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Open Source Computer Vision Library
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189 lines
7.4 KiB
189 lines
7.4 KiB
/*M/////////////////////////////////////////////////////////////////////////////////////// |
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// |
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. |
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// |
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// By downloading, copying, installing or using the software you agree to this license. |
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// If you do not agree to this license, do not download, install, |
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// copy or use the software. |
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// |
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// |
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// License Agreement |
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// For Open Source Computer Vision Library |
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// (3-clause BSD License) |
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// |
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// Copyright (C) 2017, Intel Corporation, all rights reserved. |
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// Third party copyrights are property of their respective owners. |
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// |
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// Redistribution and use in source and binary forms, with or without modification, |
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// are permitted provided that the following conditions are met: |
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// |
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// * Redistributions of source code must retain the above copyright notice, |
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// this list of conditions and the following disclaimer. |
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// |
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// * Redistributions in binary form must reproduce the above copyright notice, |
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// this list of conditions and the following disclaimer in the documentation |
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// and/or other materials provided with the distribution. |
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// |
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// * Neither the names of the copyright holders nor the names of the contributors |
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// may be used to endorse or promote products derived from this software |
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// without specific prior written permission. |
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// |
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// This software is provided by the copyright holders and contributors "as is" and |
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// any express or implied warranties, including, but not limited to, the implied |
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// warranties of merchantability and fitness for a particular purpose are disclaimed. |
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// In no event shall copyright holders or contributors be liable for any direct, |
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// indirect, incidental, special, exemplary, or consequential damages |
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// (including, but not limited to, procurement of substitute goods or services; |
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// loss of use, data, or profits; or business interruption) however caused |
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// and on any theory of liability, whether in contract, strict liability, |
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// or tort (including negligence or otherwise) arising in any way out of |
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// the use of this software, even if advised of the possibility of such damage. |
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// |
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//M*/ |
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#include "test_precomp.hpp" |
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#include "npy_blob.hpp" |
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#include <opencv2/dnn/shape_utils.hpp> |
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namespace opencv_test { namespace { |
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template<typename TString> |
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static std::string _tf(TString filename) |
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{ |
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return (getOpenCVExtraDir() + "/dnn/") + filename; |
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} |
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TEST(Test_Darknet, read_tiny_yolo_voc) |
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{ |
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Net net = readNetFromDarknet(_tf("tiny-yolo-voc.cfg")); |
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ASSERT_FALSE(net.empty()); |
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} |
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TEST(Test_Darknet, read_yolo_voc) |
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{ |
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Net net = readNetFromDarknet(_tf("yolo-voc.cfg")); |
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ASSERT_FALSE(net.empty()); |
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} |
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// Test object detection network from Darknet framework. |
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static void testDarknetModel(const std::string& cfg, const std::string& weights, |
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const std::vector<cv::String>& outNames, |
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const std::vector<int>& refClassIds, |
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const std::vector<float>& refConfidences, |
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const std::vector<Rect2d>& refBoxes, |
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int targetId, float confThreshold = 0.24) |
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{ |
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Mat sample = imread(_tf("dog416.png")); |
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Mat inp = blobFromImage(sample, 1.0/255, Size(416, 416), Scalar(), true, false); |
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Net net = readNet(findDataFile("dnn/" + cfg, false), |
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findDataFile("dnn/" + weights, false)); |
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net.setPreferableTarget(targetId); |
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net.setInput(inp); |
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std::vector<Mat> outs; |
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net.forward(outs, outNames); |
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std::vector<int> classIds; |
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std::vector<float> confidences; |
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std::vector<Rect2d> boxes; |
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for (int i = 0; i < outs.size(); ++i) |
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{ |
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Mat& out = outs[i]; |
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for (int j = 0; j < out.rows; ++j) |
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{ |
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Mat scores = out.row(j).colRange(5, out.cols); |
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double confidence; |
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Point maxLoc; |
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minMaxLoc(scores, 0, &confidence, 0, &maxLoc); |
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float* detection = out.ptr<float>(j); |
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double centerX = detection[0]; |
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double centerY = detection[1]; |
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double width = detection[2]; |
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double height = detection[3]; |
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boxes.push_back(Rect2d(centerX - 0.5 * width, centerY - 0.5 * height, |
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width, height)); |
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confidences.push_back(confidence); |
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classIds.push_back(maxLoc.x); |
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} |
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} |
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normAssertDetections(refClassIds, refConfidences, refBoxes, classIds, |
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confidences, boxes, "", confThreshold, 8e-5, 3e-5); |
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} |
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typedef testing::TestWithParam<DNNTarget> Test_Darknet_nets; |
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TEST_P(Test_Darknet_nets, YoloVoc) |
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{ |
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int targetId = GetParam(); |
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std::vector<cv::String> outNames(1, "detection_out"); |
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std::vector<int> classIds(3); |
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std::vector<float> confidences(3); |
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std::vector<Rect2d> boxes(3); |
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classIds[0] = 6; confidences[0] = 0.750469f; boxes[0] = Rect2d(0.577374, 0.127391, 0.325575, 0.173418); // a car |
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classIds[1] = 1; confidences[1] = 0.780879f; boxes[1] = Rect2d(0.270762, 0.264102, 0.461713, 0.48131); // a bycicle |
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classIds[2] = 11; confidences[2] = 0.901615f; boxes[2] = Rect2d(0.1386, 0.338509, 0.282737, 0.60028); // a dog |
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testDarknetModel("yolo-voc.cfg", "yolo-voc.weights", outNames, |
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classIds, confidences, boxes, targetId); |
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} |
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TEST_P(Test_Darknet_nets, TinyYoloVoc) |
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{ |
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int targetId = GetParam(); |
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std::vector<cv::String> outNames(1, "detection_out"); |
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std::vector<int> classIds(2); |
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std::vector<float> confidences(2); |
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std::vector<Rect2d> boxes(2); |
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classIds[0] = 6; confidences[0] = 0.761967f; boxes[0] = Rect2d(0.579042, 0.159161, 0.31544, 0.160779); // a car |
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classIds[1] = 11; confidences[1] = 0.780595f; boxes[1] = Rect2d(0.129696, 0.386467, 0.315579, 0.534527); // a dog |
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testDarknetModel("tiny-yolo-voc.cfg", "tiny-yolo-voc.weights", outNames, |
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classIds, confidences, boxes, targetId); |
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} |
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TEST_P(Test_Darknet_nets, YOLOv3) |
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{ |
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int targetId = GetParam(); |
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std::vector<cv::String> outNames(3); |
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outNames[0] = "yolo_82"; |
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outNames[1] = "yolo_94"; |
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outNames[2] = "yolo_106"; |
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std::vector<int> classIds(3); |
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std::vector<float> confidences(3); |
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std::vector<Rect2d> boxes(3); |
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classIds[0] = 7; confidences[0] = 0.952983f; boxes[0] = Rect2d(0.614622, 0.150257, 0.286747, 0.138994); // a truck |
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classIds[1] = 1; confidences[1] = 0.987908f; boxes[1] = Rect2d(0.150913, 0.221933, 0.591342, 0.524327); // a bycicle |
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classIds[2] = 16; confidences[2] = 0.998836f; boxes[2] = Rect2d(0.160024, 0.389964, 0.257861, 0.553752); // a dog (COCO) |
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testDarknetModel("yolov3.cfg", "yolov3.weights", outNames, |
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classIds, confidences, boxes, targetId); |
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} |
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INSTANTIATE_TEST_CASE_P(/**/, Test_Darknet_nets, availableDnnTargets()); |
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static void testDarknetLayer(const std::string& name, bool hasWeights = false) |
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{ |
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std::string cfg = findDataFile("dnn/darknet/" + name + ".cfg", false); |
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std::string model = ""; |
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if (hasWeights) |
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model = findDataFile("dnn/darknet/" + name + ".weights", false); |
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Mat inp = blobFromNPY(findDataFile("dnn/darknet/" + name + "_in.npy", false)); |
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Mat ref = blobFromNPY(findDataFile("dnn/darknet/" + name + "_out.npy", false)); |
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Net net = readNet(cfg, model); |
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net.setInput(inp); |
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Mat out = net.forward(); |
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normAssert(out, ref); |
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} |
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TEST(Test_Darknet, shortcut) |
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{ |
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testDarknetLayer("shortcut"); |
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} |
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TEST(Test_Darknet, upsample) |
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{ |
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testDarknetLayer("upsample"); |
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} |
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}} // namespace
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