void Core::Render()
{
#ifndef FNK_FINAL
static GpuTimer gpuTimer;
if ( m_showAnalyticsGui )
{
gpuTimer.Begin();
}
#endif
Timer timer;
{
BaseEntityGroup::Render();
VirtualMachine::Get()->Render();
}
m_msRender = timer.GetTimeMs();
VirtualMachine::Get()->Gui();
if ( m_showAnalyticsGui )
{
GuiStats();
#ifndef FNK_FINAL
gpuTimer.End();
m_msGpu = gpuTimer.GetTimeMs();
#endif
}
}
int main(int argc, char **argv) {
uchar4 *h_inputImageRGBA, *d_inputImageRGBA;
uchar4 *h_outputImageRGBA, *d_outputImageRGBA;
unsigned char *d_redBlurred, *d_greenBlurred, *d_blueBlurred;
float *h_filter;
int filterWidth;
std::string input_file;
std::string output_file;
if (argc == 3) {
input_file = std::string(argv[1]);
output_file = std::string(argv[2]);
}
else {
std::cerr << "Usage: ./hw input_file output_file" << std::endl;
exit(1);
}
//load the image and give us our input and output pointers
preProcess(&h_inputImageRGBA, &h_outputImageRGBA, &d_inputImageRGBA, &d_outputImageRGBA,
&d_redBlurred, &d_greenBlurred, &d_blueBlurred,
&h_filter, &filterWidth, input_file);
allocateMemoryAndCopyToGPU(numRows(), numCols(), h_filter, filterWidth);
GpuTimer timer;
timer.Start();
//call the students' code
your_gaussian_blur(h_inputImageRGBA, d_inputImageRGBA, d_outputImageRGBA, numRows(), numCols(),
d_redBlurred, d_greenBlurred, d_blueBlurred, filterWidth);
timer.Stop();
cudaDeviceSynchronize(); //checkCudaErrors(cudaGetLastError());
int err = printf("%f msecs.\n", timer.Elapsed());
if (err < 0) {
//Couldn't print! Probably the student closed stdout - bad news
std::cerr << "Couldn't print timing information! STDOUT Closed!" << std::endl;
exit(1);
}
cleanup();
//check results and output the blurred image
postProcess(output_file);
checkCudaErrors(cudaFree(d_redBlurred));
checkCudaErrors(cudaFree(d_greenBlurred));
checkCudaErrors(cudaFree(d_blueBlurred));
return 0;
}
int main(int argc, char **argv)
{
//uchar4 is defined in vector_types.h in the SDK
//uchar4 { unsigned char x,y,z,w; {
uchar4 *h_rgbaImage, *d_rgbaImage;
unsigned char *h_greyImage, *d_greyImage;
string input_file;
string output_file;
input_file = "cinque_terre_small.jpg";
output_file = "output_terre_small.jpg";
/*Load image and give input and output pointers*/
preProcess(&h_rgbaImage,&h_greyImage,&d_rgbaImage,&d_greyImage, input_file);
/*Start Timer*/
GpuTimer timer;
timer.Start();
/*Execute Function*/
rgb_to_grey(h_rgbaImage,d_rgbaImage,d_greyImage,numRows(),numCols());
/*Stop Timer*/
timer.Stop();
//cudaDeviceSynchronize() forces the program to wait for all previously issued commands in all streams on the device to finish before continuing
cudaDeviceSynchronize();
checkCudaErrors(cudaGetLastError());
cout<<"Code ran in: "<<timer.Elapsed())<<" seconds"<<endl;
//Copy the image from the device back to the host
size_t numPixels = numRows() * numCols();
checkCudaErrors(cudaMemCpy(h_greyImage,d_greyImage,sizeof(unsigned char) * numPixels, cudaMemcpyDeviceToHost));
//check results and output the grey image
}//end of main
int main(int argc, char **argv) {
uchar4 *h_sourceImg, *h_destImg, *h_blendedImg;
size_t numRowsSource, numColsSource;
std::string input_source_file;
std::string input_dest_file;
std::string output_file;
if (argc == 4) {
input_source_file = std::string(argv[1]);
input_dest_file = std::string(argv[2]);
output_file = std::string(argv[3]);
}
else {
std::cerr << "Usage: ./hw input_source_file input_dest_file output_file" << std::endl;
exit(1);
}
//load the image and give us our input and output pointers
preProcess(&h_sourceImg, numRowsSource, numColsSource,
&h_destImg,
&h_blendedImg, input_source_file, input_dest_file);
GpuTimer timer;
timer.Start();
//call the students' code
your_blend(h_sourceImg, numRowsSource, numColsSource,
h_destImg,
h_blendedImg);
timer.Stop();
cudaDeviceSynchronize(); checkCudaErrors(cudaGetLastError());
int err = printf("e57__TIMING__f82 %f msecs.\n", timer.Elapsed());
if (err < 0) {
//Couldn't print! Probably the student closed stdout - bad news
std::cerr << "Couldn't print timing information! STDOUT Closed!" << std::endl;
exit(1);
}
//check results and output the tone-mapped image
postProcess(h_blendedImg, numRowsSource, numColsSource, output_file);
delete[] h_destImg;
delete[] h_sourceImg;
delete[] h_blendedImg;
return 0;
}
int main(int argc, char **argv) {
float *d_luminance;
unsigned int *d_cdf;
size_t numRows, numCols;
unsigned int numBins;
std::string input_file;
std::string output_file;
if (argc == 3) {
input_file = std::string(argv[1]);
output_file = std::string(argv[2]);
}
else {
std::cerr << "Usage: ./hw input_file output_file" << std::endl;
exit(1);
}
//load the image and give us our input and output pointers
preProcess(&d_luminance, &d_cdf,
&numRows, &numCols, &numBins, input_file);
GpuTimer timer;
float min_logLum, max_logLum;
min_logLum = 0.f;
max_logLum = 1.f;
timer.Start();
//call the students' code
your_histogram_and_prefixsum(d_luminance, d_cdf, min_logLum, max_logLum,
numRows, numCols, numBins);
timer.Stop();
cudaDeviceSynchronize(); checkCudaErrors(cudaGetLastError());
int err = printf("%f msecs.\n", timer.Elapsed());
if (err < 0) {
//Couldn't print! Probably the student closed stdout - bad news
std::cerr << "Couldn't print timing information! STDOUT Closed!" << std::endl;
exit(1);
}
//check results and output the tone-mapped image
postProcess(output_file, numRows, numCols, min_logLum, max_logLum);
return 0;
}
int main(int argc, char **argv) {
uchar4 *h_rgbaImage, *d_rgbaImage;
unsigned char *h_greyImage, *d_greyImage;
std::string input_file;
std::string output_file;
if (argc == 3) {
input_file = std::string(argv[1]);
output_file = std::string(argv[2]);
}
else {
std::cerr << "Usage: ./hw input_file output_file" << std::endl;
exit(1);
}
//load the image and give us our input and output pointers
preProcess(&h_rgbaImage, &h_greyImage, &d_rgbaImage, &d_greyImage, input_file);
GpuTimer timer;
timer.Start();
//call the students' code
your_rgba_to_greyscale(h_rgbaImage, d_rgbaImage, d_greyImage, numRows(), numCols());
timer.Stop();
cudaDeviceSynchronize(); checkCudaErrors(cudaGetLastError());
printf("\n");
int err = printf("%f msecs.\n", timer.Elapsed());
if (err < 0) {
//Couldn't print! Probably the student closed stdout - bad news
std::cerr << "Couldn't print timing information! STDOUT Closed!" << std::endl;
exit(1);
}
//check results and output the grey image
postProcess(output_file);
return 0;
}
int main(int argc, char **argv) {
unsigned int *inputVals;
unsigned int *inputPos;
unsigned int *outputVals;
unsigned int *outputPos;
size_t numElems;
std::string input_file;
std::string template_file;
std::string output_file;
std::string reference_file;
double perPixelError = 0.0;
double globalError = 0.0;
bool useEpsCheck = false;
switch (argc)
{
case 3:
input_file = std::string(argv[1]);
template_file = std::string(argv[2]);
output_file = "HW4_output.png";
break;
case 4:
input_file = std::string(argv[1]);
template_file = std::string(argv[2]);
output_file = std::string(argv[3]);
break;
default:
std::cerr << "Usage: ./HW4 input_file template_file [output_filename]" << std::endl;
exit(1);
}
//load the image and give us our input and output pointers
preProcess(&inputVals, &inputPos, &outputVals, &outputPos, numElems, input_file, template_file);
GpuTimer timer;
timer.Start();
//call the students' code
your_sort(inputVals, inputPos, outputVals, outputPos, numElems);
timer.Stop();
cudaDeviceSynchronize(); checkCudaErrors(cudaGetLastError());
printf("\n");
int err = printf("Your code ran in: %f msecs.\n", timer.Elapsed());
if (err < 0) {
//Couldn't print! Probably the student closed stdout - bad news
std::cerr << "Couldn't print timing information! STDOUT Closed!" << std::endl;
exit(1);
}
//check results and output the red-eye corrected image
postProcess(outputVals, outputPos, numElems, output_file);
// check code moved from HW4.cu
/****************************************************************************
* You can use the code below to help with debugging, but make sure to *
* comment it out again before submitting your assignment for grading, *
* otherwise this code will take too much time and make it seem like your *
* GPU implementation isn't fast enough. *
* *
* This code MUST RUN BEFORE YOUR CODE in case you accidentally change *
* the input values when implementing your radix sort. *
* *
* This code performs the reference radix sort on the host and compares your *
* sorted values to the reference. *
* *
* Thrust containers are used for copying memory from the GPU *
* ************************************************************************* */
thrust::device_ptr<unsigned int> d_inputVals(inputVals);
thrust::device_ptr<unsigned int> d_inputPos(inputPos);
thrust::host_vector<unsigned int> h_inputVals(d_inputVals,
d_inputVals+numElems);
thrust::host_vector<unsigned int> h_inputPos(d_inputPos,
d_inputPos + numElems);
thrust::host_vector<unsigned int> h_outputVals(numElems);
thrust::host_vector<unsigned int> h_outputPos(numElems);
reference_calculation(&h_inputVals[0], &h_inputPos[0],
&h_outputVals[0], &h_outputPos[0],
numElems);
//postProcess(&h_outputVals[0], &h_outputPos[0], numElems, reference_file);
compareImages(reference_file, output_file, useEpsCheck, perPixelError, globalError);
thrust::device_ptr<unsigned int> d_outputVals(outputVals);
thrust::device_ptr<unsigned int> d_outputPos(outputPos);
thrust::host_vector<unsigned int> h_yourOutputVals(d_outputVals,
d_outputVals + numElems);
thrust::host_vector<unsigned int> h_yourOutputPos(d_outputPos,
d_outputPos + numElems);
checkResultsExact(&h_outputVals[0], &h_yourOutputVals[0], numElems);
checkResultsExact(&h_outputPos[0], &h_yourOutputPos[0], numElems);
checkCudaErrors(cudaFree(inputVals));
checkCudaErrors(cudaFree(inputPos));
checkCudaErrors(cudaFree(outputVals));
checkCudaErrors(cudaFree(outputPos));
return 0;
}
int main(int argc, char **argv) {
uchar4 *h_rgbaImage, *d_rgbaImage;
unsigned char *h_greyImage, *d_greyImage;
std::string input_file;
std::string output_file;
std::string reference_file;
double perPixelError = 0.0;
double globalError = 0.0;
bool useEpsCheck = false;
switch (argc)
{
case 2:
input_file = std::string(argv[1]);
output_file = "HW1_output.png";
reference_file = "HW1_reference.png";
break;
case 3:
input_file = std::string(argv[1]);
output_file = std::string(argv[2]);
reference_file = "HW1_reference.png";
break;
case 4:
input_file = std::string(argv[1]);
output_file = std::string(argv[2]);
reference_file = std::string(argv[3]);
break;
case 6:
useEpsCheck=true;
input_file = std::string(argv[1]);
output_file = std::string(argv[2]);
reference_file = std::string(argv[3]);
perPixelError = atof(argv[4]);
globalError = atof(argv[5]);
break;
default:
std::cerr << "Usage: ./HW1 input_file [output_filename] [reference_filename] [perPixelError] [globalError]" << std::endl;
exit(1);
}
//load the image and give us our input and output pointers
preProcess(&h_rgbaImage, &h_greyImage, &d_rgbaImage, &d_greyImage, input_file);
GpuTimer timer;
timer.Start();
//call the students' code
your_rgba_to_greyscale(h_rgbaImage, d_rgbaImage, d_greyImage, numRows(), numCols());
timer.Stop();
cudaDeviceSynchronize(); checkCudaErrors(cudaGetLastError());
int err = printf("Your code ran in: %f msecs.\n", timer.Elapsed());
if (err < 0) {
//Couldn't print! Probably the student closed stdout - bad news
std::cerr << "Couldn't print timing information! STDOUT Closed!" << std::endl;
exit(1);
}
size_t numPixels = numRows()*numCols();
checkCudaErrors(cudaMemcpy(h_greyImage, d_greyImage, sizeof(unsigned char) * numPixels, cudaMemcpyDeviceToHost));
//check results and output the grey image
postProcess(output_file, h_greyImage);
referenceCalculation(h_rgbaImage, h_greyImage, numRows(), numCols());
postProcess(reference_file, h_greyImage);
//generateReferenceImage(input_file, reference_file);
compareImages(reference_file, output_file, useEpsCheck, perPixelError,
globalError);
cleanup();
return 0;
}
int main(int argc, char **argv) {
float *d_luminance;
unsigned int *d_cdf;
size_t numRows, numCols;
unsigned int numBins;
std::string input_file;
std::string output_file;
std::string reference_file;
double perPixelError = 0.0;
double globalError = 0.0;
bool useEpsCheck = false;
switch (argc)
{
case 2:
input_file = std::string(argv[1]);
output_file = "HW3_output.png";
reference_file = "HW3_reference.png";
break;
case 3:
input_file = std::string(argv[1]);
output_file = std::string(argv[2]);
reference_file = "HW3_reference.png";
break;
case 4:
input_file = std::string(argv[1]);
output_file = std::string(argv[2]);
reference_file = std::string(argv[3]);
break;
case 6:
useEpsCheck=true;
input_file = std::string(argv[1]);
output_file = std::string(argv[2]);
reference_file = std::string(argv[3]);
perPixelError = atof(argv[4]);
globalError = atof(argv[5]);
break;
default:
std::cerr << "Usage: ./HW3 input_file [output_filename] [reference_filename] [perPixelError] [globalError]" << std::endl;
system("Pause");
exit(1);
}
//load the image and give us our input and output pointers
preProcess(&d_luminance, &d_cdf,
&numRows, &numCols, &numBins, input_file);
GpuTimer timer;
float min_logLum, max_logLum;
min_logLum = 0.f;
max_logLum = 1.f;
timer.Start();
//call the students' code
your_histogram_and_prefixsum(d_luminance, d_cdf, min_logLum, max_logLum,
numRows, numCols, numBins);
timer.Stop();
cudaDeviceSynchronize(); checkCudaErrors(cudaGetLastError());
int err = printf("Your code ran in: %f msecs.\n", timer.Elapsed());
if (err < 0) {
//Couldn't print! Probably the student closed stdout - bad news
std::cerr << "Couldn't print timing information! STDOUT Closed!" << std::endl;
system("Pause");
exit(1);
}
float *h_luminance = (float *) malloc(sizeof(float)*numRows*numCols);
unsigned int *h_cdf = (unsigned int *) malloc(sizeof(unsigned int)*numBins);
checkCudaErrors(cudaMemcpy(h_luminance, d_luminance, numRows*numCols*sizeof(float), cudaMemcpyDeviceToHost));
//check results and output the tone-mapped image
postProcess(output_file, numRows, numCols, min_logLum, max_logLum);
for (size_t i = 1; i < numCols * numRows; ++i) {
min_logLum = std::min(h_luminance[i], min_logLum);
max_logLum = std::max(h_luminance[i], max_logLum);
}
referenceCalculation(h_luminance, h_cdf, numRows, numCols, numBins, min_logLum, max_logLum);
checkCudaErrors(cudaMemcpy(d_cdf, h_cdf, sizeof(unsigned int) * numBins, cudaMemcpyHostToDevice));
//check results and output the tone-mapped image
postProcess(reference_file, numRows, numCols, min_logLum, max_logLum);
cleanupGlobalMemory();
compareImages(reference_file, output_file, useEpsCheck, perPixelError, globalError);
system("pause");
return 0;
}
int main(int argc, char **argv) {
uchar4 *h_inputImageRGBA, *d_inputImageRGBA;
uchar4 *h_outputImageRGBA, *d_outputImageRGBA;
unsigned char *d_redBlurred, *d_greenBlurred, *d_blueBlurred;
float *h_filter;
int filterWidth;
std::string input_file;
std::string output_file;
std::string reference_file;
double perPixelError = 0.0;
double globalError = 0.0;
bool useEpsCheck = false;
switch (argc)
{
case 2:
input_file = std::string(argv[1]);
output_file = "HW2_output.png";
reference_file = "HW2_reference.png";
break;
case 3:
input_file = std::string(argv[1]);
output_file = std::string(argv[2]);
reference_file = "HW2_reference.png";
break;
case 4:
input_file = std::string(argv[1]);
output_file = std::string(argv[2]);
reference_file = std::string(argv[3]);
break;
case 6:
useEpsCheck=true;
input_file = std::string(argv[1]);
output_file = std::string(argv[2]);
reference_file = std::string(argv[3]);
perPixelError = atof(argv[4]);
globalError = atof(argv[5]);
break;
default:
std::cerr << "Usage: ./HW2 input_file [output_filename] [reference_filename] [perPixelError] [globalError]" << std::endl;
exit(1);
}
//load the image and give us our input and output pointers
preProcess(&h_inputImageRGBA, &h_outputImageRGBA, &d_inputImageRGBA, &d_outputImageRGBA,
&d_redBlurred, &d_greenBlurred, &d_blueBlurred,
&h_filter, &filterWidth, input_file);
allocateMemoryAndCopyToGPU(numRows(), numCols(), h_filter, filterWidth);
/*
for(int i=0;i<filterWidth; ++i) {
for(int j=0;j<filterWidth; ++j) {
std::cerr<<h_filter[i*filterWidth + j]<<" ";
}
std::cerr<<std::endl;
}
*/
GpuTimer timer;
timer.Start();
//call the students' code
your_gaussian_blur(h_inputImageRGBA, d_inputImageRGBA, d_outputImageRGBA, numRows(), numCols(),
d_redBlurred, d_greenBlurred, d_blueBlurred, filterWidth);
timer.Stop();
cudaDeviceSynchronize(); checkCudaErrors(cudaGetLastError());
int err = printf("Your code ran in: %f msecs.\n", timer.Elapsed());
if (err < 0) {
//Couldn't print! Probably the student closed stdout - bad news
std::cerr << "Couldn't print timing information! STDOUT Closed!" << std::endl;
exit(1);
}
//check results and output the blurred image
size_t numPixels = numRows()*numCols();
//copy the output back to the host
checkCudaErrors(cudaMemcpy(h_outputImageRGBA, d_outputImageRGBA__, sizeof(uchar4) * numPixels, cudaMemcpyDeviceToHost));
postProcess(output_file, h_outputImageRGBA);
referenceCalculation(h_inputImageRGBA, h_outputImageRGBA,
numRows(), numCols(),
h_filter, filterWidth);
postProcess(reference_file, h_outputImageRGBA);
// Cheater easy way with OpenCV
//generateReferenceImage(input_file, reference_file, filterWidth);
compareImages(reference_file, output_file, useEpsCheck, perPixelError, globalError);
checkCudaErrors(cudaFree(d_redBlurred));
checkCudaErrors(cudaFree(d_greenBlurred));
checkCudaErrors(cudaFree(d_blueBlurred));
cleanUp();
return 0;
}
int _tmain(int argc, _TCHAR* argv[])
{
uchar4 *h_inputImageRGBA, *d_inputImageRGBA;
uchar4 *h_outputImageRGBA, *d_outputImageRGBA;
unsigned char *d_redBlurred, *d_greenBlurred, *d_blueBlurred;
float *h_filter;
int filterWidth;
//PreProcess
const std::string *filename = new std::string("./cinque_terre_small.jpg");
cv::Mat imageInputRGBA;
cv::Mat imageOutputRGBA;
//make sure the context initializes ok
checkCudaErrors(cudaFree(0));
cv::Mat image = cv::imread(filename->c_str(), CV_LOAD_IMAGE_COLOR);
if (image.empty())
{
std::cerr << "Couldn't open file: " << filename << std::endl;
cv::waitKey(0);
exit(1);
}
cv::cvtColor(image, imageInputRGBA, CV_BGR2RGBA);
//allocate memory for the output
imageOutputRGBA.create(image.rows, image.cols, CV_8UC4);
//This shouldn't ever happen given the way the images are created
//at least based upon my limited understanding of OpenCV, but better to check
if (!imageInputRGBA.isContinuous() || !imageOutputRGBA.isContinuous()) {
std::cerr << "Images aren't continuous!! Exiting." << std::endl;
exit(1);
}
h_inputImageRGBA = (uchar4 *)imageInputRGBA.ptr<unsigned char>(0);
h_outputImageRGBA = (uchar4 *)imageOutputRGBA.ptr<unsigned char>(0);
const size_t numPixels = image.rows * image.cols;
//allocate memory on the device for both input and output
checkCudaErrors(cudaMalloc(&d_inputImageRGBA, sizeof(uchar4) * numPixels));
checkCudaErrors(cudaMalloc(&d_outputImageRGBA, sizeof(uchar4) * numPixels));
checkCudaErrors(cudaMemset(d_outputImageRGBA, 0, numPixels * sizeof(uchar4))); //make sure no memory is left laying around
//copy input array to the GPU
checkCudaErrors(cudaMemcpy(d_inputImageRGBA, h_inputImageRGBA, sizeof(uchar4) * numPixels, cudaMemcpyHostToDevice));
//now create the filter that they will use
const int blurKernelWidth = 9;
const float blurKernelSigma = 2.;
filterWidth = blurKernelWidth;
//create and fill the filter we will convolve with
h_filter = new float[blurKernelWidth * blurKernelWidth];
float filterSum = 0.f; //for normalization
for (int r = -blurKernelWidth/2; r <= blurKernelWidth/2; ++r)
{
for (int c = -blurKernelWidth/2; c <= blurKernelWidth/2; ++c)
{
float filterValue = expf( -(float)(c * c + r * r) / (2.f * blurKernelSigma * blurKernelSigma));
h_filter[(r + blurKernelWidth/2) * blurKernelWidth + c + blurKernelWidth/2] = filterValue;
filterSum += filterValue;
}
}
float normalizationFactor = 1.f / filterSum;
for (int r = -blurKernelWidth/2; r <= blurKernelWidth/2; ++r)
{
for (int c = -blurKernelWidth/2; c <= blurKernelWidth/2; ++c)
{
h_filter[(r + blurKernelWidth/2) * blurKernelWidth + c + blurKernelWidth/2] *= normalizationFactor;
}
}
//blurred
checkCudaErrors(cudaMalloc(&d_redBlurred, sizeof(unsigned char) * numPixels));
checkCudaErrors(cudaMalloc(&d_greenBlurred, sizeof(unsigned char) * numPixels));
checkCudaErrors(cudaMalloc(&d_blueBlurred, sizeof(unsigned char) * numPixels));
checkCudaErrors(cudaMemset(d_redBlurred, 0, sizeof(unsigned char) * numPixels));
checkCudaErrors(cudaMemset(d_greenBlurred, 0, sizeof(unsigned char) * numPixels));
checkCudaErrors(cudaMemset(d_blueBlurred, 0, sizeof(unsigned char) * numPixels));
allocateMemoryAndCopyToGPU(image.rows, image.cols, h_filter, filterWidth);
GpuTimer timer;
timer.Start();
//call the students' code
your_gaussian_blur(h_inputImageRGBA, d_inputImageRGBA, d_outputImageRGBA, image.rows, image.cols,
d_redBlurred, d_greenBlurred, d_blueBlurred, filterWidth);
timer.Stop();
cudaDeviceSynchronize(); checkCudaErrors(cudaGetLastError());
int err = printf("%f msecs.\n", timer.Elapsed());
if (err < 0) {
//Couldn't print! Probably the student closed stdout - bad news
std::cerr << "Couldn't print timing information! STDOUT Closed!" << std::endl;
exit(1);
}
cleanup();
//check results and output the blurred image
//PostProcess
//copy the output back to the host
checkCudaErrors(cudaMemcpy(imageOutputRGBA.ptr<unsigned char>(0), d_outputImageRGBA, sizeof(uchar4) * numPixels, cudaMemcpyDeviceToHost));
cv::Mat imageOutputBGR;
cv::cvtColor(imageOutputRGBA, imageOutputBGR, CV_RGBA2BGR);
//output the image
cv::imwrite("./blurredResult.jpg", imageOutputBGR);
cv::namedWindow( "Display window", CV_WINDOW_NORMAL);
cv::imshow("Display window", imageOutputBGR);
cv::waitKey(0);
checkCudaErrors(cudaFree(d_redBlurred));
checkCudaErrors(cudaFree(d_greenBlurred));
checkCudaErrors(cudaFree(d_blueBlurred));
return 0;
}
int main(int argc, char **argv) {
float *d_luminance;
unsigned int *d_cdf;
size_t numRows, numCols;
unsigned int numBins;
std::string input_file;
std::string output_file;
std::string reference_file;
double perPixelError = 0.0;
double globalError = 0.0;
bool useEpsCheck = false;
switch (argc)
{
case 2:
input_file = std::string(argv[1]);
output_file = "output.png";
break;
case 3:
input_file = std::string(argv[1]);
output_file = std::string(argv[2]);
break;
default:
std::cerr << "Usage: ./tone input_file [output_filename] [reference_filename]" << std::endl;
exit(1);
}
//load the image and give us our input and output pointers
preProcess(&d_luminance, &d_cdf,
&numRows, &numCols, &numBins, input_file);
GpuTimer timer;
float min_logLum, max_logLum;
min_logLum = 0.f;
max_logLum = 1.f;
timer.Start();
//call the students' code
calculate_cdf(d_luminance, d_cdf, min_logLum, max_logLum,
numRows, numCols, numBins);
timer.Stop();
cudaDeviceSynchronize();
int err = printf("Your code ran in: %f msecs.\n", timer.Elapsed());
if (err < 0) {
//Couldn't print! Probably the student closed stdout - bad news
std::cerr << "Couldn't print timing information! STDOUT Closed!" << std::endl;
exit(1);
}
float *h_luminance = (float *) malloc(sizeof(float)*numRows*numCols);
unsigned int *h_cdf = (unsigned int *) malloc(sizeof(unsigned int)*numBins);
cudaMemcpy(h_luminance, d_luminance, numRows*numCols*sizeof(float), cudaMemcpyDeviceToHost);
//check results and output the tone-mapped image
postProcess(output_file, numRows, numCols, min_logLum, max_logLum);
cleanupGlobalMemory();
return 0;
}
