)
print(recognized_text)

Call from the command line:

python run.py --image ./example.jpg

Example image

  • Result license plate image

Result image

  • Result: RK115AN

API

  • license_plate_recognition(img_path: str, new_size: tuple, blurring_method: Callable, binarization_method: Callable)):
Automatic license plate recognition algorithm.
Found license plate is stored in ./results/ directiory as license_plate.jpg

Parameters
----------
img_path : str
    Path to the image
new_size  : tuple of integers
    First argument of the tuple is new width, second is the new height of the image
blurring_method : function
    Function as an object. Suggested functions from this module: gaussian_blur, median_blur, bilateral_filter
binarization_method : function
    Function as an object. Suggested functions from this module: threshold_otsu, adaptive_threshold, canny, auto_canny

Returns
-------
str
    Text recognized on the image

Blurring and filtering

  • gaussian_blur(image: np.ndarray):
Wrapper for OpenCV's Gaussian blur. Image is blurred with (3, 3) kernel.

Parameters
----------
image: numpy.ndarray
    Image as numpy array. Should be converted into grayscale.

Returns
-------
numpy.ndarray
    Blurred image using Gaussian blur

Contribute
----------
Source: https://docs.opencv.org/2.4/modules/imgproc/doc/filtering.html?highlight=gaussianblur#gaussianblur
  • median_blur(image: np.ndarray):
Wrapper for OpenCV's median blur. Aperture linear size for medianBlur is 3.

Parameters
----------
image: numpy.ndarray
    Image as numpy array. Should be converted into grayscale.

Returns
-------
numpy.ndarray
    Blurred image using median blur

Contribute
----------
Source: https://docs.opencv.org/2.4/modules/imgproc/doc/filtering.html?highlight=medianblur#medianblur
  • bilateral_filter(image: np.ndarray):
Wrapper for OpenCV's bilateral filter. Diameter of each pixel neighborhood is 11. 
Both filter sigma in the color space and filter sigma in the coordinate space are 17.

Parameters
----------
image: numpy.ndarray
    Image as numpy array. Should be converted into grayscale.

Returns
-------
numpy.ndarray
    Blurred image using bilateral filter

Contribute
----------
Source: https://docs.opencv.org/2.4/modules/imgproc/doc/filtering.html?highlight=bilateralfilter#bilateralfilter

Tresholding images

  • canny(image: np.ndarray, threshold1: int, threshold2: int):
Wrapper for OpenCV's Canny algorithm.

Parameters
----------
image : numpy.ndarray
    Image as numpy array
threshold1 : int
    Lower value of the threshold
threshold2 : int
    Upper value of the threshold

Returns
-------
numpy.ndarray
    Binarized image using Canny's algorithm.

Contribute
----------
Source: https://docs.opencv.org/2.4/doc/tutorials/imgproc/imgtrans/canny_detector/canny_detector.html
  • auto_canny(image: np.ndarray, sigma: float = 0.33):
Function automatically sets up lower and upper value of the threshold
based on sigma and median of the image

Parameters
----------
image : numpy.ndarray
    Image as numpy array
sigma : float


Returns
-------
numpy.ndarray
    Binarized image with Canny's algorithm

Contribute
----------
Source: https://docs.opencv.org/2.4/doc/tutorials/imgproc/imgtrans/canny_detector/canny_detector.html
  • threshold_otsu(image: np.ndarray):
 Wrapper for OpenCV's Otsu's threshold algorithm.

Parameters
----------
image : numpy.ndarray
    Image as numpy array

Returns
-------
numpy.ndarray
    Binarized image using Otsu's algorithm.

Contribute
----------
Source: https://docs.opencv.org/master/d7/d4d/tutorial_py_thresholding.html
  • adaptive_threshold(image: np.ndarray):
Wrapper for OpenCV's adaptive threshold algorithm.

Parameters
----------
image : numpy.ndarray
    Image as numpy array

Returns
-------
numpy.ndarray
    Binarized image using adaptive threshold.

Contribute
----------
Source: https://docs.opencv.org/master/d7/d4d/tutorial_py_thresholding.html

OCR functions

  • ocr(img_path: str):
Wrapper for Tesseract image_to_string function

Parameters
----------
img_path : str
    Path to the image

Returns
-------
str
    Text recognized on the image

Contribute
----------
PyTesseract: https://pypi.org/project/pytesseract/

Image processing

  • preprocess(image: np.ndarray, new_size: tuple, blurring_method: Callable, binarization_method: Callable):
Resizing, converting into grayscale, blurring and binarizing

Parameters
----------
image : numpy.ndarray
    Image as numpy array
new_size  : tuple of integers
    First argument of the tuple is new width, second is the new height of the image
blurring_method : function
    Function as an object. Suggested functions from this module: gaussian_blur, median_blur, bilateral_filter
binarization_method : function
    Function as an object. Suggested functions from this module: threshold_otsu, adaptive_threshold, canny, auto_canny

Returns
-------
numpy.ndarray
    Preprocessed image.

Contribute
----------
Grayscale conversion: https://docs.opencv.org/2.4/modules/imgproc/doc/miscellaneous_transformations.html
Bilateral filter: https://docs.opencv.org/2.4/modules/imgproc/doc/filtering.html
  • plate_contours(image: np.ndarray):
Finding contours on the binarized image.
Returns only 10 (or less) the biggest rectangle contours found on the image.

Parameters
----------
image : numpy.ndarray
    Binarized image as numpy array

Returns
-------
list of numpy.ndarray
    List of found OpenCV's contours.

Contribute
----------
Finding contours: https://docs.opencv.org/2.4/modules/imgproc/doc/structural_analysis_and_shape_descriptors.html?highlight=findcontours#findcontours
  • crop_image(original_img: np.ndarray, plate_cnt: np.ndarray):
Wrapper for Tesseract image_to_string function

Parameters
----------
img_path : str
    Path to the image

Returns
-------
str
    Text recognized on the image

Contribute
----------
PyTesseract: https://pypi.org/project/pytesseract/
  • prepare_ocr(image: np.ndarray):
Prepares image to the OCR process by resizing and filtering (for noise reduction)

Parameters
----------
image : numpy.ndarray
    Image as numpy array

Returns
-------
numpy.ndarray
    Image prepaired to the OCR process

Contribute
----------
Resizing: https://docs.opencv.org/2.4/modules/imgproc/doc/geometric_transformations.html#void%20resize(InputArray%20src,%20OutputArray%20dst,%20Size%20dsize,%20double%20fx,%20double%20fy,%20int%20interpolation)
Bilateral filter: https://docs.opencv.org/2.4/modules/imgproc/doc/filtering.html

License

KnowYourPlates is released under the MIT License.