rlsa.py

import numpy

def iteration(image: numpy.ndarray, value: int) -> numpy.ndarray:
    """
    This method iterates over the provided image by converting 255's to 0's if the number of consecutive 255's are
    less the "value" provided
    """

    rows, cols = image.shape
    for row in range(0,rows):
        try:
            start = image[row].tolist().index(0) # to start the conversion from the 0 pixel
        except ValueError:
            start = 0 # if '0' is not present in that row

        count = start
        for col in range(start, cols):
            if image[row, col] == 0:
                if (col-count) <= value and (col-count) > 0:
                    image[row, count:col] = 0               
                count = col  
    return image 

def rlsa(image: numpy.ndarray, horizontal: bool = True, vertical: bool = True, value: int = 0) -> numpy.ndarray:
    """
    rlsa(RUN LENGTH SMOOTHING ALGORITHM) is to extract the block-of-text or the Region-of-interest(ROI) from the
    document binary Image provided. Must pass binary image of ndarray type.
    """
    
    if isinstance(image, numpy.ndarray): # image must be binary of ndarray type
        value = int(value) if value>=0 else 0 # consecutive pixel position checker value to convert 255 to 0
        try:
            # RUN LENGTH SMOOTHING ALGORITHM working horizontally on the image
            if horizontal:
                image = iteration(image, value)   

            # RUN LENGTH SMOOTHING ALGORITHM working vertically on the image
            if vertical:
                image = image.T
                image = iteration(image, value)
                image = image.T

        except (AttributeError, ValueError) as e:
            image = None
            print("ERROR: ", e, "\n")
            print('Image must be an numpy ndarray and must be in "binary". Use Opencv/PIL to convert the image to binary.\n')
            print("import cv2;\nimage=cv2.imread('path_of_the_image');\ngray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY);\n\
                (thresh, image_binary) = cv2.threshold(gray, 150, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)\n")
            print("method usage -- rlsa.rlsa(image_binary, True, False, 10)")
    else:
        print('Image must be an numpy ndarray and must be in binary')
        image = None
    return image