Implements text_detection_ppocr java demo(opencv#251)

.gitignore

@@ -3,7 +3,12 @@
 **/__pycache__/**
 
 .vscode
+.idea
 
 build/
 **/build
 **/build/**
+
+target/
+**/target
+**/target/**

models/pom.xml

@@ -0,0 +1,76 @@
+<?xml version="1.0" encoding="UTF-8"?>
+
+<project xmlns="http://maven.apache.org/POM/4.0.0"
+         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 https://maven.apache.org/xsd/maven-4.0.0.xsd">
+    <modelVersion>4.0.0</modelVersion>
+
+    <groupId>opencv_zoo</groupId>
+    <artifactId>demo</artifactId>
+    <version>1.0.0-SNAPSHOT</version>
+    <name>opencv zoo demo application</name>
+    <packaging>pom</packaging>
+
+    <build>
+        <sourceDirectory>${project.basedir}</sourceDirectory>
+    </build>
+
+    <modules>
+        <module>text_detection_ppocr</module>
+    </modules>
+
+    <dependencies>
+        <dependency>
+            <groupId>org.bytedeco</groupId>
+            <artifactId>javacv-platform</artifactId>
+            <version>1.5.10</version>
+            <exclusions>
+                <exclusion>
+                    <groupId>org.bytedeco</groupId>
+                    <artifactId>flycapture-platform</artifactId>
+                </exclusion>
+                <exclusion>
+                    <groupId>org.bytedeco</groupId>
+                    <artifactId>libdc1394-platform</artifactId>
+                </exclusion>
+                <exclusion>
+                    <groupId>org.bytedeco</groupId>
+                    <artifactId>libfreenect-platform</artifactId>
+                </exclusion>
+                <exclusion>
+                    <groupId>org.bytedeco</groupId>
+                    <artifactId>libfreenect2-platform</artifactId>
+                </exclusion>
+                <exclusion>
+                    <groupId>org.bytedeco</groupId>
+                    <artifactId>librealsense-platform</artifactId>
+                </exclusion>
+                <exclusion>
+                    <groupId>org.bytedeco</groupId>
+                    <artifactId>librealsense2-platform</artifactId>
+                </exclusion>
+                <exclusion>
+                    <groupId>org.bytedeco</groupId>
+                    <artifactId>videoinput-platform</artifactId>
+                </exclusion>
+                <exclusion>
+                    <groupId>org.bytedeco</groupId>
+                    <artifactId>artoolkitplus-platform</artifactId>
+                </exclusion>
+                <exclusion>
+                    <groupId>org.bytedeco</groupId>
+                    <artifactId>leptonica-platform</artifactId>
+                </exclusion>
+                <exclusion>
+                    <groupId>org.bytedeco</groupId>
+                    <artifactId>tesseract-platform</artifactId>
+                </exclusion>
+            </exclusions>
+        </dependency>
+        <dependency>
+            <groupId>com.beust</groupId>
+            <artifactId>jcommander</artifactId>
+            <version>1.82</version>
+        </dependency>
+    </dependencies>
+</project>

models/text_detection_ppocr/demo.java

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+import com.beust.jcommander.JCommander;
+import com.beust.jcommander.Parameter;
+import com.beust.jcommander.UnixStyleUsageFormatter;
+import org.bytedeco.javacpp.FloatPointer;
+import org.bytedeco.javacv.CanvasFrame;
+import org.bytedeco.javacv.OpenCVFrameConverter;
+import org.bytedeco.opencv.global.opencv_dnn;
+import org.bytedeco.opencv.opencv_core.*;
+import org.bytedeco.opencv.opencv_dnn.TextDetectionModel_DB;
+import org.bytedeco.opencv.opencv_videoio.VideoCapture;
+
+import java.util.AbstractMap;
+import java.util.Map;
+
+import static org.bytedeco.opencv.global.opencv_imgcodecs.imwrite;
+import static org.bytedeco.opencv.global.opencv_imgproc.*;
+
+public class demo {
+
+    // Valid combinations of backends and targets
+    static int[][] backendTargetPairs = {
+            {opencv_dnn.DNN_BACKEND_OPENCV, opencv_dnn.DNN_TARGET_CPU},
+            {opencv_dnn.DNN_BACKEND_CUDA, opencv_dnn.DNN_TARGET_CUDA},
+            {opencv_dnn.DNN_BACKEND_CUDA, opencv_dnn.DNN_TARGET_CUDA_FP16},
+            {opencv_dnn.DNN_BACKEND_TIMVX, opencv_dnn.DNN_TARGET_NPU},
+            {opencv_dnn.DNN_BACKEND_CANN, opencv_dnn.DNN_TARGET_NPU}
+    };
+
+    static class Args {
+        @Parameter(names = {"--help", "-h"}, order = 0, help = true,
+                description = "Print help message.")
+        boolean help;
+        @Parameter(names = {"--model", "-m"}, order = 1,
+                description = "Set model type.")
+        String model = "text_detection_en_ppocrv3_2023may.onnx";
+        @Parameter(names = {"--input", "-i"}, order = 2,
+                description = "Path to input image or video file. Skip this argument to capture frames from a camera.")
+        String input;
+        @Parameter(names = "--width", order = 3,
+                description = "Resize input image to certain width, It should be multiple by 32.")
+        int width = 736;
+        @Parameter(names = "--height", order = 4,
+                description = "Resize input image to certain height, It should be multiple by 32.")
+        int height = 736;
+        @Parameter(names = "--binary_threshold", order = 5,
+                description = "Threshold of the binary map.")
+        float binaryThreshold = 0.3f;
+        @Parameter(names = "--polygon_threshold", order = 6,
+                description = "Threshold of polygons.")
+        float polygonThreshold = 0.5f;
+        @Parameter(names = "--max_candidates", order = 7,
+                description = "Set maximum number of polygon candidates.")
+        int maxCandidates = 200;
+        @Parameter(names = "--unclip_ratio", order = 8,
+                description = "The unclip ratio of the detected text region, which determines the output size.")
+        double unclipRatio = 2.0;
+        @Parameter(names = {"--save", "-s"}, order = 9, arity = 1,
+                description = "Specify to save file with results (i.e. bounding box, confidence level). Invalid in case of camera input.")
+        boolean save = true;
+        @Parameter(names = {"--viz", "-v"}, order = 10, arity = 1,
+                description = "Specify to open a new window to show results. Invalid in case of camera input.")
+        boolean viz = true;
+        @Parameter(names = {"--backend", "-bt"}, order = 11,
+                description = "Choose one of computation backends:" +
+                        " 0: OpenCV implementation + CPU," +
+                        " 1: CUDA + GPU (CUDA), " +
+                        " 2: CUDA + GPU (CUDA FP16)," +
+                        " 3: TIM-VX + NPU," +
+                        " 4: CANN + NPU")
+        int backend = 0;
+    }
+
+    static class PPOCRDet {
+        private final TextDetectionModel_DB model;
+        private final Size inputSize;
+
+        public PPOCRDet(String modelPath, Size inputSize,
+                        float binaryThreshold, float polygonThreshold, int maxCandidates, double unclipRatio,
+                        int backendId, int targetId) {
+            this.inputSize = inputSize;
+
+            model = new TextDetectionModel_DB(modelPath);
+            model.setPreferableBackend(backendId);
+            model.setPreferableTarget(targetId);
+
+            model.setBinaryThreshold(binaryThreshold);
+            model.setPolygonThreshold(polygonThreshold);
+            model.setUnclipRatio(unclipRatio);
+            model.setMaxCandidates(maxCandidates);
+
+            model.setInputParams(1.0 / 255.0, inputSize,
+                    new Scalar(122.67891434, 116.66876762, 104.00698793, 0), true, false);
+        }
+
+        public Map.Entry<PointVectorVector, FloatPointer> infer(Mat image) {
+            assert image.rows() == inputSize.height() : "height of input image != net input size";
+            assert image.cols() == inputSize.width() : "width of input image != net input size";
+            final PointVectorVector pt = new PointVectorVector();
+            final FloatPointer confidences = new FloatPointer();
+            model.detect(image, pt, confidences);
+            return new AbstractMap.SimpleEntry<>(pt, confidences);
+        }
+    }
+
+    static Mat visualize(Mat image, Map.Entry<PointVectorVector, FloatPointer> results, double fps, Scalar boxColor,
+                         Scalar textColor, boolean isClosed, int thickness) {
+        final Mat output = new Mat();
+        image.copyTo(output);
+        if (fps > 0) {
+            putText(output, String.format("FPS: %.2f", fps), new Point(0, 15), FONT_HERSHEY_SIMPLEX, 0.5, textColor);
+        }
+        final PointVectorVector pvv = results.getKey();
+        final MatVector matVector = new MatVector();
+        for (int i = 0; i < pvv.size(); i++) {
+            final PointVector pv = pvv.get(i);
+            final Point pts = new Point(pv.size());
+            for (int j = 0; j < pv.size(); j++) {
+                pts.position(j).x(pv.get(j).x()).y(pv.get(j).y());
+            }
+            matVector.push_back(new Mat(pts.position(0)));
+        }
+        polylines(output, matVector, isClosed, boxColor, thickness, LINE_AA, 0);
+        matVector.close();
+        return output;
+    }
+
+    /**
+     * Execute:
+     * mvn compile exec:java -Dexec.mainClass=demo -q -Dexec.args="--help"
+     */
+    public static void main(String[] argv) {
+        final Args args = new Args();
+        final JCommander jc = JCommander.newBuilder()
+                .addObject(args)
+                .build();
+        jc.setUsageFormatter(new UnixStyleUsageFormatter(jc));
+        jc.parse(argv);
+        if (args.help) {
+            jc.usage();
+            return;
+        }
+        final int[] backendTargetPair = backendTargetPairs[args.backend];
+        assert args.model != null && !args.model.isEmpty() : "Model name is empty";
+        final Size inpSize = new Size(args.width, args.height);
+
+        final PPOCRDet model = new PPOCRDet(args.model, inpSize,
+                args.binaryThreshold, args.polygonThreshold, args.maxCandidates, args.unclipRatio,
+                backendTargetPair[0], backendTargetPair[1]);
+
+        final VideoCapture cap = new VideoCapture();
+        if (args.input != null) {
+            cap.open(args.input);
+        } else {
+            cap.open(0);
+        }
+        assert cap.isOpened() : "Cannot open video or file";
+        Mat originalImage = new Mat();
+
+        final OpenCVFrameConverter.ToMat converter = new OpenCVFrameConverter.ToMat();
+        CanvasFrame mainframe = null;
+        if (args.input == null || args.viz) {
+            mainframe = new CanvasFrame(args.model + " Demo", CanvasFrame.getDefaultGamma() / 2.2);
+            mainframe.setDefaultCloseOperation(javax.swing.JFrame.EXIT_ON_CLOSE);
+            mainframe.setVisible(true);
+        }
+
+        final Scalar boxColor = new Scalar(0, 255, 0, 0);
+        final Scalar textColor = new Scalar(0, 0, 255, 0);
+        final TickMeter tm = new TickMeter();
+        while (cap.read(originalImage)) {
+            cap.read(originalImage);
+
+            final int originalW = originalImage.cols();
+            final int originalH = originalImage.rows();
+            final double scaleHeight = originalH / (double) inpSize.height();
+            final double scaleWidth = originalW / (double) inpSize.width();
+            final Mat image = new Mat();
+            resize(originalImage, image, inpSize);
+
+            // inference
+            tm.start();
+            Map.Entry<PointVectorVector, FloatPointer> results = model.infer(image);
+            tm.stop();
+            // Scale the results bounding box
+            final PointVectorVector pvv = results.getKey();
+            for (int i = 0; i < pvv.size(); i++) {
+                final PointVector pts = pvv.get(i);
+                for (int j = 0; j < pts.size(); j++) {
+                    pts.get(j).x((int) (pts.get(j).x() * scaleWidth));
+                    pts.get(j).y((int) (pts.get(j).y() * scaleHeight));
+                }
+            }
+
+            originalImage = visualize(originalImage, results, tm.getFPS(), boxColor, textColor, true, 2);
+            tm.reset();
+            if (args.input != null) {
+                if (args.save) {
+                    System.out.println("Result image saved to result.jpg");
+                    imwrite("result.jpg", originalImage);
+                }
+                if (args.viz) {
+                    mainframe.showImage(converter.convert(originalImage));
+                }
+            } else {
+                mainframe.showImage(converter.convert(originalImage));
+            }
+
+            // clear
+            pvv.close();
+            image.close();
+        }
+        tm.close();
+    }
+
+}

models/text_detection_ppocr/pom.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+
+<project xmlns="http://maven.apache.org/POM/4.0.0"
+         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 https://maven.apache.org/xsd/maven-4.0.0.xsd">
+    <modelVersion>4.0.0</modelVersion>
+
+    <parent>
+        <groupId>opencv_zoo</groupId>
+        <artifactId>demo</artifactId>
+        <version>1.0.0-SNAPSHOT</version>
+    </parent>
+
+    <artifactId>text_detection_ppocr</artifactId>
+
+    <build>
+        <sourceDirectory>${project.basedir}</sourceDirectory>
+    </build>
+
+</project>