KnowYourPlates 0.1.1

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

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 know_your_plates 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 know_your_plates

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 know_your_plates 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(args['image'])
print(recognized_text)

Call from the command line:

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

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

know_your_plates is released under the MIT License.