There are various syntax which is used in Matlab like: Because of this optimization, calls to reshape are almost instantaneous.Hadoop, Data Science, Statistics & others
The big advantage of doing it this way is that MATLAB makes no memory copy of data when calling reshape. Because of the way MATLAB arrangements elements in memory, these columns are exactly the elements from the red component, the green component, and the blue component of RGB. The second column of B contains the second 390,000 elements of RGB, and similarly for the third column. The first column of B contains the first 390,000 elements (in memory order) of RGB. So, each column of B has 390,000 elements. The empty matrix as the second argument to reshape signals to MATLAB to compute that size automatically. To reshape that successfully into a matrix with 3 columns, we can compute how many rows there must be: P = numel(RGB) / 3 Here are the number of elements of the array RGB: numel(RGB) You see in a call to reshape, the number of elements must not change. That line of code says, "MATLAB, reshape the array RGB so that it is Px3, and will you please just go ahead and figure out what P should be to make it work?" And, there is a big advantage to doing so, as I'll explain in a moment. Yes, as it turns out, this can all be done with a call to reshape. The array is being split apart into three components, which are then immediately put back together into a matrix. It was the next line that made me stop and scratch my head for a moment: A = I have written code just like that approximately 2.7183 bazillion times. The first three lines of code did not give me pause: red = RGB(:,:,1) The first five pixels are along the uppermost left edge of the image, and they are all black: A(1:5,:) Here is the code that I saw for transforming this array into a Px3 matrix, where each row contains the three color component values of one pixel: red = RGB(:,:,1) Compare, for example, the red and green component images: imshow(RGB(:,:,1)) The third dimension, which has length three, contains the red, green, and blue component images. RGB is a three-dimensional array: size(RGB) Historical note: This was the first "truecolor" sample image to ship with MATLAB.
I tend to use the peppers sample image a lot, so I'll switch things up and use NGC6543 instead: RGB = imread( 'ngc6543a.jpg') Today I want to show that code fragment, explain it, and then demonstrate what I think is the fastest possible to perform that transformation in MATLAB. I recently saw some code that transformed the RGB pixel values of an image into a Px3 matrix, such that each row contained the red, green, and blue color components of a single pixel.
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