Finding a Tangent Line at a Point on a Randomized Slope - random
I have a piece of processing code that I was given, which appears to be setting up a randomized Fourier series. Unfortunately, despite my efforts to improve my mathematical skills, I have no idea what it is doing and the articles I have found are not much help.
I'm trying to extend this code so that I can draw a line tangent to a point on the slope created by the code bellow. The closest I can find to answering this is in the mathematics forum. Unfortunately, I don't really understand what is being discussed or if it really is relevant to my situation.
Any assistance on how I would go about calculating a tangent line at a particular point on this curve would be much appreciated.
UPDATE As of 06/17/13
I've been trying to play around with this, but without much success. This is the best I can do, and I doubt that I'm applying the derivative correctly to find the tangent (or even if I have found the derivative at the point correctly). Also, I'm beginning to worry that I'm not drawing the line correctly even if I have everything else correct. If anyone can provide input on this I'd appreciate it.
final int w = 800;
final int h = 480;
double[] skyline;
PImage img;
int numOfDeriv = 800;
int derivModBy = 1; //Determines how many points will be checked
int time;
int timeDelay = 1000;
int iter;
double[] derivatives;
void setup() {
noStroke();
size(w, h);
fill(0,128,255);
rect(0,0,w,h);
int t[] = terrain(w,h);
fill(77,0,0);
for(int i=0; i < w; i++){
rect(i, h, 1, -1*t[i]);
}
time = millis();
timeDelay = 100;
iter =0;
img = get();
}
void draw() {
int dnum = 0; //Current position of derivatives
if(iter == numOfDeriv) iter = 0;
if (millis() > time + timeDelay){
image(img, 0, 0, width, height);
strokeWeight(4);
stroke(255,0,0);
point((float)iter*derivModBy, height-(float)skyline[iter*derivModBy]);
strokeWeight(1);
stroke(255,255,0);
print("At x = ");
print(iter);
print(", y = ");
print(skyline[iter]);
print(", derivative = ");
print((float)derivatives[iter]);
print('\n');
lineAngle(iter, (int)(height-skyline[iter]), (float)derivatives[iter], 100);
lineAngle(iter, (int)(height-skyline[iter]), (float)derivatives[iter], -100);
stroke(126);
time = millis();
iter += 1;
}
}
void lineAngle(int x, int y, float angle, float length)
{
line(x, y, x+cos(angle)*length, y-sin(angle)*length);
}
int[] terrain(int w, int h){
width = w;
height = h;
//min and max bracket the freq's of the sin/cos series
//The higher the max the hillier the environment
int min = 1, max = 6;
//allocating horizon for screen width
int[] horizon = new int[width];
skyline = new double[width];
derivatives = new double[numOfDeriv];
//ratio of amplitude of screen height to landscape variation
double r = (int) 2.0/5.0;
//number of terms to be used in sine/cosine series
int n = 4;
int[] f = new int[n*2];
//calculating omegas for sine series
for(int i = 0; i < n*2 ; i ++){
f[i] = (int) random(max - min + 1) + min;
}
//amp is the amplitude of the series
int amp = (int) (r*height);
int dnum = 0; //Current number of derivatives
for(int i = 0 ; i < width; i ++){
skyline[i] = 0;
double derivative = 0.0;
for(int j = 0; j < n; j++){
if(i % derivModBy == 0){
derivative += ( cos( (f[j]*PI*i/height) * f[j]*PI/height) -
sin(f[j+n]*PI*i/height) * f[j+n]*PI/height);
}
skyline[i] += ( sin( (f[j]*PI*i/height) ) + cos(f[j+n]*PI*i/height) );
}
skyline[i] *= amp/(n*2);
skyline[i] += (height/2);
skyline[i] = (int)skyline[i];
horizon[i] = (int)skyline[i];
derivative *= amp/(n*2);
if(i % derivModBy == 0){
derivatives[dnum++] = derivative;
derivative = 0;
}
}
return horizon;
}
void reset() {
time = millis();
}
Well it seems in this particular case that you don't need to understand much about the Fourier Series, just that it has the form:
A0 + A1*cos(x) + A2*cos(2*x) + A3*cos(3*x) +... + B1*sin(x) + B2*sin(x) +...
Normally you're given a function f(x) and you need to find the values of An and Bn such that the Fourier series converges to your function (as you add more terms) for some interval [a, b].
In this case however they want a random function that just looks like different lumps and pits (or hills and valleys as the context might suggest) so they choose random terms from the Fourier Series between min and max and set their coefficients to 1 (and conceptually 0 otherwise). They also satisfy themselves with a Fourier series of 4 sine terms and 4 cosine terms (which is certainly easier to manage than an infinite number of terms). This means that their Fourier Series ends up looking like different sine and cosine functions of different frequencies added together (and all have the same amplitude).
Finding the derivative of this is easy if you recall that:
sin(n*x)' = n * cos(x)
cos(n*x)' = -n * sin(x)
(f(x) + g(x))' = f'(x) + g'(x)
So the loop to calculate the the derivative would look like:
for(int j = 0; j < n; j++){
derivative += ( cos( (f[j]*PI*i/height) * f[j]*PI/height) - \
sin(f[j+n]*PI*i/height) * f[j+n]*PI/height);
}
At some point i (Note the derivative is being taken with respect to i since that is the variable that represents our x position here).
Hopefully with this you should be able to calculate the equation of the tangent line at a point i.
UPDATE
At the point where you do skyline[i] *= amp/(n*2); you must also adjust your derivative accordingly derivative *= amp/(n*2); however your derivative does not need adjusting when you do skyline[i] += height/2;
I received an answer to this problem via "quarks" on processing.org form. Essentially the problem is that I was taking the derivative of each term of the series instead of taking the derivative of the sum of the entire series. Also, I wasn't applying my result correctly anyway.
Here is the code that quarks provided that definitively solves this problem.
final int w = 800;
final int h = 480;
float[] skyline;
PImage img;
int numOfDeriv = 800;
int derivModBy = 1; //Determines how many points will be checked
int time;
int timeDelay = 1000;
int iter;
float[] tangents;
public void setup() {
noStroke();
size(w, h);
fill(0, 128, 255);
rect(0, 0, w, h);
terrain(w, h);
fill(77, 0, 0);
for (int i=0; i < w; i++) {
rect(i, h, 1, -1*(int)skyline[i]);
}
time = millis();
timeDelay = 100;
iter =0;
img = get();
}
public void draw() {
if (iter == numOfDeriv) iter = 0;
if (millis() > time + timeDelay) {
image(img, 0, 0, width, height);
strokeWeight(4);
stroke(255, 0, 0);
point((float)iter*derivModBy, height-(float)skyline[iter*derivModBy]);
strokeWeight(1);
stroke(255, 255, 0);
print("At x = ");
print(iter);
print(", y = ");
print(skyline[iter]);
print(", derivative = ");
print((float)tangents[iter]);
print('\n');
lineAngle(iter, (int)(height-skyline[iter]), (float)tangents[iter], 100);
lineAngle(iter, (int)(height-skyline[iter]), (float)tangents[iter], -100);
stroke(126);
time = millis();
iter += 1;
}
}
public void lineAngle(int x, int y, float angle, float length) {
line(x, y, x+cos(angle)*length, y-sin(angle)*length);
}
public void terrain(int w, int h) {
//min and max bracket the freq's of the sin/cos series
//The higher the max the hillier the environment
int min = 1, max = 6;
skyline = new float[w];
tangents = new float[w];
//ratio of amplitude of screen height to landscape variation
double r = (int) 2.0/5.0;
//number of terms to be used in sine/cosine series
int n = 4;
int[] f = new int[n*2];
//calculating omegas for sine series
for (int i = 0; i < n*2 ; i ++) {
f[i] = (int) random(max - min + 1) + min;
}
//amp is the amplitude of the series
int amp = (int) (r*h);
for (int i = 0 ; i < w; i ++) {
skyline[i] = 0;
for (int j = 0; j < n; j++) {
skyline[i] += ( sin( (f[j]*PI*i/h) ) + cos(f[j+n]*PI*i/h) );
}
skyline[i] *= amp/(n*2);
skyline[i] += (h/2);
}
for (int i = 1 ; i < w - 1; i ++) {
tangents[i] = atan2(skyline[i+1] - skyline[i-1], 2);
}
tangents[0] = atan2(skyline[1] - skyline[0], 1);
tangents[w-1] = atan2(skyline[w-2] - skyline[w-1], 1);
}
void reset() {
time = millis();
}
Related
How can I make a grid of tiles (that can be rotated randomly) in processing?
I have the following code in Processing that will produce a grid of randomly selected tiles from loaded files: static int img_count = 6; PImage[] img; void setup() { size(1200, 800); img = new PImage[img_count]; for (int i = 0; i < img_count; i++) { img[i] = loadImage("./1x/Artboard " + (i+1) + ".png"); } } void draw() { for (int i = 0; i < 12; i++) { for (int j = 0; j < 12; j++) { int rand_index = int(random(img_count)); image(img[rand_index], 100 * i, 100 * j, 100, 100 ); } } } By itself, it almost does what I want: But I need that every tile be randomly rotated as well, so I tried this: void draw() { for (int i = 0; i < 12; i++) { for (int j = 0; j < 12; j++) { float r = int(random(4)) * HALF_PI; // I added this rotate(r); // I added this int rand_index= int(random(img_count)); image(img[rand_index], 100 * i, 100 * j, 100, 100 ); } } } This second code doesn't act as I intended, as rotate() will rotate the entire image, including tiles that were already rendered. I couldn't find an appropriate way to rotate a tile the way I want, is there any way to rotate the tile before placing it?
You will probably need to translate before rotating. The order of transformations is important (e.g. translating, then rotating will be a different location than rotation, then translating). In your case image(img, x, y) makes it easy to miss that behind the scenes it's more like translate(x,y);image(img, 0, 0);. I recommend: void draw() { for (int i = 0; i < 12; i++) { for (int j = 0; j < 12; j++) { float r = int(random(4)) * HALF_PI; // I added this translate(100 * i, 100 * j); // translate first rotate(r); // I added this int rand_index= int(random(img_count)); image(img[rand_index], 0, 0, 100, 100 ); } } } (depending on your setup, you might find imageMode(CENTER); (in setup()) handy to rotate from image centre (as opposed to top left corner (default)))
Spread / Space Out the Values of Perlin Noise
I am working to create procedurally generated terrain using Perlin Noise. I have gotten it to work fairly well so far. However, the landscape produced seems more like a swampland with many smaller lakes and very few real oceans. I currently have the value noise scale value, but it only zooms the map in. Thinking about Perlin Noise like a sin wave, dividing by a value should spread it out more, like increasing the period of the sin wave. However, this is all that happens when I add this and increase that value (period is currently set at 1.25 in this picture): This is what it looks like if I increase the sea level: How can I make it so that there are more larger oceans and, or is this simply a bad way of looking at it? Here is my map generation code (as is): public static float[,] GenerateNoiseMap(int mapWidth, int mapHeight, int seed, float scale, int octaves, float persistance, float lacunarity, Vector2 offset, NormalizeMode normalizeMode, float normalizationEstimation) { float[,] noiseMap = new float[mapWidth,mapHeight]; System.Random prng = new System.Random (seed); Vector2[] octaveOffsets = new Vector2[octaves]; float maxPossibleHeight = 0; float amplitude = 1; float frequency = 1; for (int i = 0; i < octaves; i++) { float offsetX = prng.Next (-100000, 100000) + offset.x; float offsetY = prng.Next (-100000, 100000) - offset.y; octaveOffsets [i] = new Vector2 (offsetX, offsetY); maxPossibleHeight += amplitude; amplitude *= persistance; } if (scale <= 0) { scale = 0.0001f; } float maxLocalNoiseHeight = float.MinValue; float minLocalNoiseHeight = float.MaxValue; float halfWidth = mapWidth / 2f; float halfHeight = mapHeight / 2f; for (int y = 0; y < mapHeight; y++) { for (int x = 0; x < mapWidth; x++) { amplitude = 1; frequency = 1; float noiseHeight = 0; for (int i = 0; i < octaves; i++) { float sampleX = (x - halfWidth + octaveOffsets[i].x) / scale * frequency; float sampleY = (y - halfHeight + octaveOffsets[i].y) / scale * frequency; float perlinValue = ((Mathf.PerlinNoise (sampleX, sampleY) / period) * 2 - 1); noiseHeight += perlinValue * amplitude; amplitude *= persistance; frequency *= lacunarity; } if (noiseHeight > maxLocalNoiseHeight) { maxLocalNoiseHeight = noiseHeight; } else if (noiseHeight < minLocalNoiseHeight) { minLocalNoiseHeight = noiseHeight; } noiseMap [x, y] = noiseHeight; } } for (int y = 0; y < mapHeight; y++) { for (int x = 0; x < mapWidth; x++) { if (normalizeMode == NormalizeMode.Local) { noiseMap [x, y] = Mathf.InverseLerp (minLocalNoiseHeight, maxLocalNoiseHeight, noiseMap [x, y]); } else { float normalizedHeight = (noiseMap [x, y] + 1) / (2f * maxPossibleHeight / normalizationEstimation); noiseMap [x, y] = Mathf.Clamp(normalizedHeight, 0, int.MaxValue); } } } return noiseMap; }
Obtaining orientation map of fingerprint image using OpenCV
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.
I made a processing program that generates a mandelbrot set but don't know how to effectively implement a zoom method
I'm not sure if it is possible in processing but I would like to be able to zoom in on the fractal without it being extremely laggy and buggy. What I currently have is: int maxIter = 100; float zoom = 1; float x0 = width/2; float y0 = height/2; void setup(){ size(500,300); noStroke(); smooth(); } void draw(){ translate(x0, y0); scale(zoom); for(float Py = 0; Py < height; Py++){ for(float Px = 0; Px < width; Px++){ // scale pixel coordinates to Mandelbrot scale float w = width; float h = height; float xScaled = (Px * (3.5/w)) - 2.5; float yScaled = (Py * (2/h)) - 1; float x = 0; float y = 0; int iter = 0; while( x*x + y*y < 2*2 && iter < maxIter){ float tempX = x*x - y*y + xScaled; y = 2*x*y + yScaled; x = tempX; iter += 1; } // color pixels color c; c = pickColor(iter); rect(Px, Py,1,1); fill(c); } } } // pick color based on time pixel took to escape (number of iterations through loop) color pickColor(int iters){ color b = color(0,0,0); if(iters == maxIter) return b; int l = 1; color[] colors = new color[maxIter]; for(int i = 0; i < colors.length; i++){ switch(l){ case 1 : colors[i] = color(255,0,0); break; case 2 : colors[i] = color(0,0,255); break; case 3 : colors[i] = color(0,255,0); break; } if(l == 1 || l == 2) l++; else if(l == 3) l = 1; else l--; } return colors[iters]; } // allow zooming in and out void mouseWheel(MouseEvent event){ float direction = event.getCount(); if(direction < 0) zoom += .02; if(direction > 0) zoom -= .02; } // allow dragging back and forth to change view void mouseDragged(){ x0+= mouseX-pmouseX; y0+= mouseY-pmouseY; } but it doesn't work very well. It works alright at the size and max iteration I have it set to now (but still not well) and is completely unusable at larger sizes or higher maximum iterations.
The G4P library has an example that does exactly this. Download the library and go to the G4P_MandelBrot example. The example can be found online here. Hope this helps!
How to calculate the Gaussian Filter kernel
I am working on image processing project.In that I need to implement Gaussian filter.How to calculate the 3x3, 5x5,7x7 kernels? Please help me. http://s14.postimg.org/rwpyq8k5d/image.jpg
The code below illustrate how to calculate the Gaussian kernel with any filter size and Gaussian weighted parameter. enter code here public static double[,] CalculateGaussianKernel(int length, double weight) { // define an array of two dimensions based on the length value that pass it by the user from the text box. double[,] Kernel = new double[length, length]; double sumTotal = 0; int kernelRadius = length / 2; double distance = 0; double calculatedEuler = 1.0 / (2.0 * Math.PI * Math.Pow(weight, 2)); // Gaussian Function first part for (int filterY = -kernelRadius; filterY <= kernelRadius; filterY++) { for (int filterX = -kernelRadius; filterX <= kernelRadius; filterX++) { distance = ((filterX * filterX) + (filterY * filterY)) /(2 * (weight * weight)); // Gaussian Function Second part Kernel[filterY + kernelRadius,filterX + kernelRadius] = calculatedEuler * Math.Exp(-distance); sumTotal += Kernel[filterY + kernelRadius, filterX + kernelRadius]; } } for (int y = 0; y < length; y++) { for (int x = 0; x < length; x++) { Kernel[y, x] = Kernel[y, x] * (1.0 / sumTotal); } } return Kernel; }