Separator.py

import cv2

def sharp_img(img):
    gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    blur_image = cv2.blur(gray_img, (4, 4))
    gblur_img = cv2.GaussianBlur(blur_image, (0, 0), 6)
    sharp_img = cv2.addWeighted(gray_img, 1.80, gblur_img, -0.60, 0)
    sharp_not_img = cv2.bitwise_not(sharp_img)
    _, img_zeroone = cv2.threshold(sharp_not_img, 20, 255, cv2.THRESH_BINARY)
    return img_zeroone


def clear_img(img):
    im = cv2.imread("mask.png")
    mask = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
    _, t_mask = cv2.threshold(mask, 70, 255, cv2.THRESH_BINARY)
    masker = cv2.bitwise_not(t_mask)
    try:
        img = cv2.bitwise_and(img, masker)
    except:
        pass
    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (2, 2))
    opening_img = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel)
    return opening_img


def show_image(image):
    cv2.imshow("image", image)
    c = cv2.waitKey()
    if c >= 0:
        return -1
    return 0


def isContain(c, contours):
    for item in contours:
        xC, yC, wC, hC = cv2.boundingRect(c)
        xItem, yItem, wItem, hItem = cv2.boundingRect(item)
        if (xC - xItem) in range(6) and (yC - yItem) in range(6) and wC < wItem and hC < hItem:
            return True
        if wC < 5 or hC < 5:
            return True
    return False


def bestContours(contours):

    contoursDict = {}
    for index, item in enumerate(contours):
        if not isContain(item, contours):
            contoursDict[index] = len(item)
    sortedItems = sorted(contoursDict.items(), key=lambda x: x[1], reverse=True)[:5]
    keys = [item[0] for item in sortedItems]

    returnedTuple = []
    for index, item in enumerate(contours):
        if index in keys:
            returnedTuple.append(item)
    return tuple(returnedTuple)


def PredictWord(INPUT_IMAGE, OUTPUT=None):

    im = cv2.imread(INPUT_IMAGE)
    sim = sharp_img(im)

    cl_img = clear_img(sim)
    contours, hierarchy = cv2.findContours(cl_img, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
    contours = bestContours(contours)

    indices = {}
    clusters = []
    for index, c in enumerate(contours):
        x, y, w, h = cv2.boundingRect(c)
        ROI = cl_img[y : y + h, x : x + w]
        indices[index] = x
        clusters.append(ROI)
    sortedItems = sorted(indices.items(), key=lambda x: x[1])
    cluster = [clusters[item[0]] for item in sortedItems]
    return cluster
    # show_image(cl_img)