I wanted to have one dimensional PSD for an Image calculated (along rows and columns separately) using matlab.
I use the following snippet for the same.
F=fft(img,[],2);%FFT along dim2
F=fftshift(F,2);
mtf=(abs(F)).^2;
mtf_mean = mean(mtf,2);% Mean of all contents of a row
mtf_mean_norm = mtf_mean/max(max(mtf_mean)); %Normalization to 1
plot(mtf_mean_norm);
When I plot it, I expected a symmetrical plot with respect to a center (and that's what I want). But, I happen to see that two parts look asymmerical like in the attached figure.
Looks like I have a code bug, Any clues what am I missing ?
Image url: http://i.stack.imgur.com/RrLIt.jpg
I am not an image processing person, but just from my own stats knowledge I would say.
you should use mtf=abs(F)'*abs(F) instead of mtf=(abs(F)).^2. I got the following figure
here is the code that generates the figure.
> img=randn(50,50);
> F=fft(img,[],2);%FFT along dim2
> F=fftshift(F,2);
> mtf=abs(F)'*abs(F);
> mtf_mean = mean(mtf,2);% Mean of all contents of a row
> mtf_mean_norm = mtf_mean/max(max(mtf_mean)); %Normalization to 1
> plot(mtf_mean_norm);
> plot(mtf_mean_norm);
Related
In an old version of my code, I used to do a hardcopy() with a given resolution, ie:
frame = hardcopy(figHandle, ['-d' renderer], ['-r' num2str(round(pixelsperinch))]);
For reference, hardcopy saves a figure window to file.
Then I would typically perform:
ZZ = rgb2gray(frame) < 255/2;
se = strel('disk',diskSize);
ZZ2 = imdilate(ZZ,se); %perform dilation.
Surface = bwarea(ZZ2); %get estimated surface (in pixels)
This worked until I switched to Matlab 2017, in which the hardcopy() function is deprecated and we are left with the print() function instead.
I am unable to extract the data from figure handler at a specific resolution using print. I've tried many things, including:
frame = print(figHandle, '-opengl', strcat('-r',num2str(round(pixelsperinch))));
But it doesn't work. How can I overcome this?
EDIT
I don't want to 'save' nor create a figure file, my aim is to extract the data from the figure in order to mesure a surface after a dilation process. I just want to keep this information and since 'im processing a LOT of different trajectories (total is approx. 1e7 trajectories), i don't want to save each file to disk (this is costly, time execution speaking). I'm running this code on a remote server (without a graphic card).
The issue I'm struggling with is: "One or more output arguments not assigned during call to "varargout"."
getframe() does not allow for setting a specific resolution (it uses current resolution instead as far as I know)
EDIT2
Ok, figured out how to do, you need to pass the '-RGBImage' argument like this:
frame = print(figHandle, ['-' renderer], ['-r' num2str(round(pixelsperinch))], '-RGBImage');
it also accept custom resolution and renderer as specified in the documentation.
I think you must specify formattype too (-dtiff in my case). I've tried this in Matlab 2016b with no problem:
print(figHandle,'-dtiff', '-opengl', '-r600', 'nameofmyfig');
EDIT:
If you need the CData just find the handle of the corresponding axes and get its CData
f = findobj('Tag','mytag')
Then depending on your matlab version use:
mycdata = get(f,'CData');
or directly
mycdta = f.CData;
EDIT 2:
You can set the tag of your image programatically and then do what I said previously:
a = imshow('peppers.png');
set(a,'Tag','mytag');
I have a CT image of a lung as shown below :
I am trying to filter the inner of the two lungs such that i only remove the thin lines of the bronchi ,but i need to keep these small "circles " as much as i can for extraction in the next step as these small circles are nodules candidates ( cancerous structures ) . So please if you can mention to me a good filtering technique for this purpose. Thanks in advance
You can try imfilter, with Gaussian, or perhaps disk filter. Try:
img=imread('orsoR.png');
h = fspecial('disk',5);
y = imfilter(h, img);
figure;
imshow(y)
I am using Paraview 5.0.1. If any solution requires updating, I can try.
I want to programmatically obtain field plots (and corresponding PlotOverLine) of displacements and stresses in rotated coordinate systems.
What are appropriate/convenient/possible ways of doing this?
So far, I have created one Calculator filter for each component of displacements and stresses.
For instance, I used Calculators in 2D with results
(displacement.iHat)*cos(0.7853981625)+(displacement.jHat)*sin(0.7853981625)
(stress_3-stress_0)*sin(45.0*3.14159265/180)*cos(45.0*3.14159265/180)+stress_1*((cos(45.0*3.14159265/180))^2-(sin(45.0*3.14159265/180))^2)
It works fine, but it is quite cumbersome, in several aspects:
Creating them (one filter per component).
Plotting several of them in a single XY plot
Exporting them (one export per component).
Is there a simple way to do this?
PS: The Transform filter does not accomplish this. It rotates the view, not the fields.
Two solutions:
Ugly, inneficient solution
Use Transform and check "Transform All Input vectors"
Add a calculator and add a dummy array
Use transform the other way around, without checking "Transform All Input vectors"
Correct solution :
Compute the transformation yourself in a programmable filter
input = self.GetUnstructuredGridInput();
output = self.GetUnstructuredGridOutput();
output.ShallowCopy(input)
data = input.GetPointData().GetArray("YourArray")
vec = vtk.vtkDoubleArray();
vec.SetNumberOfComponents(3);
vec.SetName("TransformedVectors");
numPoints = input.GetNumberOfPoints()
for i in xrange(0, numPoints):
tuple = data.GetTuple(i)
transform(tuple) # implement the transform in python
vec.InsertNextTuple(tuple)
output.GetPointData().AddArray(vec)
I am trying to debug a custom loss function and I would like to visualize the images generated during the intermediate computation step in the objective function. A tf_summary_image or a simple imshow would be perfect, but the summary it is not working without calling a sess.run() with a proper feed_dict. For simplicity, let's say I have:
def custom_objective(y_pred, y_true):
diff = y_pred - y_true
#Here I would need something to save and/or show the diff image
square = K.square(diff)
#Here I would need something to save and/or show the square image
mean = K.mean(square, axis=-1)
return mean
Any suggestions?
I'm looking at implementing a Caffe CNN which accepts two input images and a label (later perhaps other data) and was wondering if anyone was aware of the correct syntax in the prototxt file for doing this? Is it simply an IMAGE_DATA layer with additional tops? Or should I use separate IMAGE_DATA layers for each?
Thanks,
James
Edit: I have been using the HDF5_DATA layer lately for this and it is definitely the way to go.
HDF5 is a key value store, where each key is a string, and each value is a multi-dimensional array. Thus, to use the HDF5_DATA layer, just add a new key for each top you want to use, and set the value for that key to store the image you want to use. Writing these HDF5 files from python is easy:
import h5py
import numpy as np
filelist = []
for i in range(100):
image1 = get_some_image(i)
image2 = get_another_image(i)
filename = '/tmp/my_hdf5%d.h5' % i
with hypy.File(filename, 'w') as f:
f['data1'] = np.transpose(image1, (2, 0, 1))
f['data2'] = np.transpose(image2, (2, 0, 1))
filelist.append(filename)
with open('/tmp/filelist.txt', 'w') as f:
for filename in filelist:
f.write(filename + '\n')
Then simply set the source of the HDF5_DATA param to be '/tmp/filelist.txt', and set the tops to be "data1" and "data2".
I'm leaving the original response below:
====================================================
There are two good ways of doing this. The easiest is probably to use two separate IMAGE_DATA layers, one with the first image and label, and a second with the second image. Caffe retrieves images from LMDB or LEVELDB, which are key value stores, and assuming you create your two databases with corresponding images having the same integer id key, Caffe will in fact load the images correctly, and you can proceed to construct your net with the data/labels of both layers.
The problem with this approach is that having two data layers is not really very satisfying, and it doesn't scale very well if you want to do more advanced things like having non-integer labels for things like bounding boxes, etc. If you're prepared to make a time investment in this, you can do a better job by modifying the tools/convert_imageset.cpp file to stack images or other data across channels. For example you could create a datum with 6 channels - the first 3 for your first image's RGB, and the second 3 for your second image's RGB. After reading this in using the IMAGE_DATA layer, you can split the stream into two images using a SLICE layer with a slice_point at index 3 along the slice_dim = 1 dimension. If further down the road, you decide that you want to load even more complex assortments of data, you'll understand the encoding scheme and can write your own decoding layer based off of src/caffe/layers/data_layer.cpp to gain full control of the pipeline.
You may also consider using HDF5_DATA layer with multiple "top"s