apida大改

src/test/java/coverTest/CoverTest.java

@@ -0,0 +1,110 @@
+package coverTest;
+
+import com.alibaba.fastjson.JSON;
+import com.alibaba.fastjson.JSONObject;
+import org.wlld.MatrixTools.Matrix;
+import org.wlld.config.StudyPattern;
+import org.wlld.imageRecognition.Operation;
+import org.wlld.imageRecognition.Picture;
+import org.wlld.imageRecognition.TempleConfig;
+import org.wlld.nerveEntity.ModelParameter;
+
+import java.io.InputStream;
+import java.util.HashMap;
+import java.util.Map;
+
+/**
+ * @author lidapeng
+ * @description
+ * @date 12:54 下午 2020/3/16
+ */
+public class CoverTest {
+    public static void main(String[] args) {
+
+    }
+
+    public static void insertModel(String model) throws Exception {//注入模型
+        //模型服务启动注入一次，内存长期持有静态化 TempleConfig配置类
+        //每个TempleConfig 要单例
+        //创建模版类，参数选false就可以
+        TempleConfig templeConfig = new TempleConfig(false);
+        //初始化模板 注意 width height参数是你训练图片的实际尺寸需要改，其他不用动
+        templeConfig.init(StudyPattern.Cover_Pattern, true, 320, 240, 2);
+        //反序列化成模型
+        ModelParameter modelParameter = JSONObject.parseObject(model, ModelParameter.class);
+        templeConfig.insertModel(modelParameter);//注入学习过的模型
+    }
+
+    public static void coverModel(String url, InputStream inputStream
+            , TempleConfig templeConfig) throws Exception {
+        //覆盖率计算,计算以前，内存中已经注入过模型了
+        Operation operation = new Operation(templeConfig);//初始化运算类
+        Picture picture = new Picture();
+        Matrix pic1 = picture.getImageMatrixByLocal(url);//从本地磁盘读取图片
+        Matrix pic2 = picture.getImageMatrixByIo(inputStream);//从输入流读取图片
+        double point = operation.coverPoint(pic1, 1);//获取覆盖率
+    }
+
+    public static void fireStudy() throws Exception {//土壤扰动，桔梗焚烧等识别
+        Picture picture = new Picture();
+        TempleConfig templeConfig = new TempleConfig(false);
+        //classificationNub 参数说明，识别几种东西 就写几，比如 土壤扰动，桔梗焚烧 总共2个那么就写2
+        templeConfig.init(StudyPattern.Accuracy_Pattern, true, 1000, 1000, 2);
+        Operation operation = new Operation(templeConfig);
+        for (int i = 1; i < 300; i++) {//一阶段
+            //读取本地URL地址图片,并转化成矩阵
+            Matrix a = picture.getImageMatrixByLocal("/Users/lidapeng/Desktop/myDocment/picture/a" + i + ".jpg");
+            Matrix c = picture.getImageMatrixByLocal("/Users/lidapeng/Desktop/myDocment/picture/c" + i + ".jpg");
+            //参数说明 第一个参数是 读取图片的矩阵，第二个参数是标注ID
+            //比如我认为土壤扰动是1，桔梗焚烧是2
+            operation.learning(a, 1, false);
+            operation.learning(c, 2, false);
+        }
+        for (int i = 1; i < 300; i++) {
+            Matrix a = picture.getImageMatrixByLocal("/Users/lidapeng/Desktop/myDocment/picture/a" + i + ".jpg");
+            Matrix c = picture.getImageMatrixByLocal("/Users/lidapeng/Desktop/myDocment/picture/c" + i + ".jpg");
+            //第二次学习注意标注要与第一次一致
+            operation.learning(a, 1, true);
+            operation.learning(c, 2, true);
+        }
+        templeConfig.finishStudy();//结束学习
+
+    }
+
+    public static void test(TempleConfig templeConfig, String url) throws Exception {//识别桔梗或者土壤扰动
+        Picture picture = new Picture();
+        templeConfig.init(StudyPattern.Accuracy_Pattern, true, 1000, 1000, 2);
+        Operation operation = new Operation(templeConfig);
+        Matrix a = picture.getImageMatrixByLocal(url);
+        //识别初的该图片所属的分类id,既为训练时设定的标注
+        int an = operation.toSee(a);
+
+    }
+
+    //覆盖率学习
+    public static void cover() throws Exception {
+        //创建图片解析类
+        Picture picture = new Picture();
+        //创建模版类，参数选false就可以
+        TempleConfig templeConfig = new TempleConfig(false);
+        //初始化模板 注意 width height参数是你训练图片的实际尺寸需要改，其他不用动
+        templeConfig.init(StudyPattern.Cover_Pattern, true, 320, 240, 2);
+        //创建运算类进行标注
+        Operation operation = new Operation(templeConfig);
+        Map<Integer, Double> rightTagging = new HashMap<>();//分类标注
+        Map<Integer, Double> wrongTagging = new HashMap<>();//分类标注
+        rightTagging.put(1, 1.0);//100%桔梗全覆盖标注
+        wrongTagging.put(2, 1.0);//0%桔梗无覆盖标注
+        //读取100%全覆盖桔梗图片
+        Matrix right = picture.getImageMatrixByLocal("/Users/lidapeng/Desktop/picture/yes1.jpg");
+        //读取0%无覆盖土地图片
+        Matrix wrong = picture.getImageMatrixByLocal("/Users/lidapeng/Desktop/picture/no4.jpg");
+        //开始训练覆盖率
+        operation.coverStudy(right, rightTagging, wrong, wrongTagging);
+        //学习完成，获取学习模型参数
+        ModelParameter modelParameter = templeConfig.getModel();
+        //将模型model保存数据库
+        String model = JSON.toJSONString(modelParameter);
+        System.out.println("学习完成");
+    }
+}