...
 
Commits (16)
...@@ -4276,7 +4276,7 @@ List calibration ...@@ -4276,7 +4276,7 @@ List calibration
} }
public boolean removeOutLierSets(int numOutLiers){ public boolean removeOutLierSets(int numOutLiers){
boolean removeEmptySets=false; boolean removeEmptySets=true; // false;
if (numOutLiers<0) { if (numOutLiers<0) {
GenericDialog gd = new GenericDialog("Select sets to process"); GenericDialog gd = new GenericDialog("Select sets to process");
gd.addNumericField("Series number (<0 - all images)", -1, 0); gd.addNumericField("Series number (<0 - all images)", -1, 0);
......
...@@ -1009,14 +1009,32 @@ Cv=(Cy*x-Cx*y)+(-Cy*Dx+Cx*Dy) ...@@ -1009,14 +1009,32 @@ Cv=(Cy*x-Cx*y)+(-Cy*Dx+Cx*Dy)
y0); y0);
} }
public double [] extractSimulMono ( // TODO: can use twice smaller barray
double [] localbArray,
SimulParameters simulParameters,
int outSubdiv, // subdivide output pixels - now 4
int size, // number of Bayer cells in width of the square selection (half number of pixels)
double x0, // selection center, X (in pixels)
double y0) {
return extractSimulMono ( // TODO: can use twice smaller barray
localbArray,
false, // boolean invert,
simulParameters,
outSubdiv, // subdivide output pixels - now 4
size, // number of Bayer cells in width of the square selection (half number of pixels)
x0, // selection center, X (in pixels)
y0);
}
public double [] extractSimulMono ( // TODO: can use twice smaller barray public double [] extractSimulMono ( // TODO: can use twice smaller barray
double [] localbArray, double [] localbArray,
boolean invert,
SimulParameters simulParameters, SimulParameters simulParameters,
int outSubdiv, // subdivide output pixels - now 4 int outSubdiv, // subdivide output pixels - now 4
int size, // number of Bayer cells in width of the square selection (half number of pixels) int size, // number of Bayer cells in width of the square selection (half number of pixels)
double x0, // selection center, X (in pixels) double x0, // selection center, X (in pixels)
double y0) { double y0) {
double pattern_sign = invert? -1.0 : 1.0;
int sampleWidth=(int) (Math.sqrt(simulParameters.fill)*simulParameters.subdiv); int sampleWidth=(int) (Math.sqrt(simulParameters.fill)*simulParameters.subdiv);
int sampleN=sampleWidth*sampleWidth; int sampleN=sampleWidth*sampleWidth;
if (sampleWidth<1) sampleWidth=1; if (sampleWidth<1) sampleWidth=1;
...@@ -1043,7 +1061,7 @@ Cv=(Cy*x-Cx*y)+(-Cy*Dx+Cx*Dy) ...@@ -1043,7 +1061,7 @@ Cv=(Cy*x-Cx*y)+(-Cy*Dx+Cx*Dy)
return null; return null;
} }
} }
simul_pixels[iy*size+ix]= (s-sampleAverage)/sampleAverage; simul_pixels[iy*size+ix]= pattern_sign * (s - sampleAverage) / sampleAverage;
} }
} }
if (this.debugLevel>2) { if (this.debugLevel>2) {
...@@ -1069,6 +1087,7 @@ Cv=(Cy*x-Cx*y)+(-Cy*Dx+Cx*Dy) ...@@ -1069,6 +1087,7 @@ Cv=(Cy*x-Cx*y)+(-Cy*Dx+Cx*Dy)
return rslt; return rslt;
} }
// colorComp == -1 = mono, positive, colorComp == -2 - mono, negative
public double[] extractBayerSim ( public double[] extractBayerSim (
float [][] spixels, // [0] - regular pixels, [1] - shifted by 1/2 diagonally, for checker greens float [][] spixels, // [0] - regular pixels, [1] - shifted by 1/2 diagonally, for checker greens
int full_width, int full_width,
...@@ -1105,21 +1124,28 @@ Cv=(Cy*x-Cx*y)+(-Cy*Dx+Cx*Dy) ...@@ -1105,21 +1124,28 @@ Cv=(Cy*x-Cx*y)+(-Cy*Dx+Cx*Dy)
result[index]=spixels[(ixi+iyi) & 1][iy*full_width+ix]; result[index]=spixels[(ixi+iyi) & 1][iy*full_width+ix];
} }
} else { // components 0..3 } else { // components 0..3
int ser;
double pattern_sign = (colorComp == -2)? -1.0: 1.0;
if (colorComp < 0) {
ser = 1; // offset by 1/2 pix? should it be so? // FIXME: verify and fix if needed - compare to extractSimulMono()
} else {
ser = 0;
r.x+=(bayerPeriod/2)*(colorComp &1); r.x+=(bayerPeriod/2)*(colorComp &1);
r.y+=(bayerPeriod/2)*((colorComp>>1) &1); r.y+=(bayerPeriod/2)*((colorComp>>1) &1);
}
if (debugLevel>2) System.out.println(">>> r.width="+r.width+ if (debugLevel>2) System.out.println(">>> r.width="+r.width+
" r.height="+r.height+ " r.height="+r.height+
" r.x="+r.x+ " r.x="+r.x+
" r.y="+r.y+ " r.y="+r.y+
" colorComp="+colorComp); " colorComp="+colorComp);
for (index=0;index<result.length;index++){ for (index=0;index<result.length;index++){
int iy=r.y+(index / r.width); int iy = r.y + (index / r.width);
int ix=r.x+(index % r.width); int ix = r.x + (index % r.width);
if (iy<0) iy=0; if (iy < 0) iy = 0;
else if (iy>=full_height) iy=full_height-1; else if (iy >= full_height) iy = full_height - 1;
if (ix<0) ix=0; if (ix < 0) ix = 0;
else if (ix>=full_width) iy=full_width-1; else if (ix >= full_width) iy = full_width - 1;
result[index]=spixels[0][iy*full_width+ix]; result[index] = pattern_sign * spixels[ser][iy * full_width + ix];
} }
} }
return result; return result;
......
...@@ -2,8 +2,8 @@ package com.elphel.imagej.common; ...@@ -2,8 +2,8 @@ package com.elphel.imagej.common;
/** /**
* The code below is extracted form ImageJ plugin GaussianBlur.java, stripped of ImageProcessor and used (double) instead of (float) arrays. * The code below is extracted form ImageJ plugin GaussianBlur.java, stripped of ImageProcessor and used (double) instead of (float) arrays.
* The following are notes from the original file: * The following are notes from the original file:
* *
* *
* This plug-in filter uses convolution with a Gaussian function for smoothing. * This plug-in filter uses convolution with a Gaussian function for smoothing.
* 'Radius' means the radius of decay to exp(-0.5) ~ 61%, i.e. the standard * 'Radius' means the radius of decay to exp(-0.5) ~ 61%, i.e. the standard
* deviation sigma of the Gaussian (this is the same as in Photoshop, but * deviation sigma of the Gaussian (this is the same as in Photoshop, but
...@@ -20,7 +20,7 @@ package com.elphel.imagej.common; ...@@ -20,7 +20,7 @@ package com.elphel.imagej.common;
* - For increased speed, except for small blur radii, the lines (rows or * - For increased speed, except for small blur radii, the lines (rows or
* columns of the image) are downscaled before convolution and upscaled * columns of the image) are downscaled before convolution and upscaled
* to their original length thereafter. * to their original length thereafter.
* *
* Version 03-Jun-2007 M. Schmid with preview, progressBar stack-aware, * Version 03-Jun-2007 M. Schmid with preview, progressBar stack-aware,
* snapshot via snapshot flag; restricted range for resetOutOfRoi * snapshot via snapshot flag; restricted range for resetOutOfRoi
* *
...@@ -39,12 +39,12 @@ public class DoubleGaussianBlur { ...@@ -39,12 +39,12 @@ public class DoubleGaussianBlur {
private int nPasses = 1; // The number of passes (filter directions * color channels * stack slices) private int nPasses = 1; // The number of passes (filter directions * color channels * stack slices)
// private int nChannels = 1; // The number of color channels // private int nChannels = 1; // The number of color channels
private int pass; // Current pass private int pass; // Current pass
/* Default constructor */ /* Default constructor */
public DoubleGaussianBlur() { public DoubleGaussianBlur() {
} }
public void blurDouble(double[] pixels, public void blurDouble(double[] pixels,
int width, int width,
int height, int height,
...@@ -64,7 +64,7 @@ public class DoubleGaussianBlur { ...@@ -64,7 +64,7 @@ public class DoubleGaussianBlur {
* @param sigma Standard deviation of the Gaussian * @param sigma Standard deviation of the Gaussian
* @param accuracy Accuracy of kernel, should not be > 0.02 * @param accuracy Accuracy of kernel, should not be > 0.02
* @param xDirection True for blurring in x direction, false for y direction * @param xDirection True for blurring in x direction, false for y direction
* @param extraLines Number of lines (parallel to the blurring direction) * @param extraLines Number of lines (parallel to the blurring direction)
* below and above the roi bounds that should be processed. * below and above the roi bounds that should be processed.
*/ */
public void blur1Direction(double [] pixels, public void blur1Direction(double [] pixels,
...@@ -73,7 +73,7 @@ public class DoubleGaussianBlur { ...@@ -73,7 +73,7 @@ public class DoubleGaussianBlur {
double sigma, double sigma,
double accuracy, double accuracy,
boolean xDirection boolean xDirection
// int extraLines // int extraLines
) { ) {
final int UPSCALE_K_RADIUS = 2; //number of pixels to add for upscaling final int UPSCALE_K_RADIUS = 2; //number of pixels to add for upscaling
final double MIN_DOWNSCALED_SIGMA = 4.; //minimum standard deviation in the downscaled image final double MIN_DOWNSCALED_SIGMA = 4.; //minimum standard deviation in the downscaled image
...@@ -171,7 +171,7 @@ public class DoubleGaussianBlur { ...@@ -171,7 +171,7 @@ public class DoubleGaussianBlur {
if (kRadius > maxRadius) kRadius = maxRadius; if (kRadius > maxRadius) kRadius = maxRadius;
double[][] kernel = new double[2][kRadius]; double[][] kernel = new double[2][kRadius];
for (int i=0; i<kRadius; i++) // Gaussian function for (int i=0; i<kRadius; i++) // Gaussian function
kernel[0][i] = (double)(Math.exp(-0.5*i*i/sigma/sigma)); kernel[0][i] = (Math.exp(-0.5*i*i/sigma/sigma));
if (kRadius < maxRadius && kRadius > 3) { // edge correction if (kRadius < maxRadius && kRadius > 3) { // edge correction
double sqrtSlope = Double.MAX_VALUE; double sqrtSlope = Double.MAX_VALUE;
int r = kRadius; int r = kRadius;
...@@ -184,7 +184,7 @@ public class DoubleGaussianBlur { ...@@ -184,7 +184,7 @@ public class DoubleGaussianBlur {
break; break;
} }
for (int r1 = r+2; r1 < kRadius; r1++) for (int r1 = r+2; r1 < kRadius; r1++)
kernel[0][r1] = (double)((kRadius-r1)*(kRadius-r1)*sqrtSlope*sqrtSlope); kernel[0][r1] = (kRadius-r1)*(kRadius-r1)*sqrtSlope*sqrtSlope;
} }
double sum; // sum over all kernel elements for normalization double sum; // sum over all kernel elements for normalization
if (kRadius < maxRadius) { if (kRadius < maxRadius) {
...@@ -193,13 +193,13 @@ public class DoubleGaussianBlur { ...@@ -193,13 +193,13 @@ public class DoubleGaussianBlur {
sum += 2*kernel[0][i]; sum += 2*kernel[0][i];
} else } else
sum = sigma * Math.sqrt(2*Math.PI); sum = sigma * Math.sqrt(2*Math.PI);
double rsum = 0.5 + 0.5*kernel[0][0]/sum; double rsum = 0.5 + 0.5*kernel[0][0]/sum;
for (int i=0; i<kRadius; i++) { for (int i=0; i<kRadius; i++) {
double v = (kernel[0][i]/sum); double v = (kernel[0][i]/sum);
kernel[0][i] = (double)v; kernel[0][i] = v;
rsum -= v; rsum -= v;
kernel[1][i] = (double)rsum; kernel[1][i] = rsum;
//IJ.log("k["+i+"]="+(float)v+" sum="+(float)rsum); //IJ.log("k["+i+"]="+(float)v+" sum="+(float)rsum);
} }
return kernel; return kernel;
...@@ -213,6 +213,10 @@ public class DoubleGaussianBlur { ...@@ -213,6 +213,10 @@ public class DoubleGaussianBlur {
*/ */
void downscaleLine(double[] pixels, double[] cache, double[] kernel, void downscaleLine(double[] pixels, double[] cache, double[] kernel,
int reduceBy, int pixel0, int unscaled0, int length, int pointInc, int newLength) { int reduceBy, int pixel0, int unscaled0, int length, int pointInc, int newLength) {
if (pixel0 > pixels.length) {
System.out.println("++++++ Error in DoubleGaussianBlur, pixel0="+pixel0+", pixels.length="+(pixels.length));
return;
}
double first = pixels[pixel0]; double first = pixels[pixel0];
double last = pixels[pixel0 + pointInc*(length-1)]; double last = pixels[pixel0 + pointInc*(length-1)];
int xin = unscaled0 - reduceBy/2; int xin = unscaled0 - reduceBy/2;
...@@ -241,13 +245,13 @@ public class DoubleGaussianBlur { ...@@ -241,13 +245,13 @@ public class DoubleGaussianBlur {
double[] kernel = new double[3*unitLength]; double[] kernel = new double[3*unitLength];
for (int i=0; i<=unitLength/2; i++) { for (int i=0; i<=unitLength/2; i++) {
double x = i/(double)unitLength; double x = i/(double)unitLength;
double v = (double)((0.75-x*x)/unitLength); double v = (0.75-x*x)/unitLength;
kernel[mid-i] = v; kernel[mid-i] = v;
kernel[mid+i] = v; kernel[mid+i] = v;
} }
for (int i=unitLength/2+1; i<(unitLength*3+1)/2; i++) { for (int i=unitLength/2+1; i<(unitLength*3+1)/2; i++) {
double x = i/(double)unitLength; double x = i/(double)unitLength;
double v = (double)((0.125 + 0.5*(x-1)*(x-2))/unitLength); double v = (0.125 + 0.5*(x-1)*(x-2))/unitLength;
kernel[mid-i] = v; kernel[mid-i] = v;
kernel[mid+i] = v; kernel[mid+i] = v;
} }
...@@ -284,13 +288,13 @@ public class DoubleGaussianBlur { ...@@ -284,13 +288,13 @@ public class DoubleGaussianBlur {
kernel[0] = 0; kernel[0] = 0;
for (int i=0; i<unitLength; i++) { for (int i=0; i<unitLength; i++) {
double x = i/(double)unitLength; double x = i/(double)unitLength;
double v = (double)((2./3. -x*x*(1-0.5*x))); double v = ((2./3. -x*x*(1-0.5*x)));
kernel[mid+i] = v; kernel[mid+i] = v;
kernel[mid-i] = v; kernel[mid-i] = v;
} }
for (int i=unitLength; i<2*unitLength; i++) { for (int i=unitLength; i<2*unitLength; i++) {
double x = i/(double)unitLength; double x = i/(double)unitLength;
double v = (double)((2.-x)*(2.-x)*(2.-x)/6.); double v = (2.-x)*(2.-x)*(2.-x)/6.;
kernel[mid+i] = v; kernel[mid+i] = v;
kernel[mid-i] = v; kernel[mid-i] = v;
} }
...@@ -350,7 +354,7 @@ public class DoubleGaussianBlur { ...@@ -350,7 +354,7 @@ public class DoubleGaussianBlur {
result += kern[k] * (input[i-k] + input[i+k]); result += kern[k] * (input[i-k] + input[i+k]);
pixels[p] = result; pixels[p] = result;
} }
for (; i<writeTo; i++,p+=pointInc) { //while the sum would include pixels >= length for (; i<writeTo; i++,p+=pointInc) { //while the sum would include pixels >= length
double result = input[i]*kern0; double result = input[i]*kern0;
if (i<kRadius) result += kernSum[i]*first; if (i<kRadius) result += kernSum[i]*first;
if (i+kRadius>=length) result += kernSum[length-i-1]*last; if (i+kRadius>=length) result += kernSum[length-i-1]*last;
......
package com.elphel.imagej.common;
import java.util.Properties;
public class EProperties extends Properties{
private static final long serialVersionUID = -425120416815883045L;
public int getProperty(String key, int value){
return Integer.parseInt(getProperty(key, ""+value));
}
public double getProperty(String key, double value){
return Double.parseDouble(getProperty(key, ""+value));
}
public boolean getProperty(String key, boolean value){
return Boolean.parseBoolean(getProperty(key, ""+value));
}
}