test: fix usage of findDataFile()

misused 'optional' mode

modules/dnn/perf/perf_caffe.cpp

@@ -38,7 +38,7 @@ namespace opencv_test {
 
 static caffe::Net<float>* initNet(std::string proto, std::string weights)
 {
-    proto = findDataFile(proto, false);
+    proto = findDataFile(proto);
     weights = findDataFile(weights, false);
 
 #ifdef HAVE_CLCAFFE

modules/dnn/perf/perf_net.cpp

@@ -35,7 +35,7 @@ public:
 
         weights = findDataFile(weights, false);
         if (!proto.empty())
-            proto = findDataFile(proto, false);
+            proto = findDataFile(proto);
         if (backend == DNN_BACKEND_HALIDE)
         {
             if (halide_scheduler == "disabled")
@@ -198,10 +198,10 @@ PERF_TEST_P_(DNNTestNetwork, YOLOv3)
 {
     if (backend == DNN_BACKEND_HALIDE)
         throw SkipTestException("");
-    Mat sample = imread(findDataFile("dnn/dog416.png", false));
+    Mat sample = imread(findDataFile("dnn/dog416.png"));
     Mat inp;
     sample.convertTo(inp, CV_32FC3);
-    processNet("dnn/yolov3.cfg", "dnn/yolov3.weights", "", inp / 255);
+    processNet("dnn/yolov3.weights", "dnn/yolov3.cfg", "", inp / 255);
 }
 
 PERF_TEST_P_(DNNTestNetwork, EAST_text_detection)

modules/dnn/test/test_backends.cpp

@@ -37,7 +37,7 @@ public:
 
         weights = findDataFile(weights, false);
         if (!proto.empty())
-            proto = findDataFile(proto, false);
+            proto = findDataFile(proto);
 
         // Create two networks - with default backend and target and a tested one.
         Net netDefault = readNet(weights, proto);
@@ -51,7 +51,7 @@ public:
         net.setPreferableTarget(target);
         if (backend == DNN_BACKEND_HALIDE && !halideScheduler.empty())
         {
-            halideScheduler = findDataFile(halideScheduler, false);
+            halideScheduler = findDataFile(halideScheduler);
             net.setHalideScheduler(halideScheduler);
         }
         Mat out = net.forward(outputLayer).clone();
@@ -171,7 +171,7 @@ TEST_P(DNNTestNetwork, MobileNet_SSD_Caffe)
     applyTestTag(CV_TEST_TAG_MEMORY_512MB);
     if (backend == DNN_BACKEND_HALIDE)
         throw SkipTestException("");
-    Mat sample = imread(findDataFile("dnn/street.png", false));
+    Mat sample = imread(findDataFile("dnn/street.png"));
     Mat inp = blobFromImage(sample, 1.0f / 127.5, Size(300, 300), Scalar(127.5, 127.5, 127.5), false);
     float diffScores = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 1.5e-2 : 0.0;
     float diffSquares = (target == DNN_TARGET_MYRIAD) ? 0.063  : 0.0;
@@ -190,7 +190,7 @@ TEST_P(DNNTestNetwork, MobileNet_SSD_Caffe_Different_Width_Height)
             && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
         throw SkipTestException("Test is disabled for MyriadX");
 #endif
-    Mat sample = imread(findDataFile("dnn/street.png", false));
+    Mat sample = imread(findDataFile("dnn/street.png"));
     Mat inp = blobFromImage(sample, 1.0f / 127.5, Size(300, 560), Scalar(127.5, 127.5, 127.5), false);
     float diffScores  = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.029 : 0.0;
     float diffSquares = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.09  : 0.0;
@@ -204,7 +204,7 @@ TEST_P(DNNTestNetwork, MobileNet_SSD_v1_TensorFlow)
     applyTestTag(target == DNN_TARGET_CPU ? "" : CV_TEST_TAG_MEMORY_512MB);
     if (backend == DNN_BACKEND_HALIDE)
         throw SkipTestException("");
-    Mat sample = imread(findDataFile("dnn/street.png", false));
+    Mat sample = imread(findDataFile("dnn/street.png"));
     Mat inp = blobFromImage(sample, 1.0f, Size(300, 300), Scalar(), false);
     float l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.095 : 0.0;
     float lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.09 : 0.0;
@@ -223,7 +223,7 @@ TEST_P(DNNTestNetwork, MobileNet_SSD_v1_TensorFlow_Different_Width_Height)
             && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
         throw SkipTestException("Test is disabled for MyriadX");
 #endif
-    Mat sample = imread(findDataFile("dnn/street.png", false));
+    Mat sample = imread(findDataFile("dnn/street.png"));
     Mat inp = blobFromImage(sample, 1.0f, Size(300, 560), Scalar(), false);
     float l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.012 : 0.0;
     float lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.06 : 0.0;
@@ -237,7 +237,7 @@ TEST_P(DNNTestNetwork, MobileNet_SSD_v2_TensorFlow)
     applyTestTag(target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB);
     if (backend == DNN_BACKEND_HALIDE)
         throw SkipTestException("");
-    Mat sample = imread(findDataFile("dnn/street.png", false));
+    Mat sample = imread(findDataFile("dnn/street.png"));
     Mat inp = blobFromImage(sample, 1.0f, Size(300, 300), Scalar(), false);
     float l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.013 : 2e-5;
     float lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.062 : 0.0;
@@ -254,7 +254,7 @@ TEST_P(DNNTestNetwork, SSD_VGG16)
         throw SkipTestException("");
     double scoreThreshold = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.0325 : 0.0;
     const float lInf = (target == DNN_TARGET_MYRIAD) ? 0.032 : 0.0;
-    Mat sample = imread(findDataFile("dnn/street.png", false));
+    Mat sample = imread(findDataFile("dnn/street.png"));
     Mat inp = blobFromImage(sample, 1.0f, Size(300, 300), Scalar(), false);
     processNet("dnn/VGG_ILSVRC2016_SSD_300x300_iter_440000.caffemodel",
                "dnn/ssd_vgg16.prototxt", inp, "detection_out", "", scoreThreshold, lInf);
@@ -337,7 +337,7 @@ TEST_P(DNNTestNetwork, opencv_face_detector)
 {
     if (backend == DNN_BACKEND_HALIDE)
         throw SkipTestException("");
-    Mat img = imread(findDataFile("gpu/lbpcascade/er.png", false));
+    Mat img = imread(findDataFile("gpu/lbpcascade/er.png"));
     Mat inp = blobFromImage(img, 1.0, Size(), Scalar(104.0, 177.0, 123.0), false, false);
     processNet("dnn/opencv_face_detector.caffemodel", "dnn/opencv_face_detector.prototxt",
                inp, "detection_out");
@@ -357,7 +357,7 @@ TEST_P(DNNTestNetwork, Inception_v2_SSD_TensorFlow)
 #endif
     if (backend == DNN_BACKEND_HALIDE)
         throw SkipTestException("");
-    Mat sample = imread(findDataFile("dnn/street.png", false));
+    Mat sample = imread(findDataFile("dnn/street.png"));
     Mat inp = blobFromImage(sample, 1.0f, Size(300, 300), Scalar(), false);
     float l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.015 : 0.0;
     float lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.0731 : 0.0;
@@ -400,7 +400,7 @@ TEST_P(DNNTestNetwork, FastNeuralStyle_eccv16)
 #endif
 #endif
 
-    Mat img = imread(findDataFile("dnn/googlenet_1.png", false));
+    Mat img = imread(findDataFile("dnn/googlenet_1.png"));
     Mat inp = blobFromImage(img, 1.0, Size(320, 240), Scalar(103.939, 116.779, 123.68), false, false);
     // Output image has values in range [-143.526, 148.539].
     float l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.4 : 4e-5;

modules/dnn/test/test_caffe_importer.cpp

@@ -48,7 +48,7 @@ namespace opencv_test { namespace {
 template<typename TString>
 static std::string _tf(TString filename)
 {
-    return (getOpenCVExtraDir() + "/dnn/") + filename;
+    return findDataFile(std::string("dnn/") + filename);
 }
 
 class Test_Caffe_nets : public DNNTestLayer
@@ -58,11 +58,11 @@ public:
                     double scoreDiff = 0.0, double iouDiff = 0.0)
     {
         checkBackend();
-        Net net = readNetFromCaffe(findDataFile("dnn/" + proto, false),
+        Net net = readNetFromCaffe(findDataFile("dnn/" + proto),
                                    findDataFile("dnn/" + model, false));
         net.setPreferableBackend(backend);
         net.setPreferableTarget(target);
-        Mat img = imread(findDataFile("dnn/dog416.png", false));
+        Mat img = imread(findDataFile("dnn/dog416.png"));
         resize(img, img, Size(800, 600));
         Mat blob = blobFromImage(img, 1.0, Size(), Scalar(102.9801, 115.9465, 122.7717), false, false);
         Mat imInfo = (Mat_<float>(1, 3) << img.rows, img.cols, 1.6f);
@@ -80,11 +80,12 @@ public:
 
 TEST(Test_Caffe, memory_read)
 {
-    const string proto = findDataFile("dnn/bvlc_googlenet.prototxt", false);
+    const string proto = findDataFile("dnn/bvlc_googlenet.prototxt");
     const string model = findDataFile("dnn/bvlc_googlenet.caffemodel", false);
 
     std::vector<char> dataProto;
     readFileContent(proto, dataProto);
+
     std::vector<char> dataModel;
     readFileContent(model, dataModel);
 
@@ -163,7 +164,7 @@ TEST_P(Reproducibility_AlexNet, Accuracy)
     bool readFromMemory = get<0>(GetParam());
     Net net;
     {
-        const string proto = findDataFile("dnn/bvlc_alexnet.prototxt", false);
+        const string proto = findDataFile("dnn/bvlc_alexnet.prototxt");
         const string model = findDataFile("dnn/bvlc_alexnet.caffemodel", false);
         if (readFromMemory)
         {
@@ -204,8 +205,8 @@ TEST(Reproducibility_FCN, Accuracy)
 
     Net net;
     {
-        const string proto = findDataFile("dnn/fcn8s-heavy-pascal.prototxt", false);
-        const string model = findDataFile("dnn/fcn8s-heavy-pascal.caffemodel", false);
+        const string proto = findDataFile("dnn/fcn8s-heavy-pascal.prototxt");
+        const string model = findDataFile("dnn/fcn8s-heavy-pascal.caffemodel");
         net = readNetFromCaffe(proto, model);
         ASSERT_FALSE(net.empty());
     }
@@ -233,7 +234,7 @@ TEST(Reproducibility_SSD, Accuracy)
     applyTestTag(CV_TEST_TAG_MEMORY_512MB, CV_TEST_TAG_DEBUG_LONG);
     Net net;
     {
-        const string proto = findDataFile("dnn/ssd_vgg16.prototxt", false);
+        const string proto = findDataFile("dnn/ssd_vgg16.prototxt");
         const string model = findDataFile("dnn/VGG_ILSVRC2016_SSD_300x300_iter_440000.caffemodel", false);
         net = readNetFromCaffe(proto, model);
         ASSERT_FALSE(net.empty());
@@ -331,7 +332,7 @@ TEST_P(Reproducibility_ResNet50, Accuracy)
     if (!ocl::useOpenCL() && targetId != DNN_TARGET_CPU)
         throw SkipTestException("OpenCL is disabled");
 
-    Net net = readNetFromCaffe(findDataFile("dnn/ResNet-50-deploy.prototxt", false),
+    Net net = readNetFromCaffe(findDataFile("dnn/ResNet-50-deploy.prototxt"),
                                findDataFile("dnn/ResNet-50-model.caffemodel", false));
 
     net.setPreferableBackend(DNN_BACKEND_OPENCV);
@@ -369,7 +370,7 @@ TEST_P(Reproducibility_SqueezeNet_v1_1, Accuracy)
     int targetId = GetParam();
     if(targetId == DNN_TARGET_OPENCL_FP16)
         throw SkipTestException("This test does not support FP16");
-    Net net = readNetFromCaffe(findDataFile("dnn/squeezenet_v1.1.prototxt", false),
+    Net net = readNetFromCaffe(findDataFile("dnn/squeezenet_v1.1.prototxt"),
                                findDataFile("dnn/squeezenet_v1.1.caffemodel", false));
     net.setPreferableBackend(DNN_BACKEND_OPENCV);
     net.setPreferableTarget(targetId);
@@ -400,18 +401,18 @@ TEST(Reproducibility_AlexNet_fp16, Accuracy)
     const float l1 = 1e-5;
     const float lInf = 3e-3;
 
-    const string proto = findDataFile("dnn/bvlc_alexnet.prototxt", false);
+    const string proto = findDataFile("dnn/bvlc_alexnet.prototxt");
     const string model = findDataFile("dnn/bvlc_alexnet.caffemodel", false);
 
     shrinkCaffeModel(model, "bvlc_alexnet.caffemodel_fp16");
     Net net = readNetFromCaffe(proto, "bvlc_alexnet.caffemodel_fp16");
     net.setPreferableBackend(DNN_BACKEND_OPENCV);
 
-    Mat sample = imread(findDataFile("dnn/grace_hopper_227.png", false));
+    Mat sample = imread(findDataFile("dnn/grace_hopper_227.png"));
 
-    net.setInput(blobFromImage(sample, 1.0f, Size(227, 227), Scalar(), false));
+    net.setInput(blobFromImage(sample, 1.0f, Size(227, 227), Scalar()));
     Mat out = net.forward();
-    Mat ref = blobFromNPY(findDataFile("dnn/caffe_alexnet_prob.npy", false));
+    Mat ref = blobFromNPY(findDataFile("dnn/caffe_alexnet_prob.npy"));
     normAssert(ref, out, "", l1, lInf);
 }
 
@@ -420,7 +421,7 @@ TEST(Reproducibility_GoogLeNet_fp16, Accuracy)
     const float l1 = 1e-5;
     const float lInf = 3e-3;
 
-    const string proto = findDataFile("dnn/bvlc_googlenet.prototxt", false);
+    const string proto = findDataFile("dnn/bvlc_googlenet.prototxt");
     const string model = findDataFile("dnn/bvlc_googlenet.caffemodel", false);
 
     shrinkCaffeModel(model, "bvlc_googlenet.caffemodel_fp16");
@@ -506,7 +507,7 @@ TEST_P(Test_Caffe_nets, DenseNet_121)
 
 TEST(Test_Caffe, multiple_inputs)
 {
-    const string proto = findDataFile("dnn/layers/net_input.prototxt", false);
+    const string proto = findDataFile("dnn/layers/net_input.prototxt");
     Net net = readNetFromCaffe(proto);
     net.setPreferableBackend(DNN_BACKEND_OPENCV);
 
@@ -534,8 +535,8 @@ TEST(Test_Caffe, multiple_inputs)
 
 TEST(Test_Caffe, shared_weights)
 {
-  const string proto = findDataFile("dnn/layers/shared_weights.prototxt", false);
-  const string model = findDataFile("dnn/layers/shared_weights.caffemodel", false);
+  const string proto = findDataFile("dnn/layers/shared_weights.prototxt");
+  const string model = findDataFile("dnn/layers/shared_weights.caffemodel");
 
   Net net = readNetFromCaffe(proto, model);
 
@@ -563,7 +564,7 @@ TEST_P(opencv_face_detector, Accuracy)
     dnn::Target targetId = (dnn::Target)(int)get<1>(GetParam());
 
     Net net = readNetFromCaffe(proto, model);
-    Mat img = imread(findDataFile("gpu/lbpcascade/er.png", false));
+    Mat img = imread(findDataFile("gpu/lbpcascade/er.png"));
     Mat blob = blobFromImage(img, 1.0, Size(), Scalar(104.0, 177.0, 123.0), false, false);
 
     net.setPreferableBackend(DNN_BACKEND_OPENCV);

modules/dnn/test/test_darknet_importer.cpp

@@ -82,7 +82,7 @@ TEST(Test_Darknet, read_yolo_voc_stream)
     Mat ref;
     Mat sample = imread(_tf("dog416.png"));
     Mat inp = blobFromImage(sample, 1.0/255, Size(416, 416), Scalar(), true, false);
-    const std::string cfgFile = findDataFile("dnn/yolo-voc.cfg", false);
+    const std::string cfgFile = findDataFile("dnn/yolo-voc.cfg");
     const std::string weightsFile = findDataFile("dnn/yolo-voc.weights", false);
     // Import by paths.
     {
@@ -110,12 +110,13 @@ class Test_Darknet_layers : public DNNTestLayer
 public:
     void testDarknetLayer(const std::string& name, bool hasWeights = false)
     {
-        std::string cfg = findDataFile("dnn/darknet/" + name + ".cfg", false);
+        Mat inp = blobFromNPY(findDataFile("dnn/darknet/" + name + "_in.npy"));
+        Mat ref = blobFromNPY(findDataFile("dnn/darknet/" + name + "_out.npy"));
+
+        std::string cfg = findDataFile("dnn/darknet/" + name + ".cfg");
         std::string model = "";
         if (hasWeights)
             model = findDataFile("dnn/darknet/" + name + ".weights", false);
-        Mat inp = blobFromNPY(findDataFile("dnn/darknet/" + name + "_in.npy", false));
-        Mat ref = blobFromNPY(findDataFile("dnn/darknet/" + name + "_out.npy", false));
 
         checkBackend(&inp, &ref);
 
@@ -152,7 +153,7 @@ public:
 
         Mat inp = blobFromImages(samples, 1.0/255, Size(416, 416), Scalar(), true, false);
 
-        Net net = readNet(findDataFile("dnn/" + cfg, false),
+        Net net = readNet(findDataFile("dnn/" + cfg),
                           findDataFile("dnn/" + weights, false));
         net.setPreferableBackend(backend);
         net.setPreferableTarget(target);

modules/dnn/test/test_googlenet.cpp

@@ -58,7 +58,7 @@ TEST_P(Reproducibility_GoogLeNet, Batching)
     const int targetId = GetParam();
     if(targetId == DNN_TARGET_OPENCL_FP16)
         throw SkipTestException("This test does not support FP16");
-    Net net = readNetFromCaffe(findDataFile("dnn/bvlc_googlenet.prototxt", false),
+    Net net = readNetFromCaffe(findDataFile("dnn/bvlc_googlenet.prototxt"),
                                findDataFile("dnn/bvlc_googlenet.caffemodel", false));
     net.setPreferableBackend(DNN_BACKEND_OPENCV);
     net.setPreferableTarget(targetId);
@@ -89,7 +89,7 @@ TEST_P(Reproducibility_GoogLeNet, IntermediateBlobs)
     const int targetId = GetParam();
     if(targetId == DNN_TARGET_OPENCL_FP16)
         throw SkipTestException("This test does not support FP16");
-    Net net = readNetFromCaffe(findDataFile("dnn/bvlc_googlenet.prototxt", false),
+    Net net = readNetFromCaffe(findDataFile("dnn/bvlc_googlenet.prototxt"),
                                findDataFile("dnn/bvlc_googlenet.caffemodel", false));
     net.setPreferableBackend(DNN_BACKEND_OPENCV);
     net.setPreferableTarget(targetId);
@@ -120,7 +120,7 @@ TEST_P(Reproducibility_GoogLeNet, SeveralCalls)
     const int targetId = GetParam();
     if(targetId == DNN_TARGET_OPENCL_FP16)
         throw SkipTestException("This test does not support FP16");
-    Net net = readNetFromCaffe(findDataFile("dnn/bvlc_googlenet.prototxt", false),
+    Net net = readNetFromCaffe(findDataFile("dnn/bvlc_googlenet.prototxt"),
                                findDataFile("dnn/bvlc_googlenet.caffemodel", false));
     net.setPreferableBackend(DNN_BACKEND_OPENCV);
     net.setPreferableTarget(targetId);

modules/dnn/test/test_misc.cpp

@@ -62,18 +62,18 @@ TEST(imagesFromBlob, Regression)
 
 TEST(readNet, Regression)
 {
-    Net net = readNet(findDataFile("dnn/squeezenet_v1.1.prototxt", false),
+    Net net = readNet(findDataFile("dnn/squeezenet_v1.1.prototxt"),
                       findDataFile("dnn/squeezenet_v1.1.caffemodel", false));
     EXPECT_FALSE(net.empty());
     net = readNet(findDataFile("dnn/opencv_face_detector.caffemodel", false),
-                  findDataFile("dnn/opencv_face_detector.prototxt", false));
+                  findDataFile("dnn/opencv_face_detector.prototxt"));
     EXPECT_FALSE(net.empty());
     net = readNet(findDataFile("dnn/openface_nn4.small2.v1.t7", false));
     EXPECT_FALSE(net.empty());
-    net = readNet(findDataFile("dnn/tiny-yolo-voc.cfg", false),
+    net = readNet(findDataFile("dnn/tiny-yolo-voc.cfg"),
                   findDataFile("dnn/tiny-yolo-voc.weights", false));
     EXPECT_FALSE(net.empty());
-    net = readNet(findDataFile("dnn/ssd_mobilenet_v1_coco.pbtxt", false),
+    net = readNet(findDataFile("dnn/ssd_mobilenet_v1_coco.pbtxt"),
                   findDataFile("dnn/ssd_mobilenet_v1_coco.pb", false));
     EXPECT_FALSE(net.empty());
 }

modules/dnn/test/test_onnx_importer.cpp

@@ -12,15 +12,18 @@
 namespace opencv_test { namespace {
 
 template<typename TString>
-static std::string _tf(TString filename)
+static std::string _tf(TString filename, bool required = true)
 {
-    String rootFolder = "dnn/onnx/";
-    return findDataFile(rootFolder + filename, false);
+    return findDataFile(std::string("dnn/onnx/") + filename, required);
 }
 
 class Test_ONNX_layers : public DNNTestLayer
 {
 public:
+    bool required;
+
+    Test_ONNX_layers() : required(true) { }
+
     enum Extension
     {
         npy,
@@ -31,7 +34,7 @@ public:
                         const double l1 = 0, const float lInf = 0, const bool useSoftmax = false,
                         bool checkNoFallbacks = true)
     {
-        String onnxmodel = _tf("models/" + basename + ".onnx");
+        String onnxmodel = _tf("models/" + basename + ".onnx", required);
         Mat inp, ref;
         if (ext == npy) {
             inp = blobFromNPY(_tf("data/input_" + basename + ".npy"));
@@ -275,11 +278,16 @@ TEST_P(Test_ONNX_layers, Softmax)
 
 INSTANTIATE_TEST_CASE_P(/*nothing*/, Test_ONNX_layers, dnnBackendsAndTargets());
 
-class Test_ONNX_nets : public Test_ONNX_layers {};
+class Test_ONNX_nets : public Test_ONNX_layers
+{
+public:
+    Test_ONNX_nets() { required = false; }
+};
+
 TEST_P(Test_ONNX_nets, Alexnet)
 {
     applyTestTag(target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB);
-    const String model =  _tf("models/alexnet.onnx");
+    const String model =  _tf("models/alexnet.onnx", false);
 
     Net net = readNetFromONNX(model);
     ASSERT_FALSE(net.empty());
@@ -309,7 +317,7 @@ TEST_P(Test_ONNX_nets, Googlenet)
     if (backend == DNN_BACKEND_INFERENCE_ENGINE)
         throw SkipTestException("");
 
-    const String model = _tf("models/googlenet.onnx");
+    const String model = _tf("models/googlenet.onnx", false);
 
     Net net = readNetFromONNX(model);
     ASSERT_FALSE(net.empty());
@@ -527,7 +535,7 @@ TEST_P(Test_ONNX_nets, Resnet34_kinetics)
     if (backend != DNN_BACKEND_INFERENCE_ENGINE || target != DNN_TARGET_CPU)
         throw SkipTestException("Only DLIE backend on CPU is supported");
 
-    String onnxmodel = findDataFile("dnn/resnet-34_kinetics.onnx");
+    String onnxmodel = findDataFile("dnn/resnet-34_kinetics.onnx", false);
     Mat image0 = imread(findDataFile("dnn/dog416.png"));
     Mat image1 = imread(findDataFile("dnn/street.png"));
 

modules/dnn/test/test_tf_importer.cpp

@@ -74,7 +74,7 @@ TEST(Test_TensorFlow, inception_accuracy)
 
 static std::string path(const std::string& file)
 {
-    return findDataFile("dnn/tensorflow/" + file, false);
+    return findDataFile("dnn/tensorflow/" + file);
 }
 
 class Test_TensorFlow_layers : public DNNTestLayer
@@ -360,15 +360,15 @@ TEST_P(Test_TensorFlow_nets, MobileNet_SSD)
 #endif
 
     checkBackend();
+    std::string imgPath = findDataFile("dnn/street.png");
+    std::string netConfig = findDataFile("dnn/ssd_mobilenet_v1_coco.pbtxt");
     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);
 
-    Mat ref = blobFromNPY(findDataFile("dnn/tensorflow/ssd_mobilenet_v1_coco.detection_out.npy", false));
+    Mat ref = blobFromNPY(findDataFile("dnn/tensorflow/ssd_mobilenet_v1_coco.detection_out.npy"));
 
     Net net = readNetFromTensorflow(netPath, netConfig);
     net.setPreferableBackend(backend);
@@ -397,11 +397,11 @@ TEST_P(Test_TensorFlow_nets, Inception_v2_SSD)
 #endif
 
     checkBackend();
-    std::string proto = findDataFile("dnn/ssd_inception_v2_coco_2017_11_17.pbtxt", false);
+    Mat img = imread(findDataFile("dnn/street.png"));
+    std::string proto = findDataFile("dnn/ssd_inception_v2_coco_2017_11_17.pbtxt");
     std::string model = findDataFile("dnn/ssd_inception_v2_coco_2017_11_17.pb", false);
 
     Net net = readNetFromTensorflow(model, proto);
-    Mat img = imread(findDataFile("dnn/street.png", false));
     Mat blob = blobFromImage(img, 1.0f, Size(300, 300), Scalar(), true, false);
 
     net.setPreferableBackend(backend);
@@ -434,11 +434,11 @@ TEST_P(Test_TensorFlow_nets, MobileNet_v1_SSD)
         throw SkipTestException("Test is disabled for MyriadX");
 #endif
 
+    std::string proto = findDataFile("dnn/ssd_mobilenet_v1_coco_2017_11_17.pbtxt");
     std::string model = findDataFile("dnn/ssd_mobilenet_v1_coco_2017_11_17.pb", false);
-    std::string proto = findDataFile("dnn/ssd_mobilenet_v1_coco_2017_11_17.pbtxt", false);
 
     Net net = readNetFromTensorflow(model, proto);
-    Mat img = imread(findDataFile("dnn/dog416.png", false));
+    Mat img = imread(findDataFile("dnn/dog416.png"));
     Mat blob = blobFromImage(img, 1.0f, Size(300, 300), Scalar(), true, false);
 
     net.setPreferableBackend(backend);
@@ -473,13 +473,13 @@ TEST_P(Test_TensorFlow_nets, Faster_RCNN)
     double scoresDiff = backend == DNN_BACKEND_INFERENCE_ENGINE ? 2.9e-5 : 1e-5;
     for (int i = 0; i < 2; ++i)
     {
-        std::string proto = findDataFile("dnn/" + names[i] + ".pbtxt", false);
+        std::string proto = findDataFile("dnn/" + names[i] + ".pbtxt");
         std::string model = findDataFile("dnn/" + names[i] + ".pb", false);
 
         Net net = readNetFromTensorflow(model, proto);
         net.setPreferableBackend(backend);
         net.setPreferableTarget(target);
-        Mat img = imread(findDataFile("dnn/dog416.png", false));
+        Mat img = imread(findDataFile("dnn/dog416.png"));
         Mat blob = blobFromImage(img, 1.0f, Size(800, 600), Scalar(), true, false);
 
         net.setInput(blob);
@@ -498,12 +498,12 @@ TEST_P(Test_TensorFlow_nets, MobileNet_v1_SSD_PPN)
 #endif
 
     checkBackend();
-    std::string proto = findDataFile("dnn/ssd_mobilenet_v1_ppn_coco.pbtxt", false);
+    std::string proto = findDataFile("dnn/ssd_mobilenet_v1_ppn_coco.pbtxt");
     std::string model = findDataFile("dnn/ssd_mobilenet_v1_ppn_coco.pb", false);
 
     Net net = readNetFromTensorflow(model, proto);
-    Mat img = imread(findDataFile("dnn/dog416.png", false));
-    Mat ref = blobFromNPY(findDataFile("dnn/tensorflow/ssd_mobilenet_v1_ppn_coco.detection_out.npy", false));
+    Mat img = imread(findDataFile("dnn/dog416.png"));
+    Mat ref = blobFromNPY(findDataFile("dnn/tensorflow/ssd_mobilenet_v1_ppn_coco.detection_out.npy"));
     Mat blob = blobFromImage(img, 1.0f, Size(300, 300), Scalar(), true, false);
 
     net.setPreferableBackend(backend);
@@ -521,11 +521,11 @@ TEST_P(Test_TensorFlow_nets, MobileNet_v1_SSD_PPN)
 TEST_P(Test_TensorFlow_nets, opencv_face_detector_uint8)
 {
     checkBackend();
-    std::string proto = findDataFile("dnn/opencv_face_detector.pbtxt", false);
+    std::string proto = findDataFile("dnn/opencv_face_detector.pbtxt");
     std::string model = findDataFile("dnn/opencv_face_detector_uint8.pb", false);
 
     Net net = readNetFromTensorflow(model, proto);
-    Mat img = imread(findDataFile("gpu/lbpcascade/er.png", false));
+    Mat img = imread(findDataFile("gpu/lbpcascade/er.png"));
     Mat blob = blobFromImage(img, 1.0, Size(), Scalar(104.0, 177.0, 123.0), false, false);
 
     net.setPreferableBackend(backend);
@@ -572,11 +572,11 @@ TEST_P(Test_TensorFlow_nets, EAST_text_detection)
     checkBackend();
 
     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);
+    std::string imgPath = findDataFile("cv/ximgproc/sources/08.png");
+    std::string refScoresPath = findDataFile("dnn/east_text_detection.scores.npy");
+    std::string refGeometryPath = findDataFile("dnn/east_text_detection.geometry.npy");
 
-    Net net = readNet(findDataFile("dnn/frozen_east_text_detection.pb", false));
+    Net net = readNet(netPath);
 
     net.setPreferableBackend(backend);
     net.setPreferableTarget(target);
@@ -793,11 +793,11 @@ TEST(Test_TensorFlow, two_inputs)
 TEST(Test_TensorFlow, Mask_RCNN)
 {
     applyTestTag(CV_TEST_TAG_MEMORY_1GB, CV_TEST_TAG_DEBUG_VERYLONG);
-    std::string proto = findDataFile("dnn/mask_rcnn_inception_v2_coco_2018_01_28.pbtxt", false);
+    Mat img = imread(findDataFile("dnn/street.png"));
+    std::string proto = findDataFile("dnn/mask_rcnn_inception_v2_coco_2018_01_28.pbtxt");
     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);

modules/dnn/test/test_torch_importer.cpp

@@ -53,13 +53,13 @@ using namespace cv;
 using namespace cv::dnn;
 
 template<typename TStr>
-static std::string _tf(TStr filename, bool inTorchDir = true)
+static std::string _tf(TStr filename, bool inTorchDir = true, bool required = true)
 {
     String path = "dnn/";
     if (inTorchDir)
         path += "torch/";
     path += filename;
-    return findDataFile(path, false);
+    return findDataFile(path, required);
 }
 
 TEST(Torch_Importer, simple_read)
@@ -274,7 +274,7 @@ TEST_P(Test_Torch_nets, OpenFace_accuracy)
     net.setPreferableBackend(backend);
     net.setPreferableTarget(target);
 
-    Mat sample = imread(findDataFile("cv/shared/lena.png", false));
+    Mat sample = imread(findDataFile("cv/shared/lena.png"));
     Mat sampleF32(sample.size(), CV_32FC3);
     sample.convertTo(sampleF32, sampleF32.type());
     sampleF32 /= 255;
@@ -415,7 +415,7 @@ TEST_P(Test_Torch_nets, FastNeuralStyle_accuracy)
         net.setPreferableBackend(backend);
         net.setPreferableTarget(target);
 
-        Mat img = imread(findDataFile("dnn/googlenet_1.png", false));
+        Mat img = imread(findDataFile("dnn/googlenet_1.png"));
         Mat inputBlob = blobFromImage(img, 1.0, Size(), Scalar(103.939, 116.779, 123.68), false);
 
         net.setInput(inputBlob);

modules/objdetect/test/test_qrcode.cpp

@@ -75,7 +75,7 @@ TEST_P(Objdetect_QRCode, regression)
     ASSERT_TRUE(qrcode.detect(src, corners));
 #endif
 
-    const std::string dataset_config = findDataFile(root + "dataset_config.json", false);
+    const std::string dataset_config = findDataFile(root + "dataset_config.json");
     FileStorage file_config(dataset_config, FileStorage::READ);
     ASSERT_TRUE(file_config.isOpened()) << "Can't read validation data: " << dataset_config;
     {

modules/ts/src/ts.cpp

@@ -911,25 +911,35 @@ void addDataSearchSubDirectory(const std::string& subdir)
 
 static std::string findData(const std::string& relative_path, bool required, bool findDirectory)
 {
-#define TEST_TRY_FILE_WITH_PREFIX(prefix) \
+#define CHECK_FILE_WITH_PREFIX(prefix, result) \
 { \
+    result.clear(); \
     std::string path = path_join(prefix, relative_path); \
     /*printf("Trying %s\n", path.c_str());*/ \
     if (findDirectory) \
     { \
         if (isDirectory(path)) \
-            return path; \
+            result = path; \
     } \
     else \
     { \
         FILE* f = fopen(path.c_str(), "rb"); \
         if(f) { \
             fclose(f); \
-            return path; \
+            result = path; \
         } \
     } \
 }
 
+#define TEST_TRY_FILE_WITH_PREFIX(prefix) \
+{ \
+    std::string result__; \
+    CHECK_FILE_WITH_PREFIX(prefix, result__); \
+    if (!result__.empty()) \
+        return result__; \
+}
+
+
     const std::vector<std::string>& search_path = TS::ptr()->data_search_path;
     for(size_t i = search_path.size(); i > 0; i--)
     {
@@ -956,7 +966,17 @@ static std::string findData(const std::string& relative_path, bool required, boo
             {
                 const std::string& subdir = search_subdir[i - 1];
                 std::string prefix = path_join(datapath, subdir);
-                TEST_TRY_FILE_WITH_PREFIX(prefix);
+                std::string result_;
+                CHECK_FILE_WITH_PREFIX(prefix, result_);
+#if 1  // check for misused 'optional' mode
+                if (!required && !result_.empty())
+                {
+                    std::cout << "TEST ERROR: Don't use 'optional' findData() for " << relative_path << std::endl;
+                    CV_Assert(required || result_.empty());
+                }
+#endif
+                if (!result_.empty())
+                    return result_;
             }
         }
     }