From 17c72e76724228ee0ae70e8250ed543c600180f6 Mon Sep 17 00:00:00 2001 From: seastar <2229029156@qq.com> Date: Wed, 31 Jul 2024 21:38:14 +0800 Subject: [PATCH] first commit --- .idea/.gitignore | 10 + .idea/codeStyles/Project.xml | 7 + .idea/codeStyles/codeStyleConfig.xml | 5 + .idea/compiler.xml | 14 + .idea/inspectionProfiles/Project_Default.xml | 36 ++ .idea/jarRepositories.xml | 20 + .idea/misc.xml | 17 + .idea/uiDesigner.xml | 124 +++++ LICENSE | 201 ++++++++ README.md | 136 ++++++ dbnet_crnn_java.iml | 2 + imgs/det/test1.jpg | Bin 0 -> 118297 bytes imgs/det/test2.png | Bin 0 -> 441200 bytes imgs/det/test3.jpg | Bin 0 -> 29566 bytes imgs/test_imgs/det1.jpg | Bin 0 -> 109491 bytes imgs/test_imgs/det2.jpg | Bin 0 -> 263827 bytes imgs/test_imgs/det3.jpg | Bin 0 -> 218573 bytes pom.xml | 119 +++++ src/main/java/CRNNTest.java | 30 ++ src/main/java/DBNetTest.java | 28 ++ src/main/java/DetRecTest.java | 60 +++ src/main/java/crnn/Recognition.java | 136 ++++++ src/main/java/crnn/RegModel.java | 49 ++ src/main/java/dbnet/DetModel.java | 14 + src/main/java/dbnet/DetUtils.java | 431 +++++++++++++++++ src/main/java/dbnet/Detection.java | 209 ++++++++ src/main/java/object/Box.java | 32 ++ src/main/java/object/Boxes.java | 28 ++ src/main/java/object/Model.java | 119 +++++ src/main/java/object/Text.java | 22 + src/main/java/object/Texts.java | 30 ++ .../java/utils/common/CollectionUtil.java | 51 ++ src/main/java/utils/common/ImageUtils.java | 451 ++++++++++++++++++ .../java/utils/common/PropertiesReader.java | 33 ++ src/main/java/utils/cv/NDArrayUtils.java | 225 +++++++++ target/classes/CRNNTest.class | Bin 0 -> 1942 bytes target/classes/DBNetTest.class | Bin 0 -> 2007 bytes target/classes/DetRecTest.class | Bin 0 -> 3055 bytes target/classes/crnn/Recognition.class | Bin 0 -> 5334 bytes target/classes/crnn/RegModel.class | Bin 0 -> 2161 bytes target/classes/dbnet/DetModel.class | Bin 0 -> 379 bytes target/classes/dbnet/DetUtils.class | Bin 0 -> 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.idea/uiDesigner.xml create mode 100644 LICENSE create mode 100644 README.md create mode 100644 dbnet_crnn_java.iml create mode 100644 imgs/det/test1.jpg create mode 100644 imgs/det/test2.png create mode 100644 imgs/det/test3.jpg create mode 100644 imgs/test_imgs/det1.jpg create mode 100644 imgs/test_imgs/det2.jpg create mode 100644 imgs/test_imgs/det3.jpg create mode 100644 pom.xml create mode 100644 src/main/java/CRNNTest.java create mode 100644 src/main/java/DBNetTest.java create mode 100644 src/main/java/DetRecTest.java create mode 100644 src/main/java/crnn/Recognition.java create mode 100644 src/main/java/crnn/RegModel.java create mode 100644 src/main/java/dbnet/DetModel.java create mode 100644 src/main/java/dbnet/DetUtils.java create mode 100644 src/main/java/dbnet/Detection.java create mode 100644 src/main/java/object/Box.java create mode 100644 src/main/java/object/Boxes.java create mode 100644 src/main/java/object/Model.java create mode 100644 src/main/java/object/Text.java create mode 100644 src/main/java/object/Texts.java create mode 100644 src/main/java/utils/common/CollectionUtil.java create mode 100644 src/main/java/utils/common/ImageUtils.java create mode 100644 src/main/java/utils/common/PropertiesReader.java create mode 100644 src/main/java/utils/cv/NDArrayUtils.java create mode 100644 target/classes/CRNNTest.class create mode 100644 target/classes/DBNetTest.class create mode 100644 target/classes/DetRecTest.class create mode 100644 target/classes/crnn/Recognition.class create mode 100644 target/classes/crnn/RegModel.class create mode 100644 target/classes/dbnet/DetModel.class create mode 100644 target/classes/dbnet/DetUtils.class create mode 100644 target/classes/dbnet/Detection.class create mode 100644 target/classes/object/Box.class create mode 100644 target/classes/object/Boxes.class create mode 100644 target/classes/object/Model.class create mode 100644 target/classes/object/Text.class create mode 100644 target/classes/object/Texts.class create mode 100644 target/classes/utils/common/CollectionUtil.class create mode 100644 target/classes/utils/common/ImageUtils.class create mode 100644 target/classes/utils/common/PropertiesReader.class create mode 100644 target/classes/utils/cv/NDArrayUtils.class diff --git a/.idea/.gitignore b/.idea/.gitignore new file mode 100644 index 0000000..81b2fd6 --- /dev/null +++ b/.idea/.gitignore @@ -0,0 +1,10 @@ +# Default ignored files +/shelf/ +/workspace.xml +# Datasource local storage ignored files +/dataSources/ +/dataSources.local.xml +# Zeppelin ignored files +/ZeppelinRemoteNotebooks/ +# Editor-based HTTP Client requests +/httpRequests/ diff --git a/.idea/codeStyles/Project.xml b/.idea/codeStyles/Project.xml new file mode 100644 index 0000000..919ce1f --- /dev/null +++ b/.idea/codeStyles/Project.xml @@ -0,0 +1,7 @@ + + + + + + \ No newline at end of file diff --git a/.idea/codeStyles/codeStyleConfig.xml b/.idea/codeStyles/codeStyleConfig.xml new file mode 100644 index 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dbnet_crnn_java

+ + +
+ Table of Contents +
    +
  1. + About +
  2. +
  3. + Getting Started +
  4. +
  5. + Result +
  6. +
  7. + Contact +
  8. +
+
+ +#### About + +- 本项目利用java,javacv,onnx以及djl矩阵计算等技术加载文本检测模型dbnet与文本识别模型crnn,完成ocr的识别推理。 +- 包含模型的完整项目请从右侧releases处下载。 + +#### Getting started +目前只开放了通用文本检测与通用文本识别模型。【另有印刷体文档识别、手写体文字识别以及自然场景文字识别模型未开放,如有需求可联系我。】 +- DBNetTest.java: 通用文本检测 +- CRNNTest.java:通用文本识别 +- DetRecTest.java:通用文本检测和文本识别 + +#### Result +- 通用文本检测结果展示: +

+

+ + + +

+

+ +

+

+ + + +

+

+ +

+

+ + + +

+

+ +- 通用文本识别结果展示: + +以下是imgs/det/test1.jpg的识别结果: + ``` + text: 2018人工智能未来企业排行榜 + text: 领域 + text: 排名 + text: 企业 + text: 百度 + text: 开放的人工智能服务平台 + text: 1 + text: 2 + text: 腾讯 + text: 互联网综合服务 + text: 华为 + text: 3 + text: 人工智能自动化业务、智能芯片 + text: 阿里巴巴 + text: 互联网综合服务 + text: 4 + text: 5 + text: 平安集团 + text: 人工智能金融研发平台 + text: 6 + text: 华大基因 + text: 精准检测、医疗数据运营服务 + text: 7 + text: 搜狗 + text: 综合人工智能解决方案平台 + text: 8 + text: 科大讯飞 + text: 智能语音技术 + text: 9 + text: 中科创达 + text: 智能终端平台技术 + text: 10 + text: 珍岛集团 + text: SaaS级智能营销云平台 + text: I + text: 商汤科技 + text: 人工智能视觉深度学习平台 + text: 12 + text: 神州泰岳 + text: 综合类软件产品及服务 + text: 13 + text: 寒武纪科技 + text: 深度学习专用的智能芯片 + text: 14 + text: 汉王科技 + text: 文字识别技术与智能交互 + text: 15 + text: 全志科技 + text: 智能芯片设计 + text: 16 + text: facc++旷视科技 + text: 人工智能产品和行业解决方案 + text: 17 + text: 创略科技 + text: 智能客户数据平台 + text: 18 + text: 海云数据 + text: 企业级大数据整体运营与分析服务 + text: 19 + text: 影谱科技 + text: 视觉技术、智能影像生产企业 + text: 20 + text: 智臻智能 + text: 智能机器人技术提供和平台运营 + ``` +#### Contact +如有问题,联系我: + +1、github:https://github.com/jiangnanboy + +2、QQ:2229029156 + + + diff --git a/dbnet_crnn_java.iml b/dbnet_crnn_java.iml new file mode 100644 index 0000000..78b2cc5 --- /dev/null +++ b/dbnet_crnn_java.iml @@ -0,0 +1,2 @@ + + \ No newline at end of file diff --git a/imgs/det/test1.jpg b/imgs/det/test1.jpg new file mode 100644 index 0000000000000000000000000000000000000000..3352c802feb737f0382e6bcbfa2d150aab33c3b4 GIT binary patch literal 118297 zcmdqIbyQqU*Du&euq0T3;F87(Bm~#SrEw1)+@)~|5+o$Jhv3k-y9P*b32wn%I#_Ur zIplfX?|ygPnLBG{{+q*EoO5bd?b`C&yXsU`-~YN_2H`@bWTikzNFWdr@CUk|1Brvs zP#>W^LPbM+gociehJl5Pg@uWUMSzX-1ot@sG4XQ(A|g@>Ix12!FgXzs^-F3nJp&Un z6A2aTYgWeBbc{@l4@{8I(b2Imu%2OIJ!2##B4zx4eBFNs;bEW@p@2}3UVxDCkWlcD z?mI!`AP_P#%7eK7bv;5tLqD=t#&YsGvu9Xf*ij z&j>_SjAGCUU%XL;eGpU2s(cRSHjX3W;M8z(c8UFgK`UN0`iCCAa>!+38bHUR9+*um zAz9td0PrP6`uF;;PC*ZhAE5y>COkkI=#wq2PfapsT!4#!SZSP~`s~+%S4e2t zb!xa$sp~$mUaSxkgEx&ZMrBwwxosccW`&Qq_{Q7vEG3u+K;5?th4W4k!E=zKBra=- 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1.5.7 + linux-x86 + linux-x86_64 + windows-x86 + windows-x86_64 + + + + + org.apache.commons + commons-lang3 + 3.7 + + + + org.apache.poi + poi + 4.0.0 + + + + dom4j + dom4j + 1.6.1 + + + + commons-collections + commons-collections + 3.2.2 + + + + com.alibaba.fastjson2 + fastjson2 + 2.0.20 + + + + com.microsoft.onnxruntime + onnxruntime + 1.11.0 + + + + ai.djl + api + 0.17.0 + + + + ai.djl.mxnet + mxnet-engine + 0.17.0 + + + + org.bytedeco + javacv + ${javacv.version} + + + + + org.bytedeco + javacpp + ${javacv.version} + ${javacpp.platform.linux-x86} + + + + org.bytedeco + javacpp + ${javacv.version} + ${javacpp.platform.linux-x86_64} + + + + + org.bytedeco + javacpp + ${javacv.version} + ${javacpp.platform.windows-x86} + + + + org.bytedeco + javacpp + ${javacv.version} + ${javacpp.platform.windows-x86_64} + + + + org.slf4j + slf4j-nop + 1.7.2 + + + + org.openpnp + opencv + 4.5.5-0 + + + + \ No newline at end of file diff --git a/src/main/java/CRNNTest.java b/src/main/java/CRNNTest.java new file mode 100644 index 0000000..e7333ac --- /dev/null +++ b/src/main/java/CRNNTest.java @@ -0,0 +1,30 @@ +import ai.onnxruntime.OrtException; +import object.Text; +import crnn.Recognition; +import crnn.RegModel; +import org.opencv.core.Mat; +import org.opencv.imgcodecs.Imgcodecs; + +/** + * @author sy + * @date 2024/7/5 21:31 + */ +public class CRNNTest { + public static void main(String...args) throws OrtException { + nu.pattern.OpenCV.loadLocally(); + String imgPath = "imgs/recog/test12.png"; + Mat img = Imgcodecs.imread(imgPath); + String modelPath = "models\\rec\\inference.onnx"; + String vocabPath = "models\\rec\\vocab.txt"; + RegModel regModel = new RegModel(modelPath, vocabPath); + Recognition recognition = new Recognition(regModel); + + double startTime = System.currentTimeMillis()/1000.0; + + Text text = recognition.recognize(img); + System.out.println(text); + System.out.println("时间: " + (System.currentTimeMillis()/1000.0 - startTime)); + + img.release(); + } +} diff --git a/src/main/java/DBNetTest.java b/src/main/java/DBNetTest.java new file mode 100644 index 0000000..5660695 --- /dev/null +++ b/src/main/java/DBNetTest.java @@ -0,0 +1,28 @@ +import ai.onnxruntime.OrtException; +import object.Boxes; +import dbnet.DetModel; +import dbnet.Detection; +import utils.common.ImageUtils; +import org.opencv.core.Mat; +import org.opencv.imgcodecs.Imgcodecs; + +/** + * @author sy + * @date 2024/7/1 21:26 + */ +public class DBNetTest { + public static void main(String...args) throws OrtException { + nu.pattern.OpenCV.loadLocally(); + String imgPath = "imgs/det/test4.jpg"; + Mat img = Imgcodecs.imread(imgPath); + String modelPath = "models\\det\\inference.onnx"; + DetModel detModel = new DetModel(modelPath); + Detection detection = new Detection(detModel); + double startTime = System.currentTimeMillis()/1000.0; + Boxes detBoxes = detection.detect(img); + System.out.println("时间: " + (System.currentTimeMillis()/1000.0 - startTime)); + ImageUtils.drawPredictions(img, detBoxes); + Imgcodecs.imwrite("imgs\\prediction.jpg", img); + img.release(); + } +} diff --git a/src/main/java/DetRecTest.java b/src/main/java/DetRecTest.java new file mode 100644 index 0000000..16e394f --- /dev/null +++ b/src/main/java/DetRecTest.java @@ -0,0 +1,60 @@ +import ai.onnxruntime.OrtException; +import crnn.Recognition; +import crnn.RegModel; +import dbnet.DetModel; +import dbnet.Detection; +import object.Box; +import object.Boxes; +import object.Text; +import org.opencv.core.Mat; +import org.opencv.imgcodecs.Imgcodecs; + +import java.util.List; + +/** + * @author sy + * @date 2024/7/31 21:47 + */ +public class DetRecTest { + public static void main(String...args) throws OrtException { + nu.pattern.OpenCV.loadLocally(); + + // init and load dbnet + String detModelPath = "models\\det\\inference.onnx"; + + DetModel detModel = new DetModel(detModelPath); + Detection detection = new Detection(detModel); + + // init and load crnn + String recogModelPath = "models\\rec\\inference.onnx"; + String vocabPath = "models\\rec\\vocab.txt"; + + RegModel regModel = new RegModel(recogModelPath, vocabPath); + Recognition recognition = new Recognition(regModel); + + // test + String imgPath = "imgs/det/test1.jpg"; + Mat img = Imgcodecs.imread(imgPath); + + double startTime = System.currentTimeMillis()/1000.0; + + Boxes detBoxes = detection.detect(img); + List boxList = detBoxes.getBoxes(); + int size = boxList.size(); + for(int i=size-1; i>=0; i--) { + float[] linePosition = boxList.get(i).getLinePosition(); + int x0 = (int) linePosition[0]; + int y0 = (int) linePosition[1]; + int x2 = (int) linePosition[2]; + int y2 = (int) linePosition[3]; + Mat subMat = img.submat(y0, y2, x0, x2); + Text text = recognition.recognize(subMat); + System.out.println(text); + subMat.release(); + } + + System.out.println("时间: " + (System.currentTimeMillis()/1000.0 - startTime)); + + img.release(); + } +} diff --git a/src/main/java/crnn/Recognition.java b/src/main/java/crnn/Recognition.java new file mode 100644 index 0000000..257dd8b --- /dev/null +++ b/src/main/java/crnn/Recognition.java @@ -0,0 +1,136 @@ +package crnn; + +import ai.djl.ndarray.NDArray; +import ai.djl.ndarray.NDManager; +import ai.onnxruntime.OnnxTensor; +import ai.onnxruntime.OrtException; +import ai.onnxruntime.OrtSession; +import object.Text; +import utils.common.CollectionUtil; +import utils.common.ImageUtils; +import org.opencv.core.CvType; +import org.opencv.core.Mat; +import org.opencv.core.Size; +import org.opencv.imgproc.Imgproc; + +import java.nio.FloatBuffer; +import java.util.Map; + +/** + * @author sy + * @date 2024/7/5 21:28 + */ +public class Recognition { + RegModel regModel = null; + public Recognition(RegModel regModel) { + this.regModel = regModel; + } + + /** + * @param imgMat + * @return + * @throws OrtException + */ + public Text recognize(Mat imgMat) throws OrtException { + Map inputMap = this.prepareInput(imgMat); + float[][] predictions = inference(inputMap); + Text text = this.processOutput(predictions); + return text; + } + + /** + * @param img + * @return + * @throws OrtException + */ + private Map prepareInput(Mat img) throws OrtException { + int rawImgHeight = img.height(); + int rawImgWidth = img.width(); + float curRatio = rawImgWidth / (float)rawImgHeight; + int maskHeight = 32; + int maskWidth = 640; + int curTargetHeight; + int curTargetWidth; + if(curRatio > (maskWidth / (float)maskHeight)) { + curTargetHeight = maskHeight; + curTargetWidth = maskWidth; + } else { + curTargetHeight = maskHeight; + curTargetWidth = (int) (maskHeight * curRatio); + } + + Mat inputImg = new Mat(); + Imgproc.resize(img, inputImg, new Size(curTargetWidth, curTargetHeight)); + Mat maskMat = Mat.zeros(maskHeight, maskWidth, CvType.CV_8UC3); + + //创建一行像素点的缓存数组 + byte[] data = new byte[inputImg.channels() * inputImg.width()]; + for(int row = 0; row < inputImg.height(); row++){ + inputImg.get(row,0, data);//当col为0的时候表示获取一行 + //写入 + maskMat.put(row,0, data); + } + inputImg = maskMat; + + inputImg.convertTo(inputImg, CvType.CV_32FC3, 1./255); + + Map inputMap = CollectionUtil.newHashMap(); + float[] whc = new float[(int) (maskHeight * maskWidth * 3)]; + inputImg.get(0, 0, whc); + float[] chw = ImageUtils.whc2cwh(whc); + + FloatBuffer inputBuffer= FloatBuffer.wrap(chw); + OnnxTensor inputTensor = OnnxTensor.createTensor(this.regModel.getEnv(), inputBuffer, new long[]{1, 3, maskHeight, maskWidth}); + + inputMap.put(this.regModel.getInputName(), inputTensor); + maskMat.release(); + inputImg.release(); + return inputMap; + } + + /** + * @param inputMap + * @return + * @throws OrtException + */ + private float[][] inference(Map inputMap) throws OrtException { + OrtSession.Result result = this.regModel.getSession().run(inputMap); + float[][] predicitons = ((float[][][])result.get(0).getValue())[0]; + return predicitons; + } + + /** + * @param predictions + * @return + */ + private Text processOutput(float[][] predictions) { + NDManager ndManager = NDManager.newBaseManager(); + NDArray ndArray = ndManager.create(predictions); + + NDArray ndArraySoftmax = ndArray.softmax(-1); + NDArray ndArrayArgmax = ndArraySoftmax.argMax(-1); + + long lastP = 0L; + StringBuilder sb = new StringBuilder(); + for(int i=0; i labelMapping = CollectionUtil.newHashMap(); + public RegModel (String modelPath, String vocabPath) throws OrtException { + super(modelPath); + loadVocab(vocabPath); + } + + /** + * @param vocabPath + */ + public void loadVocab(String vocabPath) { + System.out.println("load vocab..."); + try(BufferedReader br = Files.newBufferedReader(Paths.get(vocabPath), Charset.forName("utf-8"))) { + String line = null; + long cnt = 1L; + while(null != (line = br.readLine())) { + line = line.trim(); + labelMapping.put(cnt, line); + cnt ++; + } + } catch (IOException e) { + e.printStackTrace(); + } + } + + /** + * @return + */ + public Map getLabelMapping() { + return this.labelMapping; + } +} diff --git a/src/main/java/dbnet/DetModel.java b/src/main/java/dbnet/DetModel.java new file mode 100644 index 0000000..9f08972 --- /dev/null +++ b/src/main/java/dbnet/DetModel.java @@ -0,0 +1,14 @@ +package dbnet; + +import ai.onnxruntime.OrtException; +import object.Model; + +/** + * @author sy + * @date 2024/7/1 20:20 + */ +public class DetModel extends Model { + public DetModel(String modelPath) throws OrtException { + super(modelPath); + } +} diff --git a/src/main/java/dbnet/DetUtils.java b/src/main/java/dbnet/DetUtils.java new file mode 100644 index 0000000..b15a898 --- /dev/null +++ b/src/main/java/dbnet/DetUtils.java @@ -0,0 +1,431 @@ +package dbnet; + +import ai.djl.ndarray.NDArray; +import ai.djl.ndarray.NDArrays; +import ai.djl.ndarray.NDList; +import ai.djl.ndarray.NDManager; +import ai.djl.ndarray.index.NDIndex; +import ai.djl.ndarray.types.DataType; +import ai.djl.ndarray.types.Shape; +import utils.common.CollectionUtil; +import utils.cv.NDArrayUtils; +import org.apache.commons.lang3.tuple.Pair; +import org.opencv.core.*; +import org.opencv.imgproc.Imgproc; + +import java.util.ArrayList; +import java.util.List; + +/** + * @author sy + * @date 2024/7/1 20:20 + */ +public class DetUtils { + + /** + * sort the points based on their x-coordinates + * @param pts + * @return + */ + private static NDArray orderPointsClockwise(NDArray pts) { + var list = new NDList(); + long[] indexes = pts.get(":, 0").argSort().toLongArray(); + + // grab the left-most and right-most points from the sorted + // x-roodinate points + var s1 = pts.getShape(); + var leftMost1 = pts.get(indexes[0] + ",:"); + var leftMost2 = pts.get(indexes[1] + ",:"); + var leftMost = leftMost1.concat(leftMost2).reshape(2, 2); + var rightMost1 = pts.get(indexes[2] + ",:"); + var rightMost2 = pts.get(indexes[3] + ",:"); + var rightMost = rightMost1.concat(rightMost2).reshape(2, 2); + + // now, sort the left-most coordinates according to their + // y-coordinates so we can grab the top-left and bottom-left + // points, respectively + indexes = leftMost.get(":, 1").argSort().toLongArray(); + var lt = leftMost.get(indexes[0] + ",:"); + var lb = leftMost.get(indexes[1] + ",:"); + indexes = rightMost.get(":, 1").argSort().toLongArray(); + var rt = rightMost.get(indexes[0] + ",:"); + var rb = rightMost.get(indexes[1] + ",:"); + + list.add(lt); + list.add(rt); + list.add(rb); + list.add(lb); + + var rect = NDArrays.concat(list).reshape(4, 2); + return rect; + } + + /** + * get boxes from the binarized image predicted by DB + * @param manager + * @param pred the binarized image predicted by DB. + * @param mat new 'pred' after threshold filtering. + * @param boxThresh + */ + public static Pair boxesFromBitmap(NDManager manager, NDArray pred, Mat mat, float boxThresh) { + var destHeight = (int) pred.getShape().get(0); + var destWidth = (int) pred.getShape().get(1); + var height = mat.rows(); + var width = mat.cols(); + + List contours = CollectionUtil.newArrayList(); + var hierarchy = new Mat(); + + // find contours + Imgproc.findContours(mat, + contours, + hierarchy, + Imgproc.RETR_LIST, + Imgproc.CHAIN_APPROX_SIMPLE, + new Point(0, 0)); + + System.out.println("contours: " + contours.size()); + + var numContours = Math.min(contours.size(), 1000); + + var boxNDList = new NDList(); + float[] scoreList = new float[numContours]; + for (int index = 0; index < numContours; index++) { + MatOfPoint contour = contours.get(index); + float[][] pointsArr = new float[4][2]; + var sside = getMiniBoxes(contour, pointsArr); + if (sside < 3) { + continue; + } + + var points = manager.create(pointsArr); + + var score = boxScoreFast(manager, pred, points); + if (score < boxThresh) { + continue; + } + + var box = unClip(manager, points); // TODO get_mini_boxes(box) + + MatOfPoint matOfPoint = new MatOfPoint( + new Point(box.getFloat(0, 0), box.getFloat(0, 1)), + new Point(box.getFloat(1, 0), box.getFloat(1, 1)), + new Point(box.getFloat(2, 0), box.getFloat(2, 1)), + new Point(box.getFloat(3, 0), box.getFloat(3, 1)) + ); + + sside = getMiniBoxes(matOfPoint, pointsArr); + matOfPoint.release(); + if(sside < 3 + 2) { + continue; + } + + // box[:, 0] = np.clip(np.round(box[:, 0] / width * dest_width), 0, dest_width) + var boxes1 = box.get(":,0").div(width).mul(destWidth).round().clip(0, destWidth); + box.set(new NDIndex(":, 0"), boxes1); + // box[:, 1] = np.clip(np.round(box[:, 1] / height * dest_height), 0, dest_height) + var boxes2 = box.get(":,1").div(height).mul(destHeight).round().clip(0, destHeight); + box.set(new NDIndex(":, 1"), boxes2); + + boxNDList.add(box); +// boxes.set(new NDIndex(count + ",:,:"), box); + scoreList[index] = score; + + // release memory + contour.release(); + + } +// if (count < num_contours) { +// NDArray newBoxes = manager.zeros(new Shape(count, 4, 2), DataType.FLOAT32); +// newBoxes.set(new NDIndex("0,0,0"), boxes.get(":" + count + ",:,:")); +// boxes = newBoxes; +// } + + + Pair pair = null; + if((boxNDList.size() > 0) && (scoreList.length > 0)) { + NDArray boxes = NDArrays.stack(boxNDList); + pair = Pair.of(boxes, scoreList); + } + + + // release + hierarchy.release(); + + return pair; + } + + /** + * shrink or expand the boxaccording to 'unclip_ratio' + * @param points The predicted box. + * @return uncliped box + */ + public static NDArray unClip(NDManager manager, NDArray points) { + points = orderPointsClockwise(points); + float[] pointsArr = points.toFloatArray(); + float[] lt = java.util.Arrays.copyOfRange(pointsArr, 0, 2); + float[] lb = java.util.Arrays.copyOfRange(pointsArr, 6, 8); + + float[] rt = java.util.Arrays.copyOfRange(pointsArr, 2, 4); + float[] rb = java.util.Arrays.copyOfRange(pointsArr, 4, 6); + + var width = distance(lt, rt); + var height = distance(lt, lb); + + if (width > height) { + var k = (lt[1] - rt[1]) / (lt[0] - rt[0]); // y = k * x + b + + var deltaDis = height; + var deltaX = (float) Math.sqrt((deltaDis * deltaDis) / (k * k + 1)); + var deltaY = Math.abs(k * deltaX); + + if (k > 0) { + pointsArr[0] = lt[0] - deltaX + deltaY; + pointsArr[1] = lt[1] - deltaY - deltaX; + pointsArr[2] = rt[0] + deltaX + deltaY; + pointsArr[3] = rt[1] + deltaY - deltaX; + + pointsArr[4] = rb[0] + deltaX - deltaY; + pointsArr[5] = rb[1] + deltaY + deltaX; + pointsArr[6] = lb[0] - deltaX - deltaY; + pointsArr[7] = lb[1] - deltaY + deltaX; + } else { + pointsArr[0] = lt[0] - deltaX - deltaY; + pointsArr[1] = lt[1] + deltaY - deltaX; + pointsArr[2] = rt[0] + deltaX - deltaY; + pointsArr[3] = rt[1] - deltaY - deltaX; + + pointsArr[4] = rb[0] + deltaX + deltaY; + pointsArr[5] = rb[1] - deltaY + deltaX; + pointsArr[6] = lb[0] - deltaX + deltaY; + pointsArr[7] = lb[1] + deltaY + deltaX; + } + } else { + var k = (lt[1] - rt[1]) / (lt[0] - rt[0]); // y = k * x + b + + var deltaDis = width; + var deltaY = (float) Math.sqrt((deltaDis * deltaDis) / (k * k + 1)); + var deltaX = Math.abs(k * deltaY); + + if (k > 0) { + pointsArr[0] = lt[0] + deltaX - deltaY; + pointsArr[1] = lt[1] - deltaY - deltaX; + pointsArr[2] = rt[0] + deltaX + deltaY; + pointsArr[3] = rt[1] - deltaY + deltaX; + + pointsArr[4] = rb[0] - deltaX + deltaY; + pointsArr[5] = rb[1] + deltaY + deltaX; + pointsArr[6] = lb[0] - deltaX - deltaY; + pointsArr[7] = lb[1] + deltaY - deltaX; + } else { + pointsArr[0] = lt[0] - deltaX - deltaY; + pointsArr[1] = lt[1] - deltaY + deltaX; + pointsArr[2] = rt[0] - deltaX + deltaY; + pointsArr[3] = rt[1] - deltaY - deltaX; + + pointsArr[4] = rb[0] + deltaX + deltaY; + pointsArr[5] = rb[1] + deltaY - deltaX; + pointsArr[6] = lb[0] + deltaX - deltaY; + pointsArr[7] = lb[1] + deltaY + deltaX; + } + } + points = manager.create(pointsArr).reshape(4, 2); + + return points; + } + + /** + * distance between point1 and point2 + * @param point1 + * @param point2 + * @return + */ + private static float distance(float[] point1, float[] point2) { + var disX = point1[0] - point2[0]; + var disY = point1[1] - point2[1]; + var dis = (float) Math.sqrt(disX * disX + disY * disY); + return dis; + } + + /** + * get boxes from the contour or box. + * @param contour The predicted contour. + * @param pointsArr The predicted box. + * @return smaller side of box + */ + private static int getMiniBoxes(MatOfPoint contour, float[][] pointsArr) { + // https://blog.csdn.net/qq_37385726/article/details/82313558 + // bounding_box[1] - rect returns the length and width of the rectangle +// MatOfPoint2f contour2f = ImageUtils.matOfPointToMatOfPoint2f(contour); + MatOfPoint2f contour2f = new MatOfPoint2f(contour.toArray()); + var rect = Imgproc.minAreaRect(contour2f); + + var points = new Mat(); + Imgproc.boxPoints(rect, points); + + float[][] fourPoints = new float[4][2]; + for(int row=0; row fourPoints[0][1]) { + index1 = 0; + index4 = 1; + } else { + index1 = 1; + index4 = 0; + } + + if (fourPoints[3][1] > fourPoints[2][1]) { + index2 = 2; + index3 = 3; + } else { + index2 = 3; + index3 = 2; + } + + pointsArr[0] = fourPoints[index1]; + pointsArr[1] = fourPoints[index2]; + pointsArr[2] = fourPoints[index3]; + pointsArr[3] = fourPoints[index4]; + + var height = rect.boundingRect().height; + var width = rect.boundingRect().width; + var sside = Math.min(height, width); + + // release + points.release(); + contour2f.release(); + return sside; + } + + /** + * calculate the score of box. + * @param bitmap The binarized image predicted by DB. + * @param points The predicted box + * @return + */ + public static float boxScoreFast(NDManager manager, NDArray bitmap, NDArray points) { + var box = points.get(":"); + var h = bitmap.getShape().get(0); + var w = bitmap.getShape().get(1); + // xmin = np.clip(np.floor(box[:, 0].min()).astype(np.int), 0, w - 1) + var xmin = box.get(":, 0").min().floor().clip(0, w - 1).toType(DataType.INT32, true).toIntArray()[0]; + var xmax = box.get(":, 0").max().ceil().clip(0, w - 1).toType(DataType.INT32, true).toIntArray()[0]; + var ymin = box.get(":, 1").min().floor().clip(0, h - 1).toType(DataType.INT32, true).toIntArray()[0]; + var ymax = box.get(":, 1").max().ceil().clip(0, h - 1).toType(DataType.INT32, true).toIntArray()[0]; + + var mask = manager.zeros(new Shape(ymax - ymin + 1, xmax - xmin + 1), DataType.UINT8); + + box.set(new NDIndex(":, 0"), box.get(":, 0").sub(xmin)); + box.set(new NDIndex(":, 1"), box.get(":, 1").sub(ymin)); + + //mask - convert from NDArray to Mat + Mat maskMat = NDArrayUtils.uint8NDArrayToMat(mask); + + //mask - convert from NDArray to Mat - 4 rows, 2 cols + Mat boxMat = NDArrayUtils.floatNDArrayToMat(box, CvType.CV_32S); + +// boxMat.reshape(1, new int[]{1, 4, 2}); + List pts = new ArrayList<>(); + MatOfPoint matOfPoint = NDArrayUtils.matToMatOfPoint(boxMat); // new MatOfPoint(boxMat); + pts.add(matOfPoint); + Imgproc.fillPoly(maskMat, pts, new Scalar(1)); + + NDArray subBitMap = bitmap.get(ymin + ":" + (ymax + 1) + "," + xmin + ":" + (xmax + 1)); + Mat bitMapMat = NDArrayUtils.floatNDArrayToMat(subBitMap); + + Scalar score = Core.mean(bitMapMat, maskMat); + float scoreValue = (float) score.val[0]; + // release + maskMat.release(); + boxMat.release(); + bitMapMat.release(); + return scoreValue; + } + + public static NDList filterTagDetRes(NDArray dt_boxes, long width, long height) { + NDList boxesList = new NDList(); + + int num = (int) dt_boxes.getShape().get(0); + for (int i = 0; i < num; i++) { + NDArray box = dt_boxes.get(i); + box = orderPointsClockwise(box); + box = clipDetRes(box, width, height); + float[] box0 = box.get(0).toFloatArray(); + float[] box1 = box.get(1).toFloatArray(); + float[] box3 = box.get(3).toFloatArray(); + int rect_width = (int) Math.sqrt(Math.pow(box1[0] - box0[0], 2) + Math.pow(box1[1] - box0[1], 2)); + int rect_height = (int) Math.sqrt(Math.pow(box3[0] - box0[0], 2) + Math.pow(box3[1] - box0[1], 2)); + if (rect_width <= 3 || rect_height <= 3) { + continue; + } + boxesList.add(box); + } + return boxesList; + } + + public static NDArray clipDetRes(NDArray points, long width, long height) { + for (int i = 0; i < points.getShape().get(0); i++) { + int value = Math.max((int) points.get(i, 0).toFloatArray()[0], 0); + value = (int) Math.min(value, width - 1); + points.set(new NDIndex(i + ",0"), value); + value = Math.max((int) points.get(i, 1).toFloatArray()[0], 0); + value = (int) Math.min(value, height - 1); + points.set(new NDIndex(i + ",1"), value); + } + + return points; + } + + /** + * @param matrix + * @return + */ + public static float[][] transposeMatrix(float[][] matrix) { + float[][] transMatrix = new float[matrix[0].length][matrix.length]; + for(int i=0; i maxVal) { + maxVal = probabilities[i]; + idx = i; + } + } + return idx; + } + +} diff --git a/src/main/java/dbnet/Detection.java b/src/main/java/dbnet/Detection.java new file mode 100644 index 0000000..4aa6258 --- /dev/null +++ b/src/main/java/dbnet/Detection.java @@ -0,0 +1,209 @@ +package dbnet; + +import ai.djl.ndarray.NDArray; +import ai.djl.ndarray.NDList; +import ai.djl.ndarray.NDManager; +import ai.djl.ndarray.index.NDIndex; +import ai.djl.ndarray.types.DataType; +import ai.onnxruntime.OnnxTensor; +import ai.onnxruntime.OrtException; +import ai.onnxruntime.OrtSession; +import object.Box; +import object.Boxes; +import utils.common.CollectionUtil; +import utils.common.ImageUtils; +import utils.cv.NDArrayUtils; +import org.apache.commons.lang3.tuple.Pair; +import org.opencv.core.*; +import org.opencv.imgproc.Imgproc; + +import java.nio.FloatBuffer; +import java.util.List; +import java.util.Map; +import java.util.Optional; + +/** + * @author sy + * @date 2024/7/1 19:22 + */ +public class Detection { + private Boxes boxes = null; + private float thresh = 0.5f; + + DetModel detModel = null; + + long rawImgHeight; + long rawImgWidth; + float ratioH; + float ratioW; + + public Boxes getBoxes() { + return boxes; + } + + public void setBoxes(Boxes boxes) { + this.boxes = boxes; + } + + public float getThresh() { + return thresh; + } + + public void setThresh(float thresh) { + this.thresh = thresh; + } + + public Detection(DetModel detModel) { + this.detModel = detModel; + } + + public Detection(DetModel detModel, float thresh) { + this.detModel = detModel; + this.thresh = thresh; + } + + /** + * + * @param imgMat + * @return + * @throws OrtException + */ + public Boxes detect(Mat imgMat) throws OrtException { + Map inputMap = this.prepareInput(imgMat); + float[][] predictions = inference(inputMap); + Boxes detBoxes = this.processOutput(predictions); + return detBoxes; + } + + /** + * @param inputMap + * @return + * @throws OrtException + */ + private float[][] inference(Map inputMap) throws OrtException { + OrtSession.Result result = detModel.getSession().run(inputMap); + float[][] predicitons = ((float[][][][])result.get(0).getValue())[0][0]; + return predicitons; + } + + /** + * @param img + * @return + * @throws OrtException + */ + private Map prepareInput(Mat img) throws OrtException { + this.rawImgHeight = img.height(); + this.rawImgWidth = img.width(); + Mat inputImg = new Mat(); + + float ratio = 1.0f; + if (Math.max(rawImgHeight, rawImgWidth) > 960) { + if (rawImgHeight > rawImgWidth) { + ratio = (float) 960 / (float) rawImgHeight; + } else { + ratio = (float) 960 / (float) rawImgWidth; + } + } + int resize_h = (int) (rawImgHeight * ratio); + int resize_w = (int) (rawImgWidth * ratio); + + resize_h = Math.round((float) resize_h / 32f) * 32; + resize_w = Math.round((float) resize_w / 32f) * 32; + + this.ratioH = resize_h / (float) rawImgHeight; + this.ratioW = resize_w / (float) rawImgWidth; + + Imgproc.resize(img, inputImg, new Size(resize_w, resize_h)); + + inputImg.convertTo(inputImg, CvType.CV_32FC3, 1./255); + + Imgproc.cvtColor(inputImg, inputImg, Imgproc.COLOR_BGR2RGB); + + Map inputMap = CollectionUtil.newHashMap(); + float[] whc = new float[resize_h * resize_w * 3]; + + inputImg.get(0, 0, whc); + float[] chw = ImageUtils.whc2cwh(whc); + + FloatBuffer inputBuffer= FloatBuffer.wrap(chw); + OnnxTensor inputTensor = OnnxTensor.createTensor(detModel.getEnv(), inputBuffer, new long[]{1, 3, resize_h, resize_w}); + inputMap.put(detModel.getInputName(), inputTensor); + inputImg.release(); + return inputMap; + } + + /** + * @param predictions + * @return + */ + private Boxes processOutput(float[][] predictions) { + NDManager ndManager = NDManager.newBaseManager(); + NDArray ndArray = ndManager.create(predictions); + NDArray segmentation = ndArray.gt(this.thresh).toType(DataType.INT8, true); // thresh=0.3 .mul(255f) + Mat newMask = new Mat(); + Mat srcMat = NDArrayUtils.uint8NDArrayToMat(segmentation); + Scalar scalar = new Scalar(255); + Core.multiply(srcMat, scalar, newMask); + srcMat.release(); + + Pair boxScorePair = DetUtils.boxesFromBitmap(ndManager, ndArray, newMask, 0.5f); + + NDArray boxes = (NDArray) boxScorePair.getLeft(); + float[] scoreList = (float[]) boxScorePair.getRight(); + + Boxes textBoxes = null; + if(Optional.ofNullable(boxes).isPresent()) { + NDList detBoxes = null; + //boxes[:, :, 0] = boxes[:, :, 0] / ratio_w + NDArray ndArray1 = boxes.get(":, :, 0").div(this.ratioW); + boxes.set(new NDIndex(":, :, 0"), ndArray1); + + //boxes[:, :, 1] = boxes[:, :, 1] / ratio_h + NDArray ndArray2 = boxes.get(":, :, 1").div(this.ratioH); + boxes.set(new NDIndex(":, :, 1"), ndArray2); + + detBoxes = DetUtils.filterTagDetRes(boxes, this.rawImgWidth, this.rawImgHeight); + + detBoxes.detach(); + + List boxList = CollectionUtil.newArrayList(); + + for (var index=0; index< detBoxes.size(); index++) { + NDArray box = detBoxes.get(index); + float[] scoreBox = new float[4]; + scoreBox[0] = box.getFloat(0, 0); + scoreBox[1] = box.getFloat(0, 1); + scoreBox[2] = box.getFloat(2, 0); + scoreBox[3] = box.getFloat(2, 1); + float score = scoreList[index]; + Box TBox = new Box(scoreBox, score); + boxList.add(TBox); + } + + textBoxes = new Boxes(boxList); + } + + // release Mat + newMask.release(); +// mask.release(); +// structImage.release(); + + return textBoxes; + } + + /** + * @param bbox + */ + public void rescaleBoxes(float[] bbox) { + bbox[0] /= detModel.getInputWidth(); + bbox[0] *= this.rawImgWidth; + bbox[1] /= detModel.getInputHeight(); + bbox[1] *= this.rawImgHeight; + bbox[2] /= detModel.getInputWidth(); + bbox[2] *= this.rawImgWidth; + bbox[3] /= detModel.getInputHeight(); + bbox[3] *= this.rawImgHeight; + } +} + + diff --git a/src/main/java/object/Box.java b/src/main/java/object/Box.java new file mode 100644 index 0000000..9891c0d --- /dev/null +++ b/src/main/java/object/Box.java @@ -0,0 +1,32 @@ +package object; + +/** + * @author sy + * @date 2024/7/2 19:55 + */ +public class Box { + private float[] linePosition; + private float score; + + public float[] getLinePosition() { + return linePosition; + } + + public float getScore() { + return this.score; + } + + public void setLinePosition(float[] linePosition) { + this.linePosition = linePosition; + } + + public Box(float[] linePosition, float score) { + this.linePosition = linePosition; + this.score = score; + } + + @Override + public String toString() { + return "lineposition: " + linePosition[0] + "," + linePosition[1] + "," + linePosition[2] + "," + linePosition[3] + ", score: " + this.getScore(); + } +} diff --git a/src/main/java/object/Boxes.java b/src/main/java/object/Boxes.java new file mode 100644 index 0000000..a339d8f --- /dev/null +++ b/src/main/java/object/Boxes.java @@ -0,0 +1,28 @@ +package object; + +import java.util.List; + +/** + * @author sy + * @date 2024/7/1 19:33 + */ +public class Boxes { + private List boxes; + + public List getBoxes() { + return boxes; + } + + public void setBoxes(List boxes) { + this.boxes = boxes; + } + + public Boxes(List boxes) { + this.boxes = boxes; + } + + @Override + public String toString() { + return "boxes:" + this.getBoxes(); + } +} diff --git a/src/main/java/object/Model.java b/src/main/java/object/Model.java new file mode 100644 index 0000000..c81bcf8 --- /dev/null +++ b/src/main/java/object/Model.java @@ -0,0 +1,119 @@ +package object; + +import ai.onnxruntime.*; + +import java.util.Map; +import java.util.Optional; + +/** + * @author sy + * @date 2024/7/1 20:25 + */ +public class Model { + private OrtSession session; + private OrtEnvironment env; + + private int gpuDeviceId = -1; + + private long inputHeight; + private long inputWidth; + + private int numInputElements; + private String inputName; + private String outputName; + private long[] inputShape; + private OnnxJavaType inputType; + + + public OrtSession getSession() { + return session; + } + + public OrtEnvironment getEnv() { + return env; + } + + public int getGpuDeviceId() { + return gpuDeviceId; + } + + public long getInputHeight() { + return inputHeight; + } + + public long getInputWidth() { + return inputWidth; + } + + public int getNumInputElements() { + return numInputElements; + } + + public String getInputName() { + return inputName; + } + + public String getOutputName() { + return outputName; + } + + public long[] getInputShape() { + return inputShape; + } + + public OnnxJavaType getInputType() { + return inputType; + } + + public Model(String path) throws OrtException { + initModel(path); + } + + public Model(String path, int gpuDeviceId) throws OrtException { + this.gpuDeviceId = gpuDeviceId; + initModel(path); + } + + /** + * @param path + * @throws OrtException + */ + private void initModel(String path) throws OrtException { + System.out.println("init and load model..."); + this.env = OrtEnvironment.getEnvironment(); + var sessionOptions = new OrtSession.SessionOptions(); + sessionOptions.setOptimizationLevel(OrtSession.SessionOptions.OptLevel.ALL_OPT); + if(this.gpuDeviceId >= 0) { + sessionOptions.addCPU(false); + sessionOptions.addCUDA(gpuDeviceId); + } else { + sessionOptions.addCPU(true); + } + this.session = this.env.createSession(path, sessionOptions); + + Map inputMetaMap = this.session.getInputInfo(); + this.inputName = this.session.getInputNames().iterator().next(); + NodeInfo inputMeta = inputMetaMap.get(this.inputName); + this.inputType = ((TensorInfo)inputMeta.getInfo()).type; + this.inputShape = ((TensorInfo) inputMeta.getInfo()).getShape(); + + this.numInputElements = (int) (this.inputShape[1] * this.inputShape[2] * this.inputShape[3]); + this.inputHeight = this.inputShape[2]; + this.inputWidth = this.inputShape[3]; + this.outputName = this.session.getOutputNames().iterator().next(); + } + + public void closeModel() { + System.out.println("close model..."); + if (Optional.ofNullable(this.session).isPresent()) { + try { + this.session.close(); + } catch (OrtException e) { + e.printStackTrace(); + } + } + if(Optional.ofNullable(this.env).isPresent()) { + this.env.close(); + } + } +} diff --git a/src/main/java/object/Text.java b/src/main/java/object/Text.java new file mode 100644 index 0000000..3ce682a --- /dev/null +++ b/src/main/java/object/Text.java @@ -0,0 +1,22 @@ +package object; + +/** + * @author sy + * @date 2024/7/2 19:55 + */ +public class Text { + private String text; + + public String getText() { + return text; + } + + public Text(String text) { + this.text = text; + } + + @Override + public String toString() { + return "text: " + this.getText(); + } +} diff --git a/src/main/java/object/Texts.java b/src/main/java/object/Texts.java new file mode 100644 index 0000000..ea495d5 --- /dev/null +++ b/src/main/java/object/Texts.java @@ -0,0 +1,30 @@ +package object; + + +import java.util.List; + +/** + * @author sy + * @date 2024/7/1 19:23 + */ + +public class Texts { + private List texts; + + public List getTexts() { + return texts; + } + + public void setTexts(List texts) { + this.texts = texts; + } + + public Texts(List texts) { + this.texts = texts; + } + + @Override + public String toString() { + return "texts:" + this.getTexts(); + } +} diff --git a/src/main/java/utils/common/CollectionUtil.java b/src/main/java/utils/common/CollectionUtil.java new file mode 100644 index 0000000..9b70252 --- /dev/null +++ b/src/main/java/utils/common/CollectionUtil.java @@ -0,0 +1,51 @@ +package utils.common; + +import java.util.*; + +/** + * @author sy + * @date 2024/7/1 19:23 + */ +public class CollectionUtil { + + public static List newArrayList(List list) { + return new ArrayList<>(list); + } + + public static List newArrayList() { + return new ArrayList<>(); + } + + public static LinkedList newLinkedList() { + return new LinkedList<>(); + } + + public static List newArrayList(int N) { + return new ArrayList<>(N); + } + + public static List newArrayList(Set entry) { + return new ArrayList<>(entry); + } + + public static Set newHashset() { + return new HashSet<>(); + } + + public static Set newHashset(List entry) { + return new HashSet<>(entry); + } + + public static Map newHashMap() { + return new HashMap<>(); + } + + public static LinkedHashMap newLinkedHashMap() { + return new LinkedHashMap<>(); + } + + public static Map newTreeMap() { + return new TreeMap<>(); + } + +} diff --git a/src/main/java/utils/common/ImageUtils.java b/src/main/java/utils/common/ImageUtils.java new file mode 100644 index 0000000..43d1295 --- /dev/null +++ b/src/main/java/utils/common/ImageUtils.java @@ -0,0 +1,451 @@ +package utils.common; + +import ai.djl.modality.cv.Image; +import ai.djl.modality.cv.ImageFactory; +import ai.djl.modality.cv.output.BoundingBox; +import ai.djl.modality.cv.output.DetectedObjects; +import ai.djl.modality.cv.util.NDImageUtils; +import ai.djl.ndarray.NDArray; +import ai.djl.ndarray.NDList; +import ai.djl.ndarray.NDManager; +import ai.djl.ndarray.types.DataType; +import object.Box; +import object.Boxes; +import org.opencv.core.Point; +import org.opencv.core.*; +import org.opencv.imgproc.Imgproc; + +import java.awt.*; +import java.awt.image.BufferedImage; +import java.io.IOException; +import java.nio.file.Files; +import java.nio.file.Paths; +import java.util.Comparator; +import java.util.List; +import java.util.Map; +import java.util.stream.Collectors; + +/** + * @author sy + * @date 2024/7/1 19:23 + */ +public class ImageUtils { + + private static final Map COLOR_MAP = Map.of( + 0, new Color(200, 0, 0), + 1, new Color(0, 200, 0), + 2, new Color(0, 0, 200), + 3, new Color(200, 200, 0), + 4, new Color(200, 0, 200), + 5, new Color(0, 200, 200) + ); + + /** + * convert BufferedImage to DJL Image + * @param img + * @return + */ + public static Image convert(BufferedImage img) { + return ImageFactory.getInstance().fromImage(img); + } + + /** + * save BufferedImage + * @param img + * @param name + * @param path + */ + public static void saveImage(BufferedImage img, String name, String path) { + var djlImg = ImageFactory.getInstance().fromImage(img); + var outputDir = Paths.get(path); + var imagePath = outputDir.resolve(name); + try { + djlImg.save(Files.newOutputStream(imagePath), "png"); + } catch (IOException e) { + e.printStackTrace(); + } + } + + /** + * save DJL Image + * @param img + * @param name + * @param path + */ + public static void saveImage(Image img, String name, String path) { + var outputDir = Paths.get(path); + var imagePath = outputDir.resolve(name); + try { + img.save(Files.newOutputStream(imagePath), "png"); + } catch (IOException e) { + e.printStackTrace(); + } + } + + /** + * save image, including the detection box + * @param img + * @param detection + * @param name + * @param path + * @throws IOException + */ + public static void saveBoundingBoxImage(Image img, DetectedObjects detection, String name, String path) throws IOException { + // Make image copy with alpha channel because original image was jpg + img.drawBoundingBoxes(detection); + var outputDir = Paths.get(path); + Files.createDirectories(outputDir); + var imagePath = outputDir.resolve(name); + // OpenJDK can't save jpg with alpha channel + img.save(Files.newOutputStream(imagePath), "png"); + } + + /** + * draw detection box (with inclination Angle) + * @param image + * @param box + */ + public static void drawImageRect(BufferedImage image, NDArray box) { + float[] points = box.toFloatArray(); + int[] xPoints = new int[5]; + int[] yPoints = new int[5]; + + for (int i = 0; i < 4; i++) { + xPoints[i] = (int) points[2 * i]; + yPoints[i] = (int) points[2 * i + 1]; + } + xPoints[4] = xPoints[0]; + yPoints[4] = yPoints[0]; + + // convert image to Graphics2D + var g = (Graphics2D) image.getGraphics(); + try { + g.setColor(new Color(0, 255, 0)); + //declare brush properties + BasicStroke bStroke = new BasicStroke(4, BasicStroke.CAP_BUTT, BasicStroke.JOIN_MITER); + g.setStroke(bStroke); + // xPoints, yPoints, nPoints + g.drawPolyline(xPoints, yPoints, 5); + } finally { + g.dispose(); + } + } + + public static void drawPredictions(Mat img, NDList detBoxes) { + // debugging image + for (NDArray box : detBoxes) { + Imgproc.rectangle(img, //Matrix obj of the image + new Point(box.getFloat(0, 0), box.getFloat(0, 1)), //p1 + new Point(box.getFloat(2, 0), box.getFloat(2, 1)), //p2 + new Scalar(1), //Scalar object for color + 2 //Thickness of the line + ); + } + } + + public static void drawPredictions(Mat img, Boxes detBoxes) { + List boxList = detBoxes.getBoxes(); + for(Box box : boxList) { + Imgproc.rectangle(img, + new Point(box.getLinePosition()[0], box.getLinePosition()[1]), + new Point(box.getLinePosition()[2], box.getLinePosition()[3]), + new Scalar(1), + 2); + } + } + + + /** + * draw detection box (with inclination Angle) and text + * @param image + * @param box + * @param text + */ + public static void drawImageRectWithText(BufferedImage image, NDArray box, String text) { + float[] points = box.toFloatArray(); + int[] xPoints = new int[5]; + int[] yPoints = new int[5]; + + for (int i = 0; i < 4; i++) { + xPoints[i] = (int) points[2 * i]; + yPoints[i] = (int) points[2 * i + 1]; + } + xPoints[4] = xPoints[0]; + yPoints[4] = yPoints[0]; + + // convert image to Graphics2D + var g = (Graphics2D) image.getGraphics(); + try { + var fontSize = 32; + var font = new Font("楷体", Font.PLAIN, fontSize); + g.setFont(font); + g.setColor(new Color(0, 0, 255)); + // declare brush properties + BasicStroke bStroke = new BasicStroke(1, BasicStroke.CAP_BUTT, BasicStroke.JOIN_MITER); + g.setStroke(bStroke); + g.drawPolyline(xPoints, yPoints, 5); // xPoints, yPoints, nPoints + g.drawString(text, xPoints[0], yPoints[0]); + } finally { + g.dispose(); + } + } + + /** + * draw ocr results + * @param image + * @param resultBoxList + * @return + */ +// public static BufferedImage drawTextListResults(BufferedImage image, List resultBoxList) { +// var g = (Graphics2D) image.getGraphics(); +// var stroke = 2; +// g.setStroke(new BasicStroke(stroke)); +// g.setRenderingHint(RenderingHints.KEY_ANTIALIASING, RenderingHints.VALUE_ANTIALIAS_ON); +// for(Text resultBox:resultBoxList) { +// var text = resultBox.getText(); +// var ndArray = resultBox.getBox(); +// var color = COLOR_MAP.get(Math.abs(text.hashCode() % 6)); +// g.setPaint(color); +// float x = ndArray.get(0); +// var xInt = (int)x; +// float y = ndArray.get(1); +// var yInt = (int)y; +// var width = (int) (ndArray.get(2) - x); +// var height = (int) (ndArray.get(7) - y); +// g.drawRect(xInt, yInt, width, height); +// drawText(g, text, xInt, yInt, width); +// } +// g.dispose(); +// return image; +// } + + /** + * draw text + * @param g + * @param className + * @param x + * @param y + * @param width + */ + private static void drawText(Graphics2D g, String className, int x, int y, int width) { + //set the coordinates of the watermark + var showText = String.format("%s", className); +// g.fillRect(x, y - 30, width, 30); + g.setColor(Color.red); + //set font + g.setFont(new Font("Monospaced", Font.BOLD, 10)); + g.drawString(showText, x, y - 10); + } + + /** + * draw detection box + * @param image + * @param x + * @param y + * @param width + * @param height + */ + public static void drawImageRect(BufferedImage image, int x, int y, int width, int height) { + // convert image to Graphics2D + var g = (Graphics2D) image.getGraphics(); + try { + g.setColor(new Color(0, 255, 0)); + // declare brush properties + BasicStroke bStroke = new BasicStroke(2, BasicStroke.CAP_BUTT, BasicStroke.JOIN_MITER); + g.setStroke(bStroke); + g.drawRect(x, y, width, height); + } finally { + g.dispose(); + } + } + + /** + * draw detection box + * @param image + * @param x + * @param y + * @param width + * @param height + * @param c + */ + public static void drawImageRect(BufferedImage image, int x, int y, int width, int height, Color c) { + // convert image to Graphics2D + var g = (Graphics2D) image.getGraphics(); + try { + g.setColor(c); + // declare brush properties + BasicStroke bStroke = new BasicStroke(1, BasicStroke.CAP_BUTT, BasicStroke.JOIN_MITER); + g.setStroke(bStroke); + g.drawRect(x, y, width, height); + + } finally { + g.dispose(); + } + } + + /** + * resize image + * @param image + * @param width + * @param height + * @param ratio + * @return + */ + public static Image imageResize(Image image, int width, int height, float ratio) { + if((image.getHeight() > height) && (image.getWidth() > width)) { + try (var manager = NDManager.newBaseManager()) { + var ndArray = image.toNDArray(manager); + ndArray = NDImageUtils.resize(ndArray, (int)(ratio*width), (int)(ratio*height), Image.Interpolation.BICUBIC); + image = ImageFactory.getInstance().fromNDArray(ndArray.toType(DataType.UINT8, false)); + } + } + return image; + } + + /** + * resize image + * @param input + * @param maxSideLen + * @return + */ + public static Image imageResize(Image input, int maxSideLen) { + try(var manager = NDManager.newBaseManager()) { + var img = input.toNDArray(manager); + var h = input.getHeight(); + var w = input.getWidth(); + var resizeW = w; + var resizeH = h; + // limit the max side + var ratio = 1.0f; + if(Math.max(resizeH, resizeW) > maxSideLen) { + if(resizeH > resizeW) { + ratio = (float)maxSideLen / resizeH; + } else { + ratio = (float)maxSideLen / resizeW; + } + } + resizeH = (int) (resizeH * ratio); + resizeW = (int) (resizeW * ratio); + if(resizeH % 32 == 0) { +// resizeH = resizeH; + } else if(Math.floor((float) resizeH / 32) <= 1) { + resizeH = 32; + } else { + resizeH = (int) Math.floor((float) resizeH / 32) * 32; + } + if(resizeW % 32 == 0) { +// resizeW = resizeW; + } else if(Math.floor((float) resizeW / 32) <= 1) { + resizeW = 32; + } else { + resizeW = (int) Math.floor((float) resizeW / 32) * 32; + } + var resizeIDArray = NDImageUtils.resize(img, resizeW, resizeH, Image.Interpolation.BICUBIC); + input = ImageFactory.getInstance().fromNDArray(resizeIDArray.toType(DataType.UINT8, false)); + } + return input; + } + + /** + * get X + * @param img + * @param box + * @param x + * @return + */ + private static int getX(Image img, BoundingBox box, float x) { + var rect = box.getBounds(); + // pointLeftTop + var x1 = (int) (rect.getX() * img.getWidth()); + // width + var w = (int) (rect.getWidth() * img.getWidth()); + return (int) (x * w + x1); + } + + /** + * get Y + * @param img + * @param box + * @param y + * @return + */ + private static int getY(Image img, BoundingBox box, float y) { + var rect = box.getBounds(); + // pointLeftTop + var y1 = (int) (rect.getY() * img.getHeight()); + // height + var h = (int) (rect.getHeight() * img.getHeight()); + return (int) (y * h + y1); + } + + public static float[] whc2cwh(float[] src) { + float[] chw = new float[src.length]; + int j = 0; + for (int ch = 0; ch < 3; ++ch) { + for (int i = ch; i < src.length; i += 3) { + chw[j] = src[i]; + j++; + } + } + return chw; + } + + public static void xywh2xyxy(float[] bbox) { + float x = bbox[0]; + float y = bbox[1]; + float w = bbox[2]; + float h = bbox[3]; + bbox[0] = x - w * 0.5f; + bbox[1] = y - h * 0.5f; + bbox[2] = x + w * 0.5f; + bbox[3] = y + h * 0.5f; + } + + public static List nonMaxSuppression(List bboxes, float iouThreshold) { + // output boxes + List bestBboxes = CollectionUtil.newArrayList(); + // confidence + bboxes.sort(Comparator.comparing(a -> a[4])); + // standard nms + while (!bboxes.isEmpty()) { + float[] bestBbox = bboxes.remove(bboxes.size() - 1); + bestBboxes.add(bestBbox); + bboxes = bboxes.stream().filter(a -> computeIOU(a, bestBbox) < iouThreshold).collect(Collectors.toList()); + } + return bestBboxes; + } + + public static float computeIOU(float[] box1, float[] box2) { + float area1 = (box1[2] - box1[0]) * (box1[3] - box1[1]); + float area2 = (box2[2] - box2[0]) * (box2[3] - box2[1]); + + float left = Math.max(box1[0], box2[0]); + float top = Math.max(box1[1], box2[1]); + float right = Math.min(box1[2], box2[2]); + float bottom = Math.min(box1[3], box2[3]); + + float interArea = Math.max(right - left, 0) * Math.max(bottom - top, 0); + float unionArea = area1 + area2 - interArea; + return Math.max(interArea / unionArea, 1e-8f); + } + + public static MatOfPoint2f matOfPointToMatOfPoint2f(MatOfPoint src) { + MatOfPoint2f dst = new MatOfPoint2f(); + src.convertTo(dst, CvType.CV_32F); + return dst; + } + + public static MatOfPoint matOfPoint2fToMatOfPoint(MatOfPoint2f src) { + MatOfPoint dst = new MatOfPoint(); + src.convertTo(dst, CvType.CV_32S); + return dst; + } + + public static MatOfPoint matToMatOfPoint(Mat src) { + MatOfPoint dst = new MatOfPoint(); + src.convertTo(dst, CvType.CV_32S); + return dst; + } + +} + diff --git a/src/main/java/utils/common/PropertiesReader.java b/src/main/java/utils/common/PropertiesReader.java new file mode 100644 index 0000000..9ff1ff5 --- /dev/null +++ b/src/main/java/utils/common/PropertiesReader.java @@ -0,0 +1,33 @@ +package utils.common; + +import org.slf4j.Logger; +import org.slf4j.LoggerFactory; + +import java.io.IOException; +import java.io.InputStreamReader; +import java.util.Properties; + +/** + * @author sy + * @date 2024/7/1 19:23 + */ +public class PropertiesReader { + + private static final Logger LOGGER = LoggerFactory.getLogger(PropertiesReader.class); + + private static Properties properties = new Properties(); + + static { + try { + properties.load(new InputStreamReader(PropertiesReader.class.getClassLoader().getResourceAsStream("properties.properties"), "UTF-8")); + + } catch (IOException e) { + e.printStackTrace(); + } + } + + public static String get(String keyName) { + return properties.getProperty(keyName, ""); + } + +} diff --git a/src/main/java/utils/cv/NDArrayUtils.java b/src/main/java/utils/cv/NDArrayUtils.java new file mode 100644 index 0000000..b02166a --- /dev/null +++ b/src/main/java/utils/cv/NDArrayUtils.java @@ -0,0 +1,225 @@ +package utils.cv; + +import ai.djl.ndarray.NDArray; +import org.opencv.core.CvType; +import org.opencv.core.Mat; +import org.opencv.core.MatOfPoint; +import org.opencv.core.Point; + +import java.util.ArrayList; +import java.util.List; + +public class NDArrayUtils { + /** + * Mat To MatOfPoint + * @param mat + * @return + */ + public static MatOfPoint matToMatOfPoint(Mat mat) { + int rows = mat.rows(); + MatOfPoint matOfPoint = new MatOfPoint(); + + List list = new ArrayList<>(); + for (int i = 0; i < rows; i++) { + Point point = new Point((float) mat.get(i, 0)[0], (float) mat.get(i, 1)[0]); + list.add(point); + } + matOfPoint.fromList(list); + + return matOfPoint; + } + + /** + * float NDArray To float[][] Array + * @param ndArray + * @return + */ + public static float[][] floatNDArrayToArray(NDArray ndArray) { + int rows = (int) (ndArray.getShape().get(0)); + int cols = (int) (ndArray.getShape().get(1)); + float[][] arr = new float[rows][cols]; + + float[] arrs = ndArray.toFloatArray(); + for (int i = 0; i < rows; i++) { + for (int j = 0; j < cols; j++) { + arr[i][j] = arrs[i * cols + j]; + } + } + return arr; + } + + /** + * Mat To double[][] Array + * @param mat + * @return + */ + public static double[][] matToDoubleArray(Mat mat) { + int rows = mat.rows(); + int cols = mat.cols(); + + double[][] doubles = new double[rows][cols]; + + for (int i = 0; i < rows; i++) { + for (int j = 0; j < cols; j++) { + doubles[i][j] = mat.get(i, j)[0]; + } + } + + return doubles; + } + + /** + * Mat To float[][] Array + * @param mat + * @return + */ + public static float[][] matToFloatArray(Mat mat) { + int rows = mat.rows(); + int cols = mat.cols(); + + float[][] floats = new float[rows][cols]; + + for (int i = 0; i < rows; i++) { + for (int j = 0; j < cols; j++) { + floats[i][j] = (float) mat.get(i, j)[0]; + } + } + + return floats; + } + + /** + * Mat To byte[][] Array + * @param mat + * @return + */ + public static byte[][] matToUint8Array(Mat mat) { + int rows = mat.rows(); + int cols = mat.cols(); + + byte[][] bytes = new byte[rows][cols]; + + for (int i = 0; i < rows; i++) { + for (int j = 0; j < cols; j++) { + bytes[i][j] = (byte) mat.get(i, j)[0]; + } + } + + return bytes; + } + + /** + * float NDArray To float[][] Array + * @param ndArray + * @param cvType + * @return + */ + public static Mat floatNDArrayToMat(NDArray ndArray, int cvType) { + int rows = (int) (ndArray.getShape().get(0)); + int cols = (int) (ndArray.getShape().get(1)); + Mat mat = new Mat(rows, cols, cvType); + + float[] arrs = ndArray.toFloatArray(); + for (int i = 0; i < rows; i++) { + for (int j = 0; j < cols; j++) { + mat.put(i, j, arrs[i * cols + j]); + } + } + return mat; + } + + /** + * float NDArray To Mat + * @param ndArray + * @return + */ + public static Mat floatNDArrayToMat(NDArray ndArray) { + int rows = (int) (ndArray.getShape().get(0)); + int cols = (int) (ndArray.getShape().get(1)); + Mat mat = new Mat(rows, cols, CvType.CV_32F); + + float[] arrs = ndArray.toFloatArray(); + for (int i = 0; i < rows; i++) { + for (int j = 0; j < cols; j++) { + mat.put(i, j, arrs[i * cols + j]); + } + } + + return mat; + + } + + /** + * uint8 NDArray To Mat + * @param ndArray + * @return + */ + public static Mat uint8NDArrayToMat(NDArray ndArray) { + int rows = (int) (ndArray.getShape().get(0)); + int cols = (int) (ndArray.getShape().get(1)); + Mat mat = new Mat(rows, cols, CvType.CV_8U); + + byte[] arrs = ndArray.toByteArray(); + + for (int i = 0; i < rows; i++) { + for (int j = 0; j < cols; j++) { + mat.put(i, j, arrs[i * cols + j]); + } + } + return mat; + } + + /** + * float[][] Array To Mat + * @param arr + * @return + */ + public static Mat floatArrayToMat(float[][] arr) { + int rows = arr.length; + int cols = arr[0].length; + Mat mat = new Mat(rows, cols, CvType.CV_32F); + + for (int i = 0; i < rows; i++) { + for (int j = 0; j < cols; j++) { + mat.put(i, j, arr[i][j]); + } + } + + return mat; + } + + /** + * byte[][] Array To Mat + * @param arr + * @return + */ + public static Mat uint8ArrayToMat(byte[][] arr) { + int rows = arr.length; + int cols = arr[0].length; + Mat mat = new Mat(rows, cols, CvType.CV_8U); + + for (int i = 0; i < rows; i++) { + for (int j = 0; j < cols; j++) { + mat.put(i, j, arr[i][j]); + } + } + + return mat; + } + + /** + * List To Mat + * @param points + * @return + */ + public static Mat toMat(List points) { + Mat mat = new Mat(points.size(), 2, CvType.CV_32F); + for (int i = 0; i < points.size(); i++) { + 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