Is there any way to speed up this piece of code (simplified)? On my Dell m4700 laptop, it works for 1 minute and 10 seconds (the size of the canvas is 1000x1400 pixels).
pg = createGraphics(1000,1400);
pg.pixelDensity(1);
***
for(j=0;j<pg.height;j++){
for(i=0;i<pg.width;i++){
pg.stroke(cc=pg.get(i,j));
pg.point(i,j+4);
}
}
Without this line,
pg.stroke(cc=pg.get(i,j));
the code executes in milliseconds.
I made another version that works in 20 seconds, but for some reason the result is slightly different visually:
for(j=0;j<pg.height;j++){
pg.loadPixels();
for(i=0;i<pg.width;i++){
let pi = i + (j * pg.width);
let ri = pi * 4;
let cr = pg.pixels[ri];
let cg = pg.pixels[ri + 1];
let cb = pg.pixels[ri + 2];
let ca = pg.pixels[ri + 3];
pg.stroke(color(cr,cg,cb,ca));
pg.point(i,floor(j+4));
}
}
Big edit:
Ok, I miss read the question and was thinking in java's processing not p5js as the OP has properly indicated. So my answer was very wrong. Sorry.
But still the approach exists in p5js and is faster.
p5js store pixels in 1d array, 4 slots for each pixel:
[pix1R, pix1G, pix1B, pix1A, pix2R, pix2G, pix2B, pix2A...]
And also the pixel density mathers.
So the code is different, I belive you are looking for something like (no pg here, but the thinking is the same):
loadPixels();
let d = pixelDensity();
let imagesize = 4 * (width * d) * ((height) * d);
for (let i = 0; i <= imagesize; i += 4) {
let j = i + 16;//4*4
pixels[i] = pixels[j];
pixels[i + 1] = pixels[j + 1];
pixels[i + 2] = pixels[j + 2];
pixels[i + 3] = pixels[j + 3];
}
updatePixels();
Now, to access a given area in the array is a little convoluted, here an example
//the area data
const area_x = 35;
const area_y = 48;
const width_of_area = 180;
const height_of_area = 200;
//the pixel density
const d = pixelDensity();
loadPixels();
// those 2 first loops goes trough every pixel in the area
for (let x = area_x; x < width_of_area; x++) {
for (let y = area_y; y < height_of_area; y++) {
//here we go trough the pixels array to get each value of a pixel
for (let i = 0; i < d; i++) {
for (let j = 0; j < d; j++) {
// calculate the index of the 1d array for every pixel
// 4 values in the array for each pixel
// y times density times #of pixels plus
// x times density times #of pixels
index = 4 * ((y * d + j) * width * d + (x * d + i));
// You can assign raw values for rgb color
pixels[index] = 255;
pixels[index + 1] = 30;
pixels[index + 2] = 200;
pixels[index + 3] = 255;
}
}
}
}
updatePixels();
Both this examples are at p5js online editor:
1:https://editor.p5js.org/v-k-/sketches/GGVeZvCk7
2: https://editor.p5js.org/v-k-/sketches/kW9lXyK2n
Hope that helps, and sorry for the previus processing answer/code.
cheers
I'm trying to implement the method of improving fingerprint images by Anil Jain. As a starter, I encountered some difficulties while extracting the orientation image, and am strictly following those steps described in Section 2.4 of that paper.
So, this is the input image:
And this is after normalization using exactly the same method as in that paper:
I'm expecting to see something like this (an example from the internet):
However, this is what I got for displaying obtained orientation matrix:
Obviously this is wrong, and it also gives non-zero values for those zero points in the original input image.
This is the code I wrote:
cv::Mat orientation(cv::Mat inputImage)
{
cv::Mat orientationMat = cv::Mat::zeros(inputImage.size(), CV_8UC1);
// compute gradients at each pixel
cv::Mat grad_x, grad_y;
cv::Sobel(inputImage, grad_x, CV_16SC1, 1, 0, 3, 1, 0, cv::BORDER_DEFAULT);
cv::Sobel(inputImage, grad_y, CV_16SC1, 0, 1, 3, 1, 0, cv::BORDER_DEFAULT);
cv::Mat Vx, Vy, theta, lowPassX, lowPassY;
cv::Mat lowPassX2, lowPassY2;
Vx = cv::Mat::zeros(inputImage.size(), inputImage.type());
Vx.copyTo(Vy);
Vx.copyTo(theta);
Vx.copyTo(lowPassX);
Vx.copyTo(lowPassY);
Vx.copyTo(lowPassX2);
Vx.copyTo(lowPassY2);
// estimate the local orientation of each block
int blockSize = 16;
for(int i = blockSize/2; i < inputImage.rows - blockSize/2; i+=blockSize)
{
for(int j = blockSize / 2; j < inputImage.cols - blockSize/2; j+= blockSize)
{
float sum1 = 0.0;
float sum2 = 0.0;
for ( int u = i - blockSize/2; u < i + blockSize/2; u++)
{
for( int v = j - blockSize/2; v < j+blockSize/2; v++)
{
sum1 += grad_x.at<float>(u,v) * grad_y.at<float>(u,v);
sum2 += (grad_x.at<float>(u,v)*grad_x.at<float>(u,v)) * (grad_y.at<float>(u,v)*grad_y.at<float>(u,v));
}
}
Vx.at<float>(i,j) = sum1;
Vy.at<float>(i,j) = sum2;
double calc = 0.0;
if(sum1 != 0 && sum2 != 0)
{
calc = 0.5 * atan(Vy.at<float>(i,j) / Vx.at<float>(i,j));
}
theta.at<float>(i,j) = calc;
// Perform low-pass filtering
float angle = 2 * calc;
lowPassX.at<float>(i,j) = cos(angle * pi / 180);
lowPassY.at<float>(i,j) = sin(angle * pi / 180);
float sum3 = 0.0;
float sum4 = 0.0;
for(int u = -lowPassSize / 2; u < lowPassSize / 2; u++)
{
for(int v = -lowPassSize / 2; v < lowPassSize / 2; v++)
{
sum3 += inputImage.at<float>(u,v) * lowPassX.at<float>(i - u*lowPassSize, j - v * lowPassSize);
sum4 += inputImage.at<float>(u, v) * lowPassY.at<float>(i - u*lowPassSize, j - v * lowPassSize);
}
}
lowPassX2.at<float>(i,j) = sum3;
lowPassY2.at<float>(i,j) = sum4;
float calc2 = 0.0;
if(sum3 != 0 && sum4 != 0)
{
calc2 = 0.5 * atan(lowPassY2.at<float>(i, j) / lowPassX2.at<float>(i, j)) * 180 / pi;
}
orientationMat.at<float>(i,j) = calc2;
}
}
return orientationMat;
}
I've already searched a lot on the web, but almost all of them are in Matlab. And there exist very few ones using OpenCV, but they didn't help me either. I sincerely hope someone could go through my code and point out any error to help. Thank you in advance.
Update
Here are the steps that I followed according to the paper:
Obtain normalized image G.
Divide G into blocks of size wxw (16x16).
Compute the x and y gradients at each pixel (i,j).
Estimate the local orientation of each block centered at pixel (i,j) using equations:
Perform low-pass filtering to remove noise. For that, convert the orientation image into a continuous vector field defined as:
where W is a two-dimensional low-pass filter, and w(phi) x w(phi) is its size, which equals to 5.
Finally, compute the local ridge orientation at (i,j) using:
Update2
This is the output of orientationMat after changing the mat type to CV_16SC1 in Sobel operation as Micka suggested:
Maybe it's too late for me to answer, but anyway somebody could read this later and solve the same problem.
I've been working for a while in the same algorithm, same method you posted... But there's some writting errors when the papper was redacted (I guess). After fighting a lot with the equations I found this errors by looking other similar works.
Here is what worked for me...
Vy(i, j) = 2*dx(u,v)*dy(u,v)
Vx(i,j) = dx(u,v)^2 - dy(u,v)^2
O(i,j) = 0.5*arctan(Vy(i,j)/Vx(i,j)
(Excuse me I wasn't able to post images, so I wrote the modified ecuations. Remeber "u" and "v" are positions of the summation across the BlockSize by BlockSize window)
The first thing and most important (obviously) are the equations, I saw that in different works this expressions were really different and in every one they talked about the same algorithm of Hong et al.
The Key is finding the Least Mean Square (First 3 equations) of the gradients (Vx and Vy), I provided the corrected formulas above for this ation. Then you can compute angle theta for the non overlapping window (16x16 size recommended in the papper), after that the algorithm says you must calculate the magnitud of the doubled angle in "x" and "y" directions (Phi_x and Phi_y).
Phi_x(i,j) = V(i,j) * cos(2*O(i,j))
Phi_y(i,j) = V(i,j) * sin(2*O(i,j))
Magnitud is just:
V = sqrt(Vx(i,j)^2 + Vy(i,j)^2)
Note that in the related work doesn't mention that you have to use the gradient magnitud, but it make sense (for me) in doing it. After all this corrections you can apply the low pass filter to Phi_x and Phi_y, I used a simple Mask of size 5x5 to average this magnitudes (something like medianblur() of opencv).
Last thing is to calculate new angle, that is the average of the 25ith neighbors in the O(i,j) image, for this you just have to:
O'(i,j) = 0.5*arctan(Phi_y/Phi_x)
We're just there... All this just for calculating the angle of the NORMAL VECTOR TO THE RIDGES DIRECTIONS (O'(i,j)) in the BlockSize by BlockSize non overlapping window, what does it mean? it means that the angle we just calculated is perpendicular to the ridges, in simple words we just calculated the angle of the riges plus 90 degrees... To get the angle we need, we just have to substract to the obtained angle 90°.
To draw the lines we need to have an initial point (X0, Y0) and a final point(X1, Y1). For that imagine a circle centered on (X0, Y0) with a radious of "r":
x0 = i + blocksize/2
y0 = j + blocksize/2
r = blocksize/2
Note we add i and j to the first coordinates becouse the window is moving and we are gonna draw the line starting from the center of the non overlaping window, so we can't use just the center of the non overlaping window.
Then to calculate the end coordinates to draw a line we can just have to use a right triangle so...
X1 = r*cos(O'(i,j)-90°)+X0
Y1 = r*sin(O'(i,j)-90°)+Y0
X2 = X0-r*cos(O'(i,j)-90°)
Y2 = Y0-r*cos(O'(i,j)-90°)
Then just use opencv line function, where initial Point is (X0,Y0) and final Point is (X1, Y1). Additional to it, I drawed the windows of 16x16 and computed the oposite points of X1 and Y1 (X2 and Y2) to draw a line of the entire window.
Hope this help somebody.
My results...
Main function:
Mat mat = imread("nwmPa.png",0);
mat.convertTo(mat, CV_32F, 1.0/255, 0);
Normalize(mat);
int blockSize = 6;
int height = mat.rows;
int width = mat.cols;
Mat orientationMap;
orientation(mat, orientationMap, blockSize);
Normalize:
void Normalize(Mat & image)
{
Scalar mean, dev;
meanStdDev(image, mean, dev);
double M = mean.val[0];
double D = dev.val[0];
for(int i(0) ; i<image.rows ; i++)
{
for(int j(0) ; j<image.cols ; j++)
{
if(image.at<float>(i,j) > M)
image.at<float>(i,j) = 100.0/255 + sqrt( 100.0/255*pow(image.at<float>(i,j)-M,2)/D );
else
image.at<float>(i,j) = 100.0/255 - sqrt( 100.0/255*pow(image.at<float>(i,j)-M,2)/D );
}
}
}
Orientation map:
void orientation(const Mat &inputImage, Mat &orientationMap, int blockSize)
{
Mat fprintWithDirectionsSmoo = inputImage.clone();
Mat tmp(inputImage.size(), inputImage.type());
Mat coherence(inputImage.size(), inputImage.type());
orientationMap = tmp.clone();
//Gradiants x and y
Mat grad_x, grad_y;
// Sobel(inputImage, grad_x, CV_32F, 1, 0, 3, 1, 0, BORDER_DEFAULT);
// Sobel(inputImage, grad_y, CV_32F, 0, 1, 3, 1, 0, BORDER_DEFAULT);
Scharr(inputImage, grad_x, CV_32F, 1, 0, 1, 0);
Scharr(inputImage, grad_y, CV_32F, 0, 1, 1, 0);
//Vector vield
Mat Fx(inputImage.size(), inputImage.type()),
Fy(inputImage.size(), inputImage.type()),
Fx_gauss,
Fy_gauss;
Mat smoothed(inputImage.size(), inputImage.type());
// Local orientation for each block
int width = inputImage.cols;
int height = inputImage.rows;
int blockH;
int blockW;
//select block
for(int i = 0; i < height; i+=blockSize)
{
for(int j = 0; j < width; j+=blockSize)
{
float Gsx = 0.0;
float Gsy = 0.0;
float Gxx = 0.0;
float Gyy = 0.0;
//for check bounds of img
blockH = ((height-i)<blockSize)?(height-i):blockSize;
blockW = ((width-j)<blockSize)?(width-j):blockSize;
//average at block WхW
for ( int u = i ; u < i + blockH; u++)
{
for( int v = j ; v < j + blockW ; v++)
{
Gsx += (grad_x.at<float>(u,v)*grad_x.at<float>(u,v)) - (grad_y.at<float>(u,v)*grad_y.at<float>(u,v));
Gsy += 2*grad_x.at<float>(u,v) * grad_y.at<float>(u,v);
Gxx += grad_x.at<float>(u,v)*grad_x.at<float>(u,v);
Gyy += grad_y.at<float>(u,v)*grad_y.at<float>(u,v);
}
}
float coh = sqrt(pow(Gsx,2) + pow(Gsy,2)) / (Gxx + Gyy);
//smoothed
float fi = 0.5*fastAtan2(Gsy, Gsx)*CV_PI/180;
Fx.at<float>(i,j) = cos(2*fi);
Fy.at<float>(i,j) = sin(2*fi);
//fill blocks
for ( int u = i ; u < i + blockH; u++)
{
for( int v = j ; v < j + blockW ; v++)
{
orientationMap.at<float>(u,v) = fi;
Fx.at<float>(u,v) = Fx.at<float>(i,j);
Fy.at<float>(u,v) = Fy.at<float>(i,j);
coherence.at<float>(u,v) = (coh<0.85)?1:0;
}
}
}
} ///for
GaussConvolveWithStep(Fx, Fx_gauss, 5, blockSize);
GaussConvolveWithStep(Fy, Fy_gauss, 5, blockSize);
for(int m = 0; m < height; m++)
{
for(int n = 0; n < width; n++)
{
smoothed.at<float>(m,n) = 0.5*fastAtan2(Fy_gauss.at<float>(m,n), Fx_gauss.at<float>(m,n))*CV_PI/180;
if((m%blockSize)==0 && (n%blockSize)==0){
int x = n;
int y = m;
int ln = sqrt(2*pow(blockSize,2))/2;
float dx = ln*cos( smoothed.at<float>(m,n) - CV_PI/2);
float dy = ln*sin( smoothed.at<float>(m,n) - CV_PI/2);
arrowedLine(fprintWithDirectionsSmoo, Point(x, y+blockH), Point(x + dx, y + blockW + dy), Scalar::all(255), 1, CV_AA, 0, 0.06*blockSize);
// qDebug () << Fx_gauss.at<float>(m,n) << Fy_gauss.at<float>(m,n) << smoothed.at<float>(m,n);
// imshow("Orientation", fprintWithDirectionsSmoo);
// waitKey(0);
}
}
}///for2
normalize(orientationMap, orientationMap,0,1,NORM_MINMAX);
imshow("Orientation field", orientationMap);
orientationMap = smoothed.clone();
normalize(smoothed, smoothed, 0, 1, NORM_MINMAX);
imshow("Smoothed orientation field", smoothed);
imshow("Coherence", coherence);
imshow("Orientation", fprintWithDirectionsSmoo);
}
seems nothing forgot )
I have read your code thoroughly and found that you have made a mistake while calculating sum3 and sum4:
sum3 += inputImage.at<float>(u,v) * lowPassX.at<float>(i - u*lowPassSize, j - v * lowPassSize);
sum4 += inputImage.at<float>(u, v) * lowPassY.at<float>(i - u*lowPassSize, j - v * lowPassSize);
instead of inputImage you should use a low pass filter.
For windows phone app, when I am adjusting brightness by slider it works fine when I
move it to right. But when I go back to previous position, instead of image darkening, it goes brighter and brighter. Here is my code based on pixel manipulation.
private void slider1_ValueChanged(object sender, RoutedPropertyChangedEventArgs<double> e)
{
wrBmp = new WriteableBitmap(Image1, null);
for (int i = 0; i < wrBmp.Pixels.Count(); i++)
{
int pixel = wrBmp.Pixels[i];
int B = (int)(pixel & 0xFF); pixel >>= 8;
int G = (int)(pixel & 0xFF); pixel >>= 8;
int R = (int)(pixel & 0xFF); pixel >>= 8;
int A = (int)(pixel);
B += (int)slider1.Value; R += (int)slider1.Value; G += (int)slider1.Value;
if (R > 255) R = 255; if (G > 255) G = 255; if (B > 255) B = 255;
if (R < 0) R = 0; if (G < 0) G = 0; if (B < 0) B = 0;
wrBmp.Pixels[i] = B | (G << 8) | (R << 16) | (A << 24);
}
wrBmp.Invalidate();
Image1.Source = wrBmp;
}
What am I missing and is there any problem with slider value. I am working with small images as usual in mobiles. I have already tried copying original image to duplicate one. I think code is perfect, after a lot of research I found that the problem is due to slider value.Possible solution is assigning initial value to slider. I want some code help.
private double lastSlider3Vlaue;
private void slider3_ValueChanged(object sender,`RoutedPropertyChangedEventArgs e)
{
if (slider3 == null) return;
double[] contrastArray = { 1, 1.2, 1.3, 1.6, 1.7, 1.9, 2.1, 2.4, 2.6, 2.9 };
double CFactor = 0;
int nIndex = 0;
nIndex = (int)slider3.Value - (int)lastSlider3Vlaue;
if (nIndex < 0)
{
nIndex = (int)lastSlider3Vlaue - (int)slider3.Value;
this.lastSlider3Vlaue = slider3.Value;
CFactor = contrastArray[nIndex];
}
else
{
nIndex = (int)slider3.Value - (int)lastSlider3Vlaue;
this.lastSlider3Vlaue = slider3.Value;
CFactor = contrastArray[nIndex];
}
WriteableBitmap wbOriginal;
wbOriginal = new WriteableBitmap(Image1, null);
wrBmp = new WriteableBitmap(wbOriginal.PixelWidth, wbOriginal.PixelHeight);
wbOriginal.Pixels.CopyTo(wrBmp.Pixels, 0);
int h = wrBmp.PixelHeight;
int w = wrBmp.PixelWidth;
for (int i = 0; i < wrBmp.Pixels.Count(); i++)
{
int pixel = wrBmp.Pixels[i];
int B = (int)(pixel & 0xFF); pixel >>= 8;
int G = (int)(pixel & 0xFF); pixel >>= 8;
int R = (int)(pixel & 0xFF); pixel >>= 8;
int A = (int)(pixel);
R = (int)(((R - 128) * CFactor) + 128);
G = (int)(((G - 128) * CFactor) + 128);
B = (int)(((B - 128) * CFactor) + 128);
if (R > 255) R = 255; if (G > 255) G = 255; if (B > 255) B = 255;
if (R < 0) R = 0; if (G < 0) G = 0; if (B < 0) B = 0;
wrBmp.Pixels[i] = B | (G << 8) | (R << 16) | (A << 24);
}
wrBmp.Invalidate();
Image1.Source = wrBmp;
}
After debugging I found that the r g b values are decreasing continuosly when sliding forward,but when sliding backwards it is also decreasing where as it shoul increase.
please help iam working on this since past last three months.Besides this u also give me advice about how i can complete this whole image processing
Your algorithm is wrong. Each time the slider's value changes, you're adding that value to the picture's brightness. What makes your logic flawed is that the value returned by the slider will always be positive, and you're always adding the brightness to the same picture.
So, if the slider starts with a value of 10, I'll add 10 to the picture's brightness.
Then, I slide to 5. I'll add 5 to the previous picture's brightness (the one you already added 10 of brightness to).
Two ways to solve the issue:
Keep a copy of the original picture, and duplicate it every time your method is called. Then add the brightness to the copy (and not the original). That's the safest way.
Instead of adding the new absolute value of the slider, calculate the relative value (how much it changed since the last time the method was called:
private double lastSliderValue;
private void slider1_ValueChanged(object sender, RoutedPropertyChangedEventArgs<double> e)
{
var offset = slider1.Value - this.lastSliderValue;
this.lastSliderValue = slider1.Value;
// Insert your old algorithm here, but replace occurences of "slider1.Value" by "offset"
}
This second way can cause a few headaches though. Your algorithm is capping the RGB values to 255. In those cases, you are losing information and cannot revert back to the old state. For instance, take the extreme example of a slider value of 255. The algorithm sets all the pixels to 255, thus generating a white picture. Then you reduce the slider to 0, which should in theory restore the original picture. In this case, you'll subtract 255 to each pixel, but since every pixel's value is 255 you'll end up with a black picture.
Therefore, except if you find a clever way to solve the issue mentionned in the second solution, I'd recommand going with the first one.