Implements face_detection_yunet java demo(opencv#251)

models/face_detection_yunet/demo.java

@@ -0,0 +1,167 @@
+import com.beust.jcommander.JCommander;
+import com.beust.jcommander.Parameter;
+import com.beust.jcommander.UnixStyleUsageFormatter;
+import org.bytedeco.opencv.global.opencv_dnn;
+import org.bytedeco.opencv.opencv_core.*;
+import org.bytedeco.opencv.opencv_objdetect.FaceDetectorYN;
+import org.bytedeco.opencv.opencv_videoio.VideoCapture;
+
+import static org.bytedeco.opencv.global.opencv_highgui.*;
+import static org.bytedeco.opencv.global.opencv_imgcodecs.imread;
+import static org.bytedeco.opencv.global.opencv_imgcodecs.imwrite;
+import static org.bytedeco.opencv.global.opencv_imgproc.FONT_HERSHEY_SIMPLEX;
+import static org.bytedeco.opencv.global.opencv_imgproc.putText;
+import static org.bytedeco.opencv.global.opencv_videoio.CAP_PROP_FRAME_HEIGHT;
+import static org.bytedeco.opencv.global.opencv_videoio.CAP_PROP_FRAME_WIDTH;
+
+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 = {"--input", "-i"}, order = 1,
+                description = "Set input to a certain image, omit if using camera.")
+        String input;
+        @Parameter(names = {"--model", "-m"}, order = 2,
+                description = "Set model type.")
+        String model = "face_detection_yunet_2023mar.onnx";
+        @Parameter(names = {"--backend_target", "-bt"}, order = 3,
+                description = "Choose one of the backend-target pair to run this demo:" +
+                        " 0: OpenCV implementation + CPU," +
+                        " 1: CUDA + GPU (CUDA), " +
+                        " 2: CUDA + GPU (CUDA FP16)," +
+                        " 3: TIM-VX + NPU," +
+                        " 4: CANN + NPU")
+        int backendTarget = 0;
+        @Parameter(names = {"--conf_threshold"}, order = 5,
+                description = "Set the minimum needed confidence for the model to identify a face. Filter out faces of conf < conf_threshold")
+        float confThreshold = 0.9f;
+        @Parameter(names = {"--nms_threshold"}, order = 5,
+                description = "Set the threshold to suppress overlapped boxes. Suppress boxes if IoU(box1, box2) >= nms_threshold, the one of higher score is kept.")
+        float nmsThreshold = 0.3f;
+        @Parameter(names = {"--top_k"}, order = 5,
+                description = "Keep top_k bounding boxes before NMS. Set a lower value may help speed up postprocessing.")
+        int topK = 5000;
+        @Parameter(names = {"--save", "-s"}, order = 4,
+                description = "Specify to save file with results (i.e. bounding box, confidence level). Invalid in case of camera input.")
+        boolean save;
+        @Parameter(names = {"--vis", "-v"}, order = 5, arity = 1,
+                description = "Specify to open a new window to show results. Invalid in case of camera input.")
+        boolean vis = true;
+    }
+
+
+    static class YuNet {
+        private final FaceDetectorYN model;
+
+        YuNet(String modelPath, Size inputSize, float confThreshold, float nmsThreshold, int topK,
+              int backendId, int targetId) {
+            model = FaceDetectorYN.create(modelPath, "", inputSize, confThreshold, nmsThreshold, topK,
+                    backendId, targetId);
+        }
+
+        void setInputSize(Size inputSize) {
+            model.setInputSize(inputSize);
+        }
+
+        Mat infer(Mat image) {
+            final Mat res = new Mat();
+            model.detect(image, res);
+            return res;
+        }
+    }
+
+    static Mat visualize(Mat image, Mat faces, Scalar textColor, double fps) {
+        final Mat output = image.clone();
+        final int h = output.rows();
+        final int w = output.cols();
+        if (fps >= 0) {
+            putText(output, String.format("FPS: %.2f", fps), new Point(0, 15), FONT_HERSHEY_SIMPLEX, 0.5, textColor);
+        }
+
+        // TODO
+        return output;
+    }
+
+    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 backendId = backendTargetPairs[args.backendTarget][0];
+        final int targetId = backendTargetPairs[args.backendTarget][1];
+        final YuNet model = new YuNet(args.model, new Size(320, 320), args.confThreshold, args.nmsThreshold,
+                args.topK, backendId, targetId);
+
+        if (args.input != null) {
+            final Mat image = imread(args.input);
+
+            // Inference
+            model.setInputSize(image.size());
+            final Mat faces = model.infer(image);
+
+            // Print faces
+            System.out.printf("%d faces detected:\n", faces.rows());
+            for (int i = 0; i < faces.rows(); i++) {
+                // TODO
+            }
+
+            // Draw reults on the input image
+            if (args.save || args.vis) {
+                final Mat resImage = visualize(image, faces, new Scalar(0, 0, 255, 0), -1);
+                if (args.save) {
+                    System.out.println("Results are saved to result.jpg");
+                    imwrite("result.jpg", resImage);
+                }
+                if (args.vis) {
+                    namedWindow(args.input, WINDOW_AUTOSIZE);
+                    imshow(args.input, resImage);
+                    waitKey(0);
+                }
+            }
+
+        } else { // // Call default camera
+            final int deviceId = 0;
+            final VideoCapture cap = new VideoCapture(deviceId);
+            final int w = (int) cap.get(CAP_PROP_FRAME_WIDTH);
+            final int h = (int) cap.get(CAP_PROP_FRAME_HEIGHT);
+            model.setInputSize(new Size(w, h));
+
+            final TickMeter tm = new TickMeter();
+            final Mat frame = new Mat();
+            while (waitKey(1) < 0) {
+                boolean hasFrame = cap.read(frame);
+                if (!hasFrame) {
+                    System.out.println("No frames grabbed! Exiting ...");
+                    break;
+                }
+                // Inference
+                tm.start();
+                final Mat faces = model.infer(frame);
+                tm.stop();
+                final Mat resImage = visualize(frame, faces, new Scalar(0, 0, 255, 0), tm.getFPS());
+                imshow("YuNet Demo", resImage);
+
+                tm.reset();
+            }
+        }
+    }
+
+
+}

models/face_detection_yunet/pom.xml

@@ -0,0 +1,31 @@
+<?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>face_detection_yunet</artifactId>
+
+    <build>
+        <sourceDirectory>${project.basedir}</sourceDirectory>
+        <plugins>
+            <plugin>
+                <groupId>org.codehaus.mojo</groupId>
+                <artifactId>exec-maven-plugin</artifactId>
+                <version>3.3.0</version>
+                <configuration>
+                    <executable>java</executable>
+                    <mainClass>demo</mainClass>
+                </configuration>
+            </plugin>
+        </plugins>
+    </build>
+
+</project>

models/pom.xml

@@ -27,6 +27,7 @@
     </build>
 
     <modules>
+        <module>face_detection_yunet</module>
         <module>object_tracking_vittrack</module>
         <module>text_detection_ppocr</module>
     </modules>