This question already has answers here:
16-bit grayscale TIFF
(2 answers)
Closed 1 year ago.
I want to display a satellite image (16bit) that contains 4 bands with intensity levels: red, greens, blue and nir. However, I just want to display the first 3 bands. I use the following code:
RGB=imread('IMG_PHR1B_MS_202106020751594_ORT_5893575101-2_R1C1.TIF');
subset_RGB=RGB(1550:3238,343:1250,1:3);
figure(1)
imshow(subet_RGB)
impixelinfo
The pixels show clearly different values, but the image is completely black.
values range from:
red: 473-4152
green:599-4256
blue:507-3737
I transformed this image to HSV (because I also need this).
normal=im2double(subset_RGB);
hsv=rgb2hsv(normal);
imshow(hsv)
This image does result in a colour image.
I maybe thought it was because hsv is double and subset_RGB is 16-bit. so I tried the following:
test=im2double(subset_RGB)
imshow(test)
I also tried imagesc as suggested, but results in black image:
figure(2)
imagesc(subset_RGB)
How can I display the original image in true colours?
To display an RGB image using imshow, you have to rescale it from 0 to 255. I do not know what is the original bit depth of your image (even though it is saved with 16 bits, the actual bit depth may be smaller). I will assume it is 13 bits, as it is the next power of two higher than the values you have shown.
rescaled_img = uint8(double(subset_RGB) / (2^(13-8)));
imshow(rescaled_img);
Note that the colours may be off or too dark if the bit depth is wrong. An alternative, instead of dividing by 8192, you can divide by the maximum in the image. In that case, use:
rescaled_img = uint8(256 * double(subset_RGB) / max(subset_RGB, [], 'all'));
imshow(rescaled_img);
Related
I have an image with dark blue spots on a black background. I want to convert this to inverse gray scale. By inverse, I mean, I want the black ground to be white.
When I convert it to gray scale, it makes everything look black and it makes it very hard to differentiate.
Is there a way to do an inverse gray scale where the black background takes the lighter shades?
Or, another preferable option is to represent the blue as white and the black as black.
I am using img = rgb2gray(img); in MATLAB for now.
From mathworks site:
IM2 = imcomplement(IM)
Is there a way to do an inverse gray scale where the black
background takes the lighter shades?
Based on your image description I created an image sample.png:
img1 = imread('sample.png'); % Read rgb image from graphics file.
imshow(img1); % Display image.
Then, I used the imcomplement function to obtain the complement of the original image (as suggested in this answer).
img2 = imcomplement(img1); % Complement image.
imshow(img2); % Display image.
This is the result:
Or, another preferable option is to represent the blue as white and
the black as black.
In this case, the simplest option is to work with the blue channel. Now, depending on your needs, there are two approaches you can use:
Approach 1: Convert the blue channel to a binary image (B&W)
This comment suggests using the logical operation img(:,:,3) > 0, which will return a binary array of the blue channel, where every non-zero valued pixel will be mapped to 1 (white), and the rest of pixels will have a value of 0 (black).
While this approach is simple and valid, binary images have the big disadvantage of loosing intensity information. This can alter the perceptual properties of your image. Have a look at the code:
img3 = img1(:, :, 3) > 0; % Convert blue channel to binary image.
imshow(img3); % Display image.
This is the result:
Notice that the round shaped spots in the original image have become octagon shaped in the binary image, due to the loss of intensity information.
Approach 2: Convert the blue channel to grayscale image
A better approach is to use a grayscale image, because the intensity information is preserved.
The imshow function offers the imshow(I,[low high]) overload, which adjusts the color axis scaling of the grayscale image through the DisplayRange parameter.
One very cool feature of this overload, is that we can let imshow do the work for us.
From the documentation:
If you specify an empty matrix ([]), imshow uses [min(I(:)) max(I(:))]. In other words, use the minimum value in I as black, and the maximum value as white.
Have a look at the code:
img4 = img1(:, :, 3); % Extract blue channel.
imshow(img4, []); % Display image.
This is the result:
Notice that the round shape of the spots is preserved exactly as in the original image.
This question already has an answer here:
Access RGB channels in an image in MATLAB
(1 answer)
Closed 6 years ago.
I'm working with images similar to this: a cell image, and I want to extract only the red-pink sections. As of now I'm using img(:,:,1) to pull out the red values but this produces a binary image. I wanted to know if there was a way to extract the "red" values and produce a grayscale image based on their degree of "redness" or intensity. Any help would be awesome.
You are likely visualizing the result using imshow which will automatically set the color limits of the axes to be between 0 and 1. Your image is RGB and the values of the red channel are going to range from 0 to 255. Because of this, if you only specify one input to imshow, you will get an image where all values > 1 will appear as white and all zero-values will be black. So your image isn't really binary, it just appears that way.
You want to either display your image with imagesc which will automatically scale the color limits to match your data:
imagesc(img(:,:,1));
colormap gray
Or you can specify the second input to imshow to cause it to also scale to fit your data range:
imshow(img(:,:,1), [])
The reason that this isn't an issue when you are visualizing all channels is that if you specify red, green, and blue channels, this is considered a true color image and all axes color limits are ignored.
The data you capture will be correct (and is grayscale), but the visualization may be incorrect. When trying to visualize a 2D matrix (same as your result img(:,:,1)), matlab applies the default colormap and the result is:
[x,y]=meshgrid(1:200, 1:200);
z=x.^2.*sin(y/max(y(:))*pi);
figure;imagesc(z);
If you want to avoid the applied jet colormap, either change the colormap:
colormap('gray')
or change your 2D matrix into a 3D one, explicitely specifying the colors to display (must be values between 0 and 1):
z3d = z(:,:,[1 1 1]); % more efficient than repmat
z3d = (z3d - min(z(:)))./range(z(:)); % make sure values in range [0; 1]
You see banding in the colormap version, because by default a colormap contains 64 different colors; the 3d matrix version doesn't have this problem as it directly displays the colors.
If I may add to your question, it seems to me you're simply trying to isolate and visualise the red, green, and blue fluorofores separately (or in combination). I specifically think this because you mention 'pink'.
It may be nicer to just isolate the channels:
>> F_red = F; F_red(:,:,[2,3]) = 0;
>> F_green = F; F_green(:,:,[1,3]) = 0;
>> F_blue = F; F_blue(:,:,[1,2]) = 0;
>> F_pink = F; F_pink(:,:,2) = 0;
Here's a subplot of the result:
Furthermore, you should know that the 'naive' way of producing a grayscale image does not preserve the 'luminosity' of colours as perceived by the human eye, since 'green' at the same intensity as 'red' and 'blue' will actually be perceived as brighter by the human eye, and similarly 'red' is brighter than 'blue'. Matlab provides an rgb2gray function which converts an rgb image to a grayscale image that correctly preserves luminance. This is irrelevant for your pure red, green, and blue conversions, but it may be something to think about with respect to a 'pink-to-grayscale' image. For instance, compare the two images below, you will see subtle contrast differences.
>> F_pinktogray_naive = mean(F(:,:,[1,3]), 3);
>> F_pinktogray_luminance = rgb2gray(F_pink);
A subplot of the two:
In a sense, you probably care more about the left (naive) one, because you don't care about converting the pink one to a gray one "visually", but you care more about the red and blue fluorofores being "comparable" in terms of their intensity on the grayscale image instead (since they represent measurements rather than a visual scene). But it's an important distinction to keep in mind when converting rgb images to grayscale.
This question already has an answer here:
What does the index refer to when selecting a pixel on an image in Matlab?
(1 answer)
Closed 6 years ago.
I have gray scale image "lena.bmp". I want read this image in matlab using imread() function.
When i use code below to read and show image my image is dark (black).
img = imread('lena.bmp');
imshow(img);
But when i use code below, I have no problem to view.
[img map]= imread('lena.bmp');
imshow(img,map);
It seems that my first code doses not reading image in grayscale mode (like what rgb2gray function generate).
My image is as follows:
What can i do to solve this problem?
Your image is an "indexed" image. That means it contains integer values which act as "labels" more than anything, and each of those labels is mapped to a colour (i.e. an rgb triplet). Your map variable represents that mapping; at row 5 you have the rgb triplet that corresponds to 'label' "5", for instance.
To see what I mean, do unique(img) and you'll see that the values of your img array are in fact quite regular. The command rgbplot can demonstrate the actual colourmap graphically. Run rgbplot(map) on your map variable to see the mapping for each of the red green and blue colours.
Now, save and read the image below on your computer as img2 and compare the array values.
This image was generated by converting from the "indexed" image you linked to, to a "grayscale" one using photoediting software (the GIMP). The difference is that
in a grayscale image, the pixel values represent actual intensities, rather than integer 'labels'. Imread reads grayscale images as uint8 images by default, meaning it assigns intensity values to pixels ranging from 0 (black) to 255 (white). Since these values happen to be integers you could still cheat and treat them as 'labels' and force a colour-mapping on them. But if you assign a 'linear map' (i.e. value 1 = intensity 1, value 2 = intensity 2, etc) then your image will look as you would expect.
You'll see that the values from unique(img2) are quite different. If you imshow(img2) you'll see this displays as you'd expect. If you don't specify a colormap for imshow, it will assume that the map is a linear mapping from the lowest to the highest value in the image array, which explains why your indexed image looked weird, since its values were never supposed to correspond to intensities.
Also try imagesc(img2) which will show this but using the "current" colormap. imagesc causes the colormap to be "scaled", so that the lowest colour goes to the lowest value in the image, and similarly for the highest.
The default colormap is jet so you should see a psychedelic looking image but you should be able to make out lena clearly. If you try colormap gray you should see the gray version again. Also try colormap hot. Now to make sense of the colormaps, try the rgbplot command on them (e.g. rgbplot(gray), rgbplot(hot) etc).
So, going back to imshow, imshow basically allows you to display an indexed image, and specify what colormap you want to use to display it. If you don't specify the colormap, it will just use a linear interpolation from the lowest value to the highest as your map. Therefore imshow(img) will show the image pretty much in the same way as imagesc(img) with a gray colormap. And since the values in your first img represent evenly spaced 'labels' rather than actual intensities, you'll get a rubbish picture out.
EDIT: If you want to convert your indexed image to a grayscale image, matlab provides the ind2gray function, e.g.:
[img, map] = imread('lena.bmp');
img_gray = ind2gray(img, map);
This is probably what you need if you mean to process pixel values as intensities.
I have computed an image with values between 0 and 255. When I use imageview(), the image is correctly displayed, in grey levels, but when I want to save this image or display it with imshow, I have a white image, or sometimes some black pixels here and there:
Whereas with imageview():
Can some one help me?
I think that you should use imshow(uint8(image)); on the image before displaying it.
Matlab expects images of type double to be in the 0..1 range and images that are uint8 in the 0..255 range. You can convert the range yourself (but change values in the process), do an explicit cast (and potentially loose precision) or instruct Matlab to use the minimum and maximum value found in the image matrix as the white and black value to scale to when visualising.
See the following example with an uint8 image present in Matlab:
im = imread('moon.tif');
figure; imshow(im);
figure; imshow(double(im));
figure; imshow(double(im), []);
figure; imshow(im2double(im));
When i wrote these commands
out = ones(size(ben))
imshow(out)
the output is a white picture but i expect almost dark picture because the rgb values are 1,1,1. when i give 255,255,255 it also gives a white picture. Isn't this a dilemma ?
Try out = ones(size(ben), 'uint8');
ones() by default creates an array of doubles. When imshow() gets an array of doubles it assumes that the pixel values range between 0 and 1, and assigns the white color to anything greater than 1. However, if you pass an array of uint8 to imshow() it will assume the range to be between 0 and 255.
You can also try using imagesc(); instead of imshow(), but you may need to do colormap gray after wards to get a grayscale image.
Another alternative is to rescale the image before display:
imshow(out / max(out(:)));