I'm using Geometric mean filter to remove noise instead of median filter as image quality goes off in former case. code shown below is a part of m-file to remove noise.
fname = getappdata(0, 'fname');
[a, map] = imread(fname);
x = ind2rgb(a, map);
b = im2double(x);
w=fspecial('gaussian',[3,3]);
geom=exp(imfilter(log(b),ones(3,3),'replicate')).^(1/3/3);
fname=imfilter(b,w,'replicate');
axes(handles.axes1);
imshow(fname);
If i press push button which is named 'Remove Noise' above code get executed irrespective of image quality/property. In the sense even if no noise is present, image will be subjected to filter.
My question, is there any way to detect whether noise is present or not, so that the moment i pressed push button if no Noise in image it should display a message stating 'NO NOISE TO REMOVE' automatically.
There is no direct way to determine whether an image is noisy or not.
However, you can compare resulting image fname with the input image b in such a way that if their difference is lower than a certain threshold, then it can be decided that denoising operation has not changed image effectively and there is not much noise in the original image like:
threshold_ratio = 0.2; % to be decided experimentally
difference_ratio = sum(sum((fname - b).^2)) / sum(sum(b.^2));
if difference_ratio < threshold_ratio
disp('NO NOISE TO REMOVE');
end
Related
I try to determine the coordinates of a puzzle piece on the original image using the normxcorr2 function. Then I draw a rectangle on the correspondence of the two elements. Unfortunately, I notice that the coordinates that this match has given me are not good. Could someone have an idea how to improve the use of this feature and get some better results.
The puzzle piece has the name "cpiece" and the original picture has the name "bild"
clear all;
close all;
clc
cpiece = im2gray(imread('cpiece1.jpg'));
bild = im2gray(imread('original.jpg'));
figure(1)
montage({bild,cpiece})
c = normxcorr2(cpiece,bild);
figure(2)
surf(c)
shading flat
[ypeak,xpeak] = find(c==max(c(:)));
yoffSet = ypeak-size(cpiece,1);
xoffSet = xpeak-size(cpiece,2);
figure(3)
imshow(bild)
drawrectangle(gca,'Position',[xoffSet,yoffSet,size(cpiece,2),size(cpiece,1)],'FaceAlpha',0);
It seems, the problem returns back to the quality of your template image, And Check if the scales between original image and the template are exactly the same
I'm trying to combine the two images based on the information from the mask. I'm using the color information from the background image if the mask is 0 and color information from foreground image if the mask is 1. Because the mask and both
Images are of the same size, I would like to use logical indexing of matrices to achieve this.
My attempt:
mask = imread('mask.png');
foreground = imread('fg.jpg');
background = imread('bg.jpg');
[r,c,~]=size(mask);
A = zeros(size(mask));
for i=1:r
for j=1:c
if mask(i,j) == 0
A(i,j,:) = background(i,j,:);
end
if mask(i,j) > 0
A(i,j,:) = foreground(i,j,:);
end
end
end
imshow(A);
The result looks like a flickering blue image, but I don't want that. Please help.
You can do this a bit more concisely:
f = double(foreground).*double(mask);
b = double(background).*double(~mask);
blend = f+b;
imshow(blend, []);
Using logical indexing you could also do
foreground(logical(mask)) = 0;
background(logical(~mask)) = 0;
blend = foreground+background;
The ISNOT operator '~' inverts your matrix in the second line, so you cut out the area you would like for background.
NOTE: This works for black and white (one channel). For coloured images see rayryeng's solution.
There are two problems with your code. The first problem is that you are trying to assign colour pixels to the output image A, yet this image is only two-dimensional. You want an image with three channels, not two. In addition, the output image type you are specifying is wrong. By default, the output image A is of type double, yet you are copying values into it that aren't double... most likely unsigned 8-bit integer.
As such, cast the image to the same type as the input images. Assuming both input images are the same type, initialize your A so that:
A = zeros(size(foreground), class(foreground));
This correctly makes a colour image with the same type as any of the inputs, assuming that they're both the same type.
Now, your for loop is fine, but it's better if you do this in one shot with logical indexing. If you want to use logical indexing, create a new image that's initially blank like what you've done, but then make sure your mask has three channels to match the number of channels the other images have. After, you simply need to index into each image and set the right locations accordingly:
mask = imread('mask.png');
foreground = imread('fg.jpg');
background = imread('bg.jpg');
[r,c,d]=size(mask); %// Change
%// If your mask isn't three channels, make it so
%// Change
if d ~= 3
mask = cat(3, mask, mask, mask);
end
A = zeros(size(foreground), class(foreground)); %// Change
A(mask) = foreground(mask); %// Assign pixels to foreground
A(~mask) = background(~mask); %// Assign pixels to background
imshow(A);
My project is to detect human activity through stored video clips.
I am successfully able to do the following:
Get the Motion History Image (MHI) from a video using OpenCV
Train and classify the set of images using Matlab
However, I want to use Matlab in order to get the Motion History Image (MHI). Is it possible, and if yes can someone guide me? Thank you.
I have attached a sample Motion History Image (MHI)
I have used the following code for MHI:
http://www.ece.iastate.edu/~alexs/classes/2007_Fall_401/code/09_MotionHistory/motempl.c
MHI is just a ways of implementing motion detection (and uses silhouettes as the basis of it).
Let suppose that the silhouette of the most recent object has been created. It also uses a timestamp to identify if the current silhouette is recent or not. The older silhouettes have to be compared with the current silhouette in order to achieve movement detection. Hence, earlier silhouettes are also saved in the image, with an earlier timestamp.
MHI describes the changes of some moving objects over the image sequence. Basically, you should only maintain an image where every pixel encodes a time information - whether the silhouette is recent or not or where the movement occurs at a given time.
Therefore the implementation of MHI is very simple e.g.:
function MHI = MHI(fg)
% Initialize the output, MHI a.k.a. H(x,y,t,T)
MHI = fg;
% Define MHI parameter T
T = 15; % # of frames being considered; maximal value of MHI.
% Load the first frame
frame1 = fg{1};
% Get dimensions of the frames
[y_max x_max] = size(frame1);
% Compute H(x,y,1,T) (the first MHI)
MHI{1} = fg{1} .* T;
% Start global loop for each frame
for frameIndex = 2:length(fg)
%Load current frame from image cell
frame = fg{frameIndex};
% Begin looping through each point
for y = 1:y_max
for x = 1:x_max
if (frame(y,x) == 255)
MHI{frameIndex}(y,x) = T;
else
if (MHI{frameIndex-1}(y,x) > 1)
MHI{frameIndex}(y,x) = MHI{frameIndex-1}(y,x) - 1;
else
MHI{frameIndex}(y,x) = 0;
end
end
end
end
end
Code from: https://searchcode.com/codesearch/view/8509149/
Update #1:
Try to draw it as follows:
% showMHI.m
% Input frame number and motion history vector to display normalized MHI
% at the specified frame.
function showMHI(n, motion_history)
frameDisp = motion_history{n};
frameDisp = double(frameDisp);
frameDisp = frameDisp ./ 15;
figure, imshow(frameDisp)
title('MHI Image');
I want to get only leaf from an image.
The background is a normal white paper(A4) and there is some shadow.
I apply some method (structure element,edge detection using filter) but I cannot find the general way which can apply all the image.
these are examples.
Are there better methods for this problem??
thank you
another example.
and the result I got is
By using
hsv_I = rgb2hsv(I);
Is = hsv_I(:,:,2);
Is_d = imdilate(Is,strel('diamond',4));
Is_e = imerode(Is,strel('diamond',2));
Is_de = imerode(Is_d,strel('disk',2));
Is_def = imfill(Is_de,'holes');
Is_defe = imerode(Is_def,strel('disk',5));
Then Is_defe is a mask to segment
But the method that i did is very specific. I cannot use this in general.
If you have the Image Processing Toolbox, you could do as follows:
The code below first estimates the threshold with the function graythresh, thresholds the image and fills holes with the imfill function. Suppose I is a cell containing your RGB images:
for k=1:length(I)
t=graythresh(rgb2gray(I{k}));
BW{k}=imfill(~im2bw(I{k}, t), 'holes');
subplot(length(I),1,k), imshow(BW{k});
end
Let's say my image is img=zeros(100,100,3), my outputs are several ellipse which i get using a created function [ret]=draw_ellipse(x,y,a,b,angle,color,img), I can display one ellipse using imshow(ret).For the moment, I'm trying to show serval ellipse in the image. But i don't know how to code it. will ‘for loop’ work or I need to hold them?
If this is related to what you were doing in your previous question, then what you need to do is to pass the result of one iteration as input to the next.
So assuming that the function [ret]=draw_ellipse(x,y,a,b,angle,color,img) you mentioned takes as input an image img and returns the same image with an ellipse drawn on it, you could do this:
%# ellipses parameters
%#x = {..}; y = {..};
%#a = {..}; b = {..};
%#angle = {..}; color = {..};
img = zeros(200,100,'uint8'); %# image to start with
for i=1:10
img = draw_ellipse(x{i},y{i}, a{i},b{i}, angle{i}, color{i}, img);
end
imshow(img)
I'm a bit unsure of what you want. You want to show several ellipse in one image, like plotting several graphs with hold on?
There is no equivalent command for images, but a simple solution is to add the ellipses into one image and show that one:
several_ellipse = ellipse1 + ellipse2 + ellipse3;
imshow(several_ellipse)
Presumably you want to pass ret as the final input to the next call to draw_ellipse.