how could i find the centroid and area of each object in the image automatically.
MATLAB: Tiger pugmark binary image analysis
digital image processingimage processingImage Processing Toolboxpawprint
Related Solutions
Simple.
- Threshold the image to form a binary image.
- Do a morphological closing to close gaps
- Fill holes
- Throw out small blobs
- Take convex hull
- Call regionprops to find centroids.
Try this:
clc; % Clear the command window.
close all; % Close all figures (except those of imtool.)
clear; % Erase all existing variables. Or clearvars if you want.
workspace; % Make sure the workspace panel is showing.
format long g;format compact;fontSize = 20;%===============================================================================
% Read in gray scale demo image.
folder = pwd; % Determine where demo folder is (works with all versions).
baseFileName = 'imperfect circles.jpg';% Get the full filename, with path prepended.
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; endendrgbImage = imread(fullFileName);% Display the image.
subplot(2, 3, 1);imshow(rgbImage, []);title('Original Image', 'FontSize', fontSize, 'Interpreter', 'None');axis('on', 'image');hp = impixelinfo();% Get the dimensions of the image.
% numberOfColorChannels should be = 1 for a gray scale image, and 3 for an RGB color image.
[rows, columns, numberOfColorChannels] = size(rgbImage)if numberOfColorChannels > 1 % It's not really gray scale like we expected - it's color.
% Use weighted sum of ALL channels to create a gray scale image.
% grayImage = rgb2gray(rgbImage);
% ALTERNATE METHOD: Convert it to gray scale by taking only the green channel,
% which in a typical snapshot will be the least noisy channel.
grayImage = rgbImage(:, :, 2); % Take green channel.
else grayImage = rgbImage; % It's already gray scale.
end% Now it's gray scale with range of 0 to 255.
% Display the histogram of the image.
subplot(2, 3, 2);[counts, binLocations] = imhist(grayImage);% Suppress bin 1 because it's so tall
counts(1) = 0;bar(binLocations, counts);grid on;title('Histogram of Image', 'FontSize', fontSize, 'Interpreter', 'None');%------------------------------------------------------------------------------
% Set up figure properties:
% Enlarge figure to full screen.
set(gcf, 'Units', 'Normalized', 'OuterPosition', [0, 0.04, 1, 0.96]);% 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.
set(gcf, 'Name', 'Demo by ImageAnalyst', 'NumberTitle', 'Off')drawnow;% Binarize the image
% Get the mask where the region is solid.
binaryImage = grayImage > 128;% Crop off last 2 lines. For some reason, the next to the last line is all white. Set them equal to false.
binaryImage(end-1:end, :) = false; % Blacken last 2 lines.
% Do a morphological closing to connect lines
se = strel('disk', 5, 0);binaryImage = imclose(binaryImage, se);% Fill blobs:
binaryImage = imfill(binaryImage, 'holes');% Display the image.subplot(2, 3, 3);imshow(binaryImage, []);title('Initial Binary Image', 'FontSize', fontSize, 'Interpreter', 'None');axis('on', 'image');hp = impixelinfo();drawnow;% Extract only those larger than 1000 pixels in area
binaryImage = bwareaopen(binaryImage, 1000);% Display the image.subplot(2, 3, 4);imshow(binaryImage, []);title('Closed Binary Image', 'FontSize', fontSize, 'Interpreter', 'None');axis('on', 'image');drawnow;% Extract only those larger than 1000 pixels in areabinaryImage = bwconvhull(binaryImage, 'objects');% Display the image.subplot(2, 3, 5);imshow(binaryImage, []);title('Final Binary Image', 'FontSize', fontSize, 'Interpreter', 'None');axis('on', 'image');drawnow;% Use it to mask the original image.
finalImage = grayImage; % Initialize
finalImage(~binaryImage) = 0; % Erase outside the mask.
% Display the image.subplot(2, 3, 6);imshow(finalImage, []);title('Final, Masked Image', 'FontSize', fontSize, 'Interpreter', 'None');axis('on', 'image');% bwboundaries() returns a cell array, where each cell contains the row/column coordinates for an object in the image.
% Plot the borders of all the clusters on the original grayscale image using the coordinates returned by bwboundaries.
title('Outlines, from bwboundaries()', 'FontSize', fontSize); axis image; % Make sure image is not artificially stretched because of screen's aspect ratio.
hold on;boundaries = bwboundaries(binaryImage);numberOfBoundaries = size(boundaries, 1);for k = 1 : numberOfBoundaries thisBoundary = boundaries{k}; plot(thisBoundary(:,2), thisBoundary(:,1), 'g', 'LineWidth', 2);endhold off;% Find the centroids
props = regionprops(binaryImage, 'Centroid', 'EquivDiameter');xyCentroids = [props.Centroid];xCentroids = xyCentroids(1:2:end)yCentroids = xyCentroids(2:2:end)% Plot centroids over the image with a large red cross.
hold on;for k = 1 : length(xCentroids) thisX = xCentroids(k); thisY = yCentroids(k); thisDiameter = props(k).EquivDiameter; plot(thisX, thisY, 'r+', 'MarkerSize', thisDiameter, 'LineWidth', 2);end
Please define "isolate".
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