I have an image of licence plate and the numbers is marked with black squares.
what I want is to get all the coordinates of the squares, and with it to cut them from the plate.
for example this is the original image:
and this is after marking the numbers:
any help will be greatly appreciated.
Here's one way to do it in Matlab
%# read the first image
img = imread('http://i.stack.imgur.com/s9A4m.jpg');
%# convert it to a binary image
img = rgb2gray(img);
img = img > 200;
%# remove the connecting lines
img = imclose(img,strel('disk',5));
%# use bwlabel to replace the black squares with a index (1,2,3...)
lblImg = bwlabel(~img);
%# read the second image, make it binary
img2 = imread('http://i.stack.imgur.com/PtKzw.jpg');
img2 = img2 > 200;
%# create the output - each number is now labeled with an index
out = double(~img2).*lblImg;
%# visualize all
figure,imshow(label2rgb(out,'jet','k','shuffle'))
%# extract and show label #1
firstNumber = out==1;
imshow(firstNumber);
I don't do Matlab, but I can show you how to do it in Mathematica. Hopefully you can translate!
Have a look at the image processing toolbox.
The functions bwdist, imregionalmin and bwselect should be able to get you the square coordinates.
Sketch answer as I'm in a rush. You have some white dots in the squares and some black streaks connecting the squares. Look up the morphological operations such as:
http://www.mathworks.com/help/toolbox/images/ref/imclose.html
Close the image with a small structuring element (3x3 square or similar) to get rid of the bits of noise in the squares.
Open the image with a larger structuring element (10x10 square or bigger) get rid of the connecting streaky bits.
Then use a function like bwlabel to segment/label the remaining pixels. This is a little imprecise as the squares will lose some of the structure at the edges.
Related
I'm using the watershed algorithm to try and segment touching nuclei. A typical image may look like:
or this:
I'm trying to apply the watershed algorithm with this code:
show(RGB_img)
%Convert to grayscale image
I = rgb2gray(RGB_img);
%Take structuring element of a disk of size 10, for the morphological transformations
%Attempt to subtract the background from the image: top hat is the
%subtraction of the open image from the original
%Morphological transformation to subtract background noise from the image
%Tophat is the subtraction of an opened image from the original. Remove all
%images smaller than the structuring element of 10
I1 = imtophat(I, strel('disk', 10));
%Increases contrast
I2 = imadjust(I1);
%show(I2,'contrast')
%Assume we have background and foreground and assess thresh as such
level = graythresh(I2);
%Convert to binary image based on graythreshold
BW = im2bw(I2,level);
show(BW,'C');
BW = bwareaopen(BW,8);
show(BW,'C2');
BW = bwdist(BW) <= 1;
show(BW,'joined');
%Complement because we want image to be black and background white
C = ~BW;
%Use distance tranform to find nearest nonzero values from every pixel
D = -bwdist(C);
%Assign Minus infinity values to the values of C inside of the D image
% Modify the image so that the background pixels and the extended maxima
% pixels are forced to be the only local minima in the image (So you could
% hypothetically fill in water on the image
D(C) = -Inf;
%Gets 0 for all watershed lines and integers for each object (basins)
L = watershed(D);
show(L,'L');
%Takes the labels and converts to an RGB (Using hot colormap)
fin = label2rgb(L,'hot','w');
% show(fin,'fin');
im = I;
%Superimpose ridgelines,L has all of them as 0 -> so mark these as 0(black)
im(L==0)=0;
clean_img = L;
show(clean_img)
After C = ~BW; the whole image goes dark. I believe this is because the image pixels are all -inf or some smaller negative number. This is there a way around this and if so what could I change in my code to get this algorithm working? I've experimented a ton and I don't really know what's happening. Any help would be great!
The problem is with your show command. As you said in the comments this uses imshow under the hood. If you try imshow directly you'll see you also get a black image. However, if you call it with appropriate limits:
imshow(clean_img,[min(clean_img(:)), max(clean_img(:))])
you'll see everything you expect to see.
In general I usually prefer imagesc for that reason. imshow makes arbitrary judgements as to what range to represent, and I usually can't be bothered to keep up with it. I think in your case, your end image is uint16 so imshow chooses to represent the range [1, 65025]. Since all your pixel values are below 400, they look black to the naked eye for that range.
I need to find similar corners in image (for example: 4 corners of a rectangle, same corners, just different orientation?).
I have this code:
% read the image into MATLAB and convert it to grayscale
I = imread('s2.jpg');
Igray = rgb2gray(I);
figure, imshow(I);
% We can see that the image is noisy. We will clean it up with a few
% morphological operations
Ibw = im2bw(Igray,graythresh(Igray));
se = strel('line',3,90);
cleanI = imdilate(~Ibw,se);
figure, imshow(cleanI);
% Perform a Hough Transform on the image
% The Hough Transform identifies lines in an image
[H,theta,rho] = hough(cleanI);
peaks = houghpeaks(H,10);
lines = houghlines(Ibw,theta,rho,peaks);
figure, imshow(cleanI)
% Highlight (by changing color) the lines found by MATLAB
hold on
After running this code I convert my starting image into a binary image with:
binary = im2bw(I);
after this I get a product from those 2 binary images and I think I get corners..
product = binary .* cleanI;
now I imfuse this picture with grayscale starting picture and get this:
I dont know what to do to get only those 4 corners!
Ok second try. Below a code that does not finally do the job but it might help.
Edge identifies the contours and with regionprops you get the characteristica of each identified element. As soon as you know what characteristics your desired object has you can filter it and plot it. I went through the Areas in shapedata.Area and the 6th largest was the one you were searching for. If you combine Area with some of the other charateristica you might get the one you want. As I said not ideal and final but perhaps a start ...
clear all
close all
source = imread('Mobile Phone.jpg');
im = rgb2gray(source);
bw = edge(im ,'canny',[],sqrt(2));
shapedata=regionprops (bwlabel(bw,8),'all');
%index = find([shapedata.Area]== max([shapedata.Area]));
index = 213;
data = shapedata(index).PixelList;
figure
imshow(im)
hold on
plot(data(:,1),data(:,2),'ro');
I need a little help guys in Matlab in Matrix Dimensions,
I Have two images imported by imread function:
im1 = imread('1.jpg');
im2 = imread('2.jpg');
im1 is the reference image, while im2 is the Noisy image.
In the workspace window, Matlab shows the im2 Dimensions like this: 768x1024x3
while im2 displayed as: 768x1024
They are both RGB, there's no greyscale images,
In fact the second image is the a compressed image (performed compression algorithm on it ) while the first image is natural JPEG Image, untouched
and for calculating MSE/PNSR for both images, the matrix dimensions must be the same.
I Will need to transform im1 dimensions to be 3d like the first image (768x1024)
I tried this functions (squeeze, reshape) and with no success
You were on the right track with repmat. Here's the correct syntax:
im2 = repmat(im2, [1 1 3]);
This says you want 1 replicate along the first dimension, 1 replicate along the second dimension, and 3 replicates along the third dimension.
Are you sure that both are RGB images because im2 has only one channel and it looks grayscale but it can also be a colormap image in that case try
[im2, map] = imread('im2.jpg');
and see if anything is appearing in map variable. If the image is indeed colormap image, the map variable should be of size 256 X 3.
What donda has suggested is repeating the grayscale channel 3 times to make it of size 768x1024x3. Another possibility is that noisy image was created by converting RGB image to grayscale or by taking green channel of RGB image. Verify the source of the image in that case.
About PSNR computation I have a feeling that there is some problem with your code. I have given my code below use this and see if it works. Get back to me if you face any problem.
function [Psnr_DB] = psnr(I,I_out)
I = double(I);
I_out = double(I_out);
total_error = 0;
for iterz = 1:size(I,3)
for iterx = 1:size(I,1)
for itery = 1:size(I,2)
total_error = total_error + (I(iterx,itery,iterz)-I_out(iterx,itery,iterz))^2;
end
end
end
MSE = total_error/numel(I);
Psnr = (255^2)/MSE;
Psnr_DB = 10*log10(Psnr) %#ok<NOPRT>
I'm trying to read the values in this image into variables using OCR in MATLAB. I'm having trouble doing so, so I tried to split up this image into smaller parts using the white boundary lines then trying to read it, but I dont know how to do this. Any help would be appreciated, thanks.
If the blocks are always delimited by a completely vertical line, you can find where they are by comparing the original image (here transformed from RGB to grayscale to be a single plane) to a matrix that is made of repeats of the first row of the original image only. Since the lines are vertical the intensity of the pixels in the first line will be the same throughout. This generates a binary mask that can be used in conjunction with a quick thresholding to reject those lines that are all black pixels in every row. Then invert this mask and use regionprops to locate the bounding box of each region. Then you can pull these out and do what you like.
If the lines dividing the blocks of text are not always vertical or constant intensity throughout then there's a bit more work that needs to be done to locate the dividing lines, but nothing that's impossible. Some example data would be good to have in that case, though.
img = imread('http://puu.sh/cU3Nj/b020b60f0b.png');
imshow(img);
imgGray = rgb2gray(img);
imgMatch = imgGray == repmat(imgGray(1,:), size(imgGray, 1), 1);
whiteLines = imgMatch & (imgGray > 0);
boxes = regionprops(~whiteLines, 'BoundingBox');
for k = 1:6
subplot(3,2,k)
boxHere = round(boxes(k).BoundingBox);
imshow(img(boxHere(2):(boxHere(2)+boxHere(4)-1), boxHere(1):(boxHere(1)+boxHere(3)-1), :));
end
You can sum along the columns of a binary image corresponding to that input image and find peaks from the sum values. This is precisely achieved in the code here -
img = imread('http://puu.sh/cU3Nj/b020b60f0b.png');
BW = im2bw(img,0.1); %// convert to a binary image with a low threshold
peak_sum_max = 30; %// max of sum of cols to act as threshold to decide as peak
peaks_min_width = 10; %// min distance between peaks i.e. min width of each part
idx = find( sum(BW,1)>=peak_sum_max );
split_idx = [1 idx( [true diff(idx)>peaks_min_width ] )];
split_imgs = arrayfun(#(x) img(:,split_idx(x):split_idx(x+1)),...
1:numel(split_idx)-1,'Uni',0);
%// Display split images
for iter = 1:numel(split_imgs)
figure,imshow(split_imgs{iter})
end
Please note that the final output split_imgs is a cell array with each cell holding image data for each split image.
If you would like to have the split images directly without the need for messing with cell arrays, after you have split_idx, you can do this -
%// Get and display split images
for iter = 1:numel(split_idx)-1
split_img = img(:,split_idx(iter):split_idx(iter+1));
figure,imshow(split_img)
end
There is now a built-in ocr function in the Computer Vision System Toolbox.
I am writing a Matlab program to segment an image and then put a bounding box around the segmented image. My code was previously working, however I am now getting the error:
Error using rectangle
Value must be a 4 element vector
The array in question is BoundingBox created by regionprops, which should contain only four elements, however is for some reason containing more. Here is my code (the defaultSegment function returns a binary image):
function [ boundImage ] = boundSegment( input_image )
image = defaultSegment(input_image);
clear s;
s = regionprops(image, 'Area', 'BoundingBox');
numObj = numel(s);
index = 1;
for k = 1: numObj-1
if s(k+1).Area > s(index).Area
index = k+1;
else
index = index;
end
end
figure, imshow(input_image);
rectangle('Position',s(index).BoundingBox);
boundImage = null;
(I would actually prefer if my code could could directly put the bounding box on the image instead of subplotting it, but I haven't found a way to do that without the vision toolbox)
Thanks!
I suspect that image has more than two dimensions. Check that using size(image). BoundingBox will have four elements only if image has two dimensions. From Matlab's regionprops help:
'BoundingBox' — The smallest rectangle containing the region, a 1-by-Q *2 vector, where Q is the number of image dimensions: ndims(L), ndims(BW), or numel(CC.ImageSize).
The reason an image would have a third dimension is for multiple color channels. If you want to convert to grayscale for processing, use rgb2gray.
Continuing #shoelzer's answer regarding image with three channels,
I suspect you get color image because of your segmentation algorithm (defaultSegment) that paints each segment in a different color. try rgb2ind to convert image to a segmentation label 2D matrix