I have two irregular shapes in SpriteKit, and I want to calculate the vertical distance from the base of a space ship and the (irregular) terrain right below.
Is there a way to do it ?
Thanks !
Place an SKPhysicsBody that is in a shape of a line at the center of your ship with a width of 1 and the height of your scene, then in the didBeginContact method, grab the 2 contact points. You now know 2 points, just use the distance formula (in this case it is just y2-y1) and you have your answer
I found a different way to solve my problem, but I think that KnightOfDragon's one is conceptually better (although I did not manage to make it work).
The terrain's texture is essentially a bitmap with opaque and transparent pixels. So I decided to parse these pixels, storing the highest opaque pixel for each column, building a "radar altitude map". So I just have to calculate the difference between the bottom of the ship and the altitude of the column right beneath its center:
CFDataRef imageData = CGDataProviderCopyData(CGImageGetDataProvider(terrain.texture.CGImage));
const UInt32 *pixels = (const UInt32*)CFDataGetBytePtr(imageData);
NSMutableArray *radar = [NSMutableArray new];
for (long col = 0; col < terrain.size.width; col++)
[radar addObject:#(0)];
for (long ind = 0; ind < (terrain.size.height * terrain.size.width); ind++)
{
if (pixels[ind] & 0xff000000) // non-transparent pixel
{
long line = ind/terrain.size.width;
long col = ind - (line*terrain.size.width);
if ([radar[col]integerValue] <terrain.size.height-line) radar[col] = #(terrain.size.height-line);
}
}
This solution could be optimized, of course. It's just the basic idea.
I've added an image to show the original texture, its representation as opaque/transparent pixels, and a test by putting little white nodes to check where the "surface" was.
For exactly the same image
Opencv Code:
img = imread("testImg.png",0);
threshold(img, img_bw, 0, 255, CV_THRESH_BINARY | CV_THRESH_OTSU);
Mat tmp;
img_bwR.copyTo(tmp);
findContours(tmp, contours, hierarchy, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_NONE);
// Get the moment
vector<Moments> mu(contours.size() );
for( int i = 0; i < contours.size(); i++ )
{ mu[i] = moments( contours[i], false );
}
// Display area (m00)
for( int i = 0; i < contours.size(); i++ )
{
cout<<mu[i].m00 <<endl;
// I also tried the code
//cout<<contourArea(contours.at(i))<<endl;
// But the result is the same
}
Matlab code:
Img = imread('testImg.png');
lvl = graythresh(Img);
bw = im2bw(Img,lvl);
stats = regionprops(bw,'Area');
for k = 1:length(stats)
Area = stats(k).Area; %m00
end
Any one has any thought on it? How to unify them? I think they use different methods to find contours.
I uploaded the test image at the link below so that someone who is interested in this can reproduce the procedure
It is a 100 by 100 small 8 bit grayscale image with only 0 and 255 pixel intensity. For simplicity, it only has one blob on it.
For OpenCV, the area of contour (image moment m00) is 609.5 (Very odd value)
For Matlab, the area of contour (image moment m00) is 763.
Thanks
Exist many different definitions of how contours should be extracted from binary image. For example it can be polygon that is the perimeter of white object in a binary image. If this definition was used by OpenCV, then areas of contours would be the same as areas of connected components found by Matlab. But this is not the case. Contour found by findContour() function is the polygon that connects centers of neighbor "edge pixels". Edge pixel is a white pixel that has black neighbor in N4 neighborhood.
Example: suppose you have an image whose size is 100x100 pixels. Every pixel above the diagonal is black. Every pixel below or on the diagonal is white (black triangle and white triangle). Exact separation polygon will have almost 200 vertexes at distance of 1 pixel: (0,0), (1,0), (1,1), (2,1), (2,2),.... (100,99), (100,100), (0,100). As you can see this definition is not very good from practical point of view. Polygon returned by OpenCV will have exactly 3 vertexes needed to define the triangle: (0,0), (99,99), (0,99). Its area is (99 x 99 / 2) pixels. It is not equal to number of white pixels. It is not even an integer. But this polygon is more practical than previous one.
Those are not the only possible definitions for polygon extraction. Many other definitions exist. Some of them (in my opinion) may be better than the one used by OpenCV. But this is the one that was implemented and used by a lot of people.
Currently there no effective workaround for your problem. If you want to get exactly same numbers from MATLAB and OpenCV you will have to draw the contours found by foundContours on some black image, and use function moments() on image. I know that upcoming OpenCV 3 have function that finds connected components but I didn't tried it myself.
It is easy for human eyes to tell black from other colors. But how about computers?
I printed some color blocks on the normal A4 paper. Since there are three kinds of ink to compose a color image, cyan, magenta and yellow, I set the color of each block C=20%, C=30%, C=40%, C=50% and rest of two colors are 0. That is the first column of my source image. So far, no black (K of CMYK) ink is supposed to print. After that, I set the color of each dot K=100% and rest colors are 0 to print black dots.
You may feel my image is weird and awful. In fact, the image is magnified 30 times and how the ink cheat our eyes can be seen clearly. The color strips hamper me to recognize these black dots (the dot is printed as just one pixel in 800 dpi). Without the color background, I used to blur and do canny edge detector to extract the edge. However, when adding color background, simply do grayscale and edge detector cannot get good results because of the strips. How will my eyes do in order to solve such problems?
I determined to check the brightness of source image. I referred this article and formula:
brightness = sqrt( 0.299 R * R + 0.587 G * G + 0.114 B * B )
The brightness is more close to human perception and it works very well in the yellow background because the brightness of yellow is the highest compared with cyan and magenta. But how to make cyan and magenta strips as bright as possible? The expected result is that all the strips disappear.
More complicated image:
C=40%, M=40%
C=40%, Y=40%
Y=40%, M=40%
FFT result of C=40%, Y=40% brightness image
Anyone can give me some hints to remove the color strips?
#natan I tried FFT method you suggested me, but I was not lucky to get peak at both axis x and y. In order to plot the frequency as you did, I resized my image to square.
I would convert the image to the HSV colour space and then use the Value channel. This basically separates colour and brightness information.
This is the 50% cyan image
Then you can just do a simple threshold to isolate the dots.
I just did this very quickly and im sure you could get better results. Maybe find contours in the image and then remove any contours with a small area, to filter any remaining noise.
After inspecting the images, I decided that a robust threshold will be more simple than anything. For example, looking at the C=40%, M=40% photo, I first inverted the intensities so black (the signal) will be white just using
im=(abs(255-im));
we can inspect its RGB histograms using this :
hist(reshape(single(im),[],3),min(single(im(:))):max(single(im(:))));
colormap([1 0 0; 0 1 0; 0 0 1]);
so we see that there is a large contribution to some middle intensity whereas the "signal" which is now white, is mostly separated to higher value. I then applied a simple thresholds as follows:
thr = #(d) (max([min(max(d,[],1)) min(max(d,[],2))])) ;
for n=1:size(im,3)
imt(:,:,n)=im(:,:,n).*uint8(im(:,:,n)>1.1*thr(im(:,:,n)));
end
imt=rgb2gray(imt);
and got rid of objects smaller than some typical area size
min_dot_area=20;
bw=bwareaopen(imt>0,min_dot_area);
imagesc(bw);
colormap(flipud(bone));
here's the result together with the original image:
The origin of this threshold is from this code I wrote that assumed sparse signals in the form of 2-D peaks or blobs in a noisy background. By sparse I meant that there's no pile up of peaks. In that case, when projecting max(image) on the x or y axis (by (max(im,[],1) or (max(im,[],1) you get a good measure of the background. That is because you take the minimal intensity of the max(im) vector.
If you want to look at this differently you can look at the histogram of the intensities of the image. The background is supposed to be a normal distribution of some kind around some intensity, the signal should be higher than that intensity, but with much lower # of occurrences. By finding max(im) of one of the axes (x or y) you discover what was the maximal noise level.
You'll see that the threshold picks that point in the histogram where there are still some noise above it, but ALL the signal is above it too. that's why I adjusted it to be 1.1*thr. Last, there are many fancier ways to obtain a robust threshold, this is a quick and dirty way that in my view is good enough...
Thanks to everyone for posting his answer! After some search and attempt, I also come up with an adaptive method to extract these black dots from the color background. It seems that considering only the brightness could not solve the problem perfectly. Therefore natan's method which calculates and analyzes the RGB histogram is more robust. Unfortunately, I still cannot obtain a robust threshold to extract the black dots in other color samples, because things are getting more and more unpredictable when we add deeper color (e.g. Cyan > 60) or mix two colors together (e.g. Cyan = 50, Magenta = 50).
One day, I google "extract color" and TinEye's color extraction and color thief inspire me. Both of them are very cool application and the image processed by the former website is exactly what I want. So I determine to implement a similar stuff on my own. The algorithm I used here is k-means clustering. And some other related key words to search may be color palette, color quantation and getting dominant color.
I firstly apply Gaussian filter to smooth the image.
GaussianBlur(img, img, Size(5, 5), 0, 0);
OpenCV has kmeans function and it saves me a lot of time on coding. I modify this code.
// Input data should be float32
Mat samples(img.rows * img.cols, 3, CV_32F);
for (int i = 0; i < img.rows; i++) {
for (int j = 0; j < img.cols; j++) {
for (int z = 0; z < 3; z++) {
samples.at<float>(i + j * img.rows, z) = img.at<Vec3b>(i, j)[z];
}
}
}
// Select the number of clusters
int clusterCount = 4;
Mat labels;
int attempts = 1;
Mat centers;
kmeans(samples, clusterCount, labels, TermCriteria(CV_TERMCRIT_ITER|CV_TERMCRIT_EPS, 10, 0.1), attempts, KMEANS_PP_CENTERS, centers);
// Draw clustered result
Mat cluster(img.size(), img.type());
for (int i = 0; i < img.rows; i++) {
for(int j = 0; j < img.cols; j++) {
int cluster_idx = labels.at<int>(i + j * img.rows, 0);
cluster.at<Vec3b>(i, j)[0] = centers.at<float>(cluster_idx, 0);
cluster.at<Vec3b>(i, j)[1] = centers.at<float>(cluster_idx, 1);
cluster.at<Vec3b>(i, j)[2] = centers.at<float>(cluster_idx, 2);
}
}
imshow("clustered image", cluster);
// Check centers' RGB value
cout << centers;
After clustering, I convert the result to grayscale and find the darkest color which is more likely to be the color of the black dots.
// Find the minimum value
cvtColor(cluster, cluster, CV_RGB2GRAY);
Mat dot = Mat::zeros(img.size(), CV_8UC1);
cluster.copyTo(dot);
int minVal = (int)dot.at<uchar>(dot.cols / 2, dot.rows / 2);
for (int i = 0; i < dot.rows; i += 3) {
for (int j = 0; j < dot.cols; j += 3) {
if ((int)dot.at<uchar>(i, j) < minVal) {
minVal = (int)dot.at<uchar>(i, j);
}
}
}
inRange(dot, minVal - 5 , minVal + 5, dot);
imshow("dot", dot);
Let's test two images.
(clusterCount = 4)
(clusterCount = 5)
One shortcoming of the k-means clustering is one fixed clusterCount cannot be applied to every image. Also clustering is not so fast for larger images. That's the issue annoys me a lot. My dirty method for better real time performance (on iPhone) is to crop 1/16 of the image and cluster the smaller area. Then compare all the pixels in the original image with each cluster center, and pick the pixel that are the nearest to the "black" color. I simply calculate euclidean distance between two RGB colors.
A simple method is to just threshold all the pixels. Here is this idea expressed in pseudo code.
for each pixel in image
if brightness < THRESHOLD
pixel = BLACK
else
pixel = WHITE
Or if you're always dealing with cyan, magenta and yellow backgrounds then maybe you might get better results with the criteria
if pixel.r < THRESHOLD and pixel.g < THRESHOLD and pixel.b < THRESHOLD
This method will only give good results for easy images where nothing except the black dots is too dark.
You can experiment with the value of THRESHOLD to find a good value for your images.
I suggest to convert to some chroma-based color space, like LCH, and adjust simultaneous thresholds on lightness and chroma. Here is the result mask for L < 50 & C < 25 for the input image:
Seems like you need adaptive thresholds since different values work best for different areas of the image.
You may also use HSV or HSL as a color space, but they are less perceptually uniform than LCH, derived from Lab.
During the last few days i was coding a painting behavior for a game am working on, and am currently in a very advanced phase, i can say that i have 90% of the work done and working perfectly, now what i need to do is being able to draw with a "soft brush" cause for now it's like am painting with "pixel style" and that was totally expected cause that's what i wrote,
my current goal consist of using this solution :
import a brush texture, this image
create an array that contain all The alpha values of that texture
When drawing use the array elements in order to define the new pixels alpha
And this is my code to do that (it's not very long, there is too much comments)
//The main painting method
//theObject = the object to be painted
//tmpTexture = the object current texture
//targetTexture = the new texture
void paint (GameObject theObject, Texture2D tmpTexture, Texture2D targetTexture)
{
//x and y are 2 floats from another class
//they store the coordinates of the pixel
//that get hit by the RayCast
int x = (int)(coordinates.pixelPos.x);
int y = (int)(coordinates.pixelPos.y);
//iterate through a block of pixels that goes fro
//Y and X and go #brushHeight Pixels up
// and #brushWeight Pixels right
for (int tmpY = y; tmpY<y+brushHeight; tmpY++) {
for (int tmpX = x; tmpX<x+brushWidth; tmpX++) {
//check if the current pixel is different from the target pixel
if (tmpTexture.GetPixel (tmpX, tmpY) != targetTexture.GetPixel (tmpX, tmpY)) {
//create a temporary color from the target pixel at the given coordinates
Color tmpCol = targetTexture.GetPixel (tmpX, tmpY);
//change the alpha of that pixel based on the brush alpha
//myBrushAlpha is a 2 Dimensional array that contain
//the different Alpha values of the brush
//the substractions are to keep the index in range
if (myBrushAlpha [tmpY - y, tmpX - x].a > 0) {
tmpCol.a = myBrushAlpha [tmpY - y, tmpX - x].a;
}
//set the new pixel to the current texture
tmpTexture.SetPixel (tmpX, tmpY, tmpCol);
}
}
}
//Apply
tmpTexture.Apply ();
//change the object main texture
theObject.renderer.material.mainTexture = tmpTexture;
}
Now the fun (and bad) part is the code did exactly what i asked for, but there is something that i didn't think of and i couldn't solve after spend the whole night trying,
the thing is that by asking to draw anytime with the brush alpha i found myself create a very weird effect which is decreasing the alpha value of an "old" pixel, so i tried to fix that by adding an if statement that check if the current alpha of the pixel is less than the equivalent brush alpha pixel, if it is, then augment the alpha to be equal to the brush, and if the pixel alpha is bigger, then keep adding the brush alpha value to it in order to have that "soft brushing" effect, and in code it become this :
if (myBrushAlpha [tmpY - y, tmpX - x].a > tmpCol.a) {
tmpCol.a = myBrushAlpha [tmpY - y, tmpX - x].a;
} else {
tmpCol.a += myBrushAlpha [tmpY - y, tmpX - x].a;
}
But after i've done that, i got the "pixelized brush" effect back, am not sure but i think maybe it's because am making these conditions inside a for loop so everything is executed before the end of the current frame so i don't see the effect, could it be that ?
Am really lost here and hope that you can put me in the right direction,
Thank you very much and have a great day
I have an OCR based iPhone app that takes in grayscale images and thresholds them to black and white to find the text (using opencv). This works fine for images with black text on a white background. I am having an issue with automatically switching to an inverse threshold when the image is white text on a black background. Is there a widely used algorithm for checking the image to determine if it is light text on a dark background or vice versa? Can anyone recommend a clean working method? Keep in mind, I am only working with the grayscale image from the iPhone camera.
Thanks a lot.
Since I am dealing with a grayscale IplImage at this point, I could not count black or white pixels but had to count the number of pixels above a given "brightness" threshold. I just used the border pixels as this is less expensive and still gives me enough information to make a sound decision.
IplImage *image;
int sum = 0; // Number of light pixels
int threshold = 135; // Light/Dark intensity threshold
/* Count number of light pixels at border of image. Must convert to unsigned char type to make range 0-255. */
// Check every other pixel of top and bottom
for (int i=0; i<(image->width); i+=2) {
if ((unsigned char)image->imageData[i] >= threshold) { // Check top
sum++;
}
if ((unsigned char)image->imageData[(image->width)*(image->height)
- image->width + i] >= threshold) { // Check bottom
sum++;
}
}
//Check every other pixel of left and right Sides
for (int i=0; i<(image->height); i+=2) {
if ((unsigned char)image->imageData[i*(image->width)] >= threshold) { // Check left
sum++;
}
if ((unsigned char)image->imageData[i*(image->width) + (image->width) - 1] >= threshold) { // Check right
sum++;
}
}
// If more than half of the border pixels are light, use inverse threshold to find dark characters
if (sum > ((image->width/2) + (image->height/2))) {
// Use inverse binary threshold because background is light
}
else {
// Use standard binary threshold because background is dark
}
I would go over every pixel and check if it's bright or dark.
If the count of dark pixels is bigger than the bright ones, you have to invert the picture.
Look here for how to determinate the brightness:
Detect black pixel in image iOS
And that's how to draw an UIImage inverted:
[imyImage drawInRect:theImageRect blendMode:kCGBlendModeDifference alpha:1.0];