/*
* Segment a graph
*
* Returns a disjoint-set forest representing the segmentation.
*
* num_vertices: number of vertices in graph.
* num_edges: number of edges in graph
* edges: array of edges.
* c: constant for treshold function.
*/
universe *segment_graph(int num_vertices, int num_edges, edge *edges,
float c) {
// sort edges by weight
std::sort(edges, edges + num_edges);
// make a disjoint-set forest
universe *u = new universe(num_vertices);
// init thresholds
float *threshold = new float[num_vertices];
for (int i = 0; i < num_vertices; i++)
threshold[i] = THRESHOLD(1,c);
// for each edge, in non-decreasing weight order...
for (int i = 0; i < num_edges; i++) {
edge *pedge = &edges[i];
// components conected by this edge
int a = u->find(pedge->a);
int b = u->find(pedge->b);
if (a != b) {
if ((pedge->w <= threshold[a]) &&
(pedge->w <= threshold[b])) {
u->join(a, b);
a = u->find(a);
threshold[a] = pedge->w + THRESHOLD(u->size(a), c);
}
}
}
// free up
delete threshold;
return u;
}
static enumButton_t decodeLCDButton(uint16_t adcVal)
{
#define RATIO_0 /* RIGHT */ 0.0
#define RATIO_1 /* UP */ (330.0/(330.0+2000.0))
#define RATIO_2 /* DOWN */ ((330.0+620.0)/(330.0+620.0+2000.0))
#define RATIO_3 /* LEFT */ ((330.0+620.0+1000.0)/(330.0+620.0+1000.0+2000.0))
#define RATIO_4 /* SELECT */ ((330.0+620.0+1000.0+3300.0)/(330.0+620.0+1000.0+3300.0+2000.0))
#define RATIO_5 /* NONE */ 1.0
#define THRESHOLD(n,n1) (uint16_t)(((RATIO_##n1 + RATIO_##n) / 2.0) \
* ADC_NO_AVERAGED_SAMPLES * 0x0400ul)
if(adcVal > THRESHOLD(4,5))
return btnNone;
else if(adcVal > THRESHOLD(3,4))
return btnSelect;
else if(adcVal > THRESHOLD(2,3))
return btnLeft;
else if(adcVal > THRESHOLD(1,2))
return btnDown;
else if(adcVal > THRESHOLD(0,1))
return btnUp;
else
return btnRight;
#undef RATIO_0
#undef RATIO_1
#undef RATIO_2
#undef RATIO_3
#undef RATIO_4
#undef RATIO_5
#undef THRESHOLD
} /* End of decodeLCDButton */
/*
* Segment a graph
*
* Returns a disjoint-set forest representing the segmentation.
*
* num_vertices: number of vertices in graph.
* num_edges: number of edges in graph
* edges: array of edges.
* c: constant for treshold function.
*/
void segment::segment_graph(universe &u, int num_vertices,
int num_edges, std::vector<edge> edges, double c)
{
int i, a, b;
// sort edges by weight
std::sort(edges.begin(), edges.begin()+num_edges);
// init thresholds
std::vector<double> threshold;
for (i = 0; i < num_vertices; i++)
threshold.push_back( THRESHOLD(1,c) );
// for each edge, in non-decreasing weight order...
for (i = 0; i < num_edges; i++) {
edge *pedge = &edges[i];
// components conected by this edge
a = u.find(pedge->a);
b = u.find(pedge->b);
if( a==b ) continue;
if ((pedge->w <= threshold[a]) && (pedge->w <= threshold[b])) {
u.join(a, b);
a = u.find(a);
threshold[a] = pedge->w + THRESHOLD(u.size(a), c);
}
}
}
Map* map_create(size_t initial_capacity, uint64_t(*hash)(void*), bool(*equals)(void*,void*), void* pool) {
if(!equals)
equals = map_uint64_equals;
if(!hash)
hash = map_uint64_hash;
size_t capacity = 1;
while(capacity < initial_capacity)
capacity <<= 1;
Map* map = __malloc(sizeof(Map), pool);
if(!map)
return NULL;
map->table = __malloc(sizeof(List*) * capacity, pool);
if(!map->table) {
__free(map, pool);
return NULL;
}
memset(map->table, 0x0, sizeof(List*) * capacity);
map->capacity = capacity;
map->threshold = THRESHOLD(capacity);
map->size = 0;
map->hash = hash;
map->equals = equals;
map->pool = pool;
return map;
}
int hm_set ( hashmap_t *hm, char *key, void *value )
{
int hashed;
int count = 0;
int rtn;
bucket_t *b_ptr;
entry_t *e_ptr;
if ( ( hm->size + 1 ) >= THRESHOLD ( hm->capacity ) )
rehash ( hm );
hashed = hashstr ( hm, key );
if ( hm->buckets[ hashed ] == NULL )
{
/* No bucket. */
b_ptr = malloc ( sizeof ( bucket_t ) );
e_ptr = b_ptr->entries;
}
else if ( b_ptr != NULL )
{
/* Bucket exists. */
if ( b_ptr->numentries >= BUCKETTHOLD )
rehash ( hm );
for ( ; ( e_ptr = b_ptr->entries->next ) != NULL ; ) {
if ( strcmp ( e_ptr->key, key ) != 0 )
return 1;
}
e_ptr = e_ptr->next;
}
e_ptr = calloc ( 1, sizeof ( entry_t ) );
if ( e_ptr == NULL )
return 2;
e_ptr->key = calloc ( strlen( key ) + 1, sizeof( char * ) );
if ( e_ptr->key == NULL )
return 3;
strncpy( e_ptr->key, key, strlen( key ) );
e_ptr->value = value;
b_ptr->numentries++;
b_ptr->entries = e_ptr;
hm->buckets[ hashed ] = b_ptr;
hm->size++;
}
// Roberts edge detector
// inputs:
// float *input - pointer to input image
// int w, int h - width and height of input image
// float threshold - threshold of edge detection
// int padding_method - padding method for convolution
// output:
// float * - pointer to output image
float *edges_roberts(float *im, int w, int h,
float threshold, int padding_method) {
// Define operators
// 3x3 operators are used (although it is unnecessary) in order to
// simplify the convolution application, and also to unify the three
// detectors of the first family.
float roberts_1[9] = {-1, 0, 0, 0, 1, 0, 0, 0, 0}; // ROBERTS
float roberts_2[9] = { 0,-1, 0, 1, 0, 0, 0, 0, 0}; // OPERATORS
for(int z=0; z<9; z++) { // normalization
roberts_1[z] /= 2.0;
roberts_2[z] /= 2.0;
}
// Convolution with operators
float *Gx = conv2d(im, w, h, roberts_1, 3, padding_method);
float *Gy = conv2d(im, w, h, roberts_2, 3, padding_method);
// Allocate memory for output image
float *im_roberts = xmalloc(w*h*sizeof(float));
// Two images are obtained (one for each operator). Then the gradient
// magnitude image is constructed using sqrt(g_x^2+g_y^2). Also
// the absolute maximum value of the constructed images is computed
float max = 0.0;
for(int i=0; i<w; i++) {
for(int j=0; j<h; j++) {
float gx = Gx[i+1 + (j+1)*(w+2)]; // Gx and Gy are (w+2) x (h+2)
float gy = Gy[i+1 + (j+1)*(w+2)]; // there is an (+1,+1) offset
im_roberts[i+j*w] = sqrt(gx*gx + gy*gy);
max = MAX(max, im_roberts[i+j*w]);
}
}
// Threshold
for(int i=0; i<w*h; i++) {
im_roberts[i] = THRESHOLD(im_roberts[i], threshold*max);
}
// Free memory
free(Gx);
free(Gy);
return im_roberts;
}
// Sobel edge detector
// inputs:
// float *input - pointer to input image
// int w, int h - width and height of input image
// float threshold - threshold of edge detection
// int padding_method - padding method for convolution
// output:
// float * - pointer to output image
float *edges_sobel(float *im, int w, int h,
float threshold, int padding_method) {
// Define operators
float sobel_1[9] = {-1,-2,-1, 0, 0, 0, 1, 2, 1}; // SOBEL
float sobel_2[9] = {-1, 0, 1,-2, 0, 2,-1, 0, 1}; // OPERATORS
for(int z=0; z<9; z++) { // normalization
sobel_1[z] /= 8.0;
sobel_2[z] /= 8.0;
}
// Convolution with operators
float *Gx = conv2d(im, w, h, sobel_1, 3, padding_method);
float *Gy = conv2d(im, w, h, sobel_2, 3, padding_method);
// Allocate memory for output image
float *im_sobel = xmalloc(w*h*sizeof(float));
// Two images are obtained (one for each operator). Then the gradient
// magnitude image is constructed using sqrt(g_x^2+g_y^2). Also
// the absolute maximum value of the constructed images is computed
float max = 0.0;
for(int i=0; i<w; i++) {
for(int j=0; j<h; j++) {
float gx = Gx[i+1 + (j+1)*(w+2)]; // Gx and Gy are (w+2) x (h+2)
float gy = Gy[i+1 + (j+1)*(w+2)]; // there is an (+1,+1) offset
im_sobel[i+j*w] = sqrt(gx*gx + gy*gy);
max = MAX(max, im_sobel[i+j*w]);
}
}
// Threshold
for(int i=0; i<w*h; i++) {
im_sobel[i] = THRESHOLD(im_sobel[i], threshold*max);
}
// Free memory
free(Gx);
free(Gy);
return im_sobel;
}
bool map_put(Map* map, void* key, void* data) {
if(map->size + 1 > map->threshold) {
// Create new map
Map map2;
size_t capacity = map->capacity * 2;
map2.table = __malloc(sizeof(List*) * capacity, map->pool);
if(!map2.table)
return false;
memset(map2.table, 0x0, sizeof(List*) * capacity);
map2.capacity = capacity;
map2.threshold = THRESHOLD(capacity);
map2.size = 0;
map2.hash = map->hash;
map2.equals = map->equals;
map2.pool = map->pool;
// Copy
MapIterator iter;
map_iterator_init(&iter, map);
while(map_iterator_has_next(&iter)) {
MapEntry* entry = map_iterator_next(&iter);
if(!map_put(&map2, entry->key, entry->data)) {
destroy(&map2);
return false;
}
}
// Destory
destroy(map);
// Paste
memcpy(map, &map2, sizeof(Map));
}
size_t index = map->hash(key) % map->capacity;
if(!map->table[index]) {
map->table[index] = list_create(map->pool);
if(!map->table[index])
return false;
} else {
size_t size = list_size(map->table[index]);
for(size_t i = 0; i < size; i++) {
MapEntry* entry = list_get(map->table[index], i);
if(map->equals(entry->key, key))
return false;
}
}
MapEntry* entry = __malloc(sizeof(MapEntry), map->pool);
if(!entry) {
if(list_is_empty(map->table[index])) {
list_destroy(map->table[index]);
map->table[index] = NULL;
}
return false;
}
entry->key = key;
entry->data = data;
if(!list_add(map->table[index], entry)) {
__free(entry, map->pool);
if(list_is_empty(map->table[index])) {
list_destroy(map->table[index]);
map->table[index] = NULL;
}
return false;
}
map->size++;
return true;
}
// Software Guide : BeginLatex
// \vspace{0.5cm}
// \Large{Main function} \\
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
int main(int argc, char *argv[]) {
// Software Guide : EndCodeSnippet
if (argc != 4) {
printf("Usage: %s input_image threshold padding_method\n", argv[0]);
} else {
// Execution time:
double start = (double)clock();
// Load input image (using iio)
// Software Guide : BeginLatex
// Load input image (using \textit{iio}): \\
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
int w, h, pixeldim;
float *im_orig = iio_read_image_float_vec(argv[1], &w, &h, &pixeldim);
// Software Guide : EndCodeSnippet
fprintf(stderr, "Input image loaded:\t %dx%d image with %d channel(s).\n", w, h, pixeldim);
// Grayscale conversion (if necessary)
// Software Guide : BeginLatex
// Grayscale conversion (if necessary): explained in \ref{sec:grayscale}. \\ \\
// Software Guide : EndLatex
double *im = malloc(w*h*sizeof(double));
if (im == NULL){
fprintf(stderr, "Out of memory...\n");
exit(EXIT_FAILURE);
}
// allocate memory for the grayscale image <im>, output of the grayscale conversion
// and correct allocation check.
int z;
// <z> is just an integer used as array index.
int zmax = w*h; // number of elements of <im>
if (pixeldim==3){ // if the image is color (RGB, three channels)...
for(z=0;z<zmax;z++){ // for each pixel in the image <im>, calculate the gray
// value according to the expression:
// I = ( 6968*R + 23434*G + 2366*B ) / 32768.
im[z] = (double)(6968*im_orig[3*z] + 23434*im_orig[3*z + 1] + 2366*im_orig[3*z + 2])/32768;
}
fprintf(stderr, "images converted to grayscale\n");
} else { // the image was originally grayscale...
for(z=0;z<zmax;z++){
im[z] = (double)im_orig[z]; // only assign the value of im_orig to im, casting to double.
}
fprintf(stderr, "images are already in grayscale\n");
}
// Define operators:
// Software Guide : BeginLatex
// Define the normalized Roberts, Prewitt and Sobel operators: \\
// (We use $3\times 3$ operators)
// \begin{itemize}
// \item Roberts:
// $$
// R_1 = \begin{bmatrix} -1 & 0 & 0 \\ 0 & 1 & 0 \\ 0 & 0 & 0 \end{bmatrix}
// $$
// $$
// R_2 = \begin{bmatrix} 0 & -1 & 0 \\ 1 & 0 & 0 \\ 0 & 0 & 0 \end{bmatrix}
// $$
// \item Prewitt:
// $$
// P_1 = \begin{bmatrix} \frac{-1}{6} & \frac{-1}{6} & \frac{-1}{6} \\ 0 & 0 & 0 \\ \frac{1}{6} & \frac{1}{6} & \frac{1}{6} \end{bmatrix}
// $$
// $$
// P_2 = \begin{bmatrix} \frac{-1}{6} & 0 & \frac{1}{6} \\ \frac{-1}{6} & 0 & \frac{1}{6} \\ \frac{-1}{6} & 0 & \frac{1}{6} \end{bmatrix}
// $$
// \item Sobel:
// $$
// S_1 = \begin{bmatrix} \frac{-1}{8} & \frac{-1}{4} & \frac{-1}{8} \\ 0 & 0 & 0 \\ \frac{1}{8} & \frac{1}{4} & \frac{1}{8} \end{bmatrix}
// $$
// $$
// S_2 = \begin{bmatrix} \frac{-1}{8} & 0 & \frac{1}{8} \\ \frac{-1}{4} & 0 & \frac{1}{4} \\ \frac{-1}{8} & 0 & \frac{1}{8} \end{bmatrix}
// $$
// \end{itemize}
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
double roberts_1[9] = {-1, 0, 0, 0, 1, 0, 0, 0, 0}; // ROBERTS
double roberts_2[9] = { 0,-1, 0, 1, 0, 0, 0, 0, 0}; // OPERATORS
//---------------------------------------------------------------------------------
double prewitt_1[9] = {-1,-1,-1, 0, 0, 0, 1, 1, 1}; // PREWITT
double prewitt_2[9] = {-1, 0, 1,-1, 0, 1,-1, 0, 1}; // OPERATORS
//---------------------------------------------------------------------------------
double sobel_1[9] = {-1,-2,-1, 0, 0, 0, 1, 2, 1}; // SOBEL
double sobel_2[9] = {-1, 0, 1,-2, 0, 2,-1, 0, 1}; // OPERATORS
//---------------------------------------------------------------------------------
for (z=0;z<9;z++) { // NORMALIZATION
//roberts_1[z] /= 1;
//roberts_2[z] /= 1;
prewitt_1[z] /= 6;
prewitt_2[z] /= 6;
sobel_1[z] /= 8;
sobel_2[z] /= 8;
}
// Software Guide : EndCodeSnippet
// Convolve images:
// Software Guide : BeginLatex
// The input image is convolved with the defined operatos, using the \texttt{conv2d} function in \texttt{2dconvolution.c}:
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
int padding_method = atoi(argv[3]);
double *im_r1 = conv2d(im, w, h, roberts_1, 3, padding_method);
double *im_r2 = conv2d(im, w, h, roberts_2, 3, padding_method);
double *im_p1 = conv2d(im, w, h, prewitt_1, 3, padding_method);
double *im_p2 = conv2d(im, w, h, prewitt_2, 3, padding_method);
double *im_s1 = conv2d(im, w, h, sobel_1, 3, padding_method);
double *im_s2 = conv2d(im, w, h, sobel_2, 3, padding_method);
// Software Guide : EndCodeSnippet
// Allocate memory for final images:
// Software Guide : BeginLatex
// Allocate memory for final images:
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
float *im_roberts = malloc(w*h*sizeof(float));
float *im_prewitt = malloc(w*h*sizeof(float));
float *im_sobel = malloc(w*h*sizeof(float));
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
// For each method, two images are obtained (one for each operator). Then the gradient magnitude image is constructed using $M=\sqrt{g_x^2+g_y^2}$. \\
// Also the absolute maximum value of the constructed images is computed, for each method. \\
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
int i,j, fila, col;
double max_r = 0;
double max_p = 0;
double max_s = 0;
int imax = w*h;
for (i=0;i<imax;i++){
fila = (int)(i/w);
col = i - w*fila + 1;
fila += 1;
j = col + (w+2)*fila;
// Max in each case
im_roberts[i] = sqrt(im_r1[j]*im_r1[j] + im_r2[j]*im_r2[j]);
im_prewitt[i] = sqrt(im_p1[j]*im_p1[j] + im_p2[j]*im_p2[j]);
im_sobel[i] = sqrt(im_s1[j]*im_s1[j] + im_s2[j]*im_s2[j]);
// Absolute max
max_r = MAX(max_r,im_roberts[i]);
max_p = MAX(max_p,im_prewitt[i]);
max_s = MAX(max_s,im_sobel[i]);
}
// Software Guide : EndCodeSnippet
// Thresholding
// Software Guide : BeginLatex
// Thresholded images of each method are created, using the THRESHOLD macro: \\
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
float th = atof(argv[2]);
for (i=0;i<imax;i++){
im_roberts[i] = THRESHOLD(im_roberts[i],th*max_r);
im_prewitt[i] = THRESHOLD(im_prewitt[i],th*max_p);
im_sobel[i] = THRESHOLD(im_sobel[i],th*max_s);
}
// Software Guide : EndCodeSnippet
// Save outout images
// Software Guide : BeginLatex
// Save output image (using \textit{iio}): \\
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
iio_save_image_float_vec("roberts.png", im_roberts, w, h, 1);
iio_save_image_float_vec("prewitt.png", im_prewitt, w, h, 1);
iio_save_image_float_vec("sobel.png", im_sobel, w, h, 1);
// Software Guide : EndCodeSnippet
fprintf(stderr, "Roberts image saved to roberts.png.\n");
fprintf(stderr, "Prewitt image saved to prewitt.png.\n");
fprintf(stderr, "Sobel image saved to sobel.png.\n");
// Free memory:
free(im_orig);
free(im);
free(im_r1);
free(im_r2);
free(im_p1);
free(im_p2);
free(im_s1);
free(im_s2);
free(im_roberts);
free(im_prewitt);
free(im_sobel);
fprintf(stderr, "Edge detection algorithms based on first derivative computation done.\n");
// Execution time:
double finish = (double)clock();
double exectime = (finish - start)/CLOCKS_PER_SEC;
fprintf(stderr, "execution time: %1.3f s.\n", exectime);
return 0;
} // else (argc)
} // end of the program