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139 lines
6.1 KiB
C++
139 lines
6.1 KiB
C++
// This file is part of OpenCV project.
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// It is subject to the license terms in the LICENSE file found in the top-level directory
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// of this distribution and at http://opencv.org/license.html.
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//
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// Copyright (C) 2020 Intel Corporation
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#ifndef OPENCV_GAPI_PARSERS_HPP
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#define OPENCV_GAPI_PARSERS_HPP
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#include <utility> // std::tuple
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#include <opencv2/gapi/gmat.hpp>
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#include <opencv2/gapi/gkernel.hpp>
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namespace cv { namespace gapi {
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namespace nn {
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namespace parsers {
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using GRects = GArray<Rect>;
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using GDetections = std::tuple<GArray<Rect>, GArray<int>>;
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G_TYPED_KERNEL(GParseSSDBL, <GDetections(GMat, GOpaque<Size>, float, int)>,
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"org.opencv.nn.parsers.parseSSD_BL") {
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static std::tuple<GArrayDesc,GArrayDesc> outMeta(const GMatDesc&, const GOpaqueDesc&, float, int) {
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return std::make_tuple(empty_array_desc(), empty_array_desc());
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}
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};
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G_TYPED_KERNEL(GParseSSD, <GRects(GMat, GOpaque<Size>, float, bool, bool)>,
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"org.opencv.nn.parsers.parseSSD") {
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static GArrayDesc outMeta(const GMatDesc&, const GOpaqueDesc&, float, bool, bool) {
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return empty_array_desc();
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}
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};
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G_TYPED_KERNEL(GParseYolo, <GDetections(GMat, GOpaque<Size>, float, float, std::vector<float>)>,
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"org.opencv.nn.parsers.parseYolo") {
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static std::tuple<GArrayDesc, GArrayDesc> outMeta(const GMatDesc&, const GOpaqueDesc&,
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float, float, const std::vector<float>&) {
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return std::make_tuple(empty_array_desc(), empty_array_desc());
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}
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static const std::vector<float>& defaultAnchors() {
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static std::vector<float> anchors {
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0.57273f, 0.677385f, 1.87446f, 2.06253f, 3.33843f, 5.47434f, 7.88282f, 3.52778f, 9.77052f, 9.16828f
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};
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return anchors;
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}
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};
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} // namespace parsers
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} // namespace nn
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/** @brief Parses output of SSD network.
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Extracts detection information (box, confidence, label) from SSD output and
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filters it by given confidence and label.
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@note Function textual ID is "org.opencv.nn.parsers.parseSSD_BL"
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@param in Input CV_32F tensor with {1,1,N,7} dimensions.
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@param inSz Size to project detected boxes to (size of the input image).
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@param confidenceThreshold If confidence of the
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detection is smaller than confidence threshold, detection is rejected.
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@param filterLabel If provided (!= -1), only detections with
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given label will get to the output.
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@return a tuple with a vector of detected boxes and a vector of appropriate labels.
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*/
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GAPI_EXPORTS_W std::tuple<GArray<Rect>, GArray<int>> parseSSD(const GMat& in,
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const GOpaque<Size>& inSz,
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const float confidenceThreshold = 0.5f,
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const int filterLabel = -1);
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/** @brief Parses output of SSD network.
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Extracts detection information (box, confidence) from SSD output and
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filters it by given confidence and by going out of bounds.
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@note Function textual ID is "org.opencv.nn.parsers.parseSSD"
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@param in Input CV_32F tensor with {1,1,N,7} dimensions.
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@param inSz Size to project detected boxes to (size of the input image).
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@param confidenceThreshold If confidence of the
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detection is smaller than confidence threshold, detection is rejected.
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@param alignmentToSquare If provided true, bounding boxes are extended to squares.
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The center of the rectangle remains unchanged, the side of the square is
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the larger side of the rectangle.
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@param filterOutOfBounds If provided true, out-of-frame boxes are filtered.
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@return a vector of detected bounding boxes.
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*/
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GAPI_EXPORTS_W GArray<Rect> parseSSD(const GMat& in,
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const GOpaque<Size>& inSz,
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const float confidenceThreshold,
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const bool alignmentToSquare,
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const bool filterOutOfBounds);
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/** @brief Parses output of Yolo network.
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Extracts detection information (box, confidence, label) from Yolo output,
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filters it by given confidence and performs non-maximum supression for overlapping boxes.
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@note Function textual ID is "org.opencv.nn.parsers.parseYolo"
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@param in Input CV_32F tensor with {1,13,13,N} dimensions, N should satisfy:
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\f[\texttt{N} = (\texttt{num_classes} + \texttt{5}) * \texttt{5},\f]
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where num_classes - a number of classes Yolo network was trained with.
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@param inSz Size to project detected boxes to (size of the input image).
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@param confidenceThreshold If confidence of the
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detection is smaller than confidence threshold, detection is rejected.
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@param nmsThreshold Non-maximum supression threshold which controls minimum
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relative box intersection area required for rejecting the box with a smaller confidence.
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If 1.f, nms is not performed and no boxes are rejected.
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@param anchors Anchors Yolo network was trained with.
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@note The default anchor values are specified for YOLO v2 Tiny as described in Intel Open Model Zoo
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<a href="https://github.com/openvinotoolkit/open_model_zoo/blob/master/models/public/yolo-v2-tiny-tf/yolo-v2-tiny-tf.md">documentation</a>.
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@return a tuple with a vector of detected boxes and a vector of appropriate labels.
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*/
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GAPI_EXPORTS_W std::tuple<GArray<Rect>, GArray<int>> parseYolo(const GMat& in,
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const GOpaque<Size>& inSz,
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const float confidenceThreshold = 0.5f,
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const float nmsThreshold = 0.5f,
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const std::vector<float>& anchors
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= nn::parsers::GParseYolo::defaultAnchors());
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} // namespace gapi
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} // namespace cv
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// Reimport parseSSD & parseYolo under their initial namespace
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namespace cv {
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namespace gapi {
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namespace streaming {
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using cv::gapi::parseSSD;
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using cv::gapi::parseYolo;
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} // namespace streaming
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} // namespace gapi
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} // namespace cv
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#endif // OPENCV_GAPI_PARSERS_HPP
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