英特尔开发套件在JAVA环境实现ADAS道路识别演示

作者：英特尔创新大使 黄明明

简介

硬件环境

模型下载

搭建OpenVINO™ Java环境

编写测试代码

加载OpenVINO™Runtime

OpenVINO vino = OpenVINO.load();
 
//加载OpenCV Dll
 
vino.loadCvDll();
 
OvVersion version = vino.getVersion();
 
Console.println("---- OpenVINO INFO----");
 
Console.println("Description : %s", version.description);
 
Console.println("Build number: %s", version.buildNumber);

结果将输出当前系统OpenVINO™版本:

---- OpenVINO INFO----
 
Description : OpenVINO Runtime
 
Build number: 2023.2.0-13089-cfd42bd2cb0-HEAD

加载模型

String modelPath = "model/road-segmentation-adas-0001.xml";
 
Core core = new Core();
 
// -------- Step 2. Read a model --------
 
Console.println("[INFO] Loading model files: %s", modelPath);
 
Model model = core.readModel(modelPath);
 
CompiledModel compiledModel = core.compileModel(model, "AUTO");

加载图片并处理

Input inputLayerIr = compiledModel.input(0);
 
Tensor inputTensor = inferRequest.getInputTensor();
 
Mat rgbImage = new Mat();
 
Imgproc.cvtColor(mat, rgbImage, Imgproc.COLOR_BGR2RGB);
 
int imageH = mat.height();
 
int imageW = mat.width();
 
int channels = mat.channels();
 
Shape shape = inputLayerIr.getShape();
 
long N = shape.getDims().get(0);
 
long C = shape.getDims().get(1);
 
long H = shape.getDims().get(2);
 
long W = shape.getDims().get(3);
 
 
 
Mat resizedImage = new Mat();
 
Imgproc.resize(mat,resizedImage, new Size(W, H));
 
int[] data = matToIntArray(resizedImage)
 
NDArray array =  manager.create(data,new Shape(H,W,C));
 
NDArray inputImage = array.transpose(2,0,1).expandDims(0);
 
float[] floats = intToFloatArray(inputImage.toIntArray());

开始推理&处理结果

inputTensor.setData(floats);
 
inferRequest.infer();
 
Tensor outputTensor = inferRequest.getOutputTensor(0);
 
int outputLength = (int) outputTensor.getSize();
 
float[] outputData = outputTensor.getData(float[].class, outputLength);
 
NDArray ndArray = manager.create(outputData,new Shape(1,4,H,W));
 
ndArray = ndArray.argMax(1);
 
if (ndArray.getShape().get(0) == 1){
 
    ndArray = ndArray.squeeze(0);
 
}
 
ndArray = ndArray.toType(DataType.UINT8, true);
 
NDArray mask = manager.zeros(new Shape(ndArray.getShape().get(0),ndArray.getShape().get(1),3),DataType.UINT8);
 
Mat _mat = new Mat((int)ndArray.getShape().get(0),(int)ndArray.getShape().get(1), CvType.CV_8UC3);
 
byte[] b = mask.toByteArray();
 
_mat.put(0,0,b);
 
double[][] colors = new double[][]{{255, 44, 255}, {48, 255, 141}, {53, 255, 120}, {199, 216, 52}};
 
for (int i = 0 ; i < colors.length;i++) {
 
    NDArray labelIndexMap = ndArray.eq(i);
 
    labelIndexMap = labelIndexMap.toType(DataType.UINT8,true).mul(255);
 
    Mat mat1 = new Mat((int) labelIndexMap.getShape().get(0), (int) labelIndexMap.getShape().get(1),CvType.CV_8UC1);
 
    mat1.put(0,0,labelIndexMap.toByteArray());
 
    List<MatOfPoint> contours = new ArrayList<>();
 
    Mat hierarchies = new Mat();
 
    Imgproc.findContours(mat1,contours,hierarchies,Imgproc.RETR_EXTERNAL,Imgproc.CHAIN_APPROX_SIMPLE);
 
    Imgproc.drawContours(_mat,contours,-1,new Scalar(colors[i][0], colors[i][1], colors[i][2]),Imgproc.FILLED);
 
}
 
Mat resized_mask = new Mat();
 
Imgproc.resize(_mat,resized_mask, new Size(imageW, imageH));
 
Core.addWeighted(resized_mask,0.2F,mat,0.8F,0,resized_mask);

结果展示

结语