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Merge pull request #18287 from mpashchenkov:mp/ocv-gapi-blue-branch

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[G-API]: Add four kernels to parse NN outputs & provide information in Streaming scenarios

* Kernels from GL "blue" branch, acc and perf tests

* Code cleanup

* Output fix

* Comment fix

* Added new file for parsers, stylistic corrections

* Added end line

* Namespace fix

* Code cleanup

* nnparsers.hpp moved to gapi/infer/, nnparsers -> parsers

* Removed cv:: from parsers.hpp
This commit is contained in:
Maxim Pashchenkov 2020-09-18 16:31:16 +03:00 committed by GitHub
parent 830d8d6b75
commit a63cee2139
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16 changed files with 1423 additions and 1 deletions
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2
modules/gapi/CMakeLists.txt
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@ -71,6 +71,7 @@ set(gapi_srcs
src/api/kernels_core.cpp
src/api/kernels_imgproc.cpp
src/api/kernels_video.cpp
src/api/kernels_nnparsers.cpp
src/api/render.cpp
src/api/render_ocv.cpp
src/api/ginfer.cpp
@ -105,6 +106,7 @@ set(gapi_srcs
src/backends/cpu/gcpuimgproc.cpp
src/backends/cpu/gcpuvideo.cpp
src/backends/cpu/gcpucore.cpp
src/backends/cpu/gnnparsers.cpp
# Fluid Backend (also built-in, FIXME:move away)
src/backends/fluid/gfluidbuffer.cpp

32
modules/gapi/include/opencv2/gapi/core.hpp
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@ -31,7 +31,7 @@ namespace core {
using GMat2 = std::tuple<GMat,GMat>;
using GMat3 = std::tuple<GMat,GMat,GMat>; // FIXME: how to avoid this?
using GMat4 = std::tuple<GMat,GMat,GMat,GMat>;
using GMatScalar = std::tuple<GMat, GScalar>;
using GMatScalar = std::tuple<GMat, GScalar>;
G_TYPED_KERNEL(GAdd, <GMat(GMat, GMat, int)>, "org.opencv.core.math.add") {
static GMatDesc outMeta(GMatDesc a, GMatDesc b, int ddepth) {
@ -501,6 +501,18 @@ namespace core {
return in.withType(in.depth, in.chan).withSize(dsize);
}
};
G_TYPED_KERNEL(GSize, <GOpaque<Size>(GMat)>, "org.opencv.core.size") {
static GOpaqueDesc outMeta(const GMatDesc&) {
return empty_gopaque_desc();
}
};
G_TYPED_KERNEL(GSizeR, <GOpaque<Size>(GOpaque<Rect>)>, "org.opencv.core.sizeR") {
static GOpaqueDesc outMeta(const GOpaqueDesc&) {
return empty_gopaque_desc();
}
};
}
//! @addtogroup gapi_math
@ -1720,6 +1732,24 @@ GAPI_EXPORTS GMat warpAffine(const GMat& src, const Mat& M, const Size& dsize, i
int borderMode = cv::BORDER_CONSTANT, const Scalar& borderValue = Scalar());
//! @} gapi_transform
/** @brief Gets dimensions from Mat.
@note Function textual ID is "org.opencv.core.size"
@param src Input tensor
@return Size (tensor dimensions).
*/
GAPI_EXPORTS GOpaque<Size> size(const GMat& src);
/** @overload
Gets dimensions from rectangle.
@note Function textual ID is "org.opencv.core.sizeR"
@param r Input rectangle.
@return Size (rectangle dimensions).
*/
GAPI_EXPORTS GOpaque<Size> size(const GOpaque<Rect>& r);
} //namespace gapi
} //namespace cv

125
modules/gapi/include/opencv2/gapi/infer/parsers.hpp Normal file
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@ -0,0 +1,125 @@
// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
//
// Copyright (C) 2020 Intel Corporation
#ifndef OPENCV_GAPI_PARSERS_HPP
#define OPENCV_GAPI_PARSERS_HPP
#include <utility> // std::tuple
#include <opencv2/gapi/gmat.hpp>
#include <opencv2/gapi/gkernel.hpp>
namespace cv { namespace gapi {
namespace nn {
namespace parsers {
using GRects = GArray<Rect>;
using GDetections = std::tuple<GArray<Rect>, GArray<int>>;
G_TYPED_KERNEL(GParseSSDBL, <GDetections(GMat, GOpaque<Size>, float, int)>,
"org.opencv.nn.parsers.parseSSD_BL") {
static std::tuple<GArrayDesc,GArrayDesc> outMeta(const GMatDesc&, const GOpaqueDesc&, float, int) {
return std::make_tuple(empty_array_desc(), empty_array_desc());
}
};
G_TYPED_KERNEL(GParseSSD, <GRects(GMat, GOpaque<Size>, float, bool, bool)>,
"org.opencv.nn.parsers.parseSSD") {
static GArrayDesc outMeta(const GMatDesc&, const GOpaqueDesc&, float, bool, bool) {
return empty_array_desc();
}
};
G_TYPED_KERNEL(GParseYolo, <GDetections(GMat, GOpaque<Size>, float, float, std::vector<float>)>,
"org.opencv.nn.parsers.parseYolo") {
static std::tuple<GArrayDesc, GArrayDesc> outMeta(const GMatDesc&, const GOpaqueDesc&,
float, float, const std::vector<float>&) {
return std::make_tuple(empty_array_desc(), empty_array_desc());
}
static const std::vector<float>& defaultAnchors() {
static std::vector<float> anchors {
0.57273f, 0.677385f, 1.87446f, 2.06253f, 3.33843f, 5.47434f, 7.88282f, 3.52778f, 9.77052f, 9.16828f
};
return anchors;
}
};
} // namespace parsers
} // namespace nn
/** @brief Parses output of SSD network.
Extracts detection information (box, confidence, label) from SSD output and
filters it by given confidence and label.
@note Function textual ID is "org.opencv.nn.parsers.parseSSD_BL"
@param in Input CV_32F tensor with {1,1,N,7} dimensions.
@param inSz Size to project detected boxes to (size of the input image).
@param confidenceThreshold If confidence of the
detection is smaller than confidence threshold, detection is rejected.
@param filterLabel If provided (!= -1), only detections with
given label will get to the output.
@return a tuple with a vector of detected boxes and a vector of appropriate labels.
*/
GAPI_EXPORTS std::tuple<GArray<Rect>, GArray<int>> parseSSD(const GMat& in,
const GOpaque<Size>& inSz,
const float confidenceThreshold = 0.5f,
const int filterLabel = -1);
/** @overload
Extracts detection information (box, confidence) from SSD output and
filters it by given confidence and by going out of bounds.
@note Function textual ID is "org.opencv.nn.parsers.parseSSD"
@param in Input CV_32F tensor with {1,1,N,7} dimensions.
@param inSz Size to project detected boxes to (size of the input image).
@param confidenceThreshold If confidence of the
detection is smaller than confidence threshold, detection is rejected.
@param alignmentToSquare If provided true, bounding boxes are extended to squares.
The center of the rectangle remains unchanged, the side of the square is
the larger side of the rectangle.
@param filterOutOfBounds If provided true, out-of-frame boxes are filtered.
@return a vector of detected bounding boxes.
*/
GAPI_EXPORTS GArray<Rect> parseSSD(const GMat& in,
const GOpaque<Size>& inSz,
const float confidenceThreshold = 0.5f,
const bool alignmentToSquare = false,
const bool filterOutOfBounds = false);
/** @brief Parses output of Yolo network.
Extracts detection information (box, confidence, label) from Yolo output,
filters it by given confidence and performs non-maximum supression for overlapping boxes.
@note Function textual ID is "org.opencv.nn.parsers.parseYolo"
@param in Input CV_32F tensor with {1,13,13,N} dimensions, N should satisfy:
\f[\texttt{N} = (\texttt{num_classes} + \texttt{5}) * \texttt{5},\f]
where num_classes - a number of classes Yolo network was trained with.
@param inSz Size to project detected boxes to (size of the input image).
@param confidenceThreshold If confidence of the
detection is smaller than confidence threshold, detection is rejected.
@param nmsThreshold Non-maximum supression threshold which controls minimum
relative box intersection area required for rejecting the box with a smaller confidence.
If 1.f, nms is not performed and no boxes are rejected.
@param anchors Anchors Yolo network was trained with.
@note The default anchor values are taken from openvinotoolkit docs:
https://docs.openvinotoolkit.org/latest/omz_models_intel_yolo_v2_tiny_vehicle_detection_0001_description_yolo_v2_tiny_vehicle_detection_0001.html#output.
@return a tuple with a vector of detected boxes and a vector of appropriate labels.
*/
GAPI_EXPORTS std::tuple<GArray<Rect>, GArray<int>> parseYolo(const GMat& in,
const GOpaque<Size>& inSz,
const float confidenceThreshold = 0.5f,
const float nmsThreshold = 0.5f,
const std::vector<float>& anchors
= nn::parsers::GParseYolo::defaultAnchors());
} // namespace gapi
} // namespace cv
#endif // OPENCV_GAPI_PARSERS_HPP

6
modules/gapi/perf/common/gapi_core_perf_tests.hpp
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@ -10,6 +10,7 @@
#include "../../test/common/gapi_tests_common.hpp"
#include "../../test/common/gapi_parsers_tests_common.hpp"
#include <opencv2/gapi/core.hpp>
namespace opencv_test
@ -73,5 +74,10 @@ namespace opencv_test
class ConvertToPerfTest : public TestPerfParams<tuple<MatType, int, cv::Size, cv::GCompileArgs>> {};
class ResizePerfTest : public TestPerfParams<tuple<compare_f, MatType, int, cv::Size, cv::Size, cv::GCompileArgs>> {};
class ResizeFxFyPerfTest : public TestPerfParams<tuple<compare_f, MatType, int, cv::Size, double, double, cv::GCompileArgs>> {};
class ParseSSDBLPerfTest : public TestPerfParams<tuple<cv::Size, float, int, cv::GCompileArgs>>, public ParserSSDTest {};
class ParseSSDPerfTest : public TestPerfParams<tuple<cv::Size, float, bool, bool, cv::GCompileArgs>>, public ParserSSDTest {};
class ParseYoloPerfTest : public TestPerfParams<tuple<cv::Size, float, float, int, cv::GCompileArgs>>, public ParserYoloTest {};
class SizePerfTest : public TestPerfParams<tuple<MatType, cv::Size, cv::GCompileArgs>> {};
class SizeRPerfTest : public TestPerfParams<tuple<cv::Size, cv::GCompileArgs>> {};
}
#endif // OPENCV_GAPI_CORE_PERF_TESTS_HPP

182
modules/gapi/perf/common/gapi_core_perf_tests_inl.hpp
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@ -1930,5 +1930,187 @@ PERF_TEST_P_(ResizeFxFyPerfTest, TestPerformance)
//------------------------------------------------------------------------------
PERF_TEST_P_(ParseSSDBLPerfTest, TestPerformance)
{
cv::Size sz;
float confidence_threshold = 0.0f;
int filter_label = 0;
cv::GCompileArgs compile_args;
std::tie(sz, confidence_threshold, filter_label, compile_args) = GetParam();
cv::Mat in_mat = generateSSDoutput(sz);
std::vector<cv::Rect> boxes_gapi, boxes_ref;
std::vector<int> labels_gapi, labels_ref;
// Reference code //////////////////////////////////////////////////////////
parseSSDBLref(in_mat, sz, confidence_threshold, filter_label, boxes_ref, labels_ref);
// G-API code //////////////////////////////////////////////////////////////
cv::GMat in;
cv::GOpaque<cv::Size> op_sz;
auto out = cv::gapi::parseSSD(in, op_sz, confidence_threshold, filter_label);
cv::GComputation c(cv::GIn(in, op_sz), cv::GOut(std::get<0>(out), std::get<1>(out)));
// Warm-up graph engine:
auto cc = c.compile(descr_of(in_mat), descr_of(sz), std::move(compile_args));
cc(cv::gin(in_mat, sz), cv::gout(boxes_gapi, labels_gapi));
TEST_CYCLE()
{
cc(cv::gin(in_mat, sz), cv::gout(boxes_gapi, labels_gapi));
}
// Comparison ////////////////////////////////////////////////////////////
{
EXPECT_TRUE(boxes_gapi == boxes_ref);
EXPECT_TRUE(labels_gapi == labels_ref);
}
SANITY_CHECK_NOTHING();
}
//------------------------------------------------------------------------------
PERF_TEST_P_(ParseSSDPerfTest, TestPerformance)
{
cv::Size sz;
float confidence_threshold = 0;
bool alignment_to_square = false, filter_out_of_bounds = false;
cv::GCompileArgs compile_args;
std::tie(sz, confidence_threshold, alignment_to_square, filter_out_of_bounds, compile_args) = GetParam();
cv::Mat in_mat = generateSSDoutput(sz);
std::vector<cv::Rect> boxes_gapi, boxes_ref;
// Reference code //////////////////////////////////////////////////////////
parseSSDref(in_mat, sz, confidence_threshold, alignment_to_square, filter_out_of_bounds, boxes_ref);
// G-API code //////////////////////////////////////////////////////////////
cv::GMat in;
cv::GOpaque<cv::Size> op_sz;
auto out = cv::gapi::parseSSD(in, op_sz, confidence_threshold, alignment_to_square, filter_out_of_bounds);
cv::GComputation c(cv::GIn(in, op_sz), cv::GOut(out));
// Warm-up graph engine:
auto cc = c.compile(descr_of(in_mat), descr_of(sz), std::move(compile_args));
cc(cv::gin(in_mat, sz), cv::gout(boxes_gapi));
TEST_CYCLE()
{
cc(cv::gin(in_mat, sz), cv::gout(boxes_gapi));
}
// Comparison ////////////////////////////////////////////////////////////
{
EXPECT_TRUE(boxes_gapi == boxes_ref);
}
SANITY_CHECK_NOTHING();
}
//------------------------------------------------------------------------------
PERF_TEST_P_(ParseYoloPerfTest, TestPerformance)
{
cv::Size sz;
float confidence_threshold = 0.0f, nms_threshold = 0.0f;
int num_classes = 0;
cv::GCompileArgs compile_args;
std::tie(sz, confidence_threshold, nms_threshold, num_classes, compile_args) = GetParam();
cv::Mat in_mat = generateYoloOutput(num_classes);
auto anchors = cv::gapi::nn::parsers::GParseYolo::defaultAnchors();
std::vector<cv::Rect> boxes_gapi, boxes_ref;
std::vector<int> labels_gapi, labels_ref;
// Reference code //////////////////////////////////////////////////////////
parseYoloRef(in_mat, sz, confidence_threshold, nms_threshold, num_classes, anchors, boxes_ref, labels_ref);
// G-API code //////////////////////////////////////////////////////////////
cv::GMat in;
cv::GOpaque<cv::Size> op_sz;
auto out = cv::gapi::parseYolo(in, op_sz, confidence_threshold, nms_threshold, anchors);
cv::GComputation c(cv::GIn(in, op_sz), cv::GOut(std::get<0>(out), std::get<1>(out)));
// Warm-up graph engine:
auto cc = c.compile(descr_of(in_mat), descr_of(sz), std::move(compile_args));
cc(cv::gin(in_mat, sz), cv::gout(boxes_gapi, labels_gapi));
TEST_CYCLE()
{
cc(cv::gin(in_mat, sz), cv::gout(boxes_gapi, labels_gapi));
}
// Comparison ////////////////////////////////////////////////////////////
{
EXPECT_TRUE(boxes_gapi == boxes_ref);
EXPECT_TRUE(labels_gapi == labels_ref);
}
SANITY_CHECK_NOTHING();
}
//------------------------------------------------------------------------------
PERF_TEST_P_(SizePerfTest, TestPerformance)
{
MatType type;
cv::Size sz;
cv::GCompileArgs compile_args;
std::tie(type, sz, compile_args) = GetParam();
in_mat1 = cv::Mat(sz, type);
// G-API code //////////////////////////////////////////////////////////////
cv::GMat in;
auto out = cv::gapi::size(in);
cv::GComputation c(cv::GIn(in), cv::GOut(out));
cv::Size out_sz;
// Warm-up graph engine:
auto cc = c.compile(descr_of(in_mat1), std::move(compile_args));
cc(cv::gin(in_mat1), cv::gout(out_sz));
TEST_CYCLE()
{
cc(cv::gin(in_mat1), cv::gout(out_sz));
}
// Comparison ////////////////////////////////////////////////////////////
{
EXPECT_EQ(out_sz, sz);
}
SANITY_CHECK_NOTHING();
}
//------------------------------------------------------------------------------
PERF_TEST_P_(SizeRPerfTest, TestPerformance)
{
cv::Size sz;
cv::GCompileArgs compile_args;
std::tie(sz, compile_args) = GetParam();
cv::Rect rect(cv::Point(0,0), sz);
// G-API code //////////////////////////////////////////////////////////////
cv::GOpaque<cv::Rect> op_rect;
auto out = cv::gapi::size(op_rect);
cv::GComputation c(cv::GIn(op_rect), cv::GOut(out));
cv::Size out_sz;
// Warm-up graph engine:
auto cc = c.compile(descr_of(rect), std::move(compile_args));
cc(cv::gin(rect), cv::gout(out_sz));
TEST_CYCLE()
{
cc(cv::gin(rect), cv::gout(out_sz));
}
// Comparison ////////////////////////////////////////////////////////////
{
EXPECT_EQ(out_sz, sz);
}
SANITY_CHECK_NOTHING();
}
}
#endif // OPENCV_GAPI_CORE_PERF_TESTS_INL_HPP

29
modules/gapi/perf/cpu/gapi_core_perf_tests_cpu.cpp
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@ -288,4 +288,33 @@ INSTANTIATE_TEST_CASE_P(ResizeFxFyPerfTestCPU, ResizeFxFyPerfTest,
Values(0.5, 0.1),
Values(0.5, 0.1),
Values(cv::compile_args(CORE_CPU))));
INSTANTIATE_TEST_CASE_P(ParseSSDBLPerfTestCPU, ParseSSDBLPerfTest,
Combine(Values(sz720p, sz1080p),
Values(0.3f, 0.7f),
Values(0, 1),
Values(cv::compile_args(CORE_CPU))));
INSTANTIATE_TEST_CASE_P(ParseSSDPerfTestCPU, ParseSSDPerfTest,
Combine(Values(sz720p, sz1080p),
Values(0.3f, 0.7f),
testing::Bool(),
testing::Bool(),
Values(cv::compile_args(CORE_CPU))));
INSTANTIATE_TEST_CASE_P(ParseYoloPerfTestCPU, ParseYoloPerfTest,
Combine(Values(sz720p, sz1080p),
Values(0.3f, 0.7f),
Values(0.5),
Values(7, 80),
Values(cv::compile_args(CORE_CPU))));
INSTANTIATE_TEST_CASE_P(SizePerfTestCPU, SizePerfTest,
Combine(Values(CV_8UC1, CV_8UC3, CV_32FC1),
Values(szSmall128, szVGA, sz720p, sz1080p),
Values(cv::compile_args(CORE_CPU))));
INSTANTIATE_TEST_CASE_P(SizeRPerfTestCPU, SizeRPerfTest,
Combine(Values(szSmall128, szVGA, sz720p, sz1080p),
Values(cv::compile_args(CORE_CPU))));
} // opencv_test

10
modules/gapi/src/api/kernels_core.cpp
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@ -383,5 +383,15 @@ GMat warpAffine(const GMat& src, const Mat& M, const Size& dsize, int flags,
return core::GWarpAffine::on(src, M, dsize, flags, borderMode, borderValue);
}
GOpaque<Size> size(const GMat& src)
{
return core::GSize::on(src);
}
GOpaque<Size> size(const GOpaque<Rect>& r)
{
return core::GSizeR::on(r);
}
} //namespace gapi
} //namespace cv

44
modules/gapi/src/api/kernels_nnparsers.cpp Normal file
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@ -0,0 +1,44 @@
// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
//
// Copyright (C) 2020 Intel Corporation
#include "precomp.hpp"
#include <opencv2/gapi/infer/parsers.hpp>
#include <tuple>
#include <numeric>
namespace cv { namespace gapi {
nn::parsers::GDetections parseSSD(const GMat& in,
const GOpaque<Size>& inSz,
const float confidenceThreshold,
const int filterLabel)
{
return nn::parsers::GParseSSDBL::on(in, inSz, confidenceThreshold, filterLabel);
}
nn::parsers::GRects parseSSD(const GMat& in,
const GOpaque<Size>& inSz,
const float confidenceThreshold,
const bool alignmentToSquare,
const bool filterOutOfBounds)
{
return nn::parsers::GParseSSD::on(in, inSz, confidenceThreshold, alignmentToSquare, filterOutOfBounds);
}
nn::parsers::GDetections parseYolo(const GMat& in,
const GOpaque<Size>& inSz,
const float confidenceThreshold,
const float nmsThreshold,
const std::vector<float>& anchors)
{
return nn::parsers::GParseYolo::on(in, inSz, confidenceThreshold, nmsThreshold, anchors);
}
} //namespace gapi
} //namespace cv

63
modules/gapi/src/backends/cpu/gcpucore.cpp
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@ -6,6 +6,7 @@
#include "precomp.hpp"
#include "gnnparsers.hpp"
#include <opencv2/gapi/core.hpp>
#include <opencv2/gapi/cpu/core.hpp>
@ -576,6 +577,63 @@ GAPI_OCV_KERNEL(GCPUWarpAffine, cv::gapi::core::GWarpAffine)
}
};
GAPI_OCV_KERNEL(GCPUParseSSDBL, cv::gapi::nn::parsers::GParseSSDBL)
{
static void run(const cv::Mat& in_ssd_result,
const cv::Size& in_size,
const float confidence_threshold,
const int filter_label,
std::vector<cv::Rect>& out_boxes,
std::vector<int>& out_labels)
{
cv::parseSSDBL(in_ssd_result, in_size, confidence_threshold, filter_label, out_boxes, out_labels);
}
};
GAPI_OCV_KERNEL(GOCVParseSSD, cv::gapi::nn::parsers::GParseSSD)
{
static void run(const cv::Mat& in_ssd_result,
const cv::Size& in_size,
const float confidence_threshold,
const bool alignment_to_square,
const bool filter_out_of_bounds,
std::vector<cv::Rect>& out_boxes)
{
cv::parseSSD(in_ssd_result, in_size, confidence_threshold, alignment_to_square, filter_out_of_bounds, out_boxes);
}
};
GAPI_OCV_KERNEL(GCPUParseYolo, cv::gapi::nn::parsers::GParseYolo)
{
static void run(const cv::Mat& in_yolo_result,
const cv::Size& in_size,
const float confidence_threshold,
const float nms_threshold,
const std::vector<float>& anchors,
std::vector<cv::Rect>& out_boxes,
std::vector<int>& out_labels)
{
cv::parseYolo(in_yolo_result, in_size, confidence_threshold, nms_threshold, anchors, out_boxes, out_labels);
}
};
GAPI_OCV_KERNEL(GCPUSize, cv::gapi::core::GSize)
{
static void run(const cv::Mat& in, cv::Size& out)
{
out.width = in.cols;
out.height = in.rows;
}
};
GAPI_OCV_KERNEL(GCPUSizeR, cv::gapi::core::GSizeR)
{
static void run(const cv::Rect& in, cv::Size& out)
{
out.width = in.width;
out.height = in.height;
}
};
cv::gapi::GKernelPackage cv::gapi::core::cpu::kernels()
{
@ -647,6 +705,11 @@ cv::gapi::GKernelPackage cv::gapi::core::cpu::kernels()
, GCPUNormalize
, GCPUWarpPerspective
, GCPUWarpAffine
, GCPUParseSSDBL
, GOCVParseSSD
, GCPUParseYolo
, GCPUSize
, GCPUSizeR
>();
return pkg;
}

338
modules/gapi/src/backends/cpu/gnnparsers.cpp Normal file
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@ -0,0 +1,338 @@
// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
//
// Copyright (C) 2020 Intel Corporation
#include "gnnparsers.hpp"
namespace cv
{
namespace gapi
{
namespace nn
{
class YoloParser
{
public:
YoloParser(const float* out, const int side, const int lcoords, const int lclasses)
: m_out(out), m_side(side), m_lcoords(lcoords), m_lclasses(lclasses)
{}
float scale(const int i, const int b)
{
int obj_index = index(i, b, m_lcoords);
return m_out[obj_index];
}
double x(const int i, const int b)
{
int box_index = index(i, b, 0);
int col = i % m_side;
return (col + m_out[box_index]) / m_side;
}
double y(const int i, const int b)
{
int box_index = index(i, b, 0);
int row = i / m_side;
return (row + m_out[box_index + m_side * m_side]) / m_side;
}
double width(const int i, const int b, const float anchor)
{
int box_index = index(i, b, 0);
return std::exp(m_out[box_index + 2 * m_side * m_side]) * anchor / m_side;
}
double height(const int i, const int b, const float anchor)
{
int box_index = index(i, b, 0);
return std::exp(m_out[box_index + 3 * m_side * m_side]) * anchor / m_side;
}
float classConf(const int i, const int b, const int label)
{
int class_index = index(i, b, m_lcoords + 1 + label);
return m_out[class_index];
}
cv::Rect toBox(const double x, const double y, const double h, const double w, const cv::Size& in_sz)
{
auto h_scale = in_sz.height;
auto w_scale = in_sz.width;
cv::Rect r;
r.x = static_cast<int>((x - w / 2) * w_scale);
r.y = static_cast<int>((y - h / 2) * h_scale);
r.width = static_cast<int>(w * w_scale);
r.height = static_cast<int>(h * h_scale);
return r;
}
private:
const float* m_out = nullptr;
int m_side = 0, m_lcoords = 0, m_lclasses = 0;
int index(const int i, const int b, const int entry)
{
return b * m_side * m_side * (m_lcoords + m_lclasses + 1) + entry * m_side * m_side + i;
}
};
struct YoloParams
{
int num = 5;
int coords = 4;
};
struct Detection
{
Detection(const cv::Rect& in_rect, const float in_conf, const int in_label)
: rect(in_rect), conf(in_conf), label(in_label)
{}
cv::Rect rect;
float conf = 0.0f;
int label = 0;
};
class SSDParser
{
public:
SSDParser(const cv::MatSize& in_ssd_dims, const cv::Size& in_size, const float* data)
: m_dims(in_ssd_dims), m_maxProp(in_ssd_dims[2]), m_objSize(in_ssd_dims[3]),
m_data(data), m_surface(cv::Rect({0,0}, in_size)), m_size(in_size)
{
GAPI_Assert(in_ssd_dims.dims() == 4u); // Fixed output layout
GAPI_Assert(m_objSize == 7); // Fixed SSD object size
}
void adjustBoundingBox(cv::Rect& boundingBox)
{
auto w = boundingBox.width;
auto h = boundingBox.height;
boundingBox.x -= static_cast<int>(0.067 * w);
boundingBox.y -= static_cast<int>(0.028 * h);
boundingBox.width += static_cast<int>(0.15 * w);
boundingBox.height += static_cast<int>(0.13 * h);
if (boundingBox.width < boundingBox.height)
{
auto dx = (boundingBox.height - boundingBox.width);
boundingBox.x -= dx / 2;
boundingBox.width += dx;
}
else
{
auto dy = (boundingBox.width - boundingBox.height);
boundingBox.y -= dy / 2;
boundingBox.height += dy;
}
}
std::tuple<cv::Rect, float, float, int> extract(const size_t step)
{
const float* it = m_data + step * m_objSize;
float image_id = it[0];
int label = static_cast<int>(it[1]);
float confidence = it[2];
float rc_left = it[3];
float rc_top = it[4];
float rc_right = it[5];
float rc_bottom = it[6];
cv::Rect rc; // Map relative coordinates to the original image scale
rc.x = static_cast<int>(rc_left * m_size.width);
rc.y = static_cast<int>(rc_top * m_size.height);
rc.width = static_cast<int>(rc_right * m_size.width) - rc.x;
rc.height = static_cast<int>(rc_bottom * m_size.height) - rc.y;
return std::make_tuple(rc, image_id, confidence, label);
}
int getMaxProposals()
{
return m_maxProp;
}
cv::Rect getSurface()
{
return m_surface;
}
private:
const cv::MatSize m_dims;
int m_maxProp = 0, m_objSize = 0;
const float* m_data = nullptr;
const cv::Rect m_surface;
const cv::Size m_size;
};
} // namespace nn
} // namespace gapi
void parseSSDBL(const cv::Mat& in_ssd_result,
const cv::Size& in_size,
const float confidence_threshold,
const int filter_label,
std::vector<cv::Rect>& out_boxes,
std::vector<int>& out_labels)
{
cv::gapi::nn::SSDParser parser(in_ssd_result.size, in_size, in_ssd_result.ptr<float>());
out_boxes.clear();
out_labels.clear();
cv::Rect rc;
float image_id, confidence;
int label;
const size_t range = parser.getMaxProposals();
for (size_t i = 0; i < range; ++i)
{
std::tie(rc, image_id, confidence, label) = parser.extract(i);
if (image_id < 0.f)
{
break; // marks end-of-detections
}
if (confidence < confidence_threshold ||
(filter_label != -1 && label != filter_label))
{
continue; // filter out object classes if filter is specified
} // and skip objects with low confidence
out_boxes.emplace_back(rc & parser.getSurface());
out_labels.emplace_back(label);
}
}
void parseSSD(const cv::Mat& in_ssd_result,
const cv::Size& in_size,
const float confidence_threshold,
const bool alignment_to_square,
const bool filter_out_of_bounds,
std::vector<cv::Rect>& out_boxes)
{
cv::gapi::nn::SSDParser parser(in_ssd_result.size, in_size, in_ssd_result.ptr<float>());
out_boxes.clear();
cv::Rect rc;
float image_id, confidence;
int label;
const size_t range = parser.getMaxProposals();
for (size_t i = 0; i < range; ++i)
{
std::tie(rc, image_id, confidence, label) = parser.extract(i);
if (image_id < 0.f)
{
break; // marks end-of-detections
}
if (confidence < confidence_threshold)
{
continue; // skip objects with low confidence
}
if (alignment_to_square)
{
parser.adjustBoundingBox(rc);
}
const auto clipped_rc = rc & parser.getSurface();
if (filter_out_of_bounds)
{
if (clipped_rc.area() != rc.area())
{
continue;
}
}
out_boxes.emplace_back(clipped_rc);
}
}
void parseYolo(const cv::Mat& in_yolo_result,
const cv::Size& in_size,
const float confidence_threshold,
const float nms_threshold,
const std::vector<float>& anchors,
std::vector<cv::Rect>& out_boxes,
std::vector<int>& out_labels)
{
const auto& dims = in_yolo_result.size;
GAPI_Assert(dims.dims() == 4);
GAPI_Assert(dims[0] == 1);
GAPI_Assert(dims[1] == 13);
GAPI_Assert(dims[2] == 13);
GAPI_Assert(dims[3] % 5 == 0); // 5 boxes
const auto num_classes = dims[3] / 5 - 5;
GAPI_Assert(num_classes > 0);
GAPI_Assert(0 < nms_threshold && nms_threshold <= 1);
out_boxes.clear();
out_labels.clear();
gapi::nn::YoloParams params;
constexpr auto side = 13;
constexpr auto side_square = side * side;
const auto output = in_yolo_result.ptr<float>();
gapi::nn::YoloParser parser(output, side, params.coords, num_classes);
std::vector<gapi::nn::Detection> detections;
for (int i = 0; i < side_square; ++i)
{
for (int b = 0; b < params.num; ++b)
{
float scale = parser.scale(i, b);
if (scale < confidence_threshold)
{
continue;
}
double x = parser.x(i, b);
double y = parser.y(i, b);
double height = parser.height(i, b, anchors[2 * b + 1]);
double width = parser.width(i, b, anchors[2 * b]);
for (int label = 0; label < num_classes; ++label)
{
float prob = scale * parser.classConf(i,b,label);
if (prob < confidence_threshold)
{
continue;
}
auto box = parser.toBox(x, y, height, width, in_size);
detections.emplace_back(gapi::nn::Detection(box, prob, label));
}
}
}
std::stable_sort(std::begin(detections), std::end(detections),
[](const gapi::nn::Detection& a, const gapi::nn::Detection& b)
{
return a.conf > b.conf;
});
if (nms_threshold < 1.0f)
{
for (const auto& d : detections)
{
// Reject boxes which overlap with previously pushed ones
// (They are sorted by confidence, so rejected box
// always has a smaller confidence
if (std::end(out_boxes) ==
std::find_if(std::begin(out_boxes), std::end(out_boxes),
[&d, nms_threshold](const cv::Rect& r)
{
float rectOverlap = 1.f - static_cast<float>(jaccardDistance(r, d.rect));
return rectOverlap > nms_threshold;
}))
{
out_boxes. emplace_back(d.rect);
out_labels.emplace_back(d.label);
}
}
}
else
{
for (const auto& d: detections)
{
out_boxes. emplace_back(d.rect);
out_labels.emplace_back(d.label);
}
}
}
} // namespace cv

36
modules/gapi/src/backends/cpu/gnnparsers.hpp Normal file
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@ -0,0 +1,36 @@
// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
//
// Copyright (C) 2020 Intel Corporation
#include <opencv2/gapi/infer/parsers.hpp>
#ifndef OPENCV_NNPARSERS_OCV_HPP
#define OPENCV_NNPARSERS_OCV_HPP
namespace cv
{
void parseSSDBL(const cv::Mat& in_ssd_result,
const cv::Size& in_size,
const float confidence_threshold,
const int filter_label,
std::vector<cv::Rect>& out_boxes,
std::vector<int>& out_labels);
void parseSSD(const cv::Mat& in_ssd_result,
const cv::Size& in_size,
const float confidence_threshold,
const bool alignment_to_square,
const bool filter_out_of_bounds,
std::vector<cv::Rect>& out_boxes);
void parseYolo(const cv::Mat& in_yolo_result,
const cv::Size& in_size,
const float confidence_threshold,
const float nms_threshold,
const std::vector<float>& anchors,
std::vector<cv::Rect>& out_boxes,
std::vector<int>& out_labels);
}
#endif // OPENCV_NNPARSERS_OCV_HPP

10
modules/gapi/test/common/gapi_core_tests.hpp
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@ -11,6 +11,7 @@
#include <iostream>
#include "gapi_tests_common.hpp"
#include "gapi_parsers_tests_common.hpp"
namespace opencv_test
{
@ -149,6 +150,15 @@ GAPI_TEST_FIXTURE(WarpPerspectiveTest, initMatrixRandU,
GAPI_TEST_FIXTURE(WarpAffineTest, initMatrixRandU,
FIXTURE_API(CompareMats, double , double, int, int, cv::Scalar),
6, cmpF, angle, scale, flags, border_mode, border_value)
GAPI_TEST_EXT_BASE_FIXTURE(ParseSSDBLTest, ParserSSDTest, initNothing,
FIXTURE_API(float, int), 2, confidence_threshold, filter_label)
GAPI_TEST_EXT_BASE_FIXTURE(ParseSSDTest, ParserSSDTest, initNothing,
FIXTURE_API(float, bool, bool), 3, confidence_threshold, alignment_to_square, filter_out_of_bounds)
GAPI_TEST_EXT_BASE_FIXTURE(ParseYoloTest, ParserYoloTest, initNothing,
FIXTURE_API(float, float, int), 3, confidence_threshold, nms_threshold, num_classes)
GAPI_TEST_FIXTURE(SizeTest, initMatrixRandU, <>, 0)
GAPI_TEST_FIXTURE(SizeRTest, initNothing, <>, 0)
} // opencv_test
#endif //OPENCV_GAPI_CORE_TESTS_HPP

90
modules/gapi/test/common/gapi_core_tests_inl.hpp
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@ -9,6 +9,7 @@
#define OPENCV_GAPI_CORE_TESTS_INL_HPP
#include <opencv2/gapi/core.hpp>
#include <opencv2/gapi/infer/parsers.hpp>
#include "gapi_core_tests.hpp"
namespace opencv_test
@ -1578,6 +1579,95 @@ TEST_P(ReInitOutTest, TestWithAdd)
run_and_compare();
}
TEST_P(ParseSSDBLTest, ParseTest)
{
cv::Mat in_mat = generateSSDoutput(sz);
std::vector<cv::Rect> boxes_gapi, boxes_ref;
std::vector<int> labels_gapi, labels_ref;
// G-API code //////////////////////////////////////////////////////////////
cv::GMat in;
cv::GOpaque<cv::Size> op_sz;
auto out = cv::gapi::parseSSD(in, op_sz, confidence_threshold, filter_label);
cv::GComputation c(cv::GIn(in, op_sz), cv::GOut(std::get<0>(out), std::get<1>(out)));
c.apply(cv::gin(in_mat, sz), cv::gout(boxes_gapi, labels_gapi), getCompileArgs());
// Reference code //////////////////////////////////////////////////////////
parseSSDBLref(in_mat, sz, confidence_threshold, filter_label, boxes_ref, labels_ref);
// Comparison //////////////////////////////////////////////////////////////
EXPECT_TRUE(boxes_gapi == boxes_ref);
EXPECT_TRUE(labels_gapi == labels_ref);
}
TEST_P(ParseSSDTest, ParseTest)
{
cv::Mat in_mat = generateSSDoutput(sz);
std::vector<cv::Rect> boxes_gapi, boxes_ref;
// G-API code //////////////////////////////////////////////////////////////
cv::GMat in;
cv::GOpaque<cv::Size> op_sz;
auto out = cv::gapi::parseSSD(in, op_sz, confidence_threshold,
alignment_to_square, filter_out_of_bounds);
cv::GComputation c(cv::GIn(in, op_sz), cv::GOut(out));
c.apply(cv::gin(in_mat, sz), cv::gout(boxes_gapi), getCompileArgs());
// Reference code //////////////////////////////////////////////////////////
parseSSDref(in_mat, sz, confidence_threshold, alignment_to_square,
filter_out_of_bounds, boxes_ref);
// Comparison //////////////////////////////////////////////////////////////
EXPECT_TRUE(boxes_gapi == boxes_ref);
}
TEST_P(ParseYoloTest, ParseTest)
{
cv::Mat in_mat = generateYoloOutput(num_classes);
auto anchors = cv::gapi::nn::parsers::GParseYolo::defaultAnchors();
std::vector<cv::Rect> boxes_gapi, boxes_ref;
std::vector<int> labels_gapi, labels_ref;
// G-API code //////////////////////////////////////////////////////////////
cv::GMat in;
cv::GOpaque<cv::Size> op_sz;
auto out = cv::gapi::parseYolo(in, op_sz, confidence_threshold, nms_threshold, anchors);
cv::GComputation c(cv::GIn(in, op_sz), cv::GOut(std::get<0>(out), std::get<1>(out)));
c.apply(cv::gin(in_mat, sz), cv::gout(boxes_gapi, labels_gapi), getCompileArgs());
// Reference code //////////////////////////////////////////////////////////
parseYoloRef(in_mat, sz, confidence_threshold, nms_threshold, num_classes, anchors, boxes_ref, labels_ref);
// Comparison //////////////////////////////////////////////////////////////
EXPECT_TRUE(boxes_gapi == boxes_ref);
EXPECT_TRUE(labels_gapi == labels_ref);
}
TEST_P(SizeTest, ParseTest)
{
cv::GMat in;
cv::Size out_sz;
auto out = cv::gapi::size(in);
cv::GComputation c(cv::GIn(in), cv::GOut(out));
c.apply(cv::gin(in_mat1), cv::gout(out_sz), getCompileArgs());
EXPECT_EQ(out_sz, sz);
}
TEST_P(SizeRTest, ParseTest)
{
cv::Rect rect(cv::Point(0,0), sz);
cv::Size out_sz;
cv::GOpaque<cv::Rect> op_rect;
auto out = cv::gapi::size(op_rect);
cv::GComputation c(cv::GIn(op_rect), cv::GOut(out));
c.apply(cv::gin(rect), cv::gout(out_sz), getCompileArgs());
EXPECT_EQ(out_sz, sz);
}
} // opencv_test
#endif //OPENCV_GAPI_CORE_TESTS_INL_HPP

397
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@ -0,0 +1,397 @@
// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
//
// Copyright (C) 2020 Intel Corporation
#ifndef OPENCV_GAPI_PARSERS_TESTS_COMMON_HPP
#define OPENCV_GAPI_PARSERS_TESTS_COMMON_HPP
#include "gapi_tests_common.hpp"
#include "../../include/opencv2/gapi/infer/parsers.hpp"
namespace opencv_test
{
class ParserSSDTest
{
public:
cv::Mat generateSSDoutput(const cv::Size& in_sz)
{
constexpr int maxN = 200;
constexpr int objSize = 7;
std::vector<int> dims{ 1, 1, maxN, objSize };
cv::Mat mat(dims, CV_32FC1);
auto data = mat.ptr<float>();
for (int i = 0; i < maxN; ++i)
{
float* it = data + i * objSize;
auto ssdIt = generateItem(i, in_sz);
it[0] = ssdIt.image_id;
it[1] = ssdIt.label;
it[2] = ssdIt.confidence;
it[3] = ssdIt.rc_left;
it[4] = ssdIt.rc_top;
it[5] = ssdIt.rc_right;
it[6] = ssdIt.rc_bottom;
}
return mat;
}
void parseSSDref(const cv::Mat& in_ssd_result,
const cv::Size& in_size,
const float confidence_threshold,
const bool alignment_to_square,
const bool filter_out_of_bounds,
std::vector<cv::Rect>& out_boxes)
{
out_boxes.clear();
const auto &in_ssd_dims = in_ssd_result.size;
CV_Assert(in_ssd_dims.dims() == 4u);
const int MAX_PROPOSALS = in_ssd_dims[2];
const int OBJECT_SIZE = in_ssd_dims[3];
CV_Assert(OBJECT_SIZE == 7); // fixed SSD object size
const float *data = in_ssd_result.ptr<float>();
cv::Rect surface({0,0}, in_size), rc;
float image_id, confidence;
int label;
for (int i = 0; i < MAX_PROPOSALS; ++i)
{
std::tie(rc, image_id, confidence, label)
= extract(data + i*OBJECT_SIZE, in_size);
if (image_id < 0.f)
{
break; // marks end-of-detections
}
if (confidence < confidence_threshold)
{
continue; // skip objects with low confidence
}
if (alignment_to_square)
{
adjustBoundingBox(rc);
}
const auto clipped_rc = rc & surface;
if (filter_out_of_bounds)
{
if (clipped_rc.area() != rc.area())
{
continue;
}
}
out_boxes.emplace_back(clipped_rc);
}
}
void parseSSDBLref(const cv::Mat& in_ssd_result,
const cv::Size& in_size,
const float confidence_threshold,
const int filter_label,
std::vector<cv::Rect>& out_boxes,
std::vector<int>& out_labels)
{
out_boxes.clear();
out_labels.clear();
const auto &in_ssd_dims = in_ssd_result.size;
CV_Assert(in_ssd_dims.dims() == 4u);
const int MAX_PROPOSALS = in_ssd_dims[2];
const int OBJECT_SIZE = in_ssd_dims[3];
CV_Assert(OBJECT_SIZE == 7); // fixed SSD object size
cv::Rect surface({0,0}, in_size), rc;
float image_id, confidence;
int label;
const float *data = in_ssd_result.ptr<float>();
for (int i = 0; i < MAX_PROPOSALS; i++)
{
std::tie(rc, image_id, confidence, label)
= extract(data + i*OBJECT_SIZE, in_size);
if (image_id < 0.f)
{
break; // marks end-of-detections
}
if (confidence < confidence_threshold ||
(filter_label != -1 && label != filter_label))
{
continue; // filter out object classes if filter is specified
}
out_boxes.emplace_back(rc & surface);
out_labels.emplace_back(label);
}
}
private:
void adjustBoundingBox(cv::Rect& boundingBox)
{
auto w = boundingBox.width;
auto h = boundingBox.height;
boundingBox.x -= static_cast<int>(0.067 * w);
boundingBox.y -= static_cast<int>(0.028 * h);
boundingBox.width += static_cast<int>(0.15 * w);
boundingBox.height += static_cast<int>(0.13 * h);
if (boundingBox.width < boundingBox.height)
{
auto dx = (boundingBox.height - boundingBox.width);
boundingBox.x -= dx / 2;
boundingBox.width += dx;
}
else
{
auto dy = (boundingBox.width - boundingBox.height);
boundingBox.y -= dy / 2;
boundingBox.height += dy;
}
}
std::tuple<cv::Rect, float, float, int> extract(const float* it,
const cv::Size& in_size)
{
float image_id = it[0];
int label = static_cast<int>(it[1]);
float confidence = it[2];
float rc_left = it[3];
float rc_top = it[4];
float rc_right = it[5];
float rc_bottom = it[6];
cv::Rect rc; // map relative coordinates to the original image scale
rc.x = static_cast<int>(rc_left * in_size.width);
rc.y = static_cast<int>(rc_top * in_size.height);
rc.width = static_cast<int>(rc_right * in_size.width) - rc.x;
rc.height = static_cast<int>(rc_bottom * in_size.height) - rc.y;
return std::make_tuple(rc, image_id, confidence, label);
}
int randInRange(const int start, const int end)
{
GAPI_Assert(start <= end);
return start + std::rand() % (end - start + 1);
}
cv::Rect generateBox(const cv::Size& in_sz)
{
// Generated rectangle can reside outside of the initial image by border pixels
constexpr int border = 10;
constexpr int minW = 16;
constexpr int minH = 16;
cv::Rect box;
box.width = randInRange(minW, in_sz.width + 2*border);
box.height = randInRange(minH, in_sz.height + 2*border);
box.x = randInRange(-border, in_sz.width + border - box.width);
box.y = randInRange(-border, in_sz.height + border - box.height);
return box;
}
struct SSDitem
{
float image_id = 0.0f;
float label = 0.0f;
float confidence = 0.0f;
float rc_left = 0.0f;
float rc_top = 0.0f;
float rc_right = 0.0f;
float rc_bottom = 0.0f;
};
SSDitem generateItem(const int i, const cv::Size& in_sz)
{
const auto normalize = [](int v, int range) { return static_cast<float>(v) / range; };
SSDitem it;
it.image_id = static_cast<float>(i);
it.label = static_cast<float>(randInRange(0, 9));
it.confidence = static_cast<float>(std::rand()) / RAND_MAX;
auto box = generateBox(in_sz);
it.rc_left = normalize(box.x, in_sz.width);
it.rc_right = normalize(box.x + box.width, in_sz.width);
it.rc_top = normalize(box.y, in_sz.height);
it.rc_bottom = normalize(box.y + box.height, in_sz.height);
return it;
}
};
class ParserYoloTest
{
public:
cv::Mat generateYoloOutput(const int num_classes)
{
std::vector<int> dims = { 1, 13, 13, (num_classes + 5) * 5 };
cv::Mat mat(dims, CV_32FC1);
auto data = mat.ptr<float>();
const size_t range = dims[0] * dims[1] * dims[2] * dims[3];
for (size_t i = 0; i < range; ++i)
{
data[i] = static_cast<float>(std::rand()) / RAND_MAX;
}
return mat;
}
void parseYoloRef(const cv::Mat& in_yolo_result,
const cv::Size& in_size,
const float confidence_threshold,
const float nms_threshold,
const int num_classes,
const std::vector<float>& anchors,
std::vector<cv::Rect>& out_boxes,
std::vector<int>& out_labels)
{
YoloParams params;
constexpr auto side_square = 13 * 13;
this->m_out = in_yolo_result.ptr<float>();
this->m_side = 13;
this->m_lcoords = params.coords;
this->m_lclasses = num_classes;
std::vector<Detection> detections;
for (int i = 0; i < side_square; ++i)
{
for (int b = 0; b < params.num; ++b)
{
float scale = this->scale(i, b);
if (scale < confidence_threshold)
{
continue;
}
double x = this->x(i, b);
double y = this->y(i, b);
double height = this->height(i, b, anchors[2 * b + 1]);
double width = this->width(i, b, anchors[2 * b]);
for (int label = 0; label < num_classes; ++label)
{
float prob = scale * classConf(i,b,label);
if (prob < confidence_threshold)
{
continue;
}
auto box = toBox(x, y, height, width, in_size);
detections.emplace_back(Detection(box, prob, label));
}
}
}
std::stable_sort(std::begin(detections), std::end(detections),
[](const Detection& a, const Detection& b)
{
return a.conf > b.conf;
});
if (nms_threshold < 1.0f)
{
for (const auto& d : detections)
{
if (std::end(out_boxes) ==
std::find_if(std::begin(out_boxes), std::end(out_boxes),
[&d, nms_threshold](const cv::Rect& r)
{
float rectOverlap = 1.f - static_cast<float>(jaccardDistance(r, d.rect));
return rectOverlap > nms_threshold;
}))
{
out_boxes. emplace_back(d.rect);
out_labels.emplace_back(d.label);
}
}
}
else
{
for (const auto& d: detections)
{
out_boxes. emplace_back(d.rect);
out_labels.emplace_back(d.label);
}
}
}
private:
struct Detection
{
Detection(const cv::Rect& in_rect, const float in_conf, const int in_label)
: rect(in_rect), conf(in_conf), label(in_label)
{}
cv::Rect rect;
float conf = 0.0f;
int label = 0;
};
struct YoloParams
{
int num = 5;
int coords = 4;
};
float scale(const int i, const int b)
{
int obj_index = index(i, b, m_lcoords);
return m_out[obj_index];
}
double x(const int i, const int b)
{
int box_index = index(i, b, 0);
int col = i % m_side;
return (col + m_out[box_index]) / m_side;
}
double y(const int i, const int b)
{
int box_index = index(i, b, 0);
int row = i / m_side;
return (row + m_out[box_index + m_side * m_side]) / m_side;
}
double width(const int i, const int b, const float anchor)
{
int box_index = index(i, b, 0);
return std::exp(m_out[box_index + 2 * m_side * m_side]) * anchor / m_side;
}
double height(const int i, const int b, const float anchor)
{
int box_index = index(i, b, 0);
return std::exp(m_out[box_index + 3 * m_side * m_side]) * anchor / m_side;
}
float classConf(const int i, const int b, const int label)
{
int class_index = index(i, b, m_lcoords + 1 + label);
return m_out[class_index];
}
cv::Rect toBox(const double x, const double y, const double h, const double w, const cv::Size& in_sz)
{
auto h_scale = in_sz.height;
auto w_scale = in_sz.width;
cv::Rect r;
r.x = static_cast<int>((x - w / 2) * w_scale);
r.y = static_cast<int>((y - h / 2) * h_scale);
r.width = static_cast<int>(w * w_scale);
r.height = static_cast<int>(h * h_scale);
return r;
}
int index(const int i, const int b, const int entry)
{
return b * m_side * m_side * (m_lcoords + m_lclasses + 1) + entry * m_side * m_side + i;
}
const float* m_out = nullptr;
int m_side = 0, m_lcoords = 0, m_lclasses = 0;
};
} // namespace opencv_test
#endif // OPENCV_GAPI_PARSERS_TESTS_COMMON_HPP

21
modules/gapi/test/common/gapi_tests_common.hpp
View File

@ -351,6 +351,27 @@ struct TestWithParamsSpecific : public TestWithParamsBase<ParamsSpecific<Specifi
Fixture() { InitF(type, sz, dtype); } \
};
/**
* @private
* @brief Create G-API test fixture with TestWithParams base class and additional base class.
* @param Fixture test fixture name.
@param ExtBase additional base class.
* @param InitF callable that will initialize default available members (from TestFunctional)
* @param API base class API. Specifies types of user-defined parameters. If there are no such
* parameters, empty angle brackets ("<>") must be specified.
* @param Number number of user-defined parameters (corresponds to the number of types in API).
* if there are no such parameters, 0 must be specified.
* @param ... list of names of user-defined parameters. if there are no parameters, the list
* must be empty.
*/
#define GAPI_TEST_EXT_BASE_FIXTURE(Fixture, ExtBase, InitF, API, Number, ...) \
struct Fixture : public TestWithParams API, public ExtBase { \
static_assert(Number == AllParams::specific_params_size, \
"Number of user-defined parameters doesn't match size of __VA_ARGS__"); \
__WRAP_VAARGS(DEFINE_SPECIFIC_PARAMS_##Number(__VA_ARGS__)) \
Fixture() { InitF(type, sz, dtype); } \
};
/**
* @private
* @brief Create G-API test fixture with TestWithParamsSpecific base class

39
modules/gapi/test/cpu/gapi_core_tests_cpu.cpp
View File

@ -496,4 +496,43 @@ INSTANTIATE_TEST_CASE_P(ReInitOutTestCPU, ReInitOutTest,
Values(cv::Size(640, 400),
cv::Size(10, 480))));
INSTANTIATE_TEST_CASE_P(ParseTestCPU, ParseSSDBLTest,
Combine(Values(CV_8UC1, CV_8UC3, CV_32FC1),
Values(cv::Size(1920, 1080)),
Values(-1),
Values(CORE_CPU),
Values(0.3f, 0.5f, 0.7f),
Values(-1, 0, 1)));
INSTANTIATE_TEST_CASE_P(ParseTestCPU, ParseSSDTest,
Combine(Values(CV_8UC1, CV_8UC3, CV_32FC1),
Values(cv::Size(1920, 1080)),
Values(-1),
Values(CORE_CPU),
Values(0.3f, 0.5f, 0.7f),
testing::Bool(),
testing::Bool()));
INSTANTIATE_TEST_CASE_P(ParseTestCPU, ParseYoloTest,
Combine(Values(CV_8UC1, CV_8UC3, CV_32FC1),
Values(cv::Size(1920, 1080)),
Values(-1),
Values(CORE_CPU),
Values(0.3f, 0.5f, 0.7f),
Values(0.5f, 1.0f),
Values(80, 7)));
INSTANTIATE_TEST_CASE_P(SizeTestCPU, SizeTest,
Combine(Values(CV_8UC1, CV_8UC3, CV_32FC1),
Values(cv::Size(32, 32),
cv::Size(640, 320)),
Values(-1),
Values(CORE_CPU)));
INSTANTIATE_TEST_CASE_P(SizeRTestCPU, SizeRTest,
Combine(Values(CV_8UC1, CV_8UC3, CV_32FC1),
Values(cv::Size(32, 32),
cv::Size(640, 320)),
Values(-1),
Values(CORE_CPU)));
}