I want to identify the colors of this color checker chart in a little robust way. In the first step not every color has to be detected correctly.
I ectracted already the subimages, so that I have squares with only one color.
The idea for detecting the color (and print them out) was to transform the colors from RGB to the HSV color model.
Does anybody has a better solution or can help?
Best regards!
I think you want an algorithm like the following:
Compute the mean RGB value for a given subimg.
Compute the Euclidean distance from your mean RGB value to the RGB value in each of the
squares.
Return the square name that is closest to your RGB value.
Related
im trying to identify a specific shade of green leaves (e.g. navy green) from the attached image. how do i do that in the most efficient way? So far, i'm converting the RGB to HSV and then thresholding the image based on some specific range of saturation and value that will isolate my desired shade. it's working on some images and it's just all over the place on others. i want something that can isolate a specific shade of green in any different image that has slightly different saturation and value (e.g. if the picture was taken with too much light)
Image link
pic=imread('image.jpg');
q=rgb2hsv(pic);
H=q(:,:,1);
S=q(:,:,2);
V=q(:,:,3);
thresh=S>0.6111 & S<0.6666 & V>0.3888 & V<0.4583;
st=strel('diamond',20);
w=imdilate(thresh,st);
comps=bwconncomp(w,8);
num=comps.NumObjects;
fprintf('The number of leaves is %i',num)
% then i try to have some pointers on the image to show me where matlab has identified the the shade.
m = regionprops(w,'centroid');
boxes = cat(1, m.Centroid);
imshow(pic)
hold on
plot(boxes(:,1),boxes(:,2), 'b*')
hold off
Your help will be highly appreciated.
Either the HSV color space (hey, S is saturation and V value), where H will give you the hue,or CIE-Lab color space, where euclidean distance will give you how close 2 specific pixel are to each other in color.
This answer explains how to do it for HSV: Segment pixels in an image based on colour (Matlab)
Using combined with CIE-LAB may help if the colors are very close together (like the greens in each leaf), but you should give HSV a shot
I would like to convert an image from RGB space to HSV in MATLAB and use the Hue.
However, when I use 'rgb2hsv' or some other codes that I found in the internet the Hue component has block artifacts. An example of the original image and the block artifact version are shown below.
Original
Hue
I was able to reproduce your error. For those of you who are reading and want to reproduce this image on your own end, you can do this:
im = imread('http://i.stack.imgur.com/Lw8rj.jpg');
im2 = rgb2hsv(im);
imshow(im2(:,:,1));
This code will produce the output image that the OP has shown us.
You are directly using the Hue and showing the result. You should note that Hue does not have the same interpretation as grayscale intensity as per the RGB colour space.
You should probably refer to the definition of the Hue. The Hue basically refers to how humans perceive the colour to be, or the dominant colour that is interpreted by the human visual system. This is the angle that is made along the circular opening in the HSV cone. The RGB colour space can be represented as all of its colours being confined into a cube. It is a 3D space where each axis denotes the amount of each primary colour (red, green, blue) that contributes to the colour pixel in question. Converting a pixel into HSV, also known as Hue-Saturation-Value, converts the RGB colour space into a cone. The cone can be parameterized by the distance from the origin of the cone and moving upwards (value), the distance from the centre of the cone moving outwards (saturation), and the angle around the circular opening of the cone (hue).
This is what the HSV cone looks like:
Source: Wikipedia
The mapping between the angle of the Hue to the dominant / perceived colour is shown below:
Source: Wikipedia
As you can see, each angle denotes what the dominant colour would be. In MATLAB, this is scaled between [0,1]. As such, you are not visualizing the Hue properly. You are using the Hue channel to directly display this result as a grayscale image.
However, if you do a scan of the values within this image, and multiply each result by 360, then refer to the Hue colour table that I have shown above, this will give you a representation of what the dominant colours at these pixel locations would be.
The moral of this story is that you can't simply use the Hue and visualize that result. Converting to HSV can certainly be used as a pre-processing step, but you should do some more processing in this domain before anything fruitful is to happen. Looking at it directly as an image is pretty useless, as you have seen in your output image. What you can do is use a colour map that derives a relationship between hue and colour like in the Hue lookup map that I showed you, and you can then colourize your image but that's really only used as an observational tool.
Edit: July 23, 2014
As a bonus, what we can do is display the Hue as an initial grayscale image, then apply an appropriate colour map to the image so we can actually visualize what each dominant colour at each location looks like. Fortunately, there is a built-in HSV colour map that is pretty much the same as the colour lookup map that I showed above. All you would have to do is do colormap hsv right after you show the Hue channel. We can show the original image and this colourized image side-by-side by doing:
im = imread('http://i.stack.imgur.com/Lw8rj.jpg');
im2 = rgb2hsv(im);
subplot(1,2,1);
imshow(im); title('Original Image');
subplot(1,2,2);
imshow(im2(:,:,1)); title('Hue channel - Colour coded');
colormap hsv;
This is what the figure looks like:
The figure may be a bit confusing. It is labelling the sky as being blue as the dominant colour. Although this is confusing, this makes actual sense. On a clear day, the sky is blue, but the reason why the sky appears gray in this photo is probably due to the contributions in saturation and value. Saturation refers to how "pure" the colour is. As an example, true red (RGB = [255,0,0]), means that the saturation is 100%. Value refers to the intensity of the colour. Basically, it refers to how dark or how light the colour is. As such, the saturation and value would most likely play a part here which would make the colour appear gray. The few bits of colour that we see in the image is what we expect how we perceive the colours to be. For example, the red along the side of the jet carrier is perceived as red, and the green helmet is perceived to be green. The lower body of the jet carrier is (apparently) perceived to be red as well. This I can't really explain to you, but the saturation and value are contributing to the mix so that the overall output colour is about a gray or so.
The blockiness that you see in the image is most likely due to JPEG quantization. JPEG works great in that we don't perceive any discontinuities in smooth regions of the image, but the way the image is encoded is that it reconstructs it this way... in a method that will greatly reduce the size it takes to save the image, but allow it to be as visually appealing as if you were to look at the RAW image.
The moral of the story here is that you can certainly use Hue as part of your processing chain, but it is not the entire picture. You will probably need to use saturation or value (or even both) to help you discern between the colours.
I know that I can change the saturation of a RGB image with rgb2hsv, but not for grey-value images. I've already tried the neighbourhood function. Can you give me a hint?
As others have pointed out, the saturation of a gray-scale image is 0 by definition. If you are looking to improve contrast try imadjust or histeq.
As has already been mentioned, "saturation" is not a term that can apply to a grayscale image. Below is a suggestion that might approach the kind of effect you are imagining:
sat = 10;
imshow(img1,rgb2gray(map).^(exp(-0.1*sat)));
This assumes that your image img1 has a corresponding colormap in map. If you have no colormap you can replace rgb2gray(map) with gray(256).
In the example above, positive values of sat will produce brighter images, and negative values of sat will produce darker images. Really you can change the function that warps your colormap in whatever way you choose to get the desired effect.
How do I create smooth color plots in Matlab?
Here is where I am at now. I use the imagesc function and
I send you two images. One of them is smoother and better looking
and that is because I used denser meshgrid to compute the function.
But still, it is discrete looking. How do I make it smooth?
Thank you
Sounds like you need a colormap with more gradation. All the colormap generators accept an argument describing the number of discrete colors to include. Try increasing that number; I think the default is something like 64. For example:
colormap(jet(4096))
You can increase the number even further if you like, but eventually you'll hit the limits of 24-bit color space.
Incidentally, the human eye is most sensitive to color gradations in blue hues, so another thing you could do is choose an alternate colormap.
we are doing a mat lab based robotics project.which actually sorts objects based on its color so we need an algorithm to detect specific color from the image captured from a camera using mat lab.
it will be a great help if some one can help me with it.its the video of the project
In response to Amro's answer:
The five squares above all have the same Hue value in HSV space. Selecting by Hue is helpful, but you'll want to impose some constraints on Saturation and value as well.
HSV allows you to describe color in a more human-meaningful way, but you still need to look at all three values.
As a starting point, I would use the rgb space and the euclidian norm to detect if a pixel has a given color. Typically, you have 3 values for a pixel: [red green blue]. You also have also 3 values defining a target color: [255 0 0] for red. Compute the euclidian norm between those two vectors, and apply a decision threshold to classify the color of your pixel.
Eventually, you want to get rid of the luminance factor (i.e is it a bright red or a dark red?). You can switch to HSV space and use the same norm on the H value. Or you can use [red/green blue/green] vectors. Before that, apply a low pass filter to the images because divisions (also present in the hsv2rgb transform) tend to increase noise.
You probably want to convert to the HSV colorspace, and detect colors based on the Hue values. MATLAB offers the RGB2HSV function.
Here is an example submission on File Exchange that illustrate color detection based on hue.
For obtaining a single color mask, first of all convert the rgb image gray using rgb2gray. Also extract the desired color plane from the rgb image ,(eg for obtaining red plain give rgb_img(:,:,1)). Subtract the given plane from the gray image........