Chiebot-Mirror
/
opencv
mirror of https://github.com/opencv/opencv.git
8
0
Fork 0
Code Issues Projects Releases Wiki Activity

test: fix usage of findDataFile()

Browse Source 
misused 'optional' mode
pull/14847/head
Alexander Alekhin 5 years ago
parent 264679a725
commit 13a782c039
12 changed files with 114 additions and 84 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Split View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 2
      modules/dnn/perf/perf_caffe.cpp
  2. 6
      modules/dnn/perf/perf_net.cpp
  3. 22
      modules/dnn/test/test_backends.cpp
  4. 39
      modules/dnn/test/test_caffe_importer.cpp
  5. 11
      modules/dnn/test/test_darknet_importer.cpp
  6. 6
      modules/dnn/test/test_googlenet.cpp
  7. 8
      modules/dnn/test/test_misc.cpp
  8. 24
      modules/dnn/test/test_onnx_importer.cpp
  9. 42
      modules/dnn/test/test_tf_importer.cpp
  10. 8
      modules/dnn/test/test_torch_importer.cpp
  11. 2
      modules/objdetect/test/test_qrcode.cpp
  12. 28
      modules/ts/src/ts.cpp

2
modules/dnn/perf/perf_caffe.cpp
Unescape View File

@ -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

6
modules/dnn/perf/perf_net.cpp
Unescape View File

@ -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)

22
modules/dnn/test/test_backends.cpp
Unescape View File

@ -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;

39
modules/dnn/test/test_caffe_importer.cpp
Unescape View File

@ -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);

11
modules/dnn/test/test_darknet_importer.cpp
Unescape View File

@ -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);

6
modules/dnn/test/test_googlenet.cpp
Unescape View File

@ -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);

8
modules/dnn/test/test_misc.cpp
Unescape View File

@ -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());
}

24
modules/dnn/test/test_onnx_importer.cpp
Unescape View File

@ -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"));

42
modules/dnn/test/test_tf_importer.cpp
Unescape View File

@ -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);

8
modules/dnn/test/test_torch_importer.cpp
Unescape View File

@ -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);

2
modules/objdetect/test/test_qrcode.cpp
Unescape View File

@ -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;
{

28
modules/ts/src/ts.cpp
Unescape View File

@ -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_;
}
}
}

Write Preview
Loading…
Cancel
Save