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dnn(test): fix Test_Model.Keypoints* tests
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@ -83,6 +83,9 @@ public:
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model.setInputSize(size).setInputMean(mean).setInputScale(scale)
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.setInputSwapRB(swapRB).setInputCrop(crop);
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model.setPreferableBackend(backend);
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model.setPreferableTarget(target);
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points = model.estimate(frame, 0.5);
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Mat out = Mat(points).reshape(1);
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@ -242,8 +245,15 @@ TEST_P(Test_Model, DetectionMobilenetSSD)
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TEST_P(Test_Model, Keypoints_pose)
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{
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if (target == DNN_TARGET_OPENCL_FP16)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
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#ifdef HAVE_INF_ENGINE
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if (target == DNN_TARGET_MYRIAD)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
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#endif
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Mat inp = imread(_tf("pose.png"));
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std::string weights = _tf("lightweight_pose_estimation.onnx");
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std::string weights = _tf("onnx/models/lightweight_pose_estimation.onnx");
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Mat exp = blobFromNPY(_tf("keypoints_exp.npy"));
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@ -258,12 +268,17 @@ TEST_P(Test_Model, Keypoints_pose)
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TEST_P(Test_Model, Keypoints_face)
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{
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#if defined(INF_ENGINE_RELEASE)
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
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#endif
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Mat inp = imread(_tf("gray_face.png"), 0);
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std::string weights = _tf("facial_keypoints.onnx");
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std::string weights = _tf("onnx/models/facial_keypoints.onnx");
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Mat exp = blobFromNPY(_tf("facial_keypoints_exp.npy"));
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Size size{224, 224};
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float norm = 1e-4;
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float norm = (target == DNN_TARGET_OPENCL_FP16) ? 5e-3 : 1e-4;
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double scale = 1.0/255;
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Scalar mean = Scalar();
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bool swapRB = false;
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