2D Image Convolution

Overview

The 2D Image Convolution application outputs an image with the edges of the input image, saving the result as an image file on disk. The user can define what backend will be used for processing.

Note

The output will be in grayscale as convolution is currently only supported for single-channel images.

Instructions

The command line parameters are:

<backend> <input image>

where

• backend: either cpu, cuda or pva; 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_01_convolve_2d cpu ../assets/kodim08.png

• Python

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

This is using the CPU 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, edges

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','pva'],
                    help='Backend to be used for processing')
 
parser.add_argument('input',
                    help='Image to be used as input')
 
args = parser.parse_args();
 
if args.backend == 'cpu':
    backend = vpi.Backend.CPU
elif args.backend == 'cuda':
    backend = vpi.Backend.CUDA
else:
    assert args.backend == 'pva'
    backend = vpi.Backend.PVA
 
# Load input into a vpi.Image
try:
    input = vpi.asimage(np.asarray(Image.open(args.input)))
except IOError:
    sys.exit("Input file not found")
except:
    sys.exit("Error with input file")
 
# Convert it to grayscale
input = input.convert(vpi.Format.U8, backend=vpi.Backend.CUDA)
 
# Define a simple edge detection kernel
kernel = [[ 1, 0, -1],
          [ 0, 0,  0],
          [-1, 0, 1]]
 
# Using the chosen backend,
with backend:
    # Run input through the convolution filter
    output = input.convolution(kernel, border=vpi.Border.ZERO)
 
# Save result to disk
Image.fromarray(output.cpu()).save('edges_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/Convolution.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);
 
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 imageBGR = NULL;
    VPIImage gradient = NULL;
    VPIStream stream  = NULL;
 
    int retval = 0;
 
    try
    {
        if (argc != 3)
        {
            throw std::runtime_error(std::string("Usage: ") + argv[0] + " <cpu|pva|cuda> <input image>");
        }
 
        std::string strBackend       = argv[1];
        std::string strInputFileName = argv[2];
 
        // Load the input image
        cvImage = cv::imread(strInputFileName);
        if (cvImage.empty())
        {
            throw std::runtime_error("Can't open '" + strInputFileName + "'");
        }
 
        // We can't use cv::IMREAD_GRAYSCALE when opening the input file because the
        // color to grayscale conversion used differs between OpenCV-2.4 and OpenCV>=3.0,
        // yielding different image content.
 
        // Now parse the backend
        VPIBackend backend;
 
        if (strBackend == "cpu")
        {
            backend = VPI_BACKEND_CPU;
        }
        else if (strBackend == "cuda")
        {
            backend = VPI_BACKEND_CUDA;
        }
        else if (strBackend == "pva")
        {
            backend = VPI_BACKEND_PVA;
        }
        else
        {
            throw std::runtime_error("Backend '" + strBackend +
                                     "' not recognized, it must be either cpu, cuda or pva.");
        }
 
        // Create the stream for any backend.
        CHECK_STATUS(vpiStreamCreate(0, &stream));
 
        // We now wrap the loaded image into a VPIImage object to be used by VPI.
        CHECK_STATUS(vpiImageCreateWrapperOpenCVMat(cvImage, 0, &imageBGR));
 
        // Now create the input image as a single unsigned 8-bit channel
        CHECK_STATUS(vpiImageCreate(cvImage.cols, cvImage.rows, VPI_IMAGE_FORMAT_U8, 0, &image));
 
        // Convert the loaded image to grayscale
        CHECK_STATUS(vpiSubmitConvertImageFormat(stream, VPI_BACKEND_CUDA, imageBGR, image, NULL));
 
        // Now create the output image, single unsigned 8-bit channel.
        CHECK_STATUS(vpiImageCreate(cvImage.cols, cvImage.rows, VPI_IMAGE_FORMAT_U8, 0, &gradient));
 
        // Define the convolution filter, a simple edge detector.
        float kernel[3 * 3] = {1, 0, -1, 0, 0, 0, -1, 0, 1};
 
        // Submit it for processing passing the input image the result image that will store the gradient.
        CHECK_STATUS(vpiSubmitConvolution(stream, backend, image, gradient, kernel, 3, 3, VPI_BORDER_ZERO));
 
        // Wait until the algorithm finishes processing
        CHECK_STATUS(vpiStreamSync(stream));
 
        // Now let's retrieve the output image contents and output it to disk
        {
            // Lock output image to retrieve its data.
            VPIImageData outData;
            CHECK_STATUS(vpiImageLockData(gradient, VPI_LOCK_READ, VPI_IMAGE_BUFFER_HOST_PITCH_LINEAR, &outData));
 
            // Returned data consists of host-accessible memory buffers in pitch-linear layout.
            assert(outData.bufferType == VPI_IMAGE_BUFFER_HOST_PITCH_LINEAR);
 
            const VPIImageBufferPitchLinear &outPitch = outData.buffer.pitch;
 
            cv::Mat cvOut(outPitch.planes[0].height, outPitch.planes[0].width, CV_8UC1, outPitch.planes[0].data,
                          outPitch.planes[0].pitchBytes);
            imwrite("edges_" + strBackend + ".png", cvOut);
 
            // Done handling output image, don't forget to unlock it.
            CHECK_STATUS(vpiImageUnlock(gradient));
        }
    }
    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(imageBGR);
    vpiImageDestroy(gradient);
 
    vpiStreamDestroy(stream);
 
    return retval;
}