I have 2 Mat objects, overlay and background.
How do I put my overlay Mat on top of my background Mat such that only the non-transparent pixels of the overlay Mat completely obscures the background Mat?
I have tried addWeighted() which combines the 2 Mat but both "layers" are still visible.
The overlay Mat has a transparent channel while the background Mat does not.
The pixel in the overlay Mat is either completely transparent or fully obscure.
Both Mats are of the same size.
The function addWeighted won't work since it will use the same alpha value to all the pixels. To do exactly what you are saying, to only replace the non transparent values in the background, you can create a small function for that, like this:
cv::Mat blending(cv::Mat& overlay, cv::Mat& background){
//must have same size for this to work
assert(overlay.cols == background.cols && overlay.rows == background.rows);
cv::Mat result = background.clone();
for (int i = 0; i < result.rows; i++){
for (int j = 0; j < result.cols; j++){
cv::Vec4b pix = overlay.at<cv::Vec4b>(i,j);
if (pix[3] == 0){
result.at<cv::Vec3b>(i,j) = cv::Vec3b(pix[0], pix[1], pix[2]);
}
}
}
return result;
}
I am not sure if the transparent value in opencv is 0 or 255, so change it accordingly.... I think it is 0 for non-transparent adn 255 for fully transparent.
If you want to use the value of the alpha channel as a rate to blend, then change it a little to this:
cv::Mat blending(cv::Mat& overlay, cv::Mat& background){
//must have same size for this to work
assert(overlay.cols == background.cols && overlay.rows == background.rows);
cv::Mat result = background.clone();
for (int i = 0; i < result.rows; i++){
for (int j = 0; j < result.cols; j++){
cv::Vec4b pix = overlay.at<cv::Vec4b>(i,j);
double alphaRate = 1.0 - pix[3]/255.0;
result.at<cv::Vec3b>(i,j) = (1.0 - alphaRate) * cv::Vec3b(pix[0], pix[1], pix[2]) + result.at<cv::Vec3b>(i,j) * alphaRate;
}
}
return result;
}
Sorry for the code being in C++ and not in JAVA, but I think you can get an idea. Basically is just a loop in the pixels and changing the pixels in the copy of background to those of the overlay if they are not transparent.
* EDIT *
I will answer your comment with this edit, since it may take space. The problem is how OpenCV matrix works. For an image with alpha, the data is organized as an array like BGRA BGRA .... BGRA, and the basic operations like add, multiply and so on work in matrices with the same dimensions..... you can always try to separate the matrix with split (this will re write the matrix so it may be slow), then change the alpha channel to double (again, rewrite) and then do the multiplication and adding of the matrices. It should be faster since OpenCV optimizes these functions.... also you can do this in GPU....
Something like this:
cv::Mat blending(cv::Mat& overlay, cv::Mat& background){
std::vector<cv::Mat> channels;
cv::split(overlay, channels);
channels[3].convertTo(channels[3], CV_64F, 1.0/255.0);
cv::Mat newOverlay, result;
cv::merge(channels, newOverlay);
result = newOverlay * channels[3] + ((1 - channels[3]) * background);
return result;
}
Not sure if OpenCV allows a CV_8U to multiply a CV_64F, or if this will be faster or not.... but it may be.
Also, the ones with loops has no problem in threads, so it can be optimized... running this in release mode will greatly increase the speed too since the .at function of OpenCV does several asserts.... that in release mode are not done. Not sure if this can be change in JAVA though...
I was able to port api55's edited answer for java:
private void merge(Mat background, Mat overlay) {
List<Mat> backgroundChannels = new ArrayList<>();
Core.split(background, backgroundChannels);
List<Mat> overlayChannels = new ArrayList<>();
Core.split(overlay, overlayChannels);
// compute "alphaRate = 1 - overlayAlpha / 255"
Mat overlayAlphaChannel = overlayChannels.get(3);
Mat alphaRate = new Mat(overlayAlphaChannel.size(), overlayAlphaChannel.type());
Core.divide(overlayAlphaChannel, new Scalar(255), alphaRate);
Core.absdiff(alphaRate, new Scalar(1), alphaRate);
for (int i = 0; i < 3; i++) {
// compute "(1 - alphaRate) * overlay"
Mat overlayChannel = overlayChannels.get(i);
Mat temp = new Mat(alphaRate.size(), alphaRate.type());
Core.absdiff(alphaRate, new Scalar(1), temp);
Core.multiply(temp, overlayChannel, overlayChannel);
temp.release();
// compute "background * alphaRate"
Mat backgroundChannel = backgroundChannels.get(i);
Core.multiply(backgroundChannel, alphaRate, backgroundChannel);
// compute the merged channel
Core.add(backgroundChannel, overlayChannel, backgroundChannel);
}
alphaRate.release();
Core.merge(backgroundChannels, background);
}
it is a lot faster compared to the double nested loop calculation.
Related
I am now building a project based on the sample color blob tracking method. I used bounding rectangles around the contours to indicate the blobs. Now I want to improve this algorithm by using an error correction method. What I do now is simply summing up the pixels in the rect region using elemsum method and calculate the average intensity and set it as the new blob detection parameter in each frame. However, the problem is that it is not accurate since those pixels outside the contour but inside the bounding rect will be counted as well. And the result is poor.
In order to solve the problem, I used another a straightforward way to loop through each pixel in the rectangle region (which is a submat), and set all pixel values out of range to the desired (or previous) hsv scalar. Then sum up all the pixels again and calculate the average intensity. This would much more accurate and easily solves the problem. The problem is that the program runs too slow on the phone (with around 1 frame per sec), though the result is accurate.
I found some sources online on how to do it in c++ using mat.forEach. I do not want to do the ndk thing and I would like to know if there is a more efficient way to do it in Java (Android).
UPDATE:
It turned out I can solve the problem by simply reducing the sampling rate. Instead of calculating the average intensity of all pixels, just a few number of them would do the job. My code:
for (int i=0; i< bounding_rect_hsv.rows();i+=10){
for (int j=0; j<bounding_rect_hsv.cols();j+=10){
double[] data = bounding_rect_hsv.get(i, j);
for (int k = 0; k < 3; k++){
if (data[k] > new_hsvColor.val[k] + 30 || data[k] < new_hsvColor.val[k] - 30) {
data[k] = new_hsvColor.val[k];
}
}
bounding_rect_hsv.put(i, j, data); //Puts element back into matrix
}
}
My source code:
Rect rect = Imgproc.boundingRect(points);
// draw enclosing rectangle (all same color, but you could use variable i to make them unique)
Imgproc.rectangle(original_frame, new Point(rect.x, rect.y), new Point(rect.x + rect.width, rect.y + rect.height), new Scalar(255, 0, 0, 255), 3);
//Todo: use the bounding rectangular to calculate average intensity (turn the pixels out of the contour to new_hsvColor)
//Just change the boundary values would be enough
bounding_rect_rgb = original_frame.submat(rect);
Imgproc.cvtColor(bounding_rect_rgb, bounding_rect_hsv, Imgproc.COLOR_RGB2HSV_FULL);
//Todo: change the logic so that pixels outside the contour will be changed to new_hsvColor
for (int i=0; i< bounding_rect_hsv.rows();i++){
for (int j=0; j<bounding_rect_hsv.cols();j++){
double[] data = bounding_rect_hsv.get(i, j);
for (int k = 0; k < 3; k++){
if (data[k] > new_hsvColor.val[k] + 30 || data[k] < new_hsvColor.val[k] - 30)
data[k] = new_hsvColor.val[k];
}
bounding_rect_hsv.put(i, j, data); //Puts element back into matrix
}
}
If you want to compute the mean value of pixels inside a contour you can simply:
Create a mask, using drawContours with parameter CV_FILLED and color Scalar(255) on a black (Scalar(0)) initialized CV_8UC1 image with same size as the original image.
Use mean to compute the mean of pixels under the mask.
You also don't need to convert to HSV every region (Rect), but you can convert the whole image once, and then access the desired region directly on the HSV image.
In the general case you want to sum the pixel values of a lot of rectangular regions, you may prefer to compute the integral image and compute the sum as the difference of values at bottom-right and top-left rectangle positions.
I have a sequence of images for which I want to calculate the median image (as to remove moving elements). Intuitively, hard-coding a loop to go through all the pixels would have a gross running time, as well as fairly large memory usage. Is there a way to easily do this in OpenCV? (I'm not interested in averaging, I need to do a median). I'm writing this for Android (using OpenCV4Android) so obviously computing power is limited.
As far as I know, there no OpenCV function that creates median image from sequence of images. I needed the same feature couple of years ago and I had to implement this myself. It is relatively slow because for each pixel you need to extract relevant pixel from multiple images (inefficient memory access) and calculate median (also a time consuming process).
Possible ways to increase efficiency are:
There no need to compute median from all images. Small subset of images will be enough.
You can find more efficient algorithms for finding median of some small groups. For example I used algorithm that can efficiently find median in group of nine values.
If the mean is ok:
Mat result(CV_64FC3, listImages[0].size());
for(int i = 0; i < listImages.size(); i++) {
result += listImages[i];
}
result /= listImages.size();
result.convertTo(result, CV_8UC3);
EDIT:
This quick pseudo-median should make the trick:
// Following algorithm will retain the pixel which is the closest to the mean
// Computing Mean
Mat tmpResult = Mat.zeros(listImages[0].size(), CV_64FC3);
for(int i = 0; i < listImages.size(); i++) {
tmpResult += listImages[i];
}
tmpResult /= listImages.size();
tmpResult.convertTo(tmpResult, CV_8UC3);
// We will now, for each pixel retain the closest to the mean
// Initializing result with the first image
Mat result(listImages[0].clone());
Mat diff1, diff2, minDiff;
for(int i = 1; i < listImages.size(); i++) {
// Computing diff between mean/newImage and mean/lastResult
absdiff(tmpResult, listImages[i], diff1);
absdiff(tmpResult, result, diff2);
// If a pixel of the new image is closer to the mean, it replaces the old one
min(diff1, diff2, minDiff);
// Get the old pixels that are still ok
result = result & ~(minDiff - diff2);
// Get the new pixels
result += listImages[i] & (minDiff - diff2);
}
However the classic one should be also pretty fast. It is O(nb^2 * w * h) where nb is the number of images and w, h their width, height. The above is O(nb * w * h) with more operations on Mats.
The code for the classical one (almost all computations will be made in native):
Mat tmp;
// We will sorting pixels where the first mat will get the lowest pixels and the last one, the highest
for(int i = 0; i < listImages.size(); i++) {
for(int j = i + 1; j < listImages.size(); j++) {
listImages[i].copyTo(tmp);
min(listImages[i], listImages[j], listImages[i]);
max(listImages[j], tmp, listImages[j]);
}
}
// We get the median
Mat result = listImages[listImages.size() / 2];
I have code
public static void program() throws Exception{
BufferedImage input = null;
long start = System.currentTimeMillis();
while((System.currentTimeMillis() - start)/1000 < 220){
for (int i = 1; i < 13; i++){
for (int j = 1; j < 7; j++){
input = robot.createScreenCapture(new Rectangle(3+i*40, 127+j*40, 40, 40));
if ((input.getRGB(6, 3) > -7000000) && (input.getRGB(6, 3)<-5000000)){
robot.mouseMove(10+i*40, 137+j*40);
robot.mousePress(InputEvent.BUTTON1_MASK);
robot.mouseRelease(InputEvent.BUTTON1_MASK);
}
}
}
}
}
On a webpage there's a matrix (12*6) and there will randomly spawn some images. Some are bad, some are good.
I'm looking for a better way to check for good images. At the moment, on good images on location (6,3) the RGB color is different from bad images.
I'm making screenshot from every box (40 * 40) and looking at pixel in location (6,3)
Don't know how to explain my code any better
EDIT:
Picture of the webpage. External links ok?
http://i.imgur.com/B5Ev1Y0.png
I'm not sure what exactly the bottleneck is in your code, but I have a hunch it might be the repeated calls to robot.createScreenCapture.
You could try calling robot.createScreenCapture on the entire matrix (i.e. a large rectangle that covers all the smaller rectangles you are interested in) outside your nested loops, and then look up the pixel values at the points you are interested in using offsets for the x and y coordinates for the sub rectangles you are inspecting.
I have an image, and I figured out how to use robot and getPixelColor() to grab the color of a certain pixel. The image is a character that I'm controlling, and I want robot to scan around the image constantly, and tell me if the pixels around it equal a certain color. Is this at all possible? Thanks!
Myself, I'd use the Robot to extract the image that's just a little larger than the "character", and then analyze the BufferedImage obtained. The details of course will depend on the details of your program. Probably the quickest would be to get the BufferedImage's Raster, then get thats dataBuffer, then get thats data, and analyze the array returned.
For example,
// screenRect is a Rectangle the contains your "character"
// + however many images around your character that you desire
BufferedImage img = robot.createScreenCapture(screenRect);
int[] imgData = ((DataBufferInt)img.getRaster().getDataBuffer()).getData();
// now that you've got the image ints, you can analyze them as you wish.
// All I've done below is get rid of the alpha value and display the ints.
for (int i = 0; i < screenRect.height; i++) {
for (int j = 0; j < screenRect.width; j++) {
int index = i * screenRect.width + j;
int imgValue = imgData[index] & 0xffffff;
System.out.printf("%06x ", imgValue );
}
System.out.println();
}
I'm making an avatar generator where the avatar components are from PNG files with transparency. The files are things like body_1.png or legs_5.png. The transparency is around the parts but not within them and the images are all grayscale. The parts are layering fine and I can get a grayscale avatar.
I would like to be able to colorize these parts dynamically, but so far I'm not having good luck. I've tried converting the pixel data from RGB to HSL and using the original pixel's L value, while supplying the new color's H value, but the results are not great.
I've looked at Colorize grayscale image but I can't seem to make what he's saying work in Java. I end up with an image that has fairly bright, neon colors everywhere.
What I would like is for transparency to remain, while colorizing the grayscale part. The black outlines should still be black and the white highlight areas should still be white (I think).
Does anyone have a good way to do this?
EDIT:
Here's an image I might be trying to color:
Again, I want to maintain the brightness levels of the grayscale image (so the outlines stay dark, the gradients are visible, and white patches are white).
I've been able to get a LookupOp working somewhat based on Colorizing images in Java but the colors always look drab and dark.
Here's an example of my output:
The color that was being used is this one (note the brightness difference): http://www.color-hex.com/color/b124e7
This is my lookupOp
protected LookupOp createColorizeOp(short R1, short G1, short B1) {
short[] alpha = new short[256];
short[] red = new short[256];
short[] green = new short[256];
short[] blue = new short[256];
//int Y = 0.3*R + 0.59*G + 0.11*B
for (short i = 0; i < 30; i++) {
alpha[i] = i;
red[i] = i;
green[i] = i;
blue[i] = i;
}
for (short i = 30; i < 256; i++) {
alpha[i] = i;
red[i] = (short)Math.round((R1 + i*.3)/2);
green[i] = (short)Math.round((G1 + i*.59)/2);
blue[i] = (short)Math.round((B1 + i*.11)/2);
}
short[][] data = new short[][] {
red, green, blue, alpha
};
LookupTable lookupTable = new ShortLookupTable(0, data);
return new LookupOp(lookupTable, null);
}
EDIT 2: I changed my LookupOp to use the following and got much nicer looking colors:
red[i] = (short)((R1)*(float)i/255.0);
green[i] = (short)((G1)*(float)i/255.0);
blue[i] = (short)((B1)*(float)i/255.0);
It seems what will work for you is something like this:
for each pixel
if pixel is white, black or transparent then leave it alone
else
apply desired H and S and make grayscale value the L
convert new HSL back to RGB
Edit: after seeing your images I have a couple of comments:
It seems you want to special treat darker tones, since you are not colorizing anything below 30. Following the same logic, shouldn't you also exempt from colorizing the higher values? That will prevent the whites and near-whites from getting tinted with color.
You should not be setting the alpha values along with RGB. The alpha value from the original image should always be preserved. Your lookup table algorithm should only affect RGB.
While you say that you tried HSL, that is not in the code that you posted. You should do your colorizing in HSL, then convert the resulting colors to RGB for your lookup table as that will preserve the original brightness of the grayscale. Your lookup table creation could be something like this:
short H = ??; // your favorite hue
short S = ??; // your favorite saturation
for (short i = 0; i < 256; i++) {
if (i < 30 || i > 226) {
red[i] = green[i] = blue[i] = i; // don't do alpha here
}
else {
HSL_to_RGB(H, S, i, red[i], green[i], blue[i])
}
}
Note: you have to provide the HSL to RGB conversion function. See my answer on Colorize grayscale image for links to source code.