Hi trying to make a simple jpeg compressor which also decompresses the image. I use the following code to downsample the chrominance of an image for the first step of jpeg compression.
resampler = vision.ChromaResampler;
[Cb, Cr] = resampler(Cb_channel, Cr_channel);
the function is part of the computer vision toolbox for matlab.
for example before downsampling:
Y dimensions = 3024 by 4032;
Cb and Cr dimensions = 3024 by 4032
after downsampling:
Y dimensions = 3024 by 4032;
Cb and Cr dimensions = 3024 by 2016
to display the original RGB image after decompression, the dimensions of all 3 Y, Cb and Cr components need to be the same so that I can merge the channels and convert the image back to RGB. I'm using the following code to achieve this:
Cb_resized = imresize(Cb, [(size(Cb, 1)) (2*size(Cb, 2))]);
Cr_resized = imresize(Cr, [(size(Cr, 1)) (2*size(Cr, 2))]);
When I then merge the 3 channels and use imshow() to see the image it looks fine. So is the above method the correct way of reversing the downsampled chrominance when decoding a jpeg?
Using imresize for up-sampling is "almost correct".
Instead of using imresize, you better use vision.ChromaResampler for up-sampling:
up_resampler = vision.ChromaResampler();
up_resampler.Resampling = '4:2:2 to 4:4:4';
[Cb_resized, Cr_resized] = up_resampler(Cb, Cr);
The module is design such that Resampling = '4:2:2 to 4:4:4' "reverses" the result of Resampling = '4:4:4 to 4:2:2'.
The '4:4:4 to 4:2:2' ChromaResampler uses a convention that displaces the result 0.5 pixels to the right.
(I think shifting by 0.5 pixels supposes to match MPEG-1 codec standard).
The 0.5 displacement is not well documented - I had to build a short test for figuring it out.
As far as I remember, the convention of moving 0.5 a pixel is used by MPEG-1 codec, but not used by MPEG-2 and newer codecs.
I don't think it is used by JPEG, but I am not sure...
Note:
Since the human visual system is not very sensitive to the Chroma resolution, you are probably not going to see the differences if 0.5 displacement is used or not.
For getting the same result as ChromaResampler, you may use imwarp, with displacement of 1 pixel in the horizontal axis.
Understanding imwarp is a little complicated.
I am going to use imwarp for demonstrating that 1 pixel displacement, gives the same result as ChromaResampler:
The following code sample shows the equivalence:
close all
I = imread('peppers.png'); % Read sample image
YUV = rgb2ycbcr(I); % Convert RGB to Y:Cb:Cr
U = YUV(:, :, 2); % Get U color channel
V = YUV(:, :, 3); % Get V color channel
down_resampler = vision.ChromaResampler(); % 4:4:4 to 4:2:2
down_resampler.Resampling = '4:4:4 to 4:2:2';
up_resampler = vision.ChromaResampler(); % 4:2:2 to 4:4:4
up_resampler.Resampling = '4:2:2 to 4:4:4';
% Down-sample U and V using ChromaResampler
[downU, downV] = down_resampler(U, V);
%downU2 = imresize(U, [size(U, 1), size(U, 2)/2]); % Not the same as using imresize
%figure;imshow(downU);figure;imshow(downU2);
% Up-sample downU and downV using ChromaResampler
[upU, upV] = up_resampler(downU, downV);
% Result is not the same as using imresize
%resizedU = imresize(downU, [size(downU, 1), size(downU, 2)*2], 'bilinear');
%resizedV = imresize(downV, [size(downV, 1), size(downV, 2)*2], 'bilinear');
% Use transformation matrix that resize horizontally by x2 and include single pixel horizontal displacement.
tform = affine2d([ 2 0 0
0 1 0
-1 0 1]);
% Use imwarp instead of imresize (the warp includes horizontal displacement of 1 pixel)
warpU = imwarp(downU, tform, 'bilinear', 'OutputView', imref2d([size(downU, 1), size(downU, 2)*2]));
warpU(:, end) = warpU(:, end-1); % Fill the last column by duplication
%figure;imagesc(double(upU) - double(resizedU));impixelinfo
%figure;imshow(upU);figure;imshow(resizedU);
%figure;imshow(upU);figure;imshow(warpU);
% Show the differences:
figure;imagesc(double(upU) - double(warpU));title('Diff');impixelinfo
max_abs_diff = max(imabsdiff(warpU(:), upU(:)));
disp(['max_abs_diff = ', num2str(max_abs_diff)]); % Maximum absolute differenced is 1 (due to rounding).
Note: The imresize usage is kept in comments.
Note:
The default interpolation method of imresize is cubic interpolation, and the default interpolation method of ChromaResampler is linear interpolation.
Cubic interpolation is considered superior, but linear interpolation is commonly used (the visible difference is negligible).
Related
We’ve been trying to fix this program for hours, yet nothing seems to work, we just can’t figure out what the problem is. It is supposed to make the whole picture black white, besides the red pixels. (https://imgur.com/a/gxRm3N1 should look like that afterwards)
Here is the result after the using the program, the whole picture is turning red:
https://imgur.com/a/yWYVoIx
How can I fix this?
from image_helper import *
img = load_rgb_image("ara.jpg")
w, h = img.size
pixels = load_rgb_pixels("ara.jpg")
#img.show()
for i in range(w*h):
r,g,b = pixels[i]
new_r = 2*r
new_g = g // 2
new_b = b + 10
pixels[i] = (new_r, new_g, new_b)
new_img = new_rgb_image(w, h, pixels)
new_img.show()
There is an excellent solution implemented in MATLAB.
I was tempting to translate the code from MATLAB to Python (using OpenCV).
Convert the image to HSV color space.
Select "non-red" pixels. In HSV color space, the first channel is the hue - there is a range of hue for pixels that considered to be red.
Set the selected pixel saturation channel to 0. (Pixels with zero saturation are gray).
Convert the image back from HSV to BGR color space.
Here is the Python code (conversation of the original MATLAB code):
import cv2
# The following code is a Python conversion to the following MATLAB code:
# Original MATLAB code: https://stackoverflow.com/questions/4063965/how-can-i-convert-an-rgb-image-to-grayscale-but-keep-one-color
img = cv2.imread('roses.jpg') # Load image.
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) # Convert the image to HSV color space.
h = hsv[:, :, 0] # Note: in OpenCV hue range is [0,179]) The original MATLAB code is 360.*hsvImage(:, :, 1), when hue range is [0, 1].
s = hsv[:, :, 1] # Get the saturation plane.
non_red_idx = (h > 20//2) & (h < 340//2) # Select "non-red" pixels (divide the original MATLAB values by 2 due to the range differences).
s[non_red_idx] = 0 # Set the selected pixel saturations to 0.
hsv[:, :, 1] = s # Update the saturation plane.
new_img = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR) # Convert the image back to BGR space
# Show images for testing:
cv2.imshow('img', img)
cv2.imshow('new_img', new_img)
cv2.waitKey()
cv2.destroyAllWindows()
cv2.imwrite('new_img.jpg', new_img)
Result:
Notes:
For the method used for selecting the color range, refer to the original post.
The reference post has a simpler solution, using simple for loop (with inferior results), that more resembles your code.
Consider using this code as reference.
I'm trying to obtain an image which has everything but several colorful objects grayed out, as shown here:
My original image is this (the caps have slightly different colors than the example above):
I tried to apply a threshold process then binarize the image, which gave me the following result (mask is on the left, result of multiplication is on the right):
And now I'm trying to combine all of these masks. Should I use if loop to combine it into a single image or is there a better way? I tried using (&,&,&) but it turned into a black images.
Your original image has 7 distinct regions: 5 colorful tips, the hand, and the background. The question then becomes, how do we disregard the wall and the hand, which happen to be the two largest regions, and only keep the color of the tips.
If your MATLAB license permits, I would recommend using the Color Thresholder App (colorThresholder), which allows you to find a suitable representation of the colors in your image. Experimenting with it for a minute I can say that the L*a*b* color space allows good separation between the regions/colors:
We can then export this function, yielding the following:
function [BW,maskedRGBImage] = createMask(RGB)
%createMask Threshold RGB image using auto-generated code from colorThresholder app.
% [BW,MASKEDRGBIMAGE] = createMask(RGB) thresholds image RGB using
% auto-generated code from the colorThresholder app. The colorspace and
% range for each channel of the colorspace were set within the app. The
% segmentation mask is returned in BW, and a composite of the mask and
% original RGB images is returned in maskedRGBImage.
% Auto-generated by colorThresholder app on 25-Dec-2018
%------------------------------------------------------
% Convert RGB image to chosen color space
I = rgb2lab(RGB);
% Define thresholds for channel 1 based on histogram settings
channel1Min = 0.040;
channel1Max = 88.466;
% Define thresholds for channel 2 based on histogram settings
channel2Min = -4.428;
channel2Max = 26.417;
% Define thresholds for channel 3 based on histogram settings
channel3Min = -12.019;
channel3Max = 38.908;
% Create mask based on chosen histogram thresholds
sliderBW = (I(:,:,1) >= channel1Min ) & (I(:,:,1) <= channel1Max) & ...
(I(:,:,2) >= channel2Min ) & (I(:,:,2) <= channel2Max) & ...
(I(:,:,3) >= channel3Min ) & (I(:,:,3) <= channel3Max);
BW = sliderBW;
% Invert mask
BW = ~BW;
% Initialize output masked image based on input image.
maskedRGBImage = RGB;
% Set background pixels where BW is false to zero.
maskedRGBImage(repmat(~BW,[1 1 3])) = 0;
end
Now that you have the mask we can easily convert the original image to grayscale, replicate it along the 3rd dimension, then take the colorful pixels from the original image using logical indexing:
function q53922067
img = imread("https://i.stack.imgur.com/39WNm.jpg");
% Image segmentation:
BW = repmat( createMask(img), 1, 1, 3 ); % Note that this is the function shown above
% Keeping the ROI colorful and the rest gray:
gImg = repmat( rgb2gray(img), 1, 1, 3 ); % This is done for easier assignment later
gImg(BW) = img(BW);
% Final result:
figure(); imshow(gImg);
end
Which yields:
To combine masks, use | (element-wise logical OR), not & (logical AND).
mask = mask1 | mask2 | mask3;
I'm new to image processing and started using MATLAB for Astrophotography processing. I'm trying to process 10 corrupted images (same image but mixed with different noise) of the planet Saturn using MATLAB. I learned that by stacking the 10 images together leads to a noise reduced picture with high PSNR and tried the below coding to make it work.
But the output looks like an unclear saturated image with no noise reduction.
Can you please look at the code below and show me where I went wrong?
%% We are going to stack the 10 corrupted images and finally calculate the PSNR SSIM
clearvars;% Clear all the variables
close all;
load('planetdata.mat'); %to load the corrupted Image set (4-D uint8)
Clean = imread('Clean Image of Saturn.jpg');%Clean Image of Saturn.600x800x3 uint8
planet1(: , :, :) = planetdata(1, :, :, :);%One corrupted Image as reff
% Set the number of images to stack is 10
stack_number = 10;
% Lets use Clean image as reference of dimensions required
im_x = size(Clean, 1);
im_y = size(Clean, 2);
im_z = size(Clean, 3);
% Lets Generate a blank image for image stacking
resultIM = uint8(zeros(im_x, im_y, im_z));
% Iterate through the images to stack
for i = 1:1:stack_number
% Read in the target object image
CorruptIM(: , :, :) = planetdata(i, :, :, :);
% Perform image stacking using the target object image
resultIM = resultIM + CorruptIM;
end
% resultIM = resultIM / stack_number;
%% Lets Display Results
workspace; % to Make sure the work space panel is showing.
fontSize = 15;
figure;
subplot(1, 3, 1);
imshow(Clean);
title('Clean Image', 'FontSize', fontSize);
% Enlarge figure to full screen.
set(gcf, 'Position', get(0,'Screensize'));
% Give a name to the title bar.
set(gcf,'name','Stacking','numbertitle','off')
% Display one corrupt image as reference
subplot(1, 3, 2);
imshow(planet1);
title('Corrupt Image 1 : Ref', 'FontSize', fontSize);
% Display Stacked image
subplot(1, 3, 3);
imshow(resultIM);
title('Stacked Image', 'FontSize', fontSize);
%% PSNR AND SSIM Calculation
%Lets Find PSNR for For Resultant Image
[row,col] = size(Clean);
size_host = row*col;
o_double = double(Clean);
w_double = double(resultIM);
s=0;
for j = 1:size_host % the size of the original image
s = s+(w_double(j) - o_double(j))^2 ;
end
mes =s/size_host;
psnr =10*log10((255)^2/mes);
fprintf('The PSNR value for Stacked Image is %0.4f.\n',psnr);
%Lets Find SSIM for resultant Image
[ssimval, ssimmap] = ssim(uint8(resultIM),Clean);
fprintf('The SSIM value for Stacked Image is %0.4f.\n',ssimval);
I think this one statement pretty much says it all (emphasis mine):
But the output looks like an unclear saturated image with no noise reduction.
It looks like your images are in fact saturating at the upper limit for a uint8 variable, which is the data type for your result image resultIM and your data matrix planetdata. As you keep adding images, the pixel values saturate at the maximum value of 255 for unsigned 8-bit integer types.
What you'll need to do is convert to a larger data type for your intermediate calculations, such as a larger integer type or a floating point type. Then, once your calculations are complete (e.g. dividing the sum by the image stack size to get an average image), you can scale and/or round your data as needed and convert it back to a uint8 type.
I'm trying to fuse two images using SWT. But I'm getting this error :
The level of decomposition 1
and the size of the image (1,5)
are not compatible.
Suggested size: (2,6)
How to change the size of the image to make it compatible for transform?
Code I've used is :
clc
i=1;
fol=1;
n=0;
for fol=1:5
f='folder';
folder = strcat(f, num2str(fol));
cd(folder)
d= numel(D);
i=(n+1);
Fname1 = strcat(int2str(i),'.bmp');
Fname2 = strcat(int2str(i+1),'.bmp');
im1 = imread(Fname1);
im2 = imread(Fname2);
im1=double(im1);
im2=double(im2);
% image decomposition using discrete stationary wavelet transform
[A1L1,H1L1,V1L1,D1L1] = swt2(im1,1,'sym2');
[A2L1,H2L1,V2L1,D2L1] = swt2(im2,1,'sym2');
% fusion start
AfL1 = 0.5*(A1L1+A2L1);
D = (abs(H1L1)-abs(H2L1))>=0;
HfL1 = D.*H1L1 + (~D).*H2L1;
D = (abs(V1L1)-abs(V2L1))>=0;
VfL1 = D.*V1L1 + (~D).*V2L1;
D = (abs(D1L1)-abs(D2L1))>=0;
DfL1 = D.*D1L1 + (~D).*D2L1;
% fused image
imf = iswt2(AfL1,HfL1,VfL1,DfL1,'sym2');
figure;
imshow(imf,[]);
Iname= strcat(int2str(fol),'.bmp');
imwrite(imf,Iname);
end
To address your first problem, that image is really small. I'm assuming that's an image of size 1 x 5. I would suggest changing your image so that it's larger, or perhaps do an imresize on the image. However, as what Ander said in his comment to you... I wouldn't call a 1 x 5 matrix an image.
To address your second problem, once you finally load in an image, the wavelet transform will most likely give you floating point numbers that are beyond the dynamic range of any sensible floating point precision image. As such, it's good that you normalize the image first, then save it to file.
Therefore, do this right before you save the image:
%// ...
%// Your code...
imshow(imf,[]);
%// Normalize the image - Change
imf = (imf - min(imf(:))) / (max(imf(:)) - min(imf(:)));
%// Your code again
%// Now save
Iname= strcat(int2str(fol),'.bmp');
imwrite(imf,Iname);
The above transformation normalizes an image so that the minimum is 0 and the maximum is 1. Once you do that, it should be visualized properly. FWIW, doing imshow(imf,[]); does this normalization for you and displays that result, but it doesn't modify the image.
I am interested in adding a single Gaussian shaped object to an existing image, something like in the attached image. The base image that I would like to add the object to is 8-bit unsigned with values ranging from 0-255. The bright object in the attached image is actually a tree represented by normalized difference vegetation index (NDVI) data. The attached script is what I have have so far. How can I add a a Gaussian shaped abject (i.e. a tree) with values ranging from 110-155 to an existing NDVI image?
Sample data available here which can be used with this script to calculate NDVI
file = 'F:\path\to\fourband\image.tif';
[I R] = geotiffread(file);
outputdir = 'F:\path\to\output\directory\'
%% Make NDVI calculations
NIR = im2single(I(:,:,4));
red = im2single(I(:,:,1));
ndvi = (NIR - red) ./ (NIR + red);
ndvi = double(ndvi);
%% Stretch NDVI to 0-255 and convert to 8-bit unsigned integer
ndvi = floor((ndvi + 1) * 128); % [-1 1] -> [0 256]
ndvi(ndvi < 0) = 0; % not really necessary, just in case & for symmetry
ndvi(ndvi > 255) = 255; % in case the original value was exactly 1
ndvi = uint8(ndvi); % change data type from double to uint8
%% Need to add a random tree in the image here
%% Write to geotiff
tiffdata = geotiffinfo(file);
outfilename = [outputdir 'ndvi_' '.tif'];
geotiffwrite(outfilename, ndvi, R, 'GeoKeyDirectoryTag', tiffdata.GeoTIFFTags.GeoKeyDirectoryTag)
Your post is asking how to do three things:
How do we generate a Gaussian shaped object?
How can we do this so that the values range between 110 - 155?
How do we place this in our image?
Let's answer each one separately, where the order of each question builds on the knowledge from the previous questions.
How do we generate a Gaussian shaped object?
You can use fspecial from the Image Processing Toolbox to generate a Gaussian for you:
mask = fspecial('gaussian', hsize, sigma);
hsize specifies the size of your Gaussian. You have not specified it here in your question, so I'm assuming you will want to play around with this yourself. This will produce a hsize x hsize Gaussian matrix. sigma is the standard deviation of your Gaussian distribution. Again, you have also not specified what this is. sigma and hsize go hand-in-hand. Referring to my previous post on how to determine sigma, it is generally a good rule to set the standard deviation of your mask to be set to the 3-sigma rule. As such, once you set hsize, you can calculate sigma to be:
sigma = (hsize-1) / 6;
As such, figure out what hsize is, then calculate your sigma. After, invoke fspecial like I did above. It's generally a good idea to make hsize an odd integer. The reason why is because when we finally place this in your image, the syntax to do this will allow your mask to be symmetrically placed. I'll talk about this when we get to the last question.
How can we do this so that the values range between 110 - 155?
We can do this by adjusting the values within mask so that the minimum is 110 while the maximum is 155. This can be done by:
%// Adjust so that values are between 0 and 1
maskAdjust = (mask - min(mask(:))) / (max(mask(:)) - min(mask(:)));
%//Scale by 45 so the range goes between 0 and 45
%//Cast to uint8 to make this compatible for your image
maskAdjust = uint8(45*maskAdjust);
%// Add 110 to every value to range goes between 110 - 155
maskAdjust = maskAdjust + 110;
In general, if you want to adjust the values within your Gaussian mask so that it goes from [a,b], you would normalize between 0 and 1 first, then do:
maskAdjust = uint8((b-a)*maskAdjust) + a;
You'll notice that we cast this mask to uint8. The reason we do this is to make the mask compatible to be placed in your image.
How do we place this in our image?
All you have to do is figure out the row and column you would like the centre of the Gaussian mask to be placed. Let's assume these variables are stored in row and col. As such, assuming you want to place this in ndvi, all you have to do is the following:
hsizeHalf = floor(hsize/2); %// hsize being odd is important
%// Place Gaussian shape in our image
ndvi(row - hsizeHalf : row + hsizeHalf, col - hsizeHalf : col + hsizeHalf) = maskAdjust;
The reason why hsize should be odd is to allow an even placement of the shape in the image. For example, if the mask size is 5 x 5, then the above syntax for ndvi simplifies to:
ndvi(row-2:row+2, col-2:col+2) = maskAdjust;
From the centre of the mask, it stretches 2 rows above and 2 rows below. The columns stretch from 2 columns to the left to 2 columns to the right. If the mask size was even, then we would have an ambiguous choice on how we should place the mask. If the mask size was 4 x 4 as an example, should we choose the second row, or third row as the centre axis? As such, to simplify things, make sure that the size of your mask is odd, or mod(hsize,2) == 1.
This should hopefully and adequately answer your questions. Good luck!