/*
* Copyright 2013 Alibaba.com All right reserved. This software is the
* confidential and proprietary information of Alibaba.com ("Confidential
* Information"). You shall not disclose such Confidential Information and shall
* use it only in accordance with the terms of the license agreement you entered
* into with Alibaba.com.
*/
package com.alibaba.security.simpleimage.analyze.sift.detect;
import java.util.ArrayList;
import java.util.List;
import com.alibaba.security.simpleimage.analyze.sift.ImageMap;
import com.alibaba.security.simpleimage.analyze.sift.conv.GaussianConvolution;
import com.alibaba.security.simpleimage.analyze.sift.scala.DScaleSpace;
import com.alibaba.security.simpleimage.analyze.sift.scala.KeyPoint;
import com.alibaba.security.simpleimage.analyze.sift.scala.KeyPointN;
import com.alibaba.security.simpleimage.analyze.sift.scala.OctavePyramid;
import com.alibaba.security.simpleimage.analyze.sift.scala.ScalePoint;
/**
* 类LoweFeatureDetector.java的实现描述：TODO 类实现描述
*
* @author axman 2013-3-26 上午10:13:09
*/
public class LoweFeatureDetector {
OctavePyramid pyr;
public OctavePyramid getPyr() {
return pyr;
}
// Detection parameters, suggested by Lowe's research paper.
// Initial parameters
double octaveSigma = 1.6;
// Sigma for gaussian filter applied to double-scaled input image.
// sigma 英文发音西格码，用于高斯滤波器
double preprocSigma = 1.5;
// Once one of the downscaled image's dimension falls below this,
// downscaling is stopped.
int minimumRequiredPixelsize = 32;
// How many DoG levels for each octave.
int scaleSpaceLevels = 3;
boolean printWarning = true;
public void setPrintWarning(boolean printWarning) {
this.printWarning = printWarning;
}
boolean verbose = System.getProperty("_verbose") == null?false:true;
public void setVerbose(boolean verbose) {
this.verbose = verbose;
}
// Minimum absolute DoG value of a pixel to be allowed as minimum/maximum
// peak. This control how much general non-differing areas, such as the
// sky is filtered. Higher value = less peaks, lower value = more peaks.
// Good values from 0.005 to 0.01. Note this is related to
// 'dValueLowThresh', which should be a bit larger, factor 1.0 to 1.5.
double dogThresh = 0.0075;
// D-value filter highcap value, higher = less keypoints, lower = more.
// Lower: only keep keypoints with good localization properties, i.e.
// those that are precisely and easily to localize (high contrast, see
// Lowe, page 11. He recommends 0.03, but this seems way too high to
// me.)
double dValueLowThresh = 0.008;
// Required cornerness ratio level, higher = more keypoints, lower = less.
double maximumEdgeRatio = 20.0;
// The exact sub-pixel localization is done on just one DoG plane. Even
// when the scale adjustment exceeds +/- 0.5, the plane is not changed.
// With this value you can discard peaks that are localized to be too far
// from the plane. A high value will allow for peaks to be used that are
// more far away from the plane used for localization, while a low value
// will sort out more peaks, that drifted too far away.
//
// Be very careful with this value, as a too large value will lead to a
// high number of keypoints in hard to localize areas such as in photos of
// the sky.
//
// Good values seem to lie between 0.30 and 0.6.
double scaleAdjustThresh = 0.50;
// Number of maximum steps a single keypoint can make in its space.
int relocationMaximum = 4;
// Results
ArrayList<KeyPoint> globalKeypoints;
public ArrayList<KeyPoint> getGlobalKeypoints() {
return globalKeypoints;
}
// The Integer-normalized version of the globalKeypoints.
ArrayList<KeyPointN> globalNaturalKeypoints = null;
public List<KeyPointN> getGlobalNaturalKeypoints() {
if (globalNaturalKeypoints != null)
return (globalNaturalKeypoints);
if (globalKeypoints == null)
throw (new IllegalArgumentException ("No keypoints generated yet."));
globalNaturalKeypoints = new ArrayList<KeyPointN>();
for (KeyPoint kp : globalKeypoints){
globalNaturalKeypoints.add (new KeyPointN(kp));
}
return (globalNaturalKeypoints);
}
public int detectFeatures(ImageMap img) {
return (detectFeaturesDownscaled(img, -1, 1.0));
}
// Scale down the images down so that both dimensions are smaller than
// 'bothDimHi'. If 'bothDimHi' is < 0, the image is doubled before
// processing, if it is zero, nothing is done to the image.
public int detectFeaturesDownscaled(ImageMap img, int bothDimHi, double startScale) {
// globalkeypoints 是一个arraylist：数组列表
globalKeypoints = null;
globalNaturalKeypoints = null;
if (bothDimHi < 0) {
img = img.scaleDouble();// pass
startScale *= 0.5;
} else if (bothDimHi > 0) {
while (img.xDim > bothDimHi || img.yDim > bothDimHi) {
img = img.scaleHalf();// pass
startScale *= 2.0;
}
}
// Maybe the blurring has to be before double-sizing?
// better not, if we would lose more information then?
// (Lowe03, p10, "We assume that the original image has a blur of at
// least \sigma = 0.5 ...")
// So, do one initial image smoothing pass.
if (preprocSigma > 0.0) {
GaussianConvolution gaussianPre = new GaussianConvolution(preprocSigma);
img = gaussianPre.convolve(img);
}
pyr = new OctavePyramid();
pyr.setVerbose(verbose);
pyr.buildOctaves(img, startScale, scaleSpaceLevels, octaveSigma, minimumRequiredPixelsize);
globalKeypoints = new ArrayList<KeyPoint>();
// Generate keypoints from each scalespace.
for (int on = 0; on < pyr.count(); ++on) {
DScaleSpace dsp = pyr.octaves.get(on);
ArrayList<ScalePoint> peaks = dsp.findPeaks(dogThresh);// 寻找图片中的极值点
int oldCount = peaks.size();
ArrayList<ScalePoint> peaksFilt = dsp.filterAndLocalizePeaks(peaks, maximumEdgeRatio, dValueLowThresh,
scaleAdjustThresh, relocationMaximum);
if (verbose) {
System.out.printf (" filtered: %d remaining from %d, thats %5.2f %%\r\n",
peaksFilt.size(), oldCount, (100.0 * peaksFilt.size()) / oldCount);
System.out.println ("generating keypoints from peaks");
}
// Generate the actual keypoint descriptors, using pre-computed
// values for the gradient magnitude and direction.
dsp.generateMagnitudeAndDirectionMaps();
ArrayList<KeyPoint> keypoints = dsp.generateKeyPoints(peaksFilt, scaleSpaceLevels, octaveSigma);
dsp.clearMagnitudeAndDirectionMaps();
globalKeypoints.addAll(keypoints);
}
return (globalKeypoints.size());
}
}