mirror of
https://github.com/opencv/opencv.git
synced 2024-11-25 19:50:38 +08:00
61d8719b8d
Remove some assertions Replace std::ifstream to std::istream Add test for new importer Remove constructor to load file Rename cfgStream and darknetModelStream to ifile Add error notification to inform pathname to user Use FileStorage instead of std::istream Use FileNode instead of FileStorage Fix typo
237 lines
9.3 KiB
C++
237 lines
9.3 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
|
|
//
|
|
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
|
//
|
|
// By downloading, copying, installing or using the software you agree to this license.
|
|
// If you do not agree to this license, do not download, install,
|
|
// copy or use the software.
|
|
//
|
|
//
|
|
// License Agreement
|
|
// For Open Source Computer Vision Library
|
|
// (3-clause BSD License)
|
|
//
|
|
// Copyright (C) 2017, Intel Corporation, all rights reserved.
|
|
// Third party copyrights are property of their respective owners.
|
|
//
|
|
// Redistribution and use in source and binary forms, with or without modification,
|
|
// are permitted provided that the following conditions are met:
|
|
//
|
|
// * Redistributions of source code must retain the above copyright notice,
|
|
// this list of conditions and the following disclaimer.
|
|
//
|
|
// * Redistributions in binary form must reproduce the above copyright notice,
|
|
// this list of conditions and the following disclaimer in the documentation
|
|
// and/or other materials provided with the distribution.
|
|
//
|
|
// * Neither the names of the copyright holders nor the names of the contributors
|
|
// may be used to endorse or promote products derived from this software
|
|
// without specific prior written permission.
|
|
//
|
|
// This software is provided by the copyright holders and contributors "as is" and
|
|
// any express or implied warranties, including, but not limited to, the implied
|
|
// warranties of merchantability and fitness for a particular purpose are disclaimed.
|
|
// In no event shall copyright holders or contributors be liable for any direct,
|
|
// indirect, incidental, special, exemplary, or consequential damages
|
|
// (including, but not limited to, procurement of substitute goods or services;
|
|
// loss of use, data, or profits; or business interruption) however caused
|
|
// and on any theory of liability, whether in contract, strict liability,
|
|
// or tort (including negligence or otherwise) arising in any way out of
|
|
// the use of this software, even if advised of the possibility of such damage.
|
|
//
|
|
//M*/
|
|
|
|
#include "test_precomp.hpp"
|
|
#include "npy_blob.hpp"
|
|
#include <opencv2/dnn/shape_utils.hpp>
|
|
|
|
namespace opencv_test { namespace {
|
|
|
|
template<typename TString>
|
|
static std::string _tf(TString filename)
|
|
{
|
|
return (getOpenCVExtraDir() + "/dnn/") + filename;
|
|
}
|
|
|
|
TEST(Test_Darknet, read_tiny_yolo_voc)
|
|
{
|
|
Net net = readNetFromDarknet(_tf("tiny-yolo-voc.cfg"));
|
|
ASSERT_FALSE(net.empty());
|
|
}
|
|
|
|
TEST(Test_Darknet, read_yolo_voc)
|
|
{
|
|
Net net = readNetFromDarknet(_tf("yolo-voc.cfg"));
|
|
ASSERT_FALSE(net.empty());
|
|
}
|
|
|
|
TEST(Test_Darknet, read_filestorage_yolo_voc)
|
|
{
|
|
std::ifstream ifile(_tf("yolo-voc.cfg").c_str());
|
|
std::stringstream buffer;
|
|
buffer << " " << ifile.rdbuf(); // FIXME: FileStorage drops first character.
|
|
FileStorage ofs(".xml", FileStorage::WRITE | FileStorage::MEMORY);
|
|
ofs.write("cfgFile", buffer.str());
|
|
FileStorage ifs(ofs.releaseAndGetString(), FileStorage::READ | FileStorage::MEMORY | FileStorage::FORMAT_XML);
|
|
Net net = readNetFromDarknet(ifs["cfgFile"]);
|
|
ASSERT_FALSE(net.empty());
|
|
}
|
|
|
|
class Test_Darknet_layers : public DNNTestLayer
|
|
{
|
|
public:
|
|
void testDarknetLayer(const std::string& name, bool hasWeights = false)
|
|
{
|
|
std::string cfg = findDataFile("dnn/darknet/" + name + ".cfg", false);
|
|
std::string model = "";
|
|
if (hasWeights)
|
|
model = findDataFile("dnn/darknet/" + name + ".weights", false);
|
|
Mat inp = blobFromNPY(findDataFile("dnn/darknet/" + name + "_in.npy", false));
|
|
Mat ref = blobFromNPY(findDataFile("dnn/darknet/" + name + "_out.npy", false));
|
|
|
|
checkBackend(&inp, &ref);
|
|
|
|
Net net = readNet(cfg, model);
|
|
net.setPreferableBackend(backend);
|
|
net.setPreferableTarget(target);
|
|
net.setInput(inp);
|
|
Mat out = net.forward();
|
|
normAssert(out, ref, "", default_l1, default_lInf);
|
|
}
|
|
};
|
|
|
|
class Test_Darknet_nets : public DNNTestLayer
|
|
{
|
|
public:
|
|
// Test object detection network from Darknet framework.
|
|
void testDarknetModel(const std::string& cfg, const std::string& weights,
|
|
const std::vector<cv::String>& outNames,
|
|
const std::vector<int>& refClassIds,
|
|
const std::vector<float>& refConfidences,
|
|
const std::vector<Rect2d>& refBoxes,
|
|
double scoreDiff, double iouDiff, float confThreshold = 0.24)
|
|
{
|
|
checkBackend();
|
|
|
|
Mat sample = imread(_tf("dog416.png"));
|
|
Mat inp = blobFromImage(sample, 1.0/255, Size(416, 416), Scalar(), true, false);
|
|
|
|
Net net = readNet(findDataFile("dnn/" + cfg, false),
|
|
findDataFile("dnn/" + weights, false));
|
|
net.setPreferableBackend(backend);
|
|
net.setPreferableTarget(target);
|
|
net.setInput(inp);
|
|
std::vector<Mat> outs;
|
|
net.forward(outs, outNames);
|
|
|
|
std::vector<int> classIds;
|
|
std::vector<float> confidences;
|
|
std::vector<Rect2d> boxes;
|
|
for (int i = 0; i < outs.size(); ++i)
|
|
{
|
|
Mat& out = outs[i];
|
|
for (int j = 0; j < out.rows; ++j)
|
|
{
|
|
Mat scores = out.row(j).colRange(5, out.cols);
|
|
double confidence;
|
|
Point maxLoc;
|
|
minMaxLoc(scores, 0, &confidence, 0, &maxLoc);
|
|
|
|
float* detection = out.ptr<float>(j);
|
|
double centerX = detection[0];
|
|
double centerY = detection[1];
|
|
double width = detection[2];
|
|
double height = detection[3];
|
|
boxes.push_back(Rect2d(centerX - 0.5 * width, centerY - 0.5 * height,
|
|
width, height));
|
|
confidences.push_back(confidence);
|
|
classIds.push_back(maxLoc.x);
|
|
}
|
|
}
|
|
normAssertDetections(refClassIds, refConfidences, refBoxes, classIds,
|
|
confidences, boxes, "", confThreshold, scoreDiff, iouDiff);
|
|
}
|
|
};
|
|
|
|
TEST_P(Test_Darknet_nets, YoloVoc)
|
|
{
|
|
std::vector<cv::String> outNames(1, "detection_out");
|
|
|
|
std::vector<int> classIds(3);
|
|
std::vector<float> confidences(3);
|
|
std::vector<Rect2d> boxes(3);
|
|
classIds[0] = 6; confidences[0] = 0.750469f; boxes[0] = Rect2d(0.577374, 0.127391, 0.325575, 0.173418); // a car
|
|
classIds[1] = 1; confidences[1] = 0.780879f; boxes[1] = Rect2d(0.270762, 0.264102, 0.461713, 0.48131); // a bicycle
|
|
classIds[2] = 11; confidences[2] = 0.901615f; boxes[2] = Rect2d(0.1386, 0.338509, 0.282737, 0.60028); // a dog
|
|
double scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 1e-2 : 8e-5;
|
|
double iouDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.013 : 3e-5;
|
|
testDarknetModel("yolo-voc.cfg", "yolo-voc.weights", outNames,
|
|
classIds, confidences, boxes, scoreDiff, iouDiff);
|
|
}
|
|
|
|
TEST_P(Test_Darknet_nets, TinyYoloVoc)
|
|
{
|
|
std::vector<cv::String> outNames(1, "detection_out");
|
|
std::vector<int> classIds(2);
|
|
std::vector<float> confidences(2);
|
|
std::vector<Rect2d> boxes(2);
|
|
classIds[0] = 6; confidences[0] = 0.761967f; boxes[0] = Rect2d(0.579042, 0.159161, 0.31544, 0.160779); // a car
|
|
classIds[1] = 11; confidences[1] = 0.780595f; boxes[1] = Rect2d(0.129696, 0.386467, 0.315579, 0.534527); // a dog
|
|
double scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 8e-3 : 8e-5;
|
|
double iouDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 8e-3 : 3e-5;
|
|
testDarknetModel("tiny-yolo-voc.cfg", "tiny-yolo-voc.weights", outNames,
|
|
classIds, confidences, boxes, scoreDiff, iouDiff);
|
|
}
|
|
|
|
TEST_P(Test_Darknet_nets, YOLOv3)
|
|
{
|
|
std::vector<cv::String> outNames(3);
|
|
outNames[0] = "yolo_82";
|
|
outNames[1] = "yolo_94";
|
|
outNames[2] = "yolo_106";
|
|
|
|
std::vector<int> classIds(3);
|
|
std::vector<float> confidences(3);
|
|
std::vector<Rect2d> boxes(3);
|
|
classIds[0] = 7; confidences[0] = 0.952983f; boxes[0] = Rect2d(0.614622, 0.150257, 0.286747, 0.138994); // a truck
|
|
classIds[1] = 1; confidences[1] = 0.987908f; boxes[1] = Rect2d(0.150913, 0.221933, 0.591342, 0.524327); // a bicycle
|
|
classIds[2] = 16; confidences[2] = 0.998836f; boxes[2] = Rect2d(0.160024, 0.389964, 0.257861, 0.553752); // a dog (COCO)
|
|
double scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 4e-3 : 8e-5;
|
|
double iouDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.011 : 3e-5;
|
|
testDarknetModel("yolov3.cfg", "yolov3.weights", outNames,
|
|
classIds, confidences, boxes, scoreDiff, iouDiff);
|
|
}
|
|
|
|
INSTANTIATE_TEST_CASE_P(/**/, Test_Darknet_nets, dnnBackendsAndTargets());
|
|
|
|
TEST_P(Test_Darknet_layers, shortcut)
|
|
{
|
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_CPU)
|
|
throw SkipTestException("");
|
|
testDarknetLayer("shortcut");
|
|
}
|
|
|
|
TEST_P(Test_Darknet_layers, upsample)
|
|
{
|
|
testDarknetLayer("upsample");
|
|
}
|
|
|
|
TEST_P(Test_Darknet_layers, avgpool_softmax)
|
|
{
|
|
testDarknetLayer("avgpool_softmax");
|
|
}
|
|
|
|
TEST_P(Test_Darknet_layers, region)
|
|
{
|
|
testDarknetLayer("region");
|
|
}
|
|
|
|
TEST_P(Test_Darknet_layers, reorg)
|
|
{
|
|
testDarknetLayer("reorg");
|
|
}
|
|
|
|
INSTANTIATE_TEST_CASE_P(/**/, Test_Darknet_layers, dnnBackendsAndTargets());
|
|
|
|
}} // namespace
|