function secondTask(file)

    % чтение изображения
    [original] = readFile(file);

    % запись в консоль типа изображения
    sprintf(define_image(file))

    [image2, image3, image4] = contrast(rgb2gray(original));
    show(original, image2, image3, image4, noise(rgb2gray(original)), filter(nIimage));

    % функция создании гистограммы серого изображения
    gistogram(rgb2gray(original));

    writeFile(image2, image3, image4, noise(rgb2gray(original)), filter(nIimage));
    [m1, m2, m3, m4, m5, m6] = findMean(original, image_2, image_3, image_4, n_image, filtered_image);
    [s1, s2, s3, s4, s5, s6] = findStd(original, image_2, image_3, image_4, n_image, filtered_image);
    [c1, c2, c3] = findCorr(image_2, image_3, image_4, n_image, filtered_image);

        function image = readFile(file)
        image = imread(file);
    end

    function writeFile(image_2, image_3, image_4, n_image, f_image)
        imwrite(image_2, 'EdittedImage1.bmp');
        imwrite(image_3, 'EdittedImage2.bmp');
        imwrite(image_4, 'EdittedImage3.bmp');
        imwrite(n_image, 'EdittedImage4.bmp');
        imwrite(f_image, 'EdittedImage5.bmp');
    end

    function show(image, image_2, image_3, image_4, n_image, f_image)
        subplot(4,3,1);
        imshow(image)
        title('Original');

        subplot(4,3,2);
        imshow(image_2)
        title('Changed 1');

        subplot(4,3,3);
        imshow(image_3)
        title('Changed 2');

        subplot(4,3,4);
        imshow(image_4)
        title('Changed 3');

        subplot(4,3,5);
        imshow(f_image)
        title('Filter');

        subplot(4,3,6);
        imshow(n_image)
        title('Noise');


    end

    function type = define_image(file)
        f = imfinfo(file);
        im_type = f.ColorType;
        if strcmp(im_type,'indexed')
            type = ' indexed ';
        end
        if strcmp(im_type,'grayscale')
            type = ' gray scale ';
        end
        if strcmp(im_type,'truecolor')
            type = ' truecolor ';
        end

    end

    function [image_2,image_3,image_4] = contrast(image_1)
        image_2 = imadjust(image_1, [0 0.9], [0 0.7], 1);
        image_3 = imadjust(image_1, [0 0.9], [], 0.5);
        image_4 = imadjust(image_1, [0 0.9], [], 2);
    end
    function gistogram(image_1)
         subplot(4,3,8);
         imhist(image_1);
         title('Histogramm');
    end

    function n_image = noise(image_1)
         n_image = imnoise(image_1,'speckle');      %добавление шума
    end

    function f_image = filter(n_image)
         med = @(n_image) median(n_image(:));       %добавление фильтра
         f_image = nlfilter(n_image,[3 3],med);
    end

    function [m1, m2, m3, m4, m5, m6] = findMean(image, image_2, image_3, image_4, n_image, f_image)
        fprintf('Find mean:')
        m1 = mean2(image)
        m2 = mean2(image_2)
        m3 = mean2(image_3)
        m4 = mean2(image_4)
        m5 = mean2(n_image)
        m6 = mean2(f_image)
    end

    function [s1, s2, s3, s4, s5, s6] = findStd(image, image_2, image_3, image_4, n_image, f_image)
        fprintf('Find standart deviation:')
        s1 = std2(image)
        s2 = std2(image_2)
        s3 = std2(image_3)
        s4 = std2(image_4)
        s5 = std2(n_image)
        s6 = std2(f_image)
    end

    function [c1, c2, c3] = findCorr(image_2, image_3, image_4, n_image, f_image)
        fprintf('Find correlation:')
        c1 = corr2(n_image,n_image) %изображения одинаковые => с1 = 1
        c2 = corr2(image_2,image_3) %изображения разные => c2 < 1
        c3 = corr2(n_image,f_image) %изображения разные => c2 < 1
    end

end