I have this:
And I need to know all pixels in array inside the circle.
Thanks.
You are looking for the following set of pixels:
with r being the radius of your circle and (m1, m2) the center.
In order to get these pixels iterate over all positions and store those which meet the criteria in a list:
List<int> indices = new List<int>();
for (int x = 0; x < width; x++)
{
for (int y = 0; y < height; y++)
{
double dx = x - m1;
double dy = y - m2;
double distanceSquared = dx * dx + dy * dy;
if (distanceSquared <= radiusSquared)
{
indices.Add(x + y * width);
}
}
}
Shouldn´t this be an more efficient approach? (Don't iterating over the whole picture, just the desired square)
List<int> indices = new List<int>();
xmin = m1 - r;
xmax = m1 + r;
ymin = m2 - r;
ymax = m2 + r;
for (int x = xmin; x < xmax ; x++)
{
for (int y = ymin; y < ymax; y++)
{
double dx = x - m1;
double dy = y - m2;
double distanceSquared = dx * dx + dy * dy;
if (distanceSquared <= radiusSquared)
{
indices.Add(x + y * width);
}
}
}
Related
I would like to transform histograms based on images to vector graphics.
This could be a start:
function preload() {
img = loadImage("https://upload.wikimedia.org/wikipedia/commons/thumb/3/36/Cirrus_sky_panorama.jpg/1200px-Cirrus_sky_panorama.jpg");
}
function setup() {
createCanvas(400, 400);
background(255);
img.resize(0, 200);
var maxRange = 256
colorMode(HSL, maxRange);
image(img, 0, 0);
var histogram = new Array(maxRange);
for (i = 0; i <= maxRange; i++) {
histogram[i] = 0
}
loadPixels();
for (var x = 0; x < img.width; x += 5) {
for (var y = 0; y < img.height; y += 5) {
var loc = (x + y * img.width) * 4;
var h = pixels[loc];
var s = pixels[loc + 1];
var l = pixels[loc + 2];
var a = pixels[loc + 3];
b = int(l);
histogram[b]++
}
}
image(img, 0, 0);
stroke(300, 100, 80)
push()
translate(10, 0)
for (x = 0; x <= maxRange; x++) {
index = histogram[x];
y1 = int(map(index, 0, max(histogram), height, height - 300));
y2 = height
xPos = map(x, 0, maxRange, 0, width - 20)
line(xPos, y1, xPos, y2);
}
pop()
}
<script src="https://cdn.jsdelivr.net/npm/p5#1.4.1/lib/p5.js"></script>
But I would need downloadable vector graphic files as results that are closed shapes without any gaps between. It should look like that for example:
<svg viewBox="0 0 399.84 200"><polygon points="399.84 200 399.84 192.01 361.91 192.01 361.91 182.87 356.24 182.87 356.24 183.81 350.58 183.81 350.58 184.74 344.91 184.74 344.91 188.19 339.87 188.19 339.87 189.89 334.6 189.89 334.6 185.29 328.93 185.29 328.93 171.11 323.26 171.11 323.26 172.55 317.59 172.55 317.59 173.99 311.92 173.99 311.92 179.42 306.88 179.42 306.88 182.03 301.21 182.03 301.21 183.01 295.54 183.01 295.54 179.04 289.87 179.04 289.87 175.67 284.21 175.67 284.21 182.03 278.54 182.03 278.54 176 273.5 176 273.5 172.42 267.83 172.42 267.83 179.42 262.79 179.42 262.79 182.03 257.12 182.03 257.12 183.01 251.45 183.01 251.45 178.63 245.78 178.63 245.78 175.21 240.11 175.21 240.11 182.03 234.86 182.03 234.86 150.42 229.2 150.42 229.2 155.98 223.53 155.98 223.53 158.06 217.86 158.06 217.86 167.44 212.19 167.44 212.19 162.58 206.52 162.58 206.52 155.98 200.85 155.98 200.85 158.06 195.18 158.06 195.18 167.44 189.51 167.44 189.51 177.46 183.84 177.46 183.84 166.93 178.17 166.93 178.17 153.69 172.5 153.69 172.5 155.87 166.82 155.87 166.82 158.05 161.78 158.05 161.78 155.63 156.11 155.63 156.11 160.65 150.84 160.65 150.84 146.59 145.17 146.59 145.17 109.63 139.49 109.63 139.49 113.67 133.82 113.67 133.82 61.48 128.15 61.48 128.15 80.59 123.11 80.59 123.11 93.23 117.44 93.23 117.44 97.97 111.76 97.97 111.76 78.07 106.09 78.07 106.09 61.66 100.42 61.66 100.42 93.23 94.75 93.23 94.75 98.51 89.7 98.51 89.7 85.4 84.03 85.4 84.03 111.03 78.99 111.03 78.99 120.57 73.32 120.57 73.32 124.14 67.65 124.14 67.65 23.48 61.97 23.48 61.97 0 56.3 0 56.3 120.57 50.63 120.57 50.63 167.01 45.38 167.01 45.38 170.83 39.71 170.83 39.71 172.26 34.03 172.26 34.03 178.7 28.36 178.7 28.36 175.36 22.69 175.36 22.69 170.83 17.02 170.83 17.02 172.26 11.34 172.26 11.34 178.7 5.67 178.7 5.67 103.85 0 103.85 0 200 399.84 200"/></svg>
Has anyone an idea how to program that? It doesn't necessarily need to be based on p5.js, but would be cool.
Closing Gaps
In order to have a gapless histogram, you need to meet the following condition:
numberOfBars * barWidth === totalWidth
Right now you are using the p5 line() function to draw your bars. You have not explicitly set the width of your bars, so it uses the default value of 1px wide.
We know that the numberOfBars in your code is always maxRange which is 256.
Right now the total width of your histogram is width - 20, where width is set to 400 by createCanvas(400, 400). So the totalWidth is 380.
256 * 1 !== 380
If you have 256 pixels of bars in a 380 pixel space then there are going to be gaps!
We need to change the barWidth and/or the totalWidth to balance the equation.
For example, you can change your canvas size to 276 (256 + your 20px margin) and the gaps disappear!
createCanvas(276, 400);
However this is not an appropriate solution because now your image is cropped and your pixel data is wrong. But actually...it was already wrong before!
Sampling Pixels
When you call the global loadPixels() function in p5.js you are loading all of the pixels for the whole canvas. This includes the white areas outside of your image.
for (var x = 0; x < img.width; x += 5) {
for (var y = 0; y < img.height; y += 5) {
var loc = (x + y * img.width) * 4;
It is a 1-dimensional array, so your approach of limiting the x and y values here is not giving you the correct position. Your loc variable needs to use the width of the entire canvas rather than the width of just the image, since the pixels array includes the entire canvas.
var loc = (x + y * width) * 4;
Alternatively, you can look at just the pixels of the image by using img.loadPixels() and img.pixels.
img.loadPixels();
for (var x = 0; x < img.width; x += 5) {
for (var y = 0; y < img.height; y += 5) {
var loc = (x + y * img.width) * 4;
var h = img.pixels[loc];
var s = img.pixels[loc + 1];
var l = img.pixels[loc + 2];
var a = img.pixels[loc + 3];
b = int(l);
histogram[b]++;
}
}
The pixel values are always returned in RGBA regardless of the colorMode. So your third channel value is actually the blue, not the lightness. You can make use of the p5.js lightness() function to compute the lightness from the RGBA.
Updated Code
The actual lightness histogram looks dumb because 100% dwarfs all of the other bars.
function preload() {
img = loadImage("https://upload.wikimedia.org/wikipedia/commons/thumb/3/36/Cirrus_sky_panorama.jpg/1200px-Cirrus_sky_panorama.jpg");
}
function setup() {
const barCount = 100;
const imageHeight = 200;
createCanvas(400, 400);
background(255);
colorMode(HSL, barCount - 1);
img.resize(0, imageHeight);
imageMode(CENTER);
image(img, width / 2, imageHeight / 2);
img.loadPixels();
const histogram = new Array(barCount).fill(0);
for (let x = 0; x < img.width; x += 5) {
for (let y = 0; y < img.height; y += 5) {
const loc = (x + y * img.width) * 4;
const r = img.pixels[loc];
const g = img.pixels[loc + 1];
const b = img.pixels[loc + 2];
const a = img.pixels[loc + 3];
const barIndex = floor(lightness([r, g, b, a]));
histogram[barIndex]++;
}
}
fill(300, 100, 80);
strokeWeight(0);
const maxCount = max(histogram);
const barWidth = width / barCount;
const histogramHeight = height - imageHeight;
for (let i = 0; i < barCount; i++) {
const count = histogram[i];
const y1 = round(map(count, 0, maxCount, height, imageHeight));
const y2 = height;
const x1 = i * barWidth;
const x2 = x1 + barWidth;
rect(x1, y1, barWidth, height - y1);
}
}
<script src="https://cdn.jsdelivr.net/npm/p5#1.4.1/lib/p5.js"></script>
But the blue channel histogram looks pretty good!
function preload() {
img = loadImage("https://upload.wikimedia.org/wikipedia/commons/thumb/3/36/Cirrus_sky_panorama.jpg/1200px-Cirrus_sky_panorama.jpg");
}
function setup() {
const barCount = 100;
const imageHeight = 200;
createCanvas(400, 400);
background(255);
img.resize(0, imageHeight);
imageMode(CENTER);
image(img, width / 2, imageHeight / 2);
img.loadPixels();
const histogram = new Array(barCount).fill(0);
for (let x = 0; x < img.width; x += 5) {
for (let y = 0; y < img.height; y += 5) {
const loc = (x + y * img.width) * 4;
const r = img.pixels[loc];
const g = img.pixels[loc + 1];
const b = img.pixels[loc + 2];
const a = img.pixels[loc + 3];
const barIndex = floor(barCount * b / 255);
histogram[barIndex]++;
}
}
fill(100, 100, 300);
strokeWeight(0);
const maxCount = max(histogram);
const barWidth = width / barCount;
const histogramHeight = height - imageHeight;
for (let i = 0; i < barCount; i++) {
const count = histogram[i];
const y1 = round(map(count, 0, maxCount, height, imageHeight));
const y2 = height;
const x1 = i * barWidth;
const x2 = x1 + barWidth;
rect(x1, y1, barWidth, height - y1);
}
}
<script src="https://cdn.jsdelivr.net/npm/p5#1.4.1/lib/p5.js"></script>
Just to add to Linda's excellent answer(+1), you can use p5.svg to render to SVG using p5.js:
let histogram;
function setup() {
createCanvas(660, 210, SVG);
background(255);
noStroke();
fill("#ed225d");
// make an array of 256 random values in the (0, 255) range
histogram = Array.from({length: 256}, () => int(random(255)));
//console.log(histogram);
// plot the histogram
barPlot(histogram, 0, 0, width, height);
// change shape rendering so bars appear connected
document.querySelector('g').setAttribute('shape-rendering','crispEdges');
// save the plot
save("histogram.svg");
}
function barPlot(values, x, y, plotWidth, plotHeight){
let numValues = values.length;
// calculate the width of each bar in the plot
let barWidth = plotWidth / numValues;
// calculate min/max value (to map height)
let minValue = min(values);
let maxValue = max(values);
// for each value
for(let i = 0 ; i < numValues; i++){
// map the value to the plot height
let barHeight = map(values[i], minValue, maxValue, 0, plotHeight);
// render each bar, offseting y
rect(x + (i * barWidth),
y + (plotHeight - barHeight),
barWidth, barHeight);
}
}
<script src="https://unpkg.com/p5#1.3.1/lib/p5.js"></script>
<script src="https://unpkg.com/p5.js-svg#1.0.7"></script>
(In the p5 editor (or when testing locally) a save dialog should pop up.
If you use the browser's Developer Tools to inspect the bar chart it should confirm it's an SVG (not <canvas/>))
I'm trying to implement multi-level Otsu's thresholding, more specifically I need 3 thresholds/4 classes.
I'm aware of 2 similair questions on SO about it: #34856019 and #22706742.
The problem is that I don't get good results: I've read several articles with sample images and thresholds found by that code differ from the ones in these papers.
Let's say I have a picture with 3 circles on the black background, the brightness of the circles differ from very bright to dark:
Sample Image
Am I right to suppose to get as a result 4 classes: black background and 3 more classes according to circles' intensity?
My program gives me these threshold values: 226, 178, 68
As a result, the third circle is completely invisible - it's in the same class as the background.
Can someone please check these values and/or the source code? Maybe it is possible to check this image using Matlab or somehow else...
By the way, what's the best way to handle divisions by zero, which happen often with zero values in histogram?
The source code:
void MultilevelThresholding(cv::Mat& src)
{
int histogram[256] = { 0 };
int pixelsCount = src.cols * src.rows;
for (int y = 0; y < src.rows; y++)
{
for (int x = 0; x < src.cols; x++)
{
uchar value = src.at<uchar>(y, x);
histogram[value]++;
}
}
double c = 0;
double Mt = 0;
double p[256] = { 0 };
for (int i = 0; i < 256; i++)
{
p[i] = (double) histogram[i] / (double) pixelsCount;
Mt += i * p[i];
}
int optimalTreshold1 = 0;
int optimalTreshold2 = 0;
int optimalTreshold3 = 0;
double maxBetweenVar = 0;
double w0 = 0;
double m0 = 0;
double c0 = 0;
double p0 = 0;
double w1 = 0;
double m1 = 0;
double c1 = 0;
double p1 = 0;
double w2 = 0;
double m2 = 0;
double c2 = 0;
double p2 = 0;
for (int tr1 = 0; tr1 < 256; tr1++)
{
p0 += p[tr1];
w0 += (tr1 * p[tr1]);
if (p0 != 0)
{
m0 = w0 / p0;
}
c0 = p0 * (m0 - Mt) * (m0 - Mt);
c1 = 0;
w1 = 0;
m1 = 0;
p1 = 0;
for (int tr2 = tr1 + 1; tr2 < 256; tr2++)
{
p1 += p[tr2];
w1 += (tr2 * p[tr2]);
if (p1 != 0)
{
m1 = w1 / p1;
}
c1 = p1 * (m1 - Mt) * (m1 - Mt);
c2 = 0;
w2 = 0;
m2 = 0;
p2 = 0;
for (int tr3 = tr2 + 1; tr3 < 256; tr3++)
{
p2 += p[tr3];
w2 += (tr3 * p[tr3]);
if (p2 != 0)
{
m2 = w2 / p2;
}
c2 = p2 * (m2 - Mt) * (m2 - Mt);
c = c0 + c1 + c2;
if (maxBetweenVar < c)
{
maxBetweenVar = c;
optimalTreshold1 = tr1;
optimalTreshold2 = tr2;
optimalTreshold3 = tr3;
}
}
}
}
So, I've figured it out. The final source code for 4 classes (3 thresholds) Otsu thresholding:
// cv::Mat& src - source image's matrix
int histogram[256] = { 0 };
int pixelsCount = src.cols * src.rows;
for (int y = 0; y < src.rows; y++)
{
for (int x = 0; x < src.cols; x++)
{
uchar value = src.at<uchar>(y, x);
histogram[value]++;
}
}
double c = 0;
double Mt = 0;
double p[256] = { 0 };
for (int i = 0; i < 256; i++)
{
p[i] = (double) histogram[i] / (double) pixelsCount;
Mt += i * p[i];
}
int optimalTreshold1 = 0;
int optimalTreshold2 = 0;
int optimalTreshold3 = 0;
double maxBetweenVar = 0;
double w0 = 0;
double m0 = 0;
double c0 = 0;
double p0 = 0;
double w1 = 0;
double m1 = 0;
double c1 = 0;
double p1 = 0;
double w2 = 0;
double m2 = 0;
double c2 = 0;
double p2 = 0;
for (int tr1 = 0; tr1 < 256; tr1++)
{
p0 += p[tr1];
w0 += (tr1 * p[tr1]);
if (p0 != 0)
{
m0 = w0 / p0;
}
c0 = p0 * (m0 - Mt) * (m0 - Mt);
c1 = 0;
w1 = 0;
m1 = 0;
p1 = 0;
for (int tr2 = tr1 + 1; tr2 < 256; tr2++)
{
p1 += p[tr2];
w1 += (tr2 * p[tr2]);
if (p1 != 0)
{
m1 = w1 / p1;
}
c1 = p1 * (m1 - Mt) * (m1 - Mt);
c2 = 0;
w2 = 0;
m2 = 0;
p2 = 0;
for (int tr3 = tr2 + 1; tr3 < 256; tr3++)
{
p2 += p[tr3];
w2 += (tr3 * p[tr3]);
if (p2 != 0)
{
m2 = w2 / p2;
}
c2 = p2 * (m2 - Mt) * (m2 - Mt);
double p3 = 1 - (p0 + p1 + p2);
double w3 = Mt - (w0 + w1 + w2);
double m3 = w3 / p3;
double c3 = p3 * (m3 - Mt) * (m3 - Mt);
double c = c0 + c1 + c2 + c3;
if (maxBetweenVar < c)
{
maxBetweenVar = c;
optimalTreshold1 = tr1;
optimalTreshold2 = tr2;
optimalTreshold3 = tr3;
}
}
}
}
Source image
Result: 3 thresholds / 4 classes
threshold values: 179, 92, 25
I am using Aptina 5Mp sensor with Fish-eye lens for capturing an image.
I am using following algorithm to correct lens distortion.
http://www.tannerhelland.com/4743/simple-algorithm-correcting-lens-distortion/
this is not correcting the image properly.
Any help will be appreciated.
//code----
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <iostream>
#include <stdio.h>
#include <math.h>
using namespace cv;
using namespace std;
// globals
Mat src, dst;
Mat map_x, map_y;
#define REMAP_WINDOW "Remap Circle"
void make_circle_map(float , float , float , float );
int main(int argc, char** argv) {
// load image
src = imread(argv[1], 1);
float qvDepth = atof(argv[2]);
float fixStrength = atof(argv[3]);
float fixZoom = atof(argv[4]);
float lensRadius = atof(argv[5]);
// create destination and the maps
dst.create(src.size(), src.type());
map_x.create(src.size(), CV_32FC1);
map_y.create(src.size(), CV_32FC1);
// create window
// namedWindow(REMAP_WINDOW, CV_WINDOW_AUTOSIZE);
make_circle_map(qvDepth, fixStrength, fixZoom, lensRadius);
remap(src, dst, map_x, map_y, CV_INTER_LINEAR, BORDER_CONSTANT, Scalar(0,0, 0));
//imshow(REMAP_WINDOW, dst);
imwrite("got1.jpg",dst);
// while(27 != waitKey()) {
// just wait
// }
// cvDestroyWindow(REMAP_WINDOW);
return 0;
}
void make_circle_map(float qvDepth, float fixStrength, float fixZoom, float lensRadius ) {
//ApplyLensCorrection(double fixStrength, double fixZoom, double lensRadius, long long edgeHandling, long long superSamplingAmount
cout<<"qvDepth :"<<qvDepth<<" fixStrength :"<<fixStrength<<" fixZoom :"<<fixZoom<<" lensRadius :"<<lensRadius<<endl;
//float qvDepth = 32;//24;
//float fixStrength = 4.5; // has to utilized further
//float fixZoom = 0.5;
//float lensRadius =2;
//Calculate the center of the image
//double midX = 0;
//double midY = 0;
long tWidth = 1944;
long tHeight = 2580;
// the center
double midX = (double)src.cols/2;
double midY = (double)src.rows/2;
//Rotation values
double theta = 0;
double sRadius = 0;
double sRadius2 = 0;
double sDistance = 0;
double radius = 0;
double j = 0;
double k = 0;
//X and Y values, remapped around a center point of (0, 0)
double nX = 0;
double nY = 0;
double QuickVal =0;
float ssX;
float ssY;
//Source X and Y values, which may or may not be used as part of a bilinear interpolation function
double srcX = 0;
double srcY = 0;
sRadius = sqrt(tWidth * tWidth + tHeight * tHeight) / 2;
cout<<"sRadius :"<<sRadius<<endl;
double refDistance = 0;//modified 0 to 2
if (fixStrength == 0)
{
fixStrength = 0.00000001;
}
refDistance = sRadius * 2 / fixStrength;
sRadius = sRadius * (lensRadius / 100);
sRadius2 = sRadius * sRadius;
cout<<"refDistance :"<<refDistance<<" sRadius :"<<sRadius<<" sRadius2 :"<<sRadius2<<endl;
float sampleIndex =1; //has to be changed in future
for (int x = 0; x <= tWidth; x++)
{
QuickVal = x * qvDepth;
for (int y = 0; y <= tHeight; y++)
{
//Remap the coordinates around a center point of (0, 0)
nX = x - midX;
nY = y - midY;
//Offset the pixel amount by the supersampling lookup table
for(int ii = 1; ii<4;ii++){
j = nX + ii;
k = nY + ii;
//Calculate distance automatically
sDistance = (j * j) + (k * k);
//cout<<"nx :"<<nX<<" ny :"<<nY<<" j :"<<j<<" k :"<<k<<" sDistance :"<<sDistance<<" sRadius2 :"<<sRadius2<<endl;
if (sDistance <= sRadius2)
{
sDistance = sqrt(sDistance);
radius = sDistance / refDistance;
if (radius == 0)
{
theta = 1;
}
else
{
theta = atan(radius) / radius;
}
//srcX = midX + theta * j * fixZoom;
//srcY = midY + theta * k * fixZoom;
map_x.at<float>(x,y) = midX + cos(fabs(theta)) * j * fixZoom;
map_y.at<float>(x,y) = midY + sin(fabs(theta)) * k * fixZoom;
}
else
{
map_x.at<float>(x,y) = x + cos(fabs(theta)) ;//* fixZoom;//x;
map_y.at<float>(x,y) = y + sin(fabs(theta)) ;//* fixZoom;//y;
}
}
}
}
}
Image
replace the following line.
map_x.at<float>(x,y) = midX + theta * j * fixZoom;
map_y.at<float>(x,y) = midY + theta * k * fixZoom;
}
else
{
map_x.at<float>(x,y) = x ;//* fixZoom;//x;
map_y.at<float>(x,y) = y ;//* fixZoom;//y;
use argument executable [image name], BBP, correction parameter, zoom parameter, applied ratio.
ex-> ./lensdistortcorrect image.jpg 24 6.2 2.2 100
What's the best way to fit a set of points in an image one or more good lines using RANSAC using OpenCV?
Is RANSAC is the most efficient way to fit a line?
RANSAC is not the most efficient but it is better for a large number of outliers. Here is how to do it using opencv:
A useful structure-
struct SLine
{
SLine():
numOfValidPoints(0),
params(-1.f, -1.f, -1.f, -1.f)
{}
cv::Vec4f params;//(cos(t), sin(t), X0, Y0)
int numOfValidPoints;
};
Total Least squares used to make a fit for a successful pair
cv::Vec4f TotalLeastSquares(
std::vector<cv::Point>& nzPoints,
std::vector<int> ptOnLine)
{
//if there are enough inliers calculate model
float x = 0, y = 0, x2 = 0, y2 = 0, xy = 0, w = 0;
float dx2, dy2, dxy;
float t;
for( size_t i = 0; i < nzPoints.size(); ++i )
{
x += ptOnLine[i] * nzPoints[i].x;
y += ptOnLine[i] * nzPoints[i].y;
x2 += ptOnLine[i] * nzPoints[i].x * nzPoints[i].x;
y2 += ptOnLine[i] * nzPoints[i].y * nzPoints[i].y;
xy += ptOnLine[i] * nzPoints[i].x * nzPoints[i].y;
w += ptOnLine[i];
}
x /= w;
y /= w;
x2 /= w;
y2 /= w;
xy /= w;
//Covariance matrix
dx2 = x2 - x * x;
dy2 = y2 - y * y;
dxy = xy - x * y;
t = (float) atan2( 2 * dxy, dx2 - dy2 ) / 2;
cv::Vec4f line;
line[0] = (float) cos( t );
line[1] = (float) sin( t );
line[2] = (float) x;
line[3] = (float) y;
return line;
}
The actual RANSAC
SLine LineFitRANSAC(
float t,//distance from main line
float p,//chance of hitting a valid pair
float e,//percentage of outliers
int T,//number of expected minimum inliers
std::vector<cv::Point>& nzPoints)
{
int s = 2;//number of points required by the model
int N = (int)ceilf(log(1-p)/log(1 - pow(1-e, s)));//number of independent trials
std::vector<SLine> lineCandidates;
std::vector<int> ptOnLine(nzPoints.size());//is inlier
RNG rng((uint64)-1);
SLine line;
for (int i = 0; i < N; i++)
{
//pick two points
int idx1 = (int)rng.uniform(0, (int)nzPoints.size());
int idx2 = (int)rng.uniform(0, (int)nzPoints.size());
cv::Point p1 = nzPoints[idx1];
cv::Point p2 = nzPoints[idx2];
//points too close - discard
if (cv::norm(p1- p2) < t)
{
continue;
}
//line equation -> (y1 - y2)X + (x2 - x1)Y + x1y2 - x2y1 = 0
float a = static_cast<float>(p1.y - p2.y);
float b = static_cast<float>(p2.x - p1.x);
float c = static_cast<float>(p1.x*p2.y - p2.x*p1.y);
//normalize them
float scale = 1.f/sqrt(a*a + b*b);
a *= scale;
b *= scale;
c *= scale;
//count inliers
int numOfInliers = 0;
for (size_t i = 0; i < nzPoints.size(); ++i)
{
cv::Point& p0 = nzPoints[i];
float rho = abs(a*p0.x + b*p0.y + c);
bool isInlier = rho < t;
if ( isInlier ) numOfInliers++;
ptOnLine[i] = isInlier;
}
if ( numOfInliers < T)
{
continue;
}
line.params = TotalLeastSquares( nzPoints, ptOnLine);
line.numOfValidPoints = numOfInliers;
lineCandidates.push_back(line);
}
int bestLineIdx = 0;
int bestLineScore = 0;
for (size_t i = 0; i < lineCandidates.size(); i++)
{
if (lineCandidates[i].numOfValidPoints > bestLineScore)
{
bestLineIdx = i;
bestLineScore = lineCandidates[i].numOfValidPoints;
}
}
if ( lineCandidates.empty() )
{
return SLine();
}
else
{
return lineCandidates[bestLineIdx];
}
}
Take a look at Least Mean Square metod. It's faster and simplier than RANSAC.
Also take look at OpenCV's fitLine method.
RANSAC performs better when you have a lot of outliers in your data, or a complex hypothesis.
i tried to rotate Bitmap in BlackBerry in Two Ways
1 -
public static Bitmap rotateImage(Bitmap oldB, int angle) {
int w = oldB.getWidth();
int h = oldB.getHeight();
double angRad = (angle % 360) * (Math.PI / 180);
Bitmap newB = new Bitmap(w, h);
int[] oldD = new int[w * h];
int[] newD = new int[w * h];
oldB.getARGB(oldD, 0, w, 0, 0, w, h);
int axisX = w / 2;
int axisY = h / 2;
for (int x = 0; x < oldD.length; x++) {
int oldX = x % w;
int oldY = x / w;
int op = oldX - axisX;
int adj = oldY - axisY;
double oldT = MathUtilities.atan2(op, adj);
double rad = Math.sqrt((op * op) + (adj * adj));
double newT = oldT + angRad;
int newX = (int) MathUtilities.round((rad * Math.sin(newT))
+ (double) axisX);
int newY = (int) MathUtilities.round((rad * Math.cos(newT))
+ (double) axisY);
if (newX < 0 || newY < 0 || newX >= w || newY >= h) {
newD[x] = 0x00000000;
} else {
newD[x] = oldD[(newY * w) + newX];
}
}
newB.setARGB(newD, 0, w, 0, 0, w, h);
return newB;
}
2 - the second way using drawTexturedPath
------ the function
private void drawRotatedBitmap(Graphics graphics, Bitmap bm, int angle,
int x, int y) {
int w = bm.getWidth();
int h = bm.getHeight();
double a = Math.toRadians(angle);
int x1 = (int) (x - h * Math.sin(a));
int y1 = (int) (y + h * Math.cos(a));
int x2 = (int) (x1 + w * Math.cos(a));
int y2 = (int) (y1 + w * Math.sin(a));
int x3 = (int) (x + w * Math.cos(a));
int y3 = (int) (y + w * Math.sin(a));
int xPts[] = { x, x1, x2, x3 };
int yPts[] = { y, y1, y2, y3 };
int fAngle = Fixed32.toFP(angle);
int dvx = Fixed32.cosd(fAngle);
int dux = -Fixed32.sind(fAngle);
int dvy = Fixed32.sind(fAngle);
int duy = Fixed32.cosd(fAngle);
graphics.drawTexturedPath(xPts, yPts, null, null, 0, 0, dux, dvx, duy,
dvy, bm);
}
------ How to invoke
Graphics graphics = Graphics.create(circleBmp);
drawRotatedBitmap(graphics, , 45, 0, 0);
circleBitmapField.setBitmap(circleBmp);
The First way is too slow , and the second way draw the Bitmap in wrong position
can any one help me to adjust any way of them ? or have another way to rotate bitmap fast and accurate .
Thanks for help .....
You need tu use ImageManipulator class. Find here an 'how to' document.