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I would like to create a brush for drawing on a PGraphics element with Processing. I would like past brush strokes to be visible. However, since the PGraphics element is loaded every frame, previous brush strokes disappear immediatly.
My idea was then to create PGraphics pg in setup(), make a copy of it in void(), alter the original graphic pg and update the copy at every frame. This produces a NullPointerException, most likely because pg is defined locally in setup().
This is what I have got so far:
PGraphics pg;
PFont font;
void setup (){
font = createFont("Pano Bold Kopie.otf", 600);
size(800, 800, P2D);
pg = createGraphics(800, 800, P2D);
pg.beginDraw();
pg.background(0);
pg.fill(255);
pg.textFont(font);
pg.textSize(400);
pg.pushMatrix();
pg.translate(width/2, height/2-140);
pg.textAlign(CENTER, CENTER);
pg.text("a", 0 , 0);
pg.popMatrix();
pg.endDraw();
}
void draw () {
copy(pg, 0, 0, width, height, 0, 0, width, height);
loop();
int c;
loadPixels();
for (int x=0; x<width; x++) {
for (int y=0; y<height; y++) {
pg.pixels[mouseX+mouseY*width]=0;
}
}
updatePixels();
}
My last idea, which I have not attempted to implement yet, is to append pixels which have been touched by the mouse to a list and to draw from this list each frame. But this seems quite complicated to me as it might result into super long arrays needing to be processed on top of the original image. So, I hope there is another way around!
EDIT: My goal is to create a smudge brush, hence a brush which kind of copies areas from one part of the image to other parts.
There's no need to manually copy pixels like that. The PGraphics class extends PImage, which means you can simply render it with image(pg,0,0); for example.
The other thing you could do is an old trick to fade the background: instead of clearing pixels completely you can render a sketch size slightly opaque rectangle with no stroke.
Here's a quick proof of concept based on your code:
PFont font;
PGraphics pg;
void setup (){
//font = createFont("Pano Bold Kopie.otf", 600);
font = createFont("Verdana",600);
size(800, 800, P2D);
// clear main background once
background(0);
// prep fading background
noStroke();
// black fill with 10/255 transparnecy
fill(0,10);
pg = createGraphics(800, 800, P2D);
pg.beginDraw();
// leave the PGraphics instance transparent
//pg.background(0);
pg.fill(255);
pg.textFont(font);
pg.textSize(400);
pg.pushMatrix();
pg.translate(width/2, height/2-140);
pg.textAlign(CENTER, CENTER);
pg.text("a", 0 , 0);
pg.popMatrix();
pg.endDraw();
}
void draw () {
// test with mouse pressed
if(mousePressed){
// slowly fade/clear the background by drawing a slightly opaque rectangle
rect(0,0,width,height);
}
// don't clear the background, render the PGraphics layer directly
image(pg, mouseX - pg.width / 2, mouseY - pg.height / 2);
}
If you hold the mouse pressed you can see the fade effect.
(changing transparency to 10 to a higher value with make the fade quicker)
Update To create a smudge brush you can still sample pixels and then manipulate the read colours to some degree. There are many ways to implement a smudge effect based on what you want to achieve visually.
Here's a very rough proof of concept:
PFont font;
PGraphics pg;
int pressX;
int pressY;
void setup (){
//font = createFont("Pano Bold Kopie.otf", 600);
font = createFont("Verdana",600);
size(800, 800, P2D);
// clear main background once
background(0);
// prep fading background
noStroke();
// black fill with 10/255 transparnecy
fill(0,10);
pg = createGraphics(800, 800, JAVA2D);
pg.beginDraw();
// leave the PGraphics instance transparent
//pg.background(0);
pg.fill(255);
pg.noStroke();
pg.textFont(font);
pg.textSize(400);
pg.pushMatrix();
pg.translate(width/2, height/2-140);
pg.textAlign(CENTER, CENTER);
pg.text("a", 0 , 0);
pg.popMatrix();
pg.endDraw();
}
void draw () {
image(pg,0,0);
}
void mousePressed(){
pressX = mouseX;
pressY = mouseY;
}
void mouseDragged(){
// sample the colour where mouse was pressed
color sample = pg.get(pressX,pressY);
// calculate the distance from where the "smudge" started to where it is
float distance = dist(pressX,pressY,mouseX,mouseY);
// map this distance to transparency so the further the distance the less smudge (e.g. short distance, high alpha, large distnace, small alpha)
float alpha = map(distance,0,30,255,0);
// map distance to "brush size"
float size = map(distance,0,30,30,0);
// extract r,g,b values
float r = red(sample);
float g = green(sample);
float b = blue(sample);
// set new r,g,b,a values
pg.beginDraw();
pg.fill(r,g,b,alpha);
pg.ellipse(mouseX,mouseY,size,size);
pg.endDraw();
}
As the comments mention, one idea is to sample colour on press then use the sample colour and fade it as your drag away from the source area. This shows simply reading a single pixel. You may want to experiment with sampling/reading more pixels (e.g. a rectangle or ellipse).
Additionally, the code above isn't optimised.
A few things could be sped up a bit, like reading pixels, extracting colours, calculating distance, etc.
For example:
void mouseDragged(){
// sample the colour where mouse was pressed
color sample = pg.pixels[pressX + (pressY * pg.width)];
// calculate the distance from where the "smudge" started to where it is (can use manual distance squared if this is too slow)
float distance = dist(pressX,pressY,mouseX,mouseY);
// map this distance to transparency so the further the distance the less smudge (e.g. short distance, high alpha, large distnace, small alpha)
float alpha = map(distance,0,30,255,0);
// map distance to "brush size"
float size = map(distance,0,30,30,0);
// extract r,g,b values
int r = (sample >> 16) & 0xFF; // Like red(), but faster
int g = (sample >> 8) & 0xFF;
int b = sample & 0xFF;
// set new r,g,b,a values
pg.beginDraw();
pg.fill(r,g,b,alpha);
pg.ellipse(mouseX,mouseY,size,size);
pg.endDraw();
}
The idea is to start simple with clear, readable code and only at the end, if needed look into optimisations.
i am stuck on this problem for like 20h.
The quality is not every good because on 1080p video, the minimap is less than 300px / 300px
I want to detect the 10 heros circles on this images:
Like this:
For background removal, i can use this:
The heroes portrait circle radius are between 8 to 12 because a hero portrait is like 21x21px.
With this code
Mat minimapMat = mgcodecs.imread("minimap.png");
Mat minimapCleanMat = Imgcodecs.imread("minimapClean.png");
Mat minimapDiffMat = new Mat();
Core.subtract(minimapMat, minimapCleanMat, minimapDiffMat);
I obtain this:
Now i apply circles detection on it:
findCircles(minimapDiffMat);
public static void findCircles(Mat imgSrc) {
Mat img = imgSrc.clone();
Mat gray = new Mat();
Imgproc.cvtColor(img, gray, Imgproc.COLOR_BGR2GRAY);
Imgproc.blur(gray, gray, new Size(3, 3));
Mat edges = new Mat();
int lowThreshold = 40;
int ratio = 3;
Imgproc.Canny(gray, edges, lowThreshold, lowThreshold * ratio);
Mat circles = new Mat();
Vector<Mat> circlesList = new Vector<Mat>();
Imgproc.HoughCircles(edges, circles, Imgproc.CV_HOUGH_GRADIENT, 1, 10, 5, 20, 7, 15);
double x = 0.0;
double y = 0.0;
int r = 0;
for (int i = 0; i < circles.rows(); i++) {
for (int k = 0; k < circles.cols(); k++) {
double[] data = circles.get(i, k);
for (int j = 0; j < data.length; j++) {
x = data[0];
y = data[1];
r = (int) data[2];
}
Point center = new Point(x, y);
// circle center
Imgproc.circle(img, center, 3, new Scalar(0, 255, 0), -1);
// circle outline
Imgproc.circle(img, center, r, new Scalar(0, 255, 0), 1);
}
}
HighGui.imshow("cirleIn", img);
}
Results is not ok, detecting only 2 on 10:
I have tried with knn background too:
With less success.
Any tips ? Thanks a lot in advance.
The problem is that your minimap contains highlighted parts (possibly around active players) rendering your background removal inoperable. Why not threshold the highlighted color out from the image? From what I see there are just few of them. I do not use OpenCV so I gave it a shot in C++ here is the result:
int x,y;
color c0,c1,c;
picture pic0,pic1,pic2;
// pic0 - source background
// pic1 - source map
// pic2 - output
// ensure all images are the same size
pic1.resize(pic0.xs,pic0.ys);
pic2.resize(pic0.xs,pic0.ys);
// process all pixels
for (y=0;y<pic2.ys;y++)
for (x=0;x<pic2.xs;x++)
{
// get both colors without alpha
c0.dd=pic0.p[y][x].dd&0x00FFFFFF;
c1.dd=pic1.p[y][x].dd&0x00FFFFFF; c=c1;
// threshold 0xAARRGGBB distance^2
if (distance2(c1,color(0x00EEEEEE))<2000) c.dd=0; // white-ish rectangle
if (distance2(c1,color(0x00889971))<2000) c.dd=0; // gray-ish path
if (distance2(c1,color(0x005A6443))<2000) c.dd=0; // gray-ish path
if (distance2(c1,color(0x0021A2C2))<2000) c.dd=0; // aqua water
if (distance2(c1,color(0x002A6D70))<2000) c.dd=0; // aqua water
if (distance2(c1,color(0x00439D96))<2000) c.dd=0; // aqua water
if (distance2(c1,c0 )<2500) c.dd=0; // close to background
pic2.p[y][x]=c;
}
pic2.save("out0.png");
pic2.pixel_format(_pf_u); // convert to gray scale
pic2.smooth(); // blur a little
pic2.save("out1.png");
pic2.threshold(0,80,765,0x00000000); // set dark pixels (<80) to black (0) and rest to white (3*255)
pic2.pixel_format(_pf_rgba);// convert back to RGB
pic2.save("out2.png");
So you need to find OpenCV counter parts to this. The thresholds are color distance^2 (so I do not need sqrt) and looks like 50^2 is ideal for <0,255> per channel RGB vector.
I use my own picture class for images so some members are:
xs,ys is size of image in pixels
p[y][x].dd is pixel at (x,y) position as 32 bit integer type
clear(color) clears entire image with color
resize(xs,ys) resizes image to new resolution
bmp is VCL encapsulated GDI Bitmap with Canvas access
pf holds actual pixel format of the image:
enum _pixel_format_enum
{
_pf_none=0, // undefined
_pf_rgba, // 32 bit RGBA
_pf_s, // 32 bit signed int
_pf_u, // 32 bit unsigned int
_pf_ss, // 2x16 bit signed int
_pf_uu, // 2x16 bit unsigned int
_pixel_format_enum_end
};
color and pixels are encoded like this:
union color
{
DWORD dd; WORD dw[2]; byte db[4];
int i; short int ii[2];
color(){}; color(color& a){ *this=a; }; ~color(){}; color* operator = (const color *a) { dd=a->dd; return this; }; /*color* operator = (const color &a) { ...copy... return this; };*/
};
The bands are:
enum{
_x=0, // dw
_y=1,
_b=0, // db
_g=1,
_r=2,
_a=3,
_v=0, // db
_s=1,
_h=2,
};
Here also the distance^2 between colors I used for thresholding:
DWORD distance2(color &a,color &b)
{
DWORD d,dd;
d=DWORD(a.db[0])-DWORD(b.db[0]); dd =d*d;
d=DWORD(a.db[1])-DWORD(b.db[1]); dd+=d*d;
d=DWORD(a.db[2])-DWORD(b.db[2]); dd+=d*d;
d=DWORD(a.db[3])-DWORD(b.db[3]); dd+=d*d;
return dd;
}
As input I used your images:
pic0:
pic1:
And here the (sub) results:
out0.png:
out1.png:
out2.png:
Now just remove noise (by blurring or by erosion) a bit and apply your circle fitting or hough transform...
[Edit1] circle detector
I gave it a bit of taught and implemented simple detector. I just check circumference points around any pixel position with constant radius (player circle) and if number of set point is above threshold I found potential circle. It is better than use whole disc area as some of the players contain holes and there are more pixels to test also ... Then I average close circles together and render the output ... Here updated code:
int i,j,x,y,xx,yy,x0,y0,r=10,d;
List<int> cxy; // circle circumferece points
List<int> plr; // player { x,y } list
color c0,c1,c;
picture pic0,pic1,pic2;
// pic0 - source background
// pic1 - source map
// pic2 - output
// ensure all images are the same size
pic1.resize(pic0.xs,pic0.ys);
pic2.resize(pic0.xs,pic0.ys);
// process all pixels
for (y=0;y<pic2.ys;y++)
for (x=0;x<pic2.xs;x++)
{
// get both colors without alpha
c0.dd=pic0.p[y][x].dd&0x00FFFFFF;
c1.dd=pic1.p[y][x].dd&0x00FFFFFF; c=c1;
// threshold 0xAARRGGBB distance^2
if (distance2(c1,color(0x00EEEEEE))<2000) c.dd=0; // white-ish rectangle
if (distance2(c1,color(0x00889971))<2000) c.dd=0; // gray-ish path
if (distance2(c1,color(0x005A6443))<2000) c.dd=0; // gray-ish path
if (distance2(c1,color(0x0021A2C2))<2000) c.dd=0; // aqua water
if (distance2(c1,color(0x002A6D70))<2000) c.dd=0; // aqua water
if (distance2(c1,color(0x00439D96))<2000) c.dd=0; // aqua water
if (distance2(c1,c0 )<2500) c.dd=0; // close to background
pic2.p[y][x]=c;
}
// pic2.save("out0.png");
pic2.pixel_format(_pf_u); // convert to gray scale
pic2.smooth(); // blur a little
// pic2.save("out1.png");
pic2.threshold(0,80,765,0x00000000); // set dark pixels (<80) to black (0) and rest to white (3*255)
// compute player circle circumference points mask
x0=r-1; y0=r; x0*=x0; y0*=y0;
for (x=-r,xx=x*x;x<=r;x++,xx=x*x)
for (y=-r,yy=y*y;y<=r;y++,yy=y*y)
{
d=xx+yy;
if ((d>=x0)&&(d<=y0))
{
cxy.add(x);
cxy.add(y);
}
}
// get all potential player circles
x0=(5*cxy.num)/20;
for (y=r;y<pic2.ys-r;y+=2) // no need to step by single pixel ...
for (x=r;x<pic2.xs-r;x+=2)
{
for (d=0,i=0;i<cxy.num;)
{
xx=x+cxy.dat[i]; i++;
yy=y+cxy.dat[i]; i++;
if (pic2.p[yy][xx].dd>100) d++;
}
if (d>=x0) { plr.add(x); plr.add(y); }
}
// pic2.pixel_format(_pf_rgba);// convert back to RGB
// pic2.save("out2.png");
// average all circles too close together
pic2=pic1; // use original image again
pic2.bmp->Canvas->Pen->Color=TColor(0x0000FF00);
pic2.bmp->Canvas->Pen->Width=3;
pic2.bmp->Canvas->Brush->Style=bsClear;
for (i=0;i<plr.num;i+=2) if (plr.dat[i]>=0)
{
x0=plr.dat[i+0]; x=x0;
y0=plr.dat[i+1]; y=y0; d=1;
for (j=i+2;j<plr.num;j+=2) if (plr.dat[j]>=0)
{
xx=plr.dat[j+0];
yy=plr.dat[j+1];
if (((x0-xx)*(x0-xx))+((y0-yy)*(y0-yy))*10<=20*r*r) // if close
{
x+=xx; y+=yy; d++; // add to average
plr.dat[j+0]=-1; // mark as deleted
plr.dat[j+1]=-1;
}
}
x/=d; y/=d;
plr.dat[i+0]=x;
plr.dat[i+1]=y;
pic2.bmp->Canvas->Ellipse(x-r,y-r,x+r,y+r);
}
pic2.bmp->Canvas->Pen->Width=1;
pic2.bmp->Canvas->Brush->Style=bsSolid;
// pic2.save("out3.png");
As you can see the core of code is the same I just added the detector in the end.
I also use mine dynamic list template so:
List<double> xxx; is the same as double xxx[];
xxx.add(5); adds 5 to end of the list
xxx[7] access array element (safe)
xxx.dat[7] access array element (unsafe but fast direct access)
xxx.num is the actual used size of the array
xxx.reset() clears the array and set xxx.num=0
xxx.allocate(100) preallocate space for 100 items
And here the final result out3.png:
As you can see it is a bit messed up when the players are very near (due to circle averaging) with some tweaking you might get better results. But on second taught it might be due to that small red circle nearby ...
I used VCL/GDI for the circles render so just ignore/port the pic2.bmp->Canvas-> stuff to what ever you use.
As the populated image is lighter in the blue areas around the heroes, your background subtraction is of virtually no use.
I tried to improve by applying a gain of 3 to the clean image before subtraction and here is the result.
The background has disappeared, but the outlines of the heroes are severely damaged.
I looked at your case with other approaches and I consider that it is a very difficult one.
What I do when I want to do image processing is first open the image in a paint editor (I use Gimp). Then I manipulate the image the until I end up with something that defines the parts I want to detect.
Generally, RGB is bad for a lot of computer vision tasks, and making it gray scale solves only a part of the problem.
A good start is trying to decompose the image to HSL instead.
Doing so on the first image, and only looking at the Hue channel gives me this:
Several of the blobs are quite well defined.
Playing a bit with the contrast and brightness of the Hue and Luminance layers and multiplying them gives me this:
It enhances the ring around the markers, which might be useful.
These methods all have corresponding functionality in OpenCV.
It's a tricky task and you will most likely require several different filters and techniques to succeed. Hope this helps a bit. Good luck.
So, here is my situation. I have created a object detection program which is based on color object detection. My program detects the color red and it works perfectly. But here is the problems i am facing:-
Whenever there are more than one red object in the surrounding, my program detects them and it cannot really track one object at that time(i.e it tracks other red objects of various sizes in the background. It shows me the error that "too much noise in the background". As you can see in the "threshold image" attached, it detects the round object (which is my tracking object) and my cap which is red in color. I want my program to detect only my tracking object("which is a round shaped coke cap"). How can i achieve that? Please help me out. I have my engineering design contest in few days and i have to demo my program infront of my lecturers. My program should only be able to detect and track the object which i want. Thanks
My code for the objectdetection program is a little long. So, i am hereby explaining the code as follows- I captured a frame from the webcam frame-converted it to HSV- used HSV Inrange filter to filter out the other colors but red- applied morphological operations on the filtered image. This all goes in my main function
I am using a frame resolution of 1280*720 for my webcam frame. It kind of slows down my program but it was a trade off which i had to do for performing gesture controlled operations. Anyways here is my drawobjectfunction and trackfilteredobjectfunction.
int H_MIN = 0;
int H_MAX = 256;
int S_MIN = 0;
int S_MAX = 256;
int V_MIN = 0;
int V_MAX = 256;
//default capture width and height
const int FRAME_WIDTH = 1280;
const int FRAME_HEIGHT = 720;
//max number of objects to be detected in frame
const int MAX_NUM_OBJECTS=50;
//minimum and maximum object area
const int MIN_OBJECT_AREA = 20*20;
const int MAX_OBJECT_AREA = FRAME_HEIGHT*FRAME_WIDTH/1.5;
void drawObject(int x, int y,Mat &frame){
circle(frame,Point(x,y),20,Scalar(0,255,0),2);
if(y-25>0)
line(frame,Point(x,y),Point(x,y-25),Scalar(0,255,0),2);
else line(frame,Point(x,y),Point(x,0),Scalar(0,255,0),2);
if(y+25<FRAME_HEIGHT)
line(frame,Point(x,y),Point(x,y+25),Scalar(0,255,0),2);
else line(frame,Point(x,y),Point(x,FRAME_HEIGHT),Scalar(0,255,0),2);
if(x-25>0)
line(frame,Point(x,y),Point(x-25,y),Scalar(0,255,0),2);
else line(frame,Point(x,y),Point(0,y),Scalar(0,255,0),2);
if(x+25<FRAME_WIDTH)
line(frame,Point(x,y),Point(x+25,y),Scalar(0,255,0),2);
else line(frame,Point(x,y),Point(FRAME_WIDTH,y),Scalar(0,255,0),2);
putText(frame,intToString(x)+","+intToString(y),Point(x,y+30),1,1,Scalar(0,255,0),2);
}
void trackFilteredObject(int &x, int &y, Mat threshold, Mat &cameraFeed){
Mat temp;
threshold.copyTo(temp);
//these two vectors needed for output of findContours
vector< vector<Point> > contours;
vector<Vec4i> hierarchy;
//find contours of filtered image using openCV findContours function
findContours(temp,contours,hierarchy,CV_RETR_CCOMP,CV_CHAIN_APPROX_SIMPLE );
//use moments method to find our filtered object
double refArea = 0;
bool objectFound = false;
if (hierarchy.size() > 0) {
int numObjects = hierarchy.size();
//if number of objects greater than MAX_NUM_OBJECTS we have a noisy filter
if(numObjects<MAX_NUM_OBJECTS){
for (int index = 0; index >= 0; index = hierarchy[index][0]) {
Moments moment = moments((cv::Mat)contours[index]);
double area = moment.m00;
//if the area is less than 20 px by 20px then it is probably just noise
//if the area is the same as the 3/2 of the image size, probably just a bad filter
//we only want the object with the largest area so we safe a reference area each
//iteration and compare it to the area in the next iteration.
if(area>MIN_OBJECT_AREA && area<MAX_OBJECT_AREA && area>refArea){
x = moment.m10/area;
y = moment.m01/area;
objectFound = true;
refArea = area;
}else objectFound = false;
}
//let user know you found an object
if(objectFound ==true){
putText(cameraFeed,"Tracking Object",Point(0,50),2,1,Scalar(0,255,0),2);
//draw object location on screen
drawObject(x,y,cameraFeed);}
}else putText(cameraFeed,"TOO MUCH NOISE! ADJUST FILTER",Point(0,50),1,2,Scalar(0,0,255),2);
}
}
Here is the link of the image; as you can see it also detects the red hat in the background along with the red cap of the coke bottle.
My observations:- Here is what i think, to achieve my desired goal of not detecting objects of unknown sizes of red color. I think i have to edit the value of maximum object area which i declared in the above program as (const int MAX_OBJECT_AREA = FRAME_HEIGHT*FRAME_WIDTH/1.5;). I think i have to change this value, that might eliminate the detection of bigger continous red pictures. But also, there is another problem some objects are not completely red in color and they have patches of red and other colors. So, if the detected area is within the range specfied in my program then my program detects those red patches too. What i mean to say is i was wearing a tshirt which has mixed colors and when i tested my program by wearing that tshirt, my program was able to detect the red color out of the other colors. Now, how do i solve this issue?
I think you can try out the following procedure:
obtain a circular kernel having roughly the same area as your object of interest. You can do it like: Mat kernel = getStructuringElement(MORPH_ELLIPSE, Size(d, d));
where d is the diameter of the disk.
perform normalized-cross-correlation or convolution of the filtered regions image with this kernel (I think normalized-cross-correlation would be better. And add an empty boarder around the kernel).
the peak of the resulting image should give you the location of the circular region in your filtered image (if you are using normalized-cross-correlation, you'll have to add the shift).
To speed things up, you can perform this at a reduced resolution.
You can filter out non-circular shapes by detecting circles in your thresholded image. OpenCV provides a built-on method to detect circles using Hough transform, more info here. You can take advantage of this function to retain only circles that have a radius in a given range.
Another possibility is to implement connected component labeling (CCL) into your demo program.
I believe that it was removed at some point in verions 2.x of OpenCV, but a basic implementation of the two-pass version is straightforward from the Wikipedia page.
CCL will assign a unique ID for each object after thresholding. You then have to implement matching between the objects at frame (T-1) and objects in frame (T) (for example based on some nearest distance criterion) and possibly trajectory filtering or smoothing, but this would definitely give you some extra-points.
Let me start by saying that I'm still a beginner using OpenCV. Some things might seem obvious and once I learn them hopefully they also become obvious to me.
My goal is to use the floodFill feature to generate a separate image containing only the filled area. I have looked into this post but I'm a bit lost on how to convert the filled mask into an actual BGRA image with the filled color. Besides that I also need to crop the newly filled image to contain only the filled area. I'm guessing OpenCV has some magical function that could do the trick.
Here is what I'm trying to achieve:
Original image:
Filled image:
Filled area only:
UPDATE 07/07/13
Was able to do a fill on a separate image using the following code. However, I still need to figure out the best approach to get only the filled area. Also, my floodfill solution has an issue with filling an image that contains alpha values...
static int floodFillImage (cv::Mat &image, int premultiplied, int x, int y, int color)
{
cv::Mat out;
// un-multiply color
unmultiplyRGBA2BGRA(image);
// convert to no alpha
cv::cvtColor(image, out, CV_BGRA2BGR);
// create our mask
cv::Mat mask = cv::Mat::zeros(image.rows + 2, image.cols + 2, CV_8U);
// floodfill the mask
cv::floodFill(
out,
mask,
cv::Point(x,y),
255,
0,
cv::Scalar(),
cv::Scalar(),
+ (255 << 8) + cv::FLOODFILL_MASK_ONLY);
// set new image color
cv::Mat newImage(image.size(), image.type());
cv::Mat maskedImage(image.size(), image.type());
// set the solid color we will mask out of
newImage = cv::Scalar(ARGB_BLUE(color), ARGB_GREEN(color), ARGB_RED(color), ARGB_ALPHA(color));
// crop the 2 extra pixels w and h that were given before
cv::Mat maskROI = mask(cv::Rect(1,1,image.cols,image.rows));
// mask the solid color we want into new image
newImage.copyTo(maskedImage, maskROI);
// pre multiply the colors
premultiplyBGRA2RGBA(maskedImage, image);
return 0;
}
you can get the difference of those two images to get the different pixels.
pixels with no difference will be zero and other are positive value.
cv::Mat A, B, C;
A = getImageA();
B = getImageB();
C = A - B;
handle negative values in the case.(i presume not in your case)
The issue of programmatically drawing lines using XNA has been covered here. However, I want to allow a user to draw on a canvas as one would with a drawing app such as MS Paint.
This of course requires each x and/or y coordinate change in the mouse pointer position to result in another "dot" of the line being drawn on the canvas in the crayon color in real time.
In the mouse move event, what XNA API considerations come into play in order to draw the line point by point? Literally, of course, I'm not drawing a line as such, but rather a sequence of "dots". Each "dot" can, and probably should, be larger than a single pixel. Think of drawing with a felt tip pen.
The article you provided suggests a method of drawing lines with primitives; vector graphics, in other words. Applications like Paint are mostly pixel based (even though more advanced software like Photoshop has vector and rasterization features).
Bitmap editor
Since you want it to be "Paint-like" I would definitely go with the pixel based approach:
Create a grid of color values. (Extend the System.Drawing.Bitmap class or implement your own.)
Start the (game) loop:
Process input and update the color values in the grid accordingly.
Convert the Bitmap to a Texture2D.
Use a sprite batch or custom renderer to draw the texture to the screen.
Save the bitmap, if you want.
Drawing on the bitmap
I added a rough draft of the image class I am using here at the bottom of the answer. But the code should be quite self-explanatory anyways.
As mentioned before you also need to implement a method for converting the image to a Texture2D and draw it to the screen.
First we create a new 10x10 image and set all pixels to white.
var image = new Grid<Color>(10, 10);
image.Initilaize(() => Color.White);
Next we set up a brush. A brush is in essence just a function that is applied on the whole image. In this case the function should set all pixels inside the specified circle to a dark red color.
// Create a circular brush
float brushRadius = 2.5f;
int brushX = 4;
int brushY = 4;
Color brushColor = new Color(0.5f, 0, 0, 1); // dark red
Now we apply the brush. See this SO answer of mine on how to identify the pixels inside a circle.
You can use mouse input for the brush offsets and enable the user to actually draw on the bitmap.
double radiusSquared = brushRadius * brushRadius;
image.Modify((x, y, oldColor) =>
{
// Use the circle equation
int deltaX = x - brushX;
int deltaY = y - brushY;
double distanceSquared = Math.Pow(deltaX, 2) + Math.Pow(deltaY, 2);
// Current pixel lies inside the circle
if (distanceSquared <= radiusSquared)
{
return brushColor;
}
return oldColor;
});
You could also interpolate between the brush color and the old pixel. For example, you can implement a "soft" brush by letting the blend amount depend on the distance between the brush center and the current pixel.
Drawing a line
In order to draw a freehand line simply apply the brush repeatedly, each time with a different offset (depending on the mouse movement):
Custom image class
I obviously skipped some necessary properties, methods and data validation, but you get the idea:
public class Image
{
public Color[,] Pixels { get; private set; }
public Image(int width, int height)
{
Pixels= new Color[width, height];
}
public void Initialize(Func<Color> createColor)
{
for (int x = 0; x < Width; x++)
{
for (int y = 0; y < Height; y++)
{
Pixels[x, y] = createColor();
}
}
}
public void Modify(Func<int, int, Color, Color> modifyColor)
{
for (int x = 0; x < Width; x++)
{
for (int y = 0; y < Height; y++)
{
Color current = Pixels[x, y];
Pixels[x, y] = modifyColor(x, y, current);
}
}
}
}