Why when I read an image im = imread('pears.png') I see the in workspace 486x732x3 unit 8 but when I run [height, width] = size(im) I get height 486 and width 2196?. When I read m83.tif all is ok. is it related to color? which type of file will be multiply by 3 than?
Following the documentation of imread:
A = imread(FILENAME,FMT) ...
If the file contains a grayscale image, A is an M-by-N array
If the file contains a truecolor image, A is an M-by-N-by-3 array.
Thus, what you want to have are only the first dimensions. However, since im is three dimensional and size will return the size of a reshaped array when only querying two dimensions. You will see that your second dimeions is the product size(im,2)*size(im,3).
Just use
[height, width, ~] = size(im)
This will query three dimensions but drop the third output parameter. Also using:
height = size(im,1);
width = size(im,2);
will work and is a save solution.
See following code for more clarifictaion:
>> im = ones([40,20,3]);
>> [h,w] = size(im)
h =
40
w =
60
>> [h,w,~] = size(im)
h =
40
w =
20
>> h = size(im,1)
h =
40
>> w = size(im,2)
w =
20
Related
I am trying to use interpn (in python using Scipy) to replicate results from Matlab using interp3. However, I am struggling to structure my arguments. I tried the following line:
f = interpn(blur_maps, fx, fy, pyr_level)
Where blur maps is a 600 x 800 x 7 representing a grayscale image at seven levels of blur,
fx and fy are indices of the seven maps. Both fx and fy are 2d arrays. pyr_level is a 2d array that contains values from 1 to 7 representing the blur map to be interpolated.
My question is since I incorrectly arranged the arguments, how can I arrange them in a way that works? I tried to look up examples but I didn't see anything similar. Here is an example of the data I am trying to interpolate:
import numpy as np
import cv2, math
from scipy.interpolate import interpn
levels = 7
img_path = '/Users/alimahdi/Desktop/i4.jpg'
img = cv2.cvtColor(cv2.imread(img_path), cv2.COLOR_BGR2GRAY)
row, col = img.shape
x_range = np.arange(0, col)
y_range = np.arange(0, row)
fx, fy = np.meshgrid(x_range, y_range)
e = np.exp(np.sqrt(fx ** 2 + fy ** 2))
pyr_level = 7 * (e - np.min(e)) / (np.max(e) - np.min(e))
blur_maps = np.zeros((row, col, levels))
blur_maps[:, :, 0] = img
for i in range(levels - 1):
img = cv2.pyrDown(img)
r, c = img.shape
tmp = img
for j in range(int(math.log(row / r, 2))):
tmp = cv2.pyrUp(tmp)
blur_maps[:, :, i + 1] = tmp
pixelGrid = [np.arange(x) for x in blur_maps.shape]
interpPoints = np.array([fx.flatten(), fy.flatten(), pyr_level.flatten()])
interpValues = interpn(pixelGrid, blur_maps, interpPoints.T)
finalValues = np.reshape(interpValues, fx.shape)
I am now getting the following error: ValueError: One of the requested xi is out of bounds in dimension 0 I do know that the problem is in interpPoints but I am not sure how to fix it. Any suggestions?
The documentation for scipy.interpolate.interpn states that the first argument is a grid of the data you are interpolating over (which is just the integers of the pixel numbers), second argument is data (blur_maps) and third arguments is the interpolation points in the form (npoints, ndims). So you would have to do something like:
import scipy.interpolate
pixelGrid = [np.arange(x) for x in blur_maps.shape] # create grid of pixel numbers as per the docs
interpPoints = np.array([fx.flatten(), fy.flatten(), pyr_level.flatten()])
# interpolate
interpValues = scipy.interpolate.interpn(pixelGrid, blur_maps, interpPoints.T)
# now reshape the output array to get in the original format you wanted
finalValues = np.reshape(interpValues, fx.shape)
I am having issue with MatCaffe. I trained LeNet using my own dataset (2 classification, 0 or 1) in python successfully and trying to deploy it on Matlab now. Net architecture is from caffe/examples/mnist/lenet.prototxt. All the input images I fed into the net always return 1. (i tried using both positive and negative images from training).
Below is my code:
deployNet = 'lenet_deploy.prototxt';
caffeModel = 'weight.caffemodel';
caffe.set_mode_cpu();
net = caffe.Net(deployNet, caffeModel, 'test');
net.blobs('data').reshape([28 28 1 1]);
net.reshape();
patch_data = imread('cropped.jpg'); % already in greyscale
patch_data = imresize(patch_data, [28 28],'bilinear');
imshow(patch_data)
input_data = {patch_data};
scores = net.forward(input_data);
highest = max(scores{1});
disp(i);
disp(highest);
The highest always return 1 even for negative image. I tried deploying it on python and it works great. I am guessing issue with the way I pre-process the input.
Found out the issue. I forgotten to multiply the image with the training scale and transpose the width and height since Matlab is 1-indexed and column-major, the usual 4 blob dimensions in Matlab are [width, height, channels, num], and width is the fastest dimension. So just add in 2 more line of codes:
deployNet = 'lenet_deploy.prototxt';
caffeModel = 'weight.caffemodel';
caffe.set_mode_cpu();
net = caffe.Net(deployNet, caffeModel, 'test');
net.blobs('data').reshape([28 28 1 1]);
net.reshape();
patch = imread('cropped.jpg'); % already in greyscale
patch = single(patch) * 0.00390625; % multiply with scale
patch = permute(patch, [2,1,3]); %permute width and height
input_data = {patch};
scores = net.forward(input_data);
highest = scores{1};
I am trying to execute this function for image reconstruction where
ra, rh, rv, rd are reconstructed coefficients. but i am facing problem in addition and subtraction.
Please help.
Xhat = ra2 + rh2 + rv2 + rd2 + rh1 + rv1 + rd1;
sprintf('Reconstruction error (using wrcoef2) = %g', max(max(abs(X-Xhat))))
OR
XXhat = waverec2(wc,s,wname);
sprintf('Reconstruction error (using waverec2) = %g', max(max(abs(X-XXhat)))
I decomposed the image using:
>> a1 = appcoef2(wc,s,wname,1);
>> h1 = detcoef2('h',wc,s,1);
>> v1 = detcoef2('v',wc,s,1);
>> d1 = detcoef2('d',wc,s,1);
>> a2 = appcoef2(wc,s,wname,2);
>> h2 = detcoef2('h',wc,s,2);
>> v2 = detcoef2('v',wc,s,2);
>> d2 = detcoef2('d',wc,s,2);
Then reconstructed using above parameters.
Now i have to comnbine them.
I'm guessing your problem is almost certainly at the abs(X-Xhat) line.
Why? You seem to be doing some sort of wavelet decomposition/reconstruction, and if you don't pass in the right parameters your output may be larger than the original image. Therefore it makes no sense to ask for X-Xhat if these are of different sizes and you will get an error message.
The best way of fixing this is to when you reconstruct (presumably using upcoef2), is to pass size as an additional parameter to truncate. A truncated (heh) and adjusted example from the docs:
load woman;
[c,s] = wavedec2(X,2,'db4');
siz = s(size(s,1),:);
ca1 = appcoef2(c,s,'db4',1);
a = upcoef2('a',ca1,'db4',1,siz);
a2 = upcoef2('a',ca1,'db4',1);
You'll see that size(X) and size(a) are both 256 x 256, but size(a2) is larger. Therefore a-X is fine and a2-X will give you a "Matrix dimensions must agree." error.
So I'd like to resize a matrix that is of size 72x144x156 into a 180x360x156 grid. I can try to do it with this command: resizem(precip,2.5). The first two dimensions are latitude and longitude, while the last dimension is time. I don't want time to be resized.
This works if the matrix is of size 72x144. But it doesn't work for size 72x144x156. Is there a way to resize the first two dimensions without resizing the third?
Also, what is the fastest way to do this (preferably without a for loop). If a for loop is necessary, then that's fine.
I hinted in my comment, but could use interp3 like this:
outSize = [180 360 156];
[nrows,ncols,ntimes] = size(data);
scales = [nrows ncols ntimes] ./ outSize;
xq = (1:outSize(2))*scales(2) + 0.5 * (1 - scales(2));
yq = (1:outSize(1))*scales(1) + 0.5 * (1 - scales(1));
zq = (1:outSize(3))*scales(3) + 0.5 * (1 - scales(3));
[Xq,Yq,Zq] = meshgrid(xq,yq,zq);
dataLarge = interp3(data,Xq,Yq,Zq);
But the problem is simplified if you know you don't want to interpolate between time points, so you can loop as in Daniel R's answer. Although, this answer will not increase the number of time points.
D= %existing matrix
scale=2.5;
E=zeros(size(D,1)*2.5,size(D,2)*2.5,size(D,3))
for depth=1:size(D,3)
E(:,:,depth)=resizem(D(:,:,depth),scale)
end
This should provide the expected output.
% s = zeros(72, 144, 156);
% whos s;
% news = resize2D(s, 2.5);
% whos news;
function [result] = resize2D(input, multiply)
[d1, d2, d3] = size(input);
result = zeros(d1*multiply, d2*multiply, d3);
end
This question already has answers here:
eigenfaces are not showing correctly and are very dark
(2 answers)
Closed 3 years ago.
i have a set of 17 face grayscale pictures..and when try to view it i get a black images instead of ghost like pictures.
input_dir = 'images';
image_dims = [60, 60];
filenames = dir(fullfile(input_dir, '*.jpg'));
num_images = numel(filenames);
images = [];
for n = 1:num_images
filename = fullfile(input_dir, filenames(n).name);
img = imresize(imread(filename),[60,60]);
if n == 1
images = zeros(prod(image_dims), num_images);
end
images(:, n) = img(:);
end
% Trainig
% steps 1 and 2: find the mean image and the mean-shifted input images
mean_face = mean(images, 2);
shifted_images = images - repmat(mean_face, 1, num_images);
% steps 3 and 4: calculate the ordered eigenvectors and eigenvalues
[evectors, score, evalues] = princomp(images');
% step 5: only retain the top 'num_eigenfaces' eigenvectors (i.e. the principal components)
num_eigenfaces = 20;
evectors = evectors(:, 1:num_eigenfaces);
% step 6: project the images into the subspace to generate the feature vectors
features = evectors' * shifted_images;
and to see the eignevalues i used this code
figure;
for n = 1:num_eigenfaces
subplot(2, ceil(num_eigenfaces/2), n);
evector = reshape(evectors(:,n), image_dims);
imshow(evector);
end
i dont think it was suppose to be like this. can someone point out what i did wrong?
You should check each step in the code and make sure they pass sanity checks. My guess is this
features = evectors' * shifted_images;
Should be this
features = shifted_images * evectors;
Which makes me wonder if shifted_images has the correct dimensions. The evectors should be a matrix where each column represents a component vector. The matrix will be [pics x n]. The shifted images should be a [pixcount x pics] matrix. "pixcount" is the amount of pixels in each picture and "pics" is the number of pictures. If evectors' * shifted_images works without a dimensions error, I wonder if one quantity isn't being calculated correctly. I think this transpose is the culprit:
princomp(images');
Try scaling the image:
for i=1:num_eigenfaces
subplot(1,7,i);
image=reshape(evectors(:,i), image_dims);
image=image';
%scale image to full scale
imshow(image, []);
end