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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
16 changed files with 1423 additions and 1 deletions
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modules/gapi/include/opencv2/gapi/core.hpp
+31 -1
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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
modules/gapi/include/opencv2/gapi/infer/parsers.hpp
+125
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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