I'm convoluting an image (512*512) with a FFT filter (kernelsize=10), it looks good.
But when I compare it with an image which I convoluted the normal way the result was horrible.
The PSNR is about 35.
67,187/262,144 Pixel values have a difference of 1 or more(peak at ~8) (having a max pixel value of 255).
My question is, is it normal when convoluting in frequency space or might there be a problem with my convolution/transforming functions? . Because the strange thing is that I should get better results when using double as data-type. But it stays COMPLETELY the same.
When I transform an image into frequency space, DON'T convolute it, then transform it back it's fine and the PSNR is about 140 when using float.
Also, due to the pixel differences being only 1-10 I think I can rule out scaling errors
EDIT: More Details for bored interested people
I use the open source kissFFT library. With real 2dimensional input (kiss_fftndr.h)
My Image Datatype is PixelMatrix. Simply a matrix with alpha, red, green and blue values from 0.0 to 1.0 float
My kernel is also a PixelMatrix.
Here some snippets from the Convolution function
Used datatypes:
#define kiss_fft_scalar float
#define kiss_fft_cpx struct {
kiss_fft_scalar r;
kiss_fft_scalar i,
}
Configuration of the FFT:
//parameters to kiss_fftndr_alloc:
//1st param = array with the size of the 2 dimensions (in my case dim={width, height})
//2nd param = count of the dimensions (in my case 2)
//3rd param = 0 or 1 (forward or inverse FFT)
//4th and 5th params are not relevant
kiss_fftndr_cfg stf = kiss_fftndr_alloc(dim, 2, 0, 0, 0);
kiss_fftndr_cfg sti = kiss_fftndr_alloc(dim, 2, 1, 0, 0);
Padding and transforming the kernel:
I make a new array:
kiss_fft_scalar kernel[width*height];
I fill it with 0 in a loop.
Then I fill the middle of this array with the kernel I want to use.
So if I would use a 2*2 kernel with values 1/4, 1/4, 1/4 and 1/4 it would look like
0 0 0 0 0 0
0 1/4 1/4 0
0 1/4 1/4 0
0 0 0 0 0 0
The zeros are padded until they reach the size of the image.
Then I swap the quadrants of the image diagonally. It looks like:
1/4 0 0 1/4
0 0 0 0
0 0 0 0
1/4 0 0 1/4
now I transform it: kiss_fftndr(stf, floatKernel, outkernel);
outkernel is declarated as
kiss_fft_cpx outkernel= new kiss_fft_cpx[width*height]
Getting the colors into arrays:
kiss_fft_scalar *red = new kiss_fft_scalar[width*height];
kiss_fft_scalar *green = new kiss_fft_scalar[width*height];
kiss_fft-scalar *blue = new kiss_fft_scalar[width*height];
for(int i=0; i<height; i++) {
for(int j=0; i<width; j++) {
red[i*height+j] = input.get(j,i).getRed(); //input is the input image pixel matrix
green[i*height+j] = input.get(j,i).getGreen();
blue{i*height+j] = input.get(j,i).getBlue();
}
}
Then I transform the arrays:
kiss_fftndr(stf, red, outred);
kiss_fftndr(stf, green, outgreen);
kiss_fftndr(stf, blue, outblue); //the out-arrays are type kiss_fft_cpx*
The convolution:
What we have now:
3 transformed color arrays from type kiss_fft_cpx*
1 transformed kernel array from type kiss_fft_cpx*
They are both complex arrays
Now comes the convolution:
for(int m=0; m<til; m++) {
for(int n=0; n<til; n++) {
kiss_fft_scalar real = outcolor[m*til+n].r; //I do that for all 3 arrys in my code!
kiss_fft_scalar imag = outcolor[m*til+n].i; //so I have realred, realgreen, realblue
kiss_fft_scalar realMask = outkernel[m*til+n].r; // and imagred, imaggreen, etc.
kiss_fft_scalar imagMask = outkernel[m*til+n].i;
outcolor[m*til+n].r = real * realMask - imag * imagMask; //Same thing here in my code i
outcolor[m*til+n].i = real * imagMask + imag * realMask; //do it with all 3 colors
}
}
Now I transform them back:
kiss_fftndri(sti, outred, red);
kiss_fftndri(sti, outgreen, green);
kiss_fftndri(sti, outblue, blue);
and I create a new Pixel Matrix with the values from the color-arrays
PixelMatrix output;
for(int i=0; i<height; i++) {
for(int j=0; j<width; j++) {
Pixel p = new Pixel();
p.setRed( red[i*height+j] / (width*height) ); //I divide through (width*height) because of the scaling happening in the FFT;
p.setGreen( green[i*height+j] );
p.setBlue( blue[i*height+j] );
output.set(j , i , p);
}
}
Notes:
I already take care in advance that the image has a size with a power of 2 (256*256), (512*512) etc.
Examples:
kernelsize: 10
Input:
Output:
Output from normal convolution:
my console says :
142519 out of 262144 Pixels have a difference of 1 or more (maxRGB = 255)
PSNR: 32.006027221679688
MSE: 44.116752624511719
though for my eyes they look the same °.°
Maybe one person is bored and goes through the code. It's not urgent, but it's a kind of problem I just want to know what the hell I did wrong ^^
Last, but not least, my PSNR function, though I don't really think that's the problem :D
void calculateThePSNR(const PixelMatrix first, const PixelMatrix second, float* avgpsnr, float* avgmse) {
int height = first.getHeight();
int width = first.getWidth();
BMP firstOutput;
BMP secondOutput;
firstOutput.SetSize(width, height);
secondOutput.SetSize(width, height);
double rsum=0.0, gsum=0.0, bsum=0.0;
int count = 0;
int total = 0;
for(int i=0; i<height; i++) {
for(int j=0; j<width; j++) {
Pixel pixOne = first.get(j,i);
Pixel pixTwo = second.get(j,i);
double redOne = pixOne.getRed()*255;
double greenOne = pixOne.getGreen()*255;
double blueOne = pixOne.getBlue()*255;
double redTwo = pixTwo.getRed()*255;
double greenTwo = pixTwo.getGreen()*255;
double blueTwo = pixTwo.getBlue()*255;
firstOutput(j,i)->Red = redOne;
firstOutput(j,i)->Green = greenOne;
firstOutput(j,i)->Blue = blueOne;
secondOutput(j,i)->Red = redTwo;
secondOutput(j,i)->Green = greenTwo;
secondOutput(j,i)->Blue = blueTwo;
if((redOne-redTwo) > 1.0 || (redOne-redTwo) < -1.0) {
count++;
}
total++;
rsum += (redOne - redTwo) * (redOne - redTwo);
gsum += (greenOne - greenTwo) * (greenOne - greenTwo);
bsum += (blueOne - blueTwo) * (blueOne - blueTwo);
}
}
fprintf(stderr, "%d out of %d Pixels have a difference of 1 or more (maxRGB = 255)", count, total);
double rmse = rsum/(height*width);
double gmse = gsum/(height*width);
double bmse = bsum/(height*width);
double rpsnr = 20 * log10(255/sqrt(rmse));
double gpsnr = 20 * log10(255/sqrt(gmse));
double bpsnr = 20 * log10(255/sqrt(bmse));
firstOutput.WriteToFile("test.bmp");
secondOutput.WriteToFile("test2.bmp");
system("display test.bmp");
system("display test2.bmp");
*avgmse = (rmse + gmse + bmse)/3;
*avgpsnr = (rpsnr + gpsnr + bpsnr)/3;
}
Phonon had the right idea. Your images are shifted. If you shift your image by (1,1), then the MSE will be approximately zero (provided that you mask or crop the images accordingly). I confirmed this using the code (Python + OpenCV) below.
import cv
import sys
import math
def main():
fname1, fname2 = sys.argv[1:]
im1 = cv.LoadImage(fname1)
im2 = cv.LoadImage(fname2)
tmp = cv.CreateImage(cv.GetSize(im1), cv.IPL_DEPTH_8U, im1.nChannels)
cv.AbsDiff(im1, im2, tmp)
cv.Mul(tmp, tmp, tmp)
mse = cv.Avg(tmp)
print 'MSE:', mse
psnr = [ 10*math.log(255**2/m, 10) for m in mse[:-1] ]
print 'PSNR:', psnr
if __name__ == '__main__':
main()
Output:
MSE: (0.027584912741602553, 0.026742391458366047, 0.028147870144492403, 0.0)
PSNR: [63.724087463606452, 63.858801190963192, 63.636348220531396]
My advice for you to try to implement the following code :
A=double(inputS(1:10:length(inputS))); %segmentation
A(:)=-A(:);
%process the image or signal by fast fourior transformation and inverse fft
fresult=fft(inputS);
fresult(1:round(length(inputS)*2/fs))=0;
fresult(end-round(length(fresult)*2/fs):end)=0;
Y=real(ifft(fresult));
that's code help you to obtain the same size image and good for remove DC component ,the you can to convolution.
Related
Since the Corona situation characterizes my studies as self-study, as a Processing-Language newbie I don't have an easy time getting into the subject of image processing , more specifically convolution. Therefore I hope that you can help me.
My lecturer, who unfortunately is nearly never reachable, left me the following conv code. The theory behind convolution is clear to me, but I have many gaps in understanding related to the code. Could someone leave a line comment so that I can get into the code a bit more fluently?
The Code is following
color convolution (int x, int y, float[][] matrix, int matrix_size, PImage img){
float rtotal = 0.0;
float gtotal = 0.0;
float btotal = 0.0;
int offset = matrix_size / 2;
for (int i = 0; i < matrix_size; i++){
for (int j= 0; j < matrix_size; j++){
int xloc = x+i-offset;
int yloc = y+j-offset;
int loc = xloc + img.width*yloc;
rtotal += (red(img.pixels[loc]) * matrix[i][j]);
gtotal += (green(img.pixels[loc]) * matrix[i][j]);
btotal += (blue(img.pixels[loc]) * matrix[i][j]);
}
}
rtotal = constrain(rtotal, 0, 255);
gtotal = constrain(gtotal, 0, 255);
btotal = constrain(btotal, 0, 255);
return color(rtotal, gtotal, btotal);
}
I have to do a bit of guesswork since I'm not positive about all of the functions you're using and I'm not familiar with the Processing 3+ library, but here's my best shot at it.
color convolution (int x, int y, float[][] matrix, int matrix_size, PImage img){
// Note: the 'matrix' parameter here will also frequently be referred to as
// a 'window' or 'kernel' in research
// I'm not certain what your PImage class is from, but I'll assume
// you're using the Processing 3+ library and work off of that assumption
// how much of each color we see within the kernel (matrix) space
float rtotal = 0.0;
float gtotal = 0.0;
float btotal = 0.0;
// this offset is to zero-center our kernel
// the fact that we use matrix_size / 2 sort of implicitly
// alludes to the fact that our matrix_size should be an odd-number
// so that we can have a middle-pixel
int offset = matrix_size / 2;
// looping through the kernel. the fact that we use 'matrix_size'
// as our end-condition for both dimensions means that our 'matrix' kernel
// must always be a square
for (int i = 0; i < matrix_size; i++){
for (int j= 0; j < matrix_size; j++){
// calculating the index conversion from 2D to the 1D format that PImage uses
// refer to: https://processing.org/tutorials/pixels/
// for a better understanding of PImage indexing (about 1/3 of the way down the page)
// WARNING: by subtracting the offset it is possible to hit negative
// x,y values here if you pick an x or y position less than matrix_size / 2.
// the same index-out-of-bounds can occur on the high end.
// When you convolve using a kernel of N x N size (N here would be matrix_size)
// you can only convolve from [N / 2, Width - (N / 2)] for x and y
int xloc = x+i-offset;
int yloc = y+j-offset;
// this is the final 1D PImage index that corresponds to [xloc, yloc] in our 2D image
// really go back up and take a look at the link if this doesn't make sense, it's pretty good
int loc = xloc + img.width*yloc;
// I have to do some speculation again since I'm not certain what red(img.pixels[loc]) does
// I'll assume it returns the red red channel of the pixel
// this section just adds up all of the pixel colors multiplied by the value in the kernel
rtotal += (red(img.pixels[loc]) * matrix[i][j]);
gtotal += (green(img.pixels[loc]) * matrix[i][j]);
btotal += (blue(img.pixels[loc]) * matrix[i][j]);
}
}
// the fact that no further division or averaging happens after the for-loops implies
// that the kernel you feed in should have balanced values for your kernel size
// for example, a kernel that's designed to average out the color over the 3 x 3 area
// it covers (this would be like blurring the image) would be filled with 1/9
// in general: the kernel you're using should have a sum of 1 for all of the numbers inside
// this is just 'in general' you can play around with not doing that, but you'll probably notice a
// darkening effect for when the sum is less than 1, and a brightening effect if it's greater than 1
// for more info on kernels, read this: https://en.wikipedia.org/wiki/Kernel_(image_processing)
// I don't have the code for this constrain function,
// but it's almost certainly just your typical clamp (constrains the values to [0, 255])
// Note: this means that your values saturate at 0 and 255
// if you see a lot of black or white then that means your kernel
// probably isn't balanced as mentioned above
rtotal = constrain(rtotal, 0, 255);
gtotal = constrain(gtotal, 0, 255);
btotal = constrain(btotal, 0, 255);
// Finished!
return color(rtotal, gtotal, btotal);
}
I want to realize smth like tone curve.
I have predefined set of curves that I should apply to the image.
For instance:
as I understand on this chart we see dependences of current tone value to new, for example:
if we get first dot on the left - every r,g and b that = 0 will be converted to 64
or every value more than 224 will be converted to 0 and ect.
so I tried to change every pixel of image to new value
for test purpose i've simplified curve:
and here the code I have:
//init original image
cv::Mat originalMat = [self cvMatFromUIImage:inputImage];
//out image the same size
cv::Mat outMat = [self cvMatFromUIImage:inputImage];
//loop throw every row of image
for( int y = 0; y < originalMat.rows; y++ ){
//loop throw every column of image
for( int x = 0; x < originalMat.cols; x++ ){
//loop throw every color channel of image (R,G,B)
for( int c = 0; c < 3; c++ ){
if(originalMat.at<cv::Vec3b>(y,x)[c] <= 64)
outMat.at<cv::Vec3b>(y,x)[c] = 64 + ( originalMat.at<cv::Vec3b>(y,x)[c] ) -
( originalMat.at<cv::Vec3b>(y,x)[c] ) * 2 ;
if((originalMat.at<cv::Vec3b>(y,x)[c] > 64)&&(originalMat.at<cv::Vec3b>(y,x)[c] <= 128))
outMat.at<cv::Vec3b>(y,x)[c] = (( originalMat.at<cv::Vec3b>(y,x)[c] ) - 64 ) * 4
;
if((originalMat.at<cv::Vec3b>(y,x)[c] > 128))
outMat.at<cv::Vec3b>(y,x)[c] = ( originalMat.at<cv::Vec3b>(y,x)[c] ) + 128 -
(( originalMat.at<cv::Vec3b>(y,x)[c] ) - 128) * 3;
} //end of r,g,b loop
} //end of column loop
} //end of row loop
//send to output
return [self UIImageFromCVMat:outMat];
but here the result I get:
by some reason only 3/4 of image was processed
and it not matches with result i expected:
Update 0
thanks to #ACCurrent comment found errors in calculation(code and image updated), but still not understand why only 3/4 of images processed.
not sure that understand why 'noise' appears, hope it because of curve not smooth.
looks the way to avoid .at operation.
Update 1
original image:
You need to access the images with Vec4b
originalMat.type() is equals to 24
Your originalMat is of type 24, i.e. CV_8UC4. This means that the image has 4 channels, but you're accessing it with Vec3b as if it has only 3 channels. This explains why about 1/4 of the image is not modified.
So, simply replace every Vec3b in your code with Vec4b.
I want to implement stereo vision in a robot. I have calculated disparity map and point clouds. now I want to detect Dynamic Obstacles in scene.
Can anyone help me please?
Best Regards
Here is how I do that for 2d navigation.
First prepare two 2d elevation maps as 2d arrays. Set elements of one of the arrays to min height of points projected to the same cell of the 2d map, and set elements of the other array to max heights like this:
static const float c_neg_inf = -9999;
static const float c_inf = 9999;
int map_pixels_in_m_ = 40; //: for map cell size 2.5 x 2.5 cm
int map_width = 16 * map_pixels_in_m_;
int map_height = 16 * map_pixels_in_m_;
cv::Mat top_view_min_elevation(cv::Size(map_width, map_height), CV_32FC1, cv::Scalar(c_inf));
cv::Mat top_view_max_elevation(cv::Size(map_width, map_height), CV_32FC1, cv::Scalar(c_neg_inf));
//: prepare elevation maps:
for (int i = 0, v = 0; v < height; ++v) {
for (int u = 0; u < width; ++u, ++i) {
if (!pcl::isFinite(point_cloud_->points[i]))
continue;
pcl::Vector3fMap point_in_laser_frame = point_cloud_->points[i].getVector3fMap();
float z = point_in_laser_frame(2);
int map_x = map_width / 2 - point_in_laser_frame(1) * map_pixels_in_m_;
int map_y = map_height - point_in_laser_frame(0) * map_pixels_in_m_;
if (map_x >= 0 && map_x < map_width && map_y >= 0 && map_y < map_width) {
//: update elevation maps:
top_view_min_elevation.at<float>(map_x, map_y) = std::min(top_view_min_elevation.at<float>(map_x, map_y), z);
top_view_max_elevation.at<float>(map_x, map_y) = std::max(top_view_max_elevation.at<float>(map_x, map_y), z);
}
}
}
Then
//: merge values in neighboring pixels of the elevation maps:
top_view_min_elevation = cv::min(top_view_min_elevation, CvUtils::hscroll(top_view_min_elevation, -1, c_inf));
top_view_max_elevation = cv::max(top_view_max_elevation, CvUtils::hscroll(top_view_max_elevation, -1, c_neg_inf));
top_view_min_elevation = cv::min(top_view_min_elevation, CvUtils::hscroll(top_view_min_elevation, 1, c_inf));
top_view_max_elevation = cv::max(top_view_max_elevation, CvUtils::hscroll(top_view_max_elevation, 1, c_neg_inf));
top_view_min_elevation = cv::min(top_view_min_elevation, CvUtils::vscroll(top_view_min_elevation, -1, c_inf));
top_view_max_elevation = cv::max(top_view_max_elevation, CvUtils::vscroll(top_view_max_elevation, -1, c_neg_inf));
top_view_min_elevation = cv::min(top_view_min_elevation, CvUtils::vscroll(top_view_min_elevation, 1, c_inf));
top_view_max_elevation = cv::max(top_view_max_elevation, CvUtils::vscroll(top_view_max_elevation, 1, c_neg_inf));
Here CvUtils::hscroll and CvUtils::vscroll are functions that 'scroll' the content of a 2d array filling the elements on the edge that got no value in the scroll with the value of the third parameter.
Now you can make a difference between the arrays (taking care about elements with c_inf and c_neg_inf values) like this:
//: produce the top_view_elevation_diff_:
cv::Mat top_view_elevation_diff = top_view_max_elevation - top_view_min_elevation;
cv::threshold(top_view_elevation_diff, top_view_elevation_diff, c_inf, 0, cv::THRESH_TOZERO_INV);
Now all non-zero elements of top_view_elevation_diff are your potential obstacles. You can enumerate them and report 2d coordinates of those of them that are grater then some value as your 2d obstacles.
If you can wait till the middle of September, I'll put into a public repository full code of a ROS node that takes a depth image and depth camera info and generates a faked LaserScan message with measurements set to distance to found obstacles.
I need to resize an image using bilinear interpolation and create an image pyramid.I will detect corners at the different levels of the pyramid and scale the pixel co-ordinates so that they are relative to the dimensions of the largest image.
If a corner of an object is detected as a corner/keypoint/feature in all the levels,after scaling the corresponding pixel co-ordinates from the different levels so that they fall on the largest image, ideally I would like them to have the same value. Thus when resizing the images, I am trying to be as accurate as possible.
Let's assume I am resizing an image L_n_minus_1 to create a smaller image L_n. My scale factor is "ratio" (ratio>1).
*I cannot use any library.
I can resize using the pseudocode below (which is what I generally find when I search online for resizing algorithms.)
int offset = 0;
for (int i = 0; i < height_of_L_n; i++){
for (int j = 0; j < width_of_L_n; j++){
//********* This part will differ in the later version I provided below
//
int xSrcInt = (int)(ratio * j);
float xDiff = ratio * j - xSrcInt;
int ySrcInt = (int)(ratio * i);
float yDiff = ratio * i - ySrcInt;
// The above code will differ in the later version I provided below
index = (ySrcInt * width_of_L_n_minus_1 + xSrcInt);
//Get the 4 pixel values to interpolate
a = L_n_minus_1[index];
b = L_n_minus_1[index + 1];
c = L_n_minus_1[index + width_of_L_n_minus_1];
d = L_n_minus_1[index + width_of_L_n_minus_1 + 1];
//Calculate the co-efficients for interpolation
float c0 = (1 - x_diff)*(1 - y_diff);
float c1 = (x_diff)*(1 - y_diff);
float c2 = (y_diff)*(1 - x_diff);
float c3 = (x_diff*y_diff);
//half is added for rounding the pixel intensity.
int intensity = (a*c0) + (b*c1) + (c*c2) + (d*c3) + 0.5;
if (intensity > 255)
intensity = 255;
L_n[offset++] = intensity;
}
}
Or I could use this modified piece of code below :
int offset = 0;
for (int i = 0; i < height_of_L_n; i++){
for (int j = 0; j < width_of_L_n; j++){
// Here the code differs from the first piece of code
// Assume pixel centers start from (0.5,0.5). The top left pixel has co-ordinate (0.5,0.5)
// 0.5 is added to go to the co-ordinates where top left pixel has co-ordinate (0.5,0.5)
// 0.5 is subtracted to go to the generally used co-ordinates where top left pixel has co-ordinate (0,0)
// or in other words map the new co-ordinates to array indices
int xSrcInt = int((ratio * (j + 0.5)) - 0.5);
float xDiff = (ratio * (j + 0.5)) - 0.5 - xSrcInt;
int ySrcInt = int((ratio * (i + 0.5)) - 0.5);
float yDiff = (ratio * (i + 0.5)) - 0.5 - ySrcInt;
// Difference with previous code ends here
index = (ySrcInt * width_of_L_n_minus_1 + xSrcInt);
//Get the 4 pixel values to interpolate
a = L_n_minus_1[index];
b = L_n_minus_1[index + 1];
c = L_n_minus_1[index + width_of_L_n_minus_1];
d = L_n_minus_1[index + width_of_L_n_minus_1 + 1];
//Calculate the co-efficients for interpolation
float c0 = (1 - x_diff)*(1 - y_diff);
float c1 = (x_diff)*(1 - y_diff);
float c2 = (y_diff)*(1 - x_diff);
float c3 = (x_diff*y_diff);
//half is added for rounding the pixel intensity.
int intensity = (a*c0) + (b*c1) + (c*c2) + (d*c3) + 0.5;
if (intensity > 255)
intensity = 255;
L_n[offset++] = intensity;
}
}
The second piece of code was developed assuming pixel centers having co-ordinates like (0.5, 0.5) as they have in textures.
This way the top left pixel will have co-ordinate (0.5, 0.5).
Let us assume :
a 2 by 2 Destination Image is being resized from a 4 by 4 Source Image.
In the first piece of code, it is assumed that the first pixel has co-ordinates (0,0), thus for example my ratio is 2. Then
xSrcInt = (int)(0*2); // 0
ySrcInt = (int)(0*2); // 0
xDiff = (0*2) - 0; // 0
yDiff = (0*2) - 0; // 0
Thus effectively I will just be copying the first pixel value from the source, as c0 will be 1 and c1,c2 and c3 will be 0.
But in the second piece of code I will get
xSrcInt = (int)((0.5*2) - 0.5); // 0;
ySrcInt = (int)((0.5*2) - 0.5); // 0;
xDiff = ((0.5*2) - 0.5) - 0; // 0.5;
yDiff = ((0.5*2) - 0.5) - 0; // 0.5;
In this case c0,c1,c2 and c3 will all be equal to 0.25. Thus I will be using the 4 pixels at the top left.
Please let me know what do you think and if there is any bug in my second piece of code. As far as visual results go they are working perfectly.
But yes I do seem to notice better alignment of keypoints with the second piece of code. But may be that's because I am judging with prejudice :-).
Thanks in advance.
I need to calculate the standard deviation on an image I have inside a UIImage object.
I know already how to access all pixels of an image, one at a time, so somehow I can do it.
I'm wondering if there is somewhere in the framework a function to perform this in a better and more efficient way... I can't find it so maybe it doensn't exist.
Do anyone know how to do this?
bye
To further expand on my comment above. I would definitely look into using the Accelerate framework, especially depending on the size of your image. If you image is a few hundred pixels by a few hundred. You will have a ton of data to process and Accelerate along with vDSP will make all of that math a lot faster since it processes everything on the GPU. I will look into this a little more, and possibly put some code in a few minutes.
UPDATE
I will post some code to do standard deviation in a single dimension using vDSP, but this could definitely be extended to 2-D
float *imageR = [0.1,0.2,0.3,0.4,...]; // vector of values
int numValues = 100; // number of values in imageR
float mean = 0; // place holder for mean
vDSP_meanv(imageR,1,&mean,numValues); // find the mean of the vector
mean = -1*mean // Invert mean so when we add it is actually subtraction
float *subMeanVec = (float*)calloc(numValues,sizeof(float)); // placeholder vector
vDSP_vsadd(imageR,1,&mean,subMeanVec,1,numValues) // subtract mean from vector
free(imageR); // free memory
float *squared = (float*)calloc(numValues,sizeof(float)); // placeholder for squared vector
vDSP_vsq(subMeanVec,1,squared,1,numValues); // Square vector element by element
free(subMeanVec); // free some memory
float sum = 0; // place holder for sum
vDSP_sve(squared,1,&sum,numValues); sum entire vector
free(squared); // free squared vector
float stdDev = sqrt(sum/numValues); // calculated std deviation
Please explain your query so that can come up with specific reply.
If I am getting you right then you want to calculate standard deviation of RGB of pixel or HSV of color, you can frame your own method of standard deviation for circular quantities in case of HSV and RGB.
We can do this by wrapping the values.
For example: Average of [358, 2] degrees is (358+2)/2=180 degrees.
But this is not correct because its average or mean should be 0 degrees.
So we wrap 358 into -2.
Now the answer is 0.
So you have to apply wrapping and then you can calculate standard deviation from above link.
UPDATE:
Convert RGB to HSV
// r,g,b values are from 0 to 1 // h = [0,360], s = [0,1], v = [0,1]
// if s == 0, then h = -1 (undefined)
void RGBtoHSV( float r, float g, float b, float *h, float *s, float *v )
{
float min, max, delta;
min = MIN( r, MIN(g, b ));
max = MAX( r, MAX(g, b ));
*v = max;
delta = max - min;
if( max != 0 )
*s = delta / max;
else {
// r = g = b = 0
*s = 0;
*h = -1;
return;
}
if( r == max )
*h = ( g - b ) / delta;
else if( g == max )
*h=2+(b-r)/delta;
else
*h=4+(r-g)/delta;
*h *= 60;
if( *h < 0 )
*h += 360;
}
and then calculate standard deviation for hue value by this:
double calcStddev(ArrayList<Double> angles){
double sin = 0;
double cos = 0;
for(int i = 0; i < angles.size(); i++){
sin += Math.sin(angles.get(i) * (Math.PI/180.0));
cos += Math.cos(angles.get(i) * (Math.PI/180.0));
}
sin /= angles.size();
cos /= angles.size();
double stddev = Math.sqrt(-Math.log(sin*sin+cos*cos));
return stddev;
}