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https://github.com/opencv/opencv.git
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a63cee2139
[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
398 lines
13 KiB
C++
398 lines
13 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_TESTS_COMMON_HPP
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#define OPENCV_GAPI_PARSERS_TESTS_COMMON_HPP
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#include "gapi_tests_common.hpp"
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#include "../../include/opencv2/gapi/infer/parsers.hpp"
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namespace opencv_test
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{
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class ParserSSDTest
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{
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public:
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cv::Mat generateSSDoutput(const cv::Size& in_sz)
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{
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constexpr int maxN = 200;
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constexpr int objSize = 7;
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std::vector<int> dims{ 1, 1, maxN, objSize };
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cv::Mat mat(dims, CV_32FC1);
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auto data = mat.ptr<float>();
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for (int i = 0; i < maxN; ++i)
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{
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float* it = data + i * objSize;
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auto ssdIt = generateItem(i, in_sz);
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it[0] = ssdIt.image_id;
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it[1] = ssdIt.label;
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it[2] = ssdIt.confidence;
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it[3] = ssdIt.rc_left;
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it[4] = ssdIt.rc_top;
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it[5] = ssdIt.rc_right;
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it[6] = ssdIt.rc_bottom;
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}
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return mat;
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}
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void parseSSDref(const cv::Mat& in_ssd_result,
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const cv::Size& in_size,
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const float confidence_threshold,
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const bool alignment_to_square,
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const bool filter_out_of_bounds,
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std::vector<cv::Rect>& out_boxes)
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{
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out_boxes.clear();
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const auto &in_ssd_dims = in_ssd_result.size;
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CV_Assert(in_ssd_dims.dims() == 4u);
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const int MAX_PROPOSALS = in_ssd_dims[2];
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const int OBJECT_SIZE = in_ssd_dims[3];
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CV_Assert(OBJECT_SIZE == 7); // fixed SSD object size
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const float *data = in_ssd_result.ptr<float>();
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cv::Rect surface({0,0}, in_size), rc;
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float image_id, confidence;
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int label;
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for (int i = 0; i < MAX_PROPOSALS; ++i)
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{
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std::tie(rc, image_id, confidence, label)
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= extract(data + i*OBJECT_SIZE, in_size);
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if (image_id < 0.f)
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{
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break; // marks end-of-detections
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}
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if (confidence < confidence_threshold)
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{
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continue; // skip objects with low confidence
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}
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if (alignment_to_square)
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{
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adjustBoundingBox(rc);
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}
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const auto clipped_rc = rc & surface;
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if (filter_out_of_bounds)
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{
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if (clipped_rc.area() != rc.area())
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{
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continue;
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}
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}
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out_boxes.emplace_back(clipped_rc);
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}
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}
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void parseSSDBLref(const cv::Mat& in_ssd_result,
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const cv::Size& in_size,
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const float confidence_threshold,
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const int filter_label,
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std::vector<cv::Rect>& out_boxes,
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std::vector<int>& out_labels)
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{
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out_boxes.clear();
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out_labels.clear();
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const auto &in_ssd_dims = in_ssd_result.size;
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CV_Assert(in_ssd_dims.dims() == 4u);
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const int MAX_PROPOSALS = in_ssd_dims[2];
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const int OBJECT_SIZE = in_ssd_dims[3];
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CV_Assert(OBJECT_SIZE == 7); // fixed SSD object size
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cv::Rect surface({0,0}, in_size), rc;
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float image_id, confidence;
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int label;
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const float *data = in_ssd_result.ptr<float>();
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for (int i = 0; i < MAX_PROPOSALS; i++)
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{
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std::tie(rc, image_id, confidence, label)
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= extract(data + i*OBJECT_SIZE, in_size);
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if (image_id < 0.f)
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{
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break; // marks end-of-detections
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}
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if (confidence < confidence_threshold ||
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(filter_label != -1 && label != filter_label))
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{
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continue; // filter out object classes if filter is specified
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}
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out_boxes.emplace_back(rc & surface);
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out_labels.emplace_back(label);
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}
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}
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private:
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void adjustBoundingBox(cv::Rect& boundingBox)
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{
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auto w = boundingBox.width;
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auto h = boundingBox.height;
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boundingBox.x -= static_cast<int>(0.067 * w);
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boundingBox.y -= static_cast<int>(0.028 * h);
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boundingBox.width += static_cast<int>(0.15 * w);
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boundingBox.height += static_cast<int>(0.13 * h);
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if (boundingBox.width < boundingBox.height)
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{
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auto dx = (boundingBox.height - boundingBox.width);
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boundingBox.x -= dx / 2;
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boundingBox.width += dx;
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}
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else
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{
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auto dy = (boundingBox.width - boundingBox.height);
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boundingBox.y -= dy / 2;
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boundingBox.height += dy;
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}
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}
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std::tuple<cv::Rect, float, float, int> extract(const float* it,
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const cv::Size& in_size)
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{
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float image_id = it[0];
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int label = static_cast<int>(it[1]);
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float confidence = it[2];
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float rc_left = it[3];
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float rc_top = it[4];
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float rc_right = it[5];
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float rc_bottom = it[6];
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cv::Rect rc; // map relative coordinates to the original image scale
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rc.x = static_cast<int>(rc_left * in_size.width);
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rc.y = static_cast<int>(rc_top * in_size.height);
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rc.width = static_cast<int>(rc_right * in_size.width) - rc.x;
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rc.height = static_cast<int>(rc_bottom * in_size.height) - rc.y;
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return std::make_tuple(rc, image_id, confidence, label);
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}
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int randInRange(const int start, const int end)
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{
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GAPI_Assert(start <= end);
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return start + std::rand() % (end - start + 1);
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}
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cv::Rect generateBox(const cv::Size& in_sz)
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{
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// Generated rectangle can reside outside of the initial image by border pixels
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constexpr int border = 10;
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constexpr int minW = 16;
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constexpr int minH = 16;
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cv::Rect box;
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box.width = randInRange(minW, in_sz.width + 2*border);
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box.height = randInRange(minH, in_sz.height + 2*border);
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box.x = randInRange(-border, in_sz.width + border - box.width);
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box.y = randInRange(-border, in_sz.height + border - box.height);
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return box;
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}
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struct SSDitem
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{
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float image_id = 0.0f;
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float label = 0.0f;
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float confidence = 0.0f;
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float rc_left = 0.0f;
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float rc_top = 0.0f;
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float rc_right = 0.0f;
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float rc_bottom = 0.0f;
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};
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SSDitem generateItem(const int i, const cv::Size& in_sz)
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{
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const auto normalize = [](int v, int range) { return static_cast<float>(v) / range; };
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SSDitem it;
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it.image_id = static_cast<float>(i);
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it.label = static_cast<float>(randInRange(0, 9));
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it.confidence = static_cast<float>(std::rand()) / RAND_MAX;
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auto box = generateBox(in_sz);
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it.rc_left = normalize(box.x, in_sz.width);
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it.rc_right = normalize(box.x + box.width, in_sz.width);
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it.rc_top = normalize(box.y, in_sz.height);
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it.rc_bottom = normalize(box.y + box.height, in_sz.height);
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return it;
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}
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};
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class ParserYoloTest
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{
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public:
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cv::Mat generateYoloOutput(const int num_classes)
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{
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std::vector<int> dims = { 1, 13, 13, (num_classes + 5) * 5 };
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cv::Mat mat(dims, CV_32FC1);
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auto data = mat.ptr<float>();
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const size_t range = dims[0] * dims[1] * dims[2] * dims[3];
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for (size_t i = 0; i < range; ++i)
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{
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data[i] = static_cast<float>(std::rand()) / RAND_MAX;
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}
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return mat;
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}
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void parseYoloRef(const cv::Mat& in_yolo_result,
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const cv::Size& in_size,
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const float confidence_threshold,
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const float nms_threshold,
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const int num_classes,
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const std::vector<float>& anchors,
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std::vector<cv::Rect>& out_boxes,
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std::vector<int>& out_labels)
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{
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YoloParams params;
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constexpr auto side_square = 13 * 13;
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this->m_out = in_yolo_result.ptr<float>();
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this->m_side = 13;
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this->m_lcoords = params.coords;
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this->m_lclasses = num_classes;
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std::vector<Detection> detections;
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for (int i = 0; i < side_square; ++i)
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{
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for (int b = 0; b < params.num; ++b)
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{
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float scale = this->scale(i, b);
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if (scale < confidence_threshold)
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{
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continue;
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}
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double x = this->x(i, b);
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double y = this->y(i, b);
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double height = this->height(i, b, anchors[2 * b + 1]);
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double width = this->width(i, b, anchors[2 * b]);
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for (int label = 0; label < num_classes; ++label)
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{
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float prob = scale * classConf(i,b,label);
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if (prob < confidence_threshold)
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{
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continue;
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}
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auto box = toBox(x, y, height, width, in_size);
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detections.emplace_back(Detection(box, prob, label));
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}
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}
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}
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std::stable_sort(std::begin(detections), std::end(detections),
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[](const Detection& a, const Detection& b)
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{
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return a.conf > b.conf;
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});
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if (nms_threshold < 1.0f)
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{
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for (const auto& d : detections)
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{
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if (std::end(out_boxes) ==
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std::find_if(std::begin(out_boxes), std::end(out_boxes),
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[&d, nms_threshold](const cv::Rect& r)
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{
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float rectOverlap = 1.f - static_cast<float>(jaccardDistance(r, d.rect));
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return rectOverlap > nms_threshold;
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}))
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{
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out_boxes. emplace_back(d.rect);
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out_labels.emplace_back(d.label);
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}
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}
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}
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else
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{
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for (const auto& d: detections)
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{
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out_boxes. emplace_back(d.rect);
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out_labels.emplace_back(d.label);
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}
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}
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}
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private:
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struct Detection
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{
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Detection(const cv::Rect& in_rect, const float in_conf, const int in_label)
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: rect(in_rect), conf(in_conf), label(in_label)
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{}
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cv::Rect rect;
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float conf = 0.0f;
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int label = 0;
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};
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struct YoloParams
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{
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int num = 5;
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int coords = 4;
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};
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float scale(const int i, const int b)
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{
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int obj_index = index(i, b, m_lcoords);
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return m_out[obj_index];
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}
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double x(const int i, const int b)
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{
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int box_index = index(i, b, 0);
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int col = i % m_side;
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return (col + m_out[box_index]) / m_side;
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}
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double y(const int i, const int b)
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{
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int box_index = index(i, b, 0);
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int row = i / m_side;
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return (row + m_out[box_index + m_side * m_side]) / m_side;
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}
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double width(const int i, const int b, const float anchor)
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{
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int box_index = index(i, b, 0);
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return std::exp(m_out[box_index + 2 * m_side * m_side]) * anchor / m_side;
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}
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double height(const int i, const int b, const float anchor)
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{
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int box_index = index(i, b, 0);
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return std::exp(m_out[box_index + 3 * m_side * m_side]) * anchor / m_side;
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}
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float classConf(const int i, const int b, const int label)
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{
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int class_index = index(i, b, m_lcoords + 1 + label);
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return m_out[class_index];
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}
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cv::Rect toBox(const double x, const double y, const double h, const double w, const cv::Size& in_sz)
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{
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auto h_scale = in_sz.height;
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auto w_scale = in_sz.width;
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cv::Rect r;
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r.x = static_cast<int>((x - w / 2) * w_scale);
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r.y = static_cast<int>((y - h / 2) * h_scale);
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r.width = static_cast<int>(w * w_scale);
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r.height = static_cast<int>(h * h_scale);
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return r;
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}
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int index(const int i, const int b, const int entry)
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{
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return b * m_side * m_side * (m_lcoords + m_lclasses + 1) + entry * m_side * m_side + i;
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}
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const float* m_out = nullptr;
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int m_side = 0, m_lcoords = 0, m_lclasses = 0;
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};
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} // namespace opencv_test
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#endif // OPENCV_GAPI_PARSERS_TESTS_COMMON_HPP
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