I want to Nails Semantic Segmentation using the nail image from fingers

I want to segment the nails from the finger using semantic segmentation process .Then it show the segmented nail image in single window

[Mithun]

[Dyuman Joshi]

"I want to segment the nails from the finger using semantic segmentation process .Then it show the segmented nail image in single window"

Good for you.

[DGM]

I don't know anything about semantic segmentation. I don't think I even have the toolboxes for that. I believe there are some demos in the documentation.

https://www.mathworks.com/solutions/image-video-processing/semantic-segmentation.html

[Mithun]

Sir can you give normal segmentation this given image

[Image Analyst]

Aren't you running two posts that are essentially identical? Why did you post this twice? Now you have replies in each of your duplicate posts. Very inefficient. Which one should we concentrate on?

[Dyuman Joshi]

@Image Analyst, the posters appear to be different and this appears to be a homework assignment.

Answer 1

[Constantino Carlos Reyes-Aldasoro]

Hello Mithun

This is not the way Matlab Answers work. Here people will try to help you when you have a problem and you get stuck. It is not a place where people will do your work. You need to find tutorials, read documents or books, try things by yourself and then when you have tried many things and something is not working, then you ask, adding your code and the problems or errors that you cannot solve.

[Mithun]

i tried that but i can't segment the nails from the finger . I attached the code below sir.

% Demo to find leaf. By Image Analyst, Nov. 1, 2020.

clc; % Clear the command window.

close all; % Close all figures (except those of imtool.)

clearvars;

workspace; % Make sure the workspace panel is showing.

format long g;

format compact;

fontSize = 16;

fprintf('Beginning to run %s.m ...\n', mfilename);

%-----------------------------------------------------------------------------------------------------------------------------------

% Read in image.

folder = [];

baseFileName = 'nail.jpg';

fullFileName = fullfile(folder, baseFileName);

% Check if file exists.

if ~exist(fullFileName, 'file')

% The file doesn't exist -- didn't find it there in that folder.

% Check the entire search path (other folders) for the file by stripping off the folder.

fullFileNameOnSearchPath = baseFileName; % No path this time.

if ~exist(fullFileNameOnSearchPath, 'file')

% Still didn't find it. Alert user.

errorMessage = sprintf('Error: %s does not exist in the search path folders.', fullFileName);

uiwait(warndlg(errorMessage));

return;

end

end

% It's not an RGB image! It's an indexed image, so read in the indexed image...

rgbImage = imread(fullFileName);

[rows, columns, numberOfColorChannels] = size(rgbImage);

% Display the test image.

subplot(2, 2, 1);

imshow(rgbImage, []);

axis('on', 'image');

caption = sprintf('Image : "%s"', baseFileName);

title(caption, 'FontSize', fontSize, 'Interpreter', 'None');

drawnow;

hp = impixelinfo(); % Set up status line to see values when you mouse over the image.

% Set up figure properties:

% Enlarge figure to full screen.

hFig1 = gcf;

hFig1.Units = 'Normalized';

hFig1.WindowState = 'maximized';

% Get rid of tool bar and pulldown menus that are along top of figure.

% set(gcf, 'Toolbar', 'none', 'Menu', 'none');

% Give a name to the title bar.

hFig1.Name = 'Demo by Image Analyst';

[mask, maskedRGBImage] = createMask(rgbImage);

% Take just the largest regions:

mask = bwareafilt(mask, 1);

% Fill Holed.

mask = imfill(mask, 'holes');

% Display the initial mask image.

subplot(2, 2, 2);

imshow(mask, []);

hp = impixelinfo(); % Set up status line to see values when you mouse over the image.

axis('on', 'image');

title('Mask', 'FontSize', fontSize, 'Interpreter', 'None');

drawnow;

% Mask the image using bsxfun() function to multiply the mask by each channel individually. Works for gray scale as well as RGB Color images.

maskedRgbImage = bsxfun(@times, rgbImage, cast(mask, 'like', rgbImage));

% Display the final masked image.

subplot(2, 2, 3);

imshow(maskedRgbImage, []);

axis('on', 'image');

title('Masked Image', 'FontSize', fontSize, 'Interpreter', 'None');

drawnow;

hp = impixelinfo(); % Set up status line to see values when you mouse over the image.

% Display the final masked image of the background by inverting the mask.

% Mask the image using bsxfun() function to multiply the mask by each channel individually. Works for gray scale as well as RGB Color images.

backgroundImage = bsxfun(@times, rgbImage, cast(~mask, 'like', rgbImage));

subplot(2, 2, 4);

imshow(backgroundImage, []);

axis('on', 'image');

title('Background Image', 'FontSize', fontSize, 'Interpreter', 'None');

drawnow;

hp = impixelinfo(); % Set up status line to see values when you mouse over the image.

%-------------------------------------------------------------------------------------------------------------

% Make measurements

props = regionprops(mask, 'Area', 'Centroid');

allAreas = [props.Area];

xyCentroids = vertcat(props.Centroid);

subplot(2, 2, 2);

hold on;

for k = 1 : length(props)

x = xyCentroids(k, 1);

y = xyCentroids(k, 2);

txt = sprintf(' (x, y) = (%.1f, %.1f). Area = %d', ...

x, y, allAreas(k));

text(x, y, txt, 'Color', 'r', 'FontWeight', 'bold');

plot(x, y, 'r+', 'MarkerSize', 25, 'LineWidth', 2);

end

% Get boundary.

boundaries = bwboundaries(mask);

boundaries = boundaries{1}; % Extract from cell.

x = boundaries(:, 2);

y = boundaries(:, 1);

plot(x, y, 'r-', 'LineWidth', 3);

fprintf('Done running %s.m ...\n', mfilename);

msgbox('Done!');

function [BW,maskedRGBImage] = createMask(RGB)

%createMask Threshold RGB image using auto-generated code from colorThresholder app.

% [BW,MASKEDRGBIMAGE] = createMask(RGB) thresholds image RGB using

% auto-generated code from the colorThresholder app. The colorspace and

% range for each channel of the colorspace were set within the app. The

% segmentation mask is returned in BW, and a composite of the mask and

% original RGB images is returned in maskedRGBImage.

% Auto-generated by colorThresholder app on 01-Nov-2020

%------------------------------------------------------

% Convert RGB image to chosen color space

I = rgb2hsv(RGB);

% Define thresholds for channel 1 based on histogram settings

channel1Min = 0.183;

channel1Max = 0.400;

% Define thresholds for channel 2 based on histogram settings

channel2Min = 0.000;

channel2Max = 1.000;

% Define thresholds for channel 3 based on histogram settings

channel3Min = 0.000;

channel3Max = 1.000;

% Create mask based on chosen histogram thresholds

sliderBW = (I(:,:,1) >= channel1Min ) & (I(:,:,1) <= channel1Max) & ...

(I(:,:,2) >= channel2Min ) & (I(:,:,2) <= channel2Max) & ...

(I(:,:,3) >= channel3Min ) & (I(:,:,3) <= channel3Max);

BW = sliderBW;

% Initialize output masked image based on input image.

maskedRGBImage = RGB;

% Set background pixels where BW is false to zero.

maskedRGBImage(repmat(~BW,[1 1 3])) = 0;

end

[Image Analyst]

Color segmentation won't work because the nails are the same color as the fingers.

You might have some success with semantic segmentation but you'll need to have hundreds of training images from different individuals. They recommend trying Unet instead of segnet for the initial model that you do the tranfer learning on.

I'm curious, what is the use case for this? In other words, why do you need to find the fingernails?

[Mithun]

thank you sir , i will try to get that semantic segmentation