I am using MSP432P401R to do FFT of SAR ADC samples, did FFT in MATLAB and got results same as C compiler online but Code Composer Studio IDE is giving different output than MATLAB results, I thought that can be a compiler issue so tried reading same did some changes and tried but not getting results Like MATLAB.
Online C compiler was gcc 5.4.1 c99.
and in CCS TI v5.2.5 compiler is used.
float m;
float ur, ui, sr, si,tr, ti;
long double Temp_A[256],ArrayA[256]={2676,2840,2838,2832,2826,2818,2814,2808,
2804,2798,2790,2784,2778,2770,2764,2758,2752,2746,2740,2734,
2726,2720,2714,2706,2700,2692,2686,2680,2674,2668,2660,2654,
2646,2642,2634,2624,2618,2612,2604,2598,2590,2584,2576,2570,
2562,2556,2550,2542,2536,2530,2522,2512,2508,2498,2490,2484,
2478,2470,2462,2454,2448,2442,2432,2426,2420,2414,2404,2398,
2390,2382,2374,2368,2360,2352,2346,2338,2330,2322,2314,2306,
2300,2294,2286,2278,2272,2262,2258,2250,2238,2234,2228,2220,
2208,2202,2192,2186,2178,2170,2164,2156,2150,2142,2134,2126,
2116,2110,2104,2096,2088,2078,2070,2062,2054,2046,2040,2034,
2026,2018,2010,2002,1994,1986,1978,1970,1962,1954,1946,1936,
1930,1922,1914,1908,1902,1894,1886,1876,1868,1860,1852,1846,
1838,1830,1822,1814,1804,1796,1790,1784,1776,1768,1760,1754,
1746,1738,1728,1720,1714,1708,1698,1692,1684,1674,1668,1656,
1656,1644,1640,1628,1624,1612,1610,1598,1596,1584,1580,1570,
1564,1554,1546,1540,1532,1526,1520,1512,1504,1496,1490,1482,
1474,1468,1462,1454,1446,1438,1432,1424,1420,1410,1404,1398,
1392,1384,1376,1370,1364,1356,1348,1342,1336,1328,1322,1316,
1308,1300,1294,1286,1280,1276,1270,1262,1254,1248,1242,1236,
1230,1222,1216,1210,1206,1198,1192,1188,1178,1172,1168,1162,
1154,1148,1144,1138,1132,1126,1120,1114,1108,1102,1096,1090,
1084,1080,1074,1068,1062,1058,1052,1048},ArrayA_IMX[256]={0};
unsigned int jm1,i;
unsigned int ip,l;
void main(void)
{
WDT_A->CTL = WDT_A_CTL_PW |WDT_A_CTL_HOLD;
VCORE();
CLK();
P1DIR |= BIT5; //CLK--AD7352 OUTPUT DIRECTION
P1DIR |= BIT7; //CHIP SELECT--AD7352 OUTPUT DIRECTION
P5DIR &= ~BIT0; //SDATAA--AD7352 INPUT DIRECTION P5.0
P5DIR &= ~BIT2; //SDATAB--AD7352 INPUT DIRECTION P5.2
while(1)
{
bit_reversal(ArrayA);
fft(ArrayA,ArrayA_IMX);
}
}
void bit_reversal(long double REX[])
{
int i,i2,n,m;
int tx,k,j;
n = 1;
m=8;
for (i=0;i<m;i++)
{
n *= 2;
}
i2 = n >> 1;
j = 0;
for (i=0;i<n-1;i++)
{
if (i < j)
{
tx = REX[i];
//ty = IMX[i];
REX[i] = REX[j];
//IMX[i] = IMX[j];
REX[j] = tx;
//IMX[j] = ty;
}
k = i2;
while (k <= j)
{
j -= k;
k >>= 1;
}
j += k;
}
}
void fft(long double REX[],long double IMX[])
{
N = 256;
nm1 = N - 1;
nd2 = N / 2;
m = log10l(N) / log10l(2);
j = nd2;
for (l = 1; l <= m; l++)
{
le = powl(2, l);
le2 = le / 2;
ur = 1;
ui = 0;
// Calculate sine and cosine values
sr = cosl(M_PI/le2);
si = -sinl(M_PI/le2);
// Loop for each sub DFT
for (j = 1; j <= le2; j++)
{
jm1 = j - 1;
// Loop for each butterfly
for (i = jm1; i <= nm1; i += le)
{
ip = i + le2;
tr = REX[ip]*ur - IMX[ip]*ui;
ti = REX[ip]*ui + IMX[ip]*ur;
REX[ip] = REX[i] - tr;
IMX[ip] = IMX[i] - ti;
REX[i] = REX[i] + tr;
IMX[i] = IMX[i] + ti;
}
tr = ur;
ur = tr*sr - ui*si;
ui = tr*si + ui*sr;
}
}
}
Noob question. I am trying to write a for loop with a range. For example, this is what I want to produce in JavaScript:
var i, a, j, b, len = arr.length;
for (i = 0; i < len - 1; i++) {
a = arr[i];
for (j = i + 1; i < len; j++) {
b = arr[j];
doSomething(a, b);
}
}
The closest I've come so far is the following, but
It generates unnecessary and expensive slice calls
accesses the array length inside the inner loop
CoffeeScript:
for a, i in a[0...a.length-1]
for b, j in a[i+1...a.length]
doSomething a, b
Generated code:
var a, b, i, j, _i, _j, _len, _len1, _ref, _ref1;
_ref = a.slice(0, a.length - 1);
for (i = _i = 0, _len = _ref.length; _i < _len; i = ++_i) {
a = _ref[i];
_ref1 = a.slice(i + 1, a.length);
for (j = _j = 0, _len1 = _ref1.length; _j < _len1; j = ++_j) {
b = _ref1[j];
doSomething(a, b);
}
}
(How) can this be expressed in CoffeeScript?
Basically, transcribing your first JS code to CS:
len = arr.length
for i in [0...len - 1] by 1
a = arr[i]
for j in [i + 1...len] by 1
b = arr[j]
doSomething a, b
Seems like the only way to avoid the extra variables is with a while loop http://js2.coffee
i = 0
len = arr.length
while i < len - 1
a = arr[i]
j = i + 1
while j < len
b = arr[j]
doSomething a, b
j++
i++
or a bit less readable:
i = 0; len = arr.length - 1
while i < len
a = arr[i++]; j = i
while j <= len
doSomething a, arr[j++]
This is part of a code from spectral subtraction algorithm,i'm trying to optimize it for android.please help me.
this is the matlab code:
function Seg=segment(signal,W,SP,Window)
% SEGMENT chops a signal to overlapping windowed segments
% A= SEGMENT(X,W,SP,WIN) returns a matrix which its columns are segmented
% and windowed frames of the input one dimentional signal, X. W is the
% number of samples per window, default value W=256. SP is the shift
% percentage, default value SP=0.4. WIN is the window that is multiplied by
% each segment and its length should be W. the default window is hamming
% window.
% 06-Sep-04
% Esfandiar Zavarehei
if nargin<3
SP=.4;
end
if nargin<2
W=256;
end
if nargin<4
Window=hamming(W);
end
Window=Window(:); %make it a column vector
L=length(signal);
SP=fix(W.*SP);
N=fix((L-W)/SP +1); %number of segments
Index=(repmat(1:W,N,1)+repmat((0:(N-1))'*SP,1,W))';
hw=repmat(Window,1,N);
Seg=signal(Index).*hw;
and this is our java code for this function:
public class MatrixAndSegments
{
public int numberOfSegments;
public double[][] res;
public MatrixAndSegments(int numberOfSegments,double[][] res)
{
this.numberOfSegments = numberOfSegments;
this.res = res;
}
}
public MatrixAndSegments segment (double[] signal_in,int samplesPerWindow, double shiftPercentage, double[] window)
{
//default shiftPercentage = 0.4
//default samplesPerWindow = 256 //W
//default window = hanning
int L = signal_in.length;
shiftPercentage = fix(samplesPerWindow * shiftPercentage); //SP
int numberOfSegments = fix ( (L - samplesPerWindow)/ shiftPercentage + 1); //N
double[][] reprowMatrix = reprowtrans(samplesPerWindow,numberOfSegments);
double[][] repcolMatrix = repcoltrans(numberOfSegments, shiftPercentage,samplesPerWindow );
//Index=(repmat(1:W,N,1)+repmat((0:(N-1))'*SP,1,W))';
double[][] index = new double[samplesPerWindow+1][numberOfSegments+1];
for (int x = 1; x < samplesPerWindow+1; x++ )
{
for (int y = 1 ; y < numberOfSegments + 1; y++) //numberOfSegments was 3
{
index[x][y] = reprowMatrix[x][y] + repcolMatrix[x][y];
}
}
//hamming window
double[] hammingWindow = this.HammingWindow(samplesPerWindow);
double[][] HW = repvector(hammingWindow, numberOfSegments);
double[][] seg = new double[samplesPerWindow][numberOfSegments];
for (int y = 1 ; y < numberOfSegments + 1; y++)
{
for (int x = 1; x < samplesPerWindow+1; x++)
{
seg[x-1][y-1] = signal_in[ (int)index[x][y]-1 ] * HW[x-1][y-1];
}
}
MatrixAndSegments Matrixseg = new MatrixAndSegments(numberOfSegments,seg);
return Matrixseg;
}
public int fix(double val) {
if (val < 0) {
return (int) Math.ceil(val);
}
return (int) Math.floor(val);
}
public double[][] repvector(double[] vec, int replications)
{
double[][] result = new double[vec.length][replications];
for (int x = 0; x < vec.length; x++) {
for (int y = 0; y < replications; y++) {
result[x][y] = vec[x];
}
}
return result;
}
public double[][] reprowtrans(int end, int replications)
{
double[][] result = new double[end +1][replications+1];
for (int x = 1; x <= end; x++) {
for (int y = 1; y <= replications; y++) {
result[x][y] = x ;
}
}
return result;
}
public double[][] repcoltrans(int end, double multiplier, int replications)
{
double[][] result = new double[replications+1][end+1];
for (int x = 1; x <= replications; x++) {
for (int y = 1; y <= end ; y++) {
result[x][y] = (y-1)*multiplier;
}
}
return result;
}
public double[] HammingWindow(int size)
{
double[] window = new double[size];
for (int i = 0; i < size; i++)
{
window[i] = 0.54-0.46 * (Math.cos(2.0 * Math.PI * i / (size-1)));
}
return window;
}
"Porting" Matlab code statement by statement to Java is a bad approach.
Data is rarely manipulated in Matlab using loops and addressing individual elements (because the Matlab interpreter/VM is rather slow), but rather through calls to block processing functions (which have been carefully written and optimized). This leads to a very idiosyncratic programming style in which repmat, reshape, find, fancy indexing et al. are used to do operations which would be much more naturally expressed through Java loops.
For example, to multiply each column of a matrix A by a vector v, you will write in matlab:
A = diag(v) * A
or
A = repmat(v', 1, size(A, 2)) .* A
This solution:
for i = 1:size(A, 2),
A(:, i) = A(:, i) .* v';
end;
is inefficient.
But it would be terribly foolish to try to do the same thing in Java and invoke a matrix product or to build a matrix with repeated copies of v. Instead, just do:
for (int i = 0; i < rows; i++) {
for (int j = 0; j < columns; j++) {
a[i][j] *= v[i]
}
}
I suggest you to try to understand what this matlab function is actually doing, instead of focusing on how it is doing it, and reimplement it from scratch in Java, forgetting all the matlab implementation except the specifications given in the comments. Half of the code you have written is useless, indeed. Actually, it seems to me that this function wouldn't be needed at all, and what it does could be efficiently integrated in the caller's code.
I have an image of connected components(circles filled).If i want to segment them i can use watershed algorithm.I prefer writing my own function for watershed instead of using the inbuilt function in OPENCV.I have successfu How do i find the regionalmax of objects using opencv?
I wrote a function myself. My results were quite similar to MATLAB, although not exact. This function is implemented for CV_32F but it can easily be modified for other types.
I mark all the points that are not part of a minimum region by checking all the neighbors. The remaining regions are either minima, maxima or areas of inflection.
I use connected components to label each region.
I check each region for any point belonging to a maxima, if yes then I push that label into a vector.
Finally I sort the bad labels, erase all duplicates and then mark all the points in the output as not minima.
All that remains are the regions of minima.
Here is the code:
// output is a binary image
// 1: not a min region
// 0: part of a min region
// 2: not sure if min or not
// 3: uninitialized
void imregionalmin(cv::Mat& img, cv::Mat& out_img)
{
// pad the border of img with 1 and copy to img_pad
cv::Mat img_pad;
cv::copyMakeBorder(img, img_pad, 1, 1, 1, 1, IPL_BORDER_CONSTANT, 1);
// initialize binary output to 2, unknown if min
out_img = cv::Mat::ones(img.rows, img.cols, CV_8U)+2;
// initialize pointers to matrices
float* in = (float *)(img_pad.data);
uchar* out = (uchar *)(out_img.data);
// size of matrix
int in_size = img_pad.cols*img_pad.rows;
int out_size = img.cols*img.rows;
int x, y;
for (int i = 0; i < out_size; i++) {
// find x, y indexes
y = i % img.cols;
x = i / img.cols;
neighborCheck(in, out, i, x, y, img_pad.cols); // all regions are either min or max
}
cv::Mat label;
cv::connectedComponents(out_img, label);
int* lab = (int *)(label.data);
in = (float *)(img.data);
in_size = img.cols*img.rows;
std::vector<int> bad_labels;
for (int i = 0; i < out_size; i++) {
// find x, y indexes
y = i % img.cols;
x = i / img.cols;
if (lab[i] != 0) {
if (neighborCleanup(in, out, i, x, y, img.rows, img.cols) == 1) {
bad_labels.push_back(lab[i]);
}
}
}
std::sort(bad_labels.begin(), bad_labels.end());
bad_labels.erase(std::unique(bad_labels.begin(), bad_labels.end()), bad_labels.end());
for (int i = 0; i < out_size; ++i) {
if (lab[i] != 0) {
if (std::find(bad_labels.begin(), bad_labels.end(), lab[i]) != bad_labels.end()) {
out[i] = 0;
}
}
}
}
int inline neighborCleanup(float* in, uchar* out, int i, int x, int y, int x_lim, int y_lim)
{
int index;
for (int xx = x - 1; xx < x + 2; ++xx) {
for (int yy = y - 1; yy < y + 2; ++yy) {
if (((xx == x) && (yy==y)) || xx < 0 || yy < 0 || xx >= x_lim || yy >= y_lim)
continue;
index = xx*y_lim + yy;
if ((in[i] == in[index]) && (out[index] == 0))
return 1;
}
}
return 0;
}
void inline neighborCheck(float* in, uchar* out, int i, int x, int y, int x_lim)
{
int indexes[8], cur_index;
indexes[0] = x*x_lim + y;
indexes[1] = x*x_lim + y+1;
indexes[2] = x*x_lim + y+2;
indexes[3] = (x+1)*x_lim + y+2;
indexes[4] = (x + 2)*x_lim + y+2;
indexes[5] = (x + 2)*x_lim + y + 1;
indexes[6] = (x + 2)*x_lim + y;
indexes[7] = (x + 1)*x_lim + y;
cur_index = (x + 1)*x_lim + y+1;
for (int t = 0; t < 8; t++) {
if (in[indexes[t]] < in[cur_index]) {
out[i] = 0;
break;
}
}
if (out[i] == 3)
out[i] = 1;
}
The following listing is a function similar to Matlab's "imregionalmax". It looks for at most nLocMax local maxima above threshold, where the found local maxima are at least minDistBtwLocMax pixels apart. It returns the actual number of local maxima found. Notice that it uses OpenCV's minMaxLoc to find global maxima. It is "opencv-self-contained" except for the (easy to implement) function vdist, which computes the (euclidian) distance between points (r,c) and (row,col).
input is one-channel CV_32F matrix, and locations is nLocMax (rows) by 2 (columns) CV_32S matrix.
int imregionalmax(Mat input, int nLocMax, float threshold, float minDistBtwLocMax, Mat locations)
{
Mat scratch = input.clone();
int nFoundLocMax = 0;
for (int i = 0; i < nLocMax; i++) {
Point location;
double maxVal;
minMaxLoc(scratch, NULL, &maxVal, NULL, &location);
if (maxVal > threshold) {
nFoundLocMax += 1;
int row = location.y;
int col = location.x;
locations.at<int>(i,0) = row;
locations.at<int>(i,1) = col;
int r0 = (row-minDistBtwLocMax > -1 ? row-minDistBtwLocMax : 0);
int r1 = (row+minDistBtwLocMax < scratch.rows ? row+minDistBtwLocMax : scratch.rows-1);
int c0 = (col-minDistBtwLocMax > -1 ? col-minDistBtwLocMax : 0);
int c1 = (col+minDistBtwLocMax < scratch.cols ? col+minDistBtwLocMax : scratch.cols-1);
for (int r = r0; r <= r1; r++) {
for (int c = c0; c <= c1; c++) {
if (vdist(Point2DMake(r, c),Point2DMake(row, col)) <= minDistBtwLocMax) {
scratch.at<float>(r,c) = 0.0;
}
}
}
} else {
break;
}
}
return nFoundLocMax;
}
I do not know if it is what you want, but in my answer to this post, I gave some code to find local maxima (peaks) in a grayscale image (resulting from distance transform).
The approach relies on subtracting the original image from the dilated image and finding the zero pixels).
I hope it helps,
Good luck
I had the same problem some time ago, and the solution was to reimplement the imregionalmax algorithm in OpenCV/Cpp. It is not that complicated, because you can find the C++ source code of the function in the Matlab distribution. (somewhere in toolbox). All you have to do is to read carefully and understand the algorithm described there. Then rewrite it or remove the matlab-specific checks and you'll have it.