added ex10 and 12 to submission

submission/ex10/Makefile

+all: main
+
+main: main.cu aux.cu aux.h Makefile
+	nvcc -o main main.cu aux.cu --ptxas-options=-v --use_fast_math --compiler-options -Wall -lopencv_highgui -lopencv_core
+

submission/ex10/aux.cu

+// ###
+// ###
+// ### Practical Course: GPU Programming in Computer Vision
+// ###
+// ###
+// ### Technical University Munich, Computer Vision Group
+// ### Winter Semester 2013/2014, March 3 - April 4
+// ###
+// ###
+// ### Evgeny Strekalovskiy, Maria Klodt, Jan Stuehmer, Mohamed Souiai
+// ###
+// ###
+// ###
+// ### THIS FILE IS SUPPOSED TO REMAIN UNCHANGED
+// ###
+// ###
+
+
+#include "aux.h"
+#include <cstdlib>
+#include <iostream>
+using std::stringstream;
+using std::cerr;
+using std::cout;
+using std::endl;
+using std::string;
+
+
+
+
+// parameter processing: template specialization for T=bool
+template<>
+bool getParam<bool>(std::string param, bool &var, int argc, char **argv)
+{
+    const char *c_param = param.c_str();
+    for(int i=argc-1; i>=1; i--)
+    {
+        if (argv[i][0]!='-') continue;
+        if (strcmp(argv[i]+1, c_param)==0)
+        {
+            if (!(i+1<argc) || argv[i+1][0]=='-') { var = true; return true; }
+            std::stringstream ss;
+            ss << argv[i+1];
+            ss >> var;
+            return (bool)ss;
+        }
+    }
+    return false;
+}
+
+
+
+
+// opencv helpers
+void convert_layered_to_interleaved(float *aOut, const float *aIn, int w, int h, int nc)
+{
+    if (nc==1) { memcpy(aOut, aIn, w*h*sizeof(float)); return; }
+    size_t nOmega = (size_t)w*h;
+    for (int y=0; y<h; y++)
+    {
+        for (int x=0; x<w; x++)
+        {
+            for (int c=0; c<nc; c++)
+            {
+                aOut[(nc-1-c) + nc*(x + (size_t)w*y)] = aIn[x + (size_t)w*y + nOmega*c];
+            }
+        }
+    }
+}
+void convert_layered_to_mat(cv::Mat &mOut, const float *aIn)
+{
+    convert_layered_to_interleaved((float*)mOut.data, aIn, mOut.cols, mOut.rows, mOut.channels());
+}
+
+
+void convert_interleaved_to_layered(float *aOut, const float *aIn, int w, int h, int nc)
+{
+    if (nc==1) { memcpy(aOut, aIn, w*h*sizeof(float)); return; }
+    size_t nOmega = (size_t)w*h;
+    for (int y=0; y<h; y++)
+    {
+        for (int x=0; x<w; x++)
+        {
+            for (int c=0; c<nc; c++)
+            {
+                aOut[x + (size_t)w*y + nOmega*c] = aIn[(nc-1-c) + nc*(x + (size_t)w*y)];
+            }
+        }
+    }
+}
+void convert_mat_to_layered(float *aOut, const cv::Mat &mIn)
+{
+    convert_interleaved_to_layered(aOut, (float*)mIn.data, mIn.cols, mIn.rows, mIn.channels());
+}
+
+
+
+void showImage(string title, const cv::Mat &mat, int x, int y)
+{
+    const char *wTitle = title.c_str();
+    cv::namedWindow(wTitle, CV_WINDOW_AUTOSIZE);
+    cvMoveWindow(wTitle, x, y);
+    cv::imshow(wTitle, mat);
+}
+
+
+
+
+// adding Gaussian noise
+float noise(float sigma)
+{
+    float x1 = (float)rand()/RAND_MAX;
+    float x2 = (float)rand()/RAND_MAX;
+    return sigma * sqrtf(-2*log(std::max(x1,0.000001f)))*cosf(2*M_PI*x2);
+}
+void addNoise(cv::Mat &m, float sigma)
+{
+    float *data = (float*)m.data;
+    int w = m.cols;
+    int h = m.rows;
+    int nc = m.channels();
+    size_t n = (size_t)w*h*nc;
+    for(size_t i=0; i<n; i++)
+    {
+        data[i] += noise(sigma);
+    }
+}
+
+
+
+
+// cuda error checking
+string prev_file = "";
+int prev_line = 0;
+void cuda_check(string file, int line)
+{
+    cudaError_t e = cudaGetLastError();
+    if (e != cudaSuccess)
+    {
+        cout << endl << file << ", line " << line << ": " << cudaGetErrorString(e) << " (" << e << ")" << endl;
+        if (prev_line>0) cout << "Previous CUDA call:" << endl << prev_file << ", line " << prev_line << endl;
+        exit(1);
+    }
+    prev_file = file;
+    prev_line = line;
+}

submission/ex10/aux.h

+// ###
+// ###
+// ### Practical Course: GPU Programming in Computer Vision
+// ###
+// ###
+// ### Technical University Munich, Computer Vision Group
+// ### Winter Semester 2013/2014, March 3 - April 4
+// ###
+// ###
+// ### Evgeny Strekalovskiy, Maria Klodt, Jan Stuehmer, Mohamed Souiai
+// ###
+// ###
+// ###
+// ### THIS FILE IS SUPPOSED TO REMAIN UNCHANGED
+// ###
+// ###
+
+
+#ifndef AUX_H
+#define AUX_H
+
+#include <cuda_runtime.h>
+#include <ctime>
+#include <opencv2/highgui/highgui.hpp>
+#include <opencv2/imgproc/imgproc.hpp>
+#include <string>
+#include <sstream>
+
+
+
+
+// parameter processing
+template<typename T>
+bool getParam(std::string param, T &var, int argc, char **argv)
+{
+    const char *c_param = param.c_str();
+    for(int i=argc-1; i>=1; i--)
+    {
+        if (argv[i][0]!='-') continue;
+        if (strcmp(argv[i]+1, c_param)==0)
+        {
+            if (!(i+1<argc)) continue;
+            std::stringstream ss;
+            ss << argv[i+1];
+            ss >> var;
+            return (bool)ss;
+        }
+    }
+    return false;
+}
+
+
+
+
+// opencv helpers
+void convert_mat_to_layered(float *aOut, const cv::Mat &mIn);
+void convert_layered_to_mat(cv::Mat &mOut, const float *aIn);
+void showImage(std::string title, const cv::Mat &mat, int x, int y);
+
+
+
+
+// adding Gaussian noise
+void addNoise(cv::Mat &m, float sigma);
+
+
+
+
+// measuring time
+class Timer
+{
+    public:
+	Timer() : tStart(0), running(false), sec(0.f)
+	{
+	}
+	void start()
+	{
+		tStart = clock();
+		running = true;
+	}
+	void end()
+	{
+		if (!running) { sec = 0; return; }
+        cudaDeviceSynchronize();
+		clock_t tEnd = clock();
+		sec = (float)(tEnd - tStart) / CLOCKS_PER_SEC;
+		running = false;
+	}
+	float get()
+	{
+		if (running) end();
+		return sec;
+	}
+    private:
+	clock_t tStart;
+	bool running;
+	float sec;
+};
+
+
+
+
+// cuda error checking
+#define CUDA_CHECK cuda_check(__FILE__,__LINE__)
+void cuda_check(std::string file, int line);
+
+
+
+#endif  // AUX_H

submission/ex10/main.cu

+// ###
+// ###
+// ### Practical Course: GPU Programming in Computer Vision
+// ###
+// ###
+// ### Technical University Munich, Computer Vision Group
+// ### Winter Semester 2013/2014, March 3 - April 4
+// ###
+// ###
+// ### Evgeny Strekalovskiy, Maria Klodt, Jan Stuehmer, Mohamed Souiai
+// ###
+// ###
+// ###
+
+
+
+// ###
+// ###
+// ### TODO: For every student of your group, please provide here:
+// ###
+// ### Gaurav Kukreja, gaurav.kukreja@tum.de, p058
+// ### Miklos Homolya, miklos.homolya@tum.de, p056 
+// ### Ravikishore Kommajosyula, r.kommajosyula, p057
+// ###
+
+
+#include "aux.h"
+#include <iostream>
+#include <opencv2/imgproc/imgproc.hpp>
+using namespace std;
+
+// uncomment to use the camera
+//#define CAMERA
+
+template<typename T>
+__device__ T gpu_min(T a, T b)
+{
+    if (a < b)
+        return a;
+    else
+        return b;
+}
+
+template<typename T>
+__device__ T gpu_max(T a, T b)
+{
+    if (a < b)
+        return b;
+    else
+        return a;
+}
+
+__global__ void calculate_laplacian(float *image, float *jacobian, int w, int h, int nc, float tau) {
+    int x = threadIdx.x + blockDim.x * blockIdx.x;
+    int y = threadIdx.y + blockDim.y * blockIdx.y;
+    int c = threadIdx.z + blockDim.z * blockIdx.z;
+    size_t idx = x + w*y + w*h*c;
+    if (x < w && y < h && c < nc) {
+        float temp_uxy = image[idx];
+
+        jacobian[idx] = ((x+1) < (w) ? 1.0f : 0.0f) * ( image[idx + 1] - temp_uxy) +
+        		   ((x) > 0 ? 1.0f : 0.0f) * ( image[idx - 1] - temp_uxy) +
+        		   ((y+1) < (h) ? 1.0f : 0.0f) *( image[idx + w] - temp_uxy) +
+        		   ((y) > 0 ? 1.0f : 0.0f ) * ( image[idx - w] - temp_uxy);
+    }
+}
+
+
+__global__ void update_operator(float *image, float *jacobian, int w, int h, int nc, float tau) {
+    int x = threadIdx.x + blockDim.x * blockIdx.x;
+    int y = threadIdx.y + blockDim.y * blockIdx.y;
+    int c = threadIdx.z + blockDim.z * blockIdx.z;
+    size_t idx = x + w*y + w*h*c;
+    if (x < w && y < h && c < nc) {
+        image[idx] += tau * jacobian[idx];
+    }
+}
+
+inline int divc(int n, int b) { return (n + b - 1) / b; }
+
+inline dim3 make_grid(dim3 whole, dim3 block)
+{
+    return dim3(divc(whole.x, block.x),
+                divc(whole.y, block.y),
+                divc(whole.z, block.z));
+}
+
+int main(int argc, char **argv)
+{
+    // Before the GPU can process your kernels, a so called "CUDA context" must be initialized
+    // This happens on the very first call to a CUDA function, and takes some time (around half a second)
+    // We will do it right here, so that the run time measurements are accurate
+    cudaDeviceSynchronize();  CUDA_CHECK;
+
+
+
+
+    // Reading command line parameters:
+    // getParam("param", var, argc, argv) looks whether "-param xyz" is specified, and if so stores the value "xyz" in "var"
+    // If "-param" is not specified, the value of "var" remains unchanged
+    //
+    // return value: getParam("param", ...) returns true if "-param" is specified, and false otherwise
+
+#ifdef CAMERA
+#else
+    // input image
+    string image = "";
+    bool ret = getParam("i", image, argc, argv);
+    if (!ret) cerr << "ERROR: no image specified" << endl;
+    if (argc <= 1) { cout << "Usage: " << argv[0] << " -i <image> [-repeats <repeats>] [-gray]" << endl; return 1; }
+#endif
+    
+    // number of computation repetitions to get a better run time measurement
+    int repeats = 1;
+    getParam("repeats", repeats, argc, argv);
+    cout << "repeats: " << repeats << endl;
+    
+    // load the input image as grayscale if "-gray" is specifed
+    bool gray = false;
+    getParam("gray", gray, argc, argv);
+    cout << "gray: " << gray << endl;
+
+    // ### Define your own parameters here as needed    
+    float tau = 0.1;
+    getParam("tau", tau, argc, argv);
+    cout << "tau: " << tau << endl;
+
+    int iterations = 20;
+    getParam("iterations", iterations, argc, argv);
+    cout << "iterations: " << iterations << endl;
+
+    // Init camera / Load input image
+#ifdef CAMERA
+
+    // Init camera
+  	cv::VideoCapture camera(0);
+  	if(!camera.isOpened()) { cerr << "ERROR: Could not open camera" << endl; return 1; }
+    int camW = 640;
+    int camH = 480;
+  	camera.set(CV_CAP_PROP_FRAME_WIDTH,camW);
+  	camera.set(CV_CAP_PROP_FRAME_HEIGHT,camH);
+    // read in first frame to get the dimensions
+    cv::Mat mIn;
+    camera >> mIn;
+    
+#else
+    
+    // Load the input image using opencv (load as grayscale if "gray==true", otherwise as is (may be color or grayscale))
+    cv::Mat mIn = cv::imread(image.c_str(), (gray? CV_LOAD_IMAGE_GRAYSCALE : -1));
+    // check
+    if (mIn.data == NULL) { cerr << "ERROR: Could not load image " << image << endl; return 1; }
+    
+#endif
+
+    // convert to float representation (opencv loads image values as single bytes by default)
+    mIn.convertTo(mIn,CV_32F);
+    // convert range of each channel to [0,1] (opencv default is [0,255])
+    mIn /= 255.f;
+    // get image dimensions
+    int w = mIn.cols;         // width
+    int h = mIn.rows;         // height
+    int nc = mIn.channels();  // number of channels
+    cout << "image: " << w << " x " << h << endl;
+
+
+
+
+    // Set the output image format
+    cv::Mat mOut(h,w,mIn.type());  // mOut will have the same number of channels as the input image, nc layers
+    // ### Define your own output images here as needed
+    // Allocate arrays
+    // input/output image width: w
+    // input/output image height: h
+    // input image number of channels: nc
+    // output image number of channels: mOut.channels(), as defined above (nc, 3, or 1)
+
+    // allocate raw input image array
+    float *imgIn  = new float[(size_t)w*h*nc];
+
+    // allocate raw output array (the computation result will be stored in this array, then later converted to mOut for displaying)
+    float *imgOut = new float[(size_t)w*h*mOut.channels()];
+
+
+
+
+    // For camera mode: Make a loop to read in camera frames
+#ifdef CAMERA
+    // Read a camera image frame every 30 milliseconds:
+    // cv::waitKey(30) waits 30 milliseconds for a keyboard input,
+    // returns a value <0 if no key is pressed during this time, returns immediately with a value >=0 if a key is pressed
+    while (cv::waitKey(30) < 0)
+    {
+    // Get camera image
+    camera >> mIn;
+    // convert to float representation (opencv loads image values as single bytes by default)
+    mIn.convertTo(mIn,CV_32F);
+    // convert range of each channel to [0,1] (opencv default is [0,255])
+    mIn /= 255.f;
+#endif
+
+    // Init raw input image array
+    // opencv images are interleaved: rgb rgb rgb...  (actually bgr bgr bgr...)
+    // But for CUDA it's better to work with layered images: rrr... ggg... bbb...
+    // So we will convert as necessary, using interleaved "cv::Mat" for loading/saving/displaying, and layered "float*" for CUDA computations
+    convert_mat_to_layered (imgIn, mIn);
+
+    Timer timer; timer.start();
+    for (int measurement = 0; measurement < repeats; measurement++) {
+		float *d_in, *d_out;
+		size_t nbytes = (size_t)w*h*nc*sizeof(float);
+		cudaMalloc(&d_in, nbytes);
+		cudaMalloc(&d_out, nbytes);
+
+		cudaMemcpy(d_in, imgIn, nbytes, cudaMemcpyHostToDevice);
+
+		dim3 block(16, 8, 3);
+		dim3 grid = make_grid(dim3(w, h, nc), block);
+
+		for(int iter = 0; iter < iterations; iter++)
+		{
+			calculate_laplacian <<<grid, block>>> (d_in, d_out, w, h, nc, tau);
+			update_operator <<<grid, block>>> (d_in, d_out, w, h, nc, tau);
+		}
+
+		cudaMemcpy(imgOut, d_in, nbytes, cudaMemcpyDeviceToHost);
+
+		cudaFree(d_in);
+		cudaFree(d_out);
+    }
+    timer.end();  float t = timer.get();  // elapsed time in seconds
+    cout << "time: " << (t / repeats)*1000 << " ms" << endl;
+
+    // show input image
+    showImage("Input", mIn, 100, 100);  // show at position (x_from_left=100,y_from_above=100)
+
+    // show output image: first convert to interleaved opencv format from the layered raw array
+    convert_layered_to_mat(mOut, imgOut);
+    showImage("Output", mOut, 100+w+40, 100);
+
+    // ### Display your own output images here as needed
+
+#ifdef CAMERA
+    // end of camera loop
+    }
+#else
+    // wait for key inputs
+    cv::waitKey(0);
+#endif
+
+
+
+
+    // save input and result
+    cv::imwrite("image_input.png",mIn*255.f);  // "imwrite" assumes channel range [0,255]
+    cv::imwrite("image_result.png",mOut*255.f);
+
+    // free allocated arrays
+    delete[] imgIn;
+    delete[] imgOut;
+
+    // close all opencv windows
+    cvDestroyAllWindows();
+    return 0;
+}
+
+
+

submission/ex12/Makefile

+all: main
+
+main: main.cu aux.cu aux.h Makefile
+	nvcc -o main main.cu aux.cu  --ptxas-options=-v --use_fast_math --compiler-options -Wall -lopencv_highgui -lopencv_core
+

submission/ex12/aux.cu

+// ###
+// ###
+// ### Practical Course: GPU Programming in Computer Vision
+// ###
+// ###
+// ### Technical University Munich, Computer Vision Group
+// ### Winter Semester 2013/2014, March 3 - April 4
+// ###
+// ###
+// ### Evgeny Strekalovskiy, Maria Klodt, Jan Stuehmer, Mohamed Souiai
+// ###
+// ###
+// ###
+// ### THIS FILE IS SUPPOSED TO REMAIN UNCHANGED
+// ###
+// ###
+
+
+#include "aux.h"
+#include <cstdlib>
+#include <iostream>
+using std::stringstream;
+using std::cerr;
+using std::cout;
+using std::endl;
+using std::string;
+
+
+
+
+// parameter processing: template specialization for T=bool
+template<>
+bool getParam<bool>(std::string param, bool &var, int argc, char **argv)
+{
+    const char *c_param = param.c_str();
+    for(int i=argc-1; i>=1; i--)
+    {
+        if (argv[i][0]!='-') continue;
+        if (strcmp(argv[i]+1, c_param)==0)
+        {
+            if (!(i+1<argc) || argv[i+1][0]=='-') { var = true; return true; }
+            std::stringstream ss;
+            ss << argv[i+1];
+            ss >> var;
+            return (bool)ss;
+        }
+    }
+    return false;
+}
+
+
+
+
+// opencv helpers
+void convert_layered_to_interleaved(float *aOut, const float *aIn, int w, int h, int nc)
+{
+    if (nc==1) { memcpy(aOut, aIn, w*h*sizeof(float)); return; }
+    size_t nOmega = (size_t)w*h;
+    for (int y=0; y<h; y++)
+    {
+        for (int x=0; x<w; x++)
+        {
+            for (int c=0; c<nc; c++)
+            {
+                aOut[(nc-1-c) + nc*(x + (size_t)w*y)] = aIn[x + (size_t)w*y + nOmega*c];
+            }
+        }
+    }
+}
+void convert_layered_to_mat(cv::Mat &mOut, const float *aIn)
+{
+    convert_layered_to_interleaved((float*)mOut.data, aIn, mOut.cols, mOut.rows, mOut.channels());
+}
+
+
+void convert_interleaved_to_layered(float *aOut, const float *aIn, int w, int h, int nc)
+{
+    if (nc==1) { memcpy(aOut, aIn, w*h*sizeof(float)); return; }
+    size_t nOmega = (size_t)w*h;
+    for (int y=0; y<h; y++)
+    {
+        for (int x=0; x<w; x++)
+        {
+            for (int c=0; c<nc; c++)
+            {
+                aOut[x + (size_t)w*y + nOmega*c] = aIn[(nc-1-c) + nc*(x + (size_t)w*y)];
+            }
+        }
+    }
+}
+void convert_mat_to_layered(float *aOut, const cv::Mat &mIn)
+{
+    convert_interleaved_to_layered(aOut, (float*)mIn.data, mIn.cols, mIn.rows, mIn.channels());
+}
+
+
+
+void showImage(string title, const cv::Mat &mat, int x, int y)
+{
+    const char *wTitle = title.c_str();
+    cv::namedWindow(wTitle, CV_WINDOW_AUTOSIZE);
+    cvMoveWindow(wTitle, x, y);
+    cv::imshow(wTitle, mat);
+}
+
+
+
+
+// adding Gaussian noise
+float noise(float sigma)
+{
+    float x1 = (float)rand()/RAND_MAX;
+    float x2 = (float)rand()/RAND_MAX;
+    return sigma * sqrtf(-2*log(std::max(x1,0.000001f)))*cosf(2*M_PI*x2);
+}
+void addNoise(cv::Mat &m, float sigma)
+{
+    float *data = (float*)m.data;
+    int w = m.cols;
+    int h = m.rows;
+    int nc = m.channels();
+    size_t n = (size_t)w*h*nc;
+    for(size_t i=0; i<n; i++)
+    {
+        data[i] += noise(sigma);
+    }
+}
+
+
+
+
+// cuda error checking
+string prev_file = "";
+int prev_line = 0;
+void cuda_check(string file, int line)
+{
+    cudaError_t e = cudaGetLastError();
+    if (e != cudaSuccess)
+    {
+        cout << endl << file << ", line " << line << ": " << cudaGetErrorString(e) << " (" << e << ")" << endl;
+        if (prev_line>0) cout << "Previous CUDA call:" << endl << prev_file << ", line " << prev_line << endl;
+        exit(1);
+    }
+    prev_file = file;
+    prev_line = line;
+}

submission/ex12/aux.h

+// ###
+// ###
+// ### Practical Course: GPU Programming in Computer Vision
+// ###
+// ###
+// ### Technical University Munich, Computer Vision Group
+// ### Winter Semester 2013/2014, March 3 - April 4
+// ###
+// ###
+// ### Evgeny Strekalovskiy, Maria Klodt, Jan Stuehmer, Mohamed Souiai
+// ###
+// ###
+// ###
+// ### THIS FILE IS SUPPOSED TO REMAIN UNCHANGED
+// ###
+// ###
+
+
+#ifndef AUX_H
+#define AUX_H
+
+#include <cuda_runtime.h>
+#include <ctime>
+#include <opencv2/highgui/highgui.hpp>
+#include <opencv2/imgproc/imgproc.hpp>
+#include <string>
+#include <sstream>
+
+
+
+
+// parameter processing
+template<typename T>
+bool getParam(std::string param, T &var, int argc, char **argv)
+{
+    const char *c_param = param.c_str();
+    for(int i=argc-1; i>=1; i--)
+    {
+        if (argv[i][0]!='-') continue;
+        if (strcmp(argv[i]+1, c_param)==0)
+        {
+            if (!(i+1<argc)) continue;
+            std::stringstream ss;
+            ss << argv[i+1];
+            ss >> var;
+            return (bool)ss;
+        }
+    }
+    return false;
+}
+
+
+
+
+// opencv helpers
+void convert_mat_to_layered(float *aOut, const cv::Mat &mIn);
+void convert_layered_to_mat(cv::Mat &mOut, const float *aIn);
+void showImage(std::string title, const cv::Mat &mat, int x, int y);
+
+
+
+
+// adding Gaussian noise
+void addNoise(cv::Mat &m, float sigma);
+
+
+
+
+// measuring time
+class Timer
+{
+    public:
+	Timer() : tStart(0), running(false), sec(0.f)
+	{
+	}
+	void start()
+	{
+		tStart = clock();
+		running = true;
+	}
+	void end()
+	{
+		if (!running) { sec = 0; return; }
+        cudaDeviceSynchronize();
+		clock_t tEnd = clock();
+		sec = (float)(tEnd - tStart) / CLOCKS_PER_SEC;
+		running = false;
+	}
+	float get()
+	{
+		if (running) end();
+		return sec;
+	}
+    private:
+	clock_t tStart;
+	bool running;
+	float sec;
+};
+
+
+
+
+// cuda error checking
+#define CUDA_CHECK cuda_check(__FILE__,__LINE__)
+void cuda_check(std::string file, int line);
+
+
+
+#endif  // AUX_H

submission/ex12/main.cu

+// ###
+// ###
+// ### Practical Course: GPU Programming in Computer Vision
+// ###
+// ###
+// ### Technical University Munich, Computer Vision Group
+// ### Winter Semester 2013/2014, March 3 - April 4
+// ###
+// ###
+// ### Evgeny Strekalovskiy, Maria Klodt, Jan Stuehmer, Mohamed Souiai
+// ###
+// ###
+// ###
+
+
+
+// ###
+// ###
+// ### TODO: For every student of your group, please provide here:
+// ###
+// ### Gaurav Kukreja, gaurav.kukreja@tum.de, p058
+// ### Miklos Homolya, miklos.homolya@tum.de, p056 
+// ### Ravikishore Kommajosyula, r.kommajosyula, p057
+// ###
+
+
+#include "aux.h"
+#include <iostream>
+#include <math.h>
+using namespace std;
+
+// uncomment to use the camera
+//#define CAMERA
+
+#define USING_GPU
+
+__host__ __device__ float absolute_value ( float2 z ) {
+	return sqrtf((z.x * z.x) + (z.y * z.y));
+}
+
+__host__ __device__ float2 add_complex ( float2 z1, float2 z2 ) {
+	return {z1.x + z2.x, z1.y + z2.y };
+}
+
+__host__ __device__ float2 square_complex ( float2 z ) {
+	return {((z.x*z.x) - (z.y*z.y)), (2.0f * z.x * z.y) };
+}
+
+
+__global__ void callKernel(float* imgOut, int width, int height, float2 center, float radius, int iterations) {
+	  int iy = blockIdx.y * blockDim.y + threadIdx.y;  // WIDTH
+	  int ix = blockIdx.x * blockDim.x + threadIdx.x;  // HEIGHT
+	  int idx = iy * width + ix;
+	  if(ix >= width || iy >= height) return;
+
+	  float2 c, z;
+	  c.x = ((float)ix / width) * (2.0f * radius) + center.x - radius;
+	  c.y = ((float)iy / height) * (2.0f * radius) + center.y - radius;
+	  z = c;
+	  int n = 0;
+	  while( (absolute_value(z) < 2.0f) && (n < iterations))
+	  {
+		  z = add_complex ( square_complex(z), c);
+		  n++;
+	  }
+
+	  imgOut[idx] = 1 - (1.0f * n)/iterations;
+}
+
+int main(int argc, char **argv)
+{
+#ifdef USING_GPU
+	// Before the GPU can process your kernels, a so called "CUDA context" must be initialized
+	// This happens on the very first call to a CUDA function, and takes some time (around half a second)
+	// We will do it right here, so that the run time measurements are accurate
+	cudaDeviceSynchronize();  CUDA_CHECK;
+#endif // USING_GPU
+
+	// Reading command line parameters:
+	// getParam("param", var, argc, argv) looks whether "-param xyz" is specified, and if so stores the value "xyz" in "var"
+	// If "-param" is not specified, the value of "var" remains unchanged
+	//
+	// return value: getParam("param", ...) returns true if "-param" is specified, and false otherwise
+
+	// ### Define your own parameters here as needed
+	float width = 640;
+	getParam("width", width, argc, argv);
+	cout << "width = " << width << endl;
+
+	float height = 480;
+	getParam("height", height, argc, argv);
+	cout << "height = " << height<< endl;
+
+	float2 center = {-0.5f, 0.0f};
+//	getParam("center", center, argc, argv);
+//	cout << "center = " << center.x << ", " << center.y << endl;
+
+	float radius = 1.5f;
+	getParam("radius", radius, argc, argv);
+	cout << "radius = " << radius << endl;
+
+	int iterations = 100;
+	getParam("iterations", iterations, argc, argv);
+	cout << "iterations = " << iterations << endl;
+
+	int repeats = 100;
+	getParam("repeats", repeats, argc, argv);
+	cout << "repeats = " << repeats << endl;
+
+	// Set the output image format
+	// ###
+	cv::Mat mOut(height, width, CV_32FC1);    // mOut will be a grayscale image, 1 layer
+
+	// Allocate arrays
+	// input/output image width: w
+	// input/output image height: h
+	// input image number of channels: nc
+	// output image number of channels: mOut.channels(), as defined above (nc, 3, or 1)
+
+	// allocate raw output array (the computation result will be stored in this array, then later converted to mOut for displaying)
+	float *imgOut = new float[(size_t) (width*height) ];
+
+		Timer timer;
+		float t;
+		// ###
+		// ###
+		// ### TODO: Main computation
+		// ###
+		// ###
+		timer.start();
+
+		// Repetitions Loop
+		for(int rep = 0; rep < repeats; rep++)
+		{
+			size_t count = (size_t)width * height;
+
+			// Thread Dimensions
+			dim3 block = dim3(32, 8, 1);
+			dim3 grid = dim3((width + block.x - 1) / block.x, (height + block.y - 1) / block.y, 1);
+
+			// Allocating memory on the device
+			float *d_imgOut = NULL;
+			cudaMalloc(&d_imgOut, count * sizeof(float));
+
+			// Calling gaussian smoothing kernel
+			callKernel <<< grid, block >>> (d_imgOut, width, height, center, radius, iterations );
+
+			// Copying result back
+			cudaMemcpy(imgOut, d_imgOut, count * sizeof(float), cudaMemcpyDeviceToHost);
+
+			CUDA_CHECK;
+
+			// Freeing Memory
+			cudaFree(d_imgOut);
+		}
+
+		timer.end();
+		t = timer.get();
+
+		cout << "time: " << t*1000 << " ms" << endl;
+
+		// show output image: first convert to interleaved opencv format from the layered raw array
+		convert_layered_to_mat(mOut, imgOut);
+		showImage("Output", mOut, 100, 100);
+
+		// ### Display your own output images here as needed
+
+	// wait for key inputs
+	cv::waitKey(0);
+	// save input and result
+	cv::imwrite("image_result.png",mOut*255.f);
+
+	// free allocated arrays
+	delete[] imgOut;
+
+	// close all opencv windows
+	cvDestroyAllWindows();
+	return 0;
+}
+
+