creating a digital filter in android - 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).

Related

Tensorflow Lite - Input shape must be 5 dimensional error

I am trying to port a tensorflow model to tensorflow lite to use it in an android application. The conversion is successful and everything runs except for Internal error: Failed to run on the given Interpreter: input must be 5-dimensional. The input in the original model was input_shape=(20, 320, 240, 1), which is 20 320 x 240 grayscale images (therefore ...,1). Here is the important code:
List<Mat> preprocessedFrames = preprocFrames(buf);
//has length of 20 -> no problem there (shouldn't affect dimensionality either...)
int[] output = new int[2];
float[][][] inputMatrices = new float[preprocessedFrames.toArray().length][320][240];
for(int i = 0; i < preprocessedFrames.toArray().length; i++) {
Mat inpRaw = preprocessedFrames.get(i);
Bitmap data = Bitmap.createBitmap(inpRaw.cols(), inpRaw.rows(), Bitmap.Config.ARGB_8888);
Utils.matToBitmap(inpRaw, data);
int[][] pixels = pixelsFromBitmap(data);
float[][] inputMatrix = inputMatrixFromIntPixels(pixels);
// returns float[][] with floats from 0 to 1
inputMatrices[i] = inputMatrix;
}
try{
detector.run(inputMatrices, output);
Debug("results: " + output.toString());
}
The model gives me an output of 2 neurons translating into 2 labels.
The model code is the following:
model = tf.keras.Sequential(name='detector')
model.add(tf.keras.layers.Conv3D(filters=(56), input_shape=(20, 320, 240, 1), strides=(2,2,2), kernel_size=(3,11,11), padding='same', activation="relu"))
model.add(tf.keras.layers.AveragePooling3D(pool_size=(1,4,4)))
model.add(tf.keras.layers.Conv3D(filters=(72), kernel_size=(4,7,7), strides=(1,2,2), padding='same'))
model.add(tf.keras.layers.Conv3D(filters=(81), kernel_size=(2,4,4), strides=(2,2,2), padding='same'))
model.add(tf.keras.layers.Conv3D(filters=(100), kernel_size=(1,2,2), strides=(3,2,2), padding='same'))
model.add(tf.keras.layers.Conv3D(filters=(128), kernel_size=(1,2,2), padding='same'))
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(768, activation='tanh', kernel_regularizer=tf.keras.regularizers.l2(0.011)))
model.add(tf.keras.layers.Dropout(rate=0.1))
model.add(tf.keras.layers.Dense(256, activation='sigmoid', kernel_regularizer=tf.keras.regularizers.l2(0.012)))
model.add(tf.keras.layers.Dense(2, activation='softmax'))
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.00001), loss=tf.keras.losses.CategoricalCrossentropy(),
metrics=['accuracy'])
EDIT: I printed out the first input tensor as follows:
int[] shape = detector.getInputTensor(0).shape();
for(int r = 0; r < shape.length; r++){
Log.d("********" + r, "*******: " + r + " : " + shape[r]);
}
With that I first get the output [1,20,320,240,1]and after that I only get [20,320,240]. I am really quite desperate now...
So, I figured it out by myself and it seems like I really only had to make the input 5 dimensional by putting the content into a first dimension and every single pixel into a fifth dimension. I don't know why, but I will accept that xD.
float[][] output = new float[1][2];
float[][][][][] inputMatrices = new float[1][preprocessedFrames.toArray().length][320][240][1];
for(int i = 0; i < preprocessedFrames.toArray().length; i++) {
Mat inpRaw = preprocessedFrames.get(i);
Bitmap data = Bitmap.createBitmap(inpRaw.cols(), inpRaw.rows(), Bitmap.Config.ARGB_8888);
Utils.matToBitmap(inpRaw, data);
int[][] pixels = pixelsFromBitmap(data);
float[][] inputMatrix = inputMatrixFromIntPixels(pixels);
for (int j = 0; j < inputMatrix.length - 1; j++) {
for(int k = 0; k < inputMatrix[0].length - 1; k++) {
inputMatrices[0][i][k][j][0] = inputMatrix[j][k];
}
}
}

Why passing Float4 object to a RenderScript Kernel gives error?

I use the following procedure for transferring an array of float numbers to a RenderScript Kernel and it works fine.
float[] w = new float[10];
Allocation w_rs = Allocation.createSized(rs, Element.F32(rs), 10);
w_rs.copy1DRangeFrom(0, 10, w);
I want to use a similar procedure for transferring Float4 values as follows
Float4[] w = new Float4[10];
for (int i = 0; i < 10; i++) {
w[i] = new Float4(i, 2*i, 3*i, 4*i);
}
Allocation w_rs = Allocation.createSized(rs, Element.F32_4(rs), 10);
w_rs.copy1DRangeFromUnchecked(0, 10, w);
Which results in the following error
Object passed is not an Array of primitives
Apparently, w should be array of primitives. But I want w to be array of Float4.
You can simply use:
float[] w = new float[4 * 10];
for (int i = 0; i < 10; i++) {
w[i * 4 + 0] = i;
w[i * 4 + 1] = i*2;
w[i * 4 + 2] = i*3;
w[i * 4 + 3] = i*4;
}
Allocation w_rs = Allocation.createSized(rs, Element.F32_4(rs), 10);
w_rs.copyFrom(w);
// Or
w_rs.copy1DRangeFrom(0,40,w);
Painless :)
Reference: RenderScript: parallel computing on Android, the easy way
Deeper explanation
Inside RenderScript Java source code, you'll see this middleware function:
public void copy1DRangeFromUnchecked(int off, int count, Object array) {
copy1DRangeFromUnchecked(off, count, array,
validateObjectIsPrimitiveArray(array, false),
java.lang.reflect.Array.getLength(array));
}
The validateObjectIsPrimitiveArray is performed on any copy-method invocation. You can pass only raw arrays.

how to filter audio by zeroing fft data in java(android)

I'm working on an audio based application for android platform.in this code first i record my voice in wav format and then i get fft(in fftbegir method) and now i want to filter my voice in 0-4khz with zeroing fft data and then perform ifft but when i play new wav file i can hear very bad quality sound with lots of noise .the fft class is here http://introcs.cs.princeton.edu/java/97data/FFT.java.html and here is my code:
private void filter(Complex[] x, int size) throws IOException {
double d, b;
String strI;
byte[] bytes = new byte[2];
int i = 0;
double k = -3.14159;
Complex[] f = new Complex[size];
Complex[] iff;
byte[] ddd;
double[] kkk = new double[size];
FFT q = new FFT();
short shor;
double data9[] = new double[size];
d = 2 * 3.14159 / size;
totalAudioLen = size;
totalDataLen = totalAudioLen + 36;
while (i < size) {//////to make its lenght length power of 2
data9[i] = k;
k = k + d;
i++;
}
i = 0;
while (i < (size / 2) - 2000) {
f[i] = new Complex(x[i].re(), x[i].im());
i++;
}
while (i < (size / 2) + 2000) { ///i want to remov 2000 sample of fft
f[i] = new Complex(0, 0);
i++;
}
while (i < size) {
f[i] = new Complex(x[i].re(), x[i].im());
i++;
}
iff = q.ifft(f);
try {
out9 = new FileOutputStream(getridemal());
out10 = new FileOutputStream(getwavfilter());
out11 = new FileOutputStream(getkhodesh());
WriteWaveFileHeader(out10, totalAudioLen, totalDataLen,
longSampleRate, channels, byteRate);
for (i = 0; i < size; i++) {
b = iff[i].re();
shor = (short) (b * 32768.0);
bytes = ByteConvert.convertToByteArray(shor);
out10.write(bytes, 0, 2);
}
} finally {
out9.close();
out10.close();
out11.close();
}
}
private void fftbegir(String input, String output) {
double[] data8;
int i = 0;
int r, k, l;
double b;
int m = 2;
try {
in5 = new FileInputStream(input);
out5 = new FileOutputStream(output);
AppLog.logString("File size: " + totalDataLen);
totalAudioLen = in5.getChannel().size();
data8 = SoundDataUtils.load16BitPCMRawDataFileAsDoubleArray();
l = data8.length;
while (l > m) {
m = m * 2;
}
Complex[] x = new Complex[m];
while (i < l) {
x[i] = new Complex(data8[i], 0);
i++;
}
in5.close();
i--;
for (i = l; i < m; i++) {
x[i] = new Complex(0, 0);
}
FFT f = new FFT();
Complex[] y = f.fft(x);
filter(y, m);
out5.close();
}
}
thanks:)
Filtering in the frequency domain as you are doing does not work well.
Whilst applying the inverse FFT to the results of a FFT yields the same samples (that is to say, it is invertible), this no longer holds true when coefficients are modified.
There are (at least) a few issues here:
The Gibbs Phenomemum which results from the sharp transition from pass-band to stop-band
The fact that the FFT is a fairly lousy band-pass filter in the first place. Components of the frequencies in the stop-band appear in several adjacent bands, and therefore remain in the signal.
Each FFT bin contains a real and imaginary component. A complex value of (0,0) has magnitude of 0 but also loses the phase information in the process.
You'd be better off with an IIR band-stop filter, which operates in the time domain. Besides working as expected, it is far cheaper to compute too.

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;
}

Code optimization for Opencv Android

I'm using this opencv code on Android for an intensity equalization. The time of execution is around 300ms per frame (720x480). Does anybody have an idea for a possible time optimization ?
Here is the code :
cvtColor(image, hsvImage, CV_BGR2HSV);
// Get intensity
intensity = hsvImage.at<Vec3b>((int)reference.Point_::y, (int)reference.Point_::x);
float value = (float)REGULAR_INTENSITY / intensity[2];
float saturation = (float)REGULAR_SATURATION / intensity[1];
if (counter == 15 && (int)intensity[2] < REGULAR_INTENSITY) {
equalization = false;
}
// Modify intensity
float transformedSaturation, transformedValue;
for(int i = 0; i < hsvImage.rows; i++) {
unsigned char *data = hsvImage.ptr(i);
for(int j = 0; j < hsvImage.cols; j++) {
transformedSaturation = (uchar)*++data * saturation;
if (transformedSaturation > MAX_COLOR) {
transformedSaturation = MAX_COLOR;
}
*data++ = transformedSaturation;
transformedValue = (uchar)*data * value;
if (transformedValue > MAX_COLOR) {
transformedValue = MAX_COLOR;
}
*data++ = transformedValue;
}
}
cvtColor(hsvImage, image, CV_HSV2BGR);
Have you tried using OpenCV transform ? I guess it is optimised, but I dont know about the saturating cast (that is if > max_color, then = max color)

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