Develop Reference

How to fix the image preprocessing difference between tensorflow and android studio?

I'm trying to build a classification model with keras and deploy the model to my Android phone. I use the code from this website to deploy my own converted model, which is a .pb file, to my Android phone. I load a image from my phone and everything worked fine, but the prediction result is totally different from the result I got from my PC.
The procedure of testing on my PC are:
load the image with cv2, and convert to np.float32
use the keras resnet50 'preprocess_input' python function to preprocess the image
expand the image dimension for batching (batch size is 1)
forward the image to model and get the result
Relevant code:
img = cv2.imread('./my_test_image.jpg')
x = preprocess_input(img.astype(np.float32))
x = np.expand_dims(x, axis=0)
net = load_model('./my_model.h5')
prediction_result = net.predict(x)
And I noticed that the image preprocessing part of Android is different from the method I used in keras, which mode is caffe(convert the images from RGB to BGR, then zero-center each color channel with respect to the ImageNet dataset). It seems that the original code is for mode tf(will scale pixels between -1 to 1).
So I modified the following code of 'preprocessBitmap' to what I think it should be, and use a 3 channel RGB image with pixel value [127,127,127] to test it. The code predicted the same result as .h5 model did. But when I load a image to classify, the prediction result is different from .h5 model.
Does anyone has any idea? Thank you very much.
I have tried the following:
Load a 3 channel RGB image in my Phone with pixel value [127,127,127], and use the modified code below, and it will give me a prediction result that is same as prediction result using .h5 model on PC.
Test the converted .pb model on PC using tensorflow gfile module with a image, and it give me a correct prediction result (compare to .h5 model). So I think the converted .pb file does not have any problem.
Entire section of preprocessBitmap
// code of 'preprocessBitmap' section in TensorflowImageClassifier.java
TraceCompat.beginSection("preprocessBitmap");
// Preprocess the image data from 0-255 int to normalized float based
// on the provided parameters.
bitmap.getPixels(intValues, 0, bitmap.getWidth(), 0, 0, bitmap.getWidth(), bitmap.getHeight());
for (int i = 0; i < intValues.length; ++i) {
// this is a ARGB format, so we need to mask the least significant 8 bits to get blue, and next 8 bits to get green and next 8 bits to get red. Since we have an opaque image, alpha can be ignored.
final int val = intValues[i];
// original
/*
floatValues[i * 3 + 0] = (((val >> 16) & 0xFF) - imageMean) / imageStd;
floatValues[i * 3 + 1] = (((val >> 8) & 0xFF) - imageMean) / imageStd;
floatValues[i * 3 + 2] = ((val & 0xFF) - imageMean) / imageStd;
*/
// what I think it should be to do the same thing in mode caffe when using keras
floatValues[i * 3 + 0] = (((val >> 16) & 0xFF) - (float)123.68);
floatValues[i * 3 + 1] = (((val >> 8) & 0xFF) - (float)116.779);
floatValues[i * 3 + 2] = (((val & 0xFF)) - (float)103.939);
}
TraceCompat.endSection();

This question is old, but remains the top Google result for preprocess_input for ResNet50 on Android. I could not find an answer for implementing preprocess_input for Java/Android, so I came up with the following based on the original python/keras code:
/*
Preprocesses RGB bitmap IAW keras/imagenet
Port of https://github.com/tensorflow/tensorflow/blob/v2.3.1/tensorflow/python/keras/applications/imagenet_utils.py#L169
with data_format='channels_last', mode='caffe'
Convert the images from RGB to BGR, then will zero-center each color channel with respect to the ImageNet dataset, without scaling.
Returns 3D float array
*/
static float[][][] imagenet_preprocess_input_caffe( Bitmap bitmap ) {
// https://github.com/tensorflow/tensorflow/blob/v2.3.1/tensorflow/python/keras/applications/imagenet_utils.py#L210
final float[] imagenet_means_caffe = new float[]{103.939f, 116.779f, 123.68f};
float[][][] result = new float[bitmap.getHeight()][bitmap.getWidth()][3]; // assuming rgb
for (int y = 0; y < bitmap.getHeight(); y++) {
for (int x = 0; x < bitmap.getWidth(); x++) {
final int px = bitmap.getPixel(x, y);
// rgb-->bgr, then subtract means. no scaling
result[y][x][0] = (Color.blue(px) - imagenet_means_caffe[0] );
result[y][x][1] = (Color.green(px) - imagenet_means_caffe[1] );
result[y][x][2] = (Color.red(px) - imagenet_means_caffe[2] );
}
}
return result;
}
Usage with a 3D tensorflow-lite input with shape (1,224,224,3):
Bitmap bitmap = <your bitmap of size 224x224x3>;
float[][][][] imgValues = new float[1][bitmap.getHeight()][bitmap.getWidth()][3];
imgValues[0]=imagenet_preprocess_input_caffe(bitmap);
... <prep tfInput, tfOutput> ...
tfLite.run(tfInput, tfOutput);

YUV_420_888 interpretation on Samsung Galaxy S7 (Camera2)

I wrote a conversion from YUV_420_888 to Bitmap, considering the following logic (as I understand it):
To summarize the approach: the kernel’s coordinates x and y are congruent both with the x and y of the non-padded part of the Y-Plane (2d-allocation) and the x and y of the output-Bitmap. The U- and V-Planes, however, have a different structure than the Y-Plane, because they use 1 byte for coverage of 4 pixels, and, in addition, may have a PixelStride that is more than one, in addition they might also have a padding that can be different from that of the Y-Plane. Therefore, in order to access the U’s and V’s efficiently by the kernel I put them into 1-d allocations and created an index “uvIndex” that gives the position of the corresponding U- and V within that 1-d allocation, for given (x,y) coordinates in the (non-padded) Y-plane (and, so, the output Bitmap).
In order to keep the rs-Kernel lean, I excluded the padding area in the yPlane by capping the x-range via LaunchOptions (this reflects the RowStride of the y-plane which thus can be ignored WITHIN the kernel). So we just need to consider the uvPixelStride and uvRowStride within the uvIndex, i.e. the index used in order to access to the u- and v-values.
This is my code:
Renderscript Kernel, named yuv420888.rs
#pragma version(1)
#pragma rs java_package_name(com.xxxyyy.testcamera2);
#pragma rs_fp_relaxed
int32_t width;
int32_t height;
uint picWidth, uvPixelStride, uvRowStride ;
rs_allocation ypsIn,uIn,vIn;
// The LaunchOptions ensure that the Kernel does not enter the padding zone of Y, so yRowStride can be ignored WITHIN the Kernel.
uchar4 __attribute__((kernel)) doConvert(uint32_t x, uint32_t y) {
// index for accessing the uIn's and vIn's
uint uvIndex= uvPixelStride * (x/2) + uvRowStride*(y/2);
// get the y,u,v values
uchar yps= rsGetElementAt_uchar(ypsIn, x, y);
uchar u= rsGetElementAt_uchar(uIn, uvIndex);
uchar v= rsGetElementAt_uchar(vIn, uvIndex);
// calc argb
int4 argb;
argb.r = yps + v * 1436 / 1024 - 179;
argb.g = yps -u * 46549 / 131072 + 44 -v * 93604 / 131072 + 91;
argb.b = yps +u * 1814 / 1024 - 227;
argb.a = 255;
uchar4 out = convert_uchar4(clamp(argb, 0, 255));
return out;
}
Java side:
private Bitmap YUV_420_888_toRGB(Image image, int width, int height){
// Get the three image planes
Image.Plane[] planes = image.getPlanes();
ByteBuffer buffer = planes[0].getBuffer();
byte[] y = new byte[buffer.remaining()];
buffer.get(y);
buffer = planes[1].getBuffer();
byte[] u = new byte[buffer.remaining()];
buffer.get(u);
buffer = planes[2].getBuffer();
byte[] v = new byte[buffer.remaining()];
buffer.get(v);
// get the relevant RowStrides and PixelStrides
// (we know from documentation that PixelStride is 1 for y)
int yRowStride= planes[0].getRowStride();
int uvRowStride= planes[1].getRowStride(); // we know from documentation that RowStride is the same for u and v.
int uvPixelStride= planes[1].getPixelStride(); // we know from documentation that PixelStride is the same for u and v.
// rs creation just for demo. Create rs just once in onCreate and use it again.
RenderScript rs = RenderScript.create(this);
//RenderScript rs = MainActivity.rs;
ScriptC_yuv420888 mYuv420=new ScriptC_yuv420888 (rs);
// Y,U,V are defined as global allocations, the out-Allocation is the Bitmap.
// Note also that uAlloc and vAlloc are 1-dimensional while yAlloc is 2-dimensional.
Type.Builder typeUcharY = new Type.Builder(rs, Element.U8(rs));
//using safe height
typeUcharY.setX(yRowStride).setY(y.length / yRowStride);
Allocation yAlloc = Allocation.createTyped(rs, typeUcharY.create());
yAlloc.copyFrom(y);
mYuv420.set_ypsIn(yAlloc);
Type.Builder typeUcharUV = new Type.Builder(rs, Element.U8(rs));
// note that the size of the u's and v's are as follows:
// ( (width/2)*PixelStride + padding ) * (height/2)
// = (RowStride ) * (height/2)
// but I noted that on the S7 it is 1 less...
typeUcharUV.setX(u.length);
Allocation uAlloc = Allocation.createTyped(rs, typeUcharUV.create());
uAlloc.copyFrom(u);
mYuv420.set_uIn(uAlloc);
Allocation vAlloc = Allocation.createTyped(rs, typeUcharUV.create());
vAlloc.copyFrom(v);
mYuv420.set_vIn(vAlloc);
// handover parameters
mYuv420.set_picWidth(width);
mYuv420.set_uvRowStride (uvRowStride);
mYuv420.set_uvPixelStride (uvPixelStride);
Bitmap outBitmap = Bitmap.createBitmap(width, height, Bitmap.Config.ARGB_8888);
Allocation outAlloc = Allocation.createFromBitmap(rs, outBitmap, Allocation.MipmapControl.MIPMAP_NONE, Allocation.USAGE_SCRIPT);
Script.LaunchOptions lo = new Script.LaunchOptions();
lo.setX(0, width); // by this we ignore the y’s padding zone, i.e. the right side of x between width and yRowStride
//using safe height
lo.setY(0, y.length / yRowStride);
mYuv420.forEach_doConvert(outAlloc,lo);
outAlloc.copyTo(outBitmap);
return outBitmap;
}
Testing on Nexus 7 (API 22) this returns nice color Bitmaps. This device, however, has trivial pixelstrides (=1) and no padding (i.e. rowstride=width). Testing on the brandnew Samsung S7 (API 23) I get pictures whose colors are not correct - except of the green ones. But the Picture does not show a general bias towards green, it just seems that non-green colors are not reproduced correctly. Note, that the S7 applies an u/v pixelstride of 2, and no padding.
Since the most crucial code line is within the rs-code the Access of the u/v planes uint uvIndex= (...) I think, there could be the problem, probably with incorrect consideration of pixelstrides here. Does anyone see the solution? Thanks.
UPDATE: I checked everything, and I am pretty sure that the code regarding the access of y,u,v is correct. So the problem must be with the u and v values themselves. Non green colors have a purple tilt, and looking at the u,v values they seem to be in a rather narrow range of about 110-150. Is it really possible that we need to cope with device specific YUV -> RBG conversions...?! Did I miss anything?
UPDATE 2: have corrected code, it works now, thanks to Eddy's Feedback.

Look at
floor((float) uvPixelStride*(x)/2)
which calculates your U,V row offset (uv_row_offset) from the Y x-coordinate.
if uvPixelStride = 2, then as x increases:
x = 0, uv_row_offset = 0
x = 1, uv_row_offset = 1
x = 2, uv_row_offset = 2
x = 3, uv_row_offset = 3
and this is incorrect. There's no valid U/V pixel value at uv_row_offset = 1 or 3, since uvPixelStride = 2.
You want
uvPixelStride * floor(x/2)
(assuming you don't trust yourself to remember the critical round-down behavior of integer divide, if you do then):
uvPixelStride * (x/2)
should be enough
With that, your mapping becomes:
x = 0, uv_row_offset = 0
x = 1, uv_row_offset = 0
x = 2, uv_row_offset = 2
x = 3, uv_row_offset = 2
See if that fixes the color errors. In practice, the incorrect addressing here would mean every other color sample would be from the wrong color plane, since it's likely that the underlying YUV data is semiplanar (so the U plane starts at V plane + 1 byte, with the two planes interleaved)

For people who encounter error
android.support.v8.renderscript.RSIllegalArgumentException: Array too small for allocation type
use buffer.capacity() instead of buffer.remaining()
and if you already made some operations on the image, you'll need to call rewind() method on the buffer.

Furthermore for anyone else getting
android.support.v8.renderscript.RSIllegalArgumentException: Array too
small for allocation type
I fixed it by changing yAlloc.copyFrom(y); to yAlloc.copy1DRangeFrom(0, y.length, y);

Posting full solution to convert YUV->BGR (can be adopted for other formats too) and also rotate image to upright using renderscript. Allocation is used as input and byte array is used as output. It was tested on Android 8+ including Samsung devices too.
Java
/**
* Renderscript-based process to convert YUV_420_888 to BGR_888 and rotation to upright.
*/
public class ImageProcessor {
protected final String TAG = this.getClass().getSimpleName();
private Allocation mInputAllocation;
private Allocation mOutAllocLand;
private Allocation mOutAllocPort;
private Handler mProcessingHandler;
private ScriptC_yuv_bgr mConvertScript;
private byte[] frameBGR;
public ProcessingTask mTask;
private ImageListener listener;
private Supplier<Integer> rotation;
public ImageProcessor(RenderScript rs, Size dimensions, ImageListener listener, Supplier<Integer> rotation) {
this.listener = listener;
this.rotation = rotation;
int w = dimensions.getWidth();
int h = dimensions.getHeight();
Type.Builder yuvTypeBuilder = new Type.Builder(rs, Element.YUV(rs));
yuvTypeBuilder.setX(w);
yuvTypeBuilder.setY(h);
yuvTypeBuilder.setYuvFormat(ImageFormat.YUV_420_888);
mInputAllocation = Allocation.createTyped(rs, yuvTypeBuilder.create(),
Allocation.USAGE_IO_INPUT | Allocation.USAGE_SCRIPT);
//keep 2 allocations to handle different image rotations
mOutAllocLand = createOutBGRAlloc(rs, w, h);
mOutAllocPort = createOutBGRAlloc(rs, h, w);
frameBGR = new byte[w*h*3];
HandlerThread processingThread = new HandlerThread(this.getClass().getSimpleName());
processingThread.start();
mProcessingHandler = new Handler(processingThread.getLooper());
mConvertScript = new ScriptC_yuv_bgr(rs);
mConvertScript.set_inWidth(w);
mConvertScript.set_inHeight(h);
mTask = new ProcessingTask(mInputAllocation);
}
private Allocation createOutBGRAlloc(RenderScript rs, int width, int height) {
//Stored as Vec4, it's impossible to store as Vec3, buffer size will be for Vec4 anyway
//using RGB_888 as alternative for BGR_888, can be just U8_3 type
Type.Builder rgbTypeBuilderPort = new Type.Builder(rs, Element.RGB_888(rs));
rgbTypeBuilderPort.setX(width);
rgbTypeBuilderPort.setY(height);
Allocation allocation = Allocation.createTyped(
rs, rgbTypeBuilderPort.create(), Allocation.USAGE_SCRIPT
);
//Use auto-padding to be able to copy to x*h*3 bytes array
allocation.setAutoPadding(true);
return allocation;
}
public Surface getInputSurface() {
return mInputAllocation.getSurface();
}
/**
* Simple class to keep track of incoming frame count,
* and to process the newest one in the processing thread
*/
class ProcessingTask implements Runnable, Allocation.OnBufferAvailableListener {
private int mPendingFrames = 0;
private Allocation mInputAllocation;
public ProcessingTask(Allocation input) {
mInputAllocation = input;
mInputAllocation.setOnBufferAvailableListener(this);
}
#Override
public void onBufferAvailable(Allocation a) {
synchronized(this) {
mPendingFrames++;
mProcessingHandler.post(this);
}
}
#Override
public void run() {
// Find out how many frames have arrived
int pendingFrames;
synchronized(this) {
pendingFrames = mPendingFrames;
mPendingFrames = 0;
// Discard extra messages in case processing is slower than frame rate
mProcessingHandler.removeCallbacks(this);
}
// Get to newest input
for (int i = 0; i < pendingFrames; i++) {
mInputAllocation.ioReceive();
}
int rot = rotation.get();
mConvertScript.set_currentYUVFrame(mInputAllocation);
mConvertScript.set_rotation(rot);
Allocation allocOut = rot==90 || rot== 270 ? mOutAllocPort : mOutAllocLand;
// Run processing
// ain allocation isn't really used, global frame param is used to get data from
mConvertScript.forEach_yuv_bgr(allocOut);
//Save to byte array, BGR 24bit
allocOut.copyTo(frameBGR);
int w = allocOut.getType().getX();
int h = allocOut.getType().getY();
if (listener != null) {
listener.onImageAvailable(frameBGR, w, h);
}
}
}
public interface ImageListener {
/**
* Called when there is available image, image is in upright position.
*
* #param bgr BGR 24bit bytes
* #param width image width
* #param height image height
*/
void onImageAvailable(byte[] bgr, int width, int height);
}
}
RS
#pragma version(1)
#pragma rs java_package_name(com.affectiva.camera)
#pragma rs_fp_relaxed
//Script convers YUV to BGR(uchar3)
//current YUV frame to read pixels from
rs_allocation currentYUVFrame;
//input image rotation: 0,90,180,270 clockwise
uint32_t rotation;
uint32_t inWidth;
uint32_t inHeight;
//method returns uchar3 BGR which will be set to x,y in output allocation
uchar3 __attribute__((kernel)) yuv_bgr(uint32_t x, uint32_t y) {
// Read in pixel values from latest frame - YUV color space
uchar3 inPixel;
uint32_t xRot = x;
uint32_t yRot = y;
//Do not rotate if 0
if (rotation==90) {
//rotate 270 clockwise
xRot = y;
yRot = inHeight - 1 - x;
} else if (rotation==180) {
xRot = inWidth - 1 - x;
yRot = inHeight - 1 - y;
} else if (rotation==270) {
//rotate 90 clockwise
xRot = inWidth - 1 - y;
yRot = x;
}
inPixel.r = rsGetElementAtYuv_uchar_Y(currentYUVFrame, xRot, yRot);
inPixel.g = rsGetElementAtYuv_uchar_U(currentYUVFrame, xRot, yRot);
inPixel.b = rsGetElementAtYuv_uchar_V(currentYUVFrame, xRot, yRot);
// Convert YUV to RGB, JFIF transform with fixed-point math
// R = Y + 1.402 * (V - 128)
// G = Y - 0.34414 * (U - 128) - 0.71414 * (V - 128)
// B = Y + 1.772 * (U - 128)
int3 bgr;
//get red pixel and assing to b
bgr.b = inPixel.r +
inPixel.b * 1436 / 1024 - 179;
bgr.g = inPixel.r -
inPixel.g * 46549 / 131072 + 44 -
inPixel.b * 93604 / 131072 + 91;
//get blue pixel and assign to red
bgr.r = inPixel.r +
inPixel.g * 1814 / 1024 - 227;
// Write out
return convert_uchar3(clamp(bgr, 0, 255));
}

On a Samsung Galaxy Tab 5 (Tablet), android version 5.1.1 (22), with alleged YUV_420_888 format, the following renderscript math works well and produces correct colors:
uchar yValue = rsGetElementAt_uchar(gCurrentFrame, x + y * yRowStride);
uchar vValue = rsGetElementAt_uchar(gCurrentFrame, ( (x/2) + (y/4) * yRowStride ) + (xSize * ySize) );
uchar uValue = rsGetElementAt_uchar(gCurrentFrame, ( (x/2) + (y/4) * yRowStride ) + (xSize * ySize) + (xSize * ySize) / 4);
I do not understand why the horizontal value (i.e., y) is scaled by a factor of four instead of two, but it works well. I also needed to avoid use of rsGetElementAtYuv_uchar_Y|U|V. I believe the associated allocation stride value is set to zero instead of something proper. Use of rsGetElementAt_uchar() is a reasonable work-around.
On a Samsung Galaxy S5 (Smart Phone), android version 5.0 (21), with alleged YUV_420_888 format, I cannot recover the u and v values, they come through as all zeros. This results in a green looking image. Luminous is OK, but image is vertically flipped.

This code requires the use of the RenderScript compatibility library (android.support.v8.renderscript.*).
In order to get the compatibility library to work with Android API 23, I updated to gradle-plugin 2.1.0 and Build-Tools 23.0.3 as per Miao Wang's answer at How to create Renderscript scripts on Android Studio, and make them run?
If you follow his answer and get an error "Gradle version 2.10 is required" appears, do NOT change
classpath 'com.android.tools.build:gradle:2.1.0'
Instead, update the distributionUrl field of the Project\gradle\wrapper\gradle-wrapper.properties file to
distributionUrl=https\://services.gradle.org/distributions/gradle-2.10-all.zip
and change File > Settings > Builds,Execution,Deployment > Build Tools > Gradle >Gradle to Use default gradle wrapper as per "Gradle Version 2.10 is required." Error.

Re: RSIllegalArgumentException
In my case this was the case that buffer.remaining() was not multiple of stride:
The length of last line was less than stride (i.e. only up to where actual data was.)

An FYI in case someone else gets this as I was also getting "android.support.v8.renderscript.RSIllegalArgumentException: Array too small for allocation type" when trying out the code. In my case it turns out that the when allocating the buffer for Y i had to rewind the buffer because it was being left at the wrong end and wasn't copying the data. By doing buffer.rewind(); before allocation the new bytes array makes it work fine now.

optimizing my inner loop (ARM, android ndk)

I'm writing an image processing app on android, and I'm trying to speed it up using the NDK. I have the following for-loop:
int x, y, c, idx;
const int pitch3 = pitch * 3;
float adj, result;
...
// px, py, u, u_bar are all float arrays of size nx*ny*3
// theta, tau, denom are float constants
// idx >= pitch3
for(y=1;y<ny;++y)
{
for(x=1;x<nx;++x)
{
for(c=0;c<3;++c)
{
adj = -px[idx] - py[idx] + px[idx - 3] + py[idx - pitch3];
result = ((u[idx] - tau * adj) + tau * f[idx]) * denom;
u_bar[idx] = result + theta * (result - u[idx]);
u[idx] = result;
++idx;
}
}
}
I'm wondering if it is possible to speed up this loop?
I'm thinking that using fixed-point arithmetic wouldn't do much, except on really old android phone (which I'm not going to target). Would writing it in assembly give a big improvement?
EDIT: I know I could use SIMD/NEON instructions, but they are not so common I think ...

Since you're accessing the array as a flat structure, the 3 levels of looping is only increasing the value used for idx. You can loop for (idx = pitch3; idx < nx*ny*3; idx++).
Another option is to move to fixed-point math. Do you really need more than 64 bits of dynamic range?

OpenCV crop function fatal signal 11

Hello I am doing an android app which uses OpenCV to detect rectangles/squares, to detect them I am using functions (modified a bit) from squares.cpp. Points of every square found I am storing in vector> squares, then i pass it to the function which choose the biggest one and store it in vector theBiggestSq. The problem is with the cropping function which code i will paste below (i will post the link to youtube showing the problem too). If the actual square is far enough from the camera it works ok but if i will close it a bit in some point it will hang. I will post the print screen of the problem from LogCat and there are the points printed out (the boundaries points taken from theBiggestSq vector, maybe it will help to find the solution).
void cutAndSave(vector<Point> theBiggestSq, Mat image){
RotatedRect box = minAreaRect(Mat(theBiggestSq));
// Draw bounding box in the original image (debug purposes)
//cv::Point2f vertices[4];
//box.points(vertices);
//for (int i = 0; i < 4; ++i)
//{
//cv::line(img, vertices[i], vertices[(i + 1) % 4], cv::Scalar(0, 255, 0), 1, CV_AA);
//}
//cv::imshow("box", img);
//cv::imwrite("box.png", img);
// Set Region of Interest to the area defined by the box
Rect roi;
roi.x = box.center.x - (box.size.width / 2);
roi.y = box.center.y - (box.size.height / 2);
roi.width = box.size.width;
roi.height = box.size.height;
// Crop the original image to the defined ROI
//bmp=Bitmap.createBitmap(box.size.width / 2, box.size.height / 2, Bitmap.Config.ARGB_8888);
Mat crop = image(roi);
//Mat crop = image(Rect(roi.x, roi.y, roi.width, roi.height)).clone();
//Utils.matToBitmap(crop*.clone()* ,bmp);
imwrite("/sdcard/OpenCVTest/1.png", bmp);
imshow("crop", crop);
}
video of my app and its problems
cords printed respectively are: roi.x roi.y roi.width roi.height
Another problem is that the boundaries drawn should have a green colour but as you see on the video they are distorted (flexed like those boundaries would be made from glass?).
Thank you for any help. I am new in openCV doing it from only one month so please be tolerant.
EDIT:
drawing code:
//draw//
for( size_t i = 0; i < squares.size(); i++ )
{
const Point* p = &squares[i][0];
int n = (int)squares[i].size();
polylines(mBgra, &p, &n, 1, true, Scalar(255,255,0), 5, 10);
//Rect rect = boundingRect(cv::Mat(squares[i]));
//rectangle(mBgra, rect.tl(), rect.br(), cv::Scalar(0,255,0), 2, 8, 0);
}

This error basically tells you the cause - your ROI exceeds the image dimensions. This means that when you are extracting Rect roi from RotatedRect box then either x or y are smaller than zero, or the width/height pushes the dimensions outside the image. You should check this using something like
// Propose rectangle from data
int proposedX = box.center.x - (box.size.width / 2);
int proposedY = box.center.y - (box.size.height / 2);
int proposedW = box.size.width;
int proposedH = box.size.height;
// Ensure top-left edge is within image
roi.x = proposedX < 0 ? 0 : proposedX;
roi.y = proposedY < 0 ? 0 : proposedY;
// Ensure bottom-right edge is within image
roi.width =
(roi.x - 1 + proposedW) > image.cols ? // Will this roi exceed image?
(image.cols - 1 - roi.x) // YES: make roi go to image edge
: proposedW; // NO: continue as proposed
// Similar for height
roi.height = (roi.y - 1 + proposedH) > image.rows ? (image.rows - 1 - roi.y) : proposedH;

Simple Image Sharpening algorithm for Android App

I am looking for a simple image sharpening algorithm to use in my Android application. I have a grayscale image captured from video (mostly used for text) and I would like to sharpen it because my phone does not have auto focus, and the close object distance blurs the text. I don't have any background in image processing. But as a user, I am familiar with unsharp masking and other sharpening tools available in Gimp, Photoshop, etc. I didn't see any support for image processing in the Android API, and hence am looking for a method to implement myself. Thanks.

This is a simple image sharpening algorithm.
You should pass to this function width, height and byte[] array of your grayscale image and it will sharpen the image in this byte[] array.
void sharpen(int width, int height, byte* yuv) {
char *mas;
mas = (char *) malloc(width * height);
memcpy(mas, yuv, width * height);
signed int res;
int ywidth;
for (int y = 1; y < height - 1; y++) {
ywidth = y * width;
for (int x = 1; x < width - 1; x++) {
res = (
mas[x + ywidth] * 5
- mas[x - 1 + ywidth]
- mas[x + 1 + ywidth]
- mas[x + (ywidth + width)]
- mas[x + (ywidth - width)]
);
if (res > 255) {
res = 255;
};
if (res < 0) {
res = 0;
};
yuv[x + ywidth] = res;
}
}
free(mas);
}

If you have access to pixel information, your most basic option woul be a sharpening convolution kernel. Take a look at the following sites, you can learn more about sharpening kernels and how to apply kernels there.
link1
link2

ImageJ has many algorithms in Java and is freely available.