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Merge branch '4.x' into '5.x'
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@@ -100,7 +100,7 @@ public:
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void testSegmentationModel(const std::string& weights_file, const std::string& config_file,
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const std::string& inImgPath, const std::string& outImgPath,
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float norm, const Size& size = {-1, -1}, Scalar mean = Scalar(),
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double scale = 1.0, bool swapRB = false, bool crop = false)
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double scale = 1.0, bool swapRB = false, bool crop = false, const std::string outname = "")
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{
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checkBackend();
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@@ -115,6 +115,9 @@ public:
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model.setPreferableBackend(backend);
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model.setPreferableTarget(target);
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if(!outname.empty())
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model.setOutputNames({outname});
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model.segment(frame, mask);
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normAssert(mask, exp, "", norm, norm);
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}
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@@ -669,20 +672,19 @@ TEST_P(Test_Model, Segmentation)
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if ((backend == DNN_BACKEND_OPENCV && (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_CPU_FP16))
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|| (backend == DNN_BACKEND_CUDA && target == DNN_TARGET_CUDA_FP16))
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{
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norm = 2.0f; // l1 = 0.01 lInf = 2
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norm = 7.0f; // l1 = 0.01 lInf = 7
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}
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std::string inp = _tf("dog416.png");
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std::string weights_file = _tf("fcn8s-heavy-pascal.prototxt");
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std::string config_file = _tf("fcn8s-heavy-pascal.caffemodel", false);
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std::string weights_file = _tf("onnx/models/fcn-resnet50-12.onnx", false);
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std::string exp = _tf("segmentation_exp.png");
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Size size{128, 128};
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double scale = 1.0;
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Scalar mean = Scalar();
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bool swapRB = false;
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double scale = 0.019;
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Scalar mean = Scalar(0.485*255, 0.456*255, 0.406*255);
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bool swapRB = true;
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testSegmentationModel(weights_file, config_file, inp, exp, norm, size, mean, scale, swapRB);
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testSegmentationModel(weights_file, "", inp, exp, norm, size, mean, scale, swapRB, false, "out");
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}
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TEST_P(Test_Model, TextRecognition)
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