Develop Reference

Improper Computation of NDVI in Android

I am new to image processing. I am trying to implement NDVI index on an image of agricultural farm in Android. But, I am unable to get the desired result.
Original Image:
I am using this code snippet to compute NDVI:
public int[][] applyNDVI(int[][] src1, int[][] src2) {
final int pixels[][] = new int[width][height];
for (int i = 0; i < width; i++) {
for (int j = 0; j < height; j++) {
//src1 is the image in red channel and src2 is the image in the blue channel.
final int pixel1 = src1[i][j];
final int pixel2 = src2[i][j];
final double X = (pixel2 - pixel1);
final double Y = (pixel2 + pixel1);
double Z = (X / Y);
//double Z = ((double)pixel1/(double)pixel2);
int NDVI;
NDVI = (int) (Z * 255);
pixels[i][j] = Color.argb(Color.alpha(pixel1), NDVI, NDVI, NDVI);
}
}
return pixels;
}
This code snippet is giving me this output:
From what I have read about NDVI, vegetation has a higher value of NDVI (between 0.2 and 0.9) as compared to barren/dry ground (<0.2). So, plants should be light grey and ground should be dark grey in the NDVI image. But I am getting and exactly opposite image.
Also when I am using a different NDVI formula i.e. NDVI = Red Channel / Blue Channel`i.e.
double Z = ((double)pixel1/(double)pixel2);`
This formula gives me the desired output (almost).
So, I am unable to understand this discrepancy. Also, I want to know if the NDVI formula depends on the camera used and the image taken? Or is the formula of NDVI universal? If it is universal, where am I going wrong and why am not I able to get the desired output. Any kind of help would be deeply appreciated.

Library for android for Google cardboard barrel distortion

I am trying to implement a 3D app for Android that should also support cardboard like viewers. I have seen some of those images and they seem to have some kind of barrel distortion in order to be orthogonal through the cardboard lenses.
So I was looking for algorithms or libraries specifically for Java/Android that would help me achieving this.
I have found this implementation: http://www.helviojunior.com.br/fotografia/barrel-and-pincushion-distortion/
It would be great to have something like this because it has everything I'd need. Unfortunately it's for C# and it has some specific code that I just couldn't easily translate into more generic code.
Then there is a simpler Java implementation here: http://popscan.blogspot.de/2012/04/fisheye-lens-equation-simple-fisheye.html
I have changed it to:
public static Bitmap fisheye(Bitmap srcimage) {
/*
* Fish eye effect
* tejopa, 2012-04-29
* http://popscan.blogspot.com
* http://www.eemeli.de
*/
// get image pixels
double w = srcimage.getWidth();
double h = srcimage.getHeight();
int[] srcpixels = new int[(int)(w*h)];
srcimage.getPixels(srcpixels, 0, (int)w, 0, 0, (int)w, (int)h);
Bitmap resultimage = srcimage.copy(srcimage.getConfig(), true);
// create the result data
int[] dstpixels = new int[(int)(w*h)];
// for each row
for (int y=0;y<h;y++) {
// normalize y coordinate to -1 ... 1
double ny = ((2*y)/h)-1;
// pre calculate ny*ny
double ny2 = ny*ny;
// for each column
for (int x=0;x<w;x++) {
// preset to black
dstpixels[(int)(y*w+x)] = 0;
// normalize x coordinate to -1 ... 1
double nx = ((2*x)/w)-1;
// pre calculate nx*nx
double nx2 = nx*nx;
// calculate distance from center (0,0)
// this will include circle or ellipse shape portion
// of the image, depending on image dimensions
// you can experiment with images with different dimensions
double r = Math.sqrt(nx2+ny2);
// discard pixels outside from circle!
if (0.0<=r&&r<=1.0) {
double nr = Math.sqrt(1.0-r*r);
// new distance is between 0 ... 1
nr = (r + (1.0-nr)) / 2.0;
// discard radius greater than 1.0
if (nr<=1.0) {
// calculate the angle for polar coordinates
double theta = Math.atan2(ny,nx);
// calculate new x position with new distance in same angle
double nxn = nr*Math.cos(theta);
// calculate new y position with new distance in same angle
double nyn = nr*Math.sin(theta);
// map from -1 ... 1 to image coordinates
int x2 = (int)(((nxn+1)*w)/2.0);
// map from -1 ... 1 to image coordinates
int y2 = (int)(((nyn+1)*h)/2.0);
// find (x2,y2) position from source pixels
int srcpos = (int)(y2*w+x2);
// make sure that position stays within arrays
if (srcpos>=0 & srcpos < w*h) {
// get new pixel (x2,y2) and put it to target array at (x,y)
dstpixels[(int)(y*w+x)] = srcpixels[srcpos];
}
}
}
}
}
resultimage.setPixels(dstpixels, 0, (int)w, 0, 0, (int)w, (int)h);
//return result pixels
return resultimage;
}
But it doesn't have this lens factor, so the resulting image is always a full circle/ellipse.
Any chance you could point me to some working Java code or library or (maybe even better) help me to amend this code for the lens factor to be taken into account (0.0 <= factor <= 1.0)?

I managed to get it to work.
Bottom line: I created a Bitmap bigger than the original Bitmap, and then I drew the original Bitmap on the new Bitmap (and centered it there) using
Canvas canvas = new Canvas(newBitmap);
canvas.drawBitmap(originalBitmap, null, new Rect(x, y, r, b), null);
I used the Java algorithm posted in my question to create the effect on the new Bitmap. That worked great.

Converting inputFrame from Camera to HSI using OpenCV in Android

I want to convert an image from Android camera to HSI format using OpenCV.
The problem is when I use the following method
private Mat rgb2hsi(Mat rgbFrame) {
Mat hsiFrame = rgbFrame.clone();
for( int i = 0; i < rgbFrame.rows(); ++i ) {
for( int j = 0; j < rgbFrame.cols(); ++j ) {
double[] rgb = rgbFrame.get(i, j);
Log.d(MAINTAG, "rgbFrame.get(i, j) array size = " + rgb.length);
double colorR = rgb[0];
double colorG = rgb[1];
double colorB = rgb[2];
double minRGB = min(colorR, colorG, colorB);
double colorI = (colorR + colorG + colorB) / 3;
double colorS = 0.0;
if(colorI > 0) colorS = 1.0 - (minRGB / colorI);
double colorH;
double const1 = colorR - (colorG / 2) - (colorB / 2);
double const2 = Math.sqrt(Math.pow(colorR, 2) + Math.pow(colorG, 2) + Math.pow(colorR, 2)
- (colorR * colorG) - (colorR * colorB) - (colorG * colorB));
colorH = Math.acos(const1 / const2);
if(colorB > colorG) colorH = 360 - colorH;
double[] hsi = {colorH, colorS, colorI};
hsiFrame.put(i, j, hsi);
}
}
return hsiFrame;
}
It shows an error
java.lang.UnsupportedOperationException: Provided data element number (3) should be multiple of the Mat channels count (4)
I search for a while to figure out the cause of this error.
I found that I put an array of size 3 instead of 4.
Android convert byte array from Camera API to color Mat object openCV
I wonder what Type of image receive from Android Camera.
Why when I get an array of size 4?
How to convert an image received from Android camera to HSI and preview on the screen?
The following is the overrided method onCameraFrame
public Mat onCameraFrame(CameraBridgeViewBase.CvCameraViewFrame inputFrame) {
Mat outputFrame = inputFrame.rgba();
/* Get RGB color from the pixel at [index_row, index_column] */
int index_row = 0;
int index_column = 0;
final double[] mRgb_pixel = outputFrame.get(index_row, index_column);
/* Show the result */
runOnUiThread(new Runnable() {
#Override
public void run() {
int r = (int) mRgb_pixel[0];
int g = (int) mRgb_pixel[1];
int b = (int) mRgb_pixel[2];
/* Set RGB color */
mRred_textview.setText("Red\n" + Double.toString(mRgb_pixel[0]));
mGreen_textview.setText("Green\n" + Double.toString(mRgb_pixel[1]));
mBlue_textview.setText("Blue\n" + Double.toString(mRgb_pixel[2]));
mColor_textview.setBackgroundColor(Color.rgb(r, g, b));
}
});
if(mPreviewType == PreviewType.GB) {
outputFrame.convertTo(outputFrame, CvType.CV_64FC3);
return getGBColor(rgb2hsi(outputFrame));
} else if (mPreviewType == PreviewType.HSI) {
outputFrame.convertTo(outputFrame, CvType.CV_64FC3);
return rgb2hsi(outputFrame);
} else {
return outputFrame;
}
}
My MainActivity implements CameraBridgeViewBase.CvCameraViewListener2
[Edit]
I think that the reason why it return an array of size 4 is because the frame is in RGBA format, not RGB format.
Therefore, how to convert RGBA to HSI and preview the frame on the screen?

The problem here is that your hsiFrame is a 4 channel image and your hsi array has only 3 values. You need to add one term corresponding to alpha channel to your hsi array. Making either of the following changes should work for you:
1. double[] hsi = {colorH, colorS, colorI, rgb[3]};
2. Mat hsiFrame = new Mat(rgbFrame.size(), CvType.CV_8UC3);
Hope this helps.

Overlay second image on top of image within ImageViewTouch on Android

I'm having a hard time believing this isn't possible. I have a map in an ImageViewTouch view, and I'm trying to add a "You Are Here" image on the image that I move (within the map image) based on the user's location.
When the user pans around on the map, I'd like the You Are Here to pan too by being inside the ImageViewTouch. (Ideally I'd like it to scale with zooming too, but I'll take what I can get!)
I've considered using Canvas to create a new Bitmap every time the user's location changes, but considering the map is large, it wouldn't be very performant.
Is there any way to do this?

I figured it out! Instead of using Canvas and Bitmap which are slower and cause issues, I was able to use LayerDrawable. In case this helps anyone in the future, here's my code.
public void updateYouAreHere(#Nullable MapLocation location, boolean initial){
Resources resources = getResources();
Drawable drawable;
if (location == null){
drawable = MiscUtil.getDrawable(resources, R.drawable.map_image);
}else{
Drawable[] layers = new Drawable[]{
MiscUtil.getDrawable(resources, R.drawable.map_image),
MiscUtil.getDrawable(resources, R.drawable.you_are_here)
};
mapDrawable = new LayerDrawable(layers);
final int iconWidth = 50;
final int iconHeight = 86;
mapDrawable.setLayerInset(1, location.x, location.y,
(int)(Globals.Map.MapWidth) - location.x - iconWidth, (int)(Globals.Map.MapHeight) - location.y - iconHeight);
drawable = mapDrawable;
}
if (!initial) {
Matrix currentPosition = mapScrollView.getDisplayMatrix();
float minScale = mapScrollView.getMinScale();
float maxScale = mapScrollView.getMaxScale();
mapScrollView.setImageDrawable(drawable, currentPosition, minScale, maxScale);
}else{
mapScrollView.setImageDrawable(drawable);
}
}
private class MapLocation {
public int x;
public int y;
public MapLocation(int x, int y){
this.x = x;
this.y = y;
}
}
Also, MiscUtil.getDrawable is just a wrapper that calls the appropriate getResources().getDrawable() function based on SDK version.

Treshold face images in various light

I want to ask about some ideas / study materials connected to binarization. I am trying to create system that detects human emotions. I am able to get areas such as brows, eyes, nose, mouth etc. but then comes another stage -> processing...
My images are taken in various places/time of day/weather conditions. It's problematic during binarization, with the same treshold value one images are fully black, other looks well and provide me informations I want.
What I want to ask you about is:
1) If there is known way how to bring all images to the same level of brightness?
2) How to create dependency between treshold value and brightness on image?
What I have tried for now is normalize the image... but there are no effects, maybe I'm doing something wrong. I'm using OpenCV (for android)
Core.normalize(cleanFaceMatGRAY, cleanFaceMatGRAY,0, 255, Core.NORM_MINMAX, CvType.CV_8U);
EDIT:
I tried adaptive treshold, OTSU - they didnt work for me. I have problems with using CLAHE in Android but I managed to implement Niblack algorithm.
Core.normalize(cleanFaceMatGRAY, cleanFaceMatGRAY,0, 255, Core.NORM_MINMAX, CvType.CV_8U);
nibelBlackTresholding(cleanFaceMatGRAY, -0.2);
private void nibelBlackTresholding(Mat image, double parameter) {
Mat meanPowered = image.clone();
Core.multiply(image, image, meanPowered);
Scalar mean = Core.mean(image);
Scalar stdmean = Core.mean(meanPowered);
double tresholdValue = mean.val[0] + parameter * stdmean.val[0];
int totalRows = image.rows();
int totalCols = image.cols();
for (int cols=0; cols < totalCols; cols++) {
for (int rows=0; rows < totalRows; rows++) {
if (image.get(rows, cols)[0] > tresholdValue) {
image.put(rows, cols, 255);
} else {
image.put(rows, cols, 0);
}
}
}
}
The results are really good, but still not enough for some images. I paste links cuz images are big and I don't want to take too much screen:
For example this one is tresholded really fine:
https://dl.dropboxusercontent.com/u/108321090/a1.png
https://dl.dropboxusercontent.com/u/108321090/a.png
But bad light produce shadows sometimes and this gives this effect:
https://dl.dropboxusercontent.com/u/108321090/b1.png
https://dl.dropboxusercontent.com/u/108321090/b.png
Do you have any idea that could help me to improve treshold of those images with high light difference (shadows)?
EDIT2:
I found that my previous Algorithm is implemented in wrong way. Std was calculated in wrong way. In Niblack Thresholding mean is local value not global. I repaired it according to this reference http://arxiv.org/ftp/arxiv/papers/1201/1201.5227.pdf
private void niblackThresholding2(Mat image, double parameter, int window) {
int totalRows = image.rows();
int totalCols = image.cols();
int offset = (window-1)/2;
double tresholdValue = 0;
double localMean = 0;
double meanDeviation = 0;
for (int y=offset+1; y<totalCols-offset; y++) {
for (int x=offset+1; x<totalRows-offset; x++) {
localMean = calculateLocalMean(x, y, image, window);
meanDeviation = image.get(y, x)[0] - localMean;
tresholdValue = localMean*(1 + parameter * ( (meanDeviation/(1 - meanDeviation)) - 1 ));
Log.d("QWERTY","TRESHOLD " +tresholdValue);
if (image.get(y, x)[0] > tresholdValue) {
image.put(y, x, 255);
} else {
image.put(y, x, 0);
}
}
}
}
private double calculateLocalMean(int x, int y, Mat image, int window) {
int offset = (window-1)/2;
Mat tempMat;
Rect tempRect = new Rect();
Point leftTop, bottomRight;
leftTop = new Point(x - (offset + 1), y - (offset + 1));
bottomRight = new Point(x + offset, y + offset);
tempRect = new Rect(leftTop, bottomRight);
tempMat = new Mat(image, tempRect);
return Core.mean(tempMat).val[0];
}
Results for 7x7 window and proposed in reference k parameter = 0.34: I still can't get rid of shadow on faces.
https://dl.dropboxusercontent.com/u/108321090/b2.png
https://dl.dropboxusercontent.com/u/108321090/b1.png

things to look at:
http://docs.opencv.org/java/org/opencv/imgproc/CLAHE.html
http://docs.opencv.org/java/org/opencv/imgproc/Imgproc.html#adaptiveThreshold(org.opencv.core.Mat,%20org.opencv.core.Mat,%20double,%20int,%20int,%20int,%20double)
http://docs.opencv.org/java/org/opencv/imgproc/Imgproc.html#threshold(org.opencv.core.Mat,%20org.opencv.core.Mat,%20double,%20double,%20int) (THRESH_OTSU)