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copied!<p>One option which greatly improves the quality of the resulting image is to convert to a different color space in order to more easily select your colors. In particular, the <a href="http://en.wikipedia.org/wiki/HSL_and_HSV" rel="nofollow noreferrer">HSV color space</a> defines pixel colors in terms of their hue (the color), saturation (the amount of color), and value (the brightness of the color).</p> <p>For example, you can convert your RGB image to HSV space using the function <a href="https://www.mathworks.com/help/matlab/ref/rgb2hsv.html" rel="nofollow noreferrer"><code>rgb2hsv</code></a>, find pixels with hues that span what you want to define as "non-red" colors (like, say, 20 degrees to 340 degrees), set the saturation for those pixels to 0 (so they are grayscale), then convert the image back to RGB space using the function <a href="https://www.mathworks.com/help/matlab/ref/hsv2rgb.html" rel="nofollow noreferrer"><code>hsv2rgb</code></a>:</p> <pre><code>cdata = imread('EcyOd.jpg'); % Load image hsvImage = rgb2hsv(cdata); % Convert the image to HSV space hPlane = 360.*hsvImage(:, :, 1); % Get the hue plane scaled from 0 to 360 sPlane = hsvImage(:, :, 2); % Get the saturation plane nonRedIndex = (hPlane > 20) & ... % Select "non-red" pixels (hPlane < 340); sPlane(nonRedIndex) = 0; % Set the selected pixel saturations to 0 hsvImage(:, :, 2) = sPlane; % Update the saturation plane rgbImage = hsv2rgb(hsvImage); % Convert the image back to RGB space </code></pre> <p>And here is the resulting image:</p> <p><img src="https://i.stack.imgur.com/1ViZr.png" alt="alt text"></p> <p>Notice how, compared to <a href="https://stackoverflow.com/a/4064240/52738">the solution from zellus</a>, you can easily maintain the light pink tones on the flowers. Notice also that brownish tones on the stem and ground are gone as well.</p> <p>For a cool example of selecting objects from an image based on their color properties, you can check out Steve Eddins blog post <a href="http://blogs.mathworks.com/steve/2010/10/08/the-two-amigos/" rel="nofollow noreferrer">The Two Amigos</a> which describes a solution from Brett Shoelson at the MathWorks for extracting one "amigo" from an image.</p> <p><br> <strong>A note on selecting color ranges...</strong></p> <p>One additional thing you can do which can help you select ranges of colors is to look at a histogram of the hues (i.e. <code>hPlane</code> from above) present in the pixels of your HSV image. Here's an example that uses the functions <a href="https://www.mathworks.com/help/matlab/ref/histc.html" rel="nofollow noreferrer"><code>histc</code></a> (or the recommended <a href="https://www.mathworks.com/help/matlab/ref/histcounts.html" rel="nofollow noreferrer"><code>histcounts</code></a>, if available) and <a href="https://www.mathworks.com/help/matlab/ref/bar.html" rel="nofollow noreferrer"><code>bar</code></a>:</p> <pre><code>binEdges = 0:360; % Edges of histogram bins hFigure = figure(); % New figure % Bin pixel hues and plot histogram: if verLessThan('matlab', '8.4') N = histc(hPlane(:), binEdges); % Use histc in older versions hBar = bar(binEdges(1:end-1), N(1:end-1), 'histc'); else N = histcounts(hPlane(:), binEdges); hBar = bar(binEdges(1:end-1), N, 'histc'); end set(hBar, 'CData', 1:360, ... % Change the color of the bars using 'CDataMapping', 'direct', ... % indexed color mapping (360 colors) 'EdgeColor', 'none'); % and remove edge coloring colormap(hsv(360)); % Change to an HSV color map with 360 points axis([0 360 0 max(N)]); % Change the axes limits set(gca, 'Color', 'k'); % Change the axes background color set(hFigure, 'Pos', [50 400 560 200]); % Change the figure size xlabel('HSV hue (in degrees)'); % Add an x label ylabel('Bin counts'); % Add a y label </code></pre> <p>And here's the resulting pixel color histogram:</p> <p><img src="https://i.stack.imgur.com/6Ul5i.png" alt="alt text"></p> <p>Notice how the original image contains mostly red, green, and yellow colored pixels (with a few orange ones). There are almost no cyan, blue, indigo, or magenta colored pixels. Notice also that the ranges I selected above (20 to 340 degrees) do a good job of excluding most everything that isn't a part of the two large red clusters at either end.</p>

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