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增加切割参数异常报错

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pull/39/head
thenk008 5 years ago
parent 580271cda1
commit 32119aa497
1 changed files with 44 additions and 40 deletions
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src/main/java/org/wlld/imageRecognition/Operation.java
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@ -61,47 +61,51 @@ public class Operation {//进行计算
public void colorStudy(ThreeChannelMatrix threeChannelMatrix, int tag, List<Specifications> specificationsList) throws Exception { public void colorStudy(ThreeChannelMatrix threeChannelMatrix, int tag, List<Specifications> specificationsList) throws Exception {
Watershed watershed = new Watershed(threeChannelMatrix.getMatrixRGB(), specificationsList, templeConfig); Watershed watershed = new Watershed(threeChannelMatrix.getMatrixRGB(), specificationsList, templeConfig);
List<RegionBody> regionBodies = watershed.rainfall(); List<RegionBody> regionBodies = watershed.rainfall();
RegionBody regionBody = regionBodies.get(0); if (regionBodies.size() == 1) {
int minX = regionBody.getMinX() + dif; RegionBody regionBody = regionBodies.get(0);
int minY = regionBody.getMinY() + dif; int minX = regionBody.getMinX() + dif;
int maxX = regionBody.getMaxX() - dif; int minY = regionBody.getMinY() + dif;
int maxY = regionBody.getMaxY() - dif; int maxX = regionBody.getMaxX() - dif;
int xSize = maxX - minX; int maxY = regionBody.getMaxY() - dif;
int ySize = maxY - minY; int xSize = maxX - minX;
ThreeChannelMatrix threeChannelMatrix1 = convolution.getRegionMatrix(threeChannelMatrix, minX, minY, xSize, ySize); int ySize = maxY - minY;
//convolution.filtering(threeChannelMatrix1);//光照过滤 ThreeChannelMatrix threeChannelMatrix1 = convolution.getRegionMatrix(threeChannelMatrix, minX, minY, xSize, ySize);
int times = templeConfig.getFood().getTimes(); //convolution.filtering(threeChannelMatrix1);//光照过滤
for (int i = 0; i < times; i++) { int times = templeConfig.getFood().getTimes();
List<Double> feature = convolution.getCenterColor(threeChannelMatrix1, templeConfig.getPoolSize(), for (int i = 0; i < times; i++) {
templeConfig.getFeatureNub()); List<Double> feature = convolution.getCenterColor(threeChannelMatrix1, templeConfig.getPoolSize(),
if (templeConfig.isShowLog()) { templeConfig.getFeatureNub());
System.out.println(tag + ":" + feature); if (templeConfig.isShowLog()) {
} System.out.println(tag + ":" + feature);
//System.out.println("====================================="); }
int classifier = templeConfig.getClassifier(); //System.out.println("=====================================");
switch (classifier) { int classifier = templeConfig.getClassifier();
case Classifier.DNN: switch (classifier) {
Map<Integer, Double> map = new HashMap<>(); case Classifier.DNN:
map.put(tag, 1.0); Map<Integer, Double> map = new HashMap<>();
if (templeConfig.getSensoryNerves().size() == templeConfig.getFeatureNub() * 3) { map.put(tag, 1.0);
intoDnnNetwork(1, feature, templeConfig.getSensoryNerves(), true, map, null); if (templeConfig.getSensoryNerves().size() == templeConfig.getFeatureNub() * 3) {
} else { intoDnnNetwork(1, feature, templeConfig.getSensoryNerves(), true, map, null);
throw new Exception("nerves number is not equal featureNub"); } else {
} throw new Exception("nerves number is not equal featureNub");
break; }
case Classifier.LVQ: break;
Matrix vector = MatrixOperation.listToRowVector(feature); case Classifier.LVQ:
lvqStudy(tag, vector); Matrix vector = MatrixOperation.listToRowVector(feature);
break; lvqStudy(tag, vector);
case Classifier.VAvg: break;
Matrix vec = MatrixOperation.listToRowVector(feature); case Classifier.VAvg:
avgStudy(tag, vec); Matrix vec = MatrixOperation.listToRowVector(feature);
break; avgStudy(tag, vec);
case Classifier.KNN: break;
Matrix veck = MatrixOperation.listToRowVector(feature); case Classifier.KNN:
knnStudy(tag, veck); Matrix veck = MatrixOperation.listToRowVector(feature);
break; knnStudy(tag, veck);
break;
}
} }
} else {
throw new Exception("Parameter exception");
} }
} }

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