I'm trying to detect a collision between a small rectangle around the cursor and a "Connector", which is basically just a line between two points.
Now, I've decided to use the Intersector.intersectLinePolygon(p1, p2, polygon) method to do so, but when I run the code. It detects a collision everytime any of the rectangle X or Y points are in the same range as the line's bounding box and I can't really get my head around it. The desired result is the collision reporting only when the rectangle is actually touching the line.
Vector3 worldPos = cam.unproject(new Vector3(mouseX, mouseY, 0));
Rectangle rect = new Rectangle(worldPos.x-4, worldPos.y-4, 8, 8);
Boolean connectorIntersected = false;
for (int i = 0; i < nodeConnectorHandler.getAllConnectors().size(); i++) {
//Getting two points that make the connector line
Node n1 = nodeConnectorHandler.getAllConnectors().get(i).getFrom();
Node n2 = nodeConnectorHandler.getAllConnectors().get(i).getTo();
float x1 = n1.getCX();
float y1 = n1.getCY();
float x2 = n2.getCX();
float y2 = n2.getCY();
//Making a polygon out of rect
Polygon p = new Polygon(new float[] {
rect.getX(),
rect.getY(),
(rect.getX()+8f),
rect.getY(),
(rect.getX()+8f),
(rect.getY()+8f),
rect.getX(),
(rect.getY()+8f)
});
//Checking if the line intersects the polygon (representing the rectangle around the cursor)
if (Intersector.intersectLinePolygon(new Vector2(x1,y1), new Vector2(x2,y2), p))
{
selectedIndex = nodeConnectorHandler.getAllConnectors().get(i).getID();
System.out.println("ConnectorIntersected!");
connectorIntersected = true;
}
break
}
The code reports a collision everytime the rectangle is in these areas (shown in yellow, aprox):
photoshopped image link
The red line inbetween those 2 dots is the "connector"
Cursor is right below the line. It reports a collision in those yellow areas spanning across the whole game world.
I suppose I'm either not using the function properly or that I've made some obvious mistake. Or is this how the function should react? I really don't know. Thanks for any help :)
Ok, apparently I used the wrong method. intersectSegmentPolygon works as expected. My bad ¯_(ツ)_/¯.
I have a Map where i'm placing markers and connecting them with PolyLines:
PolylineOptions p = new PolylineOptions();
p.color(Color.BLUE);
p.width((float) 7.0);
Polyline polyLine = this.mMap.addPolyline(p);
p.add(actualLocation);
LatLng previousPoint = latLngs.get(latLngs.size() - 2);
p.add(previousPoint);
this.polyLines.add(polyLine);
mMap.addPolyline(p);
I'm saving the object p in an arraylist:
ArrayList<Polyline> polyLines = new ArrayList<>();
When i remove the last marker i want to remove the last polyline too. I'm doing the next:
if (polyLines.size() > 0) {
Polyline polyLine = polyLines.get(polyLines.size() - 1);
polyLine.remove();
polyLines.remove(polyLines.size() - 1);
}
I'm removing the marker but the polyline keeps in the Map. I'm removing it from the ArrayList too.
Can anyone help me to find out what is happening? I've tried to make the polyline invisible or changing the colour but it won't work.
It looks to me like you are in fact adding two polylines to the map... If PolylineOptions() are mutable once attached to a Polyline, they are in the same spot, otherwise you are adding one without positioning followed by a second. Only the second Polyline is being added to your List.
Instead of this:
PolylineOptions p = new PolylineOptions();
p.color(Color.BLUE);
p.width((float) 7.0);
Polyline polyLine = this.mMap.addPolyline(p); // Add before location set
p.add(actualLocation);
LatLng previousPoint = latLngs.get(latLngs.size() - 2);
p.add(previousPoint);
this.polyLines.add(polyLine);
mMap.addPolyline(p); // Add after location set
Did you want to do this?
PolylineOptions p = new PolylineOptions();
p.color(Color.BLUE);
p.width((float) 7.0);
p.add(actualLocation);
LatLng previousPoint = latLngs.get(latLngs.size() - 2);
p.add(previousPoint);
Polyline polyLine = mMap.addPolyline(p);
this.polyLines.add(polyLine);
I'm attempting to draw polygons on a map with location data as the user moves. I do not want any breaks between the polygons. I need one side of the next polygon to connect to the previous polygon's side. How would I go about this? I have what I've made so far, but I'm unsure how to gather the location data and use that from before to make the polygons connect.
I am using:
onLocationChanged(Location location) {
currentLocation = (new LatLng(location.getLatitude(), location.getLongitude()));
if(previousLocation != null) {
polygonCornerBackLeftCorner = SphericalUtil.computeOffset(currentLocation, widthInMeters / 2, location.getBearing() + 90);
polygonCornerBackRightCorner = SphericalUtil.computeOffset(currentLocation, widthInMeters / 2, location.getBearing() + 90);
}
previousLocation = currentLocation
}
polygonGenerator() {
PolygonOptions polygon = new PolygonOptions().add(polygonCornerBackLeftCorner, polygonCornerBackRightCorner, polygonCornerFrontRight, polyCornerFrontLeft).fillColor(Color.YELLOW);
getMap().addPolygon(whatsquare);
}
Whenever you get a new currentLocation, you'll want to compute the corner points that correspond to it, and you'll want to cache them for later. If this isn't the first location, then you'll have a pair of cached corner points, and you'll use them plus the new ones to make your polygon and add it to the map, and then cache the new corner points for the next iteration.
at the moment Iam working with Android Opencv. Iam Using the featureDetector with the Method FAST to find KeyPoints on an 240 x 320 Image.
//create a keypoint mat
MatOfKeyPoint keyPoints = new MatOfKeyPoint();
//create feature detector
FeatureDetector fd = FeatureDetector.create(FeatureDetector.FAST);
//detect feature points
fd.detect(image, keyPoints);
//return feature points
return keyPoints;
Next I will extract the points from the KeyPoints after Matching them:
DMatch[] matchesArray = matches.toArray();
Vector<Point> pointsVec1 = new Vector<Point>();
Vector<Point> pointsVec2 = new Vector<Point>();
for(int i = 0; i < matchesArray.length; i++)
{
pointsVec1.add(keyPoints1.toArray()[matchesArray[i].queryIdx].pt);
pointsVec2.add(keyPoints2.toArray()[matchesArray[i].trainIdx].pt);
}
points1.fromList(pointsVec1);
points2.fromList(pointsVec2);
Later I try to get the pixel color of these points, doing this:
Point[] pointsArray = points.toArray();
Vector<byte[]> colorVector = new Vector<byte[]>();
for(int i = 0; i< pointsArray.length; i++)
{
Point point = pointsArray[i];
byte[] color = new byte[4];
image.get((int) point.x, (int) point.y, color);
colorVector.add(color);
}
return colorVector;
Iam still wondering because there are Points out of border of the Image. For example I found this point by debugging: (308.0, 16.0). By an 240 x 340 Image this point is not in the image. There are more Point of these. I checked already the featureExtractor and the Point is already containing there. So i get a color of R = 0, G = 0, B = 0.
So my question is:
Where does this Points come from?
Have I to filter them on my own or is there something like Threshhold solution in the function?
Or I have to switch x and y or change the conversion of the Points?
In the end it is a small problem but i cant explain myself where doese they come from!
So thanks for your help!
Make sure you have not switch x <-> y or row <-> col.
This is my situation. It involves aligning a scanned image which will account for incorrect scanning. I must align the scanned image with my Java program.
These are more details:
There is a table-like form printed on a sheet of paper, which will be scanned into an image file.
I will open the picture with Java, and I will have an OVERLAY of text boxes.
The text boxes are supposed to align correctly with the scanned image.
In order to align correctly, my Java program must analyze the scanned image and detect the coordinates of the edges of the table on the scanned image, and thus position the image and the textboxes so that the textboxes and the image both align properly (in case of incorrect scanning)
You see, the guy scanning the image might not necessarily place the image in a perfectly correct position, so I need my program to automatically align the scanned image as it loads it. This program will be reusable on many of such scanned images, so I need the program to be flexible in this way.
My question is one of the following:
How can I use Java to detect the y coordinate of the upper edge of the table and the x-coordinate of the leftmost edge of the table. The table is a a regular table with many cells, with black thin border, printed on a white sheet of paper (horizontal printout)
If an easier method exists to automatically align the scanned image in such a way that all scanned images will have the graphical table align to the same x, y coordinates, then share this method :).
If you don't know the answer to the above to questions, do tell me where I should start. I don't know much about graphics java programming and I have about 1 month to finish this program. Just assume that I have a tight schedule and I have to make the graphics part as simple as possible for me.
Cheers and thank you.
Try to start from a simple scenario and then improve the approach.
Detect corners.
Find the corners in the boundaries of the form.
Using the form corners coordinates, calculate the rotation angle.
Rotate/scale the image.
Map the position of each field in the form relative to form origin coordinates.
Match the textboxes.
The program presented at the end of this post does the steps 1 to 3. It was implemented using Marvin Framework. The image below shows the output image with the detected corners.
The program also outputs: Rotation angle:1.6365770416167182
Source code:
import java.awt.Color;
import java.awt.Point;
import marvin.image.MarvinImage;
import marvin.io.MarvinImageIO;
import marvin.plugin.MarvinImagePlugin;
import marvin.util.MarvinAttributes;
import marvin.util.MarvinPluginLoader;
public class FormCorners {
public FormCorners(){
// Load plug-in
MarvinImagePlugin moravec = MarvinPluginLoader.loadImagePlugin("org.marvinproject.image.corner.moravec");
MarvinAttributes attr = new MarvinAttributes();
// Load image
MarvinImage image = MarvinImageIO.loadImage("./res/printedForm.jpg");
// Process and save output image
moravec.setAttribute("threshold", 2000);
moravec.process(image, null, attr);
Point[] boundaries = boundaries(attr);
image = showCorners(image, boundaries, 12);
MarvinImageIO.saveImage(image, "./res/printedForm_output.jpg");
// Print rotation angle
double angle = (Math.atan2((boundaries[1].y*-1)-(boundaries[0].y*-1),boundaries[1].x-boundaries[0].x) * 180 / Math.PI);
angle = angle >= 0 ? angle : angle + 360;
System.out.println("Rotation angle:"+angle);
}
private Point[] boundaries(MarvinAttributes attr){
Point upLeft = new Point(-1,-1);
Point upRight = new Point(-1,-1);
Point bottomLeft = new Point(-1,-1);
Point bottomRight = new Point(-1,-1);
double ulDistance=9999,blDistance=9999,urDistance=9999,brDistance=9999;
double tempDistance=-1;
int[][] cornernessMap = (int[][]) attr.get("cornernessMap");
for(int x=0; x<cornernessMap.length; x++){
for(int y=0; y<cornernessMap[0].length; y++){
if(cornernessMap[x][y] > 0){
if((tempDistance = Point.distance(x, y, 0, 0)) < ulDistance){
upLeft.x = x; upLeft.y = y;
ulDistance = tempDistance;
}
if((tempDistance = Point.distance(x, y, cornernessMap.length, 0)) < urDistance){
upRight.x = x; upRight.y = y;
urDistance = tempDistance;
}
if((tempDistance = Point.distance(x, y, 0, cornernessMap[0].length)) < blDistance){
bottomLeft.x = x; bottomLeft.y = y;
blDistance = tempDistance;
}
if((tempDistance = Point.distance(x, y, cornernessMap.length, cornernessMap[0].length)) < brDistance){
bottomRight.x = x; bottomRight.y = y;
brDistance = tempDistance;
}
}
}
}
return new Point[]{upLeft, upRight, bottomRight, bottomLeft};
}
private MarvinImage showCorners(MarvinImage image, Point[] points, int rectSize){
MarvinImage ret = image.clone();
for(Point p:points){
ret.fillRect(p.x-(rectSize/2), p.y-(rectSize/2), rectSize, rectSize, Color.red);
}
return ret;
}
public static void main(String[] args) {
new FormCorners();
}
}
Edge detection is something that is typically done by enhancing the contrast between neighboring pixels, such that you get a easily detectable line, which is suitable for further processing.
To do this, a "kernel" transforms a pixel according it the pixel's inital value, and the value of that pixel's neighbors. A good edge detection kernel will enhance the differences between neighboring pixels, and reduce the strength of a pixel with similar neigbors.
I would start by looking at the Sobel operator. This might not return results that are immediately useful to you; however, it will get you far closer than you would be if you were to approach the problem with little knowledge of the field.
After you have some crisp clean edges, you can use larger kernels to detect points where it seems that a 90% bend in two lines occurs, that might give you the pixel coordinates of the outer rectangle, which might be enough for your purposes.
With those outer coordinates, it still is a bit of math to make the new pixels be composted with the average values between the old pixels rotated and moved to "match". The results (especially if you do not know about anti-aliasing math) can be pretty bad, adding blur to the image.
Sharpening filters might be a solution, but they come with their own issues, mainly they make the picture sharper by adding graininess. Too much, and it is obvious that the original image is not a high-quality scan.
I researched the libraries but in the end I found it more convenient to code up my own edge detection methods.
The class below will detect black/grayed out edges of a scanned sheet of paper that contains such edges, and will return the x and y coordinate of the edges of the sheet of paper, starting from the rightmost end (reverse = true) or from lower end (reverse = true) or from the top edge (reverse = false) or from left edge (reverse = false). Also...the program will take ranges along vertical edges (rangex) measured in pixels, and horizontal ranges (rangey) measured in pixels. The ranges determine outliers in the points received.
The program does 4 vertical cuts using the specified arrays, and 4 horizontal cuts. It retrieves the values of the dark dots. It uses the ranges to eliminate outliers. Sometimes, a little spot on the paper may cause an outlier point. The smaller the range, the fewer the outliers. However, sometimes the edge is slightly tilted, so you don't want to make the range too small.
Have fun. It works perfectly for me.
import java.awt.image.BufferedImage;
import java.awt.Color;
import java.util.ArrayList;
import java.lang.Math;
import java.awt.Point;
public class EdgeDetection {
public App ap;
public int[] horizontalCuts = {120, 220, 320, 420};
public int[] verticalCuts = {300, 350, 375, 400};
public void printEdgesTest(BufferedImage image, boolean reversex, boolean reversey, int rangex, int rangey){
int[] mx = horizontalCuts;
int[] my = verticalCuts;
//you are getting edge points here
//the "true" parameter indicates that it performs a cut starting at 0. (left edge)
int[] xEdges = getEdges(image, mx, reversex, true);
int edgex = getEdge(xEdges, rangex);
for(int x = 0; x < xEdges.length; x++){
System.out.println("EDGE = " + xEdges[x]);
}
System.out.println("THE EDGE = " + edgex);
//the "false" parameter indicates you are doing your cut starting at the end (image.getHeight)
//and ending at 0
//if the parameter was true, it would mean it would start the cuts at y = 0
int[] yEdges = getEdges(image, my, reversey, false);
int edgey = getEdge(yEdges, rangey);
for(int y = 0; y < yEdges.length; y++){
System.out.println("EDGE = " + yEdges[y]);
}
System.out.println("THE EDGE = " + edgey);
}
//This function takes an array of coordinates...detects outliers,
//and computes the average of non-outlier points.
public int getEdge(int[] edges, int range){
ArrayList<Integer> result = new ArrayList<Integer>();
boolean[] passes = new boolean[edges.length];
int[][] differences = new int[edges.length][edges.length-1];
//THIS CODE SEGMENT SAVES THE DIFFERENCES BETWEEN THE POINTS INTO AN ARRAY
for(int n = 0; n<edges.length; n++){
for(int m = 0; m<edges.length; m++){
if(m < n){
differences[n][m] = edges[n] - edges[m];
}else if(m > n){
differences[n][m-1] = edges[n] - edges[m];
}
}
}
//This array determines which points are outliers or nots (fall within range of other points)
for(int n = 0; n<edges.length; n++){
passes[n] = false;
for(int m = 0; m<edges.length-1; m++){
if(Math.abs(differences[n][m]) < range){
passes[n] = true;
System.out.println("EDGECHECK = TRUE" + n);
break;
}
}
}
//Create a new array only using valid points
for(int i = 0; i<edges.length; i++){
if(passes[i]){
result.add(edges[i]);
}
}
//Calculate the rounded mean... This will be the x/y coordinate of the edge
//Whether they are x or y values depends on the "reverse" variable used to calculate the edges array
int divisor = result.size();
int addend = 0;
double mean = 0;
for(Integer i : result){
addend += i;
}
mean = (double)addend/(double)divisor;
//returns the mean of the valid points: this is the x or y coordinate of your calculated edge.
if(mean - (int)mean >= .5){
System.out.println("MEAN " + mean);
return (int)mean+1;
}else{
System.out.println("MEAN " + mean);
return (int)mean;
}
}
//this function computes "dark" points, which include light gray, to detect edges.
//reverse - when true, starts counting from x = 0 or y = 0, and ends at image.getWidth or image.getHeight()
//verticalEdge - determines whether you want to detect a vertical edge, or a horizontal edge
//arr[] - determines the coordinates of the vertical or horizontal cuts you will do
//set the arr[] array according to the graphical layout of your scanned image
//image - this is the image you want to detect black/white edges of
public int[] getEdges(BufferedImage image, int[] arr, boolean reverse, boolean verticalEdge){
int red = 255;
int green = 255;
int blue = 255;
int[] result = new int[arr.length];
for(int n = 0; n<arr.length; n++){
for(int m = reverse ? (verticalEdge ? image.getWidth():image.getHeight())-1:0; reverse ? m>=0:m<(verticalEdge ? image.getWidth():image.getHeight());){
Color c = new Color(image.getRGB(verticalEdge ? m:arr[n], verticalEdge ? arr[n]:m));
red = c.getRed();
green = c.getGreen();
blue = c.getBlue();
//determine if the point is considered "dark" or not.
//modify the range if you want to only include really dark spots.
//occasionally, though, the edge might be blurred out, and light gray helps
if(red<239 && green<239 && blue<239){
result[n] = m;
break;
}
//count forwards or backwards depending on reverse variable
if(reverse){
m--;
}else{
m++;
}
}
}
return result;
}
}
A similar such problem I've done in the past basically figured out the orientation of the form, re-aligned it, re-scaled it, and I was all set. You can use the Hough transform to to detect the angular offset of the image (ie: how much it is rotated), but you still need to detect the boundaries of the form. It also had to accommodate for the boundaries of the piece of paper itself.
This was a lucky break for me, because it basically showed a black and white image in the middle of a big black border.
Apply an aggressive, 5x5 median filter to remove some noise.
Convert from grayscale to black and white (rescale intensity values from [0,255] to [0,1]).
Calculate the Principal Component Analysis (ie: calculate the Eigenvectors of the covariance matrix for your image from the calculated Eigenvalues) (http://en.wikipedia.org/wiki/Principal_component_analysis#Derivation_of_PCA_using_the_covariance_method)
4) This gives you a basis vector. You simply use that to re-orient your image to a standard basis matrix (ie: [1,0],[0,1]).
Your image is now aligned beautifully. I did this for normalizing the orientation of MRI scans of entire human brains.
You also know that you have a massive black border around the actual image. You simply keep deleting rows from the top and bottom, and both sides of the image until they are all gone. You can temporarily apply a 7x7 median or mode filter to a copy of the image so far at this point. It helps rule out too much border remaining in the final image from thumbprints, dirt, etc.