void DS3231::setDateTime(const char* date, const char* time)
{
uint16_t year;
uint8_t month;
uint8_t day;
uint8_t hour;
uint8_t minute;
uint8_t second;
year = conv2d(date + 9);
switch (date[0])
{
case 'J': month = date[1] == 'a' ? 1 : month = date[2] == 'n' ? 6 : 7; break;
case 'F': month = 2; break;
case 'A': month = date[2] == 'r' ? 4 : 8; break;
case 'M': month = date[2] == 'r' ? 3 : 5; break;
case 'S': month = 9; break;
case 'O': month = 10; break;
case 'N': month = 11; break;
case 'D': month = 12; break;
}
day = conv2d(date + 4);
hour = conv2d(time);
minute = conv2d(time + 3);
second = conv2d(time + 6);
setDateTime(year+2000, month, day, hour, minute, second);
}
// A convenient constructor for using "the compiler's time":
// DateTime now (__DATE__, __TIME__);
// NOTE: using PSTR would further reduce the RAM footprint
DateTime::DateTime (const char* date, const char* time) {
// sample input: date = "Dec 26 2009", time = "12:34:56"
yOff = conv2d(date + 9);
// Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
switch (date[0]) {
case 'J':
m = date[1] == 'a' ? 1 : m = date[2] == 'n' ? 6 : 7;
break;
case 'F':
m = 2;
break;
case 'A':
m = date[2] == 'r' ? 4 : 8;
break;
case 'M':
m = date[2] == 'r' ? 3 : 5;
break;
case 'S':
m = 9;
break;
case 'O':
m = 10;
break;
case 'N':
m = 11;
break;
case 'D':
m = 12;
break;
}
d = conv2d(date + 4);
hh = conv2d(time);
mm = conv2d(time + 3);
ss = conv2d(time + 6);
}
void RTCDue::setTime (const char* time)
{
_hour = conv2d(time);
_minute = conv2d(time + 3);
_second = conv2d(time + 6);
RTC_SetTime (RTC, _hour, _minute, _second);
}
// Marr-Hildreth edge detector with Gaussian and Laplacian kernels
// inputs:
// float *input - pointer to input image
// int w, int h - width and height of input image
// float sigma - gaussian standard deviation
// int n - kernel size
// float tzc - threshold in zero-crossing
// int padding_method - padding method for convolution
// output:
// float * - pointer to output image
float *edges_mh(float *im, int w, int h,
float sigma, int n, float tzc, int padding_method) {
// generate Gaussian kernel
float *kernel = gaussian_kernel(n,sigma);
// smooth input image with the Gaussian kernel
float *im_smoothed = conv2d(im, w, h, kernel, n, padding_method);
// compute Laplacian of the smoothed image using a 3x3 operator
float operator[9] = {1, 1, 1, 1, -8, 1, 1, 1, 1};
float *laplacian = conv2d(im_smoothed, w+n-1, h+n-1,
operator, 3, padding_method);
// compute maximum of absolute Laplacian
float max = 0.0;
for(int i=0; i<w; i++) {
for(int j=0; j<h; j++) {
//laplacian is (w+n+1)x(h+n+1) with an offset of (n+1)/2 in x and y
float v = abs( laplacian[(i+(n+1)/2) + (j+(n+1)/2)*(w+n+1)] );
if( v > max ) max = v;
}
}
// compute laplacian zero-crossings
float *edges = xmalloc(w*h*sizeof(float));
for(int i=0; i<w; i++) {
for(int j=0; j<h; j++) {
//laplacian is (w+n+1)x(h+n+1) with an offset of (n+1)/2 in x and y
float UP_LE = laplacian[ (i-1+(n+1)/2) + (j+1+(n+1)/2)*(w+n+1) ];
float UP = laplacian[ (i +(n+1)/2) + (j+1+(n+1)/2)*(w+n+1) ];
float UP_RI = laplacian[ (i+1+(n+1)/2) + (j+1+(n+1)/2)*(w+n+1) ];
float LE = laplacian[ (i-1+(n+1)/2) + (j +(n+1)/2)*(w+n+1) ];
float RI = laplacian[ (i+1+(n+1)/2) + (j +(n+1)/2)*(w+n+1) ];
float DO_LE = laplacian[ (i-1+(n+1)/2) + (j-1+(n+1)/2)*(w+n+1) ];
float DO = laplacian[ (i +(n+1)/2) + (j-1+(n+1)/2)*(w+n+1) ];
float DO_RI = laplacian[ (i+1+(n+1)/2) + (j-1+(n+1)/2)*(w+n+1) ];
if( (LE*RI < 0.0 && abs(LE-RI) > tzc*max) ||
(UP_LE*DO_RI < 0.0 && abs(UP_LE-DO_RI) > tzc*max) ||
(DO_LE*UP_RI < 0.0 && abs(DO_LE-UP_RI) > tzc*max) ||
(UP*DO < 0.0 && abs(UP-DO) > tzc*max) ) {
edges[i+j*w] = 255.0;
} else {
edges[i+j*w] = 0.0;
}
}
}
// free memory
free(kernel);
free(im_smoothed);
free(laplacian);
return edges;
}
DateTime::DateTime (const char* date, const char* time)
: yOff(conv2d(date + 9)), m(0), d(conv2d(date + 4)), hh(conv2d(time)), mm(conv2d(time + 3)), ss(conv2d(time + 6))
{
// Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
switch (date[0]) {
case 'J': m = (date[1] == 'a') ? 1 : (date[2] == 'n') ? 6 : 7; break;
case 'F': m = 2; break;
case 'A': m = date[2] == 'r' ? 4 : 8; break;
case 'M': m = date[2] == 'r' ? 3 : 5; break;
case 'S': m = 9; break;
case 'O': m = 10; break;
case 'N': m = 11; break;
case 'D': m = 12; break;
}
}
DateTime::DateTime (const char* date, const char* time) {
yOff = conv2d(date + 9);
switch(date[0]) {
case 'J': m = date[1] == 'a' ? 1 : m = date[2] == 'n' ? 6 : 7; break;
case 'F': m = 2; break;
case 'A': m = date[2] == 'r' ? 4 : 8; break;
case 'M': m = date[2] == 'r' ? 3 : 5; break;
case 'S': m = 9; break;
case 'O': m = 10; break;
case 'N': m = 11; break;
case 'D': m = 12; break;
}
d=conv2d(date+4);
hh=conv2d(time);
mm=conv2d(time+3);
ss=conv2d(time+6);
}
// 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;
}
/**
* Geklaut von Jee-Labs.
* A convenient constructor for using "the compiler's time":
* DateTime now (__DATE__, __TIME__);
* NOTE: using PSTR would further reduce the RAM footprint
*/
void MyRTC::set(const char* date, const char* time) {
// sample input: date = "Dec 26 2009", time = "12:34:56"
_year = conv2d(date + 9);
// Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
switch (date[0]) {
case 'J':
if (date[1] == 'a') {
_month = 1;
} else if (date[2] == 'n') {
_month = 6;
} else {
_month = 7;
}
break;
case 'F':
_month = 2;
break;
case 'A':
_month = date[2] == 'r' ? 4 : 8;
break;
case 'M':
_month = date[2] == 'r' ? 3 : 5;
break;
case 'S':
_month = 9;
break;
case 'O':
_month = 10;
break;
case 'N':
_month = 11;
break;
case 'D':
_month = 12;
break;
}
_date = conv2d(date + 4);
_hours = conv2d(time);
_minutes = conv2d(time + 3);
_seconds = conv2d(time + 6);
}
// A convenient constructor for using "the compiler's time":
// DateTime now (__DATE__, __TIME__);
// NOTE: using PSTR would further reduce the RAM footprint
DateTime::DateTime(
const char *date,
const char *time,
uint32_t centisecond)
: centisecond_(centisecond) {
// sample input: date = "Dec 26 2009", time = "12:34:56"
year_ = conv2d(date + 9);
// Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
switch (date[0]) {
case 'J':
month_ = date[1] == 'a' ? 1 : (month_ = date[2] == 'n' ? 6 : 7);
break;
case 'F':
month_ = 2;
break;
case 'A':
month_ = date[2] == 'r' ? 4 : 8;
break;
case 'M':
month_ = date[2] == 'r' ? 3 : 5;
break;
case 'S':
month_ = 9;
break;
case 'O':
month_ = 10;
break;
case 'N':
month_ = 11;
break;
case 'D':
month_ = 12;
break;
default:
break;
}
day_ = conv2d(date + 4);
hour_ = conv2d(time);
minute_ = conv2d(time + 3);
second_ = conv2d(time + 6);
}
// Based on https://github.com/adafruit/RTClib/blob/master/RTClib.cpp
void RTCDue::setDate (const char* date)
{
_day = conv2d(date + 4);
//Month
switch (date[0]) {
case 'J': _month = date[1] == 'a' ? 1 : _month = date[2] == 'n' ? 6 : 7; break;
case 'F': _month = 2; break;
case 'A': _month = date[2] == 'r' ? 4 : 8; break;
case 'M': _month = date[2] == 'r' ? 3 : 5; break;
case 'S': _month = 9; break;
case 'O': _month = 10; break;
case 'N': _month = 11; break;
case 'D': _month = 12; break;
}
_year = conv2d(date + 7);
_day_of_week = calculateDayofWeek(_year, _month, _day);
RTC_SetDate (RTC, (uint16_t)_year, (uint8_t)_month, (uint8_t)_day, (uint8_t)_day_of_week);
}
// A convenient constructor for using "the compiler's time":
// This version will save RAM by using PROGMEM to store it by using the F macro.
// DateTime now (F(__DATE__), F(__TIME__));
DateTime::DateTime (const __FlashStringHelper* date, const __FlashStringHelper* time) {
// sample input: date = "Dec 26 2009", time = "12:34:56"
char buff[11];
memcpy_P(buff, date, 11);
yOff = conv2d(buff + 9);
// Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
switch (buff[0]) {
case 'J': m = buff[1] == 'a' ? 1 : m = buff[2] == 'n' ? 6 : 7; break;
case 'F': m = 2; break;
case 'A': m = buff[2] == 'r' ? 4 : 8; break;
case 'M': m = buff[2] == 'r' ? 3 : 5; break;
case 'S': m = 9; break;
case 'O': m = 10; break;
case 'N': m = 11; break;
case 'D': m = 12; break;
}
d = conv2d(buff + 4);
memcpy_P(buff, time, 8);
hh = conv2d(buff);
mm = conv2d(buff + 3);
ss = conv2d(buff + 6);
}
std::vector < Derived > lidarBoostEngine::lk_optical_flow( const MatrixBase<Derived>& I1, const MatrixBase<Derived> &I2, int win_sz)
{
//Instantiate optical flow matrices
std::vector < Derived > uv(2);
//Create masks
Matrix2d robX, robY, robT;
robX << -1, 1,
-1, 1;
robY << -1, -1,
1, 1;
robT << -1, -1,
-1, -1;
Derived Ix, Iy, It, A, solutions, x_block, y_block, t_block;
//Apply masks to images and average the result
Ix = 0.5 * ( conv2d( I1, robX ) + conv2d( I2, robX ) );
Iy = 0.5 * ( conv2d( I1, robY ) + conv2d( I2, robY ) );
It = 0.5 * ( conv2d( I1, robT ) + conv2d( I2, robT ) );
uv[0] = Derived::Zero( I1.rows(), I1.cols() );
uv[1] = Derived::Zero( I1.rows(), I1.cols() );
int hw = win_sz/2;
for( int i = hw+1; i < I1.rows()-hw; i++ )
{
for ( int j = hw+1; j < I1.cols()-hw; j++ )
{
//Take a small block of window size in the filtered images
x_block = Ix.block( i-hw, j-hw, win_sz, win_sz);
y_block = Iy.block( i-hw, j-hw, win_sz, win_sz);
t_block = It.block( i-hw, j-hw, win_sz, win_sz);
//Convert these blocks in vectors
Map<Derived> A1( x_block.data(), win_sz*win_sz, 1);
Map<Derived> A2( y_block.data(), win_sz*win_sz, 1);
Map<Derived> B( t_block.data(), win_sz*win_sz, 1);
//Organize the vectors in a matrix
A = Derived( win_sz*win_sz, 2 );
A.block(0, 0, win_sz*win_sz, 1) = A1;
A.block(0, 1, win_sz*win_sz, 1) = A2;
//Solve the linear least square system
solutions = (A.transpose() * A).ldlt().solve(A.transpose() * B);
//Insert the solutions in the optical flow matrices
uv[0](i, j) = solutions(0);
uv[1](i, j) = solutions(1);
}
}
return uv;
}
/*
* === FUNCTION ======================================================================
* Name: ConvPoolLayer::conv2d
* Description: This function is used for multiple input 2d-image-convolution with multiple kernels, in 'valid' mode
* i.e. to colvolve each input image with each kernel
* Arguments: maps_input: input images to be convolved
* kernels: convolution kernels
* maps_conved: convolved maps
* =====================================================================================
*/
void ConvPoolLayer::conv2d(const vector<MatrixXd>& maps_input, const vector<MatrixXd> kernels, vector<MatrixXd>& maps_conved)
{
maps_conved.clear();
maps_conved.reserve(kernels.size() * maps_input.size()); // number of output maps is equal to the number of input maps times the number of kernels
MatrixXd map_conved_temp; // map_conved_temp is a temporary convolved map to store one single convolution operation
for(int i = 0; i < kernels.size(); i++)
{
for(int j = 0; j < maps_input.size(); j++)
{
conv2d(maps_input[i], kernels[j], map_conved_temp);
maps_conved.push_back(map_conved_temp);
}
}
} /* ----- end of function ConvPoolLayer::conv2d ----- */
// Marr-Hildreth edge detector with Laplacian of Gaussian kernel
// inputs:
// float *input - pointer to input image
// int w, int h - width and height of input image
// float sigma - gaussian standard deviation
// int n - kernel size
// float tzc - threshold in zero-crossing
// int padding_method - padding method for convolution
// output:
// float * - pointer to output image
float *edges_mh_log(float *im, int w, int h,
float sigma, int n, float tzc, int padding_method) {
// compute Laplacian using a LoG (Laplacian of Gaussian) kernel
float *kernel = LoG_kernel(n,sigma);
float *laplacian = conv2d(im, w, h, kernel, n, padding_method);
// compute maximum of absolute Laplacian
float max = 0.0;
for(int i=0; i<w; i++) {
for(int j=0; j<h; j++) {
// laplacian is (w+n-1)x(h+n-1) with an offset of (n-1)/2 in x and y
float v = abs( laplacian[(i+(n-1)/2) + (j+(n-1)/2)*(w+n-1)] );
if( v > max ) max = v;
}
}
// compute laplacian zero-crossings
float *edges = xmalloc(w*h*sizeof(float));
for(int i=0; i<w; i++) {
for(int j=0; j<h; j++) {
// laplacian is (w+n-1)x(h+n-1) with an offset of (n-1)/2 in x and y
float UP_LE = laplacian[ (i-1+(n-1)/2) + (j+1+(n-1)/2)*(w+n-1) ];
float UP = laplacian[ (i +(n-1)/2) + (j+1+(n-1)/2)*(w+n-1) ];
float UP_RI = laplacian[ (i+1+(n-1)/2) + (j+1+(n-1)/2)*(w+n-1) ];
float LE = laplacian[ (i-1+(n-1)/2) + (j +(n-1)/2)*(w+n-1) ];
float RI = laplacian[ (i+1+(n-1)/2) + (j +(n-1)/2)*(w+n-1) ];
float DO_LE = laplacian[ (i-1+(n-1)/2) + (j-1+(n-1)/2)*(w+n-1) ];
float DO = laplacian[ (i +(n-1)/2) + (j-1+(n-1)/2)*(w+n-1) ];
float DO_RI = laplacian[ (i+1+(n-1)/2) + (j-1+(n-1)/2)*(w+n-1) ];
if( (LE*RI < 0.0 && abs(LE-RI) > tzc*max) ||
(UP_LE*DO_RI < 0.0 && abs(UP_LE-DO_RI) > tzc*max) ||
(DO_LE*UP_RI < 0.0 && abs(DO_LE-UP_RI) > tzc*max) ||
(UP*DO < 0.0 && abs(UP-DO) > tzc*max) ) {
edges[i+j*w] = 255.0;
} else {
edges[i+j*w] = 0.0;
}
}
}
// free memory
free(kernel);
free(laplacian);
return edges;
}
void ConvPoolLayer::bp_weight(const vector<MatrixXd>& grad_conved, const vector<MatrixXd>& maps_input, const int& weight_size, vector<MatrixXd>& grad_weight_single)
{
grad_weight_single.clear();
MatrixXd grad_weight_temp(weight_size, weight_size);
MatrixXd grad_weight_partial;
int num_input = maps_input.size();
int num_kernel = grad_conved.size() / num_input;
grad_weight_single.reserve(num_kernel);
for(int i = 0; i < num_kernel; i++)
{
grad_weight_partial.setZero(weight_size, weight_size);
for(int j = 0; j < num_input; j++)
{
int index = i * num_input + j;
conv2d(maps_input[j], grad_conved[index], grad_weight_temp);
grad_weight_partial += grad_weight_temp;
}
grad_weight_single.push_back(grad_weight_partial);
}
}
void RTC_set_default_time_to_compiled(void) {
uint8_t month_as_number;
switch (__DATE__[0]) {
case 'J': month_as_number = __DATE__[1] == 'a' ? 1 : __DATE__[2] == 'n' ? 6 : 7; break;
case 'F': month_as_number = 2; break;
case 'A': month_as_number = __DATE__[2] == 'r' ? 4 : 8; break;
case 'M': month_as_number = __DATE__[2] == 'r' ? 3 : 5; break;
case 'S': month_as_number = 9; break;
case 'O': month_as_number = 10; break;
case 'N': month_as_number = 11; break;
case 'D': month_as_number = 12; break;
}
RTC_time_SetTime(
conv2d(__DATE__ + 7)*100 + conv2d(__DATE__ + 9),
month_as_number,
conv2d(__DATE__ + 4),
conv2d(__TIME__),
conv2d(__TIME__ + 3),
conv2d(__TIME__ + 6),
0);
}
int main (int argc, char * argv[])
{
APPROX int * frame;
APPROX int * output;
int i;
int nFilterRowsFD = 9;
int nFilterColsFD = 9;
APPROX fltPixel_t FD[] = {
1, 3, 4, 5, 6, 5, 4, 3, 1,
3, 9, 12, 15, 18, 15, 12, 9, 3,
4, 12, 16, 20, 24, 20, 16, 12, 4,
5, 15, 20, 25, 30, 25, 20, 15, 5,
6, 18, 24, 30, 36, 30, 24, 18, 6,
5, 15, 20, 25, 30, 25, 20, 15, 5,
4, 12, 16, 20, 24, 20, 16, 12, 4,
3, 9, 12, 15, 18, 15, 12, 9, 3,
1, 3, 4, 5, 6, 5, 4, 3, 1
};
for (i = 0; i < nFilterRowsFD * nFilterColsFD; i++) // ACCEPT_FORBID
{
FD[i] /= (1024.0);
}
srand (time (NULL));
STATS_INIT ();
PRINT_STAT_STRING ("kernel", "2d_convolution");
PRINT_STAT_INT ("rows", N);
PRINT_STAT_INT ("columns", M);
PRINT_STAT_INT ("num_frames", BATCH_SIZE);
frame = calloc (M * N * BATCH_SIZE, sizeof(algPixel_t));
output = calloc (M * N * BATCH_SIZE, sizeof(algPixel_t));
if (!frame || !output) {
fprintf(stderr, "ERROR: Allocation failed.\n");
exit(-1);
}
/* load image */
tic ();
read_array_from_octave (ENDORSE(frame), N, M, FILENAME);
PRINT_STAT_DOUBLE ("time_load_image", toc ());
/* Make BATCH_SIZE-1 copies */
tic ();
for (i = 1; i < BATCH_SIZE; i++) // ACCEPT_FORBID
{
memcpy (&frame[i * M * N], frame, M * N * sizeof(algPixel_t));
}
PRINT_STAT_DOUBLE ("time_copy", toc ());
/* Perform the 2D convolution */
tic ();
accept_roi_begin();
for (i = 0; i < BATCH_SIZE; i++) // ACCEPT_FORBID
{
conv2d (&frame[i * M * N], &output[i * M * N], N, M, FD, 1.0, nFilterRowsFD, nFilterColsFD);
}
accept_roi_end();
PRINT_STAT_DOUBLE ("time_2d_convolution", toc ());
/* Write the results out to disk */
for (i = 0; i < BATCH_SIZE; i++) // ACCEPT_FORBID
{
char buffer [30];
sprintf (buffer, "2dconv_output.%d.mat", i);
write_array_to_octave (ENDORSE(&output[i * M * N]), N, M, buffer, "output_" SIZE);
}
PRINT_STAT_STRING ("output_file", "2dconv_output." SIZE ".#.mat");
STATS_END ();
free (output);
free (frame);
return 0;
}
static uint8_t conv2d(const char* p) {
uint8_t v = 0;
if ('0' <= *p && *p <= '9')
v = *p - '0';
return 10 * v + *++p - '0';
}
Adafruit_ILI9341_AS tft = Adafruit_ILI9341_AS(__cs, __dc, __rst); // Invoke custom library
float sx = 0, sy = 1, mx = 1, my = 0, hx = -1, hy = 0; // Saved H, M, S x & y multipliers
float sdeg=0, mdeg=0, hdeg=0;
uint16_t osx=120, osy=120, omx=120, omy=120, ohx=120, ohy=120; // Saved H, M, S x & y coords
uint16_t x0=0, x1=0, y0=0, y1=0;
uint32_t targetTime = 0; // for next 1 second timeout
uint8_t hh=conv2d(__TIME__), mm=conv2d(__TIME__+3), ss=conv2d(__TIME__+6); // Get H, M, S from compile time
boolean initial = 1;
void loop() {
// if (targetTime < millis()) {
// targetTime = millis()+1000;
_delay_ms(1000);
ss++; // Advance second
if (ss==60) {
ss=0;
mm++; // Advance minute
if(mm>59) {
mm=0;
hh++; // Advance hour
if (hh>23) {
hh=0;
int main(int argc, char *argv[]) {
if (argc != 6) {
printf("Usage: %s input_image_1 sigma n tzc output\n", argv[0]);
} else {
// Execution time:
double start = (double)clock();
// Parameters
float sigma = atof(argv[2]);
// <sigma> is the standard deviation of the gaussian function used to
// create the kernel.
int n = atoi(argv[3]);
// <n> is the size of the kernel (n*n).
float tzc = atof(argv[4]);
// <tzc> is the threshold of the zero-crossing method.
// Load input image (using iio)
int w, h, pixeldim;
float *im_orig = iio_read_image_float_vec(argv[1], &w, &h, &pixeldim);
fprintf(stderr, "Input image loaded:\t %dx%d image with %d channel(s).\n", w, h, pixeldim);
// Grayscale conversion (if necessary)
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");
}
// Generate gaussian kernel
// see gaussian_kernel.c
double *kernel = gaussian_kernel(n,sigma);
// Debug: save kernel to image (this is not part of the algorithm itself)
if (SAVE_KERNEL){
float *kernel_float = malloc(n*n*sizeof(float));
if (kernel_float == NULL){
fprintf(stderr, "Out of memory...\n");
exit(EXIT_FAILURE);
}
int i;
int imax = n*n;
for (i=0;i<imax;i++){
kernel_float[i] = 5000*(float)kernel[i];
}
iio_save_image_float_vec("kernel.png", kernel_float, n, n, 1);
free(kernel_float);
fprintf(stderr, "kernel saved to kernel.png\n");
}
// end of save kernel image
// Smooth input image with gaussian kernel
// see 2dconvolution.c
// <im_smoothed> is calculated convolving the grayscale image <im> with
// the gaussian kernel previously generated.
double *im_smoothed = conv2d(im, w, h, kernel, n);
// Debug: save smoothed image (this is not part of the algorithm itself)
if (SAVE_SMOOTHED_IMAGE){
float *smoothed = malloc((w+n-1)*(h+n-1)*sizeof(float));
if (smoothed == NULL){
fprintf(stderr, "Out of memory...\n");
exit(EXIT_FAILURE);
}
int i,j, fila, col;
int imax = w*h;
int dif_fila_col = (n-1)/2;
for (i=0;i<imax;i++){
fila = (int)(i/w);
col = i - w*fila + dif_fila_col;
fila += dif_fila_col;
j = col + (w+n-1)*fila;
smoothed[i] = (float)im_smoothed[j];
}
iio_save_image_float_vec("smoothed.png", smoothed, w, h, 1);
free(smoothed);
fprintf(stderr, "smoothed image saved to smoothed.png\n");
}
// end of save smoothed image
// Laplacian of the smoothed image
double operator[9] = {1, 1, 1, 1, -8, 1, 1, 1, 1};
// an approximation of the laplacian operator:
// / 1 1 1 \
// | 1 -8 1 |
// \ 1 1 1 /
// now we convolve the smoothed image with this operator,
// generating the <laplacian> image.
double *laplacian = conv2d(im_smoothed, w+n-1, h+n-1, operator, 3);
// calculate max absolute value of laplacian:
// required for thresholding in zero-crossing (next)
double max_l = 0;
int p;
int pmax = (w+n+1)*(h+n+1);
for (p=0;p<pmax;p++){
if (abs(laplacian[p])>max_l){
max_l = abs(laplacian[p]);
}
}
// Debug: save laplacian image (this is not part of the algorithm itself)
if (SAVE_LAPLACIAN_IMAGE){
float *lapl = malloc((w+n+1)*(h+n+1)*sizeof(float));
if (lapl == NULL){
fprintf(stderr, "Out of memory...\n");
exit(EXIT_FAILURE);
}
int i,j, fila, col;
int imax = w*h;
int dif_fila_col = (n+1)/2;
for (i=0;i<imax;i++){
fila = (int)(i/w);
col = i - w*fila + dif_fila_col;
fila += dif_fila_col;
j = col + (w+n+1)*fila;
lapl[i] = (float)laplacian[j];
}
iio_save_image_float_vec("laplacian.png", lapl, w, h, 1);
free(lapl);
fprintf(stderr, "laplacian image saved to laplacian.png\n");
}
// end of save laplacian image
// Zero-crossing
float *zero_cross = calloc(w*h,sizeof(float)); // this image will only content values 0 and 255
// but we use float for saving with iio.
if (zero_cross == NULL){
fprintf(stderr, "Out of memory...\n");
exit(EXIT_FAILURE);
}
int ind_en_lapl, fila, col;
int *offsets = get_neighbors_offset(w+n+1, 3);
pmax = w*h;
int dif_fila_col = (n+1)/2;
for (p=0;p<pmax;p++){
fila = ((int)(p/w));
col = p-(w*fila) + dif_fila_col;
fila += dif_fila_col;
ind_en_lapl = col + (w+n+1)*fila;
double *n3 = get_neighborhood(laplacian, ind_en_lapl, 3, offsets);
if ((n3[3]*n3[5]<0)&&(abs(n3[3]-n3[5])>(tzc*max_l))) {
// horizontal sign change
zero_cross[p] = 255;
} else if ((n3[1]*n3[7]<0)&&(abs(n3[1]-n3[7])>(tzc*max_l))) {
// vertical sign change
zero_cross[p] = 255;
} else if ((n3[2]*n3[6]<0)&&(abs(n3[2]-n3[6])>(tzc*max_l))) {
// +45deg sign change
zero_cross[p] = 255;
} else if ((n3[0]*n3[8]<0)&&(abs(n3[0]-n3[8])>(tzc*max_l))) {
// -45deg sign change
zero_cross[p] = 255;
}
free_neighborhood(n3);
}
free_neighbors_offsets(offsets);
// Save output image
iio_save_image_float_vec(argv[5], zero_cross, w, h, 1);
fprintf(stderr, "Output Image saved in %s:\t %dx%d image with %d channel(s).\n", argv[5], w, h, pixeldim);
// Free memory
free(zero_cross);
free(im_orig);
free(im);
free(im_smoothed);
free(laplacian);
free_gaussian_kernel(kernel);
fprintf(stderr, "marr-hildreth edge detector 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)
}
bool SerialLineIn::dispatch(void)
{
bool handled = false;
DateTime now = RTC.nowDateTime();
switch (toupper(*buf))
{
case 'D':
// Dyymmdd: Set date
handled = true;
if ( strlen(buf) == 1 )
{
print_time();
break;
}
if ( strlen(buf) != 7 )
{
printf_P(PSTR("SERL Format: Dyymmdd\n\r"));
break;
}
now = DateTime(2000+conv2d(buf+1),conv2d(buf+3),conv2d(buf+5),now.hour(),now.minute(),now.second());
RTC.adjust(now);
print_time();
break;
case 'T':
// Thhmmss: Set time
handled = true;
if ( strlen(buf) != 7 )
{
printf_P(PSTR("SERL Format: Dyymmdd\n\r"));
break;
}
now = DateTime(now.year(),now.month(),now.day(),conv2d(buf+1),conv2d(buf+3),conv2d(buf+5));
RTC.adjust(now);
print_time();
break;
case '@':
handled = true;
// Special time values
if ( !strcmp(buf+1,"N") )
{
uint32_t when = events.whenNext();
#ifdef HAVE_FIRE_CAMERA
if ( fire_camera.is_valid() )
when = fire_camera.whenNext();
#endif
RTC.adjust(when);
print_time();
}
else if ( !strcmp(buf+1,"1") )
{
events.reset();
#ifdef HAVE_FIRE_CAMERA
fire_camera.invalidate();
#endif
RTC.adjust(events.whenNext());
print_time();
}
else if ( !strcmp(buf+1,"0") )
{
RTC.adjust(DateTime(2011,1,1,0,0,0).unixtime());
print_time();
}
else
printf_P(PSTR("SERL Error: Unknown @ value: %s"),buf+1);
break;
case 'E':
// E: Print EEPROM
handled = true;
logger.play();
break;
case 'F':
// F: Free memory
handled = true;
printf_P(PSTR("FREE %u\n\r"),freeMemory());
break;
}
return handled;
}
/*
* === FUNCTION ======================================================================
* Name: ConvPoolLayer::flip_conv2d
* Description: This function is used for single input 2d-image-convolution with a single kernels, in 'valid' mode. Strictly this is convolution
* Arguments: map_input: input image to be convolved
* kernel: convolution kernel
* map_conved: convolved map
* =====================================================================================
*/
void ConvPoolLayer::flip_conv2d(const MatrixXd& map_input, const MatrixXd& kernel, MatrixXd& map_conved)
{
map_conved.resize(map_input.rows() + 1 - kernel.rows(), map_input.cols() + 1 - kernel.cols());
conv2d(map_input, kernel.reverse(), map_conved);
} /* ----- end of function ConvPoolLayer::flip_conv2d ----- */
// 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
// Haralick edge detector
// inputs:
// float *input - pointer to input image
// int w, int h - width and height of input image
// float rhozero - threshold
// int padding_method - padding method for convolution
// output:
// float * - pointer to output image
float *edges_haralick(float *im, int w, int h,
float rhozero, int padding_method) {
// Haralick's masks for computing k1 to k10
// New masks calculated by 2-d fitting using LS, with the function
// f(x,y) = k1 + k2*x + k3*y + k4*x^2 + k5*xy
// + k6*y^2 + k7*x^3 + k8*x^2y + k9*xy^2 + k10*y^3
float mask[10][25] = { { 425, 275, 225, 275, 425,
275, 125, 75, 125, 275,
225, 75, 25, 75, 225,
275, 125, 75, 125, 275,
425, 275, 225, 275, 425},
{ -2260, -620, 0, 620, 2260,
-1660, -320, 0, 320, 1660,
-1460, -220, 0, 220, 1460,
-1660, -320, 0, 320, 1660,
-2260, -620, 0, 620, 2260},
{ 2260, 1660, 1460, 1660, 2260,
620, 320, 220, 320, 620,
0, 0, 0, 0, 0,
-620, -320, -220, -320, -620,
-2260, -1660,-1460,-1660,-2260},
{ 1130, 620, 450, 620, 1130,
830, 320, 150, 320, 830,
730, 220, 50, 220, 730,
830, 320, 150, 320, 830,
1130, 620, 450, 620, 1130},
{ -400, -200, 0, 200, 400,
-200, -100, 0, 100, 200,
0, 0, 0, 0, 0,
200, 100, 0, -100, -200,
400, 200, 0, -200, -400},
{ 1130, 830, 730, 830, 1130,
620, 320, 220, 320, 620,
450, 150, 50, 150, 450,
620, 320, 220, 320, 620,
1130, 830, 730, 830, 1130},
{ -8260, -2180, 0, 2180, 8260,
-6220, -1160, 0, 1160, 6220,
-5540, -820, 0, 820, 5540,
-6220, -1160, 0, 1160, 6220,
-8260, -2180, 0, 2180, 8260},
{ 5640, 3600, 2920, 3600, 5640,
1800, 780, 440, 780, 1800,
0, 0, 0, 0, 0,
-1800, -780, -440, -780,-1800,
-5640, -3600,-2920,-3600,-5640},
{ -5640, -1800, 0, 1800, 5640,
-3600, -780, 0, 780, 3600,
-2920, -440, 0, 440, 2920,
-3600, -780, 0, 780, 3600,
-5640, -1800, 0, 1800, 5640},
{ 8260, 6220, 5540, 6220, 8260,
2180, 1160, 820, 1160, 2180,
0, 0, 0, 0, 0,
-2180, -1160, -820,-1160,-2180,
-8260, -6220, -5540,-6220,-8260 } };
// apply the masks operators, this will lead to coefficients k1 to k10
float *aux[10];
for(int i=0; i<10; i++) {
aux[i] = conv2d(im, w, h, mask[i], 5, padding_method);
}
float eps=1e-16;
// compute Haralick edges
float *edges = xmalloc(w*h*sizeof(float)); // get memory for output
for(int i=0; i<w; i++) {
for(int j=0; j<h; j++) {
// aux is (w+4)x(h+4) and there is an offset of (2,2)
// k1 is not used
float k2 = aux[1][i+2 + (j+2)*(w+4)];
float k3 = aux[2][i+2 + (j+2)*(w+4)];
float k4 = aux[3][i+2 + (j+2)*(w+4)];
float k5 = aux[4][i+2 + (j+2)*(w+4)];
float k6 = aux[5][i+2 + (j+2)*(w+4)];
float k7 = aux[6][i+2 + (j+2)*(w+4)];
float k8 = aux[7][i+2 + (j+2)*(w+4)];
float k9 = aux[8][i+2 + (j+2)*(w+4)];
float k10 = aux[9][i+2 + (j+2)*(w+4)];
float sintheta = -k2 / sqrt(k2*k2 + k3*k3+eps);
float costheta = -k3 / sqrt(k2*k2 + k3*k3+eps);
float C1 = k2 * sintheta + k3 * costheta;
float C2 = k4 * sintheta * sintheta
+ k5 * sintheta * costheta
+ k6 * costheta * costheta;
float C3 = k7 * sintheta * sintheta * sintheta
+ k8 * sintheta * sintheta * costheta
+ k9 * sintheta * costheta * costheta
+ k10 * costheta * costheta * costheta;
int cond1=fabs( C2 / (3.0 * C3+eps)) < rhozero;
int cond2=C3 < 0.0;
/* this condition is implicit, does not need to be checked, but we
do to provide extra safety.
*/
int cond3=C2 > 0.0;
//this one does not need to be imposed by construction, see paper.
//int cond4=fabs(C1-C2*C2/C3/3) > 0.0;
if( cond1 && cond2 && cond3) {
edges[i+j*w] = 255.0;
} else {
edges[i+j*w] = 0.0;
}
}
}
// free memory
for(int i=0; i<10; i++) {
free(aux[i]);
}
return edges;
}
