only source files

Author: leoheim

modules/java_api/bin/main/org/intel/openvino/Any.class

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+plugins {
+    id 'java'
+    id 'application'
+}
+
+import org.gradle.internal.os.OperatingSystem
+
+def opencv_path = System.getenv('OpenCV_DIR')
+def opencv_build_path = new File(opencv_path + '/bin')
+def opencv_install_path = new File(opencv_path + '/share/java')
+
+if (OperatingSystem.current().isLinux()) {
+    opencv_build_path = new File(opencv_path + '/bin')
+    opencv_install_path = new File(opencv_path + '/share/java/opencv4')
+} else if (OperatingSystem.current().isMacOsX()) {
+    opencv_build_path = new File(opencv_path + '/share/java/opencv4')
+    opencv_install_path = new File(opencv_path + '/share/OpenCV/java')
+} else if (OperatingSystem.current().isWindows()) {
+    opencv_build_path = new File(opencv_path + '/java')
+    opencv_install_path = new File(opencv_path + '/java')
+}
+
+if (opencv_build_path.exists() ) {
+  opencv_path = opencv_build_path
+} else if (opencv_install_path.exists() ) {
+  opencv_path = opencv_install_path 
+} else {
+    throw new GradleException('Incorrect OpenCV_DIR path!')
+}
+
+sourceSets {
+    main {
+        java {
+            srcDirs = ["src/main/java", "../common"]
+        }
+    }
+}
+mainClassName = 'Main'
+
+dependencies {  
+    implementation rootProject
+    implementation fileTree(dir: opencv_path, include: '*.jar')
+}

modules/java_api/bin/main/org/intel/openvino/CompiledModel.class

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+rootProject.name = 'face_detection_java_sample_async'

modules/java_api/bin/main/org/intel/openvino/Core.class

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+import org.intel.openvino.*;
+import org.opencv.core.*;
+import org.opencv.highgui.HighGui;
+import org.opencv.imgcodecs.*;
+import org.opencv.imgproc.Imgproc;
+import org.opencv.videoio.*;
+
+import java.util.ArrayList;
+import java.util.LinkedList;
+import java.util.Map;
+import java.util.Queue;
+import java.util.Vector;
+import java.util.concurrent.BlockingQueue;
+import java.util.concurrent.LinkedBlockingQueue;
+import java.util.concurrent.TimeUnit;
+
+/*
+This is async face detection Java sample (for OpenVINO Java API 2.0).
+Upon the start-up the sample application reads command line parameters and loads a network
+and an image to the Inference Engine device. When inference is done, the application will show
+the image with detected objects enclosed in rectangles in new window. It also outputs the
+confidence value and the coordinates of the rectangle to the standard output stream.
+To get the list of command line parameters run the application with `--help` parameter.
+*/
+public class Main {
+    static final float CONFIDENCE_THRESHOLD = 0.7f;
+    static int waitingTime = 1;
+
+    static BlockingQueue<Mat> processedFramesQueue = new LinkedBlockingQueue<Mat>();
+    static BlockingQueue<float[]> detectionOutput = new LinkedBlockingQueue<float[]>();
+
+    static String outputName;
+    static Queue<Integer> startedRequestsIds = new LinkedList<Integer>();
+    static Vector<InferRequest> inferRequests = new Vector<InferRequest>();
+    static Vector<Boolean> asyncInferIsFree;
+
+    static int framesCounter = 0;
+    static int resultCounter = 0;
+
+    public static Tensor imageToTensor(Mat image) {
+        int[] dimsArr = {1, image.rows(), image.cols(), 3};
+        return new Tensor(ElementType.u8, dimsArr, image.dataAddr());
+    }
+
+    static void processInferRequests() {
+        while (!startedRequestsIds.isEmpty()) {
+            int requestId = startedRequestsIds.peek();
+            InferRequest inferRequest = inferRequests.get(requestId);
+
+            if (!inferRequest.wait_for(0)) return;
+
+            Tensor outputTensor = inferRequest.get_output_tensor();
+            float[] res = outputTensor.data();
+
+            detectionOutput.add(res);
+
+            resultCounter++;
+            asyncInferIsFree.setElementAt(true, requestId);
+            startedRequestsIds.remove();
+        }
+    }
+
+    public static void main(String[] args) {
+        try {
+            System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
+        } catch (UnsatisfiedLinkError e) {
+            System.err.println("Failed to load OpenCV library\n" + e);
+            System.exit(1);
+        }
+
+        ArgumentParser parser = new ArgumentParser("This is async face detection sample");
+        parser.addArgument("-i", "path to video");
+        parser.addArgument("-m", "path to model .xml");
+        parser.addArgument("-d", "device");
+        parser.addArgument("-nireq", "number of infer requests");
+        parser.parseArgs(args);
+
+        String imgsPath = parser.get("-i", null);
+        String xmlPath = parser.get("-m", null);
+        String device = parser.get("-d", "CPU");
+        int inferRequestsSize = parser.getInteger("-nireq", 2);
+
+        if (imgsPath == null) {
+            System.out.println("Error: Missed argument: -i");
+            return;
+        }
+        if (xmlPath == null) {
+            System.out.println("Error: Missed argument: -m");
+            return;
+        }
+
+        int warmupNum = inferRequestsSize * 2;
+
+        BlockingQueue<Mat> framesQueue = new LinkedBlockingQueue<Mat>();
+
+        Runnable capture =
+                new Runnable() {
+                    @Override
+                    public void run() {
+                        Mat frame = new Mat();
+
+                        VideoCapture cam = new VideoCapture();
+                        try {
+                            int idx = Integer.valueOf(imgsPath);
+                            cam.open(idx);
+                        } catch (NumberFormatException exception) {
+                            cam.open(imgsPath);
+                        }
+
+                        while (cam.read(frame) && !Thread.interrupted()) {
+                            framesCounter++;
+                            framesQueue.add(frame.clone());
+                        }
+                        if (framesCounter == 0) {
+                            System.err.println("ERROR: Can't get any video frame!");
+                            System.exit(1);
+                        }
+                    }
+                };
+        Thread captureThread = new Thread(capture);
+
+        Runnable infer =
+                new Runnable() {
+                    @Override
+                    public void run() {
+                        try {
+                            Core core = new Core();
+                            Model net = core.read_model(xmlPath);
+
+                            PrePostProcessor p = new PrePostProcessor(net);
+                            p.input()
+                                    .tensor()
+                                    .set_element_type(ElementType.u8)
+                                    .set_layout(new Layout("NHWC"));
+                            p.input().preprocess().resize(ResizeAlgorithm.RESIZE_LINEAR);
+                            p.input().model().set_layout(new Layout("NCHW"));
+                            p.build();
+
+                            CompiledModel compiledModel = core.compile_model(net, device);
+
+                            asyncInferIsFree = new Vector<Boolean>(inferRequestsSize);
+
+                            for (int i = 0; i < inferRequestsSize; i++) {
+                                inferRequests.add(compiledModel.create_infer_request());
+                                asyncInferIsFree.add(true);
+                            }
+
+                            boolean isRunning = true;
+
+                            while (captureThread.isAlive() || !framesQueue.isEmpty()) {
+                                if (Thread.interrupted()) break;
+
+                                processInferRequests();
+                                for (int i = 0; i < inferRequestsSize; i++) {
+                                    if (!asyncInferIsFree.get(i)) continue;
+
+                                    Mat frame = framesQueue.poll(0, TimeUnit.SECONDS);
+
+                                    if (frame == null) break;
+
+                                    InferRequest request = inferRequests.get(i);
+
+                                    asyncInferIsFree.setElementAt(false, i);
+
+                                    processedFramesQueue.add(frame);
+
+                                    Tensor imgTensor = imageToTensor(frame);
+                                    request.set_input_tensor(imgTensor);
+
+                                    startedRequestsIds.add(i);
+                                    request.start_async();
+                                }
+                            }
+                            processInferRequests();
+                        } catch (InterruptedException e) {
+                            e.printStackTrace();
+
+                            for (Thread t : Thread.getAllStackTraces().keySet())
+                                if (t.getState() == Thread.State.RUNNABLE) t.interrupt();
+                        }
+                    }
+                };
+        Thread inferThread = new Thread(infer);
+
+        captureThread.start();
+        inferThread.start();
+
+        TickMeter tm = new TickMeter();
+        Scalar color = new Scalar(0, 255, 0);
+        try {
+            while (inferThread.isAlive() || !detectionOutput.isEmpty()) {
+
+                float[] detection = detectionOutput.poll(waitingTime, TimeUnit.SECONDS);
+                if (detection == null) continue;
+
+                Mat img = processedFramesQueue.poll(waitingTime, TimeUnit.SECONDS);
+                int maxProposalCount = detection.length / 7;
+
+                for (int curProposal = 0; curProposal < maxProposalCount; curProposal++) {
+                    int imageId = (int) detection[curProposal * 7];
+                    if (imageId < 0) break;
+
+                    float confidence = detection[curProposal * 7 + 2];
+
+                    // Drawing only objects with >70% probability
+                    if (confidence < CONFIDENCE_THRESHOLD) continue;
+
+                    int xmin = (int) (detection[curProposal * 7 + 3] * img.cols());
+                    int ymin = (int) (detection[curProposal * 7 + 4] * img.rows());
+                    int xmax = (int) (detection[curProposal * 7 + 5] * img.cols());
+                    int ymax = (int) (detection[curProposal * 7 + 6] * img.rows());
+
+                    // Draw rectangle around detected object.
+                    Point lt = new Point(xmin, ymin);
+                    Point br = new Point(xmax, ymax);
+                    Imgproc.rectangle(img, lt, br, color, 2);
+                }
+
+                if (resultCounter == warmupNum) {
+                    tm.start();
+                } else if (resultCounter > warmupNum) {
+                    tm.stop();
+                    double worksFps = ((double) (resultCounter - warmupNum)) / tm.getTimeSec();
+                    double readFps = ((double) (framesCounter - warmupNum)) / tm.getTimeSec();
+                    tm.start();
+
+                    String label = "Reading fps: " + String.format("%.3f", readFps);
+                    String label1 = "Inference fps: " + String.format("%.3f", worksFps);
+
+                    Imgproc.putText(img, label, new Point(10, 50), 0, 0.7, color, 1);
+                    Imgproc.putText(img, label1, new Point(10, 80), 0, 0.7, color, 1);
+                }
+                HighGui.imshow("Detection", img);
+
+                if (HighGui.waitKey(1) != -1) {
+                    inferThread.interrupt();
+                    captureThread.interrupt();
+                    break;
+                }
+            }
+
+            HighGui.waitKey(1);
+            HighGui.destroyAllWindows();
+
+            captureThread.join();
+            inferThread.join();
+        } catch (InterruptedException e) {
+            e.printStackTrace();
+            for (Thread t : Thread.getAllStackTraces().keySet())
+                if (t.getState() == Thread.State.RUNNABLE) t.interrupt();
+        }
+    }
+}