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>