5 changed files with 259 additions and 205 deletions
@ -1,23 +1,36 @@ |
|||||
SHELL := /bin/bash |
SHELL := /bin/bash |
||||
|
|
||||
|
|
||||
# ============================================
|
# ============================================
|
||||
# COMMANDS
|
# COMMANDS
|
||||
|
|
||||
CC = gcc |
CC = gcc |
||||
RM = rm -f |
RM = rm -f |
||||
CFLAGS=-lm |
CFLAGS=-lm -O3 -Wall -I. |
||||
|
OBJ=serial.o serialDeclarations.o |
||||
|
DEPS=serialDeclarations.h |
||||
|
|
||||
# ==========================================
|
# ==========================================
|
||||
# TARGETS
|
# TARGETS
|
||||
|
|
||||
|
|
||||
EXECUTABLES = serial |
EXECUTABLES = serial |
||||
|
|
||||
|
.PHONY: all clean |
||||
|
|
||||
all: $(EXECUTABLES) |
all: $(EXECUTABLES) |
||||
|
|
||||
serial: serial.c |
# ==========================================
|
||||
$(CC) $< -o $@ $(CFLAGS) |
# DEPENDENCIES (HEADERS)
|
||||
|
|
||||
|
%.o: %.c $(DEPS) |
||||
|
$(CC) -c -o $@ $< $(CFLAGS) |
||||
|
|
||||
|
.PRECIOUS: $(EXECUTABLES) $(OBJ) |
||||
|
|
||||
|
# ==========================================
|
||||
|
# EXECUTABLE (MAIN)
|
||||
|
|
||||
|
$(EXECUTABLES): $(OBJ) |
||||
|
$(CC) -o $@ $^ $(CFLAGS) |
||||
|
|
||||
clean: |
clean: |
||||
$(RM) *.o *~ $(EXECUTABLES) |
$(RM) *.o *~ $(EXECUTABLES) |
||||
|
|||||
@ -0,0 +1,33 @@ |
|||||
|
# Mean-shift |
||||
|
|
||||
|
[Mean-shift] is a mathematical procedure, adopted in algorithms, designed in the 70's by Fukunaga and Hostetler. The algorithm is used for: |
||||
|
|
||||
|
- Cluster analysis |
||||
|
- Computer vision |
||||
|
- Image processing |
||||
|
|
||||
|
## Repository |
||||
|
|
||||
|
This repository provides a serial implementation of the algorithm in C language, as well as the parallel equivalent in CUDA. The project was undertaken as part of the "Parallel and distributed systems" course of AUTH university. |
||||
|
|
||||
|
A [Gaussian] kernel was used for the weighting function. The code was tested for different data sets and information regarding the execution time and correctness were extracted. In addition, two versions of the parallel algorithm was tested and compared, with and without the usage of shared memory respectively. |
||||
|
|
||||
|
## Compilation |
||||
|
|
||||
|
To compile make sure all necessary packages and dependencies are installed. Then run: |
||||
|
|
||||
|
```sh |
||||
|
$ make |
||||
|
``` |
||||
|
|
||||
|
## Usage |
||||
|
|
||||
|
blah blah, arguments needed etc |
||||
|
|
||||
|
|
||||
|
**Free Software, Hell Yeah!** |
||||
|
|
||||
|
[//]: # (Links) |
||||
|
|
||||
|
[Mean-shift]: <https://en.wikipedia.org/wiki/Mean_shift> |
||||
|
[Gaussian]: <https://en.wikipedia.org/wiki/Gaussian_function> |
||||
@ -0,0 +1,181 @@ |
|||||
|
#include <stdio.h> |
||||
|
#include <stdlib.h> |
||||
|
#include <math.h> |
||||
|
#include <float.h> |
||||
|
|
||||
|
#include "serialDeclarations.h" |
||||
|
|
||||
|
void meanshift(double **x, int h, struct parameters *opt){ |
||||
|
|
||||
|
double **y; |
||||
|
y = alloc_2d_double(ROWS, COLUMNS); |
||||
|
y = duplicate(x, y, ROWS, COLUMNS); |
||||
|
|
||||
|
// mean shift vectors
|
||||
|
double **m; |
||||
|
m = alloc_2d_double(ROWS, COLUMNS); |
||||
|
// initialize elements of m to inf
|
||||
|
for (int i=0;i<ROWS;i++){ |
||||
|
for (int j=0;j<COLUMNS;j++){ |
||||
|
m[i][j] = DBL_MAX; |
||||
|
} |
||||
|
} |
||||
|
|
||||
|
// initialize iteration counter
|
||||
|
int iter = 0; |
||||
|
|
||||
|
// printf("%f \n", opt->epsilom);
|
||||
|
// TODO ITERATION
|
||||
|
/** iterate until convergence **/ |
||||
|
// printf("norm : %f \n", norm(m, ROWS, COLUMNS));
|
||||
|
|
||||
|
while (norm(m, ROWS, COLUMNS) > opt->epsilom) { |
||||
|
// while (iter < 1){
|
||||
|
iter = iter +1; |
||||
|
// find pairwise distance matrix (inside radius)
|
||||
|
// TODO write in C
|
||||
|
/** allocate memory for inside iteration arrays **/ |
||||
|
double ** W = alloc_2d_double(ROWS, ROWS); |
||||
|
double * l = malloc(ROWS * sizeof(double)); |
||||
|
// [I, D] = rangesearch(x,y,h);
|
||||
|
for (int i=0; i<ROWS; i++){ |
||||
|
for (int j=0; j<ROWS; j++){ |
||||
|
// TODO implement function to calculate distance calculateDistance(double *, double *)
|
||||
|
double dist = calculateDistance(y[i],x[j]); |
||||
|
|
||||
|
// 2sparse matrix
|
||||
|
if (dist < h){ |
||||
|
W[i][j] = dist; |
||||
|
//printf("%f \n", W[i][j]);
|
||||
|
}else{ |
||||
|
W[i][j] = 0; |
||||
|
} |
||||
|
} |
||||
|
} |
||||
|
|
||||
|
|
||||
|
// for each element of W (x) do x^2
|
||||
|
// size of W is [600 600]
|
||||
|
// W is a sparse matrix -> apply to non-zero elements
|
||||
|
for (int i=0; i<ROWS; i++){ |
||||
|
double sum =0; |
||||
|
for (int j=0; j < ROWS; j++){ |
||||
|
if (W[i][j] != 0){ |
||||
|
W[i][j] = W[i][j]*W[i][j]; |
||||
|
// compute kernel matrix
|
||||
|
// apply function to non zero elements of a sparse matrix
|
||||
|
double pow = ((-1)*(W[i][j]))/(2*(h*h)); |
||||
|
W[i][j] = exp(pow); |
||||
|
} |
||||
|
// make sure diagonal elements are 1
|
||||
|
if (i==j){ |
||||
|
W[i][j] = W[i][j] +1; |
||||
|
} |
||||
|
// calculate sum(W,2)
|
||||
|
sum = sum + W[i][j]; |
||||
|
} |
||||
|
/** l array is correct**/ |
||||
|
l[i] = sum; |
||||
|
// printf("l[%d] : %f \n", i, l[i]);
|
||||
|
} |
||||
|
/** W is correct**/ |
||||
|
//print_matrix(W, ROWS, ROWS);
|
||||
|
|
||||
|
|
||||
|
// create new y vector
|
||||
|
double** y_new = alloc_2d_double(ROWS, COLUMNS); |
||||
|
// TODO implement in C : y_new = W * x
|
||||
|
|
||||
|
// TODO implement function double ** multiply(double **, double **)
|
||||
|
multiply(W, x, y_new); |
||||
|
/** y_new is CORRECT **/ |
||||
|
// print_matrix(y_new, ROWS, COLUMNS);
|
||||
|
// divide element-wise
|
||||
|
for (int i=0; i<ROWS; i++){ |
||||
|
for (int j=0; j<COLUMNS; j++){ |
||||
|
y_new[i][j] = y_new[i][j] / l[i]; |
||||
|
} |
||||
|
} |
||||
|
|
||||
|
// calculate mean-shift vector
|
||||
|
for (int i=0; i<ROWS; i++){ |
||||
|
for (int j=0; j<COLUMNS; j++){ |
||||
|
m[i][j] = y_new[i][j] - y[i][j]; |
||||
|
|
||||
|
// update y
|
||||
|
y[i][j] = y_new[i][j]; |
||||
|
} |
||||
|
} |
||||
|
|
||||
|
printf("Iteration n. %d, error %f \n", iter, norm(m, ROWS, COLUMNS)); |
||||
|
// TODO maybe keep y for live display later?
|
||||
|
}; |
||||
|
|
||||
|
|
||||
|
|
||||
|
} |
||||
|
|
||||
|
// allocates a 2d array in continuous memory positions
|
||||
|
double **alloc_2d_double(int rows, int cols) { |
||||
|
double *data = (double *)malloc(rows*cols*sizeof(double)); |
||||
|
double **array= (double **)malloc(rows*sizeof(double*)); |
||||
|
for (int i=0; i<rows; i++) |
||||
|
array[i] = &(data[cols*i]); |
||||
|
return array; |
||||
|
} |
||||
|
|
||||
|
// copy the values of a 2d double array to another
|
||||
|
double **duplicate(double **a, double **b, int rows, int cols){ |
||||
|
for (int i=0;i<rows;i++){ |
||||
|
for (int j=0;j<cols;j++){ |
||||
|
b[i][j] = a[i][j]; |
||||
|
} |
||||
|
} |
||||
|
return b; |
||||
|
} |
||||
|
|
||||
|
// TODO check why there's is a difference in the norm calculate in matlab
|
||||
|
double norm(double ** m, int rows, int cols){ |
||||
|
double sum=0, a=0; |
||||
|
for (int i = 0; i < rows; i++) { |
||||
|
for (int j = 0; j < cols; j++) { |
||||
|
a = m[i][j] * m[i][j]; |
||||
|
sum = sum + a; |
||||
|
} |
||||
|
} |
||||
|
double norm = sqrt(sum); |
||||
|
return norm; |
||||
|
} |
||||
|
|
||||
|
double calculateDistance(double *y, double *x){ |
||||
|
double sum = 0, dif; |
||||
|
for (int i=0;i<COLUMNS;i++){ |
||||
|
dif = y[i]-x[i]; |
||||
|
sum += dif * dif; |
||||
|
} |
||||
|
double distance = sqrt(sum); |
||||
|
return distance; |
||||
|
} |
||||
|
|
||||
|
void multiply(double ** matrix1, double ** matrix2, double ** output){ |
||||
|
// W dims are ROWS ROWS and x dims are ROWS COLUMNS
|
||||
|
// TODO CHECK
|
||||
|
int i, j, k; |
||||
|
for (i=0; i<ROWS; i++){ |
||||
|
for (j=0; j<COLUMNS; j++){ |
||||
|
output[i][j] = 0; |
||||
|
for (k=0; k<ROWS; k++){ |
||||
|
output[i][j] += matrix1[i][k] * matrix2[k][j]; |
||||
|
} |
||||
|
} |
||||
|
} |
||||
|
} |
||||
|
|
||||
|
void print_matrix(double ** array, int rows, int cols){ |
||||
|
for (int i=0; i<cols; i++){ |
||||
|
for (int j=0; j<rows; j++){ |
||||
|
printf("%f ", array[j][i]); |
||||
|
} |
||||
|
printf("\n"); |
||||
|
} |
||||
|
} |
||||
@ -0,0 +1,25 @@ |
|||||
|
#ifndef SERIAL_DECLARATIONS_H /* Include guard */ |
||||
|
#define SERIAL_DECLARATIONS_H |
||||
|
|
||||
|
#include <stdbool.h> |
||||
|
|
||||
|
#define X "data/X.bin" |
||||
|
#define L "data/L.bin" |
||||
|
#define COLUMNS 2 |
||||
|
#define ROWS 600 |
||||
|
|
||||
|
struct parameters { |
||||
|
double epsilom; |
||||
|
bool verbose; |
||||
|
bool display; |
||||
|
}; |
||||
|
|
||||
|
double **alloc_2d_double(int rows, int cols); |
||||
|
double **duplicate(double **a, double **b, int rows, int cols); |
||||
|
void meanshift(double **x, int h, struct parameters *opt); |
||||
|
double norm(double ** m, int rows, int cols); |
||||
|
void multiply(double ** matrix1, double ** matrix2, double ** output); |
||||
|
double calculateDistance(double *, double *); |
||||
|
void print_matrix(double ** array, int rows, int cols); |
||||
|
|
||||
|
#endif //SERIAL_DECLARATIONS_H
|
||||
Loading…
Reference in new issue