MATLAB: For Complex Background can i get automatic leaf segmentation code

color segmentationImage Processing Toolboximage segmentationleaf

Sir now I am using the code below but for a complex background. This code isn't working properly.
I am facing a lot of segmentation problems.
% 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 = 'C:\Users\Manoj Prasad\Downloads\SHAREit\vivo 1601\photo\j.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;
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
and this is the complex background image where I have used for this particular code.

Best Answer

Just use the color thresholder to find hue and brightness ranges that will work across all your images.
Then call bwareafilt() to extract the largest green blob to get a single leaf.
What is the source of the images? What kind of database would have single leaves on all kinds of backgrounds? Seems highly artificial. Is there any real world use that these images came from, or are these just made up homework images for you to practice color segmentation or leaf detection on?
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