added blazeface test

src/main/java/ch/bildspur/vision/MediaPipeBlazeFaceNetwork.java

@@ -0,0 +1,90 @@
+package ch.bildspur.vision;
+
+import ch.bildspur.vision.network.ObjectDetectionNetwork;
+import ch.bildspur.vision.result.ObjectDetectionResult;
+import ch.bildspur.vision.result.ResultList;
+import ch.bildspur.vision.util.MathUtils;
+import org.bytedeco.javacpp.FloatPointer;
+import org.bytedeco.javacpp.IntPointer;
+import org.bytedeco.javacpp.indexer.FloatIndexer;
+import org.bytedeco.opencv.global.opencv_dnn;
+import org.bytedeco.opencv.opencv_core.*;
+import org.bytedeco.opencv.opencv_dnn.Net;
+import org.bytedeco.opencv.opencv_text.FloatVector;
+
+import java.nio.file.Path;
+import java.util.ArrayList;
+import java.util.List;
+
+import static org.bytedeco.opencv.global.opencv_core.CV_32F;
+import static org.bytedeco.opencv.global.opencv_dnn.*;
+
+/**
+ * Based on https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB/blob/master/caffe/ultra_face_opencvdnn_inference.py
+ * Adapted and improved a lot.
+ */
+public class MediaPipeBlazeFaceNetwork extends ObjectDetectionNetwork {
+    private Path modelPath;
+    protected Net net;
+
+    private int width;
+    private int height;
+
+    private Scalar imageMean = Scalar.all(127);
+    private float imageStd = 128.0f;
+
+    public MediaPipeBlazeFaceNetwork(Path modelPath, int width, int height) {
+        this.modelPath = modelPath;
+        this.width = width;
+        this.height = height;
+    }
+
+    @Override
+    public boolean setup() {
+        net = readNetFromONNX(modelPath.toAbsolutePath().toString());
+
+        if (DeepVision.ENABLE_CUDA_BACKEND) {
+            net.setPreferableBackend(opencv_dnn.DNN_BACKEND_CUDA);
+            net.setPreferableTarget(opencv_dnn.DNN_TARGET_CUDA);
+        }
+
+        if (net.empty()) {
+            System.out.println("Can't load network!");
+            return false;
+        }
+
+        return true;
+    }
+
+    @Override
+    public ResultList<ObjectDetectionResult> run(Mat frame) {
+        // convert image into batch of images
+        Mat inputBlob = blobFromImage(frame,
+                1 / imageStd,
+                new Size(width, height),
+                imageMean,
+                false, false, CV_32F);
+
+        // set input
+        net.setInput(inputBlob);
+
+        // create output layers
+        StringVector outNames = net.getUnconnectedOutLayersNames();
+        MatVector outs = new MatVector(outNames.size());
+
+        // run detection
+        net.forward(outs, outNames);
+
+        // extract boxes and scores
+        Mat boxesOut = outs.get(0);
+        Mat confidencesOut = outs.get(1);
+
+        // boxes
+        Mat boxes = boxesOut.reshape(0, boxesOut.size(1));
+
+        // class confidences (BACKGROUND, face)
+        Mat confidences = confidencesOut.reshape(0, confidencesOut.size(1));
+
+        return new ResultList<>();
+    }
+}

src/test/java/ch/bildspur/vision/test/BlazeFaceTest.java

@@ -0,0 +1,70 @@
+package ch.bildspur.vision.test;
+
+
+import ch.bildspur.vision.DeepVisionPreview;
+import ch.bildspur.vision.MediaPipeBlazeFaceNetwork;
+import ch.bildspur.vision.TextBoxesNetwork;
+import ch.bildspur.vision.result.ObjectDetectionResult;
+import processing.core.PApplet;
+import processing.core.PImage;
+
+import java.nio.file.Paths;
+import java.util.List;
+
+public class BlazeFaceTest extends PApplet {
+
+    public static void main(String... args) {
+        BlazeFaceTest sketch = new BlazeFaceTest();
+        sketch.runSketch();
+    }
+
+    public void settings() {
+        size(640, 480, FX2D);
+    }
+
+    PImage testImage;
+
+    DeepVisionPreview vision = new DeepVisionPreview(this);
+    MediaPipeBlazeFaceNetwork network;
+    List<ObjectDetectionResult> detections;
+
+    public void setup() {
+        colorMode(HSB, 360, 100, 100);
+
+        testImage = loadImage(sketchPath("data/faces.png"));
+
+        println("creating network...");
+        network = new MediaPipeBlazeFaceNetwork(Paths.get("networks/face_detection_back_256x256_barracuda.onnx"), 256, 256);
+
+        println("loading model...");
+        network.setup();
+
+        //network.setConfidenceThreshold(0.2f);
+
+        println("inferencing...");
+        detections = network.run(testImage);
+        println("done!");
+
+        for (ObjectDetectionResult detection : detections) {
+            System.out.println(detection.getClassName() + "\t[" + detection.getConfidence() + "]");
+        }
+
+        println("found " + detections.size() + " texts!");
+    }
+
+    public void draw() {
+        background(55);
+
+        image(testImage, 0, 0);
+
+        noFill();
+        strokeWeight(2f);
+
+        stroke(200, 80, 100);
+        for (ObjectDetectionResult detection : detections) {
+            rect(detection.getX(), detection.getY(), detection.getWidth(), detection.getHeight());
+        }
+
+        surface.setTitle("BlazeFace Test - FPS: " + Math.round(frameRate));
+    }
+}