Пример #1
0
TEST_P(Test_TensorFlow_nets, MobileNet_SSD)
{
    std::string netPath = findDataFile("dnn/ssd_mobilenet_v1_coco.pb", false);
    std::string netConfig = findDataFile("dnn/ssd_mobilenet_v1_coco.pbtxt", false);
    std::string imgPath = findDataFile("dnn/street.png", false);

    Mat inp;
    resize(imread(imgPath), inp, Size(300, 300));
    inp = blobFromImage(inp, 1.0f / 127.5, Size(), Scalar(127.5, 127.5, 127.5), true);

    std::vector<String> outNames(3);
    outNames[0] = "concat";
    outNames[1] = "concat_1";
    outNames[2] = "detection_out";

    std::vector<Mat> target(outNames.size());
    for (int i = 0; i < outNames.size(); ++i)
    {
        std::string path = findDataFile("dnn/tensorflow/ssd_mobilenet_v1_coco." + outNames[i] + ".npy", false);
        target[i] = blobFromNPY(path);
    }

    Net net = readNetFromTensorflow(netPath, netConfig);

    net.setPreferableTarget(GetParam());

    net.setInput(inp);

    std::vector<Mat> output;
    net.forward(output, outNames);

    normAssert(target[0].reshape(1, 1), output[0].reshape(1, 1), "", 1e-5, 1.5e-4);
    normAssert(target[1].reshape(1, 1), output[1].reshape(1, 1), "", 1e-5, 3e-4);
    normAssert(target[2].reshape(1, 1), output[2].reshape(1, 1), "", 4e-5, 1e-2);
}
Пример #2
0
// inp = cv.imread('opencv_extra/testdata/cv/ximgproc/sources/08.png')
// inp = inp[:,:,[2, 1, 0]].astype(np.float32).reshape(1, 512, 512, 3)
// outs = sess.run([sess.graph.get_tensor_by_name('feature_fusion/Conv_7/Sigmoid:0'),
//                  sess.graph.get_tensor_by_name('feature_fusion/concat_3:0')],
//                 feed_dict={'input_images:0': inp})
// scores = np.ascontiguousarray(outs[0].transpose(0, 3, 1, 2))
// geometry = np.ascontiguousarray(outs[1].transpose(0, 3, 1, 2))
// np.save('east_text_detection.scores.npy', scores)
// np.save('east_text_detection.geometry.npy', geometry)
TEST_P(Test_TensorFlow_nets, EAST_text_detection)
{
    checkBackend();
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE < 2018030000
    if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
        throw SkipTestException("Test is enabled starts from OpenVINO 2018R3");
#endif

    std::string netPath = findDataFile("dnn/frozen_east_text_detection.pb", false);
    std::string imgPath = findDataFile("cv/ximgproc/sources/08.png", false);
    std::string refScoresPath = findDataFile("dnn/east_text_detection.scores.npy", false);
    std::string refGeometryPath = findDataFile("dnn/east_text_detection.geometry.npy", false);

    Net net = readNet(findDataFile("dnn/frozen_east_text_detection.pb", false));

    net.setPreferableBackend(backend);
    net.setPreferableTarget(target);

    Mat img = imread(imgPath);
    Mat inp = blobFromImage(img, 1.0, Size(), Scalar(123.68, 116.78, 103.94), true, false);
    net.setInput(inp);

    std::vector<Mat> outs;
    std::vector<String> outNames(2);
    outNames[0] = "feature_fusion/Conv_7/Sigmoid";
    outNames[1] = "feature_fusion/concat_3";
    net.forward(outs, outNames);

    Mat scores = outs[0];
    Mat geometry = outs[1];

    // Scores are in range [0, 1]. Geometry values are in range [-0.23, 290]
    double l1_scores = default_l1, lInf_scores = default_lInf;
    double l1_geometry = default_l1, lInf_geometry = default_lInf;
    if (target == DNN_TARGET_OPENCL_FP16)
    {
        lInf_scores = 0.11;
        l1_geometry = 0.28; lInf_geometry = 5.94;
    }
    else if (target == DNN_TARGET_MYRIAD)
    {
        lInf_scores = 0.214;
        l1_geometry = 0.47; lInf_geometry = 15.34;
    }
    else
    {
        l1_geometry = 1e-4, lInf_geometry = 3e-3;
    }
    normAssert(scores, blobFromNPY(refScoresPath), "scores", l1_scores, lInf_scores);
    normAssert(geometry, blobFromNPY(refGeometryPath), "geometry", l1_geometry, lInf_geometry);
}
Пример #3
0
TEST(Test_TensorFlow, Mask_RCNN)
{
    std::string proto = findDataFile("dnn/mask_rcnn_inception_v2_coco_2018_01_28.pbtxt", false);
    std::string model = findDataFile("dnn/mask_rcnn_inception_v2_coco_2018_01_28.pb", false);

    Net net = readNetFromTensorflow(model, proto);
    Mat img = imread(findDataFile("dnn/street.png", false));
    Mat refDetections = blobFromNPY(path("mask_rcnn_inception_v2_coco_2018_01_28.detection_out.npy"));
    Mat refMasks = blobFromNPY(path("mask_rcnn_inception_v2_coco_2018_01_28.detection_masks.npy"));
    Mat blob = blobFromImage(img, 1.0f, Size(800, 800), Scalar(), true, false);

    net.setPreferableBackend(DNN_BACKEND_OPENCV);

    net.setInput(blob);

    // Mask-RCNN predicts bounding boxes and segmentation masks.
    std::vector<String> outNames(2);
    outNames[0] = "detection_out_final";
    outNames[1] = "detection_masks";

    std::vector<Mat> outs;
    net.forward(outs, outNames);

    Mat outDetections = outs[0];
    Mat outMasks = outs[1];
    normAssertDetections(refDetections, outDetections, "", /*threshold for zero confidence*/1e-5);

    // Output size of masks is NxCxHxW where
    // N - number of detected boxes
    // C - number of classes (excluding background)
    // HxW - segmentation shape
    const int numDetections = outDetections.size[2];

    int masksSize[] = {1, numDetections, outMasks.size[2], outMasks.size[3]};
    Mat masks(4, &masksSize[0], CV_32F);

    std::vector<cv::Range> srcRanges(4, cv::Range::all());
    std::vector<cv::Range> dstRanges(4, cv::Range::all());

    outDetections = outDetections.reshape(1, outDetections.total() / 7);
    for (int i = 0; i < numDetections; ++i)
    {
        // Get a class id for this bounding box and copy mask only for that class.
        int classId = static_cast<int>(outDetections.at<float>(i, 1));
        srcRanges[0] = dstRanges[1] = cv::Range(i, i + 1);
        srcRanges[1] = cv::Range(classId, classId + 1);
        outMasks(srcRanges).copyTo(masks(dstRanges));
    }
    cv::Range topRefMasks[] = {Range::all(), Range(0, numDetections), Range::all(), Range::all()};
    normAssert(masks, refMasks(&topRefMasks[0]));
}
Пример #4
0
TEST_P(Test_TensorFlow_nets, MobileNet_SSD)
{
    checkBackend();
    if ((backend == DNN_BACKEND_INFERENCE_ENGINE && target != DNN_TARGET_CPU) ||
        (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16))
        throw SkipTestException("");

    std::string netPath = findDataFile("dnn/ssd_mobilenet_v1_coco.pb", false);
    std::string netConfig = findDataFile("dnn/ssd_mobilenet_v1_coco.pbtxt", false);
    std::string imgPath = findDataFile("dnn/street.png", false);

    Mat inp;
    resize(imread(imgPath), inp, Size(300, 300));
    inp = blobFromImage(inp, 1.0f / 127.5, Size(), Scalar(127.5, 127.5, 127.5), true);

    std::vector<String> outNames(3);
    outNames[0] = "concat";
    outNames[1] = "concat_1";
    outNames[2] = "detection_out";

    std::vector<Mat> refs(outNames.size());
    for (int i = 0; i < outNames.size(); ++i)
    {
        std::string path = findDataFile("dnn/tensorflow/ssd_mobilenet_v1_coco." + outNames[i] + ".npy", false);
        refs[i] = blobFromNPY(path);
    }

    Net net = readNetFromTensorflow(netPath, netConfig);
    net.setPreferableBackend(backend);
    net.setPreferableTarget(target);

    net.setInput(inp);

    std::vector<Mat> output;
    net.forward(output, outNames);

    normAssert(refs[0].reshape(1, 1), output[0].reshape(1, 1), "", 1e-5, 1.5e-4);
    normAssert(refs[1].reshape(1, 1), output[1].reshape(1, 1), "", 1e-5, 3e-4);
    normAssertDetections(refs[2], output[2], "", 0.2);
}