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Watershed algorithm with touch on image to add marker OpenCV android

I am trying to save the watershed segments as image to sdcard in android.
Code tried in c++ and working fine
for (int m = 0; m < images.size(); m++) {
//wshed = wshed*0.5 + imgGray*0.5;
cv::Mat input_bgra;
cv::cvtColor(images[m], input_bgra, CV_BGR2BGRA);
// find all white pixel and set alpha value to zero:
for (int y = 0; y < input_bgra.rows; ++y)
for (int x = 0; x < input_bgra.cols; ++x)
{
cv::Vec4b & pixel = input_bgra.at<cv::Vec4b>(y, x);
// if pixel is black
if (pixel[0] == 0 && pixel[1] == 0 && pixel[2] == 0)
{
// set alpha to zero:
pixel[3] = 0;
}
}
std::ostringstream name;
name << "D:/Sathiya/res/intlayer" << m << ".png";
imwrite(name.str(), input_bgra);
}
Not sure how to achieve this in android.

I am trying to save watershed segments as png in java , is it straightforward to do.

creating a digital filter in android

I'm trying to deal with ECG signal processing in android. I want to implement simple digital filters (lowpass, highpass)
I've got a transfer function:
here is what i've found:
wikipedia - lowpass filter - it looks quite easy here.
for i from 1 to n
y[i] := y[i-1] + α * (x[i] - y[i-1])
but there is nothing about transfer function which I want to use.
I also found the following matlab code
%% Low Pass Filter H(z) = (1 - 2z^(-6) + z^(-12)) / (1 - 2z^(-1) + z^(-2))
b = [1 0 0 0 0 0 -2 0 0 0 0 0 1];
a = [1 -2 1];
h_l = filter(b,a,[1 zeros(1,12)]);
ecg_l = conv (ecg ,h_l);
but there is no function like filter and conv in java (or I missed something).
Also I was looking on stackoverflow for an answer. But I didn't found anything about transfer function.
so can someone help me? I just want to move on with my project.

Given a time-domain recurrence equation (such as the one you quoted from wikipedia), the corresponding transfer function in the z-domain can relatively easily be obtained by using the following properties:
Where X(z) and Y(z) are the z-transforms of the time-domain input sequence x and output sequence y respectively.
Going the other way around, given a transfer function which can be expressed as a ratio of polynomials in z, such as:
the recurrence equation of the transfer function can be written as:
There are of course many different ways to implement such a recurrence equation, but a simple filter implementation following the Direct Form II would be along the line of:
// Implementation of an Infinite Impulse Response (IIR) filter
// with recurrence equation:
// y[n] = -\sum_{i=1}^M a_i y[n-i] + \sum_{i=0}^N b_i x[n-i]
public class IIRFilter {
public IIRFilter(float a_[], float b_[]) {
// initialize memory elements
int N = Math.max(a_.length, b_.length);
memory = new float[N-1];
for (int i = 0; i < memory.length; i++) {
memory[i] = 0.0f;
}
// copy filter coefficients
a = new float[N];
int i = 0;
for (; i < a_.length; i++) {
a[i] = a_[i];
}
for (; i < N; i++) {
a[i] = 0.0f;
}
b = new float[N];
i = 0;
for (; i < b_.length; i++) {
b[i] = b_[i];
}
for (; i < N; i++) {
b[i] = 0.0f;
}
}
// Filter samples from input buffer, and store result in output buffer.
// Implementation based on Direct Form II.
// Works similar to matlab's "output = filter(b,a,input)" command
public void process(float input[], float output[]) {
for (int i = 0; i < input.length; i++) {
float in = input[i];
float out = 0.0f;
for (int j = memory.length-1; j >= 0; j--) {
in -= a[j+1] * memory[j];
out += b[j+1] * memory[j];
}
out += b[0] * in;
output[i] = out;
// shift memory
for (int j = memory.length-1; j > 0; j--) {
memory[j] = memory[j - 1];
}
memory[0] = in;
}
}
private float[] a;
private float[] b;
private float[] memory;
}
which you could use to implement your specific transfer function like so:
float g = 1.0f/32.0f; // overall filter gain
float[] a = {1, -2, 1};
float[] b = {g, 0, 0, 0, 0, 0, -2*g, 0, 0, 0, 0, 0, g};
IIRFilter filter = new IIRFilter(a, b);
filter.process(input, output);
Note that you can alternatively also factorize the numerator and denominator into 2nd order polynomials and obtain a cascade of 2nd order filters (known as biquad filters).

OpenCV convert color per pixel

Hello i want to convert the color in image, i'm using per-pixel methods but it seems very slow
src.getPixels(pixels, 0, width, 0, 0, width, height);
// RGB values
int R;
for (int i = 0; i < pixels.length; i++) {
// Get RGB values as ints
// Set pixel color
pixels[i] = color;
}
// Set pixels
src.setPixels(pixels, 0, width, 0, 0, width, height);
my question, is there any way i can do it using openCV? change pixel to the color i want ?

I recommend this excellent article on how to access/modify an opencv image buffer. I recommend
"the efficient way":
int i,j;
uchar* p;
for( i = 0; i < nRows; ++i)
{
p = I.ptr<uchar>(i);
for ( j = 0; j < nCols; ++j)
{
p[j] = table[p[j]];
}
Or "the iterator-safe method":
MatIterator_<Vec3b> it, end;
for( it = I.begin<Vec3b>(), end = I.end<Vec3b>(); it != end; ++it)
{
(*it)[0] = table[(*it)[0]];
(*it)[1] = table[(*it)[1]];
(*it)[2] = table[(*it)[2]];
}
For further optimizations, using cv::LUT() (where possible) can give huge speedups, but it is more intensive to design/code.

You can access Pixels by using:
img.at<Type>(y, x);
So to change an RGB Value you can use:
// read color
Vec3b intensity = img.at<Vec3b>(y, x);
// compute new color using intensity.val[0] etc. to access color values
// write new color
img.at<Vec3b>(y, x) = intensity;
#Boyko mentioned an Article from OpenCV concerning fast access to the image pixels if you want to iterate over all Pixel. The Method I would prefer from this Article is the iterator Method, as it is only slightly slower than direct pointer access but safer to use.
Example Code:
Mat& AssignNewColors(Mat& img)
{
// accept only char type matrices
CV_Assert(img.depth() != sizeof(uchar));
const int channels = img.channels();
switch(channels)
{
// case 1: skipped here
case 3:
{
// Read RGG Pixels
Mat_<Vec3b> _img = img;
for( int i = 0; i < img.rows; ++i)
for( int j = 0; j < img.cols; ++j )
{
_img(i,j)[0] = computeNewColor(_img(i,j)[0]);
_img(i,j)[1] = computeNewColor(_img(i,j)[1]);
_img(i,j)[2] = computeNewColor(_img(i,j)[2]);
}
img = _img;
break;
}
}
return img;
}

Find dominant color in a camera frame in OpenCV Android

I want to get the dominant color in an Android CvCameraViewFrame object. I use the following OpenCV Android code to do that. This code is converted from OpenCV c++ code to OpenCV Android code. In the following code I loop through all the pixels in my camera frame and find the color of each pixel and store them in a HashMap to find the dominant color at the end of the loop. To loop through each pixel it takes about 30 seconds. This is unacceptable for me. Could somebody please review this code and point me how can I find the dominant color in a camera frame.
private String[] colors = {"cBLACK", "cWHITE", "cGREY", "cRED", "cORANGE", "cYELLOW", "cGREEN", "cAQUA", "cBLUE", "cPURPLE", "cPINK", "cRED"};
public Mat onCameraFrame(CvCameraViewFrame inputFrame) {
mRgba = inputFrame.rgba();
if (mIsColorSelected) {
Imgproc.cvtColor(mRgba, mRgba, Imgproc.COLOR_BGR2HSV);
int h = mRgba.height(); // Pixel height
int w = mRgba.width(); // Pixel width
int rowSize = (int)mRgba.step1(); // Size of row in bytes, including extra padding
float initialConfidence = 1.0f;
Map<String, Integer> tallyColors = new HashMap<String, Integer>();
byte[] pixelsTotal = new byte[h*rowSize];
mRgba.get(0,0,pixelsTotal);
//This for loop takes about 30 seconds to process for my camera frame
for (int y=0; y<h; y++) {
for (int x=0; x<w; x++) {
// Get the HSV pixel components
int hVal = (int)pixelsTotal[(y*rowSize) + x + 0]; // Hue
int sVal = (int)pixelsTotal[(y*rowSize) + x + 1]; // Saturation
int vVal = (int)pixelsTotal[(y*rowSize) + x + 2]; // Value (Brightness)
// Determine what type of color the HSV pixel is.
String ctype = getPixelColorType(hVal, sVal, vVal);
// Keep count of these colors.
int totalNum = 0;
try{
totalNum = tallyColors.get(ctype);
} catch(Exception ex){
totalNum = 0;
}
totalNum++;
tallyColors.put(ctype, totalNum);
}
}
int tallyMaxIndex = 0;
int tallyMaxCount = -1;
int pixels = w * h;
for (int i=0; i<colors.length; i++) {
String v = colors[i];
int pixCount;
try{
pixCount = tallyColors.get(v);
} catch(Exception e){
pixCount = 0;
}
Log.i(TAG, v + " - " + (pixCount*100/pixels) + "%, ");
if (pixCount > tallyMaxCount) {
tallyMaxCount = pixCount;
tallyMaxIndex = i;
}
}
float percentage = initialConfidence * (tallyMaxCount * 100 / pixels);
Log.i(TAG, "Color of currency note: " + colors[tallyMaxIndex] + " (" + percentage + "% confidence).");
}
return mRgba;
}
private String getPixelColorType(int H, int S, int V)
{
String color;
if (V < 75)
color = "cBLACK";
else if (V > 190 && S < 27)
color = "cWHITE";
else if (S < 53 && V < 185)
color = "cGREY";
else { // Is a color
if (H < 14)
color = "cRED";
else if (H < 25)
color = "cORANGE";
else if (H < 34)
color = "cYELLOW";
else if (H < 73)
color = "cGREEN";
else if (H < 102)
color = "cAQUA";
else if (H < 127)
color = "cBLUE";
else if (H < 149)
color = "cPURPLE";
else if (H < 175)
color = "cPINK";
else // full circle
color = "cRED"; // back to Red
}
return color;
}
Thank you very much.

OpenCV has an Histogram method which counts all image colors. After the histogram is calculated all you would have to do is to chose the one with the biggest count...
Check here for a tutorial (C++): Histogram Calculation.
You might also the this stackoverflow answer which shows an example on how to use Android's histogram function Imgproc.calcHist().

Think about to resize your images, then you may multiply the results by the same scale:
resize( larg_image, smallerImage , interpolation=cv.CV_INTER_CUBIC );
Or,
you may check these solutions:

You could find dominant color using k-mean clustering method.
this link will be useful.
https://www.youtube.com/watch?v=f54-x3PckH8

Color detection on Vuforia frames with OpenCV for Android

I need to do color detection(ball tracking) for Augmented Reality. I want to use Qualcomms Vuforia SDK for AR and OpenCV for image processing. I found a color detection algorithm that works on desktop(OpenCV, C++) and tried to apply this to FrameMarkers(a Vuforia sample code) but no success yet.
I got a frame from Vuforia(I can only get RGB565 or GRAYSCALE frames.) and convert to OpenCV Mat object and apply same steps with desktop solution. But I got an error on HSV conversion side. Below is the code.
//HSV range for orange objects
const int H_MIN = 7;
const int S_MIN = 186;
const int V_MIN = 60;
const int H_MAX = 256;
const int S_MAX = 256;
const int V_MAX = 157;
const bool shouldUseMorphologicalOperators = true;
const int FRAME_WIDTH = 240;
const int FRAME_HEIGHT = 320;
const int MAX_NUM_OBJECTS = 50;
const int MIN_OBJECT_AREA = 20 * 20;
const int MAX_OBJECT_AREA = 320 * 240 / 1.5;
ObjectTracker::ObjectTracker()
{
x=y=0;
}
ObjectTracker::~ObjectTracker()
{
}
void ObjectTracker::track(QCAR::Frame frame)
{
int nImages = frame.getNumImages();
for(int i = 0; i < nImages; i++)
{
const QCAR::Image *image = frame.getImage(i);
if(image->getFormat() == QCAR::RGB565)
{
Mat RGB565 = Mat(image->getHeight(),image->getWidth(),CV_8UC2,(unsigned char *)image->getPixels());
Mat HSV;
//I got error an error here
cvtColor(RGB565,HSV,CV_RGB2HSV);
Mat thresholdedImage;
inRange(HSV,Scalar(H_MIN,S_MIN,V_MIN),Scalar(H_MAX,S_MAX,V_MAX),thresholdedImage);
if(shouldUseMorphologicalOperators)
applyMorphologicalOperator(thresholdedImage);
trackFilteredObject(x,y,thresholdedImage,RGB565);
//waitKey(30);
}
}
}
void ObjectTracker::applyMorphologicalOperator(Mat &thresholdedImage)
{
//create structuring element that will be used to "dilate" and "erode" image
//the element chosen here is 3px by 3px rectangle
Mat erodeElement = getStructuringElement(MORPH_RECT,Size(3,3));
//dilate with larger element so make sure object is nicely visible
Mat dilateElement = getStructuringElement(MORPH_RECT,Size(8,8));
erode(thresholdedImage,thresholdedImage,erodeElement);
erode(thresholdedImage,thresholdedImage,erodeElement);
dilate(thresholdedImage,thresholdedImage,dilateElement);
dilate(thresholdedImage,thresholdedImage,dilateElement);
}
void ObjectTracker::trackFilteredObject(int &x,int &y,Mat &thresholdedImage,Mat &cameraFeed)
{
Mat temp;
thresholdedImage.copyTo(temp);
//Two vectors needed for output of findContours
vector< vector<Point> > contours;
vector<Vec4i> hierarcy;
//find contours of filtered image using openCV findContours function
findContours(temp,contours,hierarcy,CV_RETR_CCOMP,CV_CHAIN_APPROX_SIMPLE);
//use moments method to find out filtered object
double refArea = 0;
bool objectFound = false;
if(hierarcy.size() > 0)
{
int nObjects = hierarcy.size();
//if number of objects greater than MAX_NUM_OBJECTS we have a noisy filter
if(nObjects < MAX_NUM_OBJECTS )
{
for(int index = 0; index >= 0; index = hierarcy[index][0])
{
Moments moment = moments((cv::Mat)contours[index]);
double area = moment.m00;
//if the area is less than 20 px by 20 px 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)
{
LOG("Object found");
highlightObject(x,y,cameraFeed);
}
}
else
{
LOG("Too much noise");
}
}
else
LOG("Object not found");
}
void ObjectTracker::highlightObject(int x,int y,Mat &frame)
{
}
How to do proper conversion from RGB565 to HSV color space?

Convert it to RGB888 first using some code from this SO Question.
If you have RGB888 your conversion to HSV should work fine.
EDIT: As mentioned in the Comment. In OpenCV you can do it like this:
use cvtColor(BGR565,RGB,CV_BGR5652BGR) to conver from RGB565 to RGB and then cvtColor(RGB,HSV,CV_RGB2HSV) to convert from RGB to HSV.
EDIT2: It seems that you have to use BGR5652BGR since there is no RGB5652RGB