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>