JoeAllen/DetectAnimalsFromCameras
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Signed-off-by: JoeAllen <2494136784@qq.com>
This commit is contained in:
JoeAllen
2022-07-29 09:19:34 +08:00
parent 2c8c05dedf
commit fc0185b068
3 changed files with 47 additions and 60 deletions
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src/main/java/cn/heavenlybook/utils/AnimalsClassification.java
+3 -31
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@@ -11,7 +11,6 @@ import ai.djl.training.util.ProgressBar;
import ai.djl.translate.TranslateException;
import java.io.IOException;
import java.util.ArrayList;
import java.util.List;
// https://www.paddlepaddle.org.cn/hubdetail?name=resnet50_vd_animals&en_category=ImageClassification
@@ -27,43 +26,16 @@ public final class AnimalsClassification {
public static Classifications predict(Image img)
throws IOException, ModelException, TranslateException {
Classifications classifications = AnimalsClassification.classfier(img);
Classifications classifications = AnimalsClassification.classifier(img);
List<Classifications.Classification> items = classifications.items();
double sum = 0;
double max = 0;
double[] probArr = new double[items.size()];
List<String> names = new ArrayList<>();
List<Double> probs = new ArrayList<>();
for (int i = 0; i < items.size(); i++) {
Classifications.Classification item = items.get(i);
double prob = item.getProbability();
probArr[i] = prob;
if (prob > max) {
max = prob;
}
}
for (int i = 0; i < items.size(); i++) {
probArr[i] = Math.exp(probArr[i] - max);
sum = sum + probArr[i];
}
for (int i = 0; i < items.size(); i++) {
Classifications.Classification item = items.get(i);
names.add(item.getClassName());
probs.add(probArr[i]);
}
return new Classifications(names, probs);
return ClassificationUtil.get(items);
}
static Criteria<Image, Classifications> criteria;
static ZooModel<Image, Classifications> rotateModel;
static Predictor<Image, Classifications> classifier;
public static Classifications classfier(Image img)
public static Classifications classifier(Image img)
throws IOException, ModelException, TranslateException {
criteria =
src/main/java/cn/heavenlybook/utils/ClassificationUtil.java
+43
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@@ -0,0 +1,43 @@
package cn.heavenlybook.utils;
import ai.djl.modality.Classifications;
import java.util.ArrayList;
import java.util.List;
/**
* @author JoeAllen
* @date 2022/7/29 9:16 上午
*/
public class ClassificationUtil {
public static Classifications get(List<Classifications.Classification> items) {
double sum = 0;
double max = 0;
double[] probArr = new double[items.size()];
List<String> names = new ArrayList<>();
List<Double> probs = new ArrayList<>();
for (int i = 0; i < items.size(); i++) {
Classifications.Classification item = items.get(i);
double prob = item.getProbability();
probArr[i] = prob;
if (prob > max) {
max = prob;
}
}
for (int i = 0; i < items.size(); i++) {
probArr[i] = Math.exp(probArr[i] - max);
sum = sum + probArr[i];
}
for (int i = 0; i < items.size(); i++) {
Classifications.Classification item = items.get(i);
names.add(item.getClassName());
probs.add(probArr[i]);
}
return new Classifications(names, probs);
}
}
src/main/java/cn/heavenlybook/utils/LightAnimalsClassification.java
+1 -29
View File
@@ -11,7 +11,6 @@ import ai.djl.training.util.ProgressBar;
import ai.djl.translate.TranslateException;
import java.io.IOException;
import java.util.ArrayList;
import java.util.List;
// https://www.paddlepaddle.org.cn/hubdetail?name=mobilenet_v2_animals&en_category=ImageClassification
@@ -26,34 +25,7 @@ public final class LightAnimalsClassification {
throws IOException, ModelException, TranslateException {
Classifications classifications = LightAnimalsClassification.classfier(img);
List<Classifications.Classification> items = classifications.items();
double sum = 0;
double max = 0;
double[] probArr = new double[items.size()];
List<String> names = new ArrayList<>();
List<Double> probs = new ArrayList<>();
for (int i = 0; i < items.size(); i++) {
Classifications.Classification item = items.get(i);
double prob = item.getProbability();
probArr[i] = prob;
if (prob > max) {
max = prob;
}
}
for (int i = 0; i < items.size(); i++) {
probArr[i] = Math.exp(probArr[i] - max);
sum = sum + probArr[i];
}
for (int i = 0; i < items.size(); i++) {
Classifications.Classification item = items.get(i);
names.add(item.getClassName());
probs.add(probArr[i]);
}
return new Classifications(names, probs);
return ClassificationUtil.get(items);
}
public static Classifications classfier(Image img)