image-processing-tool/binary.py at main · MelekElloumi/image-processing-tool · GitHub

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import settings as s
from utils import arrayToMatrix
from utils import clone
from stats import histogram
from stats import cumulated_histogram
import math

def binarize(image, threshold):
    new_image = clone(image)
    for h in range(s.height):
        for w in range(s.width):
            if image[h][w] > threshold:
                new_image[h][w] = s.graylevel
            else:
                new_image[h][w] = 0
    return new_image

def binarizeColour(image, thresholdR,thresholdG,thresholdB,op):
    if op =="AND":
        for h in range(s.height):
            for w in range(s.width):
                if image[h][w][0] > thresholdR and image[h][w][1] > thresholdG and image[h][w][1] > thresholdB:
                    image[h][w][0],image[h][w][1],image[h][w][2] = s.graylevel,s.graylevel,s.graylevel
                else:
                    image[h][w][0],image[h][w][1],image[h][w][2] = 0,0,0
        return image
    else:
        for h in range(s.height):
            for w in range(s.width):
                if image[h][w][0] > thresholdR or image[h][w][1] > thresholdG or image[h][w][1] > thresholdB:
                    image[h][w][0],image[h][w][1],image[h][w][2] = s.graylevel,s.graylevel,s.graylevel
                else:
                    image[h][w][0],image[h][w][1],image[h][w][2] = 0,0,0
        return image


def thresholding(image):
    hist = histogram(image)
    cum_hist = cumulated_histogram(image)
    fmin = math.inf
    thmin = -1
    for i in range(0, s.graylevel + 1):
        q1, q2 = cum_hist[i], cum_hist[s.graylevel] - cum_hist[i]
        if q1 < 1.e-6 or q2 < 1.e-6:
            continue
        m1, m2, v1, v2 = 0, 0, 0, 0
        for g in range(0, i + 1):
            m1 += g * hist[g] / s.graylevel
        m1 = m1 / q1
        for g in range(0, i + 1):
            v1 += ((g - m1) ** 2) * hist[g] / s.graylevel
        v1 = v1 / q1
        for g in range(i + 1, s.graylevel + 1):
            m2 += g * hist[g] / s.graylevel
        m2 = m2 / q2
        for g in range(i + 1, s.graylevel + 1):
            v2 += ((g - m2) ** 2) * hist[g] / s.graylevel
        v2 = v2 / q2
        f = v1 * q1 + v2 * q2
        if f < fmin:
            fmin = f
            thmin = i
    return binarize(image, thmin), thmin

def thresholdingColour(image,op):
    thminC=[]
    for c in range(3):
        imageA=image.tolist()
        imageB=[imageA[h][w][c] for w in range(s.width) for h in range(s.height)]
        imageC=arrayToMatrix(imageB,s.height,s.width,False)
        hist = histogram(imageC)
        print(hist)
        cum_hist = cumulated_histogram(imageC)
        fmin = math.inf
        thmin = -1
        for i in range(0, s.graylevel + 1):
            q1, q2 = cum_hist[i], cum_hist[s.graylevel] - cum_hist[i]
            if q1 < 1.e-6 or q2 < 1.e-6:
                continue
            m1, m2, v1, v2 = 0, 0, 0, 0
            for g in range(0, i + 1):
                m1 += g * hist[g] / s.graylevel
            m1 = m1 / q1
            for g in range(0, i + 1):
                v1 += ((g - m1) ** 2) * hist[g] / s.graylevel
            v1 = v1 / q1
            for g in range(i + 1, s.graylevel + 1):
                m2 += g * hist[g] / s.graylevel
            m2 = m2 / q2
            for g in range(i + 1, s.graylevel + 1):
                v2 += ((g - m2) ** 2) * hist[g] / s.graylevel
            v2 = v2 / q2
            f = v1 * q1 + v2 * q2
            if f < fmin:
                fmin = f
                thmin = i
        thminC.append(thmin)
        print(thmin)
    return binarizeColour(image, thminC[0], thminC[1], thminC[2], op), thminC[0], thminC[1], thminC[2]

def dilatation(image, size):
    if size % 2 == 0:
        size += 1
    new_image = clone(image)
    for h in range(s.height):
        for w in range(s.width):
            mintab = []
            for py in range(max(0, h - size // 2), min(s.height, h + size // 2 + 1)):
                for px in range(max(0, w - size // 2), min(s.width, w + size // 2 + 1)):
                    mintab.append(image[py][px])
            mintab.sort()
            new_image[h][w] = mintab[0]
    return new_image


def erosion(image, size):
    if size % 2 == 0:
        size += 1
    new_image = clone(image)
    for h in range(s.height):
        for w in range(s.width):
            maxtab = []
            for py in range(max(0, h - size // 2), min(s.height, h + size // 2 + 1)):
                for px in range(max(0, w - size // 2), min(s.width, w + size // 2 + 1)):
                    maxtab.append(image[py][px])
            maxtab.sort()
            new_image[h][w] = maxtab[len(maxtab) - 1]
    return new_image


def closing(image, size):
    return erosion(dilatation(image, size), size)

def opening(image, size):
    return dilatation(erosion(image, size), size)