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In my game,if I touch a particular object,coin objects will come out of them at random speeds and occupy random positions.
public void update(delta){
if(isTouched()&& getY()<Constants.WORLD_HEIGHT/2){
setY(getY()+(randomSpeed * delta));
setX(getX()-(randomSpeed/4 * delta));
}
}
Now I want to make this coins occupy positions in some patterns.Like if 3 coins come out,a triangle pattern or if 4 coins, rectangular pattern like that.
I tried to make it work,but coins are coming out and moved,but overlapping each other.Not able to create any patterns.
patterns like:
This is what I tried
int a = Math.abs(rndNo.nextInt() % 3)+1;//no of coins
int no =0;
float coinxPos = player.getX()-coins[0].getWidth()/2;
float coinyPos = player.getY();
int minCoinGap=20;
switch (a) {
case 1:
for (int i = 0; i < coins.length; i++) {
if (!coins[i].isCoinVisible() && no < a) {
coins[i].setCoinVisible(true);
coinxPos = coinxPos+rndNo.nextInt()%70;
coinyPos = coinyPos+rndNo.nextInt()%70;
coins[i].setPosition(coinxPos, coinyPos);
no++;
}
}
break;
case 2:
for (int i = 0; i < coins.length; i++) {
if (!coins[i].isCoinVisible() && no < a) {
coins[i].setCoinVisible(true);
coinxPos = coinxPos+minCoinGap+rndNo.nextInt()%70;
coinyPos = coinyPos+rndNo.nextInt()%150;
coins[i].setPosition(coinxPos, coinyPos);
no++;
}
}
break:
......
......
default:
break;
may be this is a simple logic to implement,but I wasted a lot of time on it and got confused of how to make it work.
Any help would be appreciated.
In my game, when I want some object at X,Y to reach some specific coordinates Xe,Ye at every frame I'm adding to it's coordinates difference between current and wanted position, divided by constant and multiplied by time passed from last frame. That way it starts moving quickly and goes slowly and slowly as it's closer, looks kinda cool.
X += ((Xe - X)* dt)/ CONST;
Y += ((Ye - Y)* dt)/ CONST;
You'll experimentally get that CONST value, bigger value means slower movement. If you want it to look even cooler you can add velocity variable and instead of changing directly coordinates depending on distance from end position you can adjust that velocity. That way even if object at some point reaches the end position it will still have some velocity and it will keep moving - it will have inertia. A bit more complex to achieve, but movement would be even wilder.
And if you want that Xe,Ye be some specific position (not random), then just set those constant values. No need to make it more complicated then that. Set like another constat OFFSET:
static final int OFFSET = 100;
Xe1 = X - OFFSET; // for first coin
Ye1 = Y - OFFSET;
Xe2 = X + OFFSET; // for second coin
Ye2 = Y - OFFSET;
...
I am using the following code to convert a Bitmap to Complex and vice versa.
Even though those were directly copied from Accord.NET framework, while testing these static methods, I have discovered that, repeated use of these static methods cause 'data-loss'. As a result, the end output/result becomes distorted.
public partial class ImageDataConverter
{
#region private static Complex[,] FromBitmapData(BitmapData bmpData)
private static Complex[,] ToComplex(BitmapData bmpData)
{
Complex[,] comp = null;
if (bmpData.PixelFormat == PixelFormat.Format8bppIndexed)
{
int width = bmpData.Width;
int height = bmpData.Height;
int offset = bmpData.Stride - (width * 1);//1 === 1 byte per pixel.
if ((!Tools.IsPowerOf2(width)) || (!Tools.IsPowerOf2(height)))
{
throw new Exception("Imager width and height should be n of 2.");
}
comp = new Complex[width, height];
unsafe
{
byte* src = (byte*)bmpData.Scan0.ToPointer();
for (int y = 0; y < height; y++)
{
for (int x = 0; x < width; x++, src++)
{
comp[y, x] = new Complex((float)*src / 255,
comp[y, x].Imaginary);
}
src += offset;
}
}
}
else
{
throw new Exception("EightBppIndexedImageRequired");
}
return comp;
}
#endregion
public static Complex[,] ToComplex(Bitmap bmp)
{
Complex[,] comp = null;
if (bmp.PixelFormat == PixelFormat.Format8bppIndexed)
{
BitmapData bmpData = bmp.LockBits( new Rectangle(0, 0, bmp.Width, bmp.Height),
ImageLockMode.ReadOnly,
PixelFormat.Format8bppIndexed);
try
{
comp = ToComplex(bmpData);
}
finally
{
bmp.UnlockBits(bmpData);
}
}
else
{
throw new Exception("EightBppIndexedImageRequired");
}
return comp;
}
public static Bitmap ToBitmap(Complex[,] image, bool fourierTransformed)
{
int width = image.GetLength(0);
int height = image.GetLength(1);
Bitmap bmp = Imager.CreateGrayscaleImage(width, height);
BitmapData bmpData = bmp.LockBits(
new Rectangle(0, 0, width, height),
ImageLockMode.ReadWrite,
PixelFormat.Format8bppIndexed);
int offset = bmpData.Stride - width;
double scale = (fourierTransformed) ? Math.Sqrt(width * height) : 1;
unsafe
{
byte* address = (byte*)bmpData.Scan0.ToPointer();
for (int y = 0; y < height; y++)
{
for (int x = 0; x < width; x++, address++)
{
double min = System.Math.Min(255, image[y, x].Magnitude * scale * 255);
*address = (byte)System.Math.Max(0, min);
}
address += offset;
}
}
bmp.UnlockBits(bmpData);
return bmp;
}
}
(The DotNetFiddle link of the complete source code)
(ImageDataConverter)
Output:
As you can see, FFT is working correctly, but, I-FFT isn't.
That is because bitmap to complex and vice versa isn't working as expected.
What could be done to correct the ToComplex() and ToBitmap() functions so that they don't loss data?
I do not code in C# so handle this answer with extreme prejudice!
Just from a quick look I spotted few problems:
ToComplex()
Is converting BMP into 2D complex matrix. When you are converting you are leaving imaginary part unchanged, but at the start of the same function you have:
Complex[,] complex2D = null;
complex2D = new Complex[width, height];
So the imaginary parts are either undefined or zero depends on your complex class constructor. This means you are missing half of the data needed for reconstruction !!! You should restore the original complex matrix from 2 images one for real and second for imaginary part of the result.
ToBitmap()
You are saving magnitude which is I think sqrt( Re*Re + Im*Im ) so it is power spectrum not the original complex values and so you can not reconstruct back... You should store Re,Im in 2 separate images.
8bit per pixel
That is not much and can cause significant round off errors after FFT/IFFT so reconstruction can be really distorted.
[Edit1] Remedy
There are more options to repair this for example:
use floating complex matrix for computations and bitmap only for visualization.
This is the safest way because you avoid additional conversion round offs. This approach has the best precision. But you need to rewrite your DIP/CV algorithms to support complex domain matrices instead of bitmaps which require not small amount of work.
rewrite your conversions to support real and imaginary part images
Your conversion is really bad as it does not store/restore Real and Imaginary parts as it should and also it does not account for negative values (at least I do not see it instead they are cut down to zero which is WRONG). I would rewrite the conversion to this:
// conversion scales
float Re_ofset=256.0,Re_scale=512.0/255.0;
float Im_ofset=256.0,Im_scale=512.0/255.0;
private static Complex[,] ToComplex(BitmapData bmpRe,BitmapData bmpIm)
{
//...
byte* srcRe = (byte*)bmpRe.Scan0.ToPointer();
byte* srcIm = (byte*)bmpIm.Scan0.ToPointer();
complex c = new Complex(0.0,0.0);
// for each line
for (int y = 0; y < height; y++)
{
// for each pixel
for (int x = 0; x < width; x++, src++)
{
complex2D[y, x] = c;
c.Real = (float)*(srcRe*Re_scale)-Re_ofset;
c.Imaginary = (float)*(srcIm*Im_scale)-Im_ofset;
}
src += offset;
}
//...
}
public static Bitmap ToBitmapRe(Complex[,] complex2D)
{
//...
float Re = (complex2D[y, x].Real+Re_ofset)/Re_scale;
Re = min(Re,255.0);
Re = max(Re, 0.0);
*address = (byte)Re;
//...
}
public static Bitmap ToBitmapIm(Complex[,] complex2D)
{
//...
float Im = (complex2D[y, x].Imaginary+Im_ofset)/Im_scale;
Re = min(Im,255.0);
Re = max(Im, 0.0);
*address = (byte)Im;
//...
}
Where:
Re_ofset = min(complex2D[,].Real);
Im_ofset = min(complex2D[,].Imaginary);
Re_scale = (max(complex2D[,].Real )-min(complex2D[,].Real ))/255.0;
Im_scale = (max(complex2D[,].Imaginary)-min(complex2D[,].Imaginary))/255.0;
or cover bigger interval then the complex matrix values.
You can also encode both Real and Imaginary parts to single image for example first half of image could be Real and next the Imaginary part. In that case you do not need to change the function headers nor names at all .. but you would need to handle the images as 2 joined squares each with different meaning ...
You can also use RGB images where R = Real, B = Imaginary or any other encoding that suites you.
[Edit2] some examples to make my points more clear
example of approach #1
The image is in form of floating point 2D complex matrix and the images are created only for visualization. There is little rounding error this way. The values are not normalized so the range is <0.0,255.0> per pixel/cell at first but after transforms and scaling it could change greatly.
As you can see I added scaling so all pixels are multiplied by 315 to actually see anything because the FFT output values are small except of few cells. But only for visualization the complex matrix is unchanged.
example of approach #2
Well as I mentioned before you do not handle negative values, normalize values to range <0,1> and back by scaling and rounding off and using only 8 bits per pixel to store the sub results. I tried to simulate that with my code and here is what I got (using complex domain instead of wrongly used power spectrum like you did). Here C++ source only as an template example as you do not have the functions and classes behind it:
transform t;
cplx_2D c;
rgb2i(bmp0);
c.ld(bmp0,bmp0);
null_im(c);
c.mul(1.0/255.0);
c.mul(255.0); c.st(bmp0,bmp1); c.ld(bmp0,bmp1); i2iii(bmp0); i2iii(bmp1); c.mul(1.0/255.0);
bmp0->SaveToFile("_out0_Re.bmp");
bmp1->SaveToFile("_out0_Im.bmp");
t. DFFT(c,c);
c.wrap();
c.mul(255.0); c.st(bmp0,bmp1); c.ld(bmp0,bmp1); i2iii(bmp0); i2iii(bmp1); c.mul(1.0/255.0);
bmp0->SaveToFile("_out1_Re.bmp");
bmp1->SaveToFile("_out1_Im.bmp");
c.wrap();
t.iDFFT(c,c);
c.mul(255.0); c.st(bmp0,bmp1); c.ld(bmp0,bmp1); i2iii(bmp0); i2iii(bmp1); c.mul(1.0/255.0);
bmp0->SaveToFile("_out2_Re.bmp");
bmp1->SaveToFile("_out2_Im.bmp");
And here the sub results:
As you can see after the DFFT and wrap the image is really dark and most of the values are rounded off. So the result after unwrap and IDFFT is really pure.
Here some explanations to code:
c.st(bmpre,bmpim) is the same as your ToBitmap
c.ld(bmpre,bmpim) is the same as your ToComplex
c.mul(scale) multiplies complex matrix c by scale
rgb2i converts RGB to grayscale intensity <0,255>
i2iii converts grayscale intensity ro grayscale RGB image
I'm not really good in this puzzles but double check this dividing.
comp[y, x] = new Complex((float)*src / 255, comp[y, x].Imaginary);
You can loose precision as it is described here
Complex class definition in Remarks section.
May be this happens in your case.
Hope this helps.
I have a heightmap. I want to efficiently compute which tiles in it are visible from an eye at any given location and height.
This paper suggests that heightmaps outperform turning the terrain into some kind of mesh, but they sample the grid using Bresenhams.
If I were to adopt that, I'd have to do a line-of-sight Bresenham's line for each and every tile on the map. It occurs to me that it ought to be possible to reuse most of the calculations and compute the heightmap in a single pass if you fill outwards away from the eye - a scanline fill kind of approach perhaps?
But the logic escapes me. What would the logic be?
Here is a heightmap with a the visibility from a particular vantagepoint (green cube) ("viewshed" as in "watershed"?) painted over it:
Here is the O(n) sweep that I came up with; I seems the same as that given in the paper in the answer below How to compute the visible area based on a heightmap? Franklin and Ray's method, only in this case I am walking from eye outwards instead of walking the perimeter doing a bresenhams towards the centre; to my mind, my approach would have much better caching behaviour - i.e. be faster - and use less memory since it doesn't have to track the vector for each tile, only remember a scanline's worth:
typedef std::vector<float> visbuf_t;
inline void map::_visibility_scan(const visbuf_t& in,visbuf_t& out,const vec_t& eye,int start_x,int stop_x,int y,int prev_y) {
const int xdir = (start_x < stop_x)? 1: -1;
for(int x=start_x; x!=stop_x; x+=xdir) {
const int x_diff = abs(eye.x-x), y_diff = abs(eye.z-y);
const bool horiz = (x_diff >= y_diff);
const int x_step = horiz? 1: x_diff/y_diff;
const int in_x = x-x_step*xdir; // where in the in buffer would we get the inner value?
const float outer_d = vec2_t(x,y).distance(vec2_t(eye.x,eye.z));
const float inner_d = vec2_t(in_x,horiz? y: prev_y).distance(vec2_t(eye.x,eye.z));
const float inner = (horiz? out: in).at(in_x)*(outer_d/inner_d); // get the inner value, scaling by distance
const float outer = height_at(x,y)-eye.y; // height we are at right now in the map, eye-relative
if(inner <= outer) {
out.at(x) = outer;
vis.at(y*width+x) = VISIBLE;
} else {
out.at(x) = inner;
vis.at(y*width+x) = NOT_VISIBLE;
}
}
}
void map::visibility_add(const vec_t& eye) {
const float BASE = -10000; // represents a downward vector that would always be visible
visbuf_t scan_0, scan_out, scan_in;
scan_0.resize(width);
vis[eye.z*width+eye.x-1] = vis[eye.z*width+eye.x] = vis[eye.z*width+eye.x+1] = VISIBLE;
scan_0.at(eye.x) = BASE;
scan_0.at(eye.x-1) = BASE;
scan_0.at(eye.x+1) = BASE;
_visibility_scan(scan_0,scan_0,eye,eye.x+2,width,eye.z,eye.z);
_visibility_scan(scan_0,scan_0,eye,eye.x-2,-1,eye.z,eye.z);
scan_out = scan_0;
for(int y=eye.z+1; y<height; y++) {
scan_in = scan_out;
_visibility_scan(scan_in,scan_out,eye,eye.x,-1,y,y-1);
_visibility_scan(scan_in,scan_out,eye,eye.x,width,y,y-1);
}
scan_out = scan_0;
for(int y=eye.z-1; y>=0; y--) {
scan_in = scan_out;
_visibility_scan(scan_in,scan_out,eye,eye.x,-1,y,y+1);
_visibility_scan(scan_in,scan_out,eye,eye.x,width,y,y+1);
}
}
Is it a valid approach?
it is using centre-points rather than looking at the slope between the 'inner' pixel and its neighbour on the side that the LoS passes
could the trig in to scale the vectors and such be replaced by factor multiplication?
it could use an array of bytes since the heights are themselves bytes
its not a radial sweep, its doing a whole scanline at a time but away from the point; it only uses only a couple of scanlines-worth of additional memory which is neat
if it works, you could imagine that you could distribute it nicely using a radial sweep of blocks; you have to compute the centre-most tile first, but then you can distribute all immediately adjacent tiles from that (they just need to be given the edge-most intermediate values) and then in turn more and more parallelism.
So how to most efficiently calculate this viewshed?
What you want is called a sweep algorithm. Basically you cast rays (Bresenham's) to each of the perimeter cells, but keep track of the horizon as you go and mark any cells you pass on the way as being visible or invisible (and update the ray's horizon if visible). This gets you down from the O(n^3) of the naive approach (testing each cell of an nxn DEM individually) to O(n^2).
More detailed description of the algorithm in section 5.1 of this paper (which you might also find interesting for other reasons if you aspire to work with really enormous heightmaps).
I need to get the total of the item prices(which are in the same column in a list view in C#) without selecting the label called lblTotal.The item prices come from the databases and need to get the sum of them within the application itself. Can you please help me and send your ideas.
I have build this using WinForms and I have designed the following code
float lblTotal = 0F;
for (int i = 0; i < orderList.Items.Count; i++)
{
if (orderList.Items[i].Selected)
{
lblTotal = float.Parse(orderList.Items[i].SubItems[1].Text);
}
}
However this is not working for my application.
Thank you
I windows forms c# the following code will total up all the integer values in column 2,
change the 2 to whatever column your values are stored in.
int column = 0;
column = 2;
int total = 0;
total = 0;
ListViewItem item = default(ListViewItem);
foreach ( item in this.ListView1.Items) {
total += int.Parse((string)item.SubItems(2).Text);
}
It is best to somehow calculate the sum either by moving the calculation to the database (SUM(x)) or your business logic/data source on the client, instead of going through all the hoops of reading it from the UI and conversion/casting.
Can anybody share code or algorithm(using pattern recognition) for image comparision in .net.
I need to compare 2 images of different resolution and textures and the find the difference . Now i have code to find the difference between 2 images using C#
// Load the images.
Bitmap bm1 = (Bitmap) (Image.FromFile(txtFile1.Text));
Bitmap bm2 = (Bitmap) (Image.FromFile(txtFile2.Text));
// Make a difference image.
int wid = Math.Min(bm1.Width, bm2.Width);
int hgt = Math.Min(bm1.Height, bm2.Height);
Bitmap bm3 = new Bitmap(wid, hgt);
// Create the difference image.
bool are_identical = true;
int r1;
int g1;
int b1;
int r2;
int g2;
int b2;
int r3;
int g3;
int b3;
Color eq_color = Color.Transparent;
Color ne_color = Color.Transparent;
for (int x = 0; x <= wid - 1; x++)
{
for (int y = 0; y <= hgt - 1; y++)
{
if (bm1.GetPixel(x, y).Equals(bm2.GetPixel(x, y)))
{
bm3.SetPixel(x, y, eq_color);
}
else
{
bm1.SetPixel(x, y, ne_color);
are_identical = false;
}
}
}
// Display the result.
picResult.Image = bm1;
Bitmap Logo = new Bitmap(picResult.Image);
Logo.MakeTransparent(Logo.GetPixel(1, 1));
picResult.Image = (Image)Logo;
//this.Cursor = Cursors.Default;
if ((bm1.Width != bm2.Width) || (bm1.Height != bm2.Height))
{
are_identical = false;
}
if (are_identical)
{
MessageBox.Show("The images are identical");
}
else
{
MessageBox.Show("The images are different");
}
//bm1.Dispose()
// bm2.Dispose()
BUT this compare if the 2 images are of same resolution and size.if some shadow is there on one image(but the 2 images are same) it shows the difference between the image..so i am trying to compare using pattern recognition.
As nailxx said, there is no "100% working free code" or something. Some years ago I helped implementing a "face recognition" app, and one of the things we used was "Locale binary patterns". Its not too easy, but it gave quite good results. Find a paper about it here:
Local binary patterns
Edit: I'm afraid I can't find the paper that I have used these days, it was shorter and fixed on the LBP itself and not how to use it with textures.
Your request is a really complex scientific (not even engineering) task.
The basic obvious algorithm is the following:
Somehow select all object on both comparing images.
This part is relatively simple and can be solved in many ways.
Compare all objects. This part is a task for scientists, considering the fact that they can be shifted, rotated, resized, and so on. :)
However, this can be solved in the case of you have a fixed number of entities to recognize. Like "circle", "triangle","rectange","line".