update:修改PCN的网络推理代码，修复存在的可能内存泄露

face-search-core/src/main/java/com/visual/face/search/core/domain/ImageMat.java

@@ -681,8 +681,12 @@ public class ImageMat implements Serializable {
      */
     public void release(){
         if(this.mat != null){
-            this.mat.release();
+            try {
-            this.mat = null;
+                this.mat.release();
+                this.mat = null;
+            }catch (Exception e){
+                e.printStackTrace();
+            }
         }
     }
 }

face-search-core/src/main/java/com/visual/face/search/core/domain/Mats.java

@@ -0,0 +1,44 @@
+package com.visual.face.search.core.domain;
+
+import org.opencv.core.Mat;
+
+import java.util.ArrayList;
+
+public class Mats extends ArrayList<Mat> {
+
+    public static Mats build(){
+        return new Mats();
+    }
+
+    public Mats append(Mat mat){
+        this.add(mat);
+        return this;
+    }
+
+    public Mats clone(){
+        Mats mats = new Mats();
+        for(Mat mat : this){
+            mats.add(mat.clone());
+        }
+        return mats;
+    }
+
+    public void release(){
+        if(this.isEmpty()){
+            return;
+        }
+        for(Mat mat : this){
+            if(null != mat){
+                try {
+                    mat.release();
+                }catch (Exception e){
+                    e.printStackTrace();
+                }finally {
+                    mat = null;
+                }
+            }
+        }
+        this.clear();
+    }
+
+}

face-search-core/src/main/java/com/visual/face/search/core/models/PcnNetworkFaceDetection.java

@@ -8,6 +8,8 @@ import com.visual.face.search.core.base.BaseOnnxInfer;
 import com.visual.face.search.core.base.FaceDetection;
 import com.visual.face.search.core.domain.FaceInfo;
 import com.visual.face.search.core.domain.ImageMat;
+import com.visual.face.search.core.domain.Mats;
+import com.visual.face.search.core.utils.ReleaseUtil;
 import org.opencv.core.*;
 import org.opencv.imgproc.Imgproc;
 
@@ -60,114 +62,116 @@ public class PcnNetworkFaceDetection extends BaseOnnxInfer implements FaceDetect
     public List<FaceInfo> inference(ImageMat image, float scoreTh, float iouTh, Map<String, Object> params) {
         Mat mat = null;
         Mat imgPad = null;
+        Mat resizeMat = null;
         ImageMat imageMat = image.clone();
         try {
             mat = imageMat.toCvMat();
-            imgPad = pad_img_not_release_mat(mat);
+            float scale = (float) ((mat.size().height > mat.size().width) ? mat.size().width/512: mat.size().height/512);
+            resizeMat = resize_img_release_mat(mat.clone(), scale);
+            imgPad = pad_img_release_mat(resizeMat.clone());
             float[] iouThs = iouTh <= 0 ? defIouThs : new float[]{iouTh, iouTh, 0.3f};
             float[] scoreThs = scoreTh <= 0 ? defScoreThs : new float[]{0.375f * scoreTh, 0.5f * scoreTh, scoreTh};
-            List<Window2> willis = detect(this.getSessions(), mat, imgPad, scoreThs, iouThs);
+            List<Window2> willis = detect_release_mat(this.getSessions(), resizeMat.clone(), imgPad.clone(), scoreThs, iouThs);
-            return trans_window(mat, imgPad, willis);
+            return trans_window(resizeMat, imgPad, willis, scale);
         } catch (Exception e) {
             throw new RuntimeException(e);
         }finally {
-            if(null != mat){
+            ReleaseUtil.release(imgPad, resizeMat, mat);
-                mat.release();
+            ReleaseUtil.release(imageMat);
-            }
-            if(null != imgPad){
-                imgPad.release();
-            }
-            if(null != imageMat){
-                imageMat.release();
-            }
         }
     }
 
     /********************************分割线*************************************/
 
-    private static Mat pad_img_not_release_mat(Mat mat){
+    private static Mat pad_img_release_mat(Mat mat){
-        int row = Math.min((int)(mat.size().height * 0.2), 100);
+        try {
-        int col = Math.min((int)(mat.size().width * 0.2), 100);
+            int row = Math.min((int)(mat.size().height * 0.2), 100);
-        Mat dst = new Mat();
+            int col = Math.min((int)(mat.size().width * 0.2), 100);
-        Core.copyMakeBorder(mat, dst, row, row, col, col, Core.BORDER_CONSTANT);
+            Mat dst = new Mat();
-        return dst;
+            Core.copyMakeBorder(mat, dst, row, row, col, col, Core.BORDER_CONSTANT);
+            return dst;
+        }finally {
+            ReleaseUtil.release(mat);
+        }
     }
 
-    private static Mat resize_img(Mat mat, float scale){
+    private static Mat resize_img_release_mat(Mat mat, float scale){
-        double h = mat.size().height;
+        Mat matF32 = null;
-        double w = mat.size().width;
+        try{
-        int h_ = (int) (h / scale);
+            double h = mat.size().height;
-        int w_ = (int) (w / scale);
+            double w = mat.size().width;
-        Mat matF32 = new Mat();
+            int h_ = (int) (h / scale);
-        if(mat.type() != CvType.CV_32FC3){
+            int w_ = (int) (w / scale);
-            mat.convertTo(matF32, CvType.CV_32FC3);
+            matF32 = new Mat();
-        }else{
+            if(mat.type() != CvType.CV_32FC3){
-            mat.copyTo(matF32);
+                mat.convertTo(matF32, CvType.CV_32FC3);
+            }else{
+                mat.copyTo(matF32);
+            }
+            Mat dst = new Mat();
+            Imgproc.resize(matF32, dst, new Size(w_, h_), 0,0, Imgproc.INTER_NEAREST);
+            return dst;
+        }finally {
+            ReleaseUtil.release(matF32, mat);
         }
-        Mat dst = new Mat();
-        Imgproc.resize(matF32, dst, new Size(w_, h_), 0,0, Imgproc.INTER_NEAREST);
-        mat.release();
-        matF32.release();
-        return dst;
     }
 
-    private static Mat preprocess_img(Mat mat, int dim){
+    private static Mat preprocess_img_release_mat(Mat mat, int dim){
-        Mat matTmp = new Mat();
+        Mat matTmp = null;
-        if(dim > 0){
+        Mat matF32 = null;
-            Imgproc.resize(mat, matTmp, new Size(dim, dim), 0, 0, Imgproc.INTER_NEAREST);
+        try {
-        }else{
+            //resize
-            mat.copyTo(matTmp);
+            matTmp = new Mat();
+            if(dim > 0){
+                Imgproc.resize(mat, matTmp, new Size(dim, dim), 0, 0, Imgproc.INTER_NEAREST);
+            }else{
+                mat.copyTo(matTmp);
+            }
+            //格式转化
+            matF32 = new Mat();
+            if(mat.type() != CvType.CV_32FC3){
+                matTmp.convertTo(matF32, CvType.CV_32FC3);
+            }else{
+                matTmp.copyTo(matF32);
+            }
+            //减法运算
+            Mat dst = new Mat();
+            Core.subtract(matF32, new Scalar(104, 117, 123), dst);
+            return dst;
+        }finally {
+            ReleaseUtil.release(matF32, matTmp, mat);
         }
 
-        //格式转化
-        Mat matF32 = new Mat();
-        if(mat.type() != CvType.CV_32FC3){
-            matTmp.convertTo(matF32, CvType.CV_32FC3);
-        }else{
-            matTmp.copyTo(matF32);
-        }
-
-        Mat dst = new Mat();
-        Core.subtract(matF32, new Scalar(104, 117, 123), dst);
-
-        mat.release();
-        matTmp.release();
-        matF32.release();
-
-        return dst;
     }
 
-    private static OnnxTensor set_input(Mat mat){
+    private static OnnxTensor set_input_release_mat(Mat mat){
         Mat dst = null;
         try {
             dst = new Mat();
             mat.copyTo(dst);
             return ImageMat.fromCVMat(dst).to4dFloatOnnxTensorAndDoReleaseMat(true);
         }finally {
-            if(null != dst){
+            ReleaseUtil.release(dst, mat);
-                dst.release();
-            }
         }
     }
 
-    private static OnnxTensor set_input(List<Mat> mats){
+    private static OnnxTensor set_input_release_mat(Mats mats){
-        float[][][][] arrays = new float[mats.size()][][][];
-        for(int i=0; i< mats.size(); i++){
-            Mat dst = new Mat();
-            mats.get(i).copyTo(dst);
-            float[][][][] array = ImageMat.fromCVMat(dst).to4dFloatArrayAndDoReleaseMat(true);
-            arrays[i] = array[0];
-            dst.release();
-        }
         try {
+            float[][][][] arrays = new float[mats.size()][][][];
+            for(int i=0; i< mats.size(); i++){
+                float[][][][] array = ImageMat.fromCVMat(mats.get(i)).to4dFloatArrayAndDoReleaseMat(true);
+                arrays[i] = array[0];
+            }
             return OnnxTensor.createTensor(OrtEnvironment.getEnvironment(), arrays);
         }catch (Exception e){
             throw new RuntimeException(e);
+        }finally {
+            ReleaseUtil.release(mats);
         }
     }
 
-    private static boolean legal(int x, int y, Mat mat){
+    private static boolean legal(int x, int y, Size size){
-        if(0 <= x && x < mat.size().width && 0 <= y && y< mat.size().height){
+        if(0 <= x && x < size.width && 0 <= y && y< size.height){
             return true;
         }else{
             return false;
@@ -191,87 +195,106 @@ public class PcnNetworkFaceDetection extends BaseOnnxInfer implements FaceDetect
     }
 
     private static List<Window2> NMS(List<Window2> winlist, boolean local, float threshold){
-        if(winlist==null || winlist.isEmpty()){
+        try{
-            return new ArrayList<>();
+            if(winlist==null || winlist.isEmpty()){
-        }
+                return new ArrayList<>();
-        //排序
-        Collections.sort(winlist);
-        int [] flag = new int[winlist.size()];
-        for(int i=0; i< winlist.size(); i++){
-            if(flag[i] > 0){
-                continue;
             }
-            for(int j=i+1; j<winlist.size(); j++){
+            //排序
-                if(local && Math.abs(winlist.get(i).scale - winlist.get(j).scale) > EPS){
+            Collections.sort(winlist);
+            int [] flag = new int[winlist.size()];
+            for(int i=0; i< winlist.size(); i++){
+                if(flag[i] > 0){
                     continue;
                 }
-                if(IoU(winlist.get(i), winlist.get(j)) > threshold){
+                for(int j=i+1; j<winlist.size(); j++){
-                    flag[j] = 1;
+                    if(local && Math.abs(winlist.get(i).scale - winlist.get(j).scale) > EPS){
+                        continue;
+                    }
+                    if(IoU(winlist.get(i), winlist.get(j)) > threshold){
+                        flag[j] = 1;
+                    }
                 }
             }
-        }
+            List<Window2> list = new ArrayList<>();
-        List<Window2> list = new ArrayList<>();
+            for(int i=0; i< flag.length; i++){
-        for(int i=0; i< flag.length; i++){
+                if(flag[i] == 0){
-            if(flag[i] == 0){
+                    list.add(winlist.get(i));
-                list.add(winlist.get(i));
+                }
+            }
+            return list;
+        }finally {
+            if(null != winlist && !winlist.isEmpty()){
+                winlist.clear();
             }
         }
-        return list;
     }
 
     private static  List<Window2> deleteFP(List<Window2> winlist){
-        if (winlist == null || winlist.isEmpty()) {
+        try {
-            return new ArrayList<>();
+            if(null == winlist || winlist.isEmpty()) {
-        }
+                return new ArrayList<>();
-        //排序
-        Collections.sort(winlist);
-        int [] flag = new int[winlist.size()];
-        for(int i=0; i< winlist.size(); i++){
-            if(flag[i] > 0){
-                continue;
             }
-            for(int j=i+1; j<winlist.size(); j++){
+            //排序
-                Window2 win = winlist.get(j);
+            Collections.sort(winlist);
-                if(inside(win.x, win.y, winlist.get(i)) && inside(win.x + win.w - 1, win.y + win.h - 1, winlist.get(i))){
+            int [] flag = new int[winlist.size()];
-                    flag[j] = 1;
+            for(int i=0; i< winlist.size(); i++){
+                if(flag[i] > 0){
+                    continue;
+                }
+                for(int j=i+1; j<winlist.size(); j++){
+                    Window2 win = winlist.get(j);
+                    if(inside(win.x, win.y, winlist.get(i)) && inside(win.x + win.w - 1, win.y + win.h - 1, winlist.get(i))){
+                        flag[j] = 1;
+                    }
                 }
             }
-        }
+            List<Window2> list = new ArrayList<>();
-        List<Window2> list = new ArrayList<>();
+            for(int i=0; i< flag.length; i++){
-        for(int i=0; i< flag.length; i++){
+                if(flag[i] == 0){
-            if(flag[i] == 0){
+                    list.add(winlist.get(i));
-                list.add(winlist.get(i));
+                }
+            }
+            return list;
+        }finally {
+            if(null != winlist && !winlist.isEmpty()){
+                winlist.clear();
             }
         }
-        return list;
     }
 
-    private static List<FaceInfo> trans_window(Mat img, Mat imgPad, List<Window2> winlist){
+    private static List<FaceInfo> trans_window(Mat img, Mat imgPad, List<Window2> winlist, float scale){
-        int row = (imgPad.size(0) - img.size(0)) / 2;
-        int col = (imgPad.size(1) - img.size(1)) / 2;
         List<FaceInfo> ret = new ArrayList<>();
-        for(Window2 win : winlist){
+        try {
-            if( win.w > 0 && win.h > 0){
+            int row = (imgPad.size(0) - img.size(0)) / 2;
-                int x1 = win.x - col;
+            int col = (imgPad.size(1) - img.size(1)) / 2;
-                int y1 = win.y - row;
+            for(Window2 win : winlist){
-                int x2 = win.x - col + win.w;
+                if( win.w > 0 && win.h > 0){
-                int y2 = win.y - row + win.w;
+                    int x1 = win.x - col;
-                int angle = (win.angle + 360) % 360;
+                    int y1 = win.y - row;
-                //扩展人脸高度
+                    int x2 = win.x - col + win.w;
-                float rw = 0f;
+                    int y2 = win.y - row + win.w;
-                float rh = 0.1f;
+                    int angle = (win.angle + 360) % 360;
-                int w = Math.abs(x2 - x1);
+                    //扩展人脸高度
-                int h = Math.abs(y2 - y1);
+                    float rw = 0f;
-                x1 = Math.max(x1 - (int)(w * rw), 1);
+                    float rh = 0.1f;
-                y1 = Math.max(y1 - (int)(h * rh), 1);
+                    int w = Math.abs(x2 - x1);
-                x2 = Math.min(x2 + (int)(w * rw), img.size(1)-1);
+                    int h = Math.abs(y2 - y1);
-                y2 = Math.min(y2 + (int)(h * rh), img.size(0)-1);
+                    x1 = Math.max(Float.valueOf((x1 - (int)(w * rw)) * scale).intValue(), 1);
-                //构建人脸信息
+                    y1 = Math.max(Float.valueOf((y1 - (int)(h * rh)) * scale).intValue(), 1);
-                FaceInfo faceInfo = FaceInfo.build(win.conf, angle, FaceInfo.FaceBox.build(x1, y1, x2, y2), FaceInfo.Points.build());
+                    x2 = Math.min(Float.valueOf((x2 + (int)(w * rw)) * scale).intValue(), Float.valueOf((img.size(1)) * scale).intValue()-1);
-                ret.add(faceInfo);
+                    y2 = Math.min(Float.valueOf((y2 + (int)(h * rh)) * scale).intValue(), Float.valueOf((img.size(0)) * scale).intValue()-1);
+                    //构建人脸信息
+                    FaceInfo faceInfo = FaceInfo.build(win.conf, angle, FaceInfo.FaceBox.build(x1, y1, x2, y2), FaceInfo.Points.build());
+                    ret.add(faceInfo);
+                }
+            }
+            return ret;
+        }finally {
+            if(null != winlist && !winlist.isEmpty()){
+                winlist.clear();
             }
         }
-        return ret;
+
     }
 
     /**
@@ -284,34 +307,30 @@ public class PcnNetworkFaceDetection extends BaseOnnxInfer implements FaceDetect
      * @throws OrtException
      * @throws IOException
      */
-    private static List<Window2> stage1(Mat img, Mat imgPad, OrtSession net, float thres) throws RuntimeException {
+    private static List<Window2> stage1_release_mat(Mat img, Mat imgPad, OrtSession net, float thres) throws RuntimeException {
-        int netSize = 24;
-        float curScale = minFace_ / netSize;
-        int row = (int) ((imgPad.size().height - img.size().height) / 2);
-        int col = (int) ((imgPad.size().width  - img.size().width)  / 2);
-
         Mat img_resized = null;
         OnnxTensor net_input1 = null;
         OrtSession.Result output = null;
         List<Window2> winlist = new ArrayList<>();
         try {
-            img_resized = resize_img(img.clone(), curScale);
+            //获取必要参数
+            int netSize = 24;
+            float curScale = minFace_ / netSize;
+            int row = (int) ((imgPad.size().height - img.size().height) / 2);
+            int col = (int) ((imgPad.size().width  - img.size().width)  / 2);
+            img_resized = resize_img_release_mat(img.clone(), curScale);
+            //循环处理
             while(Math.min(img_resized.size().height, img_resized.size().width) >= netSize){
-                img_resized = preprocess_img(img_resized, 0);
+                img_resized = preprocess_img_release_mat(img_resized, 0);
-                net_input1 = set_input(img_resized);
+                net_input1 = set_input_release_mat(img_resized.clone());
+                //推理网络
                 output = net.run(Collections.singletonMap(net.getInputNames().iterator().next(), net_input1));
                 float[][][][] cls_prob = (float[][][][]) output.get(0).getValue();
                 float[][][][] rotate = (float[][][][]) output.get(1).getValue();
                 float[][][][] bbox =   (float[][][][]) output.get(2).getValue();
                 //关闭对象
-                if(null != net_input1){
+                ReleaseUtil.release(output); output = null;
-                    net_input1.close();
+                ReleaseUtil.release(net_input1); net_input1 = null;
-                    net_input1 = null;
-                }
-                if(null != output){
-                    output.close();
-                    output = null;
-                }
                 //计算业务逻辑
                 float w = netSize * curScale;
                 for(int i=0; i< cls_prob[0][0].length; i++){
@@ -323,7 +342,7 @@ public class PcnNetworkFaceDetection extends BaseOnnxInfer implements FaceDetect
                             int rx = (int)(j * curScale * stride_ - 0.5 * sn * w + sn * xn * w + 0.5 * w) + col;
                             int ry = (int)(i * curScale * stride_ - 0.5 * sn * w + sn * yn * w + 0.5 * w) + row;
                             int rw = (int)(w * sn);
-                            if (legal(rx, ry, imgPad) && legal(rx + rw - 1, ry + rw - 1, imgPad)){
+                            if (legal(rx, ry, imgPad.size()) && legal(rx + rw - 1, ry + rw - 1, imgPad.size())){
                                 if (rotate[0][1][i][j] > 0.5){
                                     winlist.add(new Window2(rx, ry, rw, rw, 0, curScale, cls_prob[0][1][i][j]));
                                 }else{
@@ -333,21 +352,15 @@ public class PcnNetworkFaceDetection extends BaseOnnxInfer implements FaceDetect
                         }
                     }
                 }
-                img_resized = resize_img(img_resized, scale_);
+                img_resized = resize_img_release_mat(img_resized, scale_);
                 curScale = (float) (img.size().height / img_resized.size().height);
             }
         }catch (Exception e){
             throw new RuntimeException(e);
         }finally {
-            if(null != net_input1){
+            ReleaseUtil.release(output);
-                net_input1.close();
+            ReleaseUtil.release(net_input1);
-            }
+            ReleaseUtil.release(img_resized, imgPad, img);
-            if(null != output){
-                output.close();
-            }
-            if(null != img_resized){
-                img_resized.release();
-            }
         }
         //返回
         return winlist;
@@ -364,56 +377,58 @@ public class PcnNetworkFaceDetection extends BaseOnnxInfer implements FaceDetect
      * @return
      * @throws OrtException
      */
-    private static List<Window2> stage2(Mat img, Mat img180, OrtSession net, float thres, int dim, List<Window2> winlist) throws OrtException {
+    private static List<Window2> stage2_release_mat(Mat img, Mat img180, OrtSession net, float thres, int dim, List<Window2> winlist) throws OrtException {
         if(winlist==null || winlist.isEmpty()){
             return new ArrayList<>();
         }
+        //逻辑处理
+        float[][] bbox;
+        float[][] rotate;
+        float[][] cls_prob;
+        Mats datalist = null;
+        OnnxTensor input = null;
+        OrtSession.Result output = null;
+        Size size = img.size();
         int height = img.size(0);
-        List<Mat> datalist = new ArrayList<>();
+        try {
-        for(Window2 win : winlist){
+            datalist = Mats.build();
-            if(Math.abs(win.angle) < EPS){
+            for(Window2 win : winlist){
-                Mat cloneMat = img.clone();
+                if(Math.abs(win.angle) < EPS){
-                Mat corp = new Mat(cloneMat, new Rect(win.x, win.y, win.w, win.h));
+                    Mat corp = null;
-                Mat corp1 = preprocess_img(corp, dim);
+                    try {
-                datalist.add(corp1);
+                        corp = new Mat(img, new Rect(win.x, win.y, win.w, win.h));
-                if(null != corp){
+                        Mat preprocess = preprocess_img_release_mat(corp.clone(), dim);
-                    corp.release();
+                        datalist.add(preprocess);
-                }
+                    }finally {
-                if(null != cloneMat){
+                        ReleaseUtil.release(corp);
-                    cloneMat.release();
+                    }
-                }
+                }else{
-            }else{
+                    Mat corp = null;
-                int y2 = win.y + win.h - 1;
+                    try {
-                int y = height - 1 - y2;
+                        int y2 = win.y + win.h - 1;
-
+                        int y = height - 1 - y2;
-                Mat cloneMat = img180.clone();
+                        corp = new Mat(img180, new Rect(win.x, y, win.w, win.h));
-                Mat corp = new Mat(cloneMat, new Rect(win.x, y, win.w, win.h));
+                        Mat preprocess = preprocess_img_release_mat(corp.clone(), dim);
-                Mat corp1 = preprocess_img(corp, dim);
+                        datalist.add(preprocess);
-                datalist.add(corp1);
+                    }finally {
-                if(null != corp){
+                        ReleaseUtil.release(corp);
-                    corp.release();
+                    }
-                }
-                if(null != cloneMat){
-                    cloneMat.release();
                 }
             }
+            //模型推理
+            input    = set_input_release_mat(datalist.clone());
+            output   = net.run(Collections.singletonMap(net.getInputNames().iterator().next(), input));
+            cls_prob = (float[][]) output.get(0).getValue();
+            rotate   = (float[][]) output.get(1).getValue();
+            bbox     = (float[][]) output.get(2).getValue();
+        }finally {
+            ReleaseUtil.release(datalist);
+            ReleaseUtil.release(output);
+            ReleaseUtil.release(input);
+            ReleaseUtil.release(img180, img);
+            output = null; input = null;
         }
-
+        //再次后处理数据
-        OnnxTensor net_input = set_input(datalist);
-        OrtSession.Result output = net.run(Collections.singletonMap(net.getInputNames().iterator().next(), net_input));
-        float[][] cls_prob = (float[][]) output.get(0).getValue();
-        float[][] rotate   = (float[][]) output.get(1).getValue();
-        float[][] bbox     = (float[][]) output.get(2).getValue();
-        //关闭对象
-        for(Mat mat : datalist){
-            mat.release();
-        }
-        if(null != net_input){
-            net_input.close();
-        }
-        if(null != output){
-            output.close();
-        }
         List<Window2> ret = new ArrayList<>();
         for(int i=0; i<winlist.size(); i++){
             if(cls_prob[i][1] > thres){
@@ -439,7 +454,7 @@ public class PcnNetworkFaceDetection extends BaseOnnxInfer implements FaceDetect
                     }
                 }
 
-                if(legal(x, y, img) && legal(x + w - 1, y + w - 1, img)){
+                if(legal(x, y, size) && legal(x + w - 1, y + w - 1, size)){
                     int angle = 0;
                     if(Math.abs(winlist.get(i).angle) < EPS){
                         if(maxRotateIndex == 0){
@@ -479,49 +494,79 @@ public class PcnNetworkFaceDetection extends BaseOnnxInfer implements FaceDetect
      * @return
      * @throws OrtException
      */
-    private static List<Window2> stage3(Mat imgPad, Mat img180, Mat img90, Mat imgNeg90, OrtSession net, float thres, int dim, List<Window2> winlist) throws OrtException {
+    private static List<Window2> stage3_release_mat(Mat imgPad, Mat img180, Mat img90, Mat imgNeg90, OrtSession net, float thres, int dim, List<Window2> winlist) throws OrtException {
         if (winlist == null || winlist.isEmpty()) {
             return new ArrayList<>();
         }
+        //逻辑处理
+        float[][] bbox;
+        float[][] rotate;
+        float[][] cls_prob;
+        Mats datalist = null;
+        OnnxTensor input = null;
+        OrtSession.Result output = null;
+
         int height = imgPad.size(0);
         int width  = imgPad.size(1);
-        List<Mat> datalist = new ArrayList<>();
+        Size imgPadSize = imgPad.size();
-        for(Window2 win : winlist){
+        Size img180Size = img180.size();
-            if(Math.abs(win.angle) < EPS){
+        Size img90Size = img90.size();
-                Mat corp = new Mat(imgPad, new Rect(win.x, win.y, win.w, win.h));
+        Size imgNeg90Size = imgNeg90.size();
-                datalist.add(preprocess_img(corp, dim));
+
-            }else if(Math.abs(win.angle - 90) < EPS){
+        try {
-                Mat corp = new Mat(img90, new Rect(win.y, win.x, win.h, win.w));
+            datalist = Mats.build();
-                datalist.add(preprocess_img(corp, dim));
+            for(Window2 win : winlist){
-            }else if(Math.abs(win.angle + 90) < EPS){
+                if(Math.abs(win.angle) < EPS){
-                int x = win.y;
+                    Mat corp = null;
-                int y = width - 1 - (win.x + win.w - 1);
+                    try {
-                Mat corp = new Mat(imgNeg90, new Rect(x, y, win.w, win.h));
+                        corp = new Mat(imgPad, new Rect(win.x, win.y, win.w, win.h));
-                datalist.add(preprocess_img(corp, dim));
+                        datalist.add(preprocess_img_release_mat(corp.clone(), dim));
-            }else{
+                    }finally {
-                int y2 = win.y + win.h - 1;
+                        ReleaseUtil.release(corp);
-                int y = height - 1 - y2;
+                    }
-                Mat corp = new Mat(img180, new Rect(win.x, y, win.w, win.h));
+                }else if(Math.abs(win.angle - 90) < EPS){
-                datalist.add(preprocess_img(corp, dim));
+                    Mat corp = null;
+                    try {
+                        corp = new Mat(img90, new Rect(win.y, win.x, win.h, win.w));
+                        datalist.add(preprocess_img_release_mat(corp.clone(), dim));
+                    }finally {
+                        ReleaseUtil.release(corp);
+                    }
+                }else if(Math.abs(win.angle + 90) < EPS){
+                    Mat corp = null;
+                    try {
+                        int x = win.y;
+                        int y = width - 1 - (win.x + win.w - 1);
+                        corp = new Mat(imgNeg90, new Rect(x, y, win.w, win.h));
+                        datalist.add(preprocess_img_release_mat(corp.clone(), dim));
+                    }finally {
+                        ReleaseUtil.release(corp);
+                    }
+                }else{
+                    Mat corp = null;
+                    try {
+                        int y2 = win.y + win.h - 1;
+                        int y = height - 1 - y2;
+                        corp = new Mat(img180, new Rect(win.x, y, win.w, win.h));
+                        datalist.add(preprocess_img_release_mat(corp.clone(), dim));
+                    }finally {
+                        ReleaseUtil.release(corp);
+                    }
+                }
             }
+            input    = set_input_release_mat(datalist);
+            output   = net.run(Collections.singletonMap(net.getInputNames().iterator().next(), input));
+            cls_prob = (float[][]) output.get(0).getValue();
+            rotate   = (float[][]) output.get(1).getValue();
+            bbox     = (float[][]) output.get(2).getValue();
+        }finally {
+            ReleaseUtil.release(datalist);
+            ReleaseUtil.release(output);
+            ReleaseUtil.release(input);
+            ReleaseUtil.release(imgPad, img180, img90, imgNeg90);
+            output = null; input = null;
         }
-        OnnxTensor net_input = set_input(datalist);
+        //模型后处理
-        OrtSession.Result output = net.run(Collections.singletonMap(net.getInputNames().iterator().next(), net_input));
-        float[][] cls_prob = (float[][]) output.get(0).getValue();
-        float[][] rotate   = (float[][]) output.get(1).getValue();
-        float[][] bbox     = (float[][]) output.get(2).getValue();
-
-        //关闭对象
-        for(Mat mat : datalist){
-            mat.release();
-        }
-        if(null != net_input){
-            net_input.close();
-        }
-        if(null != output){
-            output.close();
-        }
-
         List<Window2> ret = new ArrayList<>();
         for(int i=0; i<winlist.size(); i++) {
             if (cls_prob[i][1] > thres) {
@@ -531,24 +576,24 @@ public class PcnNetworkFaceDetection extends BaseOnnxInfer implements FaceDetect
                 float cropX = winlist.get(i).x;
                 float cropY = winlist.get(i).y;
                 float cropW = winlist.get(i).w;
-                Mat img_tmp = imgPad;
+                Size img_tmp_size = imgPadSize;
                 if (Math.abs(winlist.get(i).angle - 180) < EPS) {
                     cropY = height - 1 - (cropY + cropW - 1);
-                    img_tmp = img180;
+                    img_tmp_size = img180Size;
                 }else if (Math.abs(winlist.get(i).angle - 90) < EPS) {
                     cropX = winlist.get(i).y;
                     cropY = winlist.get(i).x;
-                    img_tmp = img90;
+                    img_tmp_size = img90Size;
                 }else if (Math.abs(winlist.get(i).angle + 90) < EPS) {
                     cropX = winlist.get(i).y;
                     cropY = width - 1 - (winlist.get(i).x + winlist.get(i).w - 1);
-                    img_tmp = imgNeg90;
+                    img_tmp_size = imgNeg90Size;
                 }
                 int w = (int) (sn * cropW);
                 int x = (int) (cropX - 0.5 * sn * cropW + cropW * sn * xn + 0.5 * cropW);
                 int y = (int) (cropY - 0.5 * sn * cropW + cropW * sn * yn + 0.5 * cropW);
                 int angle = (int)(angleRange_ * rotate[i][0]);
-                if(legal(x, y, img_tmp) && legal(x + w - 1, y + w - 1, img_tmp)){
+                if(legal(x, y, img_tmp_size) && legal(x + w - 1, y + w - 1, img_tmp_size)){
                     if(Math.abs(winlist.get(i).angle) < EPS){
                         ret.add(new Window2(x, y, w, w, angle, winlist.get(i).scale, cls_prob[i][1]));
                     }else if(Math.abs(winlist.get(i).angle - 180) < EPS){
@@ -573,32 +618,33 @@ public class PcnNetworkFaceDetection extends BaseOnnxInfer implements FaceDetect
      * @throws OrtException
      * @throws IOException
      */
-    private static List<Window2> detect(OrtSession[] sessions, Mat img, Mat imgPad, float[] scoreThs, float iouThs[]) throws OrtException, IOException {
+    private static List<Window2> detect_release_mat(OrtSession[] sessions, Mat img, Mat imgPad, float[] scoreThs, float iouThs[]) throws OrtException, IOException {
-        Mat img180 = new Mat();
+        Mat img180 = null;
-        Core.flip(imgPad, img180, 0);
+        Mat img90 = null;
-
+        Mat imgNeg90 = null;
-        Mat img90 = new Mat();
+        try {
-        Core.transpose(imgPad, img90);
+            //前置处理
-
+            img180 = new Mat();
-        Mat imgNeg90 = new Mat();
+            Core.flip(imgPad, img180, 0);
-        Core.flip(img90, imgNeg90, 0);
+            img90 = new Mat();
-
+            Core.transpose(imgPad, img90);
-        List<Window2> winlist = stage1(img, imgPad, sessions[0], scoreThs[0]);
+            imgNeg90 = new Mat();
-        winlist = NMS(winlist, true, iouThs[0]);
+            Core.flip(img90, imgNeg90, 0);
-
+            //第一步
-        winlist = stage2(imgPad, img180, sessions[1], scoreThs[1], 24, winlist);
+            List<Window2> winlist = stage1_release_mat(img.clone(), imgPad.clone(), sessions[0], scoreThs[0]);
-        winlist = NMS(winlist, true, iouThs[1]);
+            winlist = NMS(winlist, true, iouThs[0]);
-
+            //第二步
-        winlist = stage3(imgPad, img180, img90, imgNeg90, sessions[2], scoreThs[2], 48, winlist);
+            winlist = stage2_release_mat(imgPad.clone(), img180.clone(), sessions[1], scoreThs[1], 24, winlist);
-        winlist = NMS(winlist, false, iouThs[2]);
+            winlist = NMS(winlist, true, iouThs[1]);
-
+            //第三步
-        winlist = deleteFP(winlist);
+            winlist = stage3_release_mat(imgPad.clone(), img180.clone(), img90.clone(), imgNeg90.clone(), sessions[2], scoreThs[2], 48, winlist);
-
+            winlist = NMS(winlist, false, iouThs[2]);
-        img90.release();
+            //后处理
-        img180.release();
+            winlist = deleteFP(winlist);
-        imgNeg90.release();
+            return winlist;
-
+        }finally {
-        return winlist;
+            ReleaseUtil.release(imgNeg90, img90, img180, imgPad, img);
+        }
     }
 
     /**
@@ -642,7 +688,7 @@ public class PcnNetworkFaceDetection extends BaseOnnxInfer implements FaceDetect
                     ", angle=" + angle +
                     ", scale=" + scale +
                     ", conf=" + conf +
-                    '}' +"\n";
+                    "}" +"\n";
         }
     }
 }

face-search-core/src/main/java/com/visual/face/search/core/utils/ReleaseUtil.java

@@ -0,0 +1,98 @@
+package com.visual.face.search.core.utils;
+
+import ai.onnxruntime.OnnxTensor;
+import ai.onnxruntime.OrtSession;
+import com.visual.face.search.core.domain.ImageMat;
+import com.visual.face.search.core.domain.Mats;
+import org.opencv.core.Mat;
+
+public class ReleaseUtil {
+
+    public static void release(Mat ...mats){
+        for(Mat mat : mats){
+            if(null != mat){
+                try {
+                    mat.release();
+                }catch (Exception e){
+                    e.printStackTrace();
+                }finally {
+                    mat = null;
+                }
+            }
+        }
+    }
+
+    public static void release(Mats mats){
+        if(null == mats || mats.isEmpty()){
+            return;
+        }
+        try {
+            mats.release();
+        }catch (Exception e){
+            e.printStackTrace();
+        }finally {
+            mats = null;
+        }
+    }
+
+    public static void release(ImageMat ...imageMats){
+        for(ImageMat imageMat : imageMats){
+            if(null != imageMat){
+                try {
+                    imageMat.release();
+                }catch (Exception e){
+                    e.printStackTrace();
+                }finally {
+                    imageMat = null;
+                }
+            }
+        }
+    }
+
+    public static void release(OnnxTensor ...tensors){
+        if(null == tensors || tensors.length == 0){
+            return;
+        }
+        try {
+            for(OnnxTensor tensor : tensors){
+                try {
+                    if(null != tensor){
+                        tensor.close();
+                    }
+                }catch (Exception e) {
+                    e.printStackTrace();
+                }finally {
+                    tensor = null;
+                }
+            }
+        }catch (Exception e){
+            e.printStackTrace();
+        }finally {
+            tensors = null;
+        }
+    }
+
+    public static void release(OrtSession.Result ...results){
+        if(null == results || results.length == 0){
+            return;
+        }
+        try {
+            for(OrtSession.Result result : results){
+                try {
+                    if(null != result){
+                        result.close();
+                    }
+                }catch (Exception e) {
+                    e.printStackTrace();
+                }finally {
+                    result = null;
+                }
+            }
+        }catch (Exception e){
+            e.printStackTrace();
+        }finally {
+            results = null;
+        }
+    }
+
+}