I would like to overlap two images, one grayscale and one RGB image. I would like to impose the RGB image on top of the grayscale image, but ONLY for pixels greater than a certain value. I tried using the double function in MATLAB, but this seems to change the color scheme and I cannot recover the original RGB colors. What should I do in order to retain the original RGB image instead of mapping it to one of the MATLAB colormaps? Below is my attempt at superimposing:
pixelvalues = double(imread('hello.png'));
PixelInt = mean(pixelvalues,3);
I1 = ind2rgb(Brightfield(:,:,1), gray(256)); %Brightfield
I2 = ind2rgb(PixelInt, jet(256)); %RGB Image
imshow(I2,[])
[r,c,d] = size(I2);
I1 = I1(1:r,1:c,1:d);
% Replacing those pixels below threshold with Brightfield Image
threshold = 70;
I2R = I2(:,:,1); I2G = I2(:,:,2); I2B = I2(:,:,3);
I1R = I1(:,:,1); I1G = I1(:,:,2); I1B = I1(:,:,3);
I2R(PixelInt<threshold) = I1R(PixelInt<threshold);
I2G(PixelInt<threshold) = I1G(PixelInt<threshold);
I2B(PixelInt<threshold) = I1B(PixelInt<threshold);
I2(:,:,1) = I2R; I2(:,:,2) = I2G; I2(:,:,3) = I2B;
h = figure;
imshow(I2,[])
Original RGB Image:
Brightfield:
Overlay:
Is the content of pixelvalues what you show in your first image? If so, that image does not use a jet colormap. It has pink and white values above the red values, whereas jet stops at dark red at the upper limits. When you take the mean of those values and then generate a new RGB image with ind2rgb using the jet colormap, you're creating an inherently different image. You probably want to use pixelvalues directly in generating your overlay, like so:
% Load/create your starting images:
pixelvalues = imread('hello.png'); % Color overlay
I1 = repmat(Brightfield(:, :, 1), [1 1 3]); % Grayscale underlay
[r, c, d] = size(pixelvalues);
I1 = I1(1:r, 1:c, 1:d);
% Create image mask:
PixelInt = mean(double(pixelvalues), 3);
threshold = 70;
mask = repmat((PixelInt > threshold), [1 1 3]);
% Combine images:
I1(mask) = pixelvalues(mask);
imshow(I1);
Note that you may need to do some type conversions when loading/creating the starting images. I'm assuming 'hello.png' is a uint8 RGB image and Brightfield is of type uint8. If I load your first image as pixelvalues and your second image as I1, I get the following when running the above code:
Create a mask and use it to combine the images:
onionOrig = imread('onion.png');
onionGray = rgb2gray(onionOrig);
onionMask = ~(onionOrig(:,:,1)<100 & onionOrig(:,:,2)<100 & onionOrig(:,:,3)<100);
onionMasked(:,:,1) = double(onionOrig(:,:,1)) .* onionMask + double(onionGray) .* ~onionMask;
onionMasked(:,:,2) = double(onionOrig(:,:,2)) .* onionMask + double(onionGray) .* ~onionMask;
onionMasked(:,:,3) = double(onionOrig(:,:,3)) .* onionMask + double(onionGray) .* ~onionMask;
onionFinal = uint8(onionMasked);
imshow(onionFinal)
Related
I have an image like this,
As you can see, there is a pen mark in the image. I want to remove that mark. How to do it in OpenCV.?
I tried converting it to HSV, creating a mask with blue range and removing it using the code.
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
lower_blue = np.array([110,50,50])
upper_blue = np.array([130,255,255])
mask = cv2.inRange(hsv, lower_blue , upper_blue )
res = cv2.bitwise_and(img, img, mask= mask)
It is not working as needed. All the text gets removed. How to fix this.?
You can take threshold of the first array of image. It looks like this:
Here it is clearly visible the difference in pixel values of the ink mark and the letters. After thresholding it looks like:
The ink mark can now be removed via closing. However it will reduce the size of letters as well. Therefore erosion is performed followed by a bitwise OR to obtain our mask without the ink mark.
If however you want the letters to look like the original image you can store the mask in a numpy array of 255s and perform it bitwise OR with original image.
The full code I have used is:
img = cv2.imread('ink_mark.png')
wimg = img[:, :, 0]
ret,thresh = cv2.threshold(wimg,100,255,cv2.THRESH_BINARY)
kernel = np.ones((7, 7), np.uint8)
closing = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)
erosion = cv2.erode(closing, kernel, iterations = 1)
mask = cv2.bitwise_or(erosion, thresh)
white = np.ones(img.shape,np.uint8)*255
white[:, :, 0] = mask
white[:, :, 1] = mask
white[:, :, 2] = mask
result = cv2.bitwise_or(img, white)
cv2.imshow('image', result)
cv2.waitKey(0)
cv2.destroyAllWindows()
Try using inpaint. First create a mask of the ink:
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
lower_blue = np.array([100,50,50])
upper_blue = np.array([150,255,255])
kernel = np.ones((5,5),np.uint8)
mask = cv2.inRange(hsv, lower_blue, upper_blue)
mask = cv2.dilate(mask,kernel,iterations = 4)
Use the inpaint function to paint in areas where the mask it white. OpenCV will throw away the original pixels, and use guess which pixels should go there.
dst = cv2.inpaint(img, mask, 3, cv2.INPAINT_TELEA)
How can I scale the colorbar axis of a false color image?
I read this post,and copied the code but it seems not to work correctly:
MATLAB Colorbar - Same colors, scaled values
Please see the two images below. In the first (without the scaling) the coloraxis goes
[1 2 3 4 5 6]*10^4
In the second image, it goes
[0.005 0.01 0.015 0.02 0.025]
The correct scaling (with C = 100000) would be
[0.1 0.2 0.3 0.4 0.5 0.6]
Without scaling
Wrong scaling
I want that the coloraxis is scaled by 1/C and I can freely choose C, so that when the pixel value = 10^4 and C=10^6 the scale should show 10^-2.
The reason why I multiply my image first by C is to get more decimals places, because all values below 1 will be displayed as zero without the C scaling.
When I run the code I get yticks as a workspace variable with the following values:
[500 1000 1500 2000 2500]
My code:
RGB = imread('IMG_0043.tif');% Read Image
info = imfinfo('IMG_0043.CR2'); % get Metadata
C = 1000000; % Constant to adjust image
x = info.DigitalCamera; % get EXIF
t = getfield(x, 'ExposureTime');% save ExposureTime
f = getfield(x, 'FNumber'); % save FNumber
S = getfield(x, 'ISOSpeedRatings');% save ISOSpeedRatings
date = getfield(x,'DateTimeOriginal');
I = rgb2gray(RGB); % convert Image to greyscale
K = 480; % Kamerakonstante(muss experimentel eavaluiert werden)
% N_s = K*(t*S)/power(f,2))*L
L = power(f,2)/(K*t*S)*C; %
J = immultiply(I,L); % multiply each value with constant , so the Image is Calibrated to cd/m^2
hFig = figure('Name','False Color Luminance Map', 'ToolBar','none','MenuBar','none');
% Create/initialize default colormap of jet.
cmap = jet(16); % or 256, 64, 32 or whatever.
% Now make lowest values show up as black.
cmap(1,:) = 0;
% Now make highest values show up as white.
cmap(end,:) = 1;
imshow(J,'Colormap',cmap) % show Image in false color
colorbar % add colorbar
h = colorbar; % define colorbar as variable
y_Scl = (1/C);
yticks = get(gca,'YTick');
set(h,'YTickLabel',sprintfc('%g', [yticks.*y_Scl]))
ylabel(h, 'cd/m^2')% add unit label
title(date); % Show date in image
caxis auto % set axis to auto
datacursormode on % enable datacursor
img = getframe(gcf);
nowstr = datestr(now, 'yyyy-mm-dd_HH_MM_SS');
folder = 'C:\Users\Taiko\Desktop\FalseColor\';
ImageFiles = dir( fullfile(folder, '*.jpg') );
if isempty(ImageFiles)
next_idx = 1;
else
lastfile = ImageFiles(end).name;
[~, basename, ~] = fileparts(lastfile);
file_number_str = regexp('(?<=.*_)\d+$', basename, 'match' );
last_idx = str2double(file_number_str);
next_idx = last_idx + 1;
end
newfilename = fullfile( folder, sprintf('%s_%04d.jpg', nowstr, next_idx) );
imwrite(img.cdata, newfilename);
Problems:
1) You are getting YTick of the figure (gca) but not the color bar. That would give you the "pixel" coordinates of the graph, instead of the actual values. Use yticks = get(h,'YTick');.
2) caxis auto Should come before overwriting YTicks (and after enabling the color bar); otherwise the scale and ticks will mismatch.
3) Do you mean C = 100000?
Result:
I'm trying to color only a segment of an image in Matlab. For example, I load an RGB image, then I obtain a mask with Otsu's method (graythresh). I want to keep the color only in the pixels that have value of 1 after applying im2bw with graythresh as the threshold. For example:
image = imread('peppers.png');
thr = graythresh(image);
bw = im2bw(image, thr);
With this code I obtain the following binary image:
My goal is to keep the color in the white pixels.
Thanks!
I have another suggestion on how to replace the pixels we don't care about. This works by creating linear indices for each of the slices where black pixels exist in the bw image. The summation with the result of find is done because bw is the size of just one "slice" of image and this is how we get the indices for the other 2 slices.
Starting MATLAB 2016b:
image(find(~bw)+[0 numel(bw)*[1 2]]) = NaN;
In older versions:
image(bsxfun(#plus,find(~bw),[0 numel(bw)*[1 2]])) = NaN;
Then imshow(image) gives:
Note that NaN gets converted to 0 for integer classes.
Following the clarification that the other pixels should be kept in their gray version, see the below code:
% Load image:
img = imread('peppers.png');
% Create a grayscale version:
grayimg = rgb2gray(img);
% Segment image:
if ~verLessThan('matlab','9.0') && exist('imbinarize.m','file') == 2
% R2016a onward:
bw = imbinarize(grayimg);
% Alternatively, work on just one of the color channels, e.g. red:
% bw = imbinarize(img(:,:,1));
else
% Before R2016a:
thr = graythresh(grayimg);
bw = im2bw(grayimg, thr);
end
output_img = repmat(grayimg,[1 1 3]);
colorpix = bsxfun(#plus,find(bw),[0 numel(bw)*[1 2]]);
output_img(colorpix) = img(colorpix);
figure; imshow(output_img);
The result when binarizing using only the red channel:
Your question misses "and replace the rest with black". here are two ways:
A compact solution: use bsxfun:
newImage = bsxfun(#times, Image, cast(bw, 'like', Image));
Although I am glad with the previous one, you can also take a look at this step-by-step approach:
% separate the RGB layers:
R = image(:,:,1);
G = image(:,:,2);
B = image(:,:,3);
% change the values to zero or your desired color wherever bw is false:
R(~bw) = 0;
G(~bw) = 0;
B(~bw) = 0;
% concatenate the results:
newImage = cat(3, R, G, B);
Which can give you different replacements for the black region:
UPDATE:
According to the comments, the false area of bw should be replaced with grayscale image of the same input. This is how to achieve it:
image = imread('peppers.png');
thr = graythresh(image);
bw = im2bw(image, thr);
gr = rgb2gray(image); % generate grayscale image from RGB
newImage(repmat(~bw, 1, 1, 3)) = repmat(gr(~bw), 1, 1, 3); % substitude values
% figure; imshow(newImage)
With this result:
I am new to MATLAB Image Processing and currently I have two images - one is a grayscale image of my object and the second is the scaled image generated from MATLAB using imagesc function. I am trying to overlay this scaled image on top of my grayscale image to get a spatial resolution for easier observation. Attached are the two images:
A) Grayscale Image:
B) Scaled Image:
There were a few difficulties that I encountered. Firstly, the scaled image is not saved in the same pixel dimensions, but I can get around that using the imwrite function:
im = imagesc(ScaledDiff);
imwrite(get(im,'cdata'),'scaleddiff.tif')
However, doing so will result in a loss of colorbar and the colormap. Secondly, even if I manage to shrink the scaled image to the size of the grayscale image, overlaying it is still a challenge. Ideally, I would like to set the transparency (or 'alpha') to 0 for those pixels with < 0.02 in scaled image value.
Any idea on how to do this will be greatly appreciated! Sorry if I was unclear!
UPDATE:
Thanks to Rotem, I have managed to overlay the grayscale image and a particular region of my heatmap:
However, I need to display the colorbar corresponding to the heatmap values, because otherwise the information is lost and the overlay will be useless. How should I do this? Below is a snippet of my code, where ScaledIntDiff contains the values from 0 to 0.25 that is displayed on the heatmap:
Brightfield = imread('gray.jpg'); % read background image
I1 = ind2rgb(gray2ind(Brightfield), gray); % convert indices into RGB scale
scale = 1000;
ScaledIntDiff2 = round(ScaledIntDiff.*scale);
I2 = ind2rgb(ScaledIntDiff2, jet(256)); % this is for the heatmap
threshold = 0.02;
I2R = I2(:,:,1); I2G = I2(:,:,2); I2B = I2(:,:,3);
I1R = I1(:,:,1); I1G = I1(:,:,2); I1B = I1(:,:,3);
% Replace pixels in I2 with pixels in I1 if the value of ScaledIntDiff of those pixels is below the threshold
I2R(ScaledIntDiff<threshold) = I1R([ScaledIntDiff<threshold]);
I2G(ScaledIntDiff<threshold) = I1G([ScaledIntDiff<threshold]);
I2B(ScaledIntDiff<threshold) = I1B([ScaledIntDiff<threshold]);
I2(:,:,1) = I2R; I2(:,:,2) = I2G; I2(:,:,3) = I2B;
figure
imshow(I2)
I know that the code above is highly inefficient, so suggestions on how to improve it will be very welcomed. Thank you!
Check the following:
I = imread('CKbi2Ll.jpg'); %Load input.
%Convert I to true color RGB image (all pixels R=G=B are gray color).
I1 = ind2rgb(I, gray(256));
%Convert I to true color RGB image with color map parula (instead of using imagesc)
I2 = ind2rgb(I, parula(256));
%Set the transparency (or 'alpha') to 0 for those pixels with < 0.02 in scaled image value.
%Instead of setting transparency, replace pixels with values from I1.
threshold = 0.02; %Set threshold to 0.02
I2(I1 < threshold) = I1(I1 < threshold);
%Blend I1 and I2 into J.
alpha = 0.3; %Take 0.3 from I2 and 0.7 from I1.
J = I2*alpha + I1*(1-alpha);
%Display output
imshow(J);
Adding a colorbar with ticks labels from 0 to 0.25:
Set color map to parula - it doesn't affect the displayed image, because image format is true color RGB.
Crate array of add 6 ticks from 0 to 250.
Create cell array of 6 TickLabels from 0 to 0.25.
Add colorbar with Ticks and TickLabels properties created earlier.
Add the following code after imshow(J);:
colormap(parula(256));
TLabels = cellstr(num2str((linspace(0, 0.25, 6))'));
T = linspace(1, 250, 6);
colorbar('Ticks', T', 'TickLabels', TLabels);
imshow('ClmypzU.jpg');
colormap(jet(256));
TLabels = cellstr(num2str((linspace(0, 0.25, 6))'));
T = linspace(1, 250, 6);
colorbar('Ticks', T', 'TickLabels', TLabels);
I have the following function that is successful in creating a grey circular mask over the image input, such that the new image is a grey border around a circular image. Example: Grey circular mask.
All I want to do is make the mask a very specific green, but I haven't been successful.
Here is the code:
function [newIm] = myCircularMask(im)
%Setting variables
rad = size(im,1)/2.1; %Radius of the circle window
im = double(im);
[rows, cols, planes]= size(im);
newIm = zeros(rows, cols, planes);
%Generating hard-edged circular mask with 1 inside and 0 outside
M = rows;
[X,Y] = meshgrid(-M/2:1:(M-1)/2, -M/2:1:(M-1)/2);
mask = double(zeros(M,M));
mask(X.^2 + Y.^2 < rad^2) = 1;
% Soften edge of mask
gauss = fspecial('gaussian',[12 12],0.1);
mask = conv2(mask,gauss,'same');
% Multiply image by mask, i.e. x1 inside x0 outside
for k=1:planes
newIm(:,:,k) = im(:,:,k).*mask;
end
% Make mask either 0 inside or -127 outside
mask = (abs(mask-1)*127);
% now add mask to image
for k=1:planes
newIm(:,:,k) = newIm(:,:,k)+mask;
end
newIm = floor(newIm)/255;
The type of green I would like to use is of RGB values [59 178 74].
I'm a beginner with MATLAB, so any help would be greatly appreciated.
Cheers!
Steve
After masking your image, create a color version of your mask:
% test with simple mask
mask = ones(10,10);
mask(5:7,5:7)=0;
% invert mask, multiply with rgb-values, make rgb-matrix:
r_green=59/255; g_green=178/255; b_green=74/255;
invmask=(1-mask); % use mask with ones/zeroes
rgbmask=cat(3,invmask*r_green,invmask*g_green,invmask*b_green);
Add this to your masked image.
Edit:
function [newIm] = myCircularMask(im)
%Setting variables
rad = size(im,1)/2.1; %Radius of the circle window
im = double(im);
[rows, cols, planes]= size(im);
newIm = zeros(rows, cols, planes);
%Generating hard-edged circular mask with 1 inside and 0 outside
M = rows;
[X,Y] = meshgrid(-M/2:1:(M-1)/2, -M/2:1:(M-1)/2);
mask = double(zeros(M,M));
mask(X.^2 + Y.^2 < rad^2) = 1;
% Soften edge of mask
gauss = fspecial('gaussian',[12 12],0.1);
mask = conv2(mask,gauss,'same');
% Multiply image by mask, i.e. x1 inside x0 outside
for k=1:planes
newIm(:,:,k) = im(:,:,k).*mask;
end
% Here follows the new code:
% invert mask, multiply with rgb-values, make rgb-matrix:
r_green=59/255; g_green=178/255; b_green=74/255;
invmask=(1-mask); % use mask with ones/zeroes
rgbmask=cat(3,invmask*r_green,invmask*g_green,invmask*b_green);
newIm=newIm+rgbmask;
Note that I haven't been able to test my suggestion, so there might be errors.