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value | code stringlengths 73 34.1k | code_tokens list | docstring stringlengths 3 16k | docstring_tokens list | sha stringlengths 40 40 | url stringlengths 105 339 | partition stringclasses 1
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|---|---|---|---|---|---|---|---|---|---|---|---|
lessthanoptimal/BoofCV | main/boofcv-recognition/src/main/java/boofcv/abst/fiducial/CalibrationFiducialDetector.java | CalibrationFiducialDetector.getCenter | @Override
public void getCenter(int which, Point2D_F64 location) {
CalibrationObservation view = detector.getDetectedPoints();
location.set(0,0);
for (int i = 0; i < view.size(); i++) {
PointIndex2D_F64 p = view.get(i);
location.x += p.x;
location.y += p.y;
}
location.x /= view.size();
location.y /= view.size();
} | java | @Override
public void getCenter(int which, Point2D_F64 location) {
CalibrationObservation view = detector.getDetectedPoints();
location.set(0,0);
for (int i = 0; i < view.size(); i++) {
PointIndex2D_F64 p = view.get(i);
location.x += p.x;
location.y += p.y;
}
location.x /= view.size();
location.y /= view.size();
} | [
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@param which Fiducial's index
@param location (output) Storage for the transform. modified. | [
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lessthanoptimal/BoofCV | main/boofcv-calibration/src/main/java/boofcv/alg/geo/selfcalib/SelfCalibrationLinearRotationMulti.java | SelfCalibrationLinearRotationMulti.setConstraints | public void setConstraints( boolean zeroSkew ,
boolean principlePointOrigin ,
boolean knownAspect,
double aspect )
{
if( knownAspect && !zeroSkew )
throw new IllegalArgumentException("If aspect is known then skew must be zero");
this.zeroSkew = zeroSkew;
this.principlePointOrigin = principlePointOrigin;
this.knownAspectRatio = knownAspect;
this.aspectRatio = aspect;
notZeros.resize(6);
for (int i = 0; i < 6; i++) {
notZeros.data[i] = i;
}
if( principlePointOrigin ) {
notZeros.remove(4);
notZeros.remove(2);
}
if( zeroSkew ) {
notZeros.remove(1);
}
} | java | public void setConstraints( boolean zeroSkew ,
boolean principlePointOrigin ,
boolean knownAspect,
double aspect )
{
if( knownAspect && !zeroSkew )
throw new IllegalArgumentException("If aspect is known then skew must be zero");
this.zeroSkew = zeroSkew;
this.principlePointOrigin = principlePointOrigin;
this.knownAspectRatio = knownAspect;
this.aspectRatio = aspect;
notZeros.resize(6);
for (int i = 0; i < 6; i++) {
notZeros.data[i] = i;
}
if( principlePointOrigin ) {
notZeros.remove(4);
notZeros.remove(2);
}
if( zeroSkew ) {
notZeros.remove(1);
}
} | [
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Known aspect ratio constraint can only be used if zero skew is also assumped.
@param zeroSkew Assume that skew is zero
@param principlePointOrigin Principle point is at the origin
@param knownAspect that the aspect ratio is known
@param aspect If aspect is known then this is the aspect. ratio=fy/fx Ignored otherwise. | [
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lessthanoptimal/BoofCV | main/boofcv-calibration/src/main/java/boofcv/alg/geo/selfcalib/SelfCalibrationLinearRotationMulti.java | SelfCalibrationLinearRotationMulti.extractReferenceW | void extractReferenceW(DMatrixRMaj nv ) {
W0.a11 = nv.data[0];
W0.a12 = W0.a21 = nv.data[1];
W0.a13 = W0.a31 = nv.data[2];
W0.a22 = nv.data[3];
W0.a23 = W0.a32 = nv.data[4];
W0.a33 = nv.data[5];
} | java | void extractReferenceW(DMatrixRMaj nv ) {
W0.a11 = nv.data[0];
W0.a12 = W0.a21 = nv.data[1];
W0.a13 = W0.a31 = nv.data[2];
W0.a22 = nv.data[3];
W0.a23 = W0.a32 = nv.data[4];
W0.a33 = nv.data[5];
} | [
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lessthanoptimal/BoofCV | main/boofcv-calibration/src/main/java/boofcv/alg/geo/selfcalib/SelfCalibrationLinearRotationMulti.java | SelfCalibrationLinearRotationMulti.convertW | void convertW( Homography2D_F64 w , CameraPinhole c ) {
// inv(w) = K*K'
tmp.set(w);
CommonOps_DDF3.divide(tmp,tmp.a33);
CommonOps_DDF3.cholU(tmp);
CommonOps_DDF3.invert(tmp,K);
CommonOps_DDF3.divide(K,K.a33);
c.fx = K.a11;
c.fy = knownAspectRatio ? (K.a22 + c.fx*aspectRatio)/2.0 : K.a22;
c.skew = zeroSkew ? 0 : K.a12;
c.cx = principlePointOrigin ? 0 : K.a13;
c.cy = principlePointOrigin ? 0 : K.a23;
} | java | void convertW( Homography2D_F64 w , CameraPinhole c ) {
// inv(w) = K*K'
tmp.set(w);
CommonOps_DDF3.divide(tmp,tmp.a33);
CommonOps_DDF3.cholU(tmp);
CommonOps_DDF3.invert(tmp,K);
CommonOps_DDF3.divide(K,K.a33);
c.fx = K.a11;
c.fy = knownAspectRatio ? (K.a22 + c.fx*aspectRatio)/2.0 : K.a22;
c.skew = zeroSkew ? 0 : K.a12;
c.cx = principlePointOrigin ? 0 : K.a13;
c.cy = principlePointOrigin ? 0 : K.a23;
} | [
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lessthanoptimal/BoofCV | main/boofcv-calibration/src/main/java/boofcv/alg/geo/selfcalib/SelfCalibrationLinearRotationMulti.java | SelfCalibrationLinearRotationMulti.extractCalibration | void extractCalibration( Homography2D_F64 Hinv , CameraPinhole c ) {
CommonOps_DDF3.multTransA(Hinv,W0,tmp);
CommonOps_DDF3.mult(tmp,Hinv,Wi);
convertW(Wi,c);
} | java | void extractCalibration( Homography2D_F64 Hinv , CameraPinhole c ) {
CommonOps_DDF3.multTransA(Hinv,W0,tmp);
CommonOps_DDF3.mult(tmp,Hinv,Wi);
convertW(Wi,c);
} | [
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... | Extracts calibration for the non-reference frames
w = H^-T*w*H^-1 | [
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] | f01c0243da0ec086285ee722183804d5923bc3ac | https://github.com/lessthanoptimal/BoofCV/blob/f01c0243da0ec086285ee722183804d5923bc3ac/main/boofcv-calibration/src/main/java/boofcv/alg/geo/selfcalib/SelfCalibrationLinearRotationMulti.java#L265-L270 | train |
lessthanoptimal/BoofCV | main/boofcv-calibration/src/main/java/boofcv/alg/geo/selfcalib/SelfCalibrationLinearRotationMulti.java | SelfCalibrationLinearRotationMulti.computeInverseH | public boolean computeInverseH(List<Homography2D_F64> homography0toI) {
listHInv.reset();
int N = homography0toI.size();
for (int i = 0; i < N; i++) {
Homography2D_F64 H = homography0toI.get(i);
Homography2D_F64 Hinv = listHInv.grow();
// Ensure the determinant is one
double d = CommonOps_DDF3.det(H);
if( d < 0 )
CommonOps_DDF3.divide(H,-Math.pow(-d,1.0/3),Hinv);
else
CommonOps_DDF3.divide(H,Math.pow(d,1.0/3),Hinv);
// Now invert the matrix
if( !CommonOps_DDF3.invert(Hinv,Hinv) ) {
return false;
}
}
return true;
} | java | public boolean computeInverseH(List<Homography2D_F64> homography0toI) {
listHInv.reset();
int N = homography0toI.size();
for (int i = 0; i < N; i++) {
Homography2D_F64 H = homography0toI.get(i);
Homography2D_F64 Hinv = listHInv.grow();
// Ensure the determinant is one
double d = CommonOps_DDF3.det(H);
if( d < 0 )
CommonOps_DDF3.divide(H,-Math.pow(-d,1.0/3),Hinv);
else
CommonOps_DDF3.divide(H,Math.pow(d,1.0/3),Hinv);
// Now invert the matrix
if( !CommonOps_DDF3.invert(Hinv,Hinv) ) {
return false;
}
}
return true;
} | [
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lessthanoptimal/BoofCV | integration/boofcv-android/src/main/java/boofcv/android/camera2/SimpleCamera2Activity.java | SimpleCamera2Activity.startCameraTexture | protected void startCameraTexture( TextureView view ) {
if( verbose )
Log.i(TAG,"startCamera(TextureView="+(view!=null)+")");
this.mTextureView = view;
this.mView = null;
this.mTextureView.setSurfaceTextureListener(mSurfaceTextureListener);
} | java | protected void startCameraTexture( TextureView view ) {
if( verbose )
Log.i(TAG,"startCamera(TextureView="+(view!=null)+")");
this.mTextureView = view;
this.mView = null;
this.mTextureView.setSurfaceTextureListener(mSurfaceTextureListener);
} | [
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@param view The view the camera is displayed inside or null if not displayed | [
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lessthanoptimal/BoofCV | integration/boofcv-android/src/main/java/boofcv/android/camera2/SimpleCamera2Activity.java | SimpleCamera2Activity.configureCamera | protected void configureCamera( CameraDevice device ,
CameraCharacteristics characteristics,
CaptureRequest.Builder captureRequestBuilder ) {
if( verbose )
Log.i(TAG,"configureCamera() default function");
captureRequestBuilder.set(CaptureRequest.CONTROL_AF_MODE, CaptureRequest.CONTROL_AF_MODE_CONTINUOUS_VIDEO);
captureRequestBuilder.set(CaptureRequest.CONTROL_AE_MODE, CaptureRequest.CONTROL_AE_MODE_ON);
} | java | protected void configureCamera( CameraDevice device ,
CameraCharacteristics characteristics,
CaptureRequest.Builder captureRequestBuilder ) {
if( verbose )
Log.i(TAG,"configureCamera() default function");
captureRequestBuilder.set(CaptureRequest.CONTROL_AF_MODE, CaptureRequest.CONTROL_AF_MODE_CONTINUOUS_VIDEO);
captureRequestBuilder.set(CaptureRequest.CONTROL_AE_MODE, CaptureRequest.CONTROL_AE_MODE_ON);
} | [
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@param device The camera being configured
@param characteristics Used to get information on the device
@param captureRequestBuilder used to configure the camera | [
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lessthanoptimal/BoofCV | integration/boofcv-android/src/main/java/boofcv/android/camera2/SimpleCamera2Activity.java | SimpleCamera2Activity.selectCamera | protected boolean selectCamera( String id , CameraCharacteristics characteristics ) {
if( verbose )
Log.i(TAG,"selectCamera() default function");
Integer facing = characteristics.get(CameraCharacteristics.LENS_FACING);
return facing == null || facing != CameraCharacteristics.LENS_FACING_FRONT;
} | java | protected boolean selectCamera( String id , CameraCharacteristics characteristics ) {
if( verbose )
Log.i(TAG,"selectCamera() default function");
Integer facing = characteristics.get(CameraCharacteristics.LENS_FACING);
return facing == null || facing != CameraCharacteristics.LENS_FACING_FRONT;
} | [
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lessthanoptimal/BoofCV | integration/boofcv-android/src/main/java/boofcv/android/camera2/SimpleCamera2Activity.java | SimpleCamera2Activity.reopenCameraAtResolution | protected void reopenCameraAtResolution(int cameraWidth, int cameraHeight) {
if (Looper.getMainLooper().getThread() != Thread.currentThread()) {
throw new RuntimeException("Attempted to reopenCameraAtResolution main looper thread!");
}
boolean releaseLock = true;
open.mLock.lock();
try {
if (verbose)
Log.i(TAG, "Reopening camera is null == " + (open.mCameraDevice == null)+" state="+open.state+
" activity="+getClass().getSimpleName());
if( open.state != CameraState.OPEN )
throw new RuntimeException("BUG! Attempted to re-open camera when not open");
if (null == open. mCameraDevice) {
throw new RuntimeException("Can't re-open a closed camera");
}
closePreviewSession();
open.mCameraSize = null;
firstFrame = true;
CameraManager manager = (CameraManager) getSystemService(Context.CAMERA_SERVICE);
if (manager == null)
throw new RuntimeException("Null camera manager");
try {
open.mPreviewReader = ImageReader.newInstance(
cameraWidth, cameraHeight,
ImageFormat.YUV_420_888, 2);
// Do the processing inside the the handler thread instead of the looper thread to avoid
// grinding the UI to a halt
open.mPreviewReader.setOnImageAvailableListener(onAvailableListener, mBackgroundHandler);
configureTransform(viewWidth, viewHeight);
manager.openCamera(open.cameraId, mStateCallback, null);
releaseLock = false;
} catch (IllegalArgumentException e) {
Toast.makeText(this, e.getMessage(), Toast.LENGTH_LONG).show();
finish();
} catch (CameraAccessException e) {
e.printStackTrace();
}
} finally {
if(releaseLock)
open.mLock.unlock();
}
} | java | protected void reopenCameraAtResolution(int cameraWidth, int cameraHeight) {
if (Looper.getMainLooper().getThread() != Thread.currentThread()) {
throw new RuntimeException("Attempted to reopenCameraAtResolution main looper thread!");
}
boolean releaseLock = true;
open.mLock.lock();
try {
if (verbose)
Log.i(TAG, "Reopening camera is null == " + (open.mCameraDevice == null)+" state="+open.state+
" activity="+getClass().getSimpleName());
if( open.state != CameraState.OPEN )
throw new RuntimeException("BUG! Attempted to re-open camera when not open");
if (null == open. mCameraDevice) {
throw new RuntimeException("Can't re-open a closed camera");
}
closePreviewSession();
open.mCameraSize = null;
firstFrame = true;
CameraManager manager = (CameraManager) getSystemService(Context.CAMERA_SERVICE);
if (manager == null)
throw new RuntimeException("Null camera manager");
try {
open.mPreviewReader = ImageReader.newInstance(
cameraWidth, cameraHeight,
ImageFormat.YUV_420_888, 2);
// Do the processing inside the the handler thread instead of the looper thread to avoid
// grinding the UI to a halt
open.mPreviewReader.setOnImageAvailableListener(onAvailableListener, mBackgroundHandler);
configureTransform(viewWidth, viewHeight);
manager.openCamera(open.cameraId, mStateCallback, null);
releaseLock = false;
} catch (IllegalArgumentException e) {
Toast.makeText(this, e.getMessage(), Toast.LENGTH_LONG).show();
finish();
} catch (CameraAccessException e) {
e.printStackTrace();
}
} finally {
if(releaseLock)
open.mLock.unlock();
}
} | [
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what you're doing and that this is a valid resolution.
WARNING: UNTESTED | [
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lessthanoptimal/BoofCV | integration/boofcv-android/src/main/java/boofcv/android/camera2/SimpleCamera2Activity.java | SimpleCamera2Activity.closeCamera | protected boolean closeCamera() {
if( verbose )
Log.i(TAG,"closeCamera() activity="+getClass().getSimpleName());
if (Looper.getMainLooper().getThread() != Thread.currentThread()) {
throw new RuntimeException("Attempted to close camera not on the main looper thread!");
}
boolean closed = false;
// if( verbose ) {
// StackTraceElement[] trace = new RuntimeException().getStackTrace();
// for (int i = 0; i < Math.min(trace.length, 3); i++) {
// System.out.println("[ " + i + " ] = " + trace[i].toString());
// }
// }
// NOTE: Since open can only be called in the main looper this won't be enough to prevent
// it from closing before it opens. That's why open.state exists
open.mLock.lock();
try {
if( verbose )
Log.i(TAG,"closeCamera: camera="+(open.mCameraDevice==null)+" state="+open.state);
closePreviewSession();
// close has been called while trying to open the camera!
if( open.state == CameraState.OPENING ) {
// If it's in this state that means an asych task is opening the camera. By changing the state
// to closing it will not abort that process when the task is called.
open.state = CameraState.CLOSING;
if( open.mCameraDevice != null ) {
throw new RuntimeException("BUG! Camera is opening and should be null until opened");
}
} else {
if (null != open.mCameraDevice) {
closed = true;
open.closeCamera();
}
open.state = CameraState.CLOSED;
open.clearCamera();
}
} finally {
open.mLock.unlock();
}
return closed;
} | java | protected boolean closeCamera() {
if( verbose )
Log.i(TAG,"closeCamera() activity="+getClass().getSimpleName());
if (Looper.getMainLooper().getThread() != Thread.currentThread()) {
throw new RuntimeException("Attempted to close camera not on the main looper thread!");
}
boolean closed = false;
// if( verbose ) {
// StackTraceElement[] trace = new RuntimeException().getStackTrace();
// for (int i = 0; i < Math.min(trace.length, 3); i++) {
// System.out.println("[ " + i + " ] = " + trace[i].toString());
// }
// }
// NOTE: Since open can only be called in the main looper this won't be enough to prevent
// it from closing before it opens. That's why open.state exists
open.mLock.lock();
try {
if( verbose )
Log.i(TAG,"closeCamera: camera="+(open.mCameraDevice==null)+" state="+open.state);
closePreviewSession();
// close has been called while trying to open the camera!
if( open.state == CameraState.OPENING ) {
// If it's in this state that means an asych task is opening the camera. By changing the state
// to closing it will not abort that process when the task is called.
open.state = CameraState.CLOSING;
if( open.mCameraDevice != null ) {
throw new RuntimeException("BUG! Camera is opening and should be null until opened");
}
} else {
if (null != open.mCameraDevice) {
closed = true;
open.closeCamera();
}
open.state = CameraState.CLOSED;
open.clearCamera();
}
} finally {
open.mLock.unlock();
}
return closed;
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lessthanoptimal/BoofCV | integration/boofcv-android/src/main/java/boofcv/android/camera2/SimpleCamera2Activity.java | SimpleCamera2Activity.startPreview | private void startPreview() {
// Sanity check. Parts of this code assume it's on this thread. If it has been put into a handle
// that's fine just be careful nothing assumes it's on the main looper
if (Looper.getMainLooper().getThread() != Thread.currentThread()) {
throw new RuntimeException("Not on main looper! Modify code to remove assumptions");
}
if( verbose ) {
Log.i(TAG,"startPreview()");
}
try {
open.mLock.lock();
if (null == open.mCameraDevice || null == open.mCameraSize) {
Log.i(TAG," aborting startPreview. Camera not open yet.");
return;
}
closePreviewSession();
open.surfaces = new ArrayList<>();
open.mPreviewRequestBuilder = open.mCameraDevice.createCaptureRequest(CameraDevice.TEMPLATE_PREVIEW);
if( mTextureView != null && mTextureView.isAvailable() ) {
SurfaceTexture texture = mTextureView.getSurfaceTexture();
assert texture != null;
texture.setDefaultBufferSize(open.mCameraSize.getWidth(), open.mCameraSize.getHeight());
// Display the camera preview into this texture
Surface previewSurface = new Surface(texture);
open.surfaces.add(previewSurface);
open.mPreviewRequestBuilder.addTarget(previewSurface);
}
// This is where the image for processing is extracted from
Surface readerSurface = open.mPreviewReader.getSurface();
open.surfaces.add(readerSurface);
open.mPreviewRequestBuilder.addTarget(readerSurface);
createCaptureSession();
} catch (CameraAccessException e) {
e.printStackTrace();
} finally {
open.mLock.unlock();
}
} | java | private void startPreview() {
// Sanity check. Parts of this code assume it's on this thread. If it has been put into a handle
// that's fine just be careful nothing assumes it's on the main looper
if (Looper.getMainLooper().getThread() != Thread.currentThread()) {
throw new RuntimeException("Not on main looper! Modify code to remove assumptions");
}
if( verbose ) {
Log.i(TAG,"startPreview()");
}
try {
open.mLock.lock();
if (null == open.mCameraDevice || null == open.mCameraSize) {
Log.i(TAG," aborting startPreview. Camera not open yet.");
return;
}
closePreviewSession();
open.surfaces = new ArrayList<>();
open.mPreviewRequestBuilder = open.mCameraDevice.createCaptureRequest(CameraDevice.TEMPLATE_PREVIEW);
if( mTextureView != null && mTextureView.isAvailable() ) {
SurfaceTexture texture = mTextureView.getSurfaceTexture();
assert texture != null;
texture.setDefaultBufferSize(open.mCameraSize.getWidth(), open.mCameraSize.getHeight());
// Display the camera preview into this texture
Surface previewSurface = new Surface(texture);
open.surfaces.add(previewSurface);
open.mPreviewRequestBuilder.addTarget(previewSurface);
}
// This is where the image for processing is extracted from
Surface readerSurface = open.mPreviewReader.getSurface();
open.surfaces.add(readerSurface);
open.mPreviewRequestBuilder.addTarget(readerSurface);
createCaptureSession();
} catch (CameraAccessException e) {
e.printStackTrace();
} finally {
open.mLock.unlock();
}
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lessthanoptimal/BoofCV | integration/boofcv-android/src/main/java/boofcv/android/camera2/SimpleCamera2Activity.java | SimpleCamera2Activity.cameraIntrinsicNominal | public void cameraIntrinsicNominal(CameraPinhole intrinsic ) {
open.mLock.lock();
try {
// This might be called before the camera is open
if (open.mCameraCharacterstics != null) {
SizeF physicalSize = open.mCameraCharacterstics.get(CameraCharacteristics.SENSOR_INFO_PHYSICAL_SIZE);
Rect activeSize = open.mCameraCharacterstics.get(CameraCharacteristics.SENSOR_INFO_ACTIVE_ARRAY_SIZE);
Size pixelSize = open.mCameraCharacterstics.get(CameraCharacteristics.SENSOR_INFO_PIXEL_ARRAY_SIZE);
float[] focalLengths = open.mCameraCharacterstics.get(CameraCharacteristics.LENS_INFO_AVAILABLE_FOCAL_LENGTHS);
if (focalLengths != null && focalLengths.length > 0 && physicalSize != null && activeSize != null && pixelSize != null ) {
float fl = focalLengths[0];
float widthToPixel = pixelSize.getWidth() / physicalSize.getWidth();
float heightToPixel = pixelSize.getHeight() / physicalSize.getHeight();
float s = open.mCameraSize.getWidth()/(float)activeSize.width();
intrinsic.fx = fl*widthToPixel*s;
intrinsic.fy = fl*heightToPixel*s;
intrinsic.skew = 0;
intrinsic.cx = activeSize.centerX()*s;
intrinsic.cy = activeSize.centerY()*s;
intrinsic.width = open.mCameraSize.getWidth();
intrinsic.height = open.mCameraSize.getHeight();
return;
}
}
// 60 degrees seems reasonable for a random guess
PerspectiveOps.createIntrinsic(open.mCameraSize.getWidth(),open.mCameraSize.getHeight(),
UtilAngle.radian(60));
} finally {
open.mLock.unlock();
}
} | java | public void cameraIntrinsicNominal(CameraPinhole intrinsic ) {
open.mLock.lock();
try {
// This might be called before the camera is open
if (open.mCameraCharacterstics != null) {
SizeF physicalSize = open.mCameraCharacterstics.get(CameraCharacteristics.SENSOR_INFO_PHYSICAL_SIZE);
Rect activeSize = open.mCameraCharacterstics.get(CameraCharacteristics.SENSOR_INFO_ACTIVE_ARRAY_SIZE);
Size pixelSize = open.mCameraCharacterstics.get(CameraCharacteristics.SENSOR_INFO_PIXEL_ARRAY_SIZE);
float[] focalLengths = open.mCameraCharacterstics.get(CameraCharacteristics.LENS_INFO_AVAILABLE_FOCAL_LENGTHS);
if (focalLengths != null && focalLengths.length > 0 && physicalSize != null && activeSize != null && pixelSize != null ) {
float fl = focalLengths[0];
float widthToPixel = pixelSize.getWidth() / physicalSize.getWidth();
float heightToPixel = pixelSize.getHeight() / physicalSize.getHeight();
float s = open.mCameraSize.getWidth()/(float)activeSize.width();
intrinsic.fx = fl*widthToPixel*s;
intrinsic.fy = fl*heightToPixel*s;
intrinsic.skew = 0;
intrinsic.cx = activeSize.centerX()*s;
intrinsic.cy = activeSize.centerY()*s;
intrinsic.width = open.mCameraSize.getWidth();
intrinsic.height = open.mCameraSize.getHeight();
return;
}
}
// 60 degrees seems reasonable for a random guess
PerspectiveOps.createIntrinsic(open.mCameraSize.getWidth(),open.mCameraSize.getHeight(),
UtilAngle.radian(60));
} finally {
open.mLock.unlock();
}
} | [
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lessthanoptimal/BoofCV | integration/boofcv-android/src/main/java/boofcv/android/camera2/SimpleCamera2Activity.java | SimpleCamera2Activity.displayDensityAdjusted | private float displayDensityAdjusted() {
open.mLock.lock();
try {
if (open.mCameraSize == null)
return displayMetrics.density;
int rotation = getWindowManager().getDefaultDisplay().getRotation();
int screenWidth = (rotation == 0 || rotation == 2) ? displayMetrics.widthPixels : displayMetrics.heightPixels;
int cameraWidth = open.mSensorOrientation == 0 || open.mSensorOrientation == 180 ?
open.mCameraSize.getWidth() : open.mCameraSize.getHeight();
return displayMetrics.density * cameraWidth / screenWidth;
} finally {
open.mLock.unlock();
}
} | java | private float displayDensityAdjusted() {
open.mLock.lock();
try {
if (open.mCameraSize == null)
return displayMetrics.density;
int rotation = getWindowManager().getDefaultDisplay().getRotation();
int screenWidth = (rotation == 0 || rotation == 2) ? displayMetrics.widthPixels : displayMetrics.heightPixels;
int cameraWidth = open.mSensorOrientation == 0 || open.mSensorOrientation == 180 ?
open.mCameraSize.getWidth() : open.mCameraSize.getHeight();
return displayMetrics.density * cameraWidth / screenWidth;
} finally {
open.mLock.unlock();
}
} | [
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lessthanoptimal/BoofCV | main/boofcv-feature/src/main/java/boofcv/factory/feature/disparity/FactoryStereoDisparity.java | FactoryStereoDisparity.regionSparseWta | public static <T extends ImageGray<T>> StereoDisparitySparse<T>
regionSparseWta( int minDisparity , int maxDisparity,
int regionRadiusX, int regionRadiusY ,
double maxPerPixelError ,
double texture ,
boolean subpixelInterpolation ,
Class<T> imageType ) {
double maxError = (regionRadiusX*2+1)*(regionRadiusY*2+1)*maxPerPixelError;
if( imageType == GrayU8.class ) {
DisparitySparseSelect<int[]> select;
if( subpixelInterpolation)
select = selectDisparitySparseSubpixel_S32((int) maxError, texture);
else
select = selectDisparitySparse_S32((int) maxError, texture);
DisparitySparseScoreSadRect<int[],GrayU8>
score = scoreDisparitySparseSadRect_U8(minDisparity,maxDisparity, regionRadiusX, regionRadiusY);
return new WrapDisparitySparseSadRect(score,select);
} else if( imageType == GrayF32.class ) {
DisparitySparseSelect<float[]> select;
if( subpixelInterpolation )
select = selectDisparitySparseSubpixel_F32((int) maxError, texture);
else
select = selectDisparitySparse_F32((int) maxError, texture);
DisparitySparseScoreSadRect<float[],GrayF32>
score = scoreDisparitySparseSadRect_F32(minDisparity,maxDisparity, regionRadiusX, regionRadiusY);
return new WrapDisparitySparseSadRect(score,select);
} else
throw new RuntimeException("Image type not supported: "+imageType.getSimpleName() );
} | java | public static <T extends ImageGray<T>> StereoDisparitySparse<T>
regionSparseWta( int minDisparity , int maxDisparity,
int regionRadiusX, int regionRadiusY ,
double maxPerPixelError ,
double texture ,
boolean subpixelInterpolation ,
Class<T> imageType ) {
double maxError = (regionRadiusX*2+1)*(regionRadiusY*2+1)*maxPerPixelError;
if( imageType == GrayU8.class ) {
DisparitySparseSelect<int[]> select;
if( subpixelInterpolation)
select = selectDisparitySparseSubpixel_S32((int) maxError, texture);
else
select = selectDisparitySparse_S32((int) maxError, texture);
DisparitySparseScoreSadRect<int[],GrayU8>
score = scoreDisparitySparseSadRect_U8(minDisparity,maxDisparity, regionRadiusX, regionRadiusY);
return new WrapDisparitySparseSadRect(score,select);
} else if( imageType == GrayF32.class ) {
DisparitySparseSelect<float[]> select;
if( subpixelInterpolation )
select = selectDisparitySparseSubpixel_F32((int) maxError, texture);
else
select = selectDisparitySparse_F32((int) maxError, texture);
DisparitySparseScoreSadRect<float[],GrayF32>
score = scoreDisparitySparseSadRect_F32(minDisparity,maxDisparity, regionRadiusX, regionRadiusY);
return new WrapDisparitySparseSadRect(score,select);
} else
throw new RuntimeException("Image type not supported: "+imageType.getSimpleName() );
} | [
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@param maxDisparity Maximum disparity that it will calculate. Must be > 0
@param regionRadiusX Radius of the rectangular region along x-axis.
@param regionRadiusY Radius of the rectangular region along y-axis.
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@param texture Tolerance for how similar optimal region is to other region. Closer to zero is more tolerant.
Try 0.1
@param subpixelInterpolation true to turn on sub-pixel interpolation
@param imageType Type of input image.
@param <T> Image type
@return Sparse disparity algorithm | [
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lessthanoptimal/BoofCV | main/boofcv-sfm/src/main/java/boofcv/alg/sfm/d3/direct/FeatureSpatialDiversity_F32.java | FeatureSpatialDiversity_F32.addPoint | public void addPoint( float x , float y , float z ) {
norm.grow().set(x/z, y/z);
} | java | public void addPoint( float x , float y , float z ) {
norm.grow().set(x/z, y/z);
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lessthanoptimal/BoofCV | main/boofcv-sfm/src/main/java/boofcv/alg/sfm/d3/direct/FeatureSpatialDiversity_F32.java | FeatureSpatialDiversity_F32.process | public void process() {
computeCovarince();
float eigenvalue = smallestEigenvalue();
// eigenvalue is the variance, convert to standard deviation
double stdev = Math.sqrt(eigenvalue);
// System.out.println("stdev "+stdev+" total "+norm.size()+" mean "+meanX+" "+meanY);
// approximate the spread in by doing it along the x-axis.
// Really should be along the smallest singular axis
double angle0 = Math.atan2(1.0,sigmas*(meanX-stdev));
double angle1 = Math.atan2(1.0,sigmas*(meanX+stdev));
spread = Math.abs(angle1-angle0);
} | java | public void process() {
computeCovarince();
float eigenvalue = smallestEigenvalue();
// eigenvalue is the variance, convert to standard deviation
double stdev = Math.sqrt(eigenvalue);
// System.out.println("stdev "+stdev+" total "+norm.size()+" mean "+meanX+" "+meanY);
// approximate the spread in by doing it along the x-axis.
// Really should be along the smallest singular axis
double angle0 = Math.atan2(1.0,sigmas*(meanX-stdev));
double angle1 = Math.atan2(1.0,sigmas*(meanX+stdev));
spread = Math.abs(angle1-angle0);
} | [
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lessthanoptimal/BoofCV | main/boofcv-recognition/src/main/java/boofcv/factory/scene/FactoryImageClassifier.java | FactoryImageClassifier.vgg_cifar10 | public static ClassifierAndSource vgg_cifar10() {
List<String> sources = new ArrayList<>();
sources.add( "http://boofcv.org/notwiki/largefiles/likevgg_cifar10.zip" );
ClassifierAndSource ret = new ClassifierAndSource();
ret.data0 = new ImageClassifierVggCifar10();
ret.data1 = sources;
return ret;
} | java | public static ClassifierAndSource vgg_cifar10() {
List<String> sources = new ArrayList<>();
sources.add( "http://boofcv.org/notwiki/largefiles/likevgg_cifar10.zip" );
ClassifierAndSource ret = new ClassifierAndSource();
ret.data0 = new ImageClassifierVggCifar10();
ret.data1 = sources;
return ret;
} | [
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@return The classifier and where to download the model | [
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] | f01c0243da0ec086285ee722183804d5923bc3ac | https://github.com/lessthanoptimal/BoofCV/blob/f01c0243da0ec086285ee722183804d5923bc3ac/main/boofcv-recognition/src/main/java/boofcv/factory/scene/FactoryImageClassifier.java#L41-L51 | train |
lessthanoptimal/BoofCV | main/boofcv-recognition/src/main/java/boofcv/factory/scene/FactoryImageClassifier.java | FactoryImageClassifier.nin_imagenet | public static ClassifierAndSource nin_imagenet() {
List<String> sources = new ArrayList<>();
sources.add( "http://boofcv.org/notwiki/largefiles/nin_imagenet.zip" );
ClassifierAndSource ret = new ClassifierAndSource();
ret.data0 = new ImageClassifierNiNImageNet();
ret.data1 = sources;
return ret;
} | java | public static ClassifierAndSource nin_imagenet() {
List<String> sources = new ArrayList<>();
sources.add( "http://boofcv.org/notwiki/largefiles/nin_imagenet.zip" );
ClassifierAndSource ret = new ClassifierAndSource();
ret.data0 = new ImageClassifierNiNImageNet();
ret.data1 = sources;
return ret;
} | [
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@see ImageClassifierNiNImageNet
@return The classifier and where to download the model | [
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lessthanoptimal/BoofCV | integration/boofcv-swing/src/main/java/boofcv/gui/JavaRuntimeLauncher.java | JavaRuntimeLauncher.launch | public Exit launch( Class mainClass , String ...args ) {
jvmArgs = configureArguments(mainClass,args);
try {
Runtime rt = Runtime.getRuntime();
Process pr = rt.exec(jvmArgs);
// If it exits too quickly it might not get any error messages if it crashes right away
// so the work around is to sleep
Thread.sleep(500);
BufferedReader input = new BufferedReader(new InputStreamReader(pr.getInputStream()));
BufferedReader error = new BufferedReader(new InputStreamReader(pr.getErrorStream()));
// print the output from the slave
if( !monitorSlave(pr, input, error) ) {
if( killRequested )
return Exit.REQUESTED;
else
return Exit.FROZEN;
}
if( pr.exitValue() != 0 ) {
return Exit.RETURN_NOT_ZERO;
} else {
return Exit.NORMAL;
}
} catch (IOException | InterruptedException e) {
throw new RuntimeException(e);
}
} | java | public Exit launch( Class mainClass , String ...args ) {
jvmArgs = configureArguments(mainClass,args);
try {
Runtime rt = Runtime.getRuntime();
Process pr = rt.exec(jvmArgs);
// If it exits too quickly it might not get any error messages if it crashes right away
// so the work around is to sleep
Thread.sleep(500);
BufferedReader input = new BufferedReader(new InputStreamReader(pr.getInputStream()));
BufferedReader error = new BufferedReader(new InputStreamReader(pr.getErrorStream()));
// print the output from the slave
if( !monitorSlave(pr, input, error) ) {
if( killRequested )
return Exit.REQUESTED;
else
return Exit.FROZEN;
}
if( pr.exitValue() != 0 ) {
return Exit.RETURN_NOT_ZERO;
} else {
return Exit.NORMAL;
}
} catch (IOException | InterruptedException e) {
throw new RuntimeException(e);
}
} | [
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lessthanoptimal/BoofCV | integration/boofcv-swing/src/main/java/boofcv/gui/JavaRuntimeLauncher.java | JavaRuntimeLauncher.monitorSlave | private boolean monitorSlave(Process pr,
BufferedReader input, BufferedReader error)
throws IOException, InterruptedException {
// flush the input buffer
System.in.skip(System.in.available());
// If the total amount of time allocated to the slave exceeds the maximum number of trials multiplied
// by the maximum runtime plus some fudge factor the slave is declared as frozen
boolean frozen = false;
long startTime = System.currentTimeMillis();
long lastAliveMessage = startTime;
for(;;) {
while( System.in.available() > 0 ) {
if( System.in.read() == 'q' ) {
System.out.println("User requested for the application to quit by pressing 'q'");
System.exit(0);
}
}
synchronized (streamLock) {
printBuffer(error, printErr);
}
if( input.ready() ) {
synchronized (streamLock) {
printBuffer(input, printOut);
}
} else {
Thread.sleep(500);
}
try {
// exit value throws an exception is the process has yet to stop
pr.exitValue();
break;
} catch( IllegalThreadStateException e) {
if( killRequested ) {
pr.destroy();
break;
}
// check to see if the process is frozen
if(frozenTime > 0 && System.currentTimeMillis() - startTime > frozenTime ) {
pr.destroy(); // kill the process
frozen = true;
break;
}
// let everyone know its still alive
if( System.currentTimeMillis() - lastAliveMessage > 60000 ) {
System.out.println("\nMaster is still alive: "+new Date()+" Press 'q' and enter to quit.");
lastAliveMessage = System.currentTimeMillis();
}
}
}
synchronized (streamLock) {
printBuffer(error, printErr);
printBuffer(input, printOut);
}
durationMilli = System.currentTimeMillis()-startTime;
return !frozen && !killRequested;
} | java | private boolean monitorSlave(Process pr,
BufferedReader input, BufferedReader error)
throws IOException, InterruptedException {
// flush the input buffer
System.in.skip(System.in.available());
// If the total amount of time allocated to the slave exceeds the maximum number of trials multiplied
// by the maximum runtime plus some fudge factor the slave is declared as frozen
boolean frozen = false;
long startTime = System.currentTimeMillis();
long lastAliveMessage = startTime;
for(;;) {
while( System.in.available() > 0 ) {
if( System.in.read() == 'q' ) {
System.out.println("User requested for the application to quit by pressing 'q'");
System.exit(0);
}
}
synchronized (streamLock) {
printBuffer(error, printErr);
}
if( input.ready() ) {
synchronized (streamLock) {
printBuffer(input, printOut);
}
} else {
Thread.sleep(500);
}
try {
// exit value throws an exception is the process has yet to stop
pr.exitValue();
break;
} catch( IllegalThreadStateException e) {
if( killRequested ) {
pr.destroy();
break;
}
// check to see if the process is frozen
if(frozenTime > 0 && System.currentTimeMillis() - startTime > frozenTime ) {
pr.destroy(); // kill the process
frozen = true;
break;
}
// let everyone know its still alive
if( System.currentTimeMillis() - lastAliveMessage > 60000 ) {
System.out.println("\nMaster is still alive: "+new Date()+" Press 'q' and enter to quit.");
lastAliveMessage = System.currentTimeMillis();
}
}
}
synchronized (streamLock) {
printBuffer(error, printErr);
printBuffer(input, printOut);
}
durationMilli = System.currentTimeMillis()-startTime;
return !frozen && !killRequested;
} | [
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lessthanoptimal/BoofCV | main/boofcv-feature/src/main/java/boofcv/alg/feature/dense/DescribeDenseHogAlg.java | DescribeDenseHogAlg.computeWeightBlockPixels | protected void computeWeightBlockPixels() {
int rows = cellsPerBlockY*pixelsPerCell;
int cols = cellsPerBlockX*pixelsPerCell;
weights = new double[ rows*cols ];
double offsetRow=0,offsetCol=0;
int radiusRow=rows/2,radiusCol=cols/2;
if( rows%2 == 0 ) {
offsetRow = 0.5;
}
if( cols%2 == 0 ) {
offsetCol = 0.5;
}
// use linear seperability of a Gaussian to make computation easier
// sigma is 1/2 the width along each axis
int index = 0;
for (int row = 0; row < rows; row++) {
double drow = row-radiusRow+offsetRow;
double pdfRow = UtilGaussian.computePDF(0, radiusRow, drow);
for (int col = 0; col < cols; col++) {
double dcol = col-radiusCol+offsetCol;
double pdfCol = UtilGaussian.computePDF(0, radiusCol, dcol);
weights[index++] = pdfCol*pdfRow;
}
}
// normalize so that the largest value is 1.0
double max = 0;
for (int i = 0; i < weights.length; i++) {
if( weights[i] > max ) {
max = weights[i];
}
}
for (int i = 0; i < weights.length; i++) {
weights[i] /= max;
}
} | java | protected void computeWeightBlockPixels() {
int rows = cellsPerBlockY*pixelsPerCell;
int cols = cellsPerBlockX*pixelsPerCell;
weights = new double[ rows*cols ];
double offsetRow=0,offsetCol=0;
int radiusRow=rows/2,radiusCol=cols/2;
if( rows%2 == 0 ) {
offsetRow = 0.5;
}
if( cols%2 == 0 ) {
offsetCol = 0.5;
}
// use linear seperability of a Gaussian to make computation easier
// sigma is 1/2 the width along each axis
int index = 0;
for (int row = 0; row < rows; row++) {
double drow = row-radiusRow+offsetRow;
double pdfRow = UtilGaussian.computePDF(0, radiusRow, drow);
for (int col = 0; col < cols; col++) {
double dcol = col-radiusCol+offsetCol;
double pdfCol = UtilGaussian.computePDF(0, radiusCol, dcol);
weights[index++] = pdfCol*pdfRow;
}
}
// normalize so that the largest value is 1.0
double max = 0;
for (int i = 0; i < weights.length; i++) {
if( weights[i] > max ) {
max = weights[i];
}
}
for (int i = 0; i < weights.length; i++) {
weights[i] /= max;
}
} | [
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lessthanoptimal/BoofCV | main/boofcv-feature/src/main/java/boofcv/alg/feature/dense/DescribeDenseHogAlg.java | DescribeDenseHogAlg.computePixelFeatures | private void computePixelFeatures() {
for (int y = 0; y < derivX.height; y++) {
int pixelIndex = y*derivX.width;
int endIndex = pixelIndex+derivX.width;
for (; pixelIndex < endIndex; pixelIndex++ ) {
float dx = derivX.data[pixelIndex];
float dy = derivY.data[pixelIndex];
// angle from 0 to pi radians
orientation.data[pixelIndex] = UtilAngle.atanSafe(dy,dx) + GrlConstants.F_PId2;
// gradient magnitude
magnitude.data[pixelIndex] = Math.sqrt(dx*dx + dy*dy);
}
}
} | java | private void computePixelFeatures() {
for (int y = 0; y < derivX.height; y++) {
int pixelIndex = y*derivX.width;
int endIndex = pixelIndex+derivX.width;
for (; pixelIndex < endIndex; pixelIndex++ ) {
float dx = derivX.data[pixelIndex];
float dy = derivY.data[pixelIndex];
// angle from 0 to pi radians
orientation.data[pixelIndex] = UtilAngle.atanSafe(dy,dx) + GrlConstants.F_PId2;
// gradient magnitude
magnitude.data[pixelIndex] = Math.sqrt(dx*dx + dy*dy);
}
}
} | [
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lessthanoptimal/BoofCV | main/boofcv-feature/src/main/java/boofcv/alg/feature/dense/DescribeDenseHogAlg.java | DescribeDenseHogAlg.addToHistogram | void addToHistogram(int cellX, int cellY, int orientationIndex, double magnitude) {
// see if it's being applied to a valid cell in the histogram
if( cellX < 0 || cellX >= cellsPerBlockX)
return;
if( cellY < 0 || cellY >= cellsPerBlockY)
return;
int index = (cellY*cellsPerBlockX + cellX)*orientationBins + orientationIndex;
histogram[index] += magnitude;
} | java | void addToHistogram(int cellX, int cellY, int orientationIndex, double magnitude) {
// see if it's being applied to a valid cell in the histogram
if( cellX < 0 || cellX >= cellsPerBlockX)
return;
if( cellY < 0 || cellY >= cellsPerBlockY)
return;
int index = (cellY*cellsPerBlockX + cellX)*orientationBins + orientationIndex;
histogram[index] += magnitude;
} | [
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lessthanoptimal/BoofCV | main/boofcv-ip/src/main/java/boofcv/alg/interpolate/array/Interpolate1D_F32.java | Interpolate1D_F32.setInput | public void setInput(float x[], float y[], int size) {
if (x.length < size || y.length < size) {
throw new IllegalArgumentException("Arrays too small for size.");
}
if (size < M) {
throw new IllegalArgumentException("Not enough data points for M");
}
this.x = x;
this.y = y;
this.size = size;
this.dj = Math.min(1, (int) Math.pow(size, 0.25));
ascend = x[size - 1] >= x[0];
} | java | public void setInput(float x[], float y[], int size) {
if (x.length < size || y.length < size) {
throw new IllegalArgumentException("Arrays too small for size.");
}
if (size < M) {
throw new IllegalArgumentException("Not enough data points for M");
}
this.x = x;
this.y = y;
this.size = size;
this.dj = Math.min(1, (int) Math.pow(size, 0.25));
ascend = x[size - 1] >= x[0];
} | [
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@param size The number of points used. | [
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lessthanoptimal/BoofCV | main/boofcv-ip/src/main/java/boofcv/alg/interpolate/array/Interpolate1D_F32.java | Interpolate1D_F32.process | public float process(float testX) {
if (doHunt) {
hunt(testX);
} else {
bisectionSearch(testX, 0, size - 1);
}
return compute(testX);
} | java | public float process(float testX) {
if (doHunt) {
hunt(testX);
} else {
bisectionSearch(testX, 0, size - 1);
}
return compute(testX);
} | [
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lessthanoptimal/BoofCV | main/boofcv-ip/src/main/java/boofcv/alg/interpolate/array/Interpolate1D_F32.java | Interpolate1D_F32.hunt | protected void hunt(float val) {
int lowerLimit = center;
int upperLimit;
int inc = 1;
if (val >= x[lowerLimit] && ascend) {
// hunt up
for (; ; ) {
upperLimit = lowerLimit + inc;
// see if it is outside the table
if (upperLimit >= size - 1) {
upperLimit = size - 1;
break;
} else if (val < x[upperLimit] && ascend) {
break;
} else {
lowerLimit = upperLimit;
inc += inc;
}
}
} else {
// hunt down
upperLimit = lowerLimit;
for (; ; ) {
lowerLimit = lowerLimit - inc;
if (lowerLimit <= 0) {
lowerLimit = 0;
break;
} else if (val >= x[lowerLimit] && ascend) {
break;
} else {
upperLimit = lowerLimit;
inc += inc;
}
}
}
bisectionSearch(val, lowerLimit, upperLimit);
} | java | protected void hunt(float val) {
int lowerLimit = center;
int upperLimit;
int inc = 1;
if (val >= x[lowerLimit] && ascend) {
// hunt up
for (; ; ) {
upperLimit = lowerLimit + inc;
// see if it is outside the table
if (upperLimit >= size - 1) {
upperLimit = size - 1;
break;
} else if (val < x[upperLimit] && ascend) {
break;
} else {
lowerLimit = upperLimit;
inc += inc;
}
}
} else {
// hunt down
upperLimit = lowerLimit;
for (; ; ) {
lowerLimit = lowerLimit - inc;
if (lowerLimit <= 0) {
lowerLimit = 0;
break;
} else if (val >= x[lowerLimit] && ascend) {
break;
} else {
upperLimit = lowerLimit;
inc += inc;
}
}
}
bisectionSearch(val, lowerLimit, upperLimit);
} | [
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lessthanoptimal/BoofCV | main/boofcv-ip/src/main/java/boofcv/alg/filter/derivative/impl/GradientSobel_Naive.java | GradientSobel_Naive.process | public static void process( GrayI orig,
GrayI derivX,
GrayI derivY) {
final int width = orig.getWidth();
final int height = orig.getHeight();
for (int y = 1; y < height - 1; y++) {
for (int x = 1; x < width - 1; x++) {
int dy = -(orig.get(x - 1, y - 1) + 2 * orig.get(x, y - 1) + orig.get(x + 1, y - 1));
dy += (orig.get(x - 1, y + 1) + 2 * orig.get(x, y + 1) + orig.get(x + 1, y + 1));
int dx = -(orig.get(x - 1, y - 1) + 2 * orig.get(x - 1, y) + orig.get(x - 1, y + 1));
dx += (orig.get(x + 1, y - 1) + 2 * orig.get(x + 1, y) + orig.get(x + 1, y + 1));
derivX.set(x, y, dx);
derivY.set(x, y, dy);
}
}
} | java | public static void process( GrayI orig,
GrayI derivX,
GrayI derivY) {
final int width = orig.getWidth();
final int height = orig.getHeight();
for (int y = 1; y < height - 1; y++) {
for (int x = 1; x < width - 1; x++) {
int dy = -(orig.get(x - 1, y - 1) + 2 * orig.get(x, y - 1) + orig.get(x + 1, y - 1));
dy += (orig.get(x - 1, y + 1) + 2 * orig.get(x, y + 1) + orig.get(x + 1, y + 1));
int dx = -(orig.get(x - 1, y - 1) + 2 * orig.get(x - 1, y) + orig.get(x - 1, y + 1));
dx += (orig.get(x + 1, y - 1) + 2 * orig.get(x + 1, y) + orig.get(x + 1, y + 1));
derivX.set(x, y, dx);
derivY.set(x, y, dy);
}
}
} | [
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lessthanoptimal/BoofCV | examples/src/main/java/boofcv/examples/segmentation/ExampleSegmentSuperpixels.java | ExampleSegmentSuperpixels.performSegmentation | public static <T extends ImageBase<T>>
void performSegmentation( ImageSuperpixels<T> alg , T color )
{
// Segmentation often works better after blurring the image. Reduces high frequency image components which
// can cause over segmentation
GBlurImageOps.gaussian(color, color, 0.5, -1, null);
// Storage for segmented image. Each pixel will be assigned a label from 0 to N-1, where N is the number
// of segments in the image
GrayS32 pixelToSegment = new GrayS32(color.width,color.height);
// Segmentation magic happens here
alg.segment(color,pixelToSegment);
// Displays the results
visualize(pixelToSegment,color,alg.getTotalSuperpixels());
} | java | public static <T extends ImageBase<T>>
void performSegmentation( ImageSuperpixels<T> alg , T color )
{
// Segmentation often works better after blurring the image. Reduces high frequency image components which
// can cause over segmentation
GBlurImageOps.gaussian(color, color, 0.5, -1, null);
// Storage for segmented image. Each pixel will be assigned a label from 0 to N-1, where N is the number
// of segments in the image
GrayS32 pixelToSegment = new GrayS32(color.width,color.height);
// Segmentation magic happens here
alg.segment(color,pixelToSegment);
// Displays the results
visualize(pixelToSegment,color,alg.getTotalSuperpixels());
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lessthanoptimal/BoofCV | examples/src/main/java/boofcv/examples/segmentation/ExampleSegmentSuperpixels.java | ExampleSegmentSuperpixels.visualize | public static <T extends ImageBase<T>>
void visualize(GrayS32 pixelToRegion , T color , int numSegments )
{
// Computes the mean color inside each region
ImageType<T> type = color.getImageType();
ComputeRegionMeanColor<T> colorize = FactorySegmentationAlg.regionMeanColor(type);
FastQueue<float[]> segmentColor = new ColorQueue_F32(type.getNumBands());
segmentColor.resize(numSegments);
GrowQueue_I32 regionMemberCount = new GrowQueue_I32();
regionMemberCount.resize(numSegments);
ImageSegmentationOps.countRegionPixels(pixelToRegion, numSegments, regionMemberCount.data);
colorize.process(color,pixelToRegion,regionMemberCount,segmentColor);
// Draw each region using their average color
BufferedImage outColor = VisualizeRegions.regionsColor(pixelToRegion,segmentColor,null);
// Draw each region by assigning it a random color
BufferedImage outSegments = VisualizeRegions.regions(pixelToRegion, numSegments, null);
// Make region edges appear red
BufferedImage outBorder = new BufferedImage(color.width,color.height,BufferedImage.TYPE_INT_RGB);
ConvertBufferedImage.convertTo(color, outBorder, true);
VisualizeRegions.regionBorders(pixelToRegion,0xFF0000,outBorder);
// Show the visualization results
ListDisplayPanel gui = new ListDisplayPanel();
gui.addImage(outColor,"Color of Segments");
gui.addImage(outBorder, "Region Borders");
gui.addImage(outSegments, "Regions");
ShowImages.showWindow(gui,"Superpixels", true);
} | java | public static <T extends ImageBase<T>>
void visualize(GrayS32 pixelToRegion , T color , int numSegments )
{
// Computes the mean color inside each region
ImageType<T> type = color.getImageType();
ComputeRegionMeanColor<T> colorize = FactorySegmentationAlg.regionMeanColor(type);
FastQueue<float[]> segmentColor = new ColorQueue_F32(type.getNumBands());
segmentColor.resize(numSegments);
GrowQueue_I32 regionMemberCount = new GrowQueue_I32();
regionMemberCount.resize(numSegments);
ImageSegmentationOps.countRegionPixels(pixelToRegion, numSegments, regionMemberCount.data);
colorize.process(color,pixelToRegion,regionMemberCount,segmentColor);
// Draw each region using their average color
BufferedImage outColor = VisualizeRegions.regionsColor(pixelToRegion,segmentColor,null);
// Draw each region by assigning it a random color
BufferedImage outSegments = VisualizeRegions.regions(pixelToRegion, numSegments, null);
// Make region edges appear red
BufferedImage outBorder = new BufferedImage(color.width,color.height,BufferedImage.TYPE_INT_RGB);
ConvertBufferedImage.convertTo(color, outBorder, true);
VisualizeRegions.regionBorders(pixelToRegion,0xFF0000,outBorder);
// Show the visualization results
ListDisplayPanel gui = new ListDisplayPanel();
gui.addImage(outColor,"Color of Segments");
gui.addImage(outBorder, "Region Borders");
gui.addImage(outSegments, "Regions");
ShowImages.showWindow(gui,"Superpixels", true);
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lessthanoptimal/BoofCV | examples/src/main/java/boofcv/examples/stereo/ExampleStereoDisparity.java | ExampleStereoDisparity.denseDisparity | public static GrayU8 denseDisparity(GrayU8 rectLeft , GrayU8 rectRight ,
int regionSize,
int minDisparity , int maxDisparity )
{
// A slower but more accuracy algorithm is selected
// All of these parameters should be turned
StereoDisparity<GrayU8,GrayU8> disparityAlg =
FactoryStereoDisparity.regionWta(DisparityAlgorithms.RECT_FIVE,
minDisparity, maxDisparity, regionSize, regionSize, 25, 1, 0.2, GrayU8.class);
// process and return the results
disparityAlg.process(rectLeft,rectRight);
return disparityAlg.getDisparity();
} | java | public static GrayU8 denseDisparity(GrayU8 rectLeft , GrayU8 rectRight ,
int regionSize,
int minDisparity , int maxDisparity )
{
// A slower but more accuracy algorithm is selected
// All of these parameters should be turned
StereoDisparity<GrayU8,GrayU8> disparityAlg =
FactoryStereoDisparity.regionWta(DisparityAlgorithms.RECT_FIVE,
minDisparity, maxDisparity, regionSize, regionSize, 25, 1, 0.2, GrayU8.class);
// process and return the results
disparityAlg.process(rectLeft,rectRight);
return disparityAlg.getDisparity();
} | [
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@param rectRight Rectified right camera image
@param regionSize Radius of region being matched
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lessthanoptimal/BoofCV | examples/src/main/java/boofcv/examples/stereo/ExampleStereoDisparity.java | ExampleStereoDisparity.denseDisparitySubpixel | public static GrayF32 denseDisparitySubpixel(GrayU8 rectLeft , GrayU8 rectRight ,
int regionSize ,
int minDisparity , int maxDisparity )
{
// A slower but more accuracy algorithm is selected
// All of these parameters should be turned
StereoDisparity<GrayU8,GrayF32> disparityAlg =
FactoryStereoDisparity.regionSubpixelWta(DisparityAlgorithms.RECT_FIVE,
minDisparity, maxDisparity, regionSize, regionSize, 25, 1, 0.2, GrayU8.class);
// process and return the results
disparityAlg.process(rectLeft,rectRight);
return disparityAlg.getDisparity();
} | java | public static GrayF32 denseDisparitySubpixel(GrayU8 rectLeft , GrayU8 rectRight ,
int regionSize ,
int minDisparity , int maxDisparity )
{
// A slower but more accuracy algorithm is selected
// All of these parameters should be turned
StereoDisparity<GrayU8,GrayF32> disparityAlg =
FactoryStereoDisparity.regionSubpixelWta(DisparityAlgorithms.RECT_FIVE,
minDisparity, maxDisparity, regionSize, regionSize, 25, 1, 0.2, GrayU8.class);
// process and return the results
disparityAlg.process(rectLeft,rectRight);
return disparityAlg.getDisparity();
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lessthanoptimal/BoofCV | examples/src/main/java/boofcv/examples/stereo/ExampleStereoDisparity.java | ExampleStereoDisparity.rectify | public static RectifyCalibrated rectify(GrayU8 origLeft , GrayU8 origRight ,
StereoParameters param ,
GrayU8 rectLeft , GrayU8 rectRight )
{
// Compute rectification
RectifyCalibrated rectifyAlg = RectifyImageOps.createCalibrated();
Se3_F64 leftToRight = param.getRightToLeft().invert(null);
// original camera calibration matrices
DMatrixRMaj K1 = PerspectiveOps.pinholeToMatrix(param.getLeft(), (DMatrixRMaj)null);
DMatrixRMaj K2 = PerspectiveOps.pinholeToMatrix(param.getRight(), (DMatrixRMaj)null);
rectifyAlg.process(K1,new Se3_F64(),K2,leftToRight);
// rectification matrix for each image
DMatrixRMaj rect1 = rectifyAlg.getRect1();
DMatrixRMaj rect2 = rectifyAlg.getRect2();
// New calibration matrix,
DMatrixRMaj rectK = rectifyAlg.getCalibrationMatrix();
// Adjust the rectification to make the view area more useful
RectifyImageOps.allInsideLeft(param.left, rect1, rect2, rectK);
// undistorted and rectify images
FMatrixRMaj rect1_F32 = new FMatrixRMaj(3,3);
FMatrixRMaj rect2_F32 = new FMatrixRMaj(3,3);
ConvertMatrixData.convert(rect1, rect1_F32);
ConvertMatrixData.convert(rect2, rect2_F32);
ImageDistort<GrayU8,GrayU8> imageDistortLeft =
RectifyImageOps.rectifyImage(param.getLeft(), rect1_F32, BorderType.SKIP, origLeft.getImageType());
ImageDistort<GrayU8,GrayU8> imageDistortRight =
RectifyImageOps.rectifyImage(param.getRight(), rect2_F32, BorderType.SKIP, origRight.getImageType());
imageDistortLeft.apply(origLeft, rectLeft);
imageDistortRight.apply(origRight, rectRight);
return rectifyAlg;
} | java | public static RectifyCalibrated rectify(GrayU8 origLeft , GrayU8 origRight ,
StereoParameters param ,
GrayU8 rectLeft , GrayU8 rectRight )
{
// Compute rectification
RectifyCalibrated rectifyAlg = RectifyImageOps.createCalibrated();
Se3_F64 leftToRight = param.getRightToLeft().invert(null);
// original camera calibration matrices
DMatrixRMaj K1 = PerspectiveOps.pinholeToMatrix(param.getLeft(), (DMatrixRMaj)null);
DMatrixRMaj K2 = PerspectiveOps.pinholeToMatrix(param.getRight(), (DMatrixRMaj)null);
rectifyAlg.process(K1,new Se3_F64(),K2,leftToRight);
// rectification matrix for each image
DMatrixRMaj rect1 = rectifyAlg.getRect1();
DMatrixRMaj rect2 = rectifyAlg.getRect2();
// New calibration matrix,
DMatrixRMaj rectK = rectifyAlg.getCalibrationMatrix();
// Adjust the rectification to make the view area more useful
RectifyImageOps.allInsideLeft(param.left, rect1, rect2, rectK);
// undistorted and rectify images
FMatrixRMaj rect1_F32 = new FMatrixRMaj(3,3);
FMatrixRMaj rect2_F32 = new FMatrixRMaj(3,3);
ConvertMatrixData.convert(rect1, rect1_F32);
ConvertMatrixData.convert(rect2, rect2_F32);
ImageDistort<GrayU8,GrayU8> imageDistortLeft =
RectifyImageOps.rectifyImage(param.getLeft(), rect1_F32, BorderType.SKIP, origLeft.getImageType());
ImageDistort<GrayU8,GrayU8> imageDistortRight =
RectifyImageOps.rectifyImage(param.getRight(), rect2_F32, BorderType.SKIP, origRight.getImageType());
imageDistortLeft.apply(origLeft, rectLeft);
imageDistortRight.apply(origRight, rectRight);
return rectifyAlg;
} | [
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lessthanoptimal/BoofCV | main/boofcv-feature/src/main/java/boofcv/alg/feature/color/Histogram_F64.java | Histogram_F64.isRangeSet | public boolean isRangeSet() {
for (int i = 0; i < getDimensions(); i++) {
if( valueMin[i] == 0 && valueMax[i] == 0 ) {
return false;
}
}
return true;
} | java | public boolean isRangeSet() {
for (int i = 0; i < getDimensions(); i++) {
if( valueMin[i] == 0 && valueMax[i] == 0 ) {
return false;
}
}
return true;
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lessthanoptimal/BoofCV | main/boofcv-feature/src/main/java/boofcv/alg/feature/color/Histogram_F64.java | Histogram_F64.setRange | public void setRange( int dimension , double min , double max ) {
valueMin[dimension] = min;
valueMax[dimension] = max;
} | java | public void setRange( int dimension , double min , double max ) {
valueMin[dimension] = min;
valueMax[dimension] = max;
} | [
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lessthanoptimal/BoofCV | main/boofcv-feature/src/main/java/boofcv/alg/feature/color/Histogram_F64.java | Histogram_F64.getDimensionIndex | public int getDimensionIndex( int dimension , int value ) {
double min = valueMin[dimension];
double max = valueMax[dimension];
double fraction = ((value-min)/(max-min+1.0));
return (int)(fraction*length[dimension]);
} | java | public int getDimensionIndex( int dimension , int value ) {
double min = valueMin[dimension];
double max = valueMax[dimension];
double fraction = ((value-min)/(max-min+1.0));
return (int)(fraction*length[dimension]);
} | [
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lessthanoptimal/BoofCV | main/boofcv-feature/src/main/java/boofcv/alg/feature/color/Histogram_F64.java | Histogram_F64.getIndex | public final int getIndex( int coordinate[] ) {
int index = coordinate[0]*strides[0];
for (int i = 1; i < coordinate.length; i++) {
index += strides[i]*coordinate[i];
}
return index;
} | java | public final int getIndex( int coordinate[] ) {
int index = coordinate[0]*strides[0];
for (int i = 1; i < coordinate.length; i++) {
index += strides[i]*coordinate[i];
}
return index;
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lessthanoptimal/BoofCV | main/boofcv-feature/src/main/java/boofcv/alg/feature/color/Histogram_F64.java | Histogram_F64.copy | public Histogram_F64 copy() {
Histogram_F64 out = newInstance();
System.arraycopy(value,0,out.value,0,length.length);
return out;
} | java | public Histogram_F64 copy() {
Histogram_F64 out = newInstance();
System.arraycopy(value,0,out.value,0,length.length);
return out;
} | [
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lessthanoptimal/BoofCV | main/boofcv-feature/src/main/java/boofcv/alg/shapes/polygon/RefinePolygonToGrayLine.java | RefinePolygonToGrayLine.refine | @Override
public boolean refine(Polygon2D_F64 input, Polygon2D_F64 output)
{
if( input.size() != output.size())
throw new IllegalArgumentException("Input and output sides do not match. "+input.size()+" "+output.size());
// sanity check input. If it's too small this algorithm won't work
if( checkShapeTooSmall(input) )
return false;
// see if this work space needs to be resized
if( general.length < input.size() ) {
general = new LineGeneral2D_F64[input.size() ];
for (int i = 0; i < general.length; i++) {
general[i] = new LineGeneral2D_F64();
}
}
// estimate line equations
return optimize(input,output);
} | java | @Override
public boolean refine(Polygon2D_F64 input, Polygon2D_F64 output)
{
if( input.size() != output.size())
throw new IllegalArgumentException("Input and output sides do not match. "+input.size()+" "+output.size());
// sanity check input. If it's too small this algorithm won't work
if( checkShapeTooSmall(input) )
return false;
// see if this work space needs to be resized
if( general.length < input.size() ) {
general = new LineGeneral2D_F64[input.size() ];
for (int i = 0; i < general.length; i++) {
general[i] = new LineGeneral2D_F64();
}
}
// estimate line equations
return optimize(input,output);
} | [
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lessthanoptimal/BoofCV | main/boofcv-feature/src/main/java/boofcv/alg/shapes/polygon/RefinePolygonToGrayLine.java | RefinePolygonToGrayLine.checkShapeTooSmall | private boolean checkShapeTooSmall(Polygon2D_F64 input) {
// must be longer than the border plus some small fudge factor
double minLength = cornerOffset*2 + 2;
for (int i = 0; i < input.size(); i++) {
int j = (i+1)%input.size();
Point2D_F64 a = input.get(i);
Point2D_F64 b = input.get(j);
if( a.distance2(b) < minLength*minLength )
return true;
}
return false;
} | java | private boolean checkShapeTooSmall(Polygon2D_F64 input) {
// must be longer than the border plus some small fudge factor
double minLength = cornerOffset*2 + 2;
for (int i = 0; i < input.size(); i++) {
int j = (i+1)%input.size();
Point2D_F64 a = input.get(i);
Point2D_F64 b = input.get(j);
if( a.distance2(b) < minLength*minLength )
return true;
}
return false;
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lessthanoptimal/BoofCV | main/boofcv-feature/src/main/java/boofcv/alg/shapes/polygon/RefinePolygonToGrayLine.java | RefinePolygonToGrayLine.optimize | protected boolean optimize(Polygon2D_F64 seed , Polygon2D_F64 current ) {
previous.set(seed);
// pixels squares is faster to compute
double convergeTol = convergeTolPixels*convergeTolPixels;
// initialize the lines since they are used to check for corner divergence
for (int i = 0; i < seed.size(); i++) {
int j = (i + 1) % seed.size();
Point2D_F64 a = seed.get(i);
Point2D_F64 b = seed.get(j);
UtilLine2D_F64.convert(a,b,general[i]);
}
boolean changed = false;
for (int iteration = 0; iteration < maxIterations; iteration++) {
// snap each line to the edge independently. Lines will be in local coordinates
for (int i = 0; i < previous.size(); i++) {
int j = (i + 1) % previous.size();
Point2D_F64 a = previous.get(i);
Point2D_F64 b = previous.get(j);
before.set(general[i]);
boolean failed = false;
if( !optimize(a,b,general[i]) ) {
failed = true;
} else {
int k = (i+previous.size()-1) %previous.size();
// see if the corner has diverged
if( Intersection2D_F64.intersection(general[k], general[i],tempA) != null &&
Intersection2D_F64.intersection(general[i], general[j],tempB) != null ) {
if( tempA.distance(a) > maxCornerChangePixel || tempB.distance(b) > maxCornerChangePixel ) {
failed = true;
}
} else {
failed = true;
}
}
// The line fit failed. Probably because its along the image border. Revert it
if( failed ) {
general[i].set(before);
} else {
changed = true;
}
}
// Find the corners of the quadrilateral from the lines
if( !UtilShapePolygon.convert(general,current) )
return false;
// see if it has converged
boolean converged = true;
for (int i = 0; i < current.size(); i++) {
if( current.get(i).distance2(previous.get(i)) > convergeTol ) {
converged = false;
break;
}
}
if( converged ) {
// System.out.println("Converged early at "+iteration);
break;
} else {
previous.set(current);
}
}
return changed;
} | java | protected boolean optimize(Polygon2D_F64 seed , Polygon2D_F64 current ) {
previous.set(seed);
// pixels squares is faster to compute
double convergeTol = convergeTolPixels*convergeTolPixels;
// initialize the lines since they are used to check for corner divergence
for (int i = 0; i < seed.size(); i++) {
int j = (i + 1) % seed.size();
Point2D_F64 a = seed.get(i);
Point2D_F64 b = seed.get(j);
UtilLine2D_F64.convert(a,b,general[i]);
}
boolean changed = false;
for (int iteration = 0; iteration < maxIterations; iteration++) {
// snap each line to the edge independently. Lines will be in local coordinates
for (int i = 0; i < previous.size(); i++) {
int j = (i + 1) % previous.size();
Point2D_F64 a = previous.get(i);
Point2D_F64 b = previous.get(j);
before.set(general[i]);
boolean failed = false;
if( !optimize(a,b,general[i]) ) {
failed = true;
} else {
int k = (i+previous.size()-1) %previous.size();
// see if the corner has diverged
if( Intersection2D_F64.intersection(general[k], general[i],tempA) != null &&
Intersection2D_F64.intersection(general[i], general[j],tempB) != null ) {
if( tempA.distance(a) > maxCornerChangePixel || tempB.distance(b) > maxCornerChangePixel ) {
failed = true;
}
} else {
failed = true;
}
}
// The line fit failed. Probably because its along the image border. Revert it
if( failed ) {
general[i].set(before);
} else {
changed = true;
}
}
// Find the corners of the quadrilateral from the lines
if( !UtilShapePolygon.convert(general,current) )
return false;
// see if it has converged
boolean converged = true;
for (int i = 0; i < current.size(); i++) {
if( current.get(i).distance2(previous.get(i)) > convergeTol ) {
converged = false;
break;
}
}
if( converged ) {
// System.out.println("Converged early at "+iteration);
break;
} else {
previous.set(current);
}
}
return changed;
} | [
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lessthanoptimal/BoofCV | main/boofcv-feature/src/main/java/boofcv/alg/shapes/polygon/RefinePolygonToGrayLine.java | RefinePolygonToGrayLine.optimize | protected boolean optimize( Point2D_F64 a , Point2D_F64 b , LineGeneral2D_F64 found ) {
computeAdjustedEndPoints(a, b);
return snapToEdge.refine(adjA, adjB, found);
} | java | protected boolean optimize( Point2D_F64 a , Point2D_F64 b , LineGeneral2D_F64 found ) {
computeAdjustedEndPoints(a, b);
return snapToEdge.refine(adjA, adjB, found);
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lessthanoptimal/BoofCV | integration/boofcv-swing/src/main/java/boofcv/gui/feature/VisualizeRegions.java | VisualizeRegions.watersheds | public static BufferedImage watersheds(GrayS32 segments , BufferedImage output , int radius ) {
if( output == null )
output = new BufferedImage(segments.width,segments.height,BufferedImage.TYPE_INT_RGB);
if( radius <= 0 ) {
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}
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for (int y = 0; y < segments.height; y++) {
for (int x = 0; x < segments.width; x++) {
int index = segments.unsafe_get(x, y);
if (index == 0) {
for (int i = -radius; i <= radius; i++) {
int yy = y + i;
for (int j = -radius; j <= radius; j++) {
int xx = x + j;
if (segments.isInBounds(xx, yy)) {
output.setRGB(xx, yy, 0xFF0000);
}
}
}
}
}
}
}
return output;
} | java | public static BufferedImage watersheds(GrayS32 segments , BufferedImage output , int radius ) {
if( output == null )
output = new BufferedImage(segments.width,segments.height,BufferedImage.TYPE_INT_RGB);
if( radius <= 0 ) {
for (int y = 0; y < segments.height; y++) {
for (int x = 0; x < segments.width; x++) {
int index = segments.unsafe_get(x, y);
if (index == 0)
output.setRGB(x, y, 0xFF0000);
}
}
} else {
for (int y = 0; y < segments.height; y++) {
for (int x = 0; x < segments.width; x++) {
int index = segments.unsafe_get(x, y);
if (index == 0) {
for (int i = -radius; i <= radius; i++) {
int yy = y + i;
for (int j = -radius; j <= radius; j++) {
int xx = x + j;
if (segments.isInBounds(xx, yy)) {
output.setRGB(xx, yy, 0xFF0000);
}
}
}
}
}
}
}
return output;
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lessthanoptimal/BoofCV | integration/boofcv-swing/src/main/java/boofcv/gui/feature/VisualizeRegions.java | VisualizeRegions.regions | public static BufferedImage regions(GrayS32 pixelToRegion , int numRegions , BufferedImage output ) {
return VisualizeBinaryData.renderLabeled(pixelToRegion,numRegions,output);
} | java | public static BufferedImage regions(GrayS32 pixelToRegion , int numRegions , BufferedImage output ) {
return VisualizeBinaryData.renderLabeled(pixelToRegion,numRegions,output);
} | [
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lessthanoptimal/BoofCV | main/boofcv-feature/src/main/java/boofcv/alg/feature/describe/brief/FactoryBriefDefinition.java | FactoryBriefDefinition.randomGaussian | private static void randomGaussian( Random rand , double sigma , int radius , Point2D_I32 pt ) {
int x,y;
while( true ) {
x = (int)(rand.nextGaussian()*sigma);
y = (int)(rand.nextGaussian()*sigma);
if( Math.sqrt(x*x + y*y) < radius )
break;
}
pt.set(x,y);
} | java | private static void randomGaussian( Random rand , double sigma , int radius , Point2D_I32 pt ) {
int x,y;
while( true ) {
x = (int)(rand.nextGaussian()*sigma);
y = (int)(rand.nextGaussian()*sigma);
if( Math.sqrt(x*x + y*y) < radius )
break;
}
pt.set(x,y);
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lessthanoptimal/BoofCV | main/boofcv-ip/src/main/java/boofcv/factory/transform/pyramid/FactoryPyramid.java | FactoryPyramid.discreteGaussian | public static <T extends ImageBase<T>>
PyramidDiscrete<T> discreteGaussian( int[] scaleFactors , double sigma , int radius ,
boolean saveOriginalReference, ImageType<T> imageType )
{
Class<Kernel1D> kernelType = FactoryKernel.getKernelType(imageType.getDataType(),1);
Kernel1D kernel = FactoryKernelGaussian.gaussian(kernelType,sigma,radius);
return new PyramidDiscreteSampleBlur<>(kernel, sigma, imageType, saveOriginalReference, scaleFactors);
} | java | public static <T extends ImageBase<T>>
PyramidDiscrete<T> discreteGaussian( int[] scaleFactors , double sigma , int radius ,
boolean saveOriginalReference, ImageType<T> imageType )
{
Class<Kernel1D> kernelType = FactoryKernel.getKernelType(imageType.getDataType(),1);
Kernel1D kernel = FactoryKernelGaussian.gaussian(kernelType,sigma,radius);
return new PyramidDiscreteSampleBlur<>(kernel, sigma, imageType, saveOriginalReference, scaleFactors);
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lessthanoptimal/BoofCV | main/boofcv-ip/src/main/java/boofcv/factory/transform/pyramid/FactoryPyramid.java | FactoryPyramid.floatGaussian | public static <T extends ImageGray<T>>
PyramidFloat<T> floatGaussian( double scaleFactors[], double []sigmas , Class<T> imageType ) {
InterpolatePixelS<T> interp = FactoryInterpolation.bilinearPixelS(imageType, BorderType.EXTENDED);
return new PyramidFloatGaussianScale<>(interp, scaleFactors, sigmas, imageType);
} | java | public static <T extends ImageGray<T>>
PyramidFloat<T> floatGaussian( double scaleFactors[], double []sigmas , Class<T> imageType ) {
InterpolatePixelS<T> interp = FactoryInterpolation.bilinearPixelS(imageType, BorderType.EXTENDED);
return new PyramidFloatGaussianScale<>(interp, scaleFactors, sigmas, imageType);
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lessthanoptimal/BoofCV | main/boofcv-ip/src/main/java/boofcv/alg/interpolate/array/LagrangeFormula.java | LagrangeFormula.process_F64 | public static double process_F64(double sample, double x[], double y[], int i0, int i1) {
double result = 0;
for (int i = i0; i <= i1; i++) {
double numerator = 1.0;
for (int j = i0; j <= i1; j++) {
if (i != j)
numerator *= sample - x[j];
}
double denominator = 1.0;
double a = x[i];
for (int j = i0; j <= i1; j++) {
if (i != j)
denominator *= a - x[j];
}
result += (numerator / denominator) * y[i];
}
return result;
} | java | public static double process_F64(double sample, double x[], double y[], int i0, int i1) {
double result = 0;
for (int i = i0; i <= i1; i++) {
double numerator = 1.0;
for (int j = i0; j <= i1; j++) {
if (i != j)
numerator *= sample - x[j];
}
double denominator = 1.0;
double a = x[i];
for (int j = i0; j <= i1; j++) {
if (i != j)
denominator *= a - x[j];
}
result += (numerator / denominator) * y[i];
}
return result;
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lessthanoptimal/BoofCV | integration/boofcv-swing/src/main/java/boofcv/gui/d3/Polygon3DSequenceViewer.java | Polygon3DSequenceViewer.add | public void add( Color color , Point3D_F64... polygon ) {
final Poly p = new Poly(polygon.length,color);
for( int i = 0; i < polygon.length; i++ )
p.pts[i] = polygon[i].copy();
synchronized (polygons) {
polygons.add( p );
}
} | java | public void add( Color color , Point3D_F64... polygon ) {
final Poly p = new Poly(polygon.length,color);
for( int i = 0; i < polygon.length; i++ )
p.pts[i] = polygon[i].copy();
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lessthanoptimal/BoofCV | main/boofcv-io/src/main/java/boofcv/io/image/ConvertBufferedImage.java | ConvertBufferedImage.checkDeclare | public static BufferedImage checkDeclare( int width , int height , BufferedImage image , int type ) {
if( image == null )
return new BufferedImage(width,height,type);
if( image.getType() != type )
return new BufferedImage(width,height,type);
if( image.getWidth() != width || image.getHeight() != height )
return new BufferedImage(width,height,type);
return image;
} | java | public static BufferedImage checkDeclare( int width , int height , BufferedImage image , int type ) {
if( image == null )
return new BufferedImage(width,height,type);
if( image.getType() != type )
return new BufferedImage(width,height,type);
if( image.getWidth() != width || image.getHeight() != height )
return new BufferedImage(width,height,type);
return image;
} | [
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lessthanoptimal/BoofCV | main/boofcv-io/src/main/java/boofcv/io/image/ConvertBufferedImage.java | ConvertBufferedImage.checkCopy | public static BufferedImage checkCopy( BufferedImage original , BufferedImage output ) {
ColorModel cm = original.getColorModel();
boolean isAlphaPremultiplied = cm.isAlphaPremultiplied();
if( output == null || original.getWidth() != output.getWidth() || original.getHeight() != output.getHeight() ||
original.getType() != output.getType() ) {
WritableRaster raster = original.copyData(original.getRaster().createCompatibleWritableRaster());
return new BufferedImage(cm, raster, isAlphaPremultiplied, null);
}
original.copyData(output.getRaster());
return output;
} | java | public static BufferedImage checkCopy( BufferedImage original , BufferedImage output ) {
ColorModel cm = original.getColorModel();
boolean isAlphaPremultiplied = cm.isAlphaPremultiplied();
if( output == null || original.getWidth() != output.getWidth() || original.getHeight() != output.getHeight() ||
original.getType() != output.getType() ) {
WritableRaster raster = original.copyData(original.getRaster().createCompatibleWritableRaster());
return new BufferedImage(cm, raster, isAlphaPremultiplied, null);
}
original.copyData(output.getRaster());
return output;
} | [
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lessthanoptimal/BoofCV | main/boofcv-io/src/main/java/boofcv/io/image/ConvertBufferedImage.java | ConvertBufferedImage.stripAlphaChannel | public static BufferedImage stripAlphaChannel( BufferedImage image ) {
int numBands = image.getRaster().getNumBands();
if( numBands == 4 ) {
BufferedImage output = new BufferedImage(image.getWidth(),image.getHeight(),BufferedImage.TYPE_INT_RGB);
output.createGraphics().drawImage(image,0,0,null);
return output;
} else {
return image;
}
} | java | public static BufferedImage stripAlphaChannel( BufferedImage image ) {
int numBands = image.getRaster().getNumBands();
if( numBands == 4 ) {
BufferedImage output = new BufferedImage(image.getWidth(),image.getHeight(),BufferedImage.TYPE_INT_RGB);
output.createGraphics().drawImage(image,0,0,null);
return output;
} else {
return image;
}
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lessthanoptimal/BoofCV | main/boofcv-io/src/main/java/boofcv/io/image/ConvertBufferedImage.java | ConvertBufferedImage.extractInterleavedU8 | public static InterleavedU8 extractInterleavedU8(BufferedImage img) {
DataBuffer buffer = img.getRaster().getDataBuffer();
if (buffer.getDataType() == DataBuffer.TYPE_BYTE && isKnownByteFormat(img) ) {
WritableRaster raster = img.getRaster();
InterleavedU8 ret = new InterleavedU8();
ret.width = img.getWidth();
ret.height = img.getHeight();
ret.startIndex = ConvertRaster.getOffset(raster);
ret.imageType.numBands = raster.getNumBands();
ret.numBands = raster.getNumBands();
ret.stride = ConvertRaster.stride(raster);
ret.data = ((DataBufferByte)buffer).getData();
ret.subImage = ret.startIndex != 0;
return ret;
}
throw new IllegalArgumentException("Buffered image does not have an interleaved byte raster");
} | java | public static InterleavedU8 extractInterleavedU8(BufferedImage img) {
DataBuffer buffer = img.getRaster().getDataBuffer();
if (buffer.getDataType() == DataBuffer.TYPE_BYTE && isKnownByteFormat(img) ) {
WritableRaster raster = img.getRaster();
InterleavedU8 ret = new InterleavedU8();
ret.width = img.getWidth();
ret.height = img.getHeight();
ret.startIndex = ConvertRaster.getOffset(raster);
ret.imageType.numBands = raster.getNumBands();
ret.numBands = raster.getNumBands();
ret.stride = ConvertRaster.stride(raster);
ret.data = ((DataBufferByte)buffer).getData();
ret.subImage = ret.startIndex != 0;
return ret;
}
throw new IllegalArgumentException("Buffered image does not have an interleaved byte raster");
} | [
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lessthanoptimal/BoofCV | main/boofcv-io/src/main/java/boofcv/io/image/ConvertBufferedImage.java | ConvertBufferedImage.extractGrayU8 | public static GrayU8 extractGrayU8(BufferedImage img) {
WritableRaster raster = img.getRaster();
DataBuffer buffer = raster.getDataBuffer();
if (buffer.getDataType() == DataBuffer.TYPE_BYTE && isKnownByteFormat(img) ) {
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ret.width = img.getWidth();
ret.height = img.getHeight();
ret.startIndex = ConvertRaster.getOffset(img.getRaster());
ret.stride = ConvertRaster.stride(img.getRaster());
ret.data = ((DataBufferByte)buffer).getData();
return ret;
}
throw new IllegalArgumentException("Buffered image does not have a gray scale byte raster");
} | java | public static GrayU8 extractGrayU8(BufferedImage img) {
WritableRaster raster = img.getRaster();
DataBuffer buffer = raster.getDataBuffer();
if (buffer.getDataType() == DataBuffer.TYPE_BYTE && isKnownByteFormat(img) ) {
if (raster.getNumBands() != 1)
throw new IllegalArgumentException("Input image has more than one channel");
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ret.width = img.getWidth();
ret.height = img.getHeight();
ret.startIndex = ConvertRaster.getOffset(img.getRaster());
ret.stride = ConvertRaster.stride(img.getRaster());
ret.data = ((DataBufferByte)buffer).getData();
return ret;
}
throw new IllegalArgumentException("Buffered image does not have a gray scale byte raster");
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lessthanoptimal/BoofCV | main/boofcv-io/src/main/java/boofcv/io/image/ConvertBufferedImage.java | ConvertBufferedImage.convertFromSingle | public static <T extends ImageGray<T>> T convertFromSingle(BufferedImage src, T dst, Class<T> type) {
if (type == GrayU8.class) {
return (T) convertFrom(src, (GrayU8) dst);
} else if( GrayI16.class.isAssignableFrom(type) ) {
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} else if (type == GrayF32.class) {
return (T) convertFrom(src, (GrayF32) dst);
} else {
throw new IllegalArgumentException("Unknown type " + type);
}
} | java | public static <T extends ImageGray<T>> T convertFromSingle(BufferedImage src, T dst, Class<T> type) {
if (type == GrayU8.class) {
return (T) convertFrom(src, (GrayU8) dst);
} else if( GrayI16.class.isAssignableFrom(type) ) {
return (T) convertFrom(src, (GrayI16) dst,(Class)type);
} else if (type == GrayF32.class) {
return (T) convertFrom(src, (GrayF32) dst);
} else {
throw new IllegalArgumentException("Unknown type " + type);
}
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lessthanoptimal/BoofCV | main/boofcv-io/src/main/java/boofcv/io/image/ConvertBufferedImage.java | ConvertBufferedImage.convertTo | public static BufferedImage convertTo(JComponent comp, BufferedImage storage) {
if (storage == null)
storage = new BufferedImage(comp.getWidth(), comp.getHeight(), BufferedImage.TYPE_INT_RGB);
Graphics2D g2 = storage.createGraphics();
comp.paintComponents(g2);
return storage;
} | java | public static BufferedImage convertTo(JComponent comp, BufferedImage storage) {
if (storage == null)
storage = new BufferedImage(comp.getWidth(), comp.getHeight(), BufferedImage.TYPE_INT_RGB);
Graphics2D g2 = storage.createGraphics();
comp.paintComponents(g2);
return storage;
} | [
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lessthanoptimal/BoofCV | main/boofcv-io/src/main/java/boofcv/io/image/ConvertBufferedImage.java | ConvertBufferedImage.orderBandsIntoBuffered | public static Planar orderBandsIntoBuffered(Planar src, BufferedImage dst) {
// see if no change is required
if( dst.getType() == BufferedImage.TYPE_INT_RGB )
return src;
Planar tmp = new Planar(src.type, src.getNumBands());
tmp.width = src.width;
tmp.height = src.height;
tmp.stride = src.stride;
tmp.startIndex = src.startIndex;
for( int i = 0; i < src.getNumBands(); i++ ) {
tmp.bands[i] = src.bands[i];
}
ConvertRaster.orderBandsBufferedFromRgb(tmp, dst);
return tmp;
} | java | public static Planar orderBandsIntoBuffered(Planar src, BufferedImage dst) {
// see if no change is required
if( dst.getType() == BufferedImage.TYPE_INT_RGB )
return src;
Planar tmp = new Planar(src.type, src.getNumBands());
tmp.width = src.width;
tmp.height = src.height;
tmp.stride = src.stride;
tmp.startIndex = src.startIndex;
for( int i = 0; i < src.getNumBands(); i++ ) {
tmp.bands[i] = src.bands[i];
}
ConvertRaster.orderBandsBufferedFromRgb(tmp, dst);
return tmp;
} | [
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] | f01c0243da0ec086285ee722183804d5923bc3ac | https://github.com/lessthanoptimal/BoofCV/blob/f01c0243da0ec086285ee722183804d5923bc3ac/main/boofcv-io/src/main/java/boofcv/io/image/ConvertBufferedImage.java#L930-L945 | train |
lessthanoptimal/BoofCV | demonstrations/src/main/java/boofcv/demonstrations/enhance/DenoiseVisualizeApp.java | DenoiseVisualizeApp.computeError | public static double computeError(GrayF32 imgA, GrayF32 imgB ) {
final int h = imgA.getHeight();
final int w = imgA.getWidth();
double total = 0;
for (int y = 0; y < h; y++) {
for (int x = 0; x < w; x++) {
double difference = Math.abs(imgA.get(x,y)-imgB.get(x,y));
total += difference;
}
}
return total / (w*h);
} | java | public static double computeError(GrayF32 imgA, GrayF32 imgB ) {
final int h = imgA.getHeight();
final int w = imgA.getWidth();
double total = 0;
for (int y = 0; y < h; y++) {
for (int x = 0; x < w; x++) {
double difference = Math.abs(imgA.get(x,y)-imgB.get(x,y));
total += difference;
}
}
return total / (w*h);
} | [
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lessthanoptimal/BoofCV | demonstrations/src/main/java/boofcv/demonstrations/enhance/DenoiseVisualizeApp.java | DenoiseVisualizeApp.computeWeightedError | public static double computeWeightedError(GrayF32 imgA, GrayF32 imgB ,
GrayF32 imgWeight ) {
final int h = imgA.getHeight();
final int w = imgA.getWidth();
double total = 0;
double totalWeight = 0;
for (int y = 0; y < h; y++) {
for (int x = 0; x < w; x++) {
float weight = imgWeight.get(x,y);
double difference = Math.abs(imgA.get(x,y)-imgB.get(x,y));
total += difference*weight;
totalWeight += weight;
}
}
return total / totalWeight;
} | java | public static double computeWeightedError(GrayF32 imgA, GrayF32 imgB ,
GrayF32 imgWeight ) {
final int h = imgA.getHeight();
final int w = imgA.getWidth();
double total = 0;
double totalWeight = 0;
for (int y = 0; y < h; y++) {
for (int x = 0; x < w; x++) {
float weight = imgWeight.get(x,y);
double difference = Math.abs(imgA.get(x,y)-imgB.get(x,y));
total += difference*weight;
totalWeight += weight;
}
}
return total / totalWeight;
} | [
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lessthanoptimal/BoofCV | examples/src/main/java/boofcv/examples/geometry/ExampleVideoMosaic.java | ExampleVideoMosaic.nearBorder | private static boolean nearBorder( Point2D_F64 p , StitchingFromMotion2D<?,?> stitch ) {
int r = 10;
if( p.x < r || p.y < r )
return true;
if( p.x >= stitch.getStitchedImage().width-r )
return true;
if( p.y >= stitch.getStitchedImage().height-r )
return true;
return false;
} | java | private static boolean nearBorder( Point2D_F64 p , StitchingFromMotion2D<?,?> stitch ) {
int r = 10;
if( p.x < r || p.y < r )
return true;
if( p.x >= stitch.getStitchedImage().width-r )
return true;
if( p.y >= stitch.getStitchedImage().height-r )
return true;
return false;
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lessthanoptimal/BoofCV | main/boofcv-recognition/src/main/java/boofcv/abst/fiducial/FourPointSyntheticStability.java | FourPointSyntheticStability.setShape | public void setShape(double width , double height ) {
points2D3D.get(0).location.set(-width/2,-height/2,0);
points2D3D.get(1).location.set(-width/2, height/2,0);
points2D3D.get(2).location.set( width/2, height/2,0);
points2D3D.get(3).location.set( width/2,-height/2,0);
} | java | public void setShape(double width , double height ) {
points2D3D.get(0).location.set(-width/2,-height/2,0);
points2D3D.get(1).location.set(-width/2, height/2,0);
points2D3D.get(2).location.set( width/2, height/2,0);
points2D3D.get(3).location.set( width/2,-height/2,0);
} | [
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lessthanoptimal/BoofCV | main/boofcv-recognition/src/main/java/boofcv/abst/fiducial/FourPointSyntheticStability.java | FourPointSyntheticStability.computeStability | public void computeStability(Se3_F64 targetToCamera ,
double disturbance,
FiducialStability results) {
targetToCamera.invert(referenceCameraToTarget);
maxOrientation = 0;
maxLocation = 0;
Point3D_F64 cameraPt = new Point3D_F64();
for (int i = 0; i < points2D3D.size(); i++) {
Point2D3D p23 = points2D3D.get(i);
targetToCamera.transform(p23.location,cameraPt);
p23.observation.x = cameraPt.x/cameraPt.z;
p23.observation.y = cameraPt.y/cameraPt.z;
refNorm.get(i).set(p23.observation);
normToPixel.compute(p23.observation.x,p23.observation.y,refPixels.get(i));
}
for (int i = 0; i < points2D3D.size(); i++) {
// see what happens if you tweak this observation a little bit
perturb( disturbance, refPixels.get(i), points2D3D.get(i));
// set it back to the nominal value
points2D3D.get(i).observation.set(refNorm.get(i));
}
results.location = maxLocation;
results.orientation = maxOrientation;
} | java | public void computeStability(Se3_F64 targetToCamera ,
double disturbance,
FiducialStability results) {
targetToCamera.invert(referenceCameraToTarget);
maxOrientation = 0;
maxLocation = 0;
Point3D_F64 cameraPt = new Point3D_F64();
for (int i = 0; i < points2D3D.size(); i++) {
Point2D3D p23 = points2D3D.get(i);
targetToCamera.transform(p23.location,cameraPt);
p23.observation.x = cameraPt.x/cameraPt.z;
p23.observation.y = cameraPt.y/cameraPt.z;
refNorm.get(i).set(p23.observation);
normToPixel.compute(p23.observation.x,p23.observation.y,refPixels.get(i));
}
for (int i = 0; i < points2D3D.size(); i++) {
// see what happens if you tweak this observation a little bit
perturb( disturbance, refPixels.get(i), points2D3D.get(i));
// set it back to the nominal value
points2D3D.get(i).observation.set(refNorm.get(i));
}
results.location = maxLocation;
results.orientation = maxOrientation;
} | [
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@param targetToCamera Observed target to camera pose estimate
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@param results description how how sensitive the stability estimate is
@return true if stability could be computed | [
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lessthanoptimal/BoofCV | main/boofcv-recognition/src/main/java/boofcv/abst/fiducial/FourPointSyntheticStability.java | FourPointSyntheticStability.perturb | private void perturb(double disturbance , Point2D_F64 pixel , Point2D3D p23 ) {
double x;
double y = pixel.y;
x = pixel.x + disturbance;
computeDisturbance( x,y, p23);
x = pixel.x - disturbance;
computeDisturbance( x,y, p23);
x = pixel.x;
y = pixel.y + disturbance;
computeDisturbance( x,y, p23);
y = pixel.y - disturbance;
computeDisturbance( x,y, p23);
} | java | private void perturb(double disturbance , Point2D_F64 pixel , Point2D3D p23 ) {
double x;
double y = pixel.y;
x = pixel.x + disturbance;
computeDisturbance( x,y, p23);
x = pixel.x - disturbance;
computeDisturbance( x,y, p23);
x = pixel.x;
y = pixel.y + disturbance;
computeDisturbance( x,y, p23);
y = pixel.y - disturbance;
computeDisturbance( x,y, p23);
} | [
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lessthanoptimal/BoofCV | examples/src/main/java/boofcv/examples/features/ExampleLineDetection.java | ExampleLineDetection.detectLines | public static<T extends ImageGray<T>, D extends ImageGray<D>>
void detectLines( BufferedImage image ,
Class<T> imageType ,
Class<D> derivType )
{
// convert the line into a single band image
T input = ConvertBufferedImage.convertFromSingle(image, null, imageType );
// Comment/uncomment to try a different type of line detector
DetectLineHoughPolar<T,D> detector = FactoryDetectLineAlgs.houghPolar(
new ConfigHoughPolar(3, 30, 2, Math.PI / 180,edgeThreshold, maxLines), imageType, derivType);
// DetectLineHoughFoot<T,D> detector = FactoryDetectLineAlgs.houghFoot(
// new ConfigHoughFoot(3, 8, 5, edgeThreshold,maxLines), imageType, derivType);
// DetectLineHoughFootSubimage<T,D> detector = FactoryDetectLineAlgs.houghFootSub(
// new ConfigHoughFootSubimage(3, 8, 5, edgeThreshold,maxLines, 2, 2), imageType, derivType);
List<LineParametric2D_F32> found = detector.detect(input);
// display the results
ImageLinePanel gui = new ImageLinePanel();
gui.setImage(image);
gui.setLines(found);
gui.setPreferredSize(new Dimension(image.getWidth(),image.getHeight()));
listPanel.addItem(gui, "Found Lines");
} | java | public static<T extends ImageGray<T>, D extends ImageGray<D>>
void detectLines( BufferedImage image ,
Class<T> imageType ,
Class<D> derivType )
{
// convert the line into a single band image
T input = ConvertBufferedImage.convertFromSingle(image, null, imageType );
// Comment/uncomment to try a different type of line detector
DetectLineHoughPolar<T,D> detector = FactoryDetectLineAlgs.houghPolar(
new ConfigHoughPolar(3, 30, 2, Math.PI / 180,edgeThreshold, maxLines), imageType, derivType);
// DetectLineHoughFoot<T,D> detector = FactoryDetectLineAlgs.houghFoot(
// new ConfigHoughFoot(3, 8, 5, edgeThreshold,maxLines), imageType, derivType);
// DetectLineHoughFootSubimage<T,D> detector = FactoryDetectLineAlgs.houghFootSub(
// new ConfigHoughFootSubimage(3, 8, 5, edgeThreshold,maxLines, 2, 2), imageType, derivType);
List<LineParametric2D_F32> found = detector.detect(input);
// display the results
ImageLinePanel gui = new ImageLinePanel();
gui.setImage(image);
gui.setLines(found);
gui.setPreferredSize(new Dimension(image.getWidth(),image.getHeight()));
listPanel.addItem(gui, "Found Lines");
} | [
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lessthanoptimal/BoofCV | examples/src/main/java/boofcv/examples/features/ExampleLineDetection.java | ExampleLineDetection.detectLineSegments | public static<T extends ImageGray<T>, D extends ImageGray<D>>
void detectLineSegments( BufferedImage image ,
Class<T> imageType ,
Class<D> derivType )
{
// convert the line into a single band image
T input = ConvertBufferedImage.convertFromSingle(image, null, imageType );
// Comment/uncomment to try a different type of line detector
DetectLineSegmentsGridRansac<T,D> detector = FactoryDetectLineAlgs.lineRansac(40, 30, 2.36, true, imageType, derivType);
List<LineSegment2D_F32> found = detector.detect(input);
// display the results
ImageLinePanel gui = new ImageLinePanel();
gui.setImage(image);
gui.setLineSegments(found);
gui.setPreferredSize(new Dimension(image.getWidth(),image.getHeight()));
listPanel.addItem(gui, "Found Line Segments");
} | java | public static<T extends ImageGray<T>, D extends ImageGray<D>>
void detectLineSegments( BufferedImage image ,
Class<T> imageType ,
Class<D> derivType )
{
// convert the line into a single band image
T input = ConvertBufferedImage.convertFromSingle(image, null, imageType );
// Comment/uncomment to try a different type of line detector
DetectLineSegmentsGridRansac<T,D> detector = FactoryDetectLineAlgs.lineRansac(40, 30, 2.36, true, imageType, derivType);
List<LineSegment2D_F32> found = detector.detect(input);
// display the results
ImageLinePanel gui = new ImageLinePanel();
gui.setImage(image);
gui.setLineSegments(found);
gui.setPreferredSize(new Dimension(image.getWidth(),image.getHeight()));
listPanel.addItem(gui, "Found Line Segments");
} | [
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lessthanoptimal/BoofCV | examples/src/main/java/boofcv/examples/recognition/ExampleColorHistogramLookup.java | ExampleColorHistogramLookup.coupledHueSat | public static List<double[]> coupledHueSat( List<String> images ) {
List<double[]> points = new ArrayList<>();
Planar<GrayF32> rgb = new Planar<>(GrayF32.class,1,1,3);
Planar<GrayF32> hsv = new Planar<>(GrayF32.class,1,1,3);
for( String path : images ) {
BufferedImage buffered = UtilImageIO.loadImage(path);
if( buffered == null ) throw new RuntimeException("Can't load image!");
rgb.reshape(buffered.getWidth(), buffered.getHeight());
hsv.reshape(buffered.getWidth(), buffered.getHeight());
ConvertBufferedImage.convertFrom(buffered, rgb, true);
ColorHsv.rgbToHsv(rgb, hsv);
Planar<GrayF32> hs = hsv.partialSpectrum(0,1);
// The number of bins is an important parameter. Try adjusting it
Histogram_F64 histogram = new Histogram_F64(12,12);
histogram.setRange(0, 0, 2.0*Math.PI); // range of hue is from 0 to 2PI
histogram.setRange(1, 0, 1.0); // range of saturation is from 0 to 1
// Compute the histogram
GHistogramFeatureOps.histogram(hs,histogram);
UtilFeature.normalizeL2(histogram); // normalize so that image size doesn't matter
points.add(histogram.value);
}
return points;
} | java | public static List<double[]> coupledHueSat( List<String> images ) {
List<double[]> points = new ArrayList<>();
Planar<GrayF32> rgb = new Planar<>(GrayF32.class,1,1,3);
Planar<GrayF32> hsv = new Planar<>(GrayF32.class,1,1,3);
for( String path : images ) {
BufferedImage buffered = UtilImageIO.loadImage(path);
if( buffered == null ) throw new RuntimeException("Can't load image!");
rgb.reshape(buffered.getWidth(), buffered.getHeight());
hsv.reshape(buffered.getWidth(), buffered.getHeight());
ConvertBufferedImage.convertFrom(buffered, rgb, true);
ColorHsv.rgbToHsv(rgb, hsv);
Planar<GrayF32> hs = hsv.partialSpectrum(0,1);
// The number of bins is an important parameter. Try adjusting it
Histogram_F64 histogram = new Histogram_F64(12,12);
histogram.setRange(0, 0, 2.0*Math.PI); // range of hue is from 0 to 2PI
histogram.setRange(1, 0, 1.0); // range of saturation is from 0 to 1
// Compute the histogram
GHistogramFeatureOps.histogram(hs,histogram);
UtilFeature.normalizeL2(histogram); // normalize so that image size doesn't matter
points.add(histogram.value);
}
return points;
} | [
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lessthanoptimal/BoofCV | examples/src/main/java/boofcv/examples/recognition/ExampleColorHistogramLookup.java | ExampleColorHistogramLookup.independentHueSat | public static List<double[]> independentHueSat( List<File> images ) {
List<double[]> points = new ArrayList<>();
// The number of bins is an important parameter. Try adjusting it
TupleDesc_F64 histogramHue = new TupleDesc_F64(30);
TupleDesc_F64 histogramValue = new TupleDesc_F64(30);
List<TupleDesc_F64> histogramList = new ArrayList<>();
histogramList.add(histogramHue); histogramList.add(histogramValue);
Planar<GrayF32> rgb = new Planar<>(GrayF32.class,1,1,3);
Planar<GrayF32> hsv = new Planar<>(GrayF32.class,1,1,3);
for( File f : images ) {
BufferedImage buffered = UtilImageIO.loadImage(f.getPath());
if( buffered == null ) throw new RuntimeException("Can't load image!");
rgb.reshape(buffered.getWidth(), buffered.getHeight());
hsv.reshape(buffered.getWidth(), buffered.getHeight());
ConvertBufferedImage.convertFrom(buffered, rgb, true);
ColorHsv.rgbToHsv(rgb, hsv);
GHistogramFeatureOps.histogram(hsv.getBand(0), 0, 2*Math.PI,histogramHue);
GHistogramFeatureOps.histogram(hsv.getBand(1), 0, 1, histogramValue);
// need to combine them into a single descriptor for processing later on
TupleDesc_F64 imageHist = UtilFeature.combine(histogramList,null);
UtilFeature.normalizeL2(imageHist); // normalize so that image size doesn't matter
points.add(imageHist.value);
}
return points;
} | java | public static List<double[]> independentHueSat( List<File> images ) {
List<double[]> points = new ArrayList<>();
// The number of bins is an important parameter. Try adjusting it
TupleDesc_F64 histogramHue = new TupleDesc_F64(30);
TupleDesc_F64 histogramValue = new TupleDesc_F64(30);
List<TupleDesc_F64> histogramList = new ArrayList<>();
histogramList.add(histogramHue); histogramList.add(histogramValue);
Planar<GrayF32> rgb = new Planar<>(GrayF32.class,1,1,3);
Planar<GrayF32> hsv = new Planar<>(GrayF32.class,1,1,3);
for( File f : images ) {
BufferedImage buffered = UtilImageIO.loadImage(f.getPath());
if( buffered == null ) throw new RuntimeException("Can't load image!");
rgb.reshape(buffered.getWidth(), buffered.getHeight());
hsv.reshape(buffered.getWidth(), buffered.getHeight());
ConvertBufferedImage.convertFrom(buffered, rgb, true);
ColorHsv.rgbToHsv(rgb, hsv);
GHistogramFeatureOps.histogram(hsv.getBand(0), 0, 2*Math.PI,histogramHue);
GHistogramFeatureOps.histogram(hsv.getBand(1), 0, 1, histogramValue);
// need to combine them into a single descriptor for processing later on
TupleDesc_F64 imageHist = UtilFeature.combine(histogramList,null);
UtilFeature.normalizeL2(imageHist); // normalize so that image size doesn't matter
points.add(imageHist.value);
}
return points;
} | [
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lessthanoptimal/BoofCV | examples/src/main/java/boofcv/examples/recognition/ExampleColorHistogramLookup.java | ExampleColorHistogramLookup.coupledRGB | public static List<double[]> coupledRGB( List<File> images ) {
List<double[]> points = new ArrayList<>();
Planar<GrayF32> rgb = new Planar<>(GrayF32.class,1,1,3);
for( File f : images ) {
BufferedImage buffered = UtilImageIO.loadImage(f.getPath());
if( buffered == null ) throw new RuntimeException("Can't load image!");
rgb.reshape(buffered.getWidth(), buffered.getHeight());
ConvertBufferedImage.convertFrom(buffered, rgb, true);
// The number of bins is an important parameter. Try adjusting it
Histogram_F64 histogram = new Histogram_F64(10,10,10);
histogram.setRange(0, 0, 255);
histogram.setRange(1, 0, 255);
histogram.setRange(2, 0, 255);
GHistogramFeatureOps.histogram(rgb,histogram);
UtilFeature.normalizeL2(histogram); // normalize so that image size doesn't matter
points.add(histogram.value);
}
return points;
} | java | public static List<double[]> coupledRGB( List<File> images ) {
List<double[]> points = new ArrayList<>();
Planar<GrayF32> rgb = new Planar<>(GrayF32.class,1,1,3);
for( File f : images ) {
BufferedImage buffered = UtilImageIO.loadImage(f.getPath());
if( buffered == null ) throw new RuntimeException("Can't load image!");
rgb.reshape(buffered.getWidth(), buffered.getHeight());
ConvertBufferedImage.convertFrom(buffered, rgb, true);
// The number of bins is an important parameter. Try adjusting it
Histogram_F64 histogram = new Histogram_F64(10,10,10);
histogram.setRange(0, 0, 255);
histogram.setRange(1, 0, 255);
histogram.setRange(2, 0, 255);
GHistogramFeatureOps.histogram(rgb,histogram);
UtilFeature.normalizeL2(histogram); // normalize so that image size doesn't matter
points.add(histogram.value);
}
return points;
} | [
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lessthanoptimal/BoofCV | examples/src/main/java/boofcv/examples/recognition/ExampleColorHistogramLookup.java | ExampleColorHistogramLookup.histogramGray | public static List<double[]> histogramGray( List<File> images ) {
List<double[]> points = new ArrayList<>();
GrayU8 gray = new GrayU8(1,1);
for( File f : images ) {
BufferedImage buffered = UtilImageIO.loadImage(f.getPath());
if( buffered == null ) throw new RuntimeException("Can't load image!");
gray.reshape(buffered.getWidth(), buffered.getHeight());
ConvertBufferedImage.convertFrom(buffered, gray, true);
TupleDesc_F64 imageHist = new TupleDesc_F64(150);
HistogramFeatureOps.histogram(gray, 255, imageHist);
UtilFeature.normalizeL2(imageHist); // normalize so that image size doesn't matter
points.add(imageHist.value);
}
return points;
} | java | public static List<double[]> histogramGray( List<File> images ) {
List<double[]> points = new ArrayList<>();
GrayU8 gray = new GrayU8(1,1);
for( File f : images ) {
BufferedImage buffered = UtilImageIO.loadImage(f.getPath());
if( buffered == null ) throw new RuntimeException("Can't load image!");
gray.reshape(buffered.getWidth(), buffered.getHeight());
ConvertBufferedImage.convertFrom(buffered, gray, true);
TupleDesc_F64 imageHist = new TupleDesc_F64(150);
HistogramFeatureOps.histogram(gray, 255, imageHist);
UtilFeature.normalizeL2(imageHist); // normalize so that image size doesn't matter
points.add(imageHist.value);
}
return points;
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lessthanoptimal/BoofCV | main/boofcv-ip/src/main/java/boofcv/misc/DiscretizedCircle.java | DiscretizedCircle.imageOffsets | public static int[] imageOffsets(double radius, int imgWidth) {
double PI2 = Math.PI * 2.0;
double circumference = PI2 * radius;
int num = (int) Math.ceil(circumference);
num = num - num % 4;
double angleStep = PI2 / num;
int temp[] = new int[(int) Math.ceil(circumference)];
int i = 0;
int prev = 0;
for (double ang = 0; ang < PI2; ang += angleStep) {
int x = (int) Math.round(Math.cos(ang) * radius);
int y = (int) Math.round(Math.sin(ang) * radius);
int pixel = y * imgWidth + x;
if (pixel != prev) {
// System.out.println("i = "+i+" x = "+x+" y = "+y);
temp[i++] = pixel;
}
prev = pixel;
}
if (i == temp.length)
return temp;
else {
int ret[] = new int[i];
System.arraycopy(temp, 0, ret, 0, i);
return ret;
}
} | java | public static int[] imageOffsets(double radius, int imgWidth) {
double PI2 = Math.PI * 2.0;
double circumference = PI2 * radius;
int num = (int) Math.ceil(circumference);
num = num - num % 4;
double angleStep = PI2 / num;
int temp[] = new int[(int) Math.ceil(circumference)];
int i = 0;
int prev = 0;
for (double ang = 0; ang < PI2; ang += angleStep) {
int x = (int) Math.round(Math.cos(ang) * radius);
int y = (int) Math.round(Math.sin(ang) * radius);
int pixel = y * imgWidth + x;
if (pixel != prev) {
// System.out.println("i = "+i+" x = "+x+" y = "+y);
temp[i++] = pixel;
}
prev = pixel;
}
if (i == temp.length)
return temp;
else {
int ret[] = new int[i];
System.arraycopy(temp, 0, ret, 0, i);
return ret;
}
} | [
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@return A list of offsets that describe the circle | [
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lessthanoptimal/BoofCV | examples/src/main/java/boofcv/examples/features/ExampleDetectDescribe.java | ExampleDetectDescribe.createFromPremade | public static <T extends ImageGray<T>, TD extends TupleDesc>
DetectDescribePoint<T, TD> createFromPremade( Class<T> imageType ) {
return (DetectDescribePoint)FactoryDetectDescribe.surfStable(
new ConfigFastHessian(1, 2, 200, 1, 9, 4, 4), null,null, imageType);
// return (DetectDescribePoint)FactoryDetectDescribe.sift(new ConfigCompleteSift(-1,5,300));
} | java | public static <T extends ImageGray<T>, TD extends TupleDesc>
DetectDescribePoint<T, TD> createFromPremade( Class<T> imageType ) {
return (DetectDescribePoint)FactoryDetectDescribe.surfStable(
new ConfigFastHessian(1, 2, 200, 1, 9, 4, 4), null,null, imageType);
// return (DetectDescribePoint)FactoryDetectDescribe.sift(new ConfigCompleteSift(-1,5,300));
} | [
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lessthanoptimal/BoofCV | examples/src/main/java/boofcv/examples/features/ExampleDetectDescribe.java | ExampleDetectDescribe.createFromComponents | public static <T extends ImageGray<T>, TD extends TupleDesc>
DetectDescribePoint<T, TD> createFromComponents( Class<T> imageType ) {
// create a corner detector
Class derivType = GImageDerivativeOps.getDerivativeType(imageType);
GeneralFeatureDetector corner = FactoryDetectPoint.createShiTomasi(new ConfigGeneralDetector(1000,5,1), null, derivType);
InterestPointDetector detector = FactoryInterestPoint.wrapPoint(corner, 1, imageType, derivType);
// describe points using BRIEF
DescribeRegionPoint describe = FactoryDescribeRegionPoint.brief(new ConfigBrief(true), imageType);
// Combine together.
// NOTE: orientation will not be estimated
return FactoryDetectDescribe.fuseTogether(detector, null, describe);
} | java | public static <T extends ImageGray<T>, TD extends TupleDesc>
DetectDescribePoint<T, TD> createFromComponents( Class<T> imageType ) {
// create a corner detector
Class derivType = GImageDerivativeOps.getDerivativeType(imageType);
GeneralFeatureDetector corner = FactoryDetectPoint.createShiTomasi(new ConfigGeneralDetector(1000,5,1), null, derivType);
InterestPointDetector detector = FactoryInterestPoint.wrapPoint(corner, 1, imageType, derivType);
// describe points using BRIEF
DescribeRegionPoint describe = FactoryDescribeRegionPoint.brief(new ConfigBrief(true), imageType);
// Combine together.
// NOTE: orientation will not be estimated
return FactoryDetectDescribe.fuseTogether(detector, null, describe);
} | [
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lessthanoptimal/BoofCV | main/boofcv-recognition/src/main/java/boofcv/alg/tracker/meanshift/LocalWeightedHistogramRotRect.java | LocalWeightedHistogramRotRect.computeWeights | protected void computeWeights(int numSamples, double numSigmas) {
weights = new float[ numSamples*numSamples ];
float w[] = new float[ numSamples ];
for( int i = 0; i < numSamples; i++ ) {
float x = i/(float)(numSamples-1);
w[i] = (float) UtilGaussian.computePDF(0, 1, 2f*numSigmas * (x - 0.5f));
}
for( int y = 0; y < numSamples; y++ ) {
for( int x = 0; x < numSamples; x++ ) {
weights[y*numSamples + x] = w[y]*w[x];
}
}
} | java | protected void computeWeights(int numSamples, double numSigmas) {
weights = new float[ numSamples*numSamples ];
float w[] = new float[ numSamples ];
for( int i = 0; i < numSamples; i++ ) {
float x = i/(float)(numSamples-1);
w[i] = (float) UtilGaussian.computePDF(0, 1, 2f*numSigmas * (x - 0.5f));
}
for( int y = 0; y < numSamples; y++ ) {
for( int x = 0; x < numSamples; x++ ) {
weights[y*numSamples + x] = w[y]*w[x];
}
}
} | [
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lessthanoptimal/BoofCV | main/boofcv-recognition/src/main/java/boofcv/alg/tracker/meanshift/LocalWeightedHistogramRotRect.java | LocalWeightedHistogramRotRect.createSamplePoints | protected void createSamplePoints(int numSamples) {
for( int y = 0; y < numSamples; y++ ) {
float regionY = (y/(numSamples-1.0f) - 0.5f);
for( int x = 0; x < numSamples; x++ ) {
float regionX = (x/(numSamples-1.0f) - 0.5f);
samplePts.add( new Point2D_F32(regionX,regionY));
}
}
} | java | protected void createSamplePoints(int numSamples) {
for( int y = 0; y < numSamples; y++ ) {
float regionY = (y/(numSamples-1.0f) - 0.5f);
for( int x = 0; x < numSamples; x++ ) {
float regionX = (x/(numSamples-1.0f) - 0.5f);
samplePts.add( new Point2D_F32(regionX,regionY));
}
}
} | [
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lessthanoptimal/BoofCV | main/boofcv-recognition/src/main/java/boofcv/alg/tracker/meanshift/LocalWeightedHistogramRotRect.java | LocalWeightedHistogramRotRect.computeHistogramInside | protected void computeHistogramInside( RectangleRotate_F32 region) {
for( int i = 0; i < samplePts.size(); i++ ) {
Point2D_F32 p = samplePts.get(i);
squareToImageSample(p.x, p.y, region);
interpolate.get_fast(imageX,imageY,value);
int indexHistogram = computeHistogramBin(value);
sampleHistIndex[ i ] = indexHistogram;
histogram[indexHistogram] += weights[i];
}
} | java | protected void computeHistogramInside( RectangleRotate_F32 region) {
for( int i = 0; i < samplePts.size(); i++ ) {
Point2D_F32 p = samplePts.get(i);
squareToImageSample(p.x, p.y, region);
interpolate.get_fast(imageX,imageY,value);
int indexHistogram = computeHistogramBin(value);
sampleHistIndex[ i ] = indexHistogram;
histogram[indexHistogram] += weights[i];
}
} | [
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lessthanoptimal/BoofCV | main/boofcv-recognition/src/main/java/boofcv/alg/tracker/meanshift/LocalWeightedHistogramRotRect.java | LocalWeightedHistogramRotRect.computeHistogramBorder | protected void computeHistogramBorder(T image, RectangleRotate_F32 region) {
for( int i = 0; i < samplePts.size(); i++ ) {
Point2D_F32 p = samplePts.get(i);
squareToImageSample(p.x, p.y, region);
// make sure its inside the image
if( !BoofMiscOps.checkInside(image, imageX, imageY)) {
sampleHistIndex[ i ] = -1;
} else {
// use the slower interpolation which can handle the border
interpolate.get(imageX, imageY, value);
int indexHistogram = computeHistogramBin(value);
sampleHistIndex[ i ] = indexHistogram;
histogram[indexHistogram] += weights[i];
}
}
} | java | protected void computeHistogramBorder(T image, RectangleRotate_F32 region) {
for( int i = 0; i < samplePts.size(); i++ ) {
Point2D_F32 p = samplePts.get(i);
squareToImageSample(p.x, p.y, region);
// make sure its inside the image
if( !BoofMiscOps.checkInside(image, imageX, imageY)) {
sampleHistIndex[ i ] = -1;
} else {
// use the slower interpolation which can handle the border
interpolate.get(imageX, imageY, value);
int indexHistogram = computeHistogramBin(value);
sampleHistIndex[ i ] = indexHistogram;
histogram[indexHistogram] += weights[i];
}
}
} | [
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lessthanoptimal/BoofCV | main/boofcv-recognition/src/main/java/boofcv/alg/tracker/meanshift/LocalWeightedHistogramRotRect.java | LocalWeightedHistogramRotRect.computeHistogramBin | protected int computeHistogramBin( float value[] ) {
int indexHistogram = 0;
int binStride = 1;
for( int bandIndex = 0; bandIndex < value.length; bandIndex++ ) {
int bin = (int)(numBins*value[bandIndex]/maxPixelValue);
indexHistogram += bin*binStride;
binStride *= numBins;
}
return indexHistogram;
} | java | protected int computeHistogramBin( float value[] ) {
int indexHistogram = 0;
int binStride = 1;
for( int bandIndex = 0; bandIndex < value.length; bandIndex++ ) {
int bin = (int)(numBins*value[bandIndex]/maxPixelValue);
indexHistogram += bin*binStride;
binStride *= numBins;
}
return indexHistogram;
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lessthanoptimal/BoofCV | main/boofcv-recognition/src/main/java/boofcv/alg/tracker/meanshift/LocalWeightedHistogramRotRect.java | LocalWeightedHistogramRotRect.isInFastBounds | protected boolean isInFastBounds(RectangleRotate_F32 region) {
squareToImageSample(-0.5f, -0.5f, region);
if( !interpolate.isInFastBounds(imageX, imageY))
return false;
squareToImageSample(-0.5f, 0.5f, region);
if( !interpolate.isInFastBounds(imageX, imageY))
return false;
squareToImageSample(0.5f, 0.5f, region);
if( !interpolate.isInFastBounds(imageX, imageY))
return false;
squareToImageSample(0.5f, -0.5f, region);
if( !interpolate.isInFastBounds(imageX, imageY))
return false;
return true;
} | java | protected boolean isInFastBounds(RectangleRotate_F32 region) {
squareToImageSample(-0.5f, -0.5f, region);
if( !interpolate.isInFastBounds(imageX, imageY))
return false;
squareToImageSample(-0.5f, 0.5f, region);
if( !interpolate.isInFastBounds(imageX, imageY))
return false;
squareToImageSample(0.5f, 0.5f, region);
if( !interpolate.isInFastBounds(imageX, imageY))
return false;
squareToImageSample(0.5f, -0.5f, region);
if( !interpolate.isInFastBounds(imageX, imageY))
return false;
return true;
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lessthanoptimal/BoofCV | main/boofcv-recognition/src/main/java/boofcv/alg/tracker/meanshift/LocalWeightedHistogramRotRect.java | LocalWeightedHistogramRotRect.squareToImageSample | protected void squareToImageSample(float x, float y, RectangleRotate_F32 region) {
// -1 because it starts counting at 0. otherwise width+1 samples are made
x *= region.width-1;
y *= region.height-1;
imageX = x*c - y*s + region.cx;
imageY = x*s + y*c + region.cy;
} | java | protected void squareToImageSample(float x, float y, RectangleRotate_F32 region) {
// -1 because it starts counting at 0. otherwise width+1 samples are made
x *= region.width-1;
y *= region.height-1;
imageX = x*c - y*s + region.cx;
imageY = x*s + y*c + region.cy;
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lessthanoptimal/BoofCV | main/boofcv-feature/src/main/java/boofcv/alg/feature/detect/interest/SiftDetector.java | SiftDetector.createSparseDerivatives | private void createSparseDerivatives() {
Kernel1D_F32 kernelD = new Kernel1D_F32(new float[]{-1,0,1},3);
Kernel1D_F32 kernelDD = KernelMath.convolve1D_F32(kernelD, kernelD);
Kernel2D_F32 kernelXY = KernelMath.convolve2D(kernelD, kernelD);
derivXX = FactoryConvolveSparse.horizontal1D(GrayF32.class, kernelDD);
derivXY = FactoryConvolveSparse.convolve2D(GrayF32.class, kernelXY);
derivYY = FactoryConvolveSparse.vertical1D(GrayF32.class, kernelDD);
ImageBorder<GrayF32> border = FactoryImageBorder.single(GrayF32.class, BorderType.EXTENDED);
derivXX.setImageBorder(border);
derivXY.setImageBorder(border);
derivYY.setImageBorder(border);
} | java | private void createSparseDerivatives() {
Kernel1D_F32 kernelD = new Kernel1D_F32(new float[]{-1,0,1},3);
Kernel1D_F32 kernelDD = KernelMath.convolve1D_F32(kernelD, kernelD);
Kernel2D_F32 kernelXY = KernelMath.convolve2D(kernelD, kernelD);
derivXX = FactoryConvolveSparse.horizontal1D(GrayF32.class, kernelDD);
derivXY = FactoryConvolveSparse.convolve2D(GrayF32.class, kernelXY);
derivYY = FactoryConvolveSparse.vertical1D(GrayF32.class, kernelDD);
ImageBorder<GrayF32> border = FactoryImageBorder.single(GrayF32.class, BorderType.EXTENDED);
derivXX.setImageBorder(border);
derivXY.setImageBorder(border);
derivYY.setImageBorder(border);
} | [
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lessthanoptimal/BoofCV | main/boofcv-feature/src/main/java/boofcv/alg/feature/detect/interest/SiftDetector.java | SiftDetector.process | public void process( GrayF32 input ) {
scaleSpace.initialize(input);
detections.reset();
do {
// scale from octave to input image
pixelScaleToInput = scaleSpace.pixelScaleCurrentToInput();
// detect features in the image
for (int j = 1; j < scaleSpace.getNumScales()+1; j++) {
// not really sure how to compute the scale for features found at a particular DoG image
// using the average resulted in less visually appealing circles in a test image
sigmaLower = scaleSpace.computeSigmaScale( j - 1);
sigmaTarget = scaleSpace.computeSigmaScale( j );
sigmaUpper = scaleSpace.computeSigmaScale( j + 1);
// grab the local DoG scale space images
dogLower = scaleSpace.getDifferenceOfGaussian(j-1);
dogTarget = scaleSpace.getDifferenceOfGaussian(j );
dogUpper = scaleSpace.getDifferenceOfGaussian(j+1);
detectFeatures(j);
}
} while( scaleSpace.computeNextOctave() );
} | java | public void process( GrayF32 input ) {
scaleSpace.initialize(input);
detections.reset();
do {
// scale from octave to input image
pixelScaleToInput = scaleSpace.pixelScaleCurrentToInput();
// detect features in the image
for (int j = 1; j < scaleSpace.getNumScales()+1; j++) {
// not really sure how to compute the scale for features found at a particular DoG image
// using the average resulted in less visually appealing circles in a test image
sigmaLower = scaleSpace.computeSigmaScale( j - 1);
sigmaTarget = scaleSpace.computeSigmaScale( j );
sigmaUpper = scaleSpace.computeSigmaScale( j + 1);
// grab the local DoG scale space images
dogLower = scaleSpace.getDifferenceOfGaussian(j-1);
dogTarget = scaleSpace.getDifferenceOfGaussian(j );
dogUpper = scaleSpace.getDifferenceOfGaussian(j+1);
detectFeatures(j);
}
} while( scaleSpace.computeNextOctave() );
} | [
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lessthanoptimal/BoofCV | main/boofcv-feature/src/main/java/boofcv/alg/feature/detect/interest/SiftDetector.java | SiftDetector.detectFeatures | protected void detectFeatures( int scaleIndex ) {
extractor.process(dogTarget);
FastQueue<NonMaxLimiter.LocalExtreme> found = extractor.getLocalExtreme();
derivXX.setImage(dogTarget);
derivXY.setImage(dogTarget);
derivYY.setImage(dogTarget);
for (int i = 0; i < found.size; i++) {
NonMaxLimiter.LocalExtreme e = found.get(i);
if( e.max ) {
if( isScaleSpaceExtremum(e.location.x, e.location.y, e.getValue(), 1f)) {
processFeatureCandidate(e.location.x,e.location.y,e.getValue(),e.max);
}
} else if( isScaleSpaceExtremum(e.location.x, e.location.y, e.getValue(), -1f)) {
processFeatureCandidate(e.location.x,e.location.y,e.getValue(),e.max);
}
}
} | java | protected void detectFeatures( int scaleIndex ) {
extractor.process(dogTarget);
FastQueue<NonMaxLimiter.LocalExtreme> found = extractor.getLocalExtreme();
derivXX.setImage(dogTarget);
derivXY.setImage(dogTarget);
derivYY.setImage(dogTarget);
for (int i = 0; i < found.size; i++) {
NonMaxLimiter.LocalExtreme e = found.get(i);
if( e.max ) {
if( isScaleSpaceExtremum(e.location.x, e.location.y, e.getValue(), 1f)) {
processFeatureCandidate(e.location.x,e.location.y,e.getValue(),e.max);
}
} else if( isScaleSpaceExtremum(e.location.x, e.location.y, e.getValue(), -1f)) {
processFeatureCandidate(e.location.x,e.location.y,e.getValue(),e.max);
}
}
} | [
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lessthanoptimal/BoofCV | main/boofcv-feature/src/main/java/boofcv/alg/feature/detect/interest/SiftDetector.java | SiftDetector.isScaleSpaceExtremum | boolean isScaleSpaceExtremum(int c_x, int c_y, float value, float signAdj) {
if( c_x <= 1 || c_y <= 1 || c_x >= dogLower.width-1 || c_y >= dogLower.height-1)
return false;
float v;
value *= signAdj;
for( int y = -1; y <= 1; y++ ) {
for( int x = -1; x <= 1; x++ ) {
v = dogLower.unsafe_get(c_x+x,c_y+y);
if( v*signAdj >= value )
return false;
v = dogUpper.unsafe_get(c_x+x,c_y+y);
if( v*signAdj >= value )
return false;
}
}
return true;
} | java | boolean isScaleSpaceExtremum(int c_x, int c_y, float value, float signAdj) {
if( c_x <= 1 || c_y <= 1 || c_x >= dogLower.width-1 || c_y >= dogLower.height-1)
return false;
float v;
value *= signAdj;
for( int y = -1; y <= 1; y++ ) {
for( int x = -1; x <= 1; x++ ) {
v = dogLower.unsafe_get(c_x+x,c_y+y);
if( v*signAdj >= value )
return false;
v = dogUpper.unsafe_get(c_x+x,c_y+y);
if( v*signAdj >= value )
return false;
}
}
return true;
} | [
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@param value The maximum value it is checking
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lessthanoptimal/BoofCV | main/boofcv-ip/src/main/java/boofcv/alg/distort/impl/DistortSupport.java | DistortSupport.transformScale | public static PixelTransformAffine_F32 transformScale(ImageBase from, ImageBase to,
PixelTransformAffine_F32 distort)
{
if( distort == null )
distort = new PixelTransformAffine_F32();
float scaleX = (float)(to.width)/(float)(from.width);
float scaleY = (float)(to.height)/(float)(from.height);
Affine2D_F32 affine = distort.getModel();
affine.set(scaleX,0,0,scaleY,0,0);
return distort;
} | java | public static PixelTransformAffine_F32 transformScale(ImageBase from, ImageBase to,
PixelTransformAffine_F32 distort)
{
if( distort == null )
distort = new PixelTransformAffine_F32();
float scaleX = (float)(to.width)/(float)(from.width);
float scaleY = (float)(to.height)/(float)(from.height);
Affine2D_F32 affine = distort.getModel();
affine.set(scaleX,0,0,scaleY,0,0);
return distort;
} | [
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lessthanoptimal/BoofCV | main/boofcv-geo/src/main/java/boofcv/alg/geo/f/FundamentalLinear.java | FundamentalLinear.projectOntoEssential | protected boolean projectOntoEssential( DMatrixRMaj E ) {
if( !svdConstraints.decompose(E) ) {
return false;
}
svdV = svdConstraints.getV(svdV,false);
svdU = svdConstraints.getU(svdU,false);
svdS = svdConstraints.getW(svdS);
SingularOps_DDRM.descendingOrder(svdU, false, svdS, svdV, false);
// project it into essential space
// the scale factor is arbitrary, but the first two singular values need
// to be the same. so just set them to one
svdS.unsafe_set(0, 0, 1);
svdS.unsafe_set(1, 1, 1);
svdS.unsafe_set(2, 2, 0);
// recompute F
CommonOps_DDRM.mult(svdU, svdS, temp0);
CommonOps_DDRM.multTransB(temp0,svdV, E);
return true;
} | java | protected boolean projectOntoEssential( DMatrixRMaj E ) {
if( !svdConstraints.decompose(E) ) {
return false;
}
svdV = svdConstraints.getV(svdV,false);
svdU = svdConstraints.getU(svdU,false);
svdS = svdConstraints.getW(svdS);
SingularOps_DDRM.descendingOrder(svdU, false, svdS, svdV, false);
// project it into essential space
// the scale factor is arbitrary, but the first two singular values need
// to be the same. so just set them to one
svdS.unsafe_set(0, 0, 1);
svdS.unsafe_set(1, 1, 1);
svdS.unsafe_set(2, 2, 0);
// recompute F
CommonOps_DDRM.mult(svdU, svdS, temp0);
CommonOps_DDRM.multTransB(temp0,svdV, E);
return true;
} | [
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lessthanoptimal/BoofCV | main/boofcv-geo/src/main/java/boofcv/alg/geo/f/FundamentalLinear.java | FundamentalLinear.projectOntoFundamentalSpace | protected boolean projectOntoFundamentalSpace( DMatrixRMaj F ) {
if( !svdConstraints.decompose(F) ) {
return false;
}
svdV = svdConstraints.getV(svdV,false);
svdU = svdConstraints.getU(svdU,false);
svdS = svdConstraints.getW(svdS);
SingularOps_DDRM.descendingOrder(svdU, false, svdS, svdV, false);
// the smallest singular value needs to be set to zero, unlike
svdS.set(2, 2, 0);
// recompute F
CommonOps_DDRM.mult(svdU, svdS, temp0);
CommonOps_DDRM.multTransB(temp0,svdV, F);
return true;
} | java | protected boolean projectOntoFundamentalSpace( DMatrixRMaj F ) {
if( !svdConstraints.decompose(F) ) {
return false;
}
svdV = svdConstraints.getV(svdV,false);
svdU = svdConstraints.getU(svdU,false);
svdS = svdConstraints.getW(svdS);
SingularOps_DDRM.descendingOrder(svdU, false, svdS, svdV, false);
// the smallest singular value needs to be set to zero, unlike
svdS.set(2, 2, 0);
// recompute F
CommonOps_DDRM.mult(svdU, svdS, temp0);
CommonOps_DDRM.multTransB(temp0,svdV, F);
return true;
} | [
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lessthanoptimal/BoofCV | main/boofcv-recognition/src/main/java/boofcv/alg/tracker/tld/TldFernClassifier.java | TldFernClassifier.learnFern | public void learnFern(boolean positive, ImageRectangle r) {
float rectWidth = r.getWidth();
float rectHeight = r.getHeight();
float c_x = r.x0+(rectWidth-1)/2f;
float c_y = r.y0+(rectHeight-1)/2f;
for( int i = 0; i < ferns.length; i++ ) {
// first learn it with no noise
int value = computeFernValue(c_x, c_y, rectWidth, rectHeight,ferns[i]);
TldFernFeature f = managers[i].lookupFern(value);
increment(f,positive);
}
} | java | public void learnFern(boolean positive, ImageRectangle r) {
float rectWidth = r.getWidth();
float rectHeight = r.getHeight();
float c_x = r.x0+(rectWidth-1)/2f;
float c_y = r.y0+(rectHeight-1)/2f;
for( int i = 0; i < ferns.length; i++ ) {
// first learn it with no noise
int value = computeFernValue(c_x, c_y, rectWidth, rectHeight,ferns[i]);
TldFernFeature f = managers[i].lookupFern(value);
increment(f,positive);
}
} | [
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lessthanoptimal/BoofCV | main/boofcv-recognition/src/main/java/boofcv/alg/tracker/tld/TldFernClassifier.java | TldFernClassifier.learnFernNoise | public void learnFernNoise(boolean positive, ImageRectangle r) {
float rectWidth = r.getWidth();
float rectHeight = r.getHeight();
float c_x = r.x0+(rectWidth-1)/2.0f;
float c_y = r.y0+(rectHeight-1)/2.0f;
for( int i = 0; i < ferns.length; i++ ) {
// first learn it with no noise
int value = computeFernValue(c_x, c_y, rectWidth, rectHeight,ferns[i]);
TldFernFeature f = managers[i].lookupFern(value);
increment(f,positive);
for( int j = 0; j < numLearnRandom; j++ ) {
value = computeFernValueRand(c_x, c_y, rectWidth, rectHeight,ferns[i]);
f = managers[i].lookupFern(value);
increment(f,positive);
}
}
} | java | public void learnFernNoise(boolean positive, ImageRectangle r) {
float rectWidth = r.getWidth();
float rectHeight = r.getHeight();
float c_x = r.x0+(rectWidth-1)/2.0f;
float c_y = r.y0+(rectHeight-1)/2.0f;
for( int i = 0; i < ferns.length; i++ ) {
// first learn it with no noise
int value = computeFernValue(c_x, c_y, rectWidth, rectHeight,ferns[i]);
TldFernFeature f = managers[i].lookupFern(value);
increment(f,positive);
for( int j = 0; j < numLearnRandom; j++ ) {
value = computeFernValueRand(c_x, c_y, rectWidth, rectHeight,ferns[i]);
f = managers[i].lookupFern(value);
increment(f,positive);
}
}
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lessthanoptimal/BoofCV | main/boofcv-recognition/src/main/java/boofcv/alg/tracker/tld/TldFernClassifier.java | TldFernClassifier.increment | private void increment( TldFernFeature f , boolean positive ) {
if( positive ) {
f.incrementP();
if( f.numP > maxP )
maxP = f.numP;
} else {
f.incrementN();
if( f.numN > maxN )
maxN = f.numN;
}
} | java | private void increment( TldFernFeature f , boolean positive ) {
if( positive ) {
f.incrementP();
if( f.numP > maxP )
maxP = f.numP;
} else {
f.incrementN();
if( f.numN > maxN )
maxN = f.numN;
}
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lessthanoptimal/BoofCV | main/boofcv-recognition/src/main/java/boofcv/alg/tracker/tld/TldFernClassifier.java | TldFernClassifier.lookupFernPN | public boolean lookupFernPN( TldRegionFernInfo info ) {
ImageRectangle r = info.r;
float rectWidth = r.getWidth();
float rectHeight = r.getHeight();
float c_x = r.x0+(rectWidth-1)/2.0f;
float c_y = r.y0+(rectHeight-1)/2.0f;
int sumP = 0;
int sumN = 0;
for( int i = 0; i < ferns.length; i++ ) {
TldFernDescription fern = ferns[i];
int value = computeFernValue(c_x, c_y, rectWidth, rectHeight, fern);
TldFernFeature f = managers[i].table[value];
if( f != null ) {
sumP += f.numP;
sumN += f.numN;
}
}
info.sumP = sumP;
info.sumN = sumN;
return sumN != 0 || sumP != 0;
} | java | public boolean lookupFernPN( TldRegionFernInfo info ) {
ImageRectangle r = info.r;
float rectWidth = r.getWidth();
float rectHeight = r.getHeight();
float c_x = r.x0+(rectWidth-1)/2.0f;
float c_y = r.y0+(rectHeight-1)/2.0f;
int sumP = 0;
int sumN = 0;
for( int i = 0; i < ferns.length; i++ ) {
TldFernDescription fern = ferns[i];
int value = computeFernValue(c_x, c_y, rectWidth, rectHeight, fern);
TldFernFeature f = managers[i].table[value];
if( f != null ) {
sumP += f.numP;
sumN += f.numN;
}
}
info.sumP = sumP;
info.sumN = sumN;
return sumN != 0 || sumP != 0;
} | [
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lessthanoptimal/BoofCV | main/boofcv-recognition/src/main/java/boofcv/alg/tracker/tld/TldFernClassifier.java | TldFernClassifier.computeFernValue | protected int computeFernValue(float c_x, float c_y, float rectWidth , float rectHeight , TldFernDescription fern ) {
rectWidth -= 1;
rectHeight -= 1;
int desc = 0;
for( int i = 0; i < fern.pairs.length; i++ ) {
Point2D_F32 p_a = fern.pairs[i].a;
Point2D_F32 p_b = fern.pairs[i].b;
float valA = interpolate.get_fast(c_x + p_a.x * rectWidth, c_y + p_a.y * rectHeight);
float valB = interpolate.get_fast(c_x + p_b.x * rectWidth, c_y + p_b.y * rectHeight);
desc *= 2;
if( valA < valB ) {
desc += 1;
}
}
return desc;
} | java | protected int computeFernValue(float c_x, float c_y, float rectWidth , float rectHeight , TldFernDescription fern ) {
rectWidth -= 1;
rectHeight -= 1;
int desc = 0;
for( int i = 0; i < fern.pairs.length; i++ ) {
Point2D_F32 p_a = fern.pairs[i].a;
Point2D_F32 p_b = fern.pairs[i].b;
float valA = interpolate.get_fast(c_x + p_a.x * rectWidth, c_y + p_a.y * rectHeight);
float valB = interpolate.get_fast(c_x + p_b.x * rectWidth, c_y + p_b.y * rectHeight);
desc *= 2;
if( valA < valB ) {
desc += 1;
}
}
return desc;
} | [
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lessthanoptimal/BoofCV | main/boofcv-recognition/src/main/java/boofcv/alg/tracker/tld/TldFernClassifier.java | TldFernClassifier.renormalizeP | public void renormalizeP() {
int targetMax = maxP/20;
for( int i = 0; i < managers.length; i++ ) {
TldFernManager m = managers[i];
for( int j = 0; j < m.table.length; j++ ) {
TldFernFeature f = m.table[j];
if( f == null )
continue;
f.numP = targetMax*f.numP/maxP;
}
}
maxP = targetMax;
} | java | public void renormalizeP() {
int targetMax = maxP/20;
for( int i = 0; i < managers.length; i++ ) {
TldFernManager m = managers[i];
for( int j = 0; j < m.table.length; j++ ) {
TldFernFeature f = m.table[j];
if( f == null )
continue;
f.numP = targetMax*f.numP/maxP;
}
}
maxP = targetMax;
} | [
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lessthanoptimal/BoofCV | main/boofcv-recognition/src/main/java/boofcv/alg/tracker/tld/TldFernClassifier.java | TldFernClassifier.renormalizeN | public void renormalizeN() {
int targetMax = maxN/20;
for( int i = 0; i < managers.length; i++ ) {
TldFernManager m = managers[i];
for( int j = 0; j < m.table.length; j++ ) {
TldFernFeature f = m.table[j];
if( f == null )
continue;
f.numN = targetMax*f.numN/maxN;
}
}
maxN = targetMax;
} | java | public void renormalizeN() {
int targetMax = maxN/20;
for( int i = 0; i < managers.length; i++ ) {
TldFernManager m = managers[i];
for( int j = 0; j < m.table.length; j++ ) {
TldFernFeature f = m.table[j];
if( f == null )
continue;
f.numN = targetMax*f.numN/maxN;
}
}
maxN = targetMax;
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lessthanoptimal/BoofCV | main/boofcv-feature/src/main/java/boofcv/alg/feature/describe/DescribePointSurf.java | DescribePointSurf.describe | public void describe(double x, double y, double angle, double scale, TupleDesc_F64 ret)
{
double c = Math.cos(angle),s=Math.sin(angle);
// By assuming that the entire feature is inside the image faster algorithms can be used
// the results are also of dubious value when interacting with the image border.
boolean isInBounds =
SurfDescribeOps.isInside(ii,x,y, radiusDescriptor,widthSample,scale,c,s);
// declare the feature if needed
if( ret == null )
ret = new BrightFeature(featureDOF);
else if( ret.value.length != featureDOF )
throw new IllegalArgumentException("Provided feature must have "+featureDOF+" values");
gradient.setImage(ii);
gradient.setWidth(widthSample*scale);
// use a safe method if its along the image border
SparseImageGradient gradient = isInBounds ? this.gradient : this.gradientSafe;
// extract descriptor
features(x, y, c, s, scale, gradient , ret.value);
} | java | public void describe(double x, double y, double angle, double scale, TupleDesc_F64 ret)
{
double c = Math.cos(angle),s=Math.sin(angle);
// By assuming that the entire feature is inside the image faster algorithms can be used
// the results are also of dubious value when interacting with the image border.
boolean isInBounds =
SurfDescribeOps.isInside(ii,x,y, radiusDescriptor,widthSample,scale,c,s);
// declare the feature if needed
if( ret == null )
ret = new BrightFeature(featureDOF);
else if( ret.value.length != featureDOF )
throw new IllegalArgumentException("Provided feature must have "+featureDOF+" values");
gradient.setImage(ii);
gradient.setWidth(widthSample*scale);
// use a safe method if its along the image border
SparseImageGradient gradient = isInBounds ? this.gradient : this.gradientSafe;
// extract descriptor
features(x, y, c, s, scale, gradient , ret.value);
} | [
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lessthanoptimal/BoofCV | main/boofcv-feature/src/main/java/boofcv/alg/feature/describe/DescribePointSurf.java | DescribePointSurf.computeLaplaceSign | public boolean computeLaplaceSign(int x, int y, double scale) {
int s = (int)Math.ceil(scale);
kerXX = DerivativeIntegralImage.kernelDerivXX(9*s,kerXX);
kerYY = DerivativeIntegralImage.kernelDerivYY(9*s,kerYY);
double lap = GIntegralImageOps.convolveSparse(ii,kerXX,x,y);
lap += GIntegralImageOps.convolveSparse(ii,kerYY,x,y);
return lap > 0;
} | java | public boolean computeLaplaceSign(int x, int y, double scale) {
int s = (int)Math.ceil(scale);
kerXX = DerivativeIntegralImage.kernelDerivXX(9*s,kerXX);
kerYY = DerivativeIntegralImage.kernelDerivYY(9*s,kerYY);
double lap = GIntegralImageOps.convolveSparse(ii,kerXX,x,y);
lap += GIntegralImageOps.convolveSparse(ii,kerYY,x,y);
return lap > 0;
} | [
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] | f01c0243da0ec086285ee722183804d5923bc3ac | https://github.com/lessthanoptimal/BoofCV/blob/f01c0243da0ec086285ee722183804d5923bc3ac/main/boofcv-feature/src/main/java/boofcv/alg/feature/describe/DescribePointSurf.java#L305-L313 | train |
lessthanoptimal/BoofCV | main/boofcv-feature/src/main/java/boofcv/factory/feature/detect/line/FactoryDetectLineAlgs.java | FactoryDetectLineAlgs.houghPolar | public static <I extends ImageGray<I>, D extends ImageGray<D>>
DetectLineHoughPolar<I,D> houghPolar(ConfigHoughPolar config ,
Class<I> imageType ,
Class<D> derivType ) {
if( config == null )
throw new IllegalArgumentException("This is no default since minCounts must be specified");
ImageGradient<I,D> gradient = FactoryDerivative.sobel(imageType,derivType);
return new DetectLineHoughPolar<>(config.localMaxRadius, config.minCounts, config.resolutionRange,
config.resolutionAngle, config.thresholdEdge, config.maxLines, gradient);
} | java | public static <I extends ImageGray<I>, D extends ImageGray<D>>
DetectLineHoughPolar<I,D> houghPolar(ConfigHoughPolar config ,
Class<I> imageType ,
Class<D> derivType ) {
if( config == null )
throw new IllegalArgumentException("This is no default since minCounts must be specified");
ImageGradient<I,D> gradient = FactoryDerivative.sobel(imageType,derivType);
return new DetectLineHoughPolar<>(config.localMaxRadius, config.minCounts, config.resolutionRange,
config.resolutionAngle, config.thresholdEdge, config.maxLines, gradient);
} | [
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@see DetectLineHoughPolar
@param config Configuration for line detector. Can't be null.
@param imageType Type of single band input image.
@param derivType Image derivative type.
@param <I> Input image type.
@param <D> Image derivative type.
@return Line detector. | [
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"."
] | f01c0243da0ec086285ee722183804d5923bc3ac | https://github.com/lessthanoptimal/BoofCV/blob/f01c0243da0ec086285ee722183804d5923bc3ac/main/boofcv-feature/src/main/java/boofcv/factory/feature/detect/line/FactoryDetectLineAlgs.java#L161-L173 | train |
lessthanoptimal/BoofCV | integration/boofcv-swing/src/main/java/boofcv/gui/binary/VisualizeBinaryData.java | VisualizeBinaryData.renderContours | public static BufferedImage renderContours(List<Contour> contours , int colorExternal, int colorInternal ,
int width , int height , BufferedImage out) {
if( out == null ) {
out = new BufferedImage(width,height,BufferedImage.TYPE_INT_RGB);
} else {
Graphics2D g2 = out.createGraphics();
g2.setColor(Color.BLACK);
g2.fillRect(0,0,width,height);
}
for( Contour c : contours ) {
for(Point2D_I32 p : c.external ) {
out.setRGB(p.x,p.y,colorExternal);
}
for( List<Point2D_I32> l : c.internal ) {
for( Point2D_I32 p : l ) {
out.setRGB(p.x,p.y,colorInternal);
}
}
}
return out;
} | java | public static BufferedImage renderContours(List<Contour> contours , int colorExternal, int colorInternal ,
int width , int height , BufferedImage out) {
if( out == null ) {
out = new BufferedImage(width,height,BufferedImage.TYPE_INT_RGB);
} else {
Graphics2D g2 = out.createGraphics();
g2.setColor(Color.BLACK);
g2.fillRect(0,0,width,height);
}
for( Contour c : contours ) {
for(Point2D_I32 p : c.external ) {
out.setRGB(p.x,p.y,colorExternal);
}
for( List<Point2D_I32> l : c.internal ) {
for( Point2D_I32 p : l ) {
out.setRGB(p.x,p.y,colorInternal);
}
}
}
return out;
} | [
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")",
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"out",... | Draws contours. Internal and external contours are different user specified colors.
@param contours List of contours
@param colorExternal RGB color
@param colorInternal RGB color
@param width Image width
@param height Image height
@param out (Optional) storage for output image
@return Rendered contours | [
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"."
] | f01c0243da0ec086285ee722183804d5923bc3ac | https://github.com/lessthanoptimal/BoofCV/blob/f01c0243da0ec086285ee722183804d5923bc3ac/integration/boofcv-swing/src/main/java/boofcv/gui/binary/VisualizeBinaryData.java#L77-L100 | train |
lessthanoptimal/BoofCV | integration/boofcv-swing/src/main/java/boofcv/gui/binary/VisualizeBinaryData.java | VisualizeBinaryData.render | public static void render(List<Contour> contours , int colors[] , BufferedImage out) {
colors = checkColors(colors,contours.size());
for( int i = 0; i < contours.size(); i++ ) {
Contour c = contours.get(i);
int color = colors[i];
for(Point2D_I32 p : c.external ) {
out.setRGB(p.x,p.y,color);
}
}
} | java | public static void render(List<Contour> contours , int colors[] , BufferedImage out) {
colors = checkColors(colors,contours.size());
for( int i = 0; i < contours.size(); i++ ) {
Contour c = contours.get(i);
int color = colors[i];
for(Point2D_I32 p : c.external ) {
out.setRGB(p.x,p.y,color);
}
}
} | [
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... | Renders only the external contours. Each contour is individually colored as specified by 'colors'
@param contours List of contours
@param colors List of RGB colors for each element in contours. If null then random colors will be used.
@param out (Optional) Storage for output | [
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] | f01c0243da0ec086285ee722183804d5923bc3ac | https://github.com/lessthanoptimal/BoofCV/blob/f01c0243da0ec086285ee722183804d5923bc3ac/integration/boofcv-swing/src/main/java/boofcv/gui/binary/VisualizeBinaryData.java#L149-L161 | train |
lessthanoptimal/BoofCV | integration/boofcv-swing/src/main/java/boofcv/gui/binary/VisualizeBinaryData.java | VisualizeBinaryData.renderBinary | public static BufferedImage renderBinary(GrayU8 binaryImage, boolean invert, BufferedImage out) {
if( out == null || ( out.getWidth() != binaryImage.width || out.getHeight() != binaryImage.height) ) {
out = new BufferedImage(binaryImage.getWidth(),binaryImage.getHeight(),BufferedImage.TYPE_BYTE_GRAY);
}
try {
WritableRaster raster = out.getRaster();
DataBuffer buffer = raster.getDataBuffer();
if( buffer.getDataType() == DataBuffer.TYPE_BYTE ) {
renderBinary(binaryImage, invert, (DataBufferByte)buffer, raster);
} else if( buffer.getDataType() == DataBuffer.TYPE_INT ) {
renderBinary(binaryImage, invert, (DataBufferInt)buffer, raster);
} else {
_renderBinary(binaryImage, invert, out);
}
} catch( SecurityException e ) {
_renderBinary(binaryImage, invert, out);
}
// hack so that it knows the buffer has been modified
out.setRGB(0,0,out.getRGB(0,0));
return out;
} | java | public static BufferedImage renderBinary(GrayU8 binaryImage, boolean invert, BufferedImage out) {
if( out == null || ( out.getWidth() != binaryImage.width || out.getHeight() != binaryImage.height) ) {
out = new BufferedImage(binaryImage.getWidth(),binaryImage.getHeight(),BufferedImage.TYPE_BYTE_GRAY);
}
try {
WritableRaster raster = out.getRaster();
DataBuffer buffer = raster.getDataBuffer();
if( buffer.getDataType() == DataBuffer.TYPE_BYTE ) {
renderBinary(binaryImage, invert, (DataBufferByte)buffer, raster);
} else if( buffer.getDataType() == DataBuffer.TYPE_INT ) {
renderBinary(binaryImage, invert, (DataBufferInt)buffer, raster);
} else {
_renderBinary(binaryImage, invert, out);
}
} catch( SecurityException e ) {
_renderBinary(binaryImage, invert, out);
}
// hack so that it knows the buffer has been modified
out.setRGB(0,0,out.getRGB(0,0));
return out;
} | [
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"widt... | Renders a binary image. 0 = black and 1 = white.
@param binaryImage (Input) Input binary image.
@param invert (Input) if true it will invert the image on output
@param out (Output) optional storage for output image
@return Output rendered binary image | [
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] | f01c0243da0ec086285ee722183804d5923bc3ac | https://github.com/lessthanoptimal/BoofCV/blob/f01c0243da0ec086285ee722183804d5923bc3ac/integration/boofcv-swing/src/main/java/boofcv/gui/binary/VisualizeBinaryData.java#L382-L404 | train |
lessthanoptimal/BoofCV | main/boofcv-geo/src/main/java/boofcv/alg/geo/h/HomographyInducedStereoLinePt.java | HomographyInducedStereoLinePt.process | public void process(PairLineNorm line, AssociatedPair point) {
// t0 = (F*x) cross l'
GeometryMath_F64.mult(F,point.p1,Fx);
GeometryMath_F64.cross(Fx,line.getL2(),t0);
// t1 = x' cross ((f*x) cross l')
GeometryMath_F64.cross(point.p2, t0, t1);
// t0 = x' cross e'
GeometryMath_F64.cross(point.p2,e2,t0);
double top = GeometryMath_F64.dot(t0,t1);
double bottom = t0.normSq()*(line.l1.x*point.p1.x + line.l1.y*point.p1.y + line.l1.z);
// e' * l^T
GeometryMath_F64.outerProd(e2, line.l1, el);
// cross(l')*F
GeometryMath_F64.multCrossA(line.l2, F, lf);
CommonOps_DDRM.add(lf,top/bottom,el,H);
// pick a good scale and sign for H
adjust.adjust(H, point);
} | java | public void process(PairLineNorm line, AssociatedPair point) {
// t0 = (F*x) cross l'
GeometryMath_F64.mult(F,point.p1,Fx);
GeometryMath_F64.cross(Fx,line.getL2(),t0);
// t1 = x' cross ((f*x) cross l')
GeometryMath_F64.cross(point.p2, t0, t1);
// t0 = x' cross e'
GeometryMath_F64.cross(point.p2,e2,t0);
double top = GeometryMath_F64.dot(t0,t1);
double bottom = t0.normSq()*(line.l1.x*point.p1.x + line.l1.y*point.p1.y + line.l1.z);
// e' * l^T
GeometryMath_F64.outerProd(e2, line.l1, el);
// cross(l')*F
GeometryMath_F64.multCrossA(line.l2, F, lf);
CommonOps_DDRM.add(lf,top/bottom,el,H);
// pick a good scale and sign for H
adjust.adjust(H, point);
} | [
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"Fx"... | Computes the homography based on a line and point on the plane
@param line Line on the plane
@param point Point on the plane | [
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] | f01c0243da0ec086285ee722183804d5923bc3ac | https://github.com/lessthanoptimal/BoofCV/blob/f01c0243da0ec086285ee722183804d5923bc3ac/main/boofcv-geo/src/main/java/boofcv/alg/geo/h/HomographyInducedStereoLinePt.java#L93-L115 | train |
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