OpenCV / C++ - Convert image to vector of doubles for FFT (Fast Fourier Transform)

Question

I'm trying to compute the FFT (Fast Fourier Transform) of an image to use the frequencies to determine whether or not the image is blurry.

I need to use a custom FFT algorithm that we already have in our codebase. The FFT algorithm requires a standard 1D vector of doubles or ints. I need a way to read in an image and then convert it to a vector of doubles so that I can compute the FFT of the image.

I have tried the following:

cv::Mat inputImage = cv::imread("testImage.png");
cv::Mat fImage; 

inputImage.convertTo(fImage, CV_32F); 
std::vector<double> actualImage = fImage.clone();

However, I am getting the error:

OpenCV Error: Assertion failed (channels() == CV_MAT_CN(dtype)) in copyTo,

Any ideas to how I can achieve this?

Your requirements sound suspect. The FFT of an N-dimensional input is also N-dimensional. Hence, the FFT of a 3-channel 2D image is also 3 channel and 2D. — MSalters
@MSalters I am trying to calculate whether or not an image is blurry. My idea is that I'll take the FFT (1D of complex numbers) calculate the magnitudes and then do a high pass filtering on them — Phorce
Well, that's simply not meaningful. You can take a 1D horizontal, vertical or even diagonal slice of the image, convert it to greyscale and take a 1D FFT of that. That will tell you if there's a blur in that direction. Yes, I know you said something about disliking greyscale before. But here's the point: You simply cannot take a 1D FFT given a 3-channel 2D input, just like you can't add two complex numbers and demand that the outcome is an integer. That's just now how math works. — MSalters
@MSalters - so let's say I take the grayscale of the image, convert to doubles and (as given in mikis answer) then compute the FFT and pass this through a high pass filter.. This would work? I totally understand what you mean, I must convert the image to grayscale — Phorce
Think about what that does. Say you have a 100x100 image and you turn that into a 10000 element 1D vector. Consider a vertical line - no periodic component. In the 1D vector, each pixel of this line will now be 100 pixels apart - a very clear periodic component. You introduce fake frequencies, while at the same time eliminating other real frequencies. — MSalters

Miki Miki · Accepted Answer · 2017-02-02T09:55:40

CV_32F means float, not double. You should use CV_64F instead.

You also need to specify the number of channels. This example is for 1 channel image (grayscale), and probably what you need:

// Load the image
cv::Mat inputImage = cv::imread("testImage.png");
// Convert to single channel (grayscale)
cv::cvtColor(inputImage, inputImage, cv::COLOR_BGR2GRAY);

// Or directly load as grayscale    
// cv::Mat inputImage = cv::imread("testImage.png", cv::IMREAD_GRAYSCALE);

// Convert to double
cv::Mat fImage; 
inputImage.convertTo(fImage, CV_64F); 

// Initialize the vector with the image content
std::vector<double> actualImage(fImage.begin<double>(), fImage.end<double>());

For 3 channels you can do:

cv::Mat inputImage = cv::imread("testImage.png");
cv::Mat fImage; 

inputImage.convertTo(fImage, CV_64F); 
fImage = fImage.reshape(1); // You need to reshape to single channel
std::vector<double> actualImage(fImage.begin<double>(), fImage.end<double>());

OpenCV / C++ - Convert image to vector of doubles for FFT (Fast Fourier Transform)

1 Answers