FFT

Overview

The FFT application outputs a spectrum representation of an input image, saving it into an image file on disk. You can define what backend will be used for processing.

Instructions

The command line parameters are:

<backend> <input image>

where

• backend: either cpu or cuda; it defines the backend that will perform the processing.
• input image: input image file name; it accepts png, jpeg and possibly others.

Here's one example:

• C++

  ./vpi_sample_07_fft cuda ../assets/kodim08.png

• Python

  python3 main.py cuda ../assets/kodim08.png

This is using the CUDA backend and one of the provided sample images. You can try with other images, respecting the constraints imposed by the algorithm.

Results

Input image	Output image, spectrum

Source Code

For convenience, here's the code that is also installed in the samples directory.

Language: C++ Python

import sys
import vpi
import numpy as np
from PIL import Image
from argparse import ArgumentParser
 
# ----------------------------
# Parse command line arguments
 
parser = ArgumentParser()
parser.add_argument('backend', choices=['cpu','cuda'],
                    help='Backend to be used for processing')
 
parser.add_argument('input',
                    help='Input image in space domain')
 
args = parser.parse_args();
 
if args.backend == 'cpu':
    backend = vpi.Backend.CPU
else:
    assert args.backend == 'cuda'
    backend = vpi.Backend.CUDA
 
# --------------------------------------------------------------
# Load input into a vpi.Image and convert it to float grayscale
with vpi.Backend.CUDA:
    try:
        input = vpi.asimage(np.asarray(Image.open(args.input))).convert(vpi.Format.F32)
    except IOError:
        sys.exit("Input file not found")
    except:
        sys.exit("Error with input file")
 
# --------------------------------------------------------------
# Transform input into frequency domain
with backend:
    hfreq = input.fft()
 
# --------------------------------------------------------------
# Post-process results and save to disk
 
# Transform [H,W,2] float array into [H,W] complex array
hfreq = hfreq.cpu().view(dtype=np.complex64).squeeze(2)
 
# Complete array into a full hermitian matrix
if input.width%2==0:
    wpad = input.width//2-1
    padmode = 'reflect'
else:
    wpad = input.width//2
    padmode='symmetric'
freq = np.pad(hfreq, ((0,0),(0,wpad)), mode=padmode)
freq[:,hfreq.shape[1]:] = np.conj(freq[:,hfreq.shape[1]:])
freq[1:,hfreq.shape[1]:] = freq[1:,hfreq.shape[1]:][::-1]
 
# Shift 0Hz to image center
freq = np.fft.fftshift(freq)
 
# Convert complex frequencies into log-magnitude
lmag = np.log(1+np.absolute(freq))
 
# Normalize into [0,255] range
min = lmag.min()
max = lmag.max()
lmag = ((lmag-min)*255/(max-min)).round().astype(np.uint8)
 
# -------------------
# Save result to disk
Image.fromarray(lmag).save('spectrum_python'+str(sys.version_info[0])+'_'+args.backend+'.png')

#include <opencv2/core/version.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#if CV_MAJOR_VERSION >= 3
#    include <opencv2/imgcodecs.hpp>
#else
#    include <opencv2/highgui/highgui.hpp>
#endif
 
#include <vpi/OpenCVInterop.hpp>
 
#include <vpi/Image.h>
#include <vpi/Status.h>
#include <vpi/Stream.h>
#include <vpi/algo/ConvertImageFormat.h>
#include <vpi/algo/FFT.h>
 
#include <cstring> // for memset
#include <iostream>
#include <sstream>
 
#define CHECK_STATUS(STMT)                                    \
    do                                                        \
    {                                                         \
        VPIStatus status = (STMT);                            \
        if (status != VPI_SUCCESS)                            \
        {                                                     \
            char buffer[VPI_MAX_STATUS_MESSAGE_LENGTH];       \
            vpiGetLastStatusMessage(buffer, sizeof(buffer));  \
            std::ostringstream ss;                            \
            ss << vpiStatusGetName(status) << ": " << buffer; \
            throw std::runtime_error(ss.str());               \
        }                                                     \
    } while (0);
 
// Auxiliary functions to process spectrum before saving it to disk.
cv::Mat LogMagnitude(cv::Mat cpx);
cv::Mat CompleteFullHermitian(cv::Mat in, cv::Size fullSize);
cv::Mat InplaceFFTShift(cv::Mat mag);
 
int main(int argc, char *argv[])
{
    // OpenCV image that will be wrapped by a VPIImage.
    // Define it here so that it's destroyed *after* wrapper is destroyed
    cv::Mat cvImage;
 
    // VPI objects that will be used
    VPIImage image    = NULL;
    VPIImage imageF32 = NULL;
    VPIImage spectrum = NULL;
    VPIStream stream  = NULL;
    VPIPayload fft    = NULL;
 
    int retval = 0;
 
    try
    {
        // =============================
        // Parse command line parameters
 
        if (argc != 3)
        {
            throw std::runtime_error(std::string("Usage: ") + argv[0] + " <cpu|cuda> <input image>");
        }
 
        std::string strBackend       = argv[1];
        std::string strInputFileName = argv[2];
 
        // Now parse the backend
        VPIBackend backend;
 
        if (strBackend == "cpu")
        {
            backend = VPI_BACKEND_CPU;
        }
        else if (strBackend == "cuda")
        {
            backend = VPI_BACKEND_CUDA;
        }
        else
        {
            throw std::runtime_error("Backend '" + strBackend + "' not recognized, it must be either cpu or cuda.");
        }
 
        // =====================
        // Load the input image
 
        cvImage = cv::imread(strInputFileName);
        if (cvImage.empty())
        {
            throw std::runtime_error("Can't open '" + strInputFileName + "'");
        }
 
        // =================================
        // Allocate all VPI resources needed
 
        // Create the stream for the given backend.
        CHECK_STATUS(vpiStreamCreate(backend, &stream));
 
        // We now wrap the loaded image into a VPIImage object to be used by VPI.
        // VPI won't make a copy of it, so the original
        // image must be in scope at all times.
        CHECK_STATUS(vpiImageCreateWrapperOpenCVMat(cvImage, 0, &image));
 
        // Temporary image that holds the float version of input
        CHECK_STATUS(vpiImageCreate(cvImage.cols, cvImage.rows, VPI_IMAGE_FORMAT_F32, 0, &imageF32));
 
        // Now create the output image. Note that for real inputs, the output spectrum is a Hermitian
        // matrix (conjugate-symmetric), so only the non-redundant components are output, basically the
        // left half. We adjust the output width accordingly.
        CHECK_STATUS(vpiImageCreate(cvImage.cols / 2 + 1, cvImage.rows, VPI_IMAGE_FORMAT_2F32, 0, &spectrum));
 
        // Create the FFT payload that does real (space) to complex (frequency) transformation
        CHECK_STATUS(
            vpiCreateFFT(backend, cvImage.cols, cvImage.rows, VPI_IMAGE_FORMAT_F32, VPI_IMAGE_FORMAT_2F32, &fft));
 
        // ================
        // Processing stage
 
        // Convert image to float
        CHECK_STATUS(vpiSubmitConvertImageFormat(stream, backend, image, imageF32, NULL));
 
        // Submit it for processing passing the image to be gradient and the result image
        CHECK_STATUS(vpiSubmitFFT(stream, backend, fft, imageF32, spectrum, 0));
 
        // Wait until the algorithm finishes processing
        CHECK_STATUS(vpiStreamSync(stream));
 
        // =======================================
        // Output processing and saving it to disk
 
        // Lock output image to retrieve its data on cpu memory
        VPIImageData outData;
        CHECK_STATUS(vpiImageLockData(spectrum, VPI_LOCK_READ, VPI_IMAGE_BUFFER_HOST_PITCH_LINEAR, &outData));
 
        assert(outData.bufferType == VPI_IMAGE_BUFFER_HOST_PITCH_LINEAR);
        VPIImageBufferPitchLinear &outPitch = outData.buffer.pitch;
 
        assert(outPitch.format == VPI_IMAGE_FORMAT_2F32);
 
        // Wrap spectrum to be used by OpenCV
        cv::Mat cvSpectrum(outPitch.planes[0].height, outPitch.planes[0].width, CV_32FC2, outPitch.planes[0].data,
                           outPitch.planes[0].pitchBytes);
 
        // Process it
        cv::Mat mag = InplaceFFTShift(LogMagnitude(CompleteFullHermitian(cvSpectrum, cvImage.size())));
 
        // Normalize the result to fit in 8-bits
        normalize(mag, mag, 0, 255, cv::NORM_MINMAX);
 
        // Write to disk
        imwrite("spectrum_" + strBackend + ".png", mag);
 
        // Done handling output image, don't forget to unlock it.
        CHECK_STATUS(vpiImageUnlock(spectrum));
    }
    catch (std::exception &e)
    {
        std::cerr << e.what() << std::endl;
        retval = 1;
    }
 
    // ========
    // Clean up
 
    // Make sure stream is synchronized before destroying the objects
    // that might still be in use.
    if (stream != NULL)
    {
        vpiStreamSync(stream);
    }
 
    vpiImageDestroy(image);
    vpiImageDestroy(imageF32);
    vpiImageDestroy(spectrum);
    vpiStreamDestroy(stream);
 
    // Payload is owned by the stream, so it's already destroyed
    // since the stream is now destroyed.
 
    return retval;
}
 
// Auxiliary functions --------------------------------
 
cv::Mat LogMagnitude(cv::Mat cpx)
{
    // Split spectrum into real and imaginary parts
    cv::Mat reim[2];
    assert(cpx.channels() == 2);
    split(cpx, reim);
 
    // Calculate the magnitude
    cv::Mat mag;
    magnitude(reim[0], reim[1], mag);
 
    // Convert to logarithm scale
    mag += cv::Scalar::all(1);
    log(mag, mag);
    mag = mag(cv::Rect(0, 0, mag.cols & -2, mag.rows & -2));
 
    return mag;
}
 
cv::Mat CompleteFullHermitian(cv::Mat in, cv::Size fullSize)
{
    assert(in.type() == CV_32FC2);
 
    cv::Mat out(fullSize, CV_32FC2);
    for (int i = 0; i < out.rows; ++i)
    {
        for (int j = 0; j < out.cols; ++j)
        {
            cv::Vec2f p;
            if (j < in.cols)
            {
                p = in.at<cv::Vec2f>(i, j);
            }
            else
            {
                p    = in.at<cv::Vec2f>((out.rows - i) % out.rows, (out.cols - j) % out.cols);
                p[1] = -p[1];
            }
            out.at<cv::Vec2f>(i, j) = p;
        }
    }
 
    return out;
}
 
cv::Mat InplaceFFTShift(cv::Mat mag)
{
    // Rearrange the quadrants of the fourier spectrum
    // so that the origin is at the image center.
 
    // Create a ROI for each 4 quadrants.
    int cx = mag.cols / 2;
    int cy = mag.rows / 2;
    cv::Mat qTL(mag, cv::Rect(0, 0, cx, cy));   // top-left
    cv::Mat qTR(mag, cv::Rect(cx, 0, cx, cy));  // top-right
    cv::Mat qBL(mag, cv::Rect(0, cy, cx, cy));  // bottom-left
    cv::Mat qBR(mag, cv::Rect(cx, cy, cx, cy)); // bottom-right
 
    // swap top-left with bottom-right quadrants
    cv::Mat tmp;
    qTL.copyTo(tmp);
    qBR.copyTo(qTL);
    tmp.copyTo(qBR);
 
    // swap top-right with bottom-left quadrants
    qTR.copyTo(tmp);
    qBL.copyTo(qTR);
    tmp.copyTo(qBL);
 
    return mag;
}