How can I crop several and random batches from an image? - image

I have an image of size (224 x 224) and I want to extract a number of random patches from the original image using Matlab (let say 5 patches). One of these patch should be at the centre of the original image. The patch size is (128 x 128).
I have tried this to crop just the centre patch:
II = imread('img.png')
[p3, p4] = size(II);
q1 = 50; // size of the crop box
i3_start = floor((p3-q1)/2); % or round instead of floor; using neither gives warning
i3_stop = i3_start + q1;
i4_start = floor((p4-q1)/2);
i4_stop = i4_start + q1;
II = II(i3_start:i3_stop, i4_start:i4_stop, :);
figure ,imshow(II);

I've tried to accomplish this in the following way:
A=imread('Lena.bmp');%sample image
rnd_x = randperm(size(A,1)-128,5);%choose 5 tandom unique points on x-axis
rnd_y = randperm(size(A,2)-128,5);%choose 5 tandom unique points on y-axis
for ii = 1:5
piece{ii} = A((rnd_x(ii):(rnd_x(ii)+127)),(rnd_y(ii):(rnd_y(ii)+127)),1:3);%Convert chosen numbers to image pieces
figure(ii)
imshow(piece{ii});
end
This takes image like this:
This gives 5 pics like this:
Here our image size is 512x512. So, if we want to cut the 128x128 piece from it, we need to seek from 385x385 grid (512-127). We find 5 random points on the grid expressed in rnd_x and rnd_y. Finally, we take the found points as the upper-left corners of the pieces and construct 128x128 images from them. The 5 pieces are recorded in piece cell array.
EDIT: forgot to add how to extract the center patch. The following code performs the task:
A=imread('Lena.bmp');%sample image
if mod(size(A,1),2)
A = A(1:(end-1),:,:);
end
if mod(size(A,2),2)
A = A(:,1:(end-1),:);
end
while size(A,1) > 128
A = A(2:(end-1),:,:);
end
while size(A,2) > 128
A = A(:,2:(end-1),:);
end
imshow(A)
The code removes one pixel from each side until we get the 128-pixel image.

Careful! In your code, if you load a color image (3 channels) and call size with only two outputs, you will have an incorrect value for p4.
Use three outputs when loading images to avoid this problem:
[nrows ncols nchannels] = size(II);
Your code correctly extracts a (q1 x q1) from the center of the image.
If you want a random patch just generate a random integer for the top-left column of the patch with the correct range to ensure that it doesn't fall outside the image. You can generate random integers using the function randi.
i3_start = randi(floor((p3-q1));
i4_start = randi(floor((p4-q1));
The rest of the code is the same. If you want several patches you can generate several values when calling the randi function with a second and third parameter for the desired number of rows and columns. And then process each patch inside a for loop.
BTW: In the third line you have an invalid Matlab comment (use % for comments). Also you should name your variables with more intuitive names.
Eg: [nrows ncols nchannels] = size(II);

Related

How to average a set of images and save the averaged image as the averaged image using MATLAB

I have 5 digital holograms that I recorded using a CCD at different times. I would like to average the 5.
I am able to do that by the following code in MATLAB, except that I am unable to save the file as I see in MATLAB. Instead I get a white image after saving.
I0 = imread('snap1.bmp');
sumImage = double(I0); % Inialize to first image.
for i=2:10 % Read in remaining images.
rgbImage = imread(['snap',num2str(i),'.bmp']);
sumImage = sumImage + double(rgbImage);
end;
meanImage = sumImage / 5;
figure
imshow(meanImage,[])
imwrite(double(meanImage),'snap10.bmp')
o=imread('snap10.bmp');
figure
imagesc((o))
images can be found at
If you transform the image into the uint8, it would be correct:
imwrite(uint8(meanImage),'snap10.bmp'); % instead of double
Also, the mean is wrong as you sum 1:10, but divided the sum by 5.

MATLAB: Create movie from cell array of uint8 images

I have 20 grayscale images of type uint8 stored in a 1x20 cell array named flow8. I want to generate a movie from them. My current approach is:
% Generate images.
for i = 1:20
flow8{i} = round(rand(100, 100)*255+1);
end
% Get into 4-D shape.
n = size(flow8,2);
matSize = size(flow8,1);
imageStack = reshape(cell2mat(flow8),matSize,[],n);
imageStack = permute(imageStack, [1 2 4 3]);
% Create movie.
mov = immovie(imageStack, gray)
implay(mov)
Here, I have added an image generation loop to make the code compilable.
With this code, the generated movie consists of only one horizontal line.
What do I need to do to get a proper movie? Or is there a better way to make a movie from my images?
I am using MATLAB R2015b academic on Windows 7.
If you look closely at your code, flow8 is 1 x 20. When you do your reshaping, you compute matSize with:
matSize = size(flow8, 1)
Well, that value is 1, because as we said the shape of the cell array is 1 x 20.
Instead, you likely wanted the size of each image. In which case, you'll want to index into the cell array to get the value and then take the size of that.
matSize = size(flow8{1});
Potentially another (much shorter) way to do this though, it so use cat to concatenate along the 4th dimension. Then you avoid all of the reshape and permute manipulations.
imageStack = cat(4, flow8{:});

Matlab: crop image with a sliding window?

does anybody know how to crop an image with a sliding window in Matlab?
e.g. I have an image of 1000x500 pixels, I would like to crop from this image blocks of 50x50 pixels... Of course I have to handle uneven divisions, but it is not necessary to have block of the same size.
Some details that have helped me in the past related to (i) ways to divide an image while block processing and (ii) "uneven division", as mentioned by OP.
(i) Ways to divide/process an image:
1. Process non-overlapping blocks:
Using default parameter {'BorderSize',[0 0]}, this can be handled with blockproc as below.
Example for (i)-1: Note the blocked nature of the output. Here each non-overlapping block of size 32 x 32 is used to calculate the std2() and the output std2 value is used to fill that particular block. The input and output are of size 32 x 32.
fun = #(block_struct) std2(block_struct.data) * ones(size(block_struct.data));
I2 = blockproc('moon.tif',[32 32],fun);
figure; subplot(1, 2, 1);
imshow('moon.tif'); title('input');
subplot(1,2, 2)
imshow(I2,[]); title('output');
Input and Output Image:
(i)-2: Process overlapping blocks:
Using parameter {'BorderSize',[V H]}: V rows are added above and below the block and H columns are added to the left and right of the block. The block that is processed has (N + 2*V) rows and (M + 2*H) columns. Using default parameter {'TrimBorder',true}, the border of the output is trimmed to the original input block size of N rows and M columns.
Example for (i)-2: Below code using blockproc uses {'BorderSize',[15 15]} with [N M] = [1 1]. This is similar to filtering each pixel of the image with a custom kernel. So the input to the processing unit is a block of size (1 + 2*15) rows and (1 + 2*15) columns. And since {'TrimBorder',true} by default, the std2 of the 31 rows by 31 columns block is provided as output for each pixel. The output is of size 1 by 1 after trimming border. Consequently, note that this example output is 'non-blocked' in contrast to the previous example. This code takes much longer time to process all the pixels.
fun = #(block_struct) std2(block_struct.data) * ones(size(block_struct.data));
I2 = blockproc('moon.tif',[1 1],fun,'BorderSize',[15 15]);
figure; subplot(1, 2, 1);
imshow('moon.tif'); title('input');
subplot(1,2, 2)
imshow(I2,[]); title('output');
Input and Output Image:
(ii) "Uneven division":
1. Zero/replicate/symmetric padding:
Zero padding so that an integer multiple of the blocks (N rows by M cols sized) can cover the [image + bounding zeros] in the uneven dimension. This can be achieved by using the default parameter {'PadMethod', 0} along with {'PadPartialBlocks' , true} ( which is false by default ). If a bounding region of zeros causes a high discontinuity in values computed from the bounding blocks, {'PadMethod', 'replicate'} or {'PadMethod', 'symmetric'} can be used.
2. Assume an "Active Region" within the image for block processing
For the case of processing each pixel, as in case (i)-2, we could assuming a bounding region of floor(block_size/2) pixels on all sides along the periphery of the image that is used as "Dummy" region. The Active region for block processing is contained within the Dummy region.
Something similar is used in imaging sensors where Dummy Pixels located at the periphery of an imaging array of Active Pixels allow for an operation like the color interpolation of all active area pixels. As color interpolation usually needs a 5x5 pixel mask to interpolate the color values of a pixel a bounding Dummy periphery of 2 pixels can be used.
Assuming MATLAB indexing, the region ( floor(block_size/2) + 1 ) to ( Input_Image_Rows - floor(block_size)/2) ) Rows by ( floor(block_size/2) + 1 ) to ( Input_ImageCols - floor(block_size)/2) ) Columns is considered as Active region (assuming a square block of side, block_size) which undergoes block processing for each pixel as in (i)-2.
Assuming a square block size of 5 by 5, this is shown by the following:
block_size = 5;
buffer_size = floor(block_size/2);
for i = (buffer_size+1):(image_rows-buffer_size)
for j = (buffer_size+1):(image_cols-buffer_size)
... % block processing for each pixel Image(i,j)
end
end
Matlab ver: R2013a
I would first look into the function blockproc to see if it can do what you want.
If you're sure you want to manually crop the image into blocks, you can use this script. It both writes the cropped images to .png files and saves the cropped images in the pages of a 3D array. You can modify it as you need.
This script assumes the image in evenly divisible by the block size. If it isn't, you'll need to pad it with zeros.
[rowstmp,colstmp]= size(myImage);
block_height = 50;
block_width = 50;
blocks_per_row = rows/block_height;
blocks_per_col = cols/block_width;
number_of_blocks = blocks_per_row*blocks_per_col;
%// pad image with zeros if needed
if ~(mod(rowstmp-1,block_height)==0)
rows = ceil(rowstmp/block_height)*block_height;
end
if ~(mod(colstmp-1,block_width)==0)
cols = ceil(colstmp/block_width)*block_width;
end
Im = uint8(zeros(rows,cols));
Im(1:rowstmp,1:colstmp) = myImage;
%// make sure these image have type uint8 so they save properly
cropped_image = uint8(zeros(rows,cols));
img_stack = uint8(zeros(rows,cols,number_of_blocks));
%// loop over the image blocks
for i = 1:blocks_per_row
for j = 1:blocks_per_col
%// get the cropped image from the original image
idxI = 1+(i-1)*block_height:i*block_height;
idxJ = 1+(j-1)*block_width :j*block_width;
cropped_image(idxI,idxJ) = Im(idxI,idxJ);
%//imshow(cropped_image)
%// write the cropped image to the current folder
filename = sprintf('block_row%d_col%d.png',i,j);
imwrite(cropped_image,filename);
cropped_image(idxI,idxJ) = 0;
%// keep all the blocks in a 3D array if we want to use them later
img_stack(:,:,(i-1)*blocks_per_col+j);
end
end

Resize an image with bilinear interpolation without imresize

I've found some methods to enlarge an image but there is no solution to shrink an image. I'm currently using the nearest neighbor method. How could I do this with bilinear interpolation without using the imresize function in MATLAB?
In your comments, you mentioned you wanted to resize an image using bilinear interpolation. Bear in mind that the bilinear interpolation algorithm is size independent. You can very well use the same algorithm for enlarging an image as well as shrinking an image. The right scale factors to sample the pixel locations are dependent on the output dimensions you specify. This doesn't change the core algorithm by the way.
Before I start with any code, I'm going to refer you to Richard Alan Peters' II digital image processing slides on interpolation, specifically slide #59. It has a great illustration as well as pseudocode on how to do bilinear interpolation that is MATLAB friendly. To be self-contained, I'm going to include his slide here so we can follow along and code it:
Please be advised that this only resamples the image. If you actually want to match MATLAB's output, you need to disable anti-aliasing.
MATLAB by default will perform anti-aliasing on the images to ensure the output looks visually pleasing. If you'd like to compare apples with apples, make sure you disable anti-aliasing when comparing between this implementation and MATLAB's imresize function.
Let's write a function that will do this for us. This function will take in an image (that is read in through imread) which can be either colour or grayscale, as well as an array of two elements - The image you want to resize and the output dimensions in a two-element array of the final resized image you want. The first element of this array will be the rows and the second element of this array will be the columns. We will simply go through this algorithm and calculate the output pixel colours / grayscale values using this pseudocode:
function [out] = bilinearInterpolation(im, out_dims)
%// Get some necessary variables first
in_rows = size(im,1);
in_cols = size(im,2);
out_rows = out_dims(1);
out_cols = out_dims(2);
%// Let S_R = R / R'
S_R = in_rows / out_rows;
%// Let S_C = C / C'
S_C = in_cols / out_cols;
%// Define grid of co-ordinates in our image
%// Generate (x,y) pairs for each point in our image
[cf, rf] = meshgrid(1 : out_cols, 1 : out_rows);
%// Let r_f = r'*S_R for r = 1,...,R'
%// Let c_f = c'*S_C for c = 1,...,C'
rf = rf * S_R;
cf = cf * S_C;
%// Let r = floor(rf) and c = floor(cf)
r = floor(rf);
c = floor(cf);
%// Any values out of range, cap
r(r < 1) = 1;
c(c < 1) = 1;
r(r > in_rows - 1) = in_rows - 1;
c(c > in_cols - 1) = in_cols - 1;
%// Let delta_R = rf - r and delta_C = cf - c
delta_R = rf - r;
delta_C = cf - c;
%// Final line of algorithm
%// Get column major indices for each point we wish
%// to access
in1_ind = sub2ind([in_rows, in_cols], r, c);
in2_ind = sub2ind([in_rows, in_cols], r+1,c);
in3_ind = sub2ind([in_rows, in_cols], r, c+1);
in4_ind = sub2ind([in_rows, in_cols], r+1, c+1);
%// Now interpolate
%// Go through each channel for the case of colour
%// Create output image that is the same class as input
out = zeros(out_rows, out_cols, size(im, 3));
out = cast(out, class(im));
for idx = 1 : size(im, 3)
chan = double(im(:,:,idx)); %// Get i'th channel
%// Interpolate the channel
tmp = chan(in1_ind).*(1 - delta_R).*(1 - delta_C) + ...
chan(in2_ind).*(delta_R).*(1 - delta_C) + ...
chan(in3_ind).*(1 - delta_R).*(delta_C) + ...
chan(in4_ind).*(delta_R).*(delta_C);
out(:,:,idx) = cast(tmp, class(im));
end
Take the above code, copy and paste it into a file called bilinearInterpolation.m and save it. Make sure you change your working directory where you've saved this file.
Except for sub2ind and perhaps meshgrid, everything seems to be in accordance with the algorithm. meshgrid is very easy to explain. All you're doing is specifying a 2D grid of (x,y) co-ordinates, where each location in your image has a pair of (x,y) or column and row co-ordinates. Creating a grid through meshgrid avoids any for loops as we will have generated all of the right pixel locations from the algorithm that we need before we continue.
How sub2ind works is that it takes in a row and column location in a 2D matrix (well... it can really be any amount of dimensions you want), and it outputs a single linear index. If you're not aware of how MATLAB indexes into matrices, there are two ways you can access an element in a matrix. You can use the row and column to get what you want, or you can use a column-major index. Take a look at this matrix example I have below:
A =
1 2 3 4 5
6 7 8 9 10
11 12 13 14 15
If we want to access the number 9, we can do A(2,4) which is what most people tend to default to. There is another way to access the number 9 using a single number, which is A(11)... now how is that the case? MATLAB lays out the memory of its matrices in column-major format. This means that if you were to take this matrix and stack all of its columns together in a single array, it would look like this:
A =
1
6
11
2
7
12
3
8
13
4
9
14
5
10
15
Now, if you want to access element number 9, you would need to access the 11th element of this array. Going back to the interpolation bit, sub2ind is crucial if you want to vectorize accessing the elements in your image to do the interpolation without doing any for loops. As such, if you look at the last line of the pseudocode, we want to access elements at r, c, r+1 and c+1. Note that all of these are 2D arrays, where each element in each of the matching locations in all of these arrays tell us the four pixels we need to sample from in order to produce the final output pixel. The output of sub2ind will also be 2D arrays of the same size as the output image. The key here is that each element of the 2D arrays of r, c, r+1, and c+1 will give us the column-major indices into the image that we want to access, and by throwing this as input into the image for indexing, we will exactly get the pixel locations that we want.
There are some important subtleties I'd like to add when implementing the algorithm:
You need to make sure that any indices to access the image when interpolating outside of the image are either set to 1 or the number of rows or columns to ensure you don't go out of bounds. Actually, if you extend to the right or below the image, you need to set this to one below the maximum as the interpolation requires that you are accessing pixels to one over to the right or below. This will make sure that you're still within bounds.
You also need to make sure that the output image is cast to the same class as the input image.
I ran through a for loop to interpolate each channel on its own. You could do this intelligently using bsxfun, but I decided to use a for loop for simplicity, and so that you are able to follow along with the algorithm.
As an example to show this works, let's use the onion.png image that is part of MATLAB's system path. The original dimensions of this image are 135 x 198. Let's interpolate this image by making it larger, going to 270 x 396 which is twice the size of the original image:
im = imread('onion.png');
out = bilinearInterpolation(im, [270 396]);
figure;
imshow(im);
figure;
imshow(out);
The above code will interpolate the image by increasing each dimension by twice as much, then show a figure with the original image and another figure with the scaled up image. This is what I get for both:
Similarly, let's shrink the image down by half as much:
im = imread('onion.png');
out = bilinearInterpolation(im, [68 99]);
figure;
imshow(im);
figure;
imshow(out);
Note that half of 135 is 67.5 for the rows, but I rounded up to 68. This is what I get:
One thing I've noticed in practice is that upsampling with bilinear has decent performance in comparison to other schemes like bicubic... or even Lanczos. However, when you're shrinking an image, because you're removing detail, nearest neighbour is very much sufficient. I find bilinear or bicubic to be overkill. I'm not sure about what your application is, but play around with the different interpolation algorithms and see what you like out of the results. Bicubic is another story, and I'll leave that to you as an exercise. Those slides I referred you to does have material on bicubic interpolation if you're interested.
Good luck!

Counting the squama of lizards

A biologist friend of mine asked me if I could help him make a program to count the squama (is this the right translation?) of lizards.
He sent me some images and I tried some things on Matlab. For some images it's much harder than other, for example when there are darker(black) regions. At least with my method. I'm sure I can get some useful help here. How should I improve this? Have I taken the right approach?
These are some of the images.
I got the best results by following Image Processing and Counting using MATLAB. It's basically turning the image into Black and white and then threshold it. But I did add a bit of erosion.
Here's the code:
img0=imread('C:...\pic.png');
img1=rgb2gray(img0);
%The output image BW replaces all pixels in the input image with luminance greater than level with the value 1 (white) and replaces all other pixels with the value 0 (black). Specify level in the range [0,1].
img2=im2bw(img1,0.65);%(img1,graythresh(img1));
imshow(img2)
figure;
%erode
se = strel('line',6,0);
img2 = imerode(img2,se);
se = strel('line',6,90);
img2 = imerode(img2,se);
imshow(img2)
figure;
imshow(img1, 'InitialMag', 'fit')
% Make a truecolor all-green image. I use this later to overlay it on top of the original image to show which elements were counted (with green)
green = cat(3, zeros(size(img1)),ones(size(img1)), zeros(size(img1)));
hold on
h = imshow(green);
hold off
%counts the elements now defined by black spots on the image
[B,L,N,A] = bwboundaries(img2);
%imshow(img2); hold on;
set(h, 'AlphaData', img2)
text(10,10,strcat('\color{green}Objects Found:',num2str(length(B))))
figure;
%this produces a new image showing each counted element and its count id on top of it.
imshow(img2); hold on;
colors=['b' 'g' 'r' 'c' 'm' 'y'];
for k=1:length(B),
boundary = B{k};
cidx = mod(k,length(colors))+1;
plot(boundary(:,2), boundary(:,1), colors(cidx),'LineWidth',2);
%randomize text position for better visibility
rndRow = ceil(length(boundary)/(mod(rand*k,7)+1));
col = boundary(rndRow,2); row = boundary(rndRow,1);
h = text(col+1, row-1, num2str(L(row,col)));
set(h,'Color',colors(cidx),'FontSize',14,'FontWeight','bold');
end
figure;
spy(A);
And these are some of the results. One the top-left corner you can see how many were counted.
Also, I think it's useful to have the counted elements marked in green so at least the user can know which ones have to be counted manually.
There is one route you should consider: watershed segmentation. Here is a quick and dirty example with your first image (it assumes you have the IP toolbox):
raw=rgb2gray(imread('lCeL8.jpg'));
Icomp = imcomplement(raw);
I3 = imhmin(Icomp,20);
L = watershed(I3);
%%
imagesc(L);
axis image
Result shown with a colormap:
You can then count the cells as follows:
count = numel(unique(L));
One of the advantages is that it can be directly fed to regionprops and give you all the nice details about the individual 'squama':
r=regionprops(L, 'All');
imshow(raw);
for k=2:numel(r)
if r(k).Area>100 % I chose 100 to filter out the objects with a small are.
rectangle('Position',r(k).BoundingBox, 'LineWidth',1, 'EdgeColor','b', 'Curvature', [1 1]);
end
end
Which you could use to monitor over/under segmentation:
Note: special thanks to #jucestain for helping with the proper access to the fields in the r structure here

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