Open Source Computer Vision Library https://opencv.org/
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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// copy or use the software.
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//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
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//
// Redistribution and use in source and binary forms, with or without modification,
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// this list of conditions and the following disclaimer.
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#ifndef __OPENCV_TEST_COMMON_HPP__
#define __OPENCV_TEST_COMMON_HPP__
#ifdef HAVE_OPENCL
#include "opencv2/core/ocl.hpp"
#endif
namespace cv { namespace dnn {
CV__DNN_EXPERIMENTAL_NS_BEGIN
static inline void PrintTo(const cv::dnn::Backend& v, std::ostream* os)
{
switch (v) {
case DNN_BACKEND_DEFAULT: *os << "DEFAULT"; return;
case DNN_BACKEND_HALIDE: *os << "HALIDE"; return;
case DNN_BACKEND_INFERENCE_ENGINE: *os << "DLIE"; return;
case DNN_BACKEND_OPENCV: *os << "OCV"; return;
} // don't use "default:" to emit compiler warnings
*os << "DNN_BACKEND_UNKNOWN(" << (int)v << ")";
}
static inline void PrintTo(const cv::dnn::Target& v, std::ostream* os)
{
switch (v) {
case DNN_TARGET_CPU: *os << "CPU"; return;
case DNN_TARGET_OPENCL: *os << "OCL"; return;
case DNN_TARGET_OPENCL_FP16: *os << "OCL_FP16"; return;
case DNN_TARGET_MYRIAD: *os << "MYRIAD"; return;
case DNN_TARGET_FPGA: *os << "FPGA"; return;
} // don't use "default:" to emit compiler warnings
*os << "DNN_TARGET_UNKNOWN(" << (int)v << ")";
}
using opencv_test::tuple;
using opencv_test::get;
static inline void PrintTo(const tuple<cv::dnn::Backend, cv::dnn::Target> v, std::ostream* os)
{
PrintTo(get<0>(v), os);
*os << "/";
PrintTo(get<1>(v), os);
}
CV__DNN_EXPERIMENTAL_NS_END
}} // namespace
static inline const std::string &getOpenCVExtraDir()
{
return cvtest::TS::ptr()->get_data_path();
}
static inline void normAssert(cv::InputArray ref, cv::InputArray test, const char *comment = "",
double l1 = 0.00001, double lInf = 0.0001)
{
double normL1 = cvtest::norm(ref, test, cv::NORM_L1) / ref.getMat().total();
EXPECT_LE(normL1, l1) << comment;
double normInf = cvtest::norm(ref, test, cv::NORM_INF);
EXPECT_LE(normInf, lInf) << comment;
}
static std::vector<cv::Rect2d> matToBoxes(const cv::Mat& m)
{
EXPECT_EQ(m.type(), CV_32FC1);
EXPECT_EQ(m.dims, 2);
EXPECT_EQ(m.cols, 4);
std::vector<cv::Rect2d> boxes(m.rows);
for (int i = 0; i < m.rows; ++i)
{
CV_Assert(m.row(i).isContinuous());
const float* data = m.ptr<float>(i);
double l = data[0], t = data[1], r = data[2], b = data[3];
boxes[i] = cv::Rect2d(l, t, r - l, b - t);
}
return boxes;
}
static inline void normAssertDetections(const std::vector<int>& refClassIds,
const std::vector<float>& refScores,
const std::vector<cv::Rect2d>& refBoxes,
const std::vector<int>& testClassIds,
const std::vector<float>& testScores,
const std::vector<cv::Rect2d>& testBoxes,
const char *comment = "", double confThreshold = 0.0,
double scores_diff = 1e-5, double boxes_iou_diff = 1e-4)
{
std::vector<bool> matchedRefBoxes(refBoxes.size(), false);
for (int i = 0; i < testBoxes.size(); ++i)
{
double testScore = testScores[i];
if (testScore < confThreshold)
continue;
int testClassId = testClassIds[i];
const cv::Rect2d& testBox = testBoxes[i];
bool matched = false;
for (int j = 0; j < refBoxes.size() && !matched; ++j)
{
if (!matchedRefBoxes[j] && testClassId == refClassIds[j] &&
std::abs(testScore - refScores[j]) < scores_diff)
{
double interArea = (testBox & refBoxes[j]).area();
double iou = interArea / (testBox.area() + refBoxes[j].area() - interArea);
if (std::abs(iou - 1.0) < boxes_iou_diff)
{
matched = true;
matchedRefBoxes[j] = true;
}
}
}
if (!matched)
std::cout << cv::format("Unmatched prediction: class %d score %f box ",
testClassId, testScore) << testBox << std::endl;
EXPECT_TRUE(matched) << comment;
}
// Check unmatched reference detections.
for (int i = 0; i < refBoxes.size(); ++i)
{
if (!matchedRefBoxes[i] && refScores[i] > confThreshold)
{
std::cout << cv::format("Unmatched reference: class %d score %f box ",
refClassIds[i], refScores[i]) << refBoxes[i] << std::endl;
EXPECT_LE(refScores[i], confThreshold) << comment;
}
}
}
// For SSD-based object detection networks which produce output of shape 1x1xNx7
// where N is a number of detections and an every detection is represented by
// a vector [batchId, classId, confidence, left, top, right, bottom].
static inline void normAssertDetections(cv::Mat ref, cv::Mat out, const char *comment = "",
double confThreshold = 0.0, double scores_diff = 1e-5,
double boxes_iou_diff = 1e-4)
{
CV_Assert(ref.total() % 7 == 0);
CV_Assert(out.total() % 7 == 0);
ref = ref.reshape(1, ref.total() / 7);
out = out.reshape(1, out.total() / 7);
cv::Mat refClassIds, testClassIds;
ref.col(1).convertTo(refClassIds, CV_32SC1);
out.col(1).convertTo(testClassIds, CV_32SC1);
std::vector<float> refScores(ref.col(2)), testScores(out.col(2));
std::vector<cv::Rect2d> refBoxes = matToBoxes(ref.colRange(3, 7));
std::vector<cv::Rect2d> testBoxes = matToBoxes(out.colRange(3, 7));
normAssertDetections(refClassIds, refScores, refBoxes, testClassIds, testScores,
testBoxes, comment, confThreshold, scores_diff, boxes_iou_diff);
}
static inline bool readFileInMemory(const std::string& filename, std::string& content)
{
std::ios::openmode mode = std::ios::in | std::ios::binary;
std::ifstream ifs(filename.c_str(), mode);
if (!ifs.is_open())
return false;
content.clear();
ifs.seekg(0, std::ios::end);
content.reserve(ifs.tellg());
ifs.seekg(0, std::ios::beg);
content.assign((std::istreambuf_iterator<char>(ifs)),
std::istreambuf_iterator<char>());
return true;
}
namespace opencv_test {
using namespace cv::dnn;
static inline
testing::internal::ParamGenerator< tuple<Backend, Target> > dnnBackendsAndTargets(
bool withInferenceEngine = true,
bool withHalide = false,
bool withCpuOCV = true
)
{
std::vector< tuple<Backend, Target> > targets;
std::vector< Target > available;
if (withHalide)
{
available = getAvailableTargets(DNN_BACKEND_HALIDE);
for (std::vector< Target >::const_iterator i = available.begin(); i != available.end(); ++i)
targets.push_back(make_tuple(DNN_BACKEND_HALIDE, *i));
}
if (withInferenceEngine)
{
available = getAvailableTargets(DNN_BACKEND_INFERENCE_ENGINE);
for (std::vector< Target >::const_iterator i = available.begin(); i != available.end(); ++i)
targets.push_back(make_tuple(DNN_BACKEND_INFERENCE_ENGINE, *i));
}
{
available = getAvailableTargets(DNN_BACKEND_OPENCV);
for (std::vector< Target >::const_iterator i = available.begin(); i != available.end(); ++i)
targets.push_back(make_tuple(DNN_BACKEND_OPENCV, *i));
}
return testing::ValuesIn(targets);
}
} // namespace
namespace opencv_test {
using namespace cv::dnn;
class DNNTestLayer : public TestWithParam<tuple<Backend, Target> >
{
public:
dnn::Backend backend;
dnn::Target target;
double default_l1, default_lInf;
DNNTestLayer()
{
backend = (dnn::Backend)(int)get<0>(GetParam());
target = (dnn::Target)(int)get<1>(GetParam());
getDefaultThresholds(backend, target, &default_l1, &default_lInf);
}
static void getDefaultThresholds(int backend, int target, double* l1, double* lInf)
{
if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD)
{
*l1 = 4e-3;
*lInf = 2e-2;
}
else
{
*l1 = 1e-5;
*lInf = 1e-4;
}
}
static void checkBackend(int backend, int target, Mat* inp = 0, Mat* ref = 0)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE < 2018030000
if (inp && ref && inp->size[0] != 1)
{
// Myriad plugin supports only batch size 1. Slice a single sample.
if (inp->size[0] == ref->size[0])
{
std::vector<cv::Range> range(inp->dims, Range::all());
range[0] = Range(0, 1);
*inp = inp->operator()(range);
range = std::vector<cv::Range>(ref->dims, Range::all());
range[0] = Range(0, 1);
*ref = ref->operator()(range);
}
else
throw SkipTestException("Myriad plugin supports only batch size 1");
}
#else
if (inp && ref && inp->dims == 4 && ref->dims == 4 &&
inp->size[0] != 1 && inp->size[0] != ref->size[0])
throw SkipTestException("Inconsistent batch size of input and output blobs for Myriad plugin");
#endif
}
}
protected:
void checkBackend(Mat* inp = 0, Mat* ref = 0)
{
checkBackend(backend, target, inp, ref);
}
};
} // namespace
#endif